(19)
(11)EP 3 316 875 B1

(12)EUROPEAN PATENT SPECIFICATION

(45)Mention of the grant of the patent:
17.11.2021 Bulletin 2021/46

(21)Application number: 16818801.9

(22)Date of filing:  30.06.2016
(51)International Patent Classification (IPC): 
C12Q 1/6883(2018.01)
(52)Cooperative Patent Classification (CPC):
A61P 31/12; A61K 31/00; A61P 11/00; A61P 31/04; C12Q 2600/118; A61P 37/04; C12Q 2600/106; C12Q 2600/158; C12Q 1/6883
(86)International application number:
PCT/US2016/040437
(87)International publication number:
WO 2017/004390 (05.01.2017 Gazette  2017/01)

(54)

METHODS TO DIAGNOSE ACUTE RESPIRATORY INFECTIONS

VERFAHREN ZUR DIAGNOSE VON AKUTEN ATEMWEGSINFEKTIONEN

PROCÉDÉS POUR DIAGNOSTIQUER DES INFECTIONS RESPIRATOIRES AIGUËS


(84)Designated Contracting States:
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

(30)Priority: 01.07.2015 US 201562187683 P
19.11.2015 US 201562257406 P

(43)Date of publication of application:
09.05.2018 Bulletin 2018/19

(73)Proprietor: Duke University
Durham, NC 27705 (US)

(72)Inventors:
  • TSALIK, Ephraim L.
    Cary, North Carolina 27519 (US)
  • HENAO GIRALDO, Ricardo
    Durham, North Carolina 27705 (US)
  • BURKE, Thomas W.
    Durham, North Carolina 27713 (US)
  • GINSBURG, Geoffrey S.
    Durham, North Carolina 27705 (US)
  • WOODS, Christopher W.
    Durham, North Carolina 27707 (US)
  • MCCLAIN, Micah T.
    Durham, North Carolina 27707 (US)

(74)Representative: Gibbs, Richard 
Marks & Clerk LLP Aurora 120 Bothwell Street
Glasgow G2 7JS
Glasgow G2 7JS (GB)


(56)References cited: : 
WO-A1-2015/048098
US-A1- 2008 171 323
US-A1- 2005 209 785
US-A1- 2011 318 726
  
  • RAMILO O ET AL: "Gene expression patterns in blood leukocytes discriminate patients with acute infections", BLOOD, AMERICAN SOCIETY OF HEMATOLOGY, US, vol. 109, no. 5, 1 March 2007 (2007-03-01) , pages 2066-2077, XP002580520, ISSN: 0006-4971, DOI: 10.1182/BLOOD-2006-02-002477 [retrieved on 2006-11-14] -& Octavio Ramilo ET AL: "Gene expression patterns in blood leukocytes discriminate patients with acute infections", Blood, 14 November 2006 (2006-11-14), pages 2066-2077, XP055522875, DOI: 10.1182/BLOOD-2006-02-002477 Retrieved from the Internet: URL:www.bloodjournal.org/highwire/filestre am/313625/field_highwire_adjunct_files/5/T ableS02.xls [retrieved on 2018-11-12]
  • ZAAS AIMEE K ET AL: "Gene Expression Signatures Diagnose Influenza and Other Symptomatic Respiratory Viral Infections in Humans", CELL HOST & MICROBE, ELSEVIER, NL, vol. 6, no. 3, 17 September 2009 (2009-09-17), pages 207-217, XP002670360, ISSN: 1931-3128, DOI: 10.1016/J.CHOM.2009.07.006 [retrieved on 2009-08-06] -& Aimee K Zaas ET AL: "Gene Expression Signatures Diagnose Influenza and Other Symptomatic Respiratory Viral Infections in Humans", Cell Host & Microbe, 1 January 2009 (2009-01-01), pages 207-217, XP055522860, United States DOI: 10.1016/j.chom.2009.07.006 Retrieved from the Internet: URL:https://ars.els-cdn.com/content/image/ 1-s2.0-S1931312809002510-mmc1.pdf [retrieved on 2018-11-12]
  • GRANT P PARNELL ET AL: "A distinct influenza infection signature in the blood transcriptome of patients with severe community-acquired pneumonia", CRITICAL CARE, BIOMED CENTRAL LTD., LONDON, GB, vol. 16, no. 4, 16 August 2012 (2012-08-16), page R157, XP021129459, ISSN: 1364-8535, DOI: 10.1186/CC11477
  • AIMEE K. ZAAS ET AL: "The current epidemiology and clinical decisions surrounding acute respiratory infections", TRENDS IN MOLECULAR MEDICINE, vol. 20, no. 10, 1 October 2014 (2014-10-01), pages 579-588, XP055522333, GB ISSN: 1471-4914, DOI: 10.1016/j.molmed.2014.08.001
  • EPHRAIM L. TSALIK ET AL: "Host gene expression classifiers diagnose acute respiratory illness etiology", SCIENCE TRANSLATIONAL MEDICINE, vol. 8, no. 322, 20 January 2016 (2016-01-20), pages 322ra11-322ra11, XP055478176, US ISSN: 1946-6234, DOI: 10.1126/scitranslmed.aad6873
  • ZAAS ET AL.: 'A Host-Based RT-PCR Gene Expression Signature to Identify Acute Respiratory Viral Infection,'' Science Translational Medicine' vol. 5, no. ISS. 2, 18 September 2013, pages 1 - 10, XP055342257
  
Note: Within nine months from the publication of the mention of the grant of the European patent, any person may give notice to the European Patent Office of opposition to the European patent granted. Notice of opposition shall be filed in a written reasoned statement. It shall not be deemed to have been filed until the opposition fee has been paid. (Art. 99(1) European Patent Convention).


Description

BACKGROUND



[0001] Acute respiratory infection is common in acute care environments and results in significant mortality, morbidity, and economic losses worldwide. Respiratory tract infections, or acute respiratory infections (ARI) caused 3.2 million deaths around the world and 164 million disability-adjusted life years lost in 2011, more than any other cause (World Health Organization., 2013a, 2013b). In 2012, the fourth leading cause of death worldwide was lower respiratory tract infections, and in low and middle income countries, where less supportive care is available, lower respiratory tract infections are the leading cause of death (WHO factsheet, accessed August 22, 2014). These illnesses are also problematic in developed countries. In the United States in 2010, the Centers for Disease Control (CDC) determined that pneumonia and influenza alone caused 15.1 deaths for every 100,000 people in the US population. The aged and children under the age of 5 years are particularly vulnerable to poor outcomes due to ARIs. For example, in 2010, pneumonia accounted for 18.3% of all deaths, or almost 1.4 million deaths, worldwide in children aged 5 years or younger.

[0002] Pneumonia and other lower respiratory tract infections can be due to many different pathogens that are primarily viral, bacterial, or less frequently fungal. Among viral pathogens, influenza is among the most notorious based on numbers of affected individuals, variable severity from season to season, and the ever-present worry about new strains causing much higher morbidity and mortality (e.g., Avian flu). However, among viral pathogens, influenza is only one of many that cause significant human disease. Respiratory Syncytial Virus (RSV) is the leading cause of hospitalization of children in developed countries during the winter months. Worldwide, about 33 million new cases of RSV infections were reported in 2005 in children under 5, with 3.4 million severe enough for hospitalization. It is estimated that this viral infection alone kills between 66,000 and 199,000 children each year. And, in the United States alone, about 10,000 deaths annually are associated with RSV infections in the over-65 population. In addition to known viral pathogens, history has shown that new and emerging infections can manifest at any time, spreading globally within days or weeks. Recent examples include SARS-coronavirus, which had a 10% mortality rate when it appeared in 2003-2004. More recently, Middle East respiratory syndrome (MERS) coronavirus continues to simmer in the Middle East and has been associated with a 30% mortality rate. Both of these infections present with respiratory symptoms and may at first be indistinguishable from any other ARI.

[0003] Although viral infections cause the majority of ARI, bacterial etiologies are also prominent especially in the context of lower respiratory tract infections. Specific causes of bacterial ARI vary geographically and by clinical context but include Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Chlamydia pneumoniae, Mycoplasma pneumoniae, Klebsiella pneumoniae, Escherichia coli, and Pseudomonas aeruginosa. The identification of these pathogens relies on their growth in culture, which typically requires days and has limited sensitivity for detection of the infectious agent. Obtaining an adequate sample to test is problematic: In a study of 1669 patients with community-acquired pneumonia, only 14% of patients could provide a "good-quality" sputum sample that resulted in a positive culture (Garcia-Vazquez et al., 2004). Clinicians are aware of the limitations of these tests, which drives uncertainty and, consequently, antibacterial therapies are frequently prescribed without any confirmation of a bacterial infection.

[0004] The ability to rapidly diagnose the etiology of ARIs is an urgent global problem with far-reaching consequences at multiple levels: optimizing treatment for individual patients; epidemiological surveillance to identify and track outbreaks; and guiding appropriate use of antimicrobials to stem the rising tide of antimicrobial resistance. It has been well established that early and appropriate antimicrobial therapy improves outcomes in patients with severe infection. This in part drives the over-utilization of antimicrobial therapies. Up to 73% of ambulatory care patients with acute respiratory illness are prescribed an antibiotic, accounting for approximately 40% of all antibiotics prescribed to adults in this setting. It has, however, been estimated that only a small fraction of these patients require anti-bacterial treatment (Cantrell et al. 2003, Clin. Ther. Jan;24(1): 170-82). A similar trend is observed in emergency departments. Even if the presence of a viral pathogen has been microbiologically confirmed, it does not preclude the possibility of a concurrent bacterial infection. As a result, antibacterials are often prescribed "just in case." This spiraling empiricism contributes to the rising tide of antimicrobial resistance (Gould, 2009; Kim & Gallis, 1989), which is itself associated with higher mortality, length of hospitalization, and costs of health care (Cosgrove 2006, Clin. Infect. Dis., Jan 15;42 Suppl 2:S82-9). In addition, the inappropriate use of antibiotics may lead to drug-related adverse effects and other complications, e.g., Clostridium difficile-associated diarrhea (Zaas et al., 2014).

[0005] Acute respiratory infections are frequently characterized by non-specific symptoms (such as fever or cough) that are common to many different illnesses, including illnesses that are not caused by an infection. Existing diagnostics for ARI fall short in a number of ways. Conventional microbiological testing is limited by poor sensitivity and specificity, slow turnaround times, or by the complexity of the test (Zaas et al. 2014, Trends Mol Med 20(10):579-88). One limitation of current tests that detect specific viral pathogens, for example the multiplex PCR-based assays, is the inability to detect emergent or pandemic viral strains. Influenza pandemics arise when new viruses circulate against which populations have no natural resistance. Influenza pandemics are frequently devastating. For example, in 1918-1919 the Spanish flu affected about 20% to 40% of the world's population and killed about 50 million people; in 1957-1958, Asian flu killed about 2 million people; in 1968-1969 the Hong Kong flu killed about 1 million people; and in 2009-2010, the Centers for Disease Control estimates that approximately 43 million to 89 million people contracted swine flu resulting in 8,870 to 18,300 related deaths. The emergence of these new strains challenges existing diagnostics which are not designed to detect them. This was particularly evident during the 2009 influenza pandemic where confirmation of infection required days and only occurred at specialized testing centers such as state health departments or the CDC (Kumar & Henrickson 2012, Clin Microbiol Rev 25(2):344-61). The Ebola virus disease outbreak in West Africa poses similar challenges at the present time. Moreover, there is every expectation we will continue to face this issue as future outbreaks of infectious diseases are inevitable.

[0006] A further limitation of diagnostics that use the paradigm of testing for specific viruses or bacteria is that even though a pathogenic microbe may be detected, this is not proof that the patient's symptoms are due to the detected pathogen. A microorganism may be present as part of the individual's normal flora, known as colonization, or it may be detected due to contamination of the tested sample (e.g., a nasal swab or wash). Although recently-approved multiplex PCR assays, including those that detect viruses and bacteria, offer high sensitivity, these tests do not differentiate between asymptomatic carriage of a virus and true infection. For example, there is a high rate of asymptomatic viral shedding in ARI, particularly in children (Jansen et al. 2011, J Clin Microbiol 49(7):2631-2636). Similarly, even though one pathogen is detected, illness may be due to a second pathogen for which there was no test available or performed.

[0007] Reports have described host gene expression profiles differentiating viral ARI from healthy controls (Huang et al. 2011 PLoS Genetics 7(8): e1002234; Mejias et al., 2013; Thach et al. 2005 Genes and Immunity 6:588-595; Woods et al., 2013; A. K. Zaas et al., 2013; A. K. Zaas et al., 2009). However, few among these differentiate viral from bacterial ARI, which is a more clinically meaningful distinction than is detection of viral infection versus healthy or bacterial infection versus healthy (Hu, Yu, Crosby, & Storch, 2013; Parnell et al., 2012; Ramilo et al., 2007). For example, Ramilo et al (Blood American Society of Hematology, US, vol. 109, no. 5, 1 March 2007) describes that gene expression profiles were obtained from pediatric patients with acute infections caused by influenza A virus, Gram-negative (Escherichia coli) or Gram-positive (Staphylococcus aureus and Streptococcus pneumoniae) bacteria. Thirty-five genes were identified that could be used to discriminate patients with influenza A virus infections from patients with either E coli or S pneumoniae infection.

[0008] Current diagnostics methods are thus limited in their ability to differentiate between a bacterial and viral infection, and symptoms arising from non-infectious causes, or to identify co-infections with bacteria and virus.

SUMMARY



[0009] Aspects of the present invention are set out in the appended claims.
The present disclosure provides, in part, a molecular diagnostic test that overcomes many of the limitations of current methods for the determination of the etiology of respiratory symptoms. The test detects the host's response to an infectious agent or agents by measuring and analyzing the patterns of co-expressed genes, or signatures. These gene expression signatures may be measured in a blood sample in a human or animal presenting with symptoms that are consistent with an acute respiratory infection or in a human or animal that is at risk of developing (e.g., presymptomatic) an acute respiratory infection (e.g., during an epidemic or local disease outbreak). Measurement of the host response as taught herein differentiates between bacterial ARI, viral ARI, and a non-infectious cause of illness, and may also detect ARI resulting from co-infection with bacteria and virus.

[0010] This multi-component test performs with unprecedented accuracy and clinical applicability, allowing health care providers to use the response of the host (the subject or patient) to reliably determine the nature of the infectious agent, to the level of pathogen class, or to exclude an infectious cause of symptoms in an individual patient presenting with symptoms that, by themselves, are not specific. In some embodiments, the results are agnostic to the species of respiratory virus or bacteria (i.e., while differentiating between virus or bacteria, it does not differentiate between particular genus or species of virus or bacteria). This offers an advantage over current tests that include probes or reagents directed to specific pathogens and thus are limited to detecting only those specific pathogens.

[0011] One aspect of the present disclosure provides a method for determining whether acute respiratory symptoms in a subject are bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of: (a) providing a biological sample that has been obtained from the subject; (b) measuring on a platform gene expression levels of a pre-defined sets of genes, termed signatures in said biological sample; (c) normalizing gene expression levels for the technology (i.e., platform) used to make said measurement to generate normalized gene expression values; (d) entering the normalized gene expression values into a bacterial acute respiratory infection (ARI) classifier, a viral ARI classifier and optionally a non-infectious illness classifier that have pre-defined weighting values (coefficients) for each of the genes in each signature, wherein the bacterial acute respiratory infection (ARI) classifier comprises expression levels of 5 to 200 of the genes listed as part of a bacterial classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12, the viral ARI classifier comprises expression levels of 5 to 200 of the genes listed as part of a viral classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12, and the non-infectious illness classifier comprises expression levels of 5 to 200 of the genes listed as part of a non-infectious illness classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12; (e) calculating an etiology probability for a bacterial ARI, a viral ARI and, optionally non-infectious illness based upon said normalized gene expression values and said classifier(s), to thereby determine whether the patient providing the sample has an infection of bacterial origin, viral origin, or has a non-infectious illness, or some combination of these conditions, optionally the method further comprising:
(f) comparing the probability to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection.
In some embodiments, if the sample does not indicate a likelihood of bacterial ARI or viral ARI, the method may further comprise repeating steps (d) and (e) using only the non-infectious classifier, to determine whether the acute respiratory illness in the subject is non-infectious in origin.

[0012] Yet another aspect of the present disclosure that is outwith the scope of the appended claims provides a method of treating an acute respiratory infection (ARI) whose etiology is unknown in a subject, said method comprising, consisting of, or consisting essentially of: (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes (e.g., one, two or three or more signatures); (c) normalizing gene expression levels for the technology (i.e., platform) used to make said measurement to generate a normalized value; (d) entering the normalized values into a bacterial classifier, a viral classifier and non-infectious illness classifier that have pre-defined weighting values for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) classifying the sample as being of bacterial etiology, viral etiology, or noninfectious illness; and (g) administering to the subject an appropriate treatment regimen as identified by step (e). In some examples, step (g) comprises administering an antibacterial therapy when the etiology of the ARI is determined to be bacterial. In other examples, step (g) comprises administering an antiviral therapy when the etiology of the ARI is determined to be viral.

[0013] Another aspect of the disclosure that is outwith the scope of the appended claims is a method of monitoring response to a vaccine or a drug in a subject suffering from or at risk of an acute respiratory illness selected from bacterial, viral and/or non-infectious, comprising determining a host response of said subject, said determining carried out by a method as taught herein. In some examples, the drug is an antibacterial drug or an antiviral drug.

[0014] In some embodiments, the methods further comprise generating a report assigning the subject a score indicating the probability of the etiology of the ARI

[0015] Further provided is a system for determining an etiology of an acute respiratory illness in a subject selected from bacterial, viral and/or non-infectious, comprising : at least one processor; a sample input circuit configured to receive a biological sample from the subject; a sample analysis circuit coupled to the at least one processor and configured to determine gene expression levels of the biological sample; an input/output circuit coupled to the at least one processor; a storage circuit coupled to the at least one processor and configured to store data, parameters, and/or classifiers; and a memory coupled to the processor and comprising computer readable program code embodied in the memory that when executed by the at least one processor causes the at least one processor to perform operations comprising: controlling/performing measurement via the sample analysis circuit of gene expression levels of a pre-defined set of genes (i.e., signature) in said biological sample; normalizing the gene expression levels to generate normalized gene expression values; retrieving from the storage circuit a bacterial acute respiratory infection (ARI) classifier, a viral ARI classifier and optionally a non-infectious illness classifier, said classifier(s) comprising pre-defined weighting values (i.e., coefficients) for each of the genes of the pre-defined set of genes, wherein the bacterial acute respiratory infection (ARI) classifier comprises expression levels of 5 to 200 of the genes listed as part of a bacterial classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12, the viral ARI classifier comprises expression levels of 5 to 200 of the genes listed as part of a viral classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12, and the non-infectious illness classifier comprises expression levels of 5 to 200 of the genes listed as part of a non-infectious illness classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12; entering the normalized gene expression values into the bacterial acute respiratory infection (ARI) classifier, the viral ARI classifier, and optionally the non-infectious illness classifier; calculating an etiology probability for a bacterial ARI, a viral ARI, and optionally non-infectious illness based upon said classifier(s); and controlling output via the input/output circuit of a determination whether the acute respiratory illness in the subject is bacterial in origin, viral in origin, non-infectious in origin, or some combination thereof.

[0016] In some embodiments, the system comprises computer readable code to transform quantitative, or semi-quantitative, detection of gene expression to a cumulative score or probability of the etiology of the ARI

[0017] In some embodiments, the system comprises an array platform, a thermal cycler platform (e.g., multiplexed and/or real-time PCR platform), a hybridization and multi-signal coded (e.g., fluorescence) detector platform, a nucleic acid mass spectrometry platform, a nucleic acid sequencing platform, or a combination thereof.

[0018] In some embodiments of the aspects, the pre-defined sets of genes comprise at least three genetic signatures.

[0019] In some embodiments of the aspects, the biological sample comprises a sample selected from the group consisting of peripheral blood, sputum, nasopharyngeal swab, nasopharyngeal wash, bronchoalveolar lavage, endotracheal aspirate, and combinations thereof.

[0020] The bacterial classifier comprises expression levels of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes (measurable, e.g., with oligonucleotide probes homologous to said genes or gene transcripts) listed as part of a bacterial classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12. The viral classifier comprises expression levels of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes (measurable, e.g., with oligonucleotide probes homologous to said genes or gene transcripts) listed as part of a viral classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12. The non-infectious illness classifier comprises expression levels of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes (measurable, e.g., with oligonucleotide probes homologous to said genes or gene transcripts) listed as part of a non-infectious illness classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12.

[0021] According to a further aspect of the disclosure that is outwith the scope of the claims, there is provided a kit for determining the etiology of an acute respiratory infection (ARI) in a subject is also provided, comprising, consisting of, or consisting essentially of (a) a means for extracting mRNA from a biological sample; (b) a means for generating one or more arrays consisting of a plurality of synthetic oligonucleotides with regions homologous to transcripts from of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes from Table 1, Table 2, Table 9, Table 10 and/or Table 12; and (c) instructions for use.

[0022] Another aspect of the present disclosure that is outwith the scope of the claims provides a method of using a kit for assessing the acute respiratory infection (ARI) classifier comprising, consisting of, or consisting essentially of: (a) generating one or more arrays consisting of a plurality of synthetic oligonucleotides with regions homologous to of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes from Table 1, Table 2, Table 9, Table 10 and/or Table 12; (b) adding to said array oligonucleotides with regions homologous to normalizing genes; (c) obtaining a biological sample from a subject suffering from an acute respiratory infection (ARI); (d) isolating RNA from said sample to create a transcriptome; (e) measuring said transcriptome on said array (e.g., by measuring fluorescence or electric current proportional to the level of gene expression, etc.); (f) normalizing the measurements of said transcriptome to the normalizing genes, electronically transferring normalized measurements to a computer to implement the classifier(s), (g) generating a report; and optionally (h) administering an appropriate treatment based on the results.

[0023] In some examples, the method further comprises externally validating an ARI classifier against a known dataset comprising at least two relevant clinical attributes. In some embodiments, the dataset is selected from the group consisting of GSE6269, GSE42026, GSE40396, GSE20346, GSE42834 and combinations thereof.

[0024] An ARI classifier as taught herein may be used in a method of treatment for acute respiratory infection (ARI) in a subject of unknown etiology.

BRIEF DESCRIPTION OF THE DRAWINGS



[0025] The foregoing aspects and other features of the disclosure are explained in the following description, taken in connection with the accompanying drawings, herein:

FIG. 1 is a schematic showing a method of obtaining classifiers (training 10) according to some embodiments of the present disclosure, where each classifier is composed of a weighted sum of all or a subset of normalized gene expression levels. This weighted sum defines a probability that allows for a decision (classification), particularly when compared to a threshold value or a confidence interval. The exact combination of genes, their weights and the threshold for each classifier obtained by the training are particular to a specific platform. The classifier (or more precisely its components, namely weights and threshold or confidence interval (values)) go to a database. Weights with a nonzero value determine the subset of genes used by the classifier. Repeat to obtain all three classifiers (bacterial ARI, viral ARI and non-infectious ARI) within a specified platform matching the gene expression values.

FIG. 2 is a diagram showing an example of generating and/or using classifiers in accordance with some embodiments of the present disclosure.

FIG. 3 is a schematic showing a method of classification 20 of an etiology of acute respiratory symptoms suffered by a subject making use of classifiers according to some embodiments of the present disclosure.

FIG. 4 presents schematics showing the decision pattern for using secondary classification to determine the etiology of an ARI in a subject in accordance with some embodiments of the present disclosure.

FIG. 5 is a diagram of an example training method presented in Example 1. A cohort of patients encompassing bacterial ARI, viral ARI, or non-infectious illness was used to develop classifiers of each condition. This combined ARI classifier was validated using leave one out cross-validation and compared to three published classifiers of bacterial vs. viral infection. The combined ARI classifier was also externally validated in six publically available datasets. In one experiment, healthy volunteers were included in the training set to determine their suitability as "no-infection" controls. All subsequent experiments were performed without the use of this healthy subject cohort.

FIG. 6 presents graphs showing the results of leave-one-out cross-validation of three classifiers (bacterial ARI, viral ARI and noninfectious illness) according an example training method presented in Example 1. Each patient is assigned probabilities of having bacterial ARI (triangle), viral ARI (circle), and non-infectious illness (square). Patients clinically adjudicated as having bacterial ARI, viral ARI, or non-infectious illness, are presented in the top, center, and bottom panels, respectively. Overall classification accuracy was 87%.

FIG. 7 is a graph showing the evaluation of healthy adults as a no-infection control, rather than an ill-but-uninfected control. This figure demonstrates the unexpected superiority of the use of ill-but-not infected subjects as the control.

FIG. 8 shows the positive and negative predictive values for A) Bacterial and B) Viral ARI classification as a function of prevalence.

FIG. 9 is a Venn diagram representing overlap in the Bacterial ARI, Viral ARI, and Non-infectious Illness Classifiers. There are 71 genes in the Bacterial ARI Classifier, 33 in the Viral ARI Classifier, and 26 in the Non-infectious Illness Classifier. One gene overlaps between the Bacterial and Viral ARI Classifiers. Five genes overlap between the Bacterial ARI and Non-infectious Illness Classifiers. Four genes overlap between the Viral ARI and Non-infectious Illness Classifiers.

FIG. 10 is a graph showing Classifier performance in patients with co-infection by the identification of bacterial and viral pathogens. Bacterial and Viral ARI classifiers were trained on subjects with bacterial (n=22) or viral (n=71) infection (GSE60244). This same dataset also included 25 subjects with bacterial/viral co-infection. Bacterial and viral classifier predictions were normalized to the same scale, as shown in the figure. Each subject receives two probabilities: that of a bacterial ARI host response and a viral ARI host response. A probability score of 0.5 or greater was considered positive. Subjects 1-6 have a bacterial host response. Subjects 7-9 have both bacterial and viral host responses which may indicate true co-infection. Subjects 10-23 have a viral host response. Subjects 24-25 do not have bacterial or viral host responses.

FIG. 11 is a block diagram of a classification system and/or computer program product that may be used in a platform. A classification system and/or computer program product 1100 may include a processor subsystem 1140, including one or more Central Processing Units (CPU) on which one or more operating systems and/or one or more applications run. While one processor 1140 is shown, it will be understood that multiple processors 1140 may be present, which may be either electrically interconnected or separate. Processor(s) 1140 are configured to execute computer program code from memory devices, such as memory 1150, to perform at least some of the operations and methods described herein. The storage circuit 1170 may store databases which provide access to the data/parameters/classifiers used by the classification system 1110 such as the signatures, weights, thresholds, etc. An input/output circuit 1160 may include displays and/or user input devices, such as keyboards, touch screens and/or pointing devices. Devices attached to the input/output circuit 1160 may be used to provide information to the processor 1140 by a user of the classification system 1100. Devices attached to the input/output circuit 1160 may include networking or communication controllers, input devices (keyboard, a mouse, touch screen, etc.) and output devices (printer or display). An optional update circuit 1180 may be included as an interface for providing updates to the classification system 1100 such as updates to the code executed by the processor 1140 that are stored in the memory 1150 and/or the storage circuit 1170. Updates provided via the update circuit 1180 may also include updates to portions of the storage circuit 1170 related to a database and/or other data storage format which maintains information for the classification system 1100, such as the signatures, weights, thresholds, etc. The sample input circuit 1110 provides an interface for the classification system 1100 to receive biological samples to be analyzed. The sample processing circuit 1120 may further process the biological sample within the classification system 1100 so as to prepare the biological sample for automated analysis.


DETAILED DESCRIPTION



[0026] For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to preferred embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended, such alteration and further modifications of the disclosure as illustrated herein, being contemplated as would normally occur to one skilled in the art to which the disclosure relates.

[0027] Articles "a" and "an" are used herein to refer to one or to more than one (i.e., at least one) of the grammatical object of the article. By way of example, "an element" means at least one element and can include more than one element.

[0028] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0029] The present disclosure provides that alterations in gene, protein and metabolite expression in blood in response to pathogen exposure that causes acute respiratory infections can be used to identify and characterize the etiology of the ARI in a subject with a high degree of accuracy.

Definitions



[0030] As used herein, the term "acute respiratory infection" or "ARI" refers to an infection, or an illness showing symptoms and/or physical findings consistent with an infection (e.g., symptoms such as coughing, wheezing, fever, sore throat, congestion; physical findings such as elevated heart rate, elevated breath rate, abnormal white blood cell count, low arterial carbon dioxide tension (PaCO2), etc.), of the upper or lower respiratory tract, often due to a bacterial or viral pathogen, and characterized by rapid progression of symptoms over hours to days. ARIs may primarily be of the upper respiratory tract (URIs), the lower respiratory tract (LRIs), or a combination of the two. ARIs may have systemic effects due to spread of the infection beyond the respiratory tract or due to collateral damage induced by the immune response. An example of the former includes Staphylococcus aureus pneumonia that has spread to the blood stream and can result in secondary sites of infection, including endocarditis (infection of the heart valves), septic arthritis (joint infection), or osteomyelitis (bone infection). An example of the latter includes influenza pneumonia leading to acute respiratory distress syndrome and respiratory failure.

[0031] The term "signature" as used herein refers to a set of biological analytes and the measurable quantities of said analytes whose particular combination signifies the presence or absence of the specified biological state. These signatures are discovered in a plurality of subjects with known status (e.g., with a confirmed respiratory bacterial infection, respiratory viral infection, or suffering from non-infectious illness), and are discriminative (individually or jointly) of one or more categories or outcomes of interest. These measurable analytes, also known as biological markers, can be (but are not limited to) gene expression levels, protein or peptide levels, or metabolite levels. See also US 2015/0227681 to Courchesne et al.; US 2016/0153993 to Eden et al.

[0032] In some embodiments as disclosed herein, the "signature" is a particular combination of genes whose expression levels, when incorporated into a classifier as taught herein, discriminate a condition such as a bacterial ARI, viral ARI or non-infectious illness. See, for example, Table 1, Table 2, Table 9, Table 10 and Table 12 hereinbelow. In some embodiments, the signature is agnostic to the species of respiratory virus or bacteria (i.e., while differentiating between virus or bacteria, it does not differentiate between particular genus or species of virus or bacteria) and/or agnostic to the particular cause of the non-infectious illness.

[0033] As used herein, the terms "classifier" and "predictor" are used interchangeably and refer to a mathematical function that uses the values of the signature (e.g., gene expression levels for a defined set of genes) and a pre-determined coefficient (or weight) for each signature component to generate scores for a given observation or individual patient for the purpose of assignment to a category. The classifier may be linear and/or probabilistic. A classifier is linear if scores are a function of summed signature values weighted by a set of coefficients. Furthermore, a classifier is probabilistic if the function of signature values generates a probability, a value between 0 and 1.0 (or 0 and 100%) quantifying the likelihood that a subject or observation belongs to a particular category or will have a particular outcome, respectively. Probit regression and logistic regression are examples of probabilistic linear classifiers that use probit and logistic link functions, respectively, to generate a probability.

[0034] A classifier as taught herein may be obtained by a procedure known as "training," which makes use of a set of data containing observations with known category membership (e.g., bacterial ARI, viral ARI, and/or non-infection illness). See FIG. 1. Specifically, training seeks to find the optimal coefficient (i.e., weight) for each component of a given signature (e.g., gene expression level components), as well as an optimal signature, where the optimal result is determined by the highest achievable classification accuracy.

[0035] "Classification" refers to a method of assigning a subject suffering from or at risk for acute respiratory symptoms to one or more categories or outcomes (e.g., a patient is infected with a pathogen or is not infected, another categorization may be that a patient is infected with a virus and/or infected with a bacterium). See FIG. 3. In some cases, a subject may be classified to more than one category, e.g., in case of bacterial and viral co-infection. The outcome, or category, is determined by the value of the scores provided by the classifier, which may be compared to a cut-off or threshold value, confidence level, or limit. In other scenarios, the probability of belonging to a particular category may be given (e.g., if the classifier reports probabilities).

[0036] As used herein, the term "indicative" when used with gene expression levels, means that the gene expression levels are up-regulated or down-regulated, altered, or changed compared to the expression levels in alternative biological states (e.g., bacterial ARI or viral ARI) or control. The term "indicative" when used with protein levels means that the protein levels are higher or lower, increased or decreased, altered, or changed compared to the standard protein levels or levels in alternative biological states.

[0037] The term "subject" and "patient" are used interchangeably and refer to any animal being examined, studied or treated. It is not intended that the present disclosure be limited to any particular type of subject. In some embodiments of the present invention, humans are the preferred subject, while in other embodiments non-human animals are the preferred subject, including, but not limited to, mice, monkeys, ferrets, cattle, sheep, goats, pigs, chicken, turkeys, dogs, cats, horses and reptiles. In certain embodiments, the subject is suffering from an ARI or is displaying ARI-like symptoms.

[0038] "Platform" or "technology" as used herein refers to an apparatus (e.g., instrument and associated parts, computer, computer-readable media comprising one or more databases as taught herein, reagents, etc.) that may be used to measure a signature, e.g., gene expression levels, in accordance with the present disclosure. Examples of platforms include, but are not limited to, an array platform, a thermal cycler platform (e.g., multiplexed and/or real-time PCR platform), a nucleic acid sequencing platform, a hybridization and multi-signal coded (e.g., fluorescence) detector platform, etc., a nucleic acid mass spectrometry platform, a magnetic resonance platform, and combinations thereof.

[0039] In some embodiments, the platform is configured to measure gene expression levels semi-quantitatively, that is, rather than measuring in discrete or absolute expression, the expression levels are measured as an estimate and/or relative to each other or a specified marker or markers (e.g., expression of another, "standard" or "reference," gene).

[0040] In some embodiments, semi-quantitative measuring includes "real-time PCR" by performing PCR cycles until a signal indicating the specified mRNA is detected, and using the number of PCR cycles needed until detection to provide the estimated or relative expression levels of the genes within the signature.

[0041] A real-time PCR platform includes, for example, a TaqMan® Low Density Array (TLDA), in which samples undergo multiplexed reverse transcription, followed by real-time PCR on an array card with a collection of wells in which real-time PCR is performed. See Kodani et al. 2011, J. Clin. Microbiol. 49(6):2175-2182. A real-time PCR platform also includes, for example, a Biocartis Idylla™ sample-to-result technology, in which cells are lysed, DNA/RNA extracted and real-time PCR is performed and results detected.

[0042] A magnetic resonance platform includes, for example, T2 Biosystems® T2 Magnetic Resonance (T2MR®) technology, in which molecular targets may be identified in biological samples without the need for purification.

[0043] The terms "array," "microarray" and "micro array" are interchangeable and refer to an arrangement of a collection of nucleotide sequences presented on a substrate. Any type of array can be utilized in the methods provided herein. For example, arrays can be on a solid substrate (a solid phase array), such as a glass slide, or on a semi-solid substrate, such as nitrocellulose membrane. Arrays can also be presented on beads, i.e., a bead array. These beads are typically microscopic and may be made of, e.g., polystyrene. The array can also be presented on nanoparticles, which may be made of, e.g., particularly gold, but also silver, palladium, or platinum. See, e.g., Nanosphere Verigene® System, which uses gold nanoparticle probe technology. Magnetic nanoparticles may also be used. Other examples include nuclear magnetic resonance microcoils. The nucleotide sequences can be DNA, RNA, or any permutations thereof (e.g., nucleotide analogues, such as locked nucleic acids (LNAs), and the like). In some embodiments, the nucleotide sequences span exon/intron boundaries to detect gene expression of spliced or mature RNA species rather than genomic DNA. The nucleotide sequences can also be partial sequences from a gene, primers, whole gene sequences, non-coding sequences, coding sequences, published sequences, known sequences, or novel sequences. The arrays may additionally comprise other compounds, such as antibodies, peptides, proteins, tissues, cells, chemicals, carbohydrates, and the like that specifically bind proteins or metabolites.

[0044] An array platform includes, for example, the TaqMan® Low Density Array (TLDA) mentioned above, and an Affymetrix® microarray platform.

[0045] A hybridization and multi-signal coded detector platform includes, for example, NanoString nCounter® technology, in which hybridization of a color-coded barcode attached to a target-specific probe (e.g., corresponding to a gene expression transcript of interest) is detected; and Luminex® xMAP® technology, in which microsphere beads are color coded and coated with a target-specific (e.g., gene expression transcript) probe for detection; and Illumina® BeadArray, in which microbeads are assembled onto fiber optic bundles or planar silica slides and coated with a target-specific (e.g., gene expression transcript) probe for detection.

[0046] A nucleic acid mass spectrometry platform includes, for example, the Ibis Biosciences Plex-ID® Detector, in which DNA mass spectrometry is used to detect amplified DNA using mass profiles.

[0047] A thermal cycler platform includes, for example, the FilmArray® multiplex PCR system, which extract and purifies nucleic acids from an unprocessed sample and performs nested multiplex PCR; and the RainDrop Digital PCR System, which is a droplet-based PCR platform using microfluidic chips.

[0048] The term "computer readable medium" refers to any device or system for storing and providing information (e.g., data and instructions) to a computer processor. Examples of computer readable media include, but are not limited to, DVDs, CDs hard disk drives, magnetic tape and servers for streaming media over networks, and applications, such as those found on smart phones and tablets. In various embodiments, aspects of the present invention including data structures and methods may be stored on a computer readable medium. Processing and data may also be performed on numerous device types, including but not limited to, desk top and lap top computers, tablets, smart phones, and the like.

[0049] As used herein, the term "biological sample" comprises any sample that may be taken from a subject that contains genetic material that can be used in the methods provided herein. For example, a biological sample may comprise a peripheral blood sample. The term "peripheral blood sample" refers to a sample of blood circulating in the circulatory system or body taken from the system of body. Other samples may comprise those taken from the upper respiratory tract, including but not limited to, sputum, nasopharyngeal swab and nasopharyngeal wash. A biological sample may also comprise those samples taken from the lower respiratory tract, including but not limited to, bronchoalveolar lavage and endotracheal aspirate. A biological sample may also comprise any combinations thereof.

[0050] The term "genetic material" refers to a material used to store genetic information in the nuclei or mitochondria of an organism's cells. Examples of genetic material include, but are not limited to, double-stranded and single-stranded DNA, cDNA, RNA, and mRNA.

[0051] The term "plurality of nucleic acid oligomers" refers to two or more nucleic acid oligomers, which can be DNA or RNA.

[0052] As used herein, the terms "treat", "treatment" and "treating" refer to the reduction or amelioration of the severity, duration and/or progression of a disease or disorder or one or more symptoms thereof resulting from the administration of one or more therapies. Such terms refer to a reduction in the replication of a virus or bacteria, or a reduction in the spread of a virus or bacteria to other organs or tissues in a subject or to other subjects. Treatment may also include therapies for ARIs resulting from non-infectious illness, such as allergy treatment, asthma treatments, and the like.

[0053] The term "effective amount" refers to an amount of a therapeutic agent that is sufficient to exert a physiological effect in the subject. The term "responsivity" refers to a change in gene expression levels of genes in a subject in response to the subject being infected with a virus or bacteria or suffering from a non-infectious illness compared to the gene expression levels of the genes in a subject that is not infected with a virus, bacteria or suffering from a non-infectious illness or a control subject.

[0054] The term "appropriate treatment regimen" refers to the standard of care needed to treat a specific disease or disorder. Often such regimens require the act of administering to a subject a therapeutic agent(s) capable of producing a curative effect in a disease state. For example, a therapeutic agent for treating a subject having bacteremia is an antibiotic which include, but are not limited to, penicillins, cephalosporins, fluroquinolones, tetracyclines, macrolides, and aminoglycosides. A therapeutic agent for treating a subject having a viral respiratory infection includes, but is not limited to, oseltamivir, RNAi antivirals, inhaled ribavirin, monoclonal antibody respigam, zanamivir, and neuraminidase blocking agents. The invention contemplates the use of the methods of the invention to determine treatments with antivirals or antibiotics that are not yet available. Appropriate treatment regimes also include treatments for ARIs resulting from non-infectious illness, such as allergy treatments, including but not limited to, administration of antihistamines, decongestants, anticholinergic nasal sprays, leukotriene inhibitors, mast cell inhibitors, steroid nasal sprays, etc.; and asthma treatments, including, but not limited to, inhaled corticosteroids, leukotriene modifiers, long-acting beta agonists, combinations inhalers (e.g., fluticasone-salmeterol; budesonide-formoterol; mometasone-formoterol, etc.), theophylline, short-acting beta agonists, ipratropium, oral and intravenous corticosteroids, omalizumab, and the like.

[0055] Often such regimens require the act of administering to a subject a therapeutic agent(s) capable of producing reduction of symptoms associated with a disease state. Examples such therapeutic agents include, but are not limited to, NSAIDS, acetaminophen, anti-histamines, beta-agonists, anti-tussives or other medicaments that reduce the symptoms associated with the disease process.

Methods of Generating Classifiers (Training)



[0056] The present disclosure provides methods of generating classifiers (also referred to as training 10) for use in the methods of determining the etiology of an acute respiratory illness in a subject. Gene expression-based classifiers are developed that can be used to identify and characterize the etiology of an ARI in a subject with a high degree of accuracy.

[0057] Hence, and as shown in FIG. 1, one aspect of the present disclosure provides a method of making an acute respiratory infection (ARI) classifier comprising, consisting of, or consisting essentially of: (i) providing a biological sample (e.g., a peripheral blood sample) that has been obtained from a plurality of subjects suffering from bacterial, viral or non-infectious acute respiratory infection 100; (ii) optionally, isolating RNA from said sample (e.g., total RNA to create a transcriptome) (105, not shown in FIG. 1); (iii) measuring gene expression levels of a plurality of genes 110 (i.e., some or all of the genes expressed in the RNA); (iv) normalizing the gene expression levels 120; and (v) generating a bacterial ARI classifier, a viral ARI classifier or a non-infectious illness classifier 130 based on the results.

[0058] In some embodiments, the sample is not purified after collection. In some embodiments, the sample may be purified to remove extraneous material, before or after lysis of cells. In some embodiments, the sample is purified with cell lysis and removal of cellular materials, isolation of nucleic acids, and/or reduction of abundant transcripts such as globin or ribosomal RNAs.

[0059] In some embodiments, measuring gene expression levels may include generating one or more microarrays using said transcriptomes; measuring said transcriptomes using a plurality of primers; analyzing and correcting batch differences.

[0060] In some embodiments, the method further includes uploading 140 the final gene target list for the generated classifier, the associated weights (wn), and threshold values to one or more databases.

[0061] An example of generating said classifiers is detailed in FIG. 2. As shown in FIG. 2, biological samples from a cohort of patients encompassing bacterial ARI, viral ARI, or non-infectious illness are used to develop gene expression-based classifiers for each condition (i.e., bacterial acute respiratory infection, viral acute respiratory infection, or non-infectious cause of illness). Specifically, the bacterial ARI classifier is obtained to positively identifying those with bacterial ARI vs. either viral ARI or non-infectious illnesses. The viral ARI classifier is obtained to positively identifying those with viral ARI vs. bacterial ARI or non-infectious illness (NI). The non-infectious illness classifier is generated to improve bacterial and viral ARI classifier specificity. Next, signatures for bacterial ARI classifiers, viral ARI classifiers, and non-infectious illness classifiers are generated (e.g., by applying a sparse logistic regression model).

[0062] These three classifiers may then be combined, if desired, into a single classifier termed "the ARI classifier" by following a one-versus-all scheme whereby largest membership probability assigns class label. See also FIG. 5. The combined ARI classifier may be validated in some embodiments using leave-one-out cross-validation in the same population from which it was derived and/or may be validated in some embodiments using publically available human gene expression datasets of samples from subjects suffering from illness of known etiology. For example, validation may be performed using publically available human gene expression datasets (e.g., GSE6269, GSE42026, GSE40396, GSE20346, and/or GSE42834), the datasets chosen if they included at least two clinical groups (bacterial ARI, viral ARI, or non-infectious illness).

[0063] The classifier may be validated in a standard set of samples from subjects suffering from illness of known etiology, i.e., bacterial ARI, viral ARI, or non-infectious illness.

[0064] The methodology for training described herein may be readily translated by one of ordinary skill in the art to different gene expression detection (e.g., mRNA detection and quantification) platforms.

[0065] The methods and assays of the present disclosure may be based upon gene expression, for example, through direct measurement of RNA, measurement of derived materials (e.g., cDNA), and measurement of RNA products (e.g., encoded proteins or peptides). Any method of extracting and screening gene expression may be used and is within the scope of the present disclosure.

[0066] In some embodiments, the measuring comprises the detection and quantification (e.g., semi-quantification) of mRNA in the sample. In some embodiments, the gene expression levels are adjusted relative to one or more standard gene level(s) ("normalized"). As known in the art, normalizing is done to remove technical variability inherent to a platform to give a quantity or relative quantity (e.g., of expressed genes).

[0067] In some embodiments, detection and quantification of mRNA may first involve a reverse transcription and/or amplification step, e.g., RT-PCR such as quantitative RT-PCR. In some embodiments, detection and quantification may be based upon the unamplified mRNA molecules present in or purified from the biological sample. Direct detection and measurement of RNA molecules typically involves hybridization to complementary primers and/or labeled probes. Such methods include traditional northern blotting and surface-enhanced Raman spectroscopy (SERS), which involves shooting a laser at a sample exposed to surfaces of plasmonic-active metal structures with gene-specific probes, and measuring changes in light frequency as it scatters.

[0068] Similarly, detection of RNA derivatives, such as cDNA, typically involves hybridization to complementary primers and/or labeled probes. This may include high-density oligonucleotide probe arrays (e.g., solid state microarrays and bead arrays) or related probe-hybridization methods, and polymerase chain reaction (PCR)-based amplification and detection, including real-time, digital, and end-point PCR methods for relative and absolute quantitation of specific RNA molecules.

[0069] Additionally, sequencing-based methods can be used to detect and quantify RNA or RNA-derived material levels. When applied to RNA, sequencing methods are referred to as RNAseq, and provide both qualitative (sequence, or presence/absence of an RNA, or its cognate cDNA, in a sample) and quantitative (copy number) information on RNA molecules from a sample. See, e.g., Wang et al. 2009 Nat. Rev. Genet. 10(1):57-63. Another sequence-based method, serial analysis of gene expression (SAGE), uses cDNA "tags" as a proxy to measure expression levels of RNA molecules.

[0070] Moreover, use of proprietary platforms for mRNA detection and quantification may also be used to complete the methods of the present disclosure. Examples of these are Pixel ™ System, incorporating Molecular Indexing™, developed by CELLULAR RESEARCH, INC., NanoString® Technologies nCounter gene expression system; mRNA-Seq, Tag-Profiling, BeadArrayTM technology and VeraCode from Illumina, the ICEPlex System from PrimeraDx, and the QuantiGene 2.0 Multiplex Assay from Affymetrix.

[0071] As an example, RNA from whole blood from a subject can be collected using RNA preservation reagents such as PAXgene™ RNA tubes (PreAnalytiX, Valencia, Calif.). The RNA can be extracted using a standard PAXgene™ or Versagene™ (Gentra Systems, Inc, Minneapolis, Minn.) RNA extraction protocol. The Versagene™ kit produces greater yields of higher quality RNA from the PAXgene™ RNA tubes. Following RNA extraction, one can use GLOBINCIear™ (Ambion, Austin, Tex.) for whole blood globin reduction. (This method uses a bead-oligonucleotide construct to bind globin mRNA and, in our experience, we are able to remove over 90% of the globin mRNA.) Depending on the technology, removal of abundant and non-interesting transcripts may increase the sensitivity of the assay, such as with a microarray platform.

[0072] Quality of the RNA can be assessed by several means. For example, RNA quality can be assessed using an Agilent 2100 Bioanalyzer immediately following extraction. This analysis provides an RNA Integrity Number (RIN) as a quantitative measure of RNA quality. Also, following globin reduction the samples can be compared to the globin-reduced standards. In addition, the scaling factors and background can be assessed following hybridization to microarrays.

[0073] Real-time PCR may be used to quickly identify gene expression from a whole blood sample. For example, the isolated RNA can be reverse transcribed and then amplified and detected in real time using non-specific fluorescent dyes that intercalate with the resulting ds-DNA, or sequence-specific DNA probes labeled with a fluorescent reporter which permits detection only after hybridization of the probe with its complementary DNA target.

[0074] Hence, it should be understood that there are many methods of mRNA quantification and detection that may be used by a platform in accordance with the methods disclosed herein.

[0075] The expression levels are typically normalized following detection and quantification as appropriate for the particular platform using methods routinely practiced by those of ordinary skill in the art.

[0076] With mRNA detection and quantification and a matched normalization methodology in place for platform, it is simply a matter of using carefully selected and adjudicated patient samples for the training methods. For example, the cohort described hereinbelow was used to generate the appropriate weighting values (coefficients) to be used in conjunction with the genes in the three signatures in the classifier for a platform. These subject-samples could also be used to generate coefficients and cut-offs for a test implemented using a different mRNA detection and quantification platform.

[0077] In some embodiments, the individual categories of classifiers (i.e., bacterial ARI, viral ARI, non-infectious illness) are formed from a cohort inclusive of a variety of such causes thereof. For instance, the bacterial ARI classifier is obtained from a cohort having bacterial infections from multiple bacterial genera and/or species, the viral ARI classifier is obtained from a cohort having viral infections from multiple viral genera and/or species, and the non-infectious illness classifier is obtained from a cohort having a non-infectious illness due to multiple non-infectious causes. See, e.g., Table 8. In this way, the respective classifiers obtained are agnostic to the underlying bacteria, virus, and non-infectious cause. In some embodiments, some or all of the subjects with non-infectious causes of illness in the cohort have symptoms consistent with a respiratory infection.

[0078] In some embodiments, the signatures may be obtained using a supervised statistical approach known as sparse linear classification in which sets of genes are identified by the model according to their ability to separate phenotypes during a training process that uses the selected set of patient samples. The outcomes of training are gene signatures and classification coefficients for the three comparisons. Together the signatures and coefficients provide a classifier or predictor. Training may also be used to establish threshold or cut-off values. Threshold or cut-off values can be adjusted to change test performance, e.g., test sensitivity and specificity. For example, the threshold for bacterial ARI may be intentionally lowered to increase the sensitivity of the test for bacterial infection, if desired.

[0079] In some embodiments, the classifier generating comprises iteratively: (i) assigning a weight for each normalized gene expression value, entering the weight and expression value for each gene into a classifier (e.g., a linear regression classifier) equation and determining a score for outcome for each of the plurality of subjects, then (ii) determining the accuracy of classification for each outcome across the plurality of subjects, and then (iii) adjusting the weight until accuracy of classification is optimized. Genes having a non-zero weight are included in the respective classifier.

[0080] In some embodiments, the classifier is a linear regression classifier and said generating comprises converting a score of said classifier to a probability using a link function. As known in the art, the link function specifies the link between the target/output of the model (e.g., probability of bacterial infection) and systematic components (in this instance, the combination of explanatory variables that comprise the predictor) of the linear model. It says how the expected value of the response relates to the linear predictor of explanatory variable.

Methods of Classification



[0081] The present disclosure further provides methods for determining whether a patient has a respiratory illness due to a bacterial infection, a viral infection, or a non-infectious cause. The method for making this determination relies upon the use of classifiers obtained as taught herein. The methods may include: a) measuring the expression levels of pre-defined sets of genes (i.e., for one or more of the three signatures); b) normalizing gene expression levels for the technology used to make said measurement; c) taking those values and entering them into a bacterial classifier, a viral classifier, and optionally a non-infectious illness classifier (i.e., predictors) that have pre-defined weighting values (coefficients) for each of the genes in each signature; d) comparing the output of the classifiers to pre-defined thresholds, cut-off values, confidence intervals or ranges of values that indicate likelihood of infection; and optionally e) jointly reporting the results of the classifiers.

[0082] A simple overview of such methods is provided in FIG. 3. In this representation, each of the three gene signatures is informative of the patient's host response to a different ARI etiology (bacterial or viral) or to an ill, but not infected, state (NI). These signatures are groups of gene transcripts which have consistent and coordinated increased or decreased levels of expression in response to one of three clinical states: bacterial ARI, viral ARI, or a non-infected but ill state. These signatures are derived using carefully adjudicated groups of patient samples with the condition(s) of interest (training 10).

[0083] With reference to FIG. 3, after obtaining a biological sample from the patient (e.g., a blood sample), in some embodiments the mRNA is extracted. The mRNA (or a defined region of each mRNA), is quantified for all, or a subset, of the genes in the signatures. Depending upon the apparatus that is used for quantification, the mRNA may have to be first purified from the sample.

[0084] The signature is reflective of a clinical state and is defined relative to at least one of the other two possibilities. For example, the bacterial ARI signature is identified as a group of biomarkers (here, represented by gene mRNA transcripts) that distinguish patients with bacterial ARI and those without bacterial ARI (including patients with viral ARI or non-infectious illness as it pertains to this application). The viral ARI signature is defined by a group of biomarkers that distinguish patients with viral ARI from those without viral ARI (including patients with either bacterial ARI or non-infectious illness). The non-infectious illness signature is defined by a group of biomarkers that distinguish patients with non-infectious causes of illness relative to those with either bacterial or viral ARI.

[0085] The normalized expression levels of each gene of the signature (e.g., first column Table 9) are the explanatory or independent variables or features used in the classifier. As an example, the classifier may have a general form as a probit regression formulation:

where the condition is bacterial ARI, viral ARI, or non-infection illness; Φ(.) is the probit (or logistic, etc.) link function; {β12,...,βd} are the coefficients obtained during training (e.g., second, third and fourth columns from Table 9) (coefficients may also be denoted {w1,w2,...,wd} as "weights" herein); {X1,X2,...,Xd} are the normalized gene expression levels of the signature; and d is the size of the signature (i.e., number of genes).

[0086] As would be understood by one skilled in the art, the value of the coefficients for each explanatory variable will change for each technology platform used to measure the expression of the genes or a subset of genes used in the probit regression model. For example, for gene expression measured by Affymetrix U133A 2.0 microarray, the coefficients for each of the features in the classifier algorithm are shown in Table 9.

[0087] The sensitivity, specificity, and overall accuracy of each classifier may be optimized by changing the threshold for classification using receiving operating characteristic (ROC) curves.

[0088] Another aspect of the present disclosure provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of a) obtaining a biological sample from the subject; b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes (i.e., three signatures); c) normalizing gene expression levels for the technology used to make said measurement to generate a normalized value; d) entering the normalized value into a bacterial classifier, a viral classifier and non-infectious illness classifier (i.e., predictors) that have pre-defined weighting values (coefficients) for each of the genes in each signature; e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; and e) classifying the sample as being of bacterial etiology, viral etiology, or noninfectious illness. In some embodiments, the method further comprises generating a report assigning the patient a score indicating the probability of the etiology of the ARI

[0089] The classifiers that are developed during training and using a training set of samples are applied for prediction purposes to diagnose new individuals ("classification"). For each subject or patient, a biological sample is taken and the normalized levels of expression (i.e., the relative amount of mRNA expression) in the sample of each of the genes specified by the signatures found during training are the input for the classifiers. The classifiers also use the weighting coefficients discovered during training for each gene. As outputs, the classifiers are used to compute three probability values. Each probability value may be used to determine the likelihood of the three considered clinical states: bacterial ARI, viral ARI, and non-infectious illness.

[0090] In some embodiments, the results of each of the classifiers - the probability a new subject or patient has a bacterial ARI, viral ARI, or non-infectious illness - are reported. In final form, the three signatures with their corresponding coefficients are applied to an individual patient to obtain three probability values, namely probability of having a bacterial ARI, viral ARI, and a non-infectious illness. In some embodiments, these values may be reported relative to a reference range that indicates the confidence with which the classification is made. In some embodiments, the output of the classifier may be compared to a threshold value, for example, to report a "positive" in the case that the classifier score or probability exceeds the threshold indicating the presence of one or more of a bacterial ARI, viral ARI, or non-infectious illness. If the classifier score or probability fails to reach the threshold, the result would be reported as "negative" for the respective condition. Optionally, the values for bacterial and viral ARI alone are reported and the report is silent on the likelihood of ill but not infected.

[0091] It should be noted that a classifier obtained with one platform may not show optimal performance on another platform. This could be due to the promiscuity of probes or other technical issues particular to the platform. Accordingly, also described herein are methods to adapt a signature as taught herein from one platform for another.

[0092] For example, a signature obtained from an Affymetrix platform may be adapted to a TLDA platform by the use of corresponding TLDA probes for the genes in the signature and/or substitute genes correlated with those in the signature, for the Affymetrix platform. Table 1 shows a list of Affymetrix probes and the genes they measure, plus "replacement genes" that are introduced as resplacements for gene probes that either may not perform well on the TLDA platform for technical reasons or to replace those Affymetrix probes for which there is no cognate TLDA probe. These replacements may indicate highly correlated genes or may be probes that bind to a different location in the same gene transcript. Additional genes may be included, such as pan-viral gene probes. The weights shown in Table 1 are weights calculated for a classifier implemented on the microarray platform. Weights that have not been estimated are indicated by "NA" in the table. (Example 4 below provides the completed translation of these classifiers to the TLDA platform.) Reference probes for TLDA (i.e., normalization genes, e.g., TRAP1, PPIB, GAPDH and 18S) also have "NA" in the columns for weights and Affymetrix probeset ID (these are not part of the classifier). Additional gene probes that do not necessarily correspond to the Affymetrix probeset also have "NA" in the Affymetrix probeset ID column.
Table 1: Preliminary Gene List for TLDA platform Columns are as follows:
Column 1: Affymetrix probeset ID - this was the probeset identified in the Affy discovery analyses (primary probeset)
Columns 2.3.4: estimated coefficients (weights) for contribution of each probates to the 3 classifiers from Affymetrix weights
Column 5: Gene name
AFFXProbeSetBacterialViralNIGene
216867_s_at 0.0534745 0 0 PDGFA
203313 s at 1.09463 0 0 TGIF1
NA NA NA NA TRAP1
NA NA NA NA PPIB
202720_at 0 0.0787402 0 TES
210657_s_at NA NA NA SEPT4
NA NA NA NA EPHB3
NA NA NA NA SYDE1
202864_s_at 0 0.100019 0 SP100
213633 at 1.01336 0 0 SH3BP1
NA NA NA NA 18S
NA NA NA NA 18S
NA NA NA NA GIT2
205153_s_at 0.132886 0 0 CD40
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215804_at 1.94364 0 0 EPHA1
215268_at 0.0381782 0 0 KIAA0754
203153_at NA NA NA IFIT1
217502_at NA NA NA IFIT2
205569_at NA NA NA LAMP3
218943_s_at NA NA NA DDX58
NA NA NA NA GAPDH
213300_at 0.578303 0 0 ATG2A
200663_at 0.176027 0 0 CD63
216303_s_at 0.31126 0 0 MTMR1
NA NA NA NA ICAM2
NA NA NA NA EXOSC4
208702_x_at 0 0 0.0426262 APLP2
NA NA NA NA 18S
NA NA NA NA 18S
NA NA NA NA FPGS
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208029_s_at 0.020511 0 0.394049 LAPTM4B
203153_at 0.133743 0 0 IFIT1
NA NA NA NA DECR1
200986_at NA NA NA SERPING1
214097_at 0.211804 0.576801 0 RPS21
204392_at 0 0.129465 0 CAMK1
219382_at 0.866643 0 0 SERTAD3
205048_s_at 0.0114514 0 0 PSPH
205552_s_at NA NA NA OAS1
219684_at NA NA NA RTP4
221491_x_at 0.651431 0 0 HLA-DRB3
NA NA NA NA TRAP1
NA NA NA NA PPIB
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215606 s at 0.479765 0 0 ERC1
44673_at 0.0307987 0 0 SIGLEC1
222059_at 0 0.112261 0 ZNF335
NA NA NA NA MRC2
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214085_x_at 0.367611 0 0 GLIPR1
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200947_s_at 1.78944 0 0 GLUD1
206676 at 0 0 0.0774651 CEACAM8
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218306_s_at 0 0 0.784894 HERC1
205008_s_at 0 0.223868 0 CIB2
219777_at 0 0.25509 0 GIMAP6
218812_s_at 0.967987 0 0 ORAI2
NA NA NA NA GAPDH
208736_at 0 0.582264 0.0862941 ARPC3
203455_s_at 0 0 0.0805395 SAT1
208545_x_at 0.265408 0 0 TAF4
NA NA NA NA TLDC1
202509_s_at NA NA NA TNFAIP2
205098_at 0.116414 0 0 CCR1
222154_s_at NA NA NA SPATS2L
201188_s_at 0.606326 0 0 ITPR3
NA NA NA NA FPGS
205483_s_at NA NA NA ISG15
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220059_at 0.86817 0 0 STAP1
214955_at 0.100645 0 0 TMPRSS6
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205001_s_at 0 0.067117 0 DDX3Y
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202688_at 0 0.0050837 0 TNFSF10
NA NA NA NA TRAP1
NA NA NA NA PPIB
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204490_s_at 0.00732794 0 0 CD44
206207_at 0.0852924 0 0 CLC
216289_at 0 0.00074607 0 GPR144
201949_x_at 0 0 0.034093 CAPZB
NA NA NA NA EXOG
216473_x_at 0 0.0769736 0 DUX4
212900_at 0.0573273 0 0 SEC24A
204439_at NA NA NA IFI44L
212162_at 0 0.0102331 0 KIDINS220
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214175_x_at 0 0 0.266628 PDLIM4
219863_at NA NA NA HERC5
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212697_at 0 0 1.02451 FAM134C
NA NA NA NA FNBP4
202672_s_at NA NA NA ATF3
201341_at 0.109677 0 0 ENC1
210797_s_at 0 0.188667 0 OASL
206647_at 0.0650386 0 0 HBZ
215848_at 0 0.326241 0 SCAPER
213573_at 0 0 0.50859 KPNB1
NA NA NA NA GAPDH
NA NA NA NA POLR1C
214582_at 0 0 0.0377349 PDE3B
218700_s_at 0 0.00086067 0 RAB7L1
203045_at 0.850903 0 0 NINJ1
NA NA NA NA ZER1
206133_at NA NA NA XAF1
213797_at NA NA NA RSAD2
219437_s_at 0 0.405445 0.217428 ANKRD11
NA NA NA NA FPGS
212947_at 0.286979 0 0 SLC9A8
NA NA NA NA SOX4
202145_at 0 0.166043 0 LY6E
213633_at 1.01336 0 0 SH3BP1
NA NA NA NA DECR1
210724_at 0 0 0.482166 EMR3
220122_at 0.399475 0 0 MCTP1
218400_at NA NA NA OAS3
201659_s_at 0.110991 0 0 ARL1
214326_x_at 0.698109 0 0.261075 JUND
NA NA NA NA MRPS31
217717_s_at 0.638943 0 0 YWHAB
218095_s_at 0.00541128 0.613773 0 TMEM165
NA NA NA NA TRAP1
NA NA NA NA PPIB
219066_at 0 0.221446 0 PPCDC
214022_s_at 0 0 0.0380438 IFITM1
214453_s_at NA NA NA IFI44
215342_s_at 0.0497241 0 0 RABGAP1L
204545_at 0.342478 0 0 PEX6
220935_s_at 0.170358 0 0 CDK5RAP2
201802_at 0.00859629 0 0 SLC29A1
202086_at NA NA NA MX1
209360_s_at 0.319632 0 0 RUNX1
NA NA NA NA LY75-CD302
203275_at 0 0.118256 0 IRF2
NA NA NA NA MYL10
203882_at 0 0.0776936 0 IRF9
206934_at 0.151959 0 0 SIRPB1
207860_at 0.376517 0 0 NCR1
207194_s_at 0.3162 0 0 ICAM4
209396_s_at 0 0 0.0355749 CHI3L1
204750_s_at 0.537475 0 0 DSC2
207840_at 0 0.118889 0 CD160
202411_at 0.0522361 0 0 IFI27
215184_at 0 0.0650331 0 DAPK2
202005_at 0.680527 0 0 ST14
214800_x_at 0 0.103261 0 BTF3
NA NA NA NA GAPDH
207075_at 0.0627344 0 0 NLRP3
206026_s_at NA NA NA TNFAIP6
219523_s_at 0 0 0.07715 TENM3
217593_at 0.0747507 0 0 ZSCAN 18
204747_at NA NA NA IFIT3
212657_s_at 0 0 0.254507 IL1RN
204972_at NA NA NA OAS2
207606_s_at 0.299775 0 0 ARHGAP12
NA NA NA NA FPGS
205033_s_at 0 0.0878603 0 DEFA3
219143_s_at 0.415444 0 0 RPP25
208601_s_at 0.270581 0 0 TUBB1
216713_at 0.510039 0 0 KRIT1
NA NA NA NA DECR1
214617_at 0.261957 0 0 PRF1
201055_s_at 0 0 1.25363 HNRNPAO
219055_at 0.0852367 0 0 SRBD1
219130_at 0 0.150771 0 TRMT13
202644_s_at 0.340624 0 0 TNFAIP3
205164_at 0.46638 0 0 GCAT


[0093] Further discussion of this example signature for a TLDA platform is provided below in Examples 3 and 4.

[0094] This method of determining the etiology of an ARI may be combined with other tests. For example, if the patient is determined to have a viral ARI, a follow-up test may be to determine if influenza A or B can be directly detected or if a host response indicative of such an infection can be detected. Similarly, a follow-up test to a result of bacterial ARI may be to determine if a Gram positive or a Gram negative bacterium can be directly detected or if a host response indicative of such an infection can be detected. In some embodiments, simultaneous testing may be performed to determine the class of infection using the classifiers, and also to test for specific pathogens using pathogen-specific probes or detection methods. See, e.g., US 2015/0284780 to Eley et al. (method for detecting active tuberculosis); US 2014/0323391 to Tsalik et al. (method for classification of bacterial infection).

Methods of Determining a Secondary Classification of an ARI in a Subject



[0095] The present disclosure also provides methods of classifying a subject using a secondary classification scheme.

[0096] In some embodiments, the method further comprises generating a report assigning the patient a score indicating the probability of the etiology of the ARI

[0097] Classifying the status of a patient using a secondary classification scheme is shown in FIG. 4. In this example, the bacterial ARI classifier will distinguish between patients with a bacterial ARI from those without a bacterial ARI, which could, instead, be a viral ARI or a non-infectious cause of illness. A secondary classification can then be imposed on those patients with non-bacterial ARI to further discriminate between viral ARI and non-infectious illness. This same process of primary and secondary classification can also be applied to the viral ARI classifier where patients determined not to have a viral infection would then be secondarily classified as having a bacterial ARI or non-infectious cause of illness. Likewise, applying the non-infectious illness classifier as a primary test will determine whether patients have such a non-infectious illness or instead have an infectious cause of symptoms. The secondary classification step would determine if that infectious is due to bacterial or viral pathogens.

[0098] Results from the three primary and three secondary classifications can be summed through various techniques by those skilled in the art (such as summation, counts, or average) to produce an actionable report for the provider. In some embodiments, the genes used for this secondary level of classification can be some or all of those presented in Table 2.

[0099] In such examples, the three classifiers described above (bacteria classifier, virus classifier and non-infectious illness classifier) are used to perform the 1st level classification. Then for those patients with non-bacterial infection, a secondary classifier is defined to distinguish viral ARI from those with non-infectious illness (FIG. 4, left panel). Similarly, for those patients with non-viral infection, a new classifier is used to distinguish viral from non-infectious illness (FIG. 4, middle panel), and for those patients who are not classified as having a non-infectious illness in the first step, a new classifier is used to distinguish between viral and bacterial ARI (FIG. 4, right panel).

[0100] In this two-tier method, nine probabilities may be generated, and those probabilities may be combined in a number of ways. Two strategies are described here as a way to reconcile the three sets of predictions, where each has a probability of bacterial ARI, viral ARI, and non-infectious illness. For example: Highest predicted average probability: All predicted probabilities for bacterial ARI are averaged, as are all the predicted probabilities of viral ARI and, similarly, all predicted probabilities of non-infectious illness. The greatest averaged probability denotes the diagnosis.

[0101] Greatest number of predictions: Instead of averaging the predicted probabilities of each condition, the number of times a particular diagnosis is predicted for that patient sample (i.e., bacterial ARI, viral ARI or non-infectious illness) is counted. The best-case scenario is when the three classification schemes give the same answer (e.g., bacterial ARI for scheme 1, bacterial ARI for scheme 2, and bacterial ARI for scheme 3). The worst case is that each scheme nominates a different diagnosis, resulting in a 3-way tie.

[0102] Using the training set of patient samples previously described, the Result of Tier 1 classification could be, for example (clinical classification presented in rows; diagnostic test prediction presented in columns) similar to that presented in Table 3.
Table 3
 bacterialviralni counts 
bacterial 82.8 12.8 4.2 58 9 3
viral 3.4 90.4 6.0 4 104 7
ni 9.0 4.5 86.3 8 4 76


[0103] Following Tier 2 classification using the highest predicted average probability strategy (clinical classification presented in rows; diagnostic test prediction presented in columns), results may be similar to Table 4.
Table 4 - Mean (average predictions than max):
 bacterialviralni counts 
bacterial 82.8 11.4 5.7 58 8 4
viral 1.7 91.3 6.9 2 105 8
ni 7.9 7.9 84.0 7 7 74


[0104] Following Tier 2 classification using the greatest number of predictions strategy (clinical classification presented in rows; diagnostic test prediction presented in columns), results may be similar to Table 5.
Table 5 - Max (max predictions then count votes, 7 ties):
  bacterial viral ni   counts  
bacterial 84.2 11.4 4.2 59 8 3
viral 4.3 89.5 6.0 5 103 7
ni 11.3 7.9 80.6 10 7 71


[0105] Classification can be achieved, for example, as described above, and/or as summarized in Table 2. Table 2 summarizes the gene membership in three distinct classification strategies that solve different diagnostic questions. There are a total of 270 probes that collectively comprise three complex classifiers. The first is referred to as BVS (Bacterial ARI, Viral ARI, SIRS), which is the same as that presented below in Example 1. These probes are the same as those presented in Table 9, which offers probe/gene weights used in classification. They also correspond to the genes presented in Table 10.

[0106] The second is referred to as 2L for 2-layer or 2-tier. This is the hierarchical scheme presented in FIG. 4.

[0107] The third is a one-tier classification scheme, BVSH, which is similar to BVS but also includes a population of healthy controls (similarly described in Example 1). This group has been shown to be a poor control for non-infection, but there are use cases in which discrimination from healthy may be clinically important. For example, this can include the serial measurement of signatures to correlate with convalescence. It may also be used to discriminate patients who have been exposed to an infectious agent and are presymptomatic vs. asymptomatic. In the BVSH scheme, four groups are represented in the training cohort - those with bacterial ARI, viral ARI, SIRS (non-infectious illness), and Healthy. These four groups are used to generate four distinct signatures that distinguish each class from all other possibilities.

Table 2 legend:



[0108] 

Probe = Affymetrix probe ID

BVS = Three-classifier model trained on patients with Bacterial ARI, Viral ARI, and Non-Infectious Illness (with respiratory symptoms). 1 denotes this probe is included in this three-classifier model. 0 denotes the probe is not present in this classification scheme.

BVS-BO = Genes or probes included in the Bacterial ARI classifier as part of the BVS classification scheme. This classifier specifically discriminates patients with bacterial ARI from other etiologies (viral ARI or or 10)

BVS-VO = As for BVS-BO except this column identifies genes included in the Viral ARI classifier. This classifier specifically discriminates patients with viral ARI from other etiologies (bacterial ARI or non-infectious illness)

BVS-SO = As for BVS-BO or BVS-VO, except this column identifies genes included in the non-infectious illness classifier. This classifier specifically discriminates patients with non-infectious illness from other etiologies (bacterial or viral ARI)

2L refers to the two-tier hierarchical classification scheme. A 1 in this column indicates the specified probe or gene was included in the classification task. This 2-tier classification scheme is itself comprised of three separate tiered tasks. The first applies a one vs. others, where one can be Bacterial ARI, Viral ARI, or non-infectious illness. If a given subject falls into the "other" category, a 2nd tier classification occurs that distinguishes between the remaining possibilities. 2L-SO is the 1st tier for a model that determines with a given subject has a non-infectious illness or not, followed by SL-BV which discriminates between bacterial and viral ARI as possibilities. A 1 in these columns indicates that gene or probe are included in that specified classification model. 2L-BO and 2L-VS make another 2-tier classification scheme. 2L-VO and 2L-SB comprise the 3rd model in the 2-tier classification scheme.

Finally, BVSH refers to a one-level classification scheme that includes healthy individuals in the training cohort and therefore includes a classifier for the healthy state as compared to bacterial ARI, viral ARI, or non-infectious illness. The dark grey BVSH column identifies any gene or probe included in this classification scheme. This scheme is itself comprised by BVSH-BO, BVSH-VO, BVSH-SO, and BVSH-HO with their respective probe/gene compositions denoted by '1' in these columns.



[0109] Table 2 provides a summary of use of members of the gene sets for viral, bacterial, and non-infectious illness classifiers that are constructed according to the required task. A '1' indicates membership of the gene in the classifier.






























Methods of Treating a Subject with an ARI



[0110] Another aspect of the present disclosure that is outwith the scope of the appended claims provides a method of treating an acute respiratory infection (ARI) whose etiology is unknown in a subject, said method comprising, consisting of, or consisting essentially of (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes (e.g., one, two or three or more signatures); (c) normalizing gene expression levels as required for the technology used to make said measurement to generate a normalized value; (d) entering the normalized value into a bacterial classifier, a viral classifier and non-infectious illness classifier (i.e., predictors) that have pre-defined weighting values (coefficients) for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) classifying the sample as being of bacterial etiology, viral etiology, or noninfectious illness; and (g) administering to the subject an appropriate treatment regimen as identified by step (f).

[0111] In some examples, step (g) comprises administering an antibacterial therapy when the etiology of the ARI is determined to be bacterial. In other embodiments, step (g) comprises administering an antiviral therapy when the etiology of the ARI is determined to be viral.

[0112] After the etiology of the ARI of the subject has been determined, she may undergo treatment, for example anti-viral therapy if the ARI is determined to be viral, and/or she may be quarantined to her home for the course of the infection. Alternatively, bacterial therapy regimens may be administered (e.g., administration of antibiotics) if the ARI is determined to be bacterial. Those subjects classified as non-infectious illness may be sent home or seen for further diagnosis and treatment (e.g., allergy, asthma, etc.).

[0113] The person performing the peripheral blood sample need not perform the comparison, however, as it is contemplated that a laboratory may communicate the gene expression levels of the classifiers to a medical practitioner for the purpose of identifying the etiology of the ARI and for the administration of appropriate treatment. Additionally, it is contemplated that a medical professional, after examining a patient, would order an agent to obtain a peripheral blood sample, have the sample assayed for the classifiers, and have the agent report patient's etiological status to the medical professional. Once the medical professional has obtained the etiology of the ARI, the medical professional could order suitable treatment and/or quarantine.

[0114] The methods provided herein can be effectively used to diagnose the etiology of illness in order to correctly treat the patient and reduce inappropriate use of antibiotics. Further, the methods provided herein have a variety of other uses, including but not limited to, (1) a host-based test to detect individuals who have been exposed to a pathogen and have impending, but not symptomatic, illness (e.g., in scenarios of natural spread of diseases through a population but also in the case of bioterrorism); (2) a host-based test for monitoring response to a vaccine or a drug, either in a clinical trial setting or for population monitoring of immunity; (3) a host-based test for screening for impending illness prior to deployment (e.g., a military deployment or on a civilian scenario such as embarkation on a cruise ship); and (4) a host-based test for the screening of livestock for ARIs (e.g., avian flu and other potentially pandemic viruses).

[0115] Another aspect of the present disclosure that is outwith the scope of the appended claims provides a kit for determining the etiology of an acute respiratory infection (ARI) in a subject comprising, consisting of, or consisting essentially of (a) a means for extracting a biological sample; (b) a means for generating one or more arrays consisting of a plurality of synthetic oligonucleotides with regions homologous to a group of gene transcripts as taught herein; and (c) instructions for use.

[0116] Yet another aspect of the present disclosure that is outwith the scope of the appended claims provides a method of using a kit for assessing the acute respiratory infection (ARI) classifier comprising, consisting of, or consisting essentially of: (a) generating one or more arrays consisting of a plurality of synthetic oligonucleotides with regions homologous to a a group of gene transcripts as taught herein; (b) adding to said array oligonucleotides with regions homologous to normalizing genes; (c) obtaining a biological sample from a subject suffering from an acute respiratory infection (ARI); (d) isolating RNA from said sample to create a transcriptome; (e) measuring said transcriptome on said array; (f) normalizing the measurements of said transcriptome to the normalizing genes, electronically transferring normalized measurements to a computer to implement the classifier algorithm(s), (g) generating a report; and optionally (h) administering an appropriate treatment based on the results.

Classification Systems



[0117] With reference to FIG. 11, a classification system and/or computer program product 1100 may be used in or by a platform, according to various embodiments described herein. A classification system and/or computer program product 1100 may be embodied as one or more enterprise, application, personal, pervasive and/or embedded computer systems that are operable to receive, transmit, process and store data using any suitable combination of software, firmware and/or hardware and that may be standalone and/or interconnected by any conventional, public and/or private, real and/or virtual, wired and/or wireless network including all or a portion of the global communication network known as the Internet, and may include various types of tangible, non-transitory computer readable medium.

[0118] As shown in FIG. 11, the classification system 1100 may include a processor subsystem 1140, including one or more Central Processing Units (CPU) on which one or more operating systems and/or one or more applications run. While one processor 1140 is shown, it will be understood that multiple processors 1140 may be present, which may be either electrically interconnected or separate. Processor(s) 1140 are configured to execute computer program code from memory devices, such as memory 1150, to perform at least some of the operations and methods described herein, and may be any conventional or special purpose processor, including, but not limited to, digital signal processor (DSP), field programmable gate array (FPGA), application specific integrated circuit (ASIC), and multi-core processors.

[0119] The memory subsystem 1150 may include a hierarchy of memory devices such as Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read-Only Memory (EPROM) or flash memory, and/or any other solid state memory devices.

[0120] A storage circuit 1170 may also be provided, which may include, for example, a portable computer diskette, a hard disk, a portable Compact Disk Read-Only Memory (CDROM), an optical storage device, a magnetic storage device and/or any other kind of disk- or tape-based storage subsystem. The storage circuit 1170 may provide non-volatile storage of data/parameters/classifiers for the classification system 1100. The storage circuit 1170 may include disk drive and/or network store components. The storage circuit 1170 may be used to store code to be executed and/or data to be accessed by the processor 1140. In some embodiments, the storage circuit 1170 may store databases which provide access to the data/parameters/classifiers used for the classification system 1110 such as the signatures, weights, thresholds, etc. Any combination of one or more computer readable media may be utilized by the storage circuit 1170. The computer readable media may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. As used herein, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0121] An input/output circuit 1160 may include displays and/or user input devices, such as keyboards, touch screens and/or pointing devices. Devices attached to the input/output circuit 1160 may be used to provide information to the processor 1140 by a user of the classification system 1100. Devices attached to the input/output circuit 1160 may include networking or communication controllers, input devices (keyboard, a mouse, touch screen, etc.) and output devices (printer or display). The input/output circuit 1160 may also provide an interface to devices, such as a display and/or printer, to which results of the operations of the classification system 1100 can be communicated so as to be provided to the user of the classification system 1100.

[0122] An optional update circuit 1180 may be included as an interface for providing updates to the classification system 1100. Updates may include updates to the code executed by the processor 1140 that are stored in the memory 1150 and/or the storage circuit 1170. Updates provided via the update circuit 1180 may also include updates to portions of the storage circuit 1170 related to a database and/or other data storage format which maintains information for the classification system 1100, such as the signatures, weights, thresholds, etc.

[0123] The sample input circuit 1110 of the classification system 1100 may provide an interface for the platform as described hereinabove to receive biological samples to be analyzed. The sample input circuit 1110 may include mechanical elements, as well as electrical elements, which receive a biological sample provided by a user to the classification system 1100 and transport the biological sample within the classification system 1100 and/or platform to be processed. The sample input circuit 1110 may include a bar code reader that identifies a bar-coded container for identification of the sample and/or test order form. The sample processing circuit 1120 may further process the biological sample within the classification system 1100 and/or platform so as to prepare the biological sample for automated analysis. The sample analysis circuit 1130 may automatically analyze the processed biological sample. The sample analysis circuit 1130 may be used in measuring, e.g., gene expression levels of a pre-defined set of genes with the biological sample provided to the classification system 1100. The sample analysis circuit 1130 may also generate normalized gene expression values by normalizing the gene expression levels. The sample analysis circuit 1130 may retrieve from the storage circuit 1170 a bacterial acute respiratory infection (ARI) classifier, a viral ARI classifier, and optionally a non-infectious illness classifier, these classifier(s) comprising pre-defined weighting values (i.e., coefficients) for each of the genes of the pre-defined set of genes. The sample analysis circuit 1130 may enter the normalized gene expression values into one or more acute respiratory illness classifiers selected from the bacterial acute respiratory infection (ARI) classifier, the viral ARI classifier and the non-infectious illness classifier. The sample analysis circuit 1130 may calculate an etiology probability for one or more of a bacterial ARI, viral ARI, and optionally non-infectious illness based upon said classifier(s) and control output, via the input/output circuit 1160, of a determination whether the acute respiratory illness in the subject is bacterial in origin, viral in origin, non-infectious in origin, or some combination thereof.

[0124] The sample input circuit 1110, the sample processing circuit 1120, the sample analysis circuit 1130, the input/output circuit 1160, the storage circuit 1170, and/or the update circuit 1180 may execute at least partially under the control of the one or more processors 1140 of the classification system 1100. As used herein, executing "under the control" of the processor 1140 means that the operations performed by the sample input circuit 1110, the sample processing circuit 1120, the sample analysis circuit 1130, the input/output circuit 1160, the storage circuit 1170, and/or the update circuit 1180 may be at least partially executed and/or directed by the processor 1140, but does not preclude at least a portion of the operations of those components being separately electrically or mechanically automated. The processor 1140 may control the operations of the classification system 1100, as described herein, via the execution of computer program code.

[0125] Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python or the like, conventional procedural programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages. The program code may execute entirely on the classification system 1100, partly on the classification system 1100, as a stand-alone software package, partly on the classification system 1100 and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the classification system 1100 through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider) or in a cloud computer environment or offered as a service such as a Software as a Service (SaaS).

[0126] In some embodiments, the system includes computer readable code that can transform quantitative, or semi-quantitative, detection of gene expression to a cumulative score or probability of the etiology of the ARI.

[0127] In some embodiments, the system is a sample-to-result system, with the components integrated such that a user can simply insert a biological sample to be tested, and some time later (preferably a short amount of time, e.g., 30 or 45 minutes, or 1, 2, or 3 hours, up to 8, 12, 24 or 48 hours) receive a result output from the system.

[0128] It is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways.

[0129] Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., "such as") provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any nonclaimed element as essential to the practice of the invention.

[0130] It also is understood that any numerical range recited herein includes all values from the lower value to the upper value. For example, if a concentration range is stated as 1% to 50%, it is intended that values such as 2% to 40%, 10% to 30%, or 1% to 3%, etc., are expressly enumerated in this specification. These are only examples of what is specifically intended, and all possible combinations of numerical values between and including the lowest value and the highest value enumerated are to be considered to be expressly stated in this application.

[0131] The following examples are illustrative only and are not intended to be limiting in scope.

EXAMPLES


Example 1. Host Gene Expression classifiers Diagnose Acute Respiratory Illness Etiology



[0132] Acute respiratory infections due to bacterial or viral pathogens are among the most common reasons for seeking medical care. Current pathogen-based diagnostic approaches are not reliable or timely, thus most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use.

[0133] We asked whether host gene expression patterns discriminate infectious from non-infectious causes of illness in the acute care setting. Among those with acute respiratory infection, we determined whether infectious illness is due to viral or bacterial pathogens.

[0134] The samples that formed the basis for discovery were drawn from an observational, cohort study conducted at four tertiary care hospital emergency departments and a student health facility. 44 healthy controls and 273 patients with community-onset acute respiratory infection or non-infectious illness were selected from a larger cohort of patients with suspected sepsis (CAPSOD study). Mean age was 45 years and 45% of participants were male. Further demographic information may be found in Table 1 of Tsalik et al. (2016) Sci Transl Med 9(322): 1-9.

[0135] Clinical phenotypes were adjudicated through manual chart review. Routine microbiological testing and multiplex PCR for respiratory viral pathogens were performed. Peripheral whole blood gene expression was measured using microarrays. Sparse logistic regression was used to develop classifiers of bacterial vs. viral vs. non-infectious illness. Five independently derived datasets including 328 individuals were used for validation.

[0136] Gene expression-based classifiers were developed for bacterial acute respiratory infection (71 probes), viral acute respiratory infection (33 probes), or a non-infectious cause of illness (26 probes). The three classifiers were applied to 273 patients where class assignment was determined by the highest predicted probability. Overall accuracy was 87% (238/273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, p<0.03) and three published classifiers of bacterial vs. viral infection (78-83%). The classifiers developed here externally validated in five publicly available datasets (AUC 0.90-0.99). We compared the classification accuracy of the host gene expression-based tests to procalcitonin and clinically adjudicated diagnoses, which included bacterial or viral acute respiratory infection or non-infectious illness.

[0137] The host's peripheral blood gene expression response to infection offers a diagnostic strategy complementary to those already in use. 8_ This strategy has successfully characterized the host response to viral 8-13 and bacterial ARI11,14. Despite these advances, several issues preclude their use as diagnostics in patient care settings. An important consideration in the development of host-based molecular signatures is that they be developed in the intended use population.15 However, nearly all published gene expression-based ARI classifiers used healthy individuals as controls and focused on small or homogeneous populations and are thus not optimized for use in acute care settings where patients present with undifferentiated symptoms. Furthermore, the statistical methods used to identify gene-expression classifiers often include redundant genes based on clustering, univariate testing, or pathway association. These strategies identify relevant biology but do not maximize diagnostic performance. An alternative, as exemplified here, is to combine genes from unrelated pathways to generate a more informative classifier.

Methods


Classifier Derivation Cohorts



[0138] Studies were approved by relevant Institutional Review Boards, and in accord with the Declaration of Helsinki. All subjects or their legally authorized representatives provided written informed consent.

[0139] Patients with community-onset, suspected infection were enrolled in the Emergency Departments of Duke University Medical Center (DUMC; Durham, NC), the Durham VA Medical Center (DVAMC; Durham, NC), or Henry Ford Hospital (Detroit, MI) as part of the Community Acquired Pneumonia & Sepsis Outcome Diagnostics study (Clinical Trials Identifier No. NCT00258869).16-19 Additional patients were enrolled through UNC Health Care Emergency Department (UNC; Chapel Hill, NC) as part of the Community Acquired Pneumonia and Sepsis Study. Patients were eligible if they had a known or suspected infection and if they exhibited two or more Systemic Inflammatory Response Syndrome (SIRS) criteria.20 ARI cases included patients with upper or lower respiratory tract symptoms, as adjudicated by emergency medicine (SWG, EBQ) or infectious diseases (ELT) physicians. Adjudications were based on retrospective, manual chart reviews performed at least 28 days after enrollment and prior to any gene expression-based categorization, using previously published criteria.17 The totality of information used to support these adjudications would not have been available to clinicians at the time of their evaluation. Seventy patients with microbiologically confirmed bacterial ARI were identified including four with pharyngitis and 66 with pneumonia. Microbiological etiologies were determined using conventional culture of blood or respiratory samples, urinary antigen testing (Streptococcus or Legionella), or with serological testing (Mycoplasma). Patients with viral ARI (n=115) were ascertained based on identification of a viral etiology and compatible symptoms. In addition, 48 students at Duke University as part of the DARPA Predicting Health and Disease study with definitive viral ARI using the same adjudication methods were included. The ResPlex II v2.0 viral PCR multiplex assay (Qiagen; Hilden, Germany) augmented clinical testing for viral etiology identification. This panel detects influenza A and B, adenovirus (B, E), parainfluenza 1-4, respiratory syncytial virus A and B, human metapneumovirus, human rhinovirus, coronavirus (229E, OC43, NL63, HKU1), coxsackie/echo virus, and bocavirus. Upon adjudication, a subset of enrolled patients were determined to have non-infectious illness (n=88) (Table 8). The determination of "non-infectious illness" was made only when an alternative diagnosis was established and results of any routinely ordered microbiological testing failed to support an infectious etiology. Lastly, healthy controls (n=44; median age 30 years; range 23-59) were enrolled as part of a study on the effect of aspirin on platelet function among healthy volunteers without symptoms, where gene expression analyses was performed on pre-aspirin challenge time points.21

Procalcitonin Measurement



[0140] Concentrations were measured at different stages during the study and as a result, different platforms were utilized based on availability. Some serum measurements were made on a Roche Elecsys 2010 analyzer (Roche Diagnostics, Laval, Canada) by electrochemiluminescent immunoassay. Additional serum measurements were made using the miniVIDAS immunoassay (bioMerieux, Durham NC, USA). When serum was unavailable, measurements were made by the Phadia Immunology Reference Laboratory in plasma-EDTA by immunofluorescence using the B·R·A·H·M·S PCT sensitive KRYPTOR (Thermo Fisher Scientific, Portage MI, USA). Replicates were performed for some paired serum and plasma samples, revealing equivalence in concentrations. Therefore, all procalcitonin measurements were treated equivalently, regardless of testing platform.

Microarray Generation



[0141] At initial clinical presentation, patients were enrolled and samples collected for analysis. After adjudications were performed as described above, 317 subjects with clear clinical phenotypes were selected for gene expression analysis. Total RNA was extracted from human blood using the PAXgene Blood RNA Kit (Qiagen, Valencia, CA) according to the manufacturer's protocol. RNA quantity and quality were assessed using the Nanodrop spectrophotometer (Thermo Scientific, Waltham, MA) and Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA), respectively. Microarrays were RMA-normalized. Hybridization and data collection were performed at Expression Analysis (Durham, NC) using the GeneChip Human Genome U133A 2.0 Array (Affymetrix, Santa Clara, CA) according to the Affymetrix Technical Manual.

Statistical Analysis



[0142] The transcriptomes of 317 subjects (273 ill patients and 44 healthy volunteers) were measured in two microarray batches with seven overlapping samples (GSE63990). Exploratory principal component analysis and hierarchical clustering revealed substantial batch differences. These were corrected by first estimating and removing probe-wise mean batch effects using the Bayesian fixed effects model. Next, we fitted a robust linear regression model with Huber loss function using seven overlapping samples, which was used to adjust the remaining expression values.

[0143] Sparse classification methods such as sparse logistic regression perform classification and variable selection simultaneously while reducing over-fitting risk.21 Therefore, separate gene selection strategies such as univariate testing or sparse factor models are unnecessary. Here, a sparse logistic regression model was fitted independently to each of the binary tasks using the 40% of probes with the largest variance after batch correction.22 Specifically, we used a Lasso regularized generalized linear model with binomial likelihood with nested cross-validation to select for the regularization parameters. Code was written in Matlab using the Glmnet toolbox. This generated Bacterial ARI, Viral ARI, and Non-Infectious Illness classifiers. Provided that each binary classifier estimates class membership probabilities (e.g., probability of bacterial vs. either viral or non-infectious in the case of the Bacterial ARI classifier), we can combine the three classifiers into a single decision model (termed the ARI classifier) by following a one-versus-all scheme whereby largest membership probability assigns class label.21 Classification performance metrics included area-under-the-receiving-operating-characteristic-curve (AUC) for binary outcomes and confusion matrices for ternary outcomes.23

Validation



[0144] The ARI classifier was validated using leave-one-out cross-validation in the same population from which it was derived. Independent, external validation occurred using publically available human gene expression datasets from 328 individuals (GSE6269, GSE42026, GSE40396, GSE20346, and GSE42834). Datasets were chosen if they included at least two clinical groups (bacterial ARI, viral ARI, or non-infectious illness). To match probes across different microarray platforms, each ARI classifier probe was converted to gene symbols, which were used to identify corresponding target microarray probes.

Results


Bacterial ARI, Viral ARI, and Non-Infectious Illness classifiers



[0145] In generating host gene expression-based classifiers that distinguish between clinical states, all relevant clinical phenotypes should be represented during the model training process. This imparts specificity, allowing the model to be applied to these included clinical groups but not to clinical phenotypes that were absent from model training.15 The target population for an ARI diagnostic not only includes patients with viral and bacterial etiologies, but must also distinguish from the alternative - those without bacterial or viral ARI. Historically, healthy individuals have served as the uninfected control group. However, this fails to consider how patients with non-infectious illness, which can present with similar clinical symptoms, would be classified, serving as a potential source of diagnostic error. To our knowledge, no ARI gene-expression based classifier has included ill, uninfected controls in its derivation. We therefore enrolled a large, heterogeneous population of patients at initial clinical presentation with community-onset viral ARI (n=115), bacterial ARI (n=70), or non-infectious illness (n=88) (Table 8). We also included a healthy adult control cohort (n=44) to define the most appropriate control population for ARI classifier development.

[0146] We first determined whether a gene expression classifier derived with healthy individuals as controls could accurately classify patients with non-infectious illness. Array data from patients with bacterial ARI, viral ARI, and healthy controls were used to generate gene expression classifiers for these conditions. Leave-one-out cross validation revealed highly accurate discrimination between bacterial ARI (AUC 0.96), viral ARI (AUC 0.95), and healthy (AUC 1.0) subjects for a combined accuracy of 90% (FIG. 7). However, when the classifier was applied to ill-uninfected patients, 48/88 were identified as bacterial, 35/88 as viral, and 5/88 as healthy. This highlighted that healthy individuals are a poor substitute for patients with non-infectious illness in the biomarker discovery process.

[0147] Consequently, we re-derived an ARI classifier using a non-infectious illness control rather than healthy. Specifically, array data from these three groups was used to generate three gene-expression classifiers of host response to bacterial ARI, viral ARI, and non-infectious illness (FIG. 5). Specifically, the Bacterial ARI classifier was tasked with positively identifying those with bacterial ARI vs. either viral ARI or non-infectious illnesses. The Viral ARI classifier was tasked with positively identifying those with viral ARI vs. bacterial ARI or non-infectious illnesses. The Non-Infectious Illness classifier was not generated with the intention of positively identifying all non-infectious illnesses, which would require an adequate representation of all such cases.

[0148] Rather, it was generated as an alternative category, so that patients without bacterial or viral ARI could be assigned accordingly. Moreover, we hypothesized that such ill but non-infected patients were more clinically relevant controls because healthy people are unlikely to be the target for such a classification task.

[0149] Six statistical strategies were employed to generate these gene-expression classifiers: linear support vector machines, supervised factor models, sparse multinomial logistic regression, elastic nets, K-nearest neighbor, and random forests. All performed similarly although sparse logistic regression required the fewest number of classifier genes and outperformed other strategies by a small margin (data not shown). We also compared a strategy that generated three separate binary classifiers to a single multinomial classifier that would simultaneously assign a given subject to one of the three clinical categories. This latter approach required more genes and achieved an inferior accuracy. Consequently, we applied a sparse logistic regression model to define Bacterial ARI, Viral ARI, and Non-Infectious Illness classifiers containing 71, 33 and 26 probe signatures, respectively. Probe and classifier weights are shown in Table 9.

[0150] Clinical decision making is infrequently binary, requiring the simultaneous distinction of multiple diagnostic possibilities. We applied all three classifiers, collectively defined as the ARI classifier, using leave-one-out cross-validation to assign probabilities of bacterial ARI, viral ARI, and non-infectious illness (FIG. 6). These conditions are not mutually exclusive. For example, the presence of a bacterial ARI does not preclude a concurrent viral ARI or non-infectious disease. Moreover, the assigned probability represents the extent to which the patient's gene expression response matches that condition's canonical signature. Since each signature intentionally functions independently of the others, the probabilities are not expected to sum to one. To simplify classification, the highest predicted probability determined class assignment. Overall classification accuracy was 87% (238/273 were concordant with adjudicated phenotype).

[0151] Bacterial ARI was identified in 58/70 (83%) patients and excluded 179/191 (94%) without bacterial infection. Viral ARI was identified in 90% (104/115) and excluded in 92% (145/158) of cases. Using the non-infectious illness classifier, infection was excluded in 86% of cases (76/88). Sensitivity analyses was performed for positive and negative predictive values for all three classifiers given that prevalence can vary for numerous reasons including infection type, patient characteristics, or location (FIG. 8). For both bacterial and viral classification, predictive values remained high across a range of prevalence estimates, including those typically found for ARI

[0152] To determine if there was any effect of age, we included it as a variable in the classification scheme. This resulted in two additional correct classifications, likely due to the over-representation of young people in the viral ARI cohort. However, we observed no statistically significant differences between correctly and incorrectly classified subjects due to age (Wilcoxon rank sum p=0.17).

[0153] We compared this performance to procalcitonin, a widely used biomarker specific for bacterial infection. Procalcitonin concentrations were determined for the 238 subjects where samples were available and compared to ARI classifier performance for this subgroup. Procalcitonin concentrations >0.25µg/L assigned patients as having bacterial ARI, whereas values ≤0.25µg/L assigned patients as non-bacterial, which could be either viral ARI or non-infectious illness. Procalcitonin correctly classified 186 of 238 patients (78%) compared to 204/238 (86%) using the ARI classifier (p=0.03). However, accuracy for the two strategies varied depending on the classification task. For example, performance was similar in discriminating viral from bacterial ARI. Procalcitonin correctly classified 136/155 (AUC 0.89) compared to 140/155 for the ARI classifier (p-value=0.65 using McNemar's test with Yates correction). However, the ARI classifier was significantly better than procalcitonin in discriminating bacterial ARI from non-infectious illness [105/124 vs. 79/124 (AUC 0.72); p-value<0.001], and discriminating bacterial ARI from all other etiologies including viral and non-infectious etiologies [215/238 vs. 186/238 (AUC 0.82); p-value=0.02].

[0154] We next compared the ARI classifier to three published gene expression classifiers of bacterial vs. viral infection, each of which was derived without uninfected ill controls. These included a 35-probe classifier (Ramilo) derived from children with influenza or bacterial sepsis11; a 33-probe classifier (Hu) derived from children with febrile viral illness or bacterial infection14; and a 29-probe classifier (Parnell) derived from adult ICU patients with community-acquired pneumonia or influenza12. We hypothesized that classifiers generated using only patients with viral or bacterial infection would perform poorly when applied to a clinically relevant population that included ill but uninfected patients. Specifically, when presented with an individual with neither a bacterial nor a viral infection, the previously published classifiers would be unable to accurately assign that individual to a third, alternative category. We therefore applied the derived as well as published classifiers to our 273-patient cohort. Discrimination between bacterial ARI, viral ARI, and non-infectious illness was better with the derived ARI classifier (McNemar's test with Yates correction, p=0.002 vs. Ramilo; p=0.0001 vs. Parnell; and p=0.08 vs. Hu) (Table 6).24,25 This underscores the importance of deriving gene-expression classifiers in a cohort representative of the intended use population, which in the case of ARI should include non-infectious illness.15

Discordant classifications



[0155] To better understand ARI classifier performance, we individually reviewed the 35 discordant cases. Nine adjudicated bacterial infections were classified as viral and three as non-infectious illness. Four viral infections were classified as bacterial and seven as non-infectious. Eight non-infectious cases were classified as bacterial and four as viral. We did not observe a consistent pattern among discordant cases, however, notable examples included atypical bacterial infections. One patient with M. pneumoniae based on serological conversion and one of three patients with Legionella pneumonia were classified as viral ARI. Of six patients with non-infectious illness due to autoimmune or inflammatory diseases, only one adjudicated to have Still's disease was classified as having bacterial infection. See also eTable 3 of Tsalik et al. (2016) Sci Transl Med 9(322): 1-9.

External validation



[0156] Generating classifiers from high dimensional, gene expression data can result in over-fitting. We therefore validated the ARI classifier in silico using gene expression data from 328 individuals, represented in five available datasets (GSE6269, GSE42026, GSE40396, GSE20346, and GSE42834). These were chosen because they included at least two relevant clinical groups, varying in age, geographic distribution, and illness severity (Table 7). Applying the ARI classifier to four datasets with bacterial and viral ARI, AUC ranged from 0.90-0.99. Lastly, GSE42834 included patients with bacterial pneumonia (n=19), lung cancer (n=16), and sarcoidosis (n=68). Overall classification accuracy was 96% (99/103) corresponding to an AUC of 0.99. GSE42834 included five subjects with bacterial pneumonia pre- and post-treatment. All five demonstrated a treatment-dependent resolution of the bacterial infection. See also Figures 3-8 of Tsalik et al. (2016) Sci Transl Med 9(322):1-9.

Biological pathways



[0157] The sparse logistic regression model that generated the classifiers penalizes selection of genes from a given pathway if there is no additive diagnostic value. Consequently, conventional gene enrichment pathway analysis is not appropriate to perform. Moreover, such conventional gene enrichment analyses have been described.9,12,14,28,29 Instead a literature review was performed for all classifier genes (Table 10). Overlap between Bacterial, Viral, and Non-infectious Illness Classifiers is shown in FIG. 9.

[0158] The Viral classifier included known anti-viral response categories such as interferon response, T-cell signaling, and RNA processing. The Viral classifier had the greatest representation of RNA processing pathways such as KPNB1, which is involved in nuclear transport and is co-opted by viruses for transport of viral proteins and genomes.26,27 Its downregulation suggests it may play an antiviral role in the host response.

[0159] The Bacterial classifier encompassed the greatest breadth of cellular processes, notably cell cycle regulation, cell growth, and differentiation. The Bacterial classifier included genes important in T-, B-, and NK-cell signaling. Unique to the Bacterial classifier were genes involved in oxidative stress, and fatty acid and amino acid metabolism, consistent with sepsis-related metabolic perturbations.28

Summary of clinical applicability



[0160] We determined that host gene expression changes are exquisitely specific to the offending pathogen class and can be used to discriminate common etiologies of respiratory illness. This creates an opportunity to develop and utilize gene expression classifiers as novel diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance. Using sparse logistic regression, we developed host gene expression profiles that accurately distinguished between bacterial and viral etiologies in patients with acute respiratory symptoms (external validation AUC 0.90-0.99). Deriving the ARI classifier with a non-infectious illness control group imparted a high negative predictive value across a wide range of prevalence estimates.

[0161] Respiratory tract infections caused 3.2 million deaths worldwide and 164 million disability-adjusted life years lost in 2011, more than any other cause. 1,2 Despite a viral etiology in the majority of cases, 73% of ambulatory care patients in the U.S. with acute respiratory infection (ARI) are prescribed an antibiotic, accounting for 41% of all antibiotics prescribed in this setting.3,4 Even when a viral pathogen is microbiologically confirmed, this does not exclude a possible concurrent bacterial infection leading to antimicrobial prescribing "just in case". This empiricism drives antimicrobial resistance5,6, recognized as a national security priority.7 The encouraging metrics provided in this example provide an opportunity to provide clinically actionable results which will optimize treatment and mitigate emerging antibiotic resistance.

[0162] Several studies made notable inroads in developing host-response diagnostics for ARI This includes response to respiratory viruses8,10-12,14, bacterial etiologies in an ICU population12,30, and tuberculosis31-33. Typically, these define host response profiles compared to the healthy state, offering valuable insights into host biology. 16,34,35 However, these gene lists are suboptimal with respect to a diagnostic application because the gene expression profiles that are a component of the diagnostic is not representative of the population for which the test will be applied. 15 Healthy individuals do not present with acute respiratory complaints, thus they are excluded from the host-response diagnostic development reported herein.

[0163] Including patients with bacterial and viral infections allows for the distinction between these two states but does not address how to classify non-infectious illness. This phenotype is important to include because patients present with infectious and non-infectious etiologies that may share symptoms. That is, symptoms may not provide a clinician with a high degree of diagnostic certainty. The current approach, which uniquely appreciates the necessity of including the three most likely states for ARI symptoms, can be applied to an undifferentiated clinical population where such a test is in greatest need.

[0164] The small number of discordant classifications occurred may have arisen either from errors in classification or clinical phenotyping. Errors in clinical phenotyping can arise from a failure to identify causative pathogens due to limitations in current microbiological diagnostics. Alternatively, some non-infectious disease processes may in fact be infection-related through mechanisms that have yet to be discovered. Discordant cases were not clearly explained by a unifying variable such as pathogen type, syndrome, or patient characteristic. As such, the gene expression classifiers presented herein may be impacted by other factors including patient-specific variables (e.g., treatment, comorbidity, duration of illness); test-specific variables (e.g., sample processing, assay conditions, RNA quality and yield); or as-of-yet unidentified variables.

Example 2: Classification Performance in Patients with Co-Infection Defined by the Identification of Bacterial and Viral Pathogens



[0165] In addition to determining that age did not significantly impact classification accuracy, we assessed whether severity of illness or etiology of SIRS affected classification. Patients with viral ARI tended to be less ill, as evidenced by lower rate of hospitalization. In the various cohorts, hospitalization was used as a marker of disease severity and its impact on classification performance was assessed. This test revealed no difference (Fisher's exact test p-value of 1). In addition, the SIRS control cohort included subjects with both respiratory and non-respiratory etiologies. We assessed whether classification was different in subjects with respiratory vs. non-respiratory SIRS and determined it was not (Fisher's exact test p-value of 0.1305).

[0166] Some patients with ARI will have both bacterial and viral pathogens identified, often termed co-infection. However, it is unclear how the host responds in such situations. Illness may be driven by the bacteria, the virus, both, or neither at different times in the patient's clinical course. We therefore determined how the bacterial and viral ARI classifiers performed in a population with bacterial and viral co-identification. GSE60244 included bacterial pneumonia (n=22), viral respiratory tract infection (n=71), and bacterial/viral co-identification (n=25). The co-identification group was defined by the presence of both bacterial and viral pathogens without further subcategorization as to the likelihood of bacterial or viral disease. We trained classifiers on subjects in GSE60244 with bacterial or viral infection and then validated in those with co-identification (FIG. 10). A host response was considered positive above a probability threshold of 0.5. We observed all four possible categories. Six of 25 subjects had a positive bacterial signature; 14/25 had a viral response; 3/25 had positive bacterial and viral signatures; and 2/25 had neither.

[0167] The major clinical decision faced by clinicians is whether or not to prescribe antibacterials. A simpler diagnostic strategy might focus only on the probability of bacterial ARI according to the result from the Bacterial ARI classifier. However, there is value in providing information about viral or non-infectious alternatives. For example, the confidence to withhold antibacterials in a patient with a low probability of bacterial ARI can be enhanced by a high probability of an alternative diagnosis. Further, a full diagnostic report could identify concurrent illness that a single classifier would miss. We observed this when validating in a population with bacterial and viral co-identification. These patients are more commonly referred to as "co-infected." To have infection, there must be a pathogen, a host, and a maladaptive interaction between the two. Simply identifying bacterial and viral pathogens should not imply co-infection. Although we cannot know the true infection status in the 25 subjects tested, who had evidence of bacterial/viral co-identification, the host response classifiers suggest the existence of multiple host-response states. FIG. 10 is an informative representation of infection status, which could be used by a clinician to diagnose the etiology of ARI.

References



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Table 8. Etiological causes of illness for subjects with viral ARI, bacterial ARI, and non-infectious illness.
 Number of subjects
Total Cohort 273
All Viral ARI 115
  Coronavirus 7
  Coxsackievirus/Echovirus 3
  Cytomegalovirus 1
  Enterovirus 20
  Human Metapneumovirus 9
  Influenza, non-typed 7
  Influenza A, non-subtyped 6
  Influenza A, 2009 H1N1 37
  Parainfluenza 1
  Polymicrobial (Coronavirus, Rhinovirus, Coxsackievirus/Echovirus) 1
  Rhinovirus 19
  Respiratory Syncitial Virus 6
All Bacterial ARI 70
  Bacillus species a 1
  Bordetella bronchiseptica 1
  Enterobacter aerogenes 1
  Escherichia coli 1
  Haemophilus influenza 3
  Legionella sp. 3
  Mycoplasma pneumoniae 1
  Pasteurella multocida 1
  Polymicrobial 11
  Pantoea sp.; Coagulase negative Staphylococcus 1
  Pseudomonas aeruginosa; Alcaligenes xylosoxidans 1
  Pseudomonas aeruginosa; Serratia marcescens 1
  Staphylococcus aureus; Haemophilus influenzae 2
  Staphylococcus aureus; Proteus mirabilis 1
  Staphylococcus aureus; Viridans Group Streptococcus; Escherichia coli 1
  Streptococcus pneumoniae; Haemophilus sp. 1
  Streptococcus pneumoniae; Staphylococcus aureus 3
  Proteus mirabilis 1
  Pseudomonas aeruginosa 4
  Staphylococcus aureus 7
  Streptococcus pneumoniae 30
  Streptococcus pyogenes 4
  Viridans Group Streptococcus 1
All Non-Infectious Illness 88
  Acute Renal Failure; Hypovolemia 1
  Alcohol intoxication; Spinal cord stenosis; Hyperglycemia 1
  Arrhythmia 2
  Asthma 1
  AV Graft Pseudoaneurysm and Thrombus 1
  Brain Metastases with Vasogenic Edema 1
  Cerebrovascular Accident 1
  Chest Pain 2
  Cocaine Intoxication 1
  Congestive Heart Failure 13
  Congestive Heart Failure; Amiodarone Toxicity 1
  Congestive Heart Failure; Arrhythmia 1  
  Chronic Obstructive Pulmonary Disease 5  
  Cryptogenic Organizing Pneumonia 1  
  Emphysema 1  
  Gastrointestinal Hemorrhage 3  
  Hematoma in Leg 1  
  Hemochromatosis; Abdominal Pain and Peritoneal Dialysis 1  
  Hemothorax 1  
  Heroin Overdose 1  
  Hyperglycemia 2  
  Hypertensive Emergency 3  
  Hypertensive Emergency with Pulmonary Edema 1  
  Hypovolemia 2  
  Infarcted Uterine Fibroid 1  
  Lung Cancer; Coronary Artery Disease 1  
  Lung Cancer; Hemoptysis 1  
  Mitochondrial Disorder; Acidosis 1  
  Myocardial Infarction 2  
  Myocardial Infarction; Hypovolemia 1  
  Nephrolithiasis 2  
  Pancreatitis 4  
  Post-operative Vocal Cord Paralysis 1  
  Hyperemesis Gravidarum; Allergic Rhinitis 1  
  Pulmonary Edema 2  
  Pulmonary Edema; Hypertensive Crisis 1  
  Pulmonary Embolism 5  
  Pulmonary Embolism; Myocardial Infarction 1  
  Pulmonary Embolism; Pulmonary Artery Hypertension 1  
  Pulmonary Fibrosis 2  
  Pulmonary Mass 1  
  Reactive Arthritis 1  
  Rhabdomyolysis 1  
  Ruptured Aneurysm; Hypovolemic Shock 1  
  Severe Aortic Stenosis 1  
  Small Bowel Obstruction 1  
  Stills Disease 1  
  Pulmonary Artery Hypertension; Congestive Heart Failure 1  
  Systemic Lupus Erythematosis 1  
  Tracheobronchomalacia 1  
  Transient Ischemic Attack 1  
  Ulcerative Colitis 1  
  Urethral Obstruction 1  
a This patient was adjudicated as having a bacterial ARI with Bacillus species identified as the etiologic agent. We later recognized Bacillus species was not the correct microbiological etiology although the clinical history was otherwise consistent with bacterial pneumonia. As this error was identified after model derivation, we included the subject in all subsequent analyses.










Table 10. Genes in the Bacterial ARI, Viral ARI, and Non-infectious Illness (NI) Classifiers, grouped by biologic process. Gene accession numbers are provided in Table 9.
Biologic processBacterialViralNI
Cell cycle regulation JUND* (-), NINJ1, IFI27, CDKN1A, C7orf19, SERTAD3 ZNF291 JUND* (+)
Regulation of cell growth YWHAB, PDGFA   APLP2
Development/ Differentiation GLIPR1, RUNX1, ST14, TGIF, EPHA1 CTBP1 SP1, CEACAM8, ODZ3
RNA transcription, processing FLJ10379, RPS21* (+), RPL28, TAF4, RPP25 DDX3Y, POLR2F, RPS21* (-), BTF3, MRPS18B* (+), HSPC117, FLJ10287 HEATR1, MRPS18B* (-)
Role in nuclear transport   KPNB1 KPNB1
Role in cell and membrane trafficking RAB6IP2, SH3BP1, EXOC7* (+), LAPTM4B, CPNE1, GNG7, TPARL, KIAA1324 TPARL EXOC7* (-), HERC1, LAPTM4B, KIAA1324, APLP2
Cell structure/ adhesion TMPRSS6, TUBB1, ARHGAP12, ICAM4, DSC2, FMOD TES, ARPC3* (+), KIDINS220 PDLIM4, IGSF4, PDE3B, ARPC3* (-), CHI3L1
Role in cell stress response KIAA1324, KRIT1, ENC1   CBX7, APLP2, KIAA1324
Role in autophagy LAPTM4B* (-), KIAA1324* (-)   KIAA1324* (+), LAPTM4B* (+)
Role in apoptosis KRIT1, GLIPR1, CIAS1 DAPK2, TNFSF10  
General Inflammatory response TNFA1P3, FMOD, ITPR3, CIAS1, GNG7, CLC, IFI27, CCR1 TNFSF10 HNRPA0, EMR3, IL1RN, TNFAIP2, CHI3L1
Interferon response IFIT1 SP100, IRF2, OASL, ISG F3G  
Cytotoxic response PRF1 DefA1/3  
Toxin response P450 gene cluster, CYP2A6, ENC1, GGT1, TST    
T-cell signaling TRA/D@, CD44 Ly6E, CAMK1, CD160  
B-cell signaling BRDG1, HLA-DRB1/3/4, CD40    
NK-cell response NCR1 CD160  
Phospholipid and calcium signaling MTMR1, CPNE1, PSPH, ITPR3, CLC, MCTP1    
Fatty acid metabolism PEX6, GLUD1    
Cholesterol metabolism CYP27A1* (-)   CYP27A1* (+)
Amino acid metabolism GLUD1, PSPH, GCAT    
* Genes listed in more than one classifier. In cases where such overlapping genes have different directions of expression, increased expression is denoted by (+) and decreased expression is denoted by (-).

Example 3: The Bacterial/Viral/SIRS assay contemplated on a TLDA platform



[0169] We will develop a custom multianalyte, quantitative real-time PCR (RT-PCR) assay on the 384-well TaqMan Low Density Array (TLDA, Applied Biosystems) platform. TLDA cards will be manufactured with one or more TaqMan primer/probe sets specific for a gene mRNA transcript in the classifier(s) in each well, along with multiple endogenous control RNA targets (primer/probe sets) for data normalization. For each patient sample, purified total RNA is reverse transcribed into cDNA, loaded into a master well and distributed into each assay well via centrifugation through microfluidic channels. TaqMan hydrolysis probes rely on 5' to 3' exonuclease activity to cleave the dual-labeled probe during hybridization to complementary target sequence with each amplification round, resulting in fluorescent signal production. In this manner, quantitative detection of the accumulated PCR products in "real-time" is possible. During exponential amplification and detection, the number of PCR cycles at which the fluorescent signal exceeds a detection threshold is the threshold cycle (Ct) or quantification cycle (Cq) - as determined by commercial software for the RT-PCR instrument. To quantify gene expression, the Ct for a target RNA is subtracted from the Ct of endogenous normalization RNA (or the geometric mean of multiple normalization RNAs), providing a deltaCt value for each RNA target within a sample which indicates relative expression of a target RNA normalized for variability in amount or quality of input sample RNA or cDNA.

[0170] The data for the quantified gene signatures are then processed using a computer and according to the probit classifier described above (equation 1) and reproduce here. Normalized gene expression levels of each gene of the signature are the explanatory or independent variables or features used in the classifier, in this example the general form of the classifier is a probit regression formulation:

where the condition is bacterial ARI, viral ARI, or non-infection illness; Φ(.) is the probit link function; {β12,...,βd} are the coefficients obtained during training; {X1,X2,...,Xd} are the normalized genes expression values of the signature; and d is the size of the signature (number of genes). The value of the coefficients for each explanatory variable are specific to the technology platform used to measure the expression of the genes or a subset of genes used in the probit regression model. The computer program computes a score, or probability, and compares the score to a threshold value. The sensitivity, specificity, and overall accuracy of each classifier is optimized by changing the threshold for classification using receiving operating characteristic (ROC) curves.

[0171] A preliminary list of genes for the TLDA platform based on the signature from the Affymetrix platform (Affy signature) as well as from other sources is provided below in Table 1A. Weights appropriate for the TLDA platform for the respective classifiers were thereafter determined as described below in Example 4.
Table 1A: Preliminary list of genes for development of classifiers for TLDA platform.
Original Affy IDAlternate Affy IDGROUPBacterialViralNon-infectiousGENETLDA assay identifier
219437_s_at 212332_at Affy signature - - - ANKRD11 Hs00331872_s1
208702_x_at 201642_at Affy signature - - - APLP2 Hs00155778_m1
207606_s_at 212633_at Affy signature - - - ARHGAP12 Hs00367895_m1
201659_s_at 209444_at Affy signature - - - ARL1 Hs01029870_m1
208736_at 201132_at Affy signature - - - ARPC3 Hs00855185_g1
205965_at 218695_at Affy signature - - - BATF Hs00232390_m1
214800_x_at 209876_at Affy signature - - - BTF3 Hs00852566_g1
209031_at 209340_at Affy signature - - - CADM1 Hs00296064_s1
204392_at 214054_at Affy signature - - - CAMK1 Hs00269334_m1
201949_x_at 37012_at Affy signature - - - CAPZB Hs00191827_m1
207840_at 213830_at Affy signature - - - CD160 Hs00199894_m1
200663_at 203234_at Affy signature - - - CD63 Hs00156390_m1
220935_s_at 219271_at Affy signature - - - CDK5RAP2 Hs01001427_m1
206676_at 207269_at Affy signature - - - CEACAM8 Hs00266198_m1
209396_s_at 209395_at Affy signature - - - CHI3L1 Hs01072230_g1
205008_s_at 58900_at Affy signature - - - CIB2 Hs00197280_m1
205200_at 206034_at Affy signature - - - CLEC3B Hs00162844_m1
203979_at 49111_at Affy signature - - - CYP27A1 Hs01017992_g1
207244_x_at 209280_at Affy signature - - - CYP2A13 Hs00711162_s1
215184_at 217521_at Affy signature - - - DAPK2 Hs00204888_m1
205001_s_at 214131_at Affy signature - - - DDX3Y Hs00965254_gH
205033_s_at 207269_at Affy signature - - - DEFA3 Hs00414018_m1
204750_s_at 205418_at Affy signature - - - DSC2 Hs00951428_m1
216473_x_at 221660_at Affy signature - - - DUX4 Hs03037970_g1
210724_at 220246_at Affy signature - - - EMR3 Hs01128745_m1
215804_at 206903_at Affy signature - - - EPHA1 Hs00975876_g1
212035_s_at 200935_at Affy signature - - - EXOC7 Hs01117053_m1
212697_at 46665_at Affy signature - - - FAM134C Hs00738661_m1
209919_x_at 218695_at Affy signature - - - GGT1 Hs00980756_m1
219777_at 202963_at Affy signature - - - GIMAP6 Hs00226776_m1
200947_s_at 202126_at Affy signature - - - GLUD1 Hs03989560_s1
218595_s_at 217103_at Affy signature - - - HEATR1 Hs00985319_m1
218306_s_at 212232_at Affy signature - - - HERC1 Hs01032528_m1
221491_x_at 203290_at Affy signature - - - HLA-DRB3 Hs00734212_m1
201055_s_at 37012_at Affy signature - - - HNRNPAO Hs00246543_s1
203153_at 219863_at Affy signature - - - IFIT1 Hs01911452_s1
214022_s_at 35254_at Affy signature - - - IFITM1 Hs00705137_s1
212657_s_at 202837_at Affy signature - - - IL1RN Hs00893626_m1
203275_at 213038_at Affy signature - - - IRF2 Hs01082884_m1
203882_at 201649_at Affy signature - - - IRF9 Hs00196051_m1
215268_at 200837_at Affy signature - - - KIAA0754 Hs03055204_s1
221874_at 203063_at Affy signature - - - KIAA1324 Hs00381767_m1
213573_at 31845_at Affy signature - - - KPNB1 Hs00158514_m1
208029_s_at 212573_at Affy signature - - - LAPTM4B Hs00363282_m1
202145_at 204972_at Affy signature - - - LY6E Hs03045111_g1
220122_at 218323_at Affy signature - - - MCTP1 Hs01115711_m1
217408_at 212846_at Affy signature - - - MRPS18B Hs00204096_m1
207860_at 212318_at Affy signature - - - NCR1 Hs00950814_g1
203045_at 213038_at Affy signature - - - NINJ1 Hs00982607_m1
210797_s_at 205660_at Affy signature - - - OASL Hs00984390_m1
214175_x_at 204600_at Affy signature - - - PDGFA Hs00184792_m1
219066_at 217497_at Affy signature - - - PPCDC Hs00222418_m1
214617_at 212070_at Affy signature - - - PRF1 Hs00169473_m1
218700_s_at 203816_at Affy signature - - - RAB7L1 Hs00187510_m1
215342_s_at 218695_at Affy signature - - - RABGAP1L Hs02567906_s1
219143_s_at 204683_at Affy signature - - - RPP25 Hs00706565_s1
214097_at 201094_at Affy signature - - - RPS21 Hs00963477_g1
210365_at 222307_at Affy signature - - - SAT1 Hs00971739_g1
215848_at 81811_at Affy signature - - - SCAPER Hs02569575_s1
212900_at 204496_at Affy signature - - - SEC24A Hs00378456_m1
44673_at 219211_at Affy signature - - - SIGLEC1 Hs00988063_m1
201802_at 206361_at Affy signature - - - SLC29A1 Hs01085704_g1
202864_s_at 202863_at Affy signature - - - SP100 Hs00162109_m1
205312_at 205707_at Affy signature - - - SPI1 Hs00231368_m1
202005_at 205418_at Affy signature - - - ST14 Hs04330394_g1
220059_at 202478_at Affy signature - - - STAP1 Hs01038134_m1
219523_s_at 206903_at Affy signature - - - TENM3 Hs01111787_m1
202720_at 201344_at Affy signature - - - TES Hs00210319_m1
203313_s_at 212232_at Affy signature - - - TGIF1 Hs00820148_g1
218095_s_at 219157_at Affy signature - - - TMEM165 Hs00218461_m1
202509_s_at 212603_at Affy signature - - - TNFAIP2 Hs00196800_m1
219130_at 200685_at Affy signature - - - TRMT13 Hs00219487_m1
208601_s_at 205127_at Affy signature - - - TUBB1 Hs00258236_m1
217717_s_at 205037_at Affy signature - - - YWHAB Hs00793604_m1
217593_at 222141_at Affy signature - - - ZSCAN18 Hs00225073_m1
213300_at 219014_at Affy signature - - - ATG2A Hs00390076_m1
212914_at 211938_at Affy signature - - - CBX7 Hs00545603_m1
220308_at 202452_at Affy signature - - - CCDC19 Hs01099244_m1
205098_at 213361_at Affy signature - - - CCR1 Hs00928897_s1
205153_s_at 215346_at Affy signature - - - CD40 Hs01002913_g1
204490_s_at 205026_at Affy signature - - - CD44 Hs00153304_m1
202284_s_at 213324_at Affy signature - - - CDKN1A Hs00355782_m1
206207_at 206361_at Affy signature - - - CLC Hs01055743_m1
206918_s_at 200964_at Affy signature - - - CPNE1 Hs00537765_m1
203392_s_at 222265_at Affy signature - - - CTBP1 Hs00972289_g1
207718_x_at 44702_at Affy signature - - - CYP2A6 Hs00711162_s1
207718_x_at 44702_at Affy signature - - - CYP2A7 Hs00711162_s1
201341_at 209717_at Affy signature - - - ENC1 Hs00171580_m1
215606_s_at 211999_at Affy signature - - - ERC1 Hs00327390_s1
202973_x_at 201417_at Affy signature - - - FAM13A Hs01040170_m1
202709_at 222265_at Affy signature - - - FMOD Hs00157619_m1
206371_at 205844_at Affy signature - - - FOLR3 Hs01549264_m1
205164_at 209391_at Affy signature - - - GCAT Hs00606568_gH
214085_x_at 203799_at Affy signature - - - GLIPR1 Hs00199268_m1
206896_s_at 206126_at Affy signature - - - GNG7 Hs00192999_m1
216289_at 206338_at Affy signature - - - GPR144 Hs01369282_m1
208886_at 213096_at Affy signature - - - H1F0 Hs00961932_s1
206647_at 40850_at Affy signature - - - HBZ Hs00744391_s1
207194_s_at 218225_at Affy signature - - - ICAM4 Hs00169941_m1
202411_at 213797_at Affy signature - - - IFI27 Hs01086373_g1
201188_s_at 213958_at Affy signature - - - ITPR3 Hs00609948_m1
212162_at 210148_at Affy signature - - - KIDINS220 Hs01057000_m1
216713_at 213049_at Affy signature - - - KRIT1 Hs01090981_m1
212708_at 202897_at Affy signature - - - MSL1 Hs00290567_s1
216303_s_at 222265_at Affy signature - - - MTMR1 Hs01021250_m1
207075_at 203906_at Affy signature - - - NLRP3 Hs00366465_m1
214582_at 222317_at Affy signature - - - ORAI2 Hs01057217_m1
216867_s_at 202909_at Affy signature - - - PDE3B Hs00236997_m1
204545_at 320_at Affy signature - - - PDLIM4 Hs00165457_m1
209511_at 218333_at Affy signature - - - POLR1C Hs00191646_m1
209511_at 218333_at Affy signature - - - POLR2F Hs00222679_m1
213633_at 204632_at Affy signature - - - PSG4 Hs00978711_m1
213633_at 204632_at Affy signature - - - PSG4 Hs01652476_m1
205048_s_at 203303_at Affy signature - - - PSPH Hs00190154_m1
213223_at 210607_at Affy signature - - - RPL28 Hs00357189_g1
200042_at 212247_at Affy signature - - - RTCB Hs00204783_m1
209360_s_at 203916_at Affy signature - - - RUNX1 Hs00231079_m1
219382_at 209575_at Affy signature - - - SERTAD3 Hs00705989_s1
213633_at 204632_at Affy signature - - - SH3BP1 Hs00978711_m1
213633_at 204632_at Affy signature - - - SH3BP1 Hs01652476_m1
206934_at 202545_at Affy signature - - - SIRPB1 Hs01092173_m1
212947_at 220404_at Affy signature - - - SLC9A8 Hs00905708_m1
216571_at 202396_at Affy signature - - - SMPD1 Hs01086851_m1
219055_at 219439_at Affy signature - - - SRBD1 Hs01005222_m1
208545_x_at 204600_at Affy signature - - - TAF4 Hs01122669_m1
214955_at 217162_at Affy signature - - - TMPRSS6 Hs00541789_s1
202644_s_at 55692_at Affy signature - - - TNFAIP3 Hs01568119_m1
202688_at 219684_at Affy signature - - - TNFSF10 Hs00234356_m1
209605_at 212897_at Affy signature - - - TST Hs04187383_m1
222059_at 216076_at Affy signature - - - ZNF335 Hs00223060_m1
202509_s_at NA InTxAlternate - - - TNFAIP2 Hs00969305_m1
202672_s_at NA PanViralArray - - - ATF3 Hs00910173_m1
218943_s_at NA PanViralArray - - - DDX58 Hs01061436_m1
219863_at NA PanViralArray - - - HERC5 Hs01061821_m1
214059_at NA PanViralArray - - - IFI44 Hs00951349_m1
204439_at NA PanViralArray - - - IFI44L Hs00915294_g1
204415_at NA PanViralArray - - - IFI6 Hs00242571_m1
203153_at NA PanViralArray - - - IFIT1 Hs03027069_s1
217502_at NA PanViralArray - - - IFIT2 Hs01922738_s1
204747_at NA PanViralArray - - - IFIT3 Hs01922752_s1
205483_s_at NA PanViralArray - - - ISG15 Hs01921425_s1
205569_at NA PanViralArray - - - LAMP3 Hs00180880_m1
202145_at NA PanViralArray - - - LY6E Hs03045111_g1
202086_at NA PanViralArray - - - MX1 Hs00182073_m1
205552_s_at NA PanViralArray - - - OAS1 Hs00973637_m1
202869_at NA PanViralArray - - - OAS2 Hs00973637_m1
218400_at NA PanViralArray - - - OAS3 Hs00934282_g1
205660_at NA PanViralArray - - - OASL Hs00984390_m1
213797_at NA PanViralArray - - - RSAD2 Hs00369813_m1
219684_at NA PanViralArray - - - RTP4 Hs00223342_m1
210657_s_at NA PanViralArray - - - SEPT4 Hs00910209_g1
200986_at NA PanViralArray - - - SERPING1 Hs00934330_m1
222154_s_at NA PanViralArray - - - SPATS2L Hs01016364_m1
206026_s_at NA PanViralArray - - - TNFAIP6 Hs01113602_m1
219211_at NA PanViralArray - - - USP18 Hs00276441_m1
206133_at NA PanViralArray - - - XAF1 Hs01550142_m1
NA NA Reference - - - FPGS Hs00191956_m1
NA NA Reference - - - PPIB Hs00168719_m1
NA NA Reference - - - TRAP1 Hs00972326_m1
NA NA Reference - - - DECR1 Hs00154728_m1
NA NA Reference - - - GAPDH Hs99999905_m1
NA NA Reference - - - 18S Hs99999901_s1
NA 203799_at Replacement - - - CD302 Hs00208436_m1
NA 31845_at Replacement - - - ELF4 Hs01086126_m1
NA 204600_at Replacement - - - EPHB3 Hs01082563_g1
NA 206903_at Replacement - - - EXOG Hs01035290_m1
NA 218695_at Replacement - - - EXOSC4 Hs00363401_g1
NA 212232_at Replacement - - - FNBP4 Hs01553131_m1
NA 209876_at Replacement - - - GIT2 Hs00331902_s1
NA 204683_at Replacement - - - ICAM2 Hs01015796_m1
NA 201642_at Replacement - - - IFNGR2 Hs00985251_m1
NA 203799_at Replacement - - - LY75-CD302 Hs00208436_m1
NA 209280_at Replacement - - - MRC2 Hs00195862_m1
NA 212603_at Replacement - - - MRPS31 Hs00960912_m1
NA 221660_at Replacement - - - MYL10 Hs00540809_m1
NA 203290_at Replacement - - - PEX6 Hs00165457_m1
NA 201417_at Replacement - - - SOX4 Hs00268388_s1
NA 44702_at Replacement - - - SYDE1 Hs00973080_m1
NA 222261_at Replacement - - - TLDC1 Hs00297285_m1
NA 202452_at Replacement - - - ZER1 Hs01115240_m1

Example 4: Bacterial/Viral/SIRS classification using gene expression measured by RT-qPCR implemented on the TLDA platform



[0172] The genes of the three signatures that compose the Host Response-ARI (HR-ARI) test were transitioned to a Custom TaqMan® Low Density Array Cards from ThermoFisher Scientific (Waltham, MA). Expression of these gene signatures were measured using custom multianalyte quantitative real time PCR (RT-qPCR) assays on the 384-well TaqMan Low Density Array (TLDA; Thermo-Fisher) platform. TLDA cards were designed and manufactured with one or more TaqMan primer/probe sets per well, each representing a specific RNA transcript in the ARI signatures, along with multiple endogenous control RNA targets (TRAP1, PPIB, GAPDH, FPGS, DECR1 and 18S) that are used to normalize for RNA loading and to control for plate-to-plate variability. In practice, two reference genes (out of five available), which have the smallest coefficient of variation across samples for the normalization procedure, were selected and primer/probe sets with more than 33% missing values (below limits of quantification) were discarded. The remaining missing values (if any), are set to 1 + max(Cq), where Cq is the quantification cycle for RT-qPCR. Normalized expression values were then calculated as the average of the selected references minus the observed Cq values for any given primer/probe set. See Hellemans et al. (2007) Genome Biol 2007;8(2):R19.

[0173] A total of 174 unique primer/probe sets were assayed per sample. Of these primer/probes, 144 primer/probe sets measure gene targets representative of the 132 previously described Affymetrix (microarray) probes of the three ARI gene signatures (i.e., the genes in the bacterial gene expression signature, the viral gene expression signature and the non-infectious gene expression signature); 6 probe sets are for reference genes, and we additionally assayed 24 probe sets from a previously-discovered pan-viral gene signature. See U.S. Patent No. 8,821,876; Zaas et al. Cell Host Microbe (2009) 6(3):207-217. In addition, a number of primer/probe sets for "replacement" genes were added for training, the expression of these genes being correlated with the expression of some genes from the Affymetrix signature. Some genes are replaced because the RT-qPCR assays for these genes, when performed using TLDA probes, did not perform well.

[0174] For each sample, total RNA was purified from PAXgene Blood RNA tubes (PreAnalytix) and reverse transcribed into cDNA using the Superscript VILO cDNA synthesis kit (Thermo-Fisher) according to the manufacturer's recommended protocol. A standard amount of cDNA for each sample was loaded per master well, and distributed into each TaqMan assay well via centrifugation through microfluidic channels. The TaqMan hydrolysis probes rely on 5' to 3' exonuclease activity to cleave the dual-labeled probe during hybridization to complementary target sequence with each amplification round, resulting in fluorescent signal production. Quantitative detection of the fluorescence indicates accumulated PCR products in "real-time." During exponential amplification and detection, the number of PCR cycles at which the fluorescent signal exceeds a detection threshold is the threshold cycle (Ct) or quantification cycle (Cq) - as determined by commercial software for the RT-qPCR instrument.

Sample/cohort selection:



[0175] Under an IRB-approved protocol, we enrolled patients presenting to the emergency department with acute respiratory illness (See Table 11, below). The patients in this cohort are a subset of those reported in Table 1 of Tsalik et al. (2016) Sci Transl Med 9(322):1-9. Retrospective clinical adjudication of the clinical and other test data for these patients leads to one of three assignments: bacterial ARI, viral ARI, or non-infectious illness.
Table 11: Demographic information for the enrolled cohort
CohortNumber of subjectsaGender (M/F)Mean age, years (Range)bEthnicity (B/W/O)Admitted# Samples (Viral/ Bacterial/ Non-Infectious Illness)
Enrolled Derivation Cohort 317 122/151 45 (6-88) 135/116/22 61% 115/70/88
Viral 115 44/71 45 (6-88) 40/59/16 21%  
Bacterial 70 35/35 49 (14-88) 46/22/2 94%  
Non-infectious Illnessc 88 43/45 49 (14-88) 49/35/4 88%  
Healthy 44 23/21 30 (20-59) 8/27/6d 0%  
a Only subjects with viral, bacterial, or non-infectious illness were included (when available) from each validation cohort.
b When mean age was unavailable or could not be calculated, data is presented as either Adult or Pediatric.
c Non-infectious illness was defined by the presence of SIRS criteria, which includes at least two of the following four features: Temperature <36° or >38°C; Heart rate >90 beats per minute; Respiratory rate >20 breaths per minute or arterial partial pressure of CO2 <32mmHg; and white blood cell count <4000 or >12,000 cells/mm3 or >10% band form neutrophils.
d Three subjects did not report ethnicity.
M, Male. F, Female. B, Black. W, White. O, Other/Unknown. GSE numbers refer to NCBI Gene Expression Omnibus datasets. N/A, Not available based on published data.

Data analysis methods:



[0176] During the data preprocessing stage, we select a subset of at least two reference gene targets (out of five available) with the smallest coefficient of variation across samples and plates. We discard targets with more than 33% missing values (17 targets below the limit of quantification), only if these values are not over represented in any particular class, e.g., bacterial ARI. Next we impute the remaining missing values to 1 + max(Cq), then normalize the expression values for all targets using the reference combination previously selected. In particular, we compute normalized expression values as the mean of the selected references (DECR1 and PPIB) minus the Cq values of any given target.

[0177] Once the data has been normalized, we proceed to build the classification model by fitting a sparse logistic regression model to the data (Friedman et al. (2010) J. Stat. Softw. 33, 1-22). This model estimates the probability that a subject belongs to a particular class as a weighted sum of normalized gene targets. Specifically, we write, p(subject is of class) = σ (w1x1 + ... + wpxp), where σ is the logistic function, w1, ..., wp are classification weights estimated during the fitting procedure, x1, ..., xp represent the p gene targets containing normalized expression values.

[0178] Similar to the array-based classifier, we build three binary classifiers: (1) bacterial ARI vs. viral ARI and non-infectious illness; (2) viral ARI vs. bacterial ARI and non-infectious illness; and (3) non-infectious illness vs. bacterial and viral ARI. After having fitted the three classifiers, we have estimates for p(bacterial ARI), p(viral ARI) and p(non-infectious illness). The thresholds for each of the classifiers are selected from Receiving Operating Characteristic (ROC) curves using a symmetric cost function (expected sensitivity and specificity are approximately equal) (Fawcett (2006) Pattern Recogn Lett 27:861-874). As a result, a subject is predicted as bacterial ARI if p(bacterial ARI) > tb, where tb is the threshold for the bacterial ARI classifier. We similarly select thresholds for the viral ARI and non-infectious illness classifiers, tv and tn, respectively. If desired, a combined prediction can be made by selecting the most likely condition, i.e., the one with largest probability, specifically we write, argmax{p(bacterial ARI),p(viral ARI),p(non-infectious illness)}.

Results:



[0179] During the initial transition of the microarray-discovered genomic classifiers onto the TLDA platform, we assayed 32 samples that also had been assayed by microarray. This group served to confirm that TLDA-based RT-qPCR measurement of the gene transcripts that compose the ARI classifier recapitulates the results obtained for microarray-based measurement of gene transcripts, and is therefore a valid methodology for classifying patients as having bacterial or viral ARI, or having non-infectious illness. We found that from the 32 samples tested both on TLDA and microarray platforms, when assessed using their corresponding classifiers, there is agreement of 84.4%, which means that 27 of 32 subjects had the same combined prediction in both microarray and TLDA-based classification models.

[0180] After demonstrating concordance between microarray and TLDA-based classification, we tested an additional 63 samples, using the TLDA-based classification, from patients with clinical adjudication of ARI status but without previously-characterized gene expression patterns. In total, therefore, 95 samples were assessed using the TLDA-based classification test. This dataset from 95 samples allowed us to evaluate how the TLDA-based RT-qPCR platform classifies new patients, using only the clinical adjudication as the reference standard. In this experiment, we observed an overall accuracy of 81.1%, which corresponds to 77/95 correctly classified samples. More specifically, the model yielded bacterial ARI, viral ARI, and non-infectious illness accuracies of 80% (24 correct of 30), 77.4% (24 correct of 31) and 85.3% (29 correct of 34), respectively. In terms of the performance of the individual classifiers, we observed area under the ROC curves of 0.92, 0.86 and 0.91, for the bacterial ARI, viral ARI and non-infectious illness classifier, respectively. Provided that we do not count with a validation dataset for any of the classifiers, yet we want unbiased estimates of classification performance (accuracies and areas under the ROC curve), we are reporting leave-one-out cross-validated performance metrics.

[0181] The weights and thresholds for each of the classifiers (bacterial ARI, viral ARI and non-infectious illness) are shown in the Table 12, shown below. Note that this Table lists 151 gene targets instead of 174 gene targets because the reference genes were removed in the preprocessing stage, as described above, as were 17 targets for which there were missing values. These 17 targets were also removed during the preprocessing stage.

[0182] If the panviral signature genes are removed, we see a slight decreased performance, no larger than 5% across AUC, accuracies and percent of agreement values.

Summary:



[0183] The composite host-response ARI classifier is composed of gene expression signatures that are diagnostic of bacterial ARI versus viral ARI, versus non-infectious illness and a mathematical classification framework. The mathematical classifiers provide three discrete probabilities: that a subject has a bacterial ARI, viral ARI, or non-infectious illness. In each case, a cutoff or threshold may be specified above which threshold one would determine that a patient has the condition. In addition, one may modify the threshold to alter the sensitive and specificity of the test.

[0184] The measurement of these gene expression levels can occur on a variety of technical platforms. Here, we describe the measurement of these signatures using a TLDA-based RT-qPCR platform. Moreover, the mathematical framework that determines ARI etiology probabilities is adapted to the platform by platform-specific training to accommodate transcript measurement methods (i.e., establishing platform-specific weights, w1, ..., wp). Similar, straightforward, methodology could be conducted to translate the gene signatures to other gene expression detection platforms, and then train the associated classifiers. This Example also demonstrates good concordance between TLDA-based and microarray-based classification of etiology of ARI. Finally, we show the use of the TLDA-based RT-qPCR platform and associated mathematical classifier to diagnose new patients with acute respiratory illness.
Table 12: Genes, TLDA probe/primers, and classifier weights for the bacterial, viral and non-infectious illness classifiers.
TLDA Assay IDBacterialViralNon-infectiousGroupGene SymbolRefSeq IDGene Name
Hs00153304_m1 0.44206 -0.19499 0   CD44 NM_000610.3; NM_001202555.1; hCG1811182 Celera Annotation; CD44 molecule (Indian blood group)
NM_001001392.1; NM_001202556.1;
NM_001001391.1; NM_001001390.1;
NM_001001389.1
Hs00155778-m1 0 0 0   APLP2 NM_001142278.1; NM_001142277.1; hCG2032871 Celera Annotation; amyloid beta (A4) precursor-like protein 2
NM_001142276.1; NR_024515.1;
NR_024516.1; NM_001642.2;
NM_001243299.1
Hs00156390_m1 0.07707 -0.15022 0   CD63 NM_001780.5; NM_001267698.1; CD63 molecule; hCG20743 Celera Annotation
NM_001257389.1; NM_001257390.1;
NM_001257391.1
Hs00158514-m1 0 0 0   KPNB1 NM_002265.5 hCG1773668 Celera Annotation; karyopherin (importin) beta 1
Hs00162109-m1 0 0.012558 0   SP100 NM_003113.3; NM_001080391.1; SP100 nuclear antigen; hCG34336 Celera Annotation
NM_001206702.1; NM_001206703.1;
NM_001206701.1; NM_001206704.1
Hs00165457_m1 0.14396 -0.00784 0   PEX6 NM_000287.3 peroxisomal biogenesis factor 6; hCG17647 Celera Annotation
Hs00169473-m1 0 -0.04883 0.135154   PRF1 NM_005041.4; NM_001083116.1 hCG22817 Celera Annotation; perforin 1 (pore forming protein)
Hs00169941-m1 0 -0.33225 0   ICAM4 NM_001544.4; NM_022377.3 intercellular adhesion molecule 4 (Landsteiner-Wiener blood group); hCG28480 Celera Annotation
Hs00171580_m1 0 -0.04133 0   ENC1 NM_001256575.1; NM_001256576.1; hCG37104 Celera Annotation; ectodermal-neural cortex 1 (with BTB domain)
NM_003633.3; NM_001256574.1
Hs00187510-m1 0.38204 -0.19399 -0.242396   RAB7L1 NM_001135662.1; NM_003929.2 hCG19156 Celera Annotation; RAB7; member RAS oncogene family-like 1
Hs00190154-m1 0.0726 0 -0.128456   PSPH NM_004577.3 phosphoserine phosphatase; hCG1811513 Celera Annotation
Hs00191827_m1 0 0 0   CAPZB NM_001282162.1; NM_004930.4 capping protein (actin filament) muscle Z-line; beta; hCG41078 Celera Annotation
Hs00192999_m1 0.08266 0 -0.127277   GNG7 NM_052847.2 guanine nucleotide binding protein (G protein); gamma 7; hCG20107 Celera Annotation
Hs00196051_m1 0.05 -0.4723 0   IRF9 NM_006084.4 interferon regulatory factor 9; hCG40171 Celera Annotation
Hs00196800_m1 0 0 0   TNFAIP2 NM_006291.2 tumor necrosis factor; alpha-induced protein 2; hCG22889 Celera Annotation
Hs00197280_m1 -0.14204 0.089619 0.147283   CIB2 NM_006383.3; NM_001271888.1 calcium and integrin binding family member 2; hCG38933 Celera Annotation
Hs00199268_m1 0 -0.10536 0.38895   GLIPR1 NM_006851.2 hCG26513 Celera Annotation; GLI pathogenesis-related 1
Hs00199894_m1 0 -0.10571 0.02064   CD160 NR_103845.1; NM_007053.3 hCG1762288 Celera Annotation; CD160 molecule
Hs00204096_m1 0 0 0   MRPS18B NM_014046.3 hCG2039591 Celera Annotation; mitochondrial ribosomal protein S18B
Hs00204783_m1 -0.12369 0.330219 0   RTCB NM_014306.4 RNA 2'; 3'-cyclic phosphate and 5'-OH ligase; hCG41412 Celera Annotation
Hs00204888_m1 0 0 0   DAPK2 NM_014326.3 death-associated protein kinase 2; hCG32392 Celera Annotation
Hs00210319_m1 0 0.061489 0   TES NM_015641.3; NM_152829.2 testis derived transcript (3 LIM domains); hCG39086 Celera Annotation
Hs00218461_m1 0.18667 0 -0.125865   TMEM165 NR_073070.1; NM_018475.4 hCG20603 Celera Annotation; transmembrane protein 165
Hs00219487_m1 0.32643 0 -0.350154   TRMT13 NM_019083.2 hCG31836 Celera Annotation; tRNA methyltransferase 13 homolog (S. cerevisiae)
Hs00222418_m1 -0.08795 0.254466 0   PPCDC NM_021823.3 phosphopantothenoylcysteine decarboxylase; hCG21917 Celera Annotation
Hs00222679_m1 0 0.072372 0   POLR2F; LOC100131530 NM_021974.3 polymerase (RNA) II (DNA directed) polypeptide F; hCG41858 Celera Annotation; uncharacterized LOC100131530
Hs00223060_m1 0 -0.12877 0.034889   ZNF335 NM_022095.3 zinc finger protein 335; hCG40026 Celera Annotation
Hs00225073_m1 0 0.661155 -0.183337   ZSCAN 18 NM_001145544.1; NM_001145543.1; hCG201365 Celera Annotation; zinc finger and SCAN domain containing 18
NM_023926.4; NM_001145542.1
Hs00226776_m1 0 0.198622 -0.254653   GIMAP6 NM_001244072.1; NM_001244071.1; hCG1655100 Celera Annotation; GTPase; IMAP family member 6
NM_024711.5
Hs00231079_m1 0.0787 0 -0.089259   RUNX1 NM_001001890.2; NM_001754.4 runt-related transcription factor 1; hCG2007747 Celera Annotation
Hs00231368_m1 0.30434 0 -0.130472   SPI1 NM_001080547.1; NM_003120.2 spleen focus forming virus (SFFV) proviral integration oncogene; hCG25181 Celera Annotation
Hs00232390_m1 0.22771 -0.39445 0   BATF NM_006399.3 hCG22346 Celera Annotation; basic leucine zipper transcription factor; ATF-like
Hs00234356_m1 0 0 -0.005804   TNFSF10 NR_033994.1; NM_003810.3 tumor necrosis factor (ligand) superfamily; member 10; hCG20249 Celera Annotation
Hs00246543_s1 0 0.096747 0   HNRNPAO NM_006805.3 hCG1639951 Celera Annotation; heterogeneous nuclear ribonucleoprotein A0
Hs00258236_m1 0 0.067758 -0.014686   TUBB1 NM_030773.3 tubulin; beta 1 class VI; hCG28550 Celera Annotation
Hs00259863_m1 -0.03861 0.156335 0   ORAI2 NM_001126340.2; NM_001271818.1; hCG1736771 Celera Annotation; ORAI calcium release-activated calcium modulator 2
NM_032831.3
Hs00266198_m1 -0.03709 0.174789 0   CEACAM8 NM_001816.3 carcinoembryonic antigen-related cell adhesion molecule 8; hCG21882 Celera Annotation
Hs00269334_m1 0 0.11804 -0.054795   CAMK1 NM_003656.4 calcium/calmodulin-dependent protein kinase I; hCG21548 Celera Annotation
Hs00290567_s1 0.10454 -0.57285 0   MSL1 NM_001012241.1 hCG31740 Celera Annotation; male-specific lethal 1 homolog (Drosophila)
Hs00296064_s1 -0.11096 0.162636 0   CADM1 NM_014333.3; NM_001098517.1 cell adhesion molecule 1
Hs00327390_s1 -0.27728 0.219012 0.023246   ERC1 NM_178040.2; NR_027949.1; ELKS/RAB6-interacting/CAST family member 1
NR_027946.1; NR_027948.1;
NM_178039.2
Hs00331872_s1 0 -0.04877 0   ANKRD11 NM_013275.5; NM_001256182.1; hCG1980824 Celera Annotation; ankyrin repeat domain 11
NM_001256183.1
Hs00355782_m1 0 0 0   CDKN1A NM_001220778.1; NM_001220777.1; cyclin-dependent kinase inhibitor 1A (p21; Cip1); hCG15367 Celera Annotation
NM_000389.4; NM_078467.2
Hs00357189_g1 0 0 0   RPL28 NM_001136137.1; NM_000991.4; ribosomal protein L28; hCG38234 Celera Annotation
NM_001136134.1; NM_001136135.1;
NM_001136136.1
Hs00363282_m1 0 -0.39826 0.298323   LAPTM4B NM_018407.4 lysosomal protein transmembrane 4 beta; hCG2008559 Celera Annotation
Hs00366465_m1 0 0 0   NLRP3 NM_001127461.2; NM_001079821.2; NLR family; pyrin domain containing 3; hCG1982559 Celera Annotation
NM_001243133.1; NM_004895.4;
NM_001127462.2; NM_183395.2
Hs00367895_m1 0 0 0   ARHGAP12 NM_001270698.1; NM_001270697.1; Rho GTPase activating protein 12; hCG2017264 Celera Annotation
NM_018287.6; NM_001270699.1;
NM_001270696.1; NM_001270695.1
Hs00378456_m1 0 0 0   SEC24A NM_021982.2; NM_001252231.1 SEC24 family; member A (S. cerevisiae); hCG1981418 Celera Annotation
Hs00381767_m1 -0.08167 -0.02155 0.251085   KIAA1324 NR_049774.1; NM_020775.4; hCG1997600 Celera Annotation; KIAA1324
NM_001267049.1; NM_001267048.1
Hs00390076_m1 -0.4019 0 0.306895   ATG2A NM_015104.2 hCG2039982 Celera Annotation; autophagy related 2A
Hs00414018_m1 0 0 0   DEFA3; DEFA1; DEFA1B NM_004084.3; NM_005217.3; NM_001042500.1 defensin; alpha 3; neutrophil-specific; defensin; alpha 1; defensin; alpha 1B
Hs00537765_m1 0.12016 0 -0.311567   CPNE1 NM_001198863.1; NM_152926.2; copine I; hCG38213 Celera Annotation
NR_037188.1; NM_152927.2;
NM_152925.2; NM_152928.2;
NM_003915.5
Hs00541789_s1 0 0 0   TMPRSS6 NM_153609.2 hCG2011224 Celera Annotation; transmembrane protease; serine 6
Hs00545603_m1 -0.15652 0 0.157219   CBX7 NM_175709.3 chromobox homolog 7; hCG41710 Celera Annotation
Hs00606568_gH 0 0.024977 0   GCAT NM_014291.3; NM_001171690.1 hCG41842 Celera Annotation; glycine C-acetyltransferase
Hs00609948_m1 -0.1261 0 0.132035   ITPR3 NM_002224.3 hCG40301 Celera Annotation; inositol 1; 4; 5-trisphosphate receptor; type 3
Hs00705137_s1 0 0.190805 -0.207955   IFITM1 NM_003641.3 interferon induced transmembrane protein 1; hCG1741134 Celera Annotation
Hs00705989_s1 0 0.264586 -0.237834   SERTAD3 NM_203344.2; NM_013368.3 SERTA domain containing 3; hCG201413 Celera Annotation
Hs00706565_s1 0 0.247956 -0.127891   RPP25 NM_017793.2 ribonuclease P/MRP 25kDa subunit; hCG1643228 Celera Annotation
Hs00711162_s1 -0.01602 0.105815 0   CYP2A13; NM_000764.2; NM_030589.2; cytochrome P450; family 2; subfamily A; polypeptide 13;
CYP2A7; NM_000766.4; NM_000762.5 cytochrome P450; family 2; subfamily A; polypeptide 7;
CYP2A6 cytochrome P450; family 2; subfamily A; polypeptide 6;
hCG2039740 Celera Annotation; hCG1780445 Celera Annotation
Hs00734212_m1 0.03633 -0.10881 0   HLA-DRB3; NM_022555.3 hCG2001518 Celera Annotation; major histocompatibility complex; class II; DR beta 3; major histocompatibility complex; class II; DR beta 1
HLA-DRB1
Hs00738661_m1 -0.2813 0 0.255274   FAM134C NR_026697.1; NM_178126.3 family with sequence similarity 134; member C; hCG2043027 Celera Annotation
Hs00793604_m1 0 0 -0.392469   YWHAB NM_003404.4; NM_139323.3 hCG38378 Celera Annotation; tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein; beta polypeptide
Hs00820148_g1 0 0 0.082524   TGIF1 NM_173207.2; NM_003244.3; TGFB-induced factor homeobox 1; hCG1994498 Celera Annotation
NM_001278682.1; NM_170695.3;
NM_001278686.1; NM_001278684.1;
NM_173210.2; NM_173209.2;
NM_173208.2; NM_174886.2;
NM_173211.1
Hs00852566_g1 0 0 0.090784   BTF3 NM_001207.4; NM_001037637.1 hCG37844 Celera Annotation; basic transcription factor 3
Hs00855185_g1 0.22884 -0.16129 0   ARPC3 NM_001278556.1; NM_005719.2 hCG1787850 Celera Annotation; hCG1730237 Celera Annotation; actin related protein 2/3 complex; subunit 3; 21kDa
Hs00893626_m1 0 0 -0.131321   IL1RN NM_000577.4; NM_173841.2; hCG1733963 Celera Annotation; interleukin 1 receptor antagonist
NM_173842.2; NM_173843.2
Hs00905708_m1 0 0 0   SLC9A8 NM_001260491.1; NR_048537.1; solute carrier family 9; subfamily A (NHE8; cation proton antiporter 8); member 8; hCG37890 Celera Annotation
NR_048538.1; NR_048539.1;
NR_048540.1; NM_015266.2
Hs00928897_s1 0 0 0   CCR1 NM_001295.2 hCG15324 Celera Annotation; chemokine (C-C motif) receptor 1
Hs00950814_g1 0 0 0.035502   NCR1 NM_001145457.2; NM_001242356.2; hCG19670 Celera Annotation; natural cytotoxicity triggering receptor 1
NM_004829.6
Hs00951428_m1 0 0.113402 0   DSC2 NM_024422.3; NM_004949.3 hCG24896 Celera Annotation; desmocollin 2
Hs00961932_s1 0 0 0   H1F0 NM_005318.3 hCG1641126 Celera Annotation; H1 histone family; member 0
Hs00963477_g1 0 -0.00884 0   RPS21 NM_001024.3 hCG41768 Celera Annotation; ribosomal protein S21
Hs00971739_g1 0 0.128754 0   SAT1 NR_027783.1; NM_002970.2 hCG17885 Celera Annotation; spermidine/spermine N1-acetyltransferase 1
Hs00972289_g1 -0.36317 0.301793 0.148178   CTBP1 NM_001012614.1; NM_001328.2 hCG1981976 Celera Annotation; C-terminal binding protein 1
Hs00978711_m1 0 -0.19534 0.079881   SH3BP1 NM_018957.3 hCG41861 Celera Annotation; SH3-domain binding protein 1
Hs00980756_m1 0 -0.27613 0.042497   GGT1 NM_001032364.2; NM_001032365.2; gamma-glutamyltransferase 1; hCG2010666 Celera Annotation
NM_005265.2; NM_013430.2
Hs00982607_m1 0 0 0   NINJ1 NM_004148.3 ninjurin 1; hCG18015 Celera Annotation
Hs00984390 _m1 0 0.074028 -0.022201   OASL NM_198213.2; NM_003733.3 hCG27362 Celera Annotation; 2'-5'-oligoadenylate synthetase-like
Hs00985319 _m1 -0.01147 0.079048 0   HEATR1 NM_018072.5 HEAT repeat containing 1; hCG25461 Celera Annotation
Hs00988063_m1 -0.08452 0.168519 0   SIGLEC1 NM_023068.3 hCG39260 Celera Annotation; sialic acid binding Ig-like lectin 1; sialoadhesin
Hs01001427_m1 0.04332 -0.60556 0   CDK5RAP2 NR_073558.1; NR_073554.1; hCG27455 Celera Annotation; CDK5 regulatory subunit associated protein 2
NR_073555.1; NR_073556.1;
NM_001272039.1; NR_073557.1;
NM_001011649.2; NM_018249.5
Hs01002913_g1 0 0 0   CD40 NM_152854.2; NM_001250.4 hCG40016 Celera Annotation; CD40 molecule; TNF receptor superfamily member 5
Hs01005222 _m1 0 0.326033 0   SRBD1 NM_018079.4 S1 RNA binding domain 1; hCG1987258 Celera Annotation
Hs01017992_g1 0 0 0.179899   CYP27A1 NM_000784.3 hCG15569 Celera Annotation; cytochrome P450; family 27; subfamily A; polypeptide 1
Hs01021250 _m1 0.01799 0.196899 -0.140181   MTMR1 NM_003828.2 hCG1640369 Celera Annotation; myotubularin related protein 1
Hs01029870 _m1 0 -0.58215 0.22929   ARL1 NM_001177.4 hCG1782029 Celera Annotation; ADP-ribosylation factor-like 1
  0 -0.36595 Hs01032528_m1 0.410577   HERC1 NM_003922.3 hCG1818283 Celera Annotation; HECT and RLD domain containing E3 ubiquitin protein ligase family member 1
Hs01038134_m1 -0.13717 0.004773 0.049685   STAP1 NM_012108.2 signal transducing adaptor family member 1; hCG40344 Celera Annotation
Hs01040170 _m1 0.04344 -0.17845 -0.052769   FAM13A NM_014883.3; NM_001265578.1; hCG39059 Celera Annotation; family with sequence similarity 13; member A
NM_001015045.2; NM_001265580.1;
NM_001265579.1
Hs01055743 _m1 -0.30697 0 0.257693   CLC NM_001828.5 hCG43348 Celera Annotation; Charcot-Leyden crystal galectin
Hs01057000_m1 0 -0.68353 0.082116   KIDINS220 NM_020738.2 hCG23067 Celera Annotation; kinase D-interacting substrate; 220kDa
Hs01057217_m1 -0.45125 0.327746 0.070281   PDE3B NM_000922.3 phosphodiesterase 3B; cGMP-inhibited; hCG23682 Celera Annotation
Hs01072230_g1 0 -0.00364 0.169878   CHI3L1 NM_001276.2 chitinase 3-like 1 (cartilage glycoprotein-39); hCG24326 Celera Annotation
Hs01082884 _m1 0.29147 -0.1223 0   IRF2 NM_002199.3 hCG16244 Celera Annotation; interferon regulatory factor 2
Hs01085704_g1 0 0 0   SLC29A1 NM_001078174.1; NM_004955.2; hCG19000 Celera Annotation; solute carrier family 29 (equilibrative nucleoside transporter); member 1
NM_001078177.1; NM_001078176.2;
NM_001078175.2
Hs01086373_g1 -0.11199 0.274551 -0.063877   IFI27 NM_005532.3; NM_001130080.1 interferon; alpha-inducible protein 27; hCG22330 Celera Annotation
Hs01086851_m1 0.37999 -0.28298 0   SMPD1 NM_001007593.2; NM_000543.4 sphingomyelin phosphodiesterase 1; acid lysosomal; hCG24080 Celera Annotation
Hs01090981_m1 0 0 0   KRIT1 NM_194456.1; NM_194454.1; hCG1812017 Celera Annotation; KRIT1; ankyrin repeat containing
NM_004912.3; NM_001013406.1;
NM_194455.1
Hs01092173_m1 0.09825 0 0   SIRPB1 NM_001083910.2; NM_006065.3 signal-regulatory protein beta 1; hCG39419 Celera Annotation
Hs01099244_m1 0.01588 -0.22063 0.055484   CCDC19 NM_012337.2 hCG39740 Celera Annotation; coiled-coil domain containing 19
Hs01115711_m1 0.2568 0 -0.127859   MCTP1 NM_001002796.2; NM_024717.4 multiple C2 domains; transmembrane 1; hCG1811111 Celera Annotation
Hs01117053_m1 0 0 0   EXOC7 NR_028133.1 exocyst complex component 7; hCG40887 Celera Annotation
Hs01122669_m1 0 -0.03893 0.066177   TAF4 NM_003185.3 hCG41771 Celera Annotation; TAF4 RNA polymerase II; TATA box binding protein (TBP)-associated factor; 135kDa
Hs01128745_m1 0 0.031228 0   EMR3 NM_032571.3 hCG95683 Celera Annotation; egf-like module containing; mucin-like; hormone receptor-like 3
Hs01549264_m1 0.02825 -0.12496 0     NM_000804.2 hCG1640300 Celera Annotation; folate receptor 3 (gamma)
Hs01568119_m1 0 0.181259 -0.076525   TNFAIP3 NM_001270508.1; NM_006290.3; hCG16787 Celera Annotation; tumor necrosis factor; alpha-induced protein 3
NM_001270507.1
Hs01911452_s1 0 0 0   IFIT1 NM_001548.4; NM_001270928.1; hCG24571 Celera Annotation; interferon-induced protein with tetratricopeptide repeats 1
NM_001270927.1; NM_001270930.1;
NM_001270929.1
Hs02567906_s1 -0.22881 0.019641 0   RABGAP1L NM_001243763.1; NM_014857.4; hCG2024869 Celera Annotation; RAB GTPase activating protein 1-like
NM_001035230.2
Hs02569575_s1 0 0 -0.12916   SCAPER NM_001145923.1; NM_020843.2 hCG40799 Celera Annotation; S-phase cyclin A-associated protein in the ER
Hs03037970_g1 0 0 0   DUX4L7; DUX4L5; DUX4L6; DUX4L2; DUX2; DUX4; LOC100653046 ; DUX4L; DUX4L4; DUX4L3 NM_001278056.1; NM_001164467.2; double homeobox 4 like 7; double homeobox 4 like 5; double homeobox 2; double homeobox 4 like 2; double homeobox 4 like 6; double homeobox 4; double homeobox protein 4-like; double homeobox 4-like; double homeobox 4 like 4; double homeobox 4 like 3
NR_038191.1; NM_001177376.2;
NM_012147.4; NM_001127389.2;
NM_001127388.2; NM_001127387.2;
NM_033178.4; NM_001127386.2
 
Hs03045111_g1 -0.02913 0.054676 0   LY6E NM_002346.2; NM_001127213.1 hCG1765592 Celera Annotation; lymphocyte antigen 6 complex; locus E
Hs03055204_s1 0 0 0   KIAA0754 NM_015038.1 KIAA0754
Hs03989560_s1 -0.28689 0.169135 0.040358   GLUD1 NM_005271.3 glutamate dehydrogenase 1
Hs04187383-m1 0 0 0   TST NM_003312.5; NM_001270483.1 thiosulfate sulfurtransferase (rhodanese); hCG41451 Celera Annotation
Hs00969305_m1 0 -0.50526 0 InTxAlternate TNFAIP2 NM_006291.2 tumor necrosis factor; alpha-induced protein 2; hCG22889 Celera Annotation
Hs00180880_m1 0 0 0 PanViral LAMP3 NM_014398.3 lysosomal-associated membrane protein 3; hCG16067 Celera Annotation
Hs00182073_m1 0 0.043305 0 PanViral MX1 NM_002462.3; NM_001144925.1; myxovirus (influenza virus) resistance 1; interferon-inducible protein p78 (mouse); hCG401239 Celera Annotation
NM_001178046.1
Hs00213443_m1 0 0.009468 -0.051318 PanViral OAS2 NM_016817.2 2'-5'-oligoadenylate synthetase 2; 69/71kDa; hCG38536 Celera Annotation
Hs00223342_m1 0 0 0 PanViral RTP4 NM_022147.2 hCG1653633 Celera Annotation; receptor (chemosensory) transporter protein 4
Hs00242571_m1 0 0 -0.078103 PanViral IFI6 NM_022873.2; NM_002038.3; interferon; alpha-inducible protein 6; hCG1727099 Celera Annotation
NM_022872.2
Hs00276441_m1 0 0.033981 -0.048548 PanViral USP18 NM_017414.3 ubiquitin specific peptidase 18; hCG21533 Celera Annotation
Hs00369813_m1 -0.02854 0 0 PanViral RSAD2 NM_080657.4 hCG23898 Celera Annotation; radical S-adenosyl methionine domain containing 2
Hs00910173_m1 0 0.065635 -0.003951 PanViral ATF3 NM_001030287.3; NM_001206484.2; hCG37734 Celera Annotation; activating transcription factor 3
NM_001206488.2; NM_001674.3
Hs00910209_g1 -0.00172 0.07212 0 PanViral SEP4 NM_080416.2; NM_004574.3; septin 4; hCG30696 Celera Annotation
NM_001256822.1; NM_080415.2;
NM_001256782.1; NR_037155.1;
NM_001198713.1
Hs00915294_g1 0 0 0 PanViral IFI44L NM_006820.2 hCG24062 Celera Annotation; interferon-induced protein 44-like
Hs00934282_g1 0 0 0 PanViral OAS3 NM_006187.2 2'-5'-oligoadenylate synthetase 3; 100kDa; hCG40370 Celera Annotation
Hs00934330_m1 0 0.065027 0 PanViral SERPING1 NM_000062.2; NM_001032295.1 serpin peptidase inhibitor; clade G (C1 inhibitor); member 1; hCG39766 Celera Annotation
Hs00951349_m1 0 0 0 PanViral IFI44 NM_006417.4 interferon-induced protein 44; hCG24065 Celera Annotation
Hs00973637_m1 0 0 -0.060351 PanViral OAS1 NM_001032409.1; NM_016816.2; 2'-5'-oligoadenylate synthetase 1; 40/46kDa; hCG40366 Celera Annotation
NM_002534.2
Hs01016364_m1 0 0 0 PanViral SPATS2L NM_001100422.1; NM_015535.2; spermatogenesis associated; serine-rich 2-like; hCG1811464 Celera Annotation
NM_001100424.1; NM_001100423.1
Hs01061436_m1 0 0.01828 -0.042268 PanViral DDX58 NM_014314.3 DEAD (Asp-Glu-Ala-Asp) box polypeptide 58; hCG1811781 Celera Annotation
Hs01061821_m1 0 0 0 PanViral HERC5 NM_016323.3 HECT and RLD domain containing E3 ubiquitin protein ligase 5; hCG1813153 Celera Annotation
Hs01113602_m1 0.05847 0 -0.206842 PanViral TNFAIP6 NM_007115.3 hCG41965 Celera Annotation; tumor necrosis factor; alpha-induced protein 6
Hs01550142_m1 0 -0.06086 0 PanViral XAF1 NR_046398.1; NM_199139.2; hCG1777063 Celera Annotation; XIAP associated factor 1
NM_017523.3; NR_046396.1;
NR_046397.1
Hs01921425_s1 0 0.018167 -0.032153 PanViral ISG15 NM_005101.3 ISG15 ubiquitin-like modifier; hCG1771418 Celera Annotation
Hs01922738_s1 -0.0409 0.185197 -0.007029 PanViral IFIT2 NM_001547.4 interferon-induced protein with tetratricopeptide repeats 2; hCG1643352 Celera Annotation
Hs01922752_s1 0 0 0 PanViral IFIT3 NM_001549.4; NM_001031683.2 hCG24570 Celera Annotation; interferon-induced protein with tetratricopeptide repeats 3
Hs03027069_s1 -0.00733 0 0 PanViral IFIT1 NM_001548.4; NM_001270928.1; interferon-induced protein with tetratricopeptide repeats 1; hCG24571 Celera Annotation
NM_001270927.1; NM_001270930.1;
NM_001270929.1
Hs00191646_m1 0 0 0 Replacement POLR1C NM_203290.2 polymerase (RNA) I polypeptide C; 30kDa; hCG18995 Celera Annotation
Hs00208436_m1 0 0.013116 0 Replacement CD302; LY75-CD302 NM_014880.4; NM_001198763.1; CD302 molecule; hCG40834 Celera Annotation; LY75-CD302 readthrough
NM_001198760.1; NM_001198759.1
Hs00297285_m1 0 -0.46905 0 Replacement TLDC1 NM_020947.3 TBC/LysM-associated domain containing 1; hCG39793 Celera Annotation
Hs00331902_s1 0 -0.45598 0.236611 Replacement GIT2 NM_057170.3; NM_014776.3; hCG38510 Celera Annotation; G protein-coupled receptor kinase interacting ArfGAP 2
NM_001135213.1; NM_001135214.1;
NM_057169.3
Hs00363401_g1 0 0 -0.077823 Replacement EXOSC4 NM_019037.2 hCG1747868 Celera Annotation; exosome component 4
Hs00960912_m1 0 0.26766 0 Replacement MRPS31 NM_005830.3 mitochondrial ribosomal protein S31; hCG32763 Celera Annotation
Hs00985251_m1 0.10711 -0.17404 0 Replacement IFNGR2 NM_005534.3 interferon gamma receptor 2 (interferon gamma transducer 1); hCG401179 Celera Annotation
Hs01015796_m1 0 0.189857 0 Replacement ICAM2 NM_001099786.1; NM_001099787.1; intercellular adhesion molecule 2; hCG41817 Celera Annotation
NM_001099788.1; NM_001099789.1;
NM_000873.3
Hs01035290_m1 -0.05606 0.248968 0 Replacement EXOG NM_005107.3; NM_001145464.1 endo/exonuclease (5'-3'); endonuclease G-like; hCG40337 Celera Annotation
Hs01086126_m1 0 0 0 Replacement ELF4 NM_001421.3; NM_001127197.1 E74-like factor 4 (ets domain transcription factor); hCG21000 Celera Annotation
Hs01115240_m1 0 -0.79464 0.589673 Replacement ZER1 NM_006336.3 zyg-11 related; cell cycle regulator; hCG1788209 Celera Annotation
Hs01553131_m1 0 -0.26139 0.697495 Replacement FNBP4 NM_015308.2 formin binding protein 4; hCG25190 Celera Annotation


[0185] Any patents or publications mentioned in this specification are indicative of the levels of those skilled in the art to which the invention pertains. In case of conflict, the present specification, including definitions, will control.


Claims

1. A method for making acute respiratory illness classifiers for a platform, wherein the classifiers comprise a bacterial ARI classifier, a viral ARI classifier and a non-infectious illness classifier for the platform, said method comprising:

(a) providing biological samples that have been obtained from a plurality of subjects known to be suffering from a bacterial acute respiratory infection;

(b) providing biological samples that have been obtained from a plurality of subjects known to be suffering from a viral acute respiratory infection;

(c) providing biological samples that have been obtained from a plurality of subjects known to be suffering from a non-infectious illness;

(d) measuring on said platform the gene expression levels of a plurality of genes (e.g., all expressed genes or transcriptome, or a subset thereof) in each of said biological samples from steps (a), (b) and (c);

(e) normalizing the gene expression levels obtained in step (d) to generate normalized gene expression values; and

(f) generating a bacterial ARI classifier, a viral ARI classifier and a non-infectious illness classifier for the platform based upon said normalized gene expression values, to thereby make the acute respiratory illness classifiers for the platform, optionally wherein said measuring comprises or is preceded by one or more steps of: purifying cells from said sample, breaking the cells of said sample, and isolating RNA from said sample.


 
2. The method of claim 1, wherein said measuring comprises semi-quantitative PCR and/or nucleic acid probe hybridization, or wherein said platform comprises an array platform, a thermal cycler platform (e.g., multiplexed and/or real-time PCR platform), a hybridization and multi-signal coded (e.g., fluorescence) detector platform, a nucleic acid mass spectrometry platform, a nucleic acid sequencing platform, or a combination thereof.
 
3. The method of claim 1, wherein said generating comprises iteratively:

(i) assigning a weight for each normalized gene expression value, entering the weight and expression value for each gene into a classifier (e.g., a linear regression classifier) equation and determining a score for outcome for each of the plurality of subjects, then

(ii) determining the accuracy of classification for each outcome across the plurality of subjects, and then

(iii) adjusting the weight until accuracy of classification is optimized,

to provide said bacterial ARI classifier, viral ARI classifier and non-infectious illness classifier for the platform,
wherein genes having a non-zero weight are included in the respective classifier,

and optionally uploading components of each classifier (genes, weights and/or etiology threshold value) onto one or more databases, optionally wherein the classifier is a linear regression classifier and said generating comprises converting a score of said classifier to a probability.


 
4. The method according to any one of claims 1 to 3 further comprising validating said ARI classifier against a known dataset comprising at least two relevant clinical attributes.
 
5. The method of any one of claims 1 to 4, wherein the bacterial ARI classifier comprises expression levels of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes (measurable, e.g., with oligonucleotide probes homologous to said genes) listed as part of a bacterial ARI classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12; or

wherein the viral classifier comprises expression levels of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes (measurable, e.g., with oligonucleotide probes homologous to said genes) listed as part of a viral ARI classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12; or

wherein the non-infectious classifier comprises expression levels of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes (measurable, e.g., with oligonucleotide probes homologous to said genes) listed as part of a non-infectious illness classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12.


 
6. A method for determining an etiology of an acute respiratory illness in a subject suffering therefrom, or at risk thereof, selected from bacterial, viral and/or non-infectious, comprising:

(a) providing a biological sample that has been obtained from the subject;

(b) measuring on a platform gene expression levels of a pre-defined set of genes (i.e., signature) in said biological sample;

(c) normalizing the gene expression levels to generate normalized gene expression values;

(d) entering the normalized gene expression values into a bacterial acute respiratory infection (ARI) classifier, a viral ARI classifier, and optionally a non-infectious illness classifier, said classifier(s) comprising pre-defined weighting values (i.e., coefficients) for each of the genes of the pre-defined set of genes for the platform, wherein the bacterial acute respiratory infection (ARI) classifier comprises expression levels of 5 to 200 of the genes listed as part of a bacterial classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12, the viral ARI classifier comprises expression levels of 5 to 200 of the genes listed as part of a viral classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12, and the non-infectious illness classifier comprises expression levels of 5 to 200 of the genes listed as part of a non-infectious illness classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12, optionally wherein said classifier(s) are retrieved from one or more databases; and

(e) calculating an etiology probability for a bacterial ARI, a viral ARI and, optionally non-infectious illness based upon said normalized gene expression values and said classifier(s),
to thereby determine whether the acute respiratory illness in the subject is bacterial in origin, viral in origin, non-infectious in origin, or some combination thereof,
optionally the method further comprising:

(f) comparing the probability to pre-defined thresholds, cut-off values, or ranges of values (e.g., a confidence interval) that indicate likelihood of infection.


 
7. The method of claim 6, wherein the subject is suffering from acute respiratory illness symptoms, and optionally wherein said subject is suspected of having a bacterial infection or a viral infection.
 
8. The method of claim 6 or claim 7, wherein, if the sample does not indicate a likelihood of bacterial ARI or viral ARI, further comprises repeating steps (d) and (e) using only the non-infectious classifier, to determine whether the acute respiratory illness in the subject is non-infectious in origin.
 
9. The method of any one of claims 6 to 8 in which the method further comprises generating a report assigning the subject a score indicating the probability of the etiology of the acute respiratory illness.
 
10. The method as in any one of claims 6 to 9 in which the pre-defined set of genes comprises from 30 to 200 genes listed in Table 1, Table 2, Table 9, Table 10 and/or Table 12.
 
11. The method as in any one of claims 6 to 10 in which the biological sample is selected from the group consisting of peripheral blood, sputum, nasopharyngeal swab, nasopharyngeal wash, bronchoalveolar lavage, endotracheal aspirate, and combinations thereof, and optionally in which the biological sample is a peripheral blood sample.
 
12. The method of any one of claims 6 to 11, wherein a subject determined as suffering from an acute respiratory illness when the etiology is determined to comprise a bacterial ARI, is intended to be administered an antibacterial therapy.
 
13. The method of any one of claims 6 to 11, wherein a subject determined as suffering from an acute respiratory illness when the etiology is determined to comprise a viral ARI, is intended to be administered an antiviral therapy.
 
14. The method of any one of claims 6 to 11, further comprising monitoring a response to a vaccine or a drug in the subject.
 
15. The method of claim 14, wherein the drug is an antibacterial drug or an antiviral drug.
 
16. A system for determining an etiology of an acute respiratory illness in a subject selected from bacterial, viral and/or non-infectious, comprising:

at least one processor;

a sample input circuit configured to receive a biological sample from the subject; a sample analysis circuit coupled to the at least one processor and configured to determine gene expression levels of the biological sample;

an input/output circuit coupled to the at least one processor;

a storage circuit coupled to the at least one processor and configured to store data, parameters, and/or classifiers; and

a memory coupled to the processor and comprising computer readable program code embodied in the memory that when executed by the at least one processor causes the at least one processor to perform operations comprising:

controlling/performing measurement via the sample analysis circuit of gene expression levels of a pre-defined set of genes (i.e., signature) in said biological sample; normalizing the gene expression levels to generate normalized gene expression values; retrieving from the storage circuit a bacterial acute respiratory infection (ARI) classifier, a viral ARI classifier, and optionally a non-infectious illness classifier, said classifier(s) comprising pre- defined weighting values (i.e., coefficients) for each of the genes of the pre-defined set of genes, wherein the bacterial acute respiratory infection (ARI) classifier comprises expression levels of 5 to 200 of the genes listed as part of a bacterial classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12, the viral ARI classifier comprises expression levels of 5 to 200 of the genes listed as part of a viral classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12, and the non-infectious illness classifier comprises expression levels of 5 to 200 of the genes listed as part of a non-infectious illness classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12;

entering the normalized gene expression values into the bacterial acute respiratory infection (ARI) classifier, the viral ARI classifier, and optionally the non-infectious illness classifier;

calculating an etiology probability for a bacterial ARI, a viral ARI, and optionally non- infectious illness based upon said classifier(s); and

controlling output via the input/output circuit of a determination whether the acute respiratory illness in the subject is bacterial in origin, viral in origin, non-infectious in origin, or some combination thereof,

optionally where said system comprises computer readable code to transform quantitative, or semi-quantitative, detection of gene expression to a cumulative score or probability of the etiology of the ARI,

optionally wherein said system comprises an array platform, a thermal cycler platform (e.g., multiplexed and/or real-time PCR platform), a hybridization and multi-signal coded (e.g., fluorescence) detector platform, a nucleic acid mass spectrometry platform, a nucleic acid sequencing platform, or a combination thereof,

optionally wherein the pre-defined set of genes comprises from 30 to 200 genes listed in Table 1, Table 2, Table 9, Table 10 and/or Table 12.


 


Ansprüche

1. Verfahren zur Schaffung von Klassifizierern für akute Atemwegserkrankungen für eine Plattform, wobei die Klassifizierer einen bakteriellen ARI-Klassifizierer, einen viralen ARI-Klassifizierer und einen Klassifizierer für nicht infektiöse Erkrankungen für die Plattform umfassen, wobei das Verfahren Folgendes umfasst:

(a) Bereitstellen biologischer Proben, welche von einer Vielzahl von Subjekten erhalten wurden, welche bekanntermaßen an einer akuten bakteriellen Atemwegsinfektion leiden;

(b) Bereitstellen biologischer Proben, welche von einer Vielzahl von Subjekten erhalten wurden, welche bekanntermaßen an einer akuten viralen Atemwegsinfektion leiden;

(c) Bereitstellen biologischer Proben, welche von einer Vielzahl von Subjekten erhalten wurden, welche bekanntermaßen an einer nicht infektiösen Erkrankung leiden;

(d) Messen, auf der Plattform, der Genexpressionsniveaus einer Vielzahl von Genen (zum Beispiel, aller exprimierten Gene oder des Transkriptoms, oder einer Teilmenge davon) in jeder der biologischen Proben aus den Schritten (a), (b) und (c);

(e) Normalisieren der in Schritt (d) erhaltenen Genexpressionsniveaus zum Erzeugen normalisierter Genexpressionswerte; und

(f) Erzeugen eines bakteriellen ARI-Klassifizierers, eines viralen ARI-Klassifizierers und eines Klassifizierers für nicht infektiöse Erkrankung für die Plattform, basierend auf den normalisierten Genexpressionswerten, um dadurch die Klassifiziere für akute Atemwegserkrankungen zu schaffen, wobei optionsweise das Messen einen der folgenden Schritte umfasst oder diese Schritte dem Messen vorausgehen: Reinigen von Zellen aus der Probe, Aufschluss der Zellen der Probe und Isolieren von ARN aus der Probe.


 
2. Verfahren nach Anspruch 1, wobei das Messen halbquantitative PCR- und/oder Nukleinsäure-Sonden-Hybridisierung umfasst, oder wobei die Plattform eine Array-Plattform, eine Thermocycler-Plattform (beispielsweise Multiplex- und/oder Echtzeit-PCR-Plattform), eine Hybridisierungs- und multisignalcodierte (beispielsweise Fluoreszenz-) Detektorplattform, eine Nukleinsäure-Massenspektrometrieplattform, eine Nukleinsäure-Sequenzierungsplattform oder eine Kombination davon umfasst.
 
3. Verfahren nach Anspruch 1, wobei das Erzeugen wiederholend Folgendes umfasst:

(i) Zuweisen eines Gewichts für jeden normalisierten Genexpressionswert, Eingeben des Gewichts und des Expressionswertes für jedes Gen in eine Klassifizierer-Gleichung (beispielsweise, einen Klassifizierer mit linearer Regression) und Bestimmen eines Ergebnis-Scores für jedes der Vielzahl von Subjekten, und anschließend

(ii) Bestimmen der Klassifizierungsgenauigkeit für jedes Ergebnis über die Vielzahl von Subjekten, und anschließend

(iii) Anpassen des Gewichts, bis die Klassifizierungsgenauigkeit optimiert ist, um den bakteriellen ARI-Klassifizierer, den viralen ARI-Klassifizierer und den Klassifizierer für nicht infektiöse Erkrankungen an die Plattform bereitzustellen,

wobei Gene, welche ein Gewicht ungleich null aufweisen, in dem jeweiligen Klassifizierer eingeschlossen sind,

und optionsweise, Hochladen von Komponenten eines jeden Klassifizierers (Gene, Gewichte und/oder Ätiologie-Schwellenwert) in eine oder mehrere Datenbanken, wobei optionsweise der Klassifizierer ein Klassifizierer mit linearer Regression ist und das Erzeugen Umwandeln eines Scores des Klassifizierers in eine Wahrscheinlichkeit umfasst.


 
4. Verfahren nach einem der Ansprüche 1 bis 3, ferner umfassend das Abgleichen des ARI-Klassifizierers mit einem bekannten Datensatz, welcher mindestens zwei relevante klinische Attribute umfasst.
 
5. Verfahren nach einem der Ansprüche 1 bis 4, wobei der bakterielle ARI-Klassifizierer Expressionsniveaus von 5, 10, 20, 30 oder 50, bis 80, 100, 150 oder 200 derjenigen Gene (messbar beispielsweise mit zu diesen Genen homologen Oligonukleotid-Sonden), welche als Bestandteil eines bakteriellen ARI-Klassifizierers in Tabelle 1, Tabelle 2, Tabelle 9, Tabelle 10 und/oder Tabelle 12 aufgelistet sind, umfasst; oder

wobei der virale Klassifizierer Expressionsniveaus von 5, 10, 20, 30 oder 50, bis 80, 100, 150 oder 200 derjenigen Gene (messbar beispielsweise mit zu diesen Genen homologen Oligonukleotid-Sonden), welche als Bestandteil eines viralen ARI-Klassifizierers in Tabelle 1, Tabelle 2, Tabelle 9, Tabelle 10 und/oder Tabelle 12 aufgelistet sind, umfasst; oder

wobei der nicht infektiöse Klassifizierer Expressionsniveaus von 5, 10, 20, 30 oder 50, bis 80, 100, 150 oder 200 derjenigen Gene (messbar beispielsweise mit zu diesen Genen homologen Oligonukleotid-Sonden), welche als Bestandteil eines Klassifizierers für nicht infektiöse Erkrankungen in Tabelle 1, Tabelle 2, Tabelle 9, Tabelle 10 und/oder Tabelle 12 aufgelistet sind, umfasst.


 
6. Verfahren zum Bestimmen einer Ätiologie einer akuten Atemwegserkrankung bei einem daran leidenden oder davon gefährdeten Subjekt, gewählt aus bakteriell, viral und/oder nicht infektiös, Folgendes umfassend:

(a) Bereitstellen einer biologischen Probe, welche von dem Subjekt erhalten wurde;

(b) Messen, an einer Plattform, von Genexpressionsniveaus einer vorbestimmten Menge an Genen (d. h., einer Signatur) in der biologischen Probe;

(e) Normalisieren der Genexpressionsniveaus zum Erzeugen normalisierter Genexpressionswerte;

(d) Eingeben der normalisierten Genexpressionswerte in einen bakteriellen Klassifizierer für akute Atemwegsinfektion (ARI), einen viralen ARI-Klassifizierer und optionsweise einen Klassifizierer für nicht infektiöse Erkrankungen, wobei der bzw. die Klassifizierer vorbestimmte Gewichtungswerte (d. h., Koeffizienten) für jeden der Gene der vorbestimmten Menge an Genen für die Plattform umfassen, wobei der bakterielle Klassifizierer für akute Atemwegsinfektion (ARI) Expressionsniveaus von 5 bis 200 derjenigen Gene umfasst, welche als Bestandteil eines bakteriellen Klassifizierers in Tabelle 1, Tabelle 2, Tabelle 9, Tabelle 10 und/oder Tabelle 12 aufgelistet sind, wobei der virale ARI-Klassifizierer Expressionsniveaus von 5 bis 200 derjenigen Gene umfasst, welche als Bestandteil eines viralen Klassifizierers in Tabelle 1, Tabelle 2, Tabelle 9, Tabelle 10 und/oder Tabelle 12 aufgelistet sind, und der Klassifizierer für nicht infektiöse Erkrankungen Expressionsniveaus von 5 bis 200 derjenigen Gene umfasst, welche als Bestandteil eines Klassifizierers für nicht infektiöse Erkrankungen in Tabelle 1, Tabelle 2, Tabelle 9, Tabelle 10 und/oder Tabelle 12 aufgelistet sind, wobei optionsweise der bzw. die Klassifizierer aus einer oder mehreren Datenbanken abgerufen werden; und

(e) Berechnen einer Ätiologie-Wahrscheinlichkeit für eine bakterielle ARI, eine virale ARI und optionsweise für eine nicht infektiöse Erkrankung, basierend auf den normalisierten Genexpressionswerten und dem bzw. den Klassifizierer(n),
um hierdurch zu bestimmen, ob eine akute Atemwegserkrankung bei dem Subjekt bakteriellen Ursprungs, viralen Ursprungs oder nicht infektiösen Ursprungs oder eine Kombination davon ist, wobei das Verfahren ferner Folgendes umfasst:

(f) Vergleichen der Wahrscheinlichkeit mit vorbestimmten Schwellenwerten, Cut-off-Werten oder Wertbereichen (beispielsweise, einem Vertrauensintervall), welche die Infektionswahrscheinlichkeit angeben.


 
7. Verfahren nach Anspruch 6, wobei das Subjekt an Symptomen einer akuten Atemwegserkrankung leidet, und wobei optionsweise das Subjekt verdächtigt wird, an einer bakteriellen Infektion oder einer viralen Infektion zu leiden.
 
8. Verfahren nach Anspruch 6 oder Anspruch 7, wobei, wenn die Probe keine Wahrscheinlichkeit einer bakteriellen ARI oder einer viralen ARI anzeigt, das Verfahren ferner das Wiederholen der Schritte (d) und (e) umfasst mit nur dem nicht infektiösen Klassifizierer, um zu bestimmen, ob die akute Atemwegserkrankung des Subjekts nicht infektiösen Ursprungs ist.
 
9. Verfahren nach einem der Ansprüche 6 bis 8, wobei das Verfahren ferner Erzeugen eines Berichts umfasst, welcher dem Subjekt einen Score zuweist, welcher die Wahrscheinlichkeit der Ätiologie der akuten Atemwegserkrankung anzeigt.
 
10. Verfahren nach einem der Ansprüche 6 bis 9, wobei die vorbestimmte Menge an Genen 30 bis 200 Gene umfasst, welche in Tabelle 1, Tabelle 2, Tabelle 9, Tabelle 10 und/oder Tabelle 12 aufgelistet sind.
 
11. Verfahren nach einem der Ansprüche 6 bis 10, wobei die biologische Probe aus der Gruppe gewählt ist, bestehend aus peripherem Blut, Auswurf, Nasen-/Rachenabstrich, Nasen-/Rachenspülung, bronchoalveolärer Lavage, endotrachealem Aspirat und Kombinationen davon, und wobei optionsweise die biologische Probe eine Probe von peripherem Blut ist.
 
12. Verfahren nach einem der Ansprüche 6 bis 11, wobei ein Subjekt, welches als an einer akuten Atemwegserkrankung leidend bestimmt wurde, wenn die Ätiologie dahingehend bestimmt wurde, dass sie eine bakterielle ARI umfasst, dazu bestimmt ist, eine antibakterielle Therapie verabreicht zu bekommen.
 
13. Verfahren nach einem der Ansprüche 6 bis 11, wobei ein Subjekt, welches als an einer akuten Atemwegserkrankung leidend bestimmt wurde, wenn die Ätiologie dahingehend bestimmt wurde, dass sie eine virale ARI umfasst, dazu bestimmt ist, eine antivirale Therapie verabreicht zu bekommen.
 
14. Verfahren nach einem der Ansprüche 6 bis 11, ferner umfassend das Überwachen einer Reaktion auf eine Impfung oder ein Medikament bei dem Subjekt.
 
15. Verfahren nach Anspruch 14, wobei das Medikament ein antibakterielles Medikament oder ein antivirales Medikament ist.
 
16. System zum Bestimmen einer Ätiologie einer akuten Atemwegserkrankung bei einem Subjekt, gewählt aus bakteriell, viral und/oder nicht infektiös, Folgendes umfassend:

mindestens einen Prozessor;

einen Proben-Eingabeschaltkreis, welcher konfiguriert ist, um eine biologische Probe von einem Subjekt aufzunehmen;

einen Probenanalyseschaltkreis, welcher mit dem mindestens einen Prozessor gekoppelt und konfiguriert ist, um Genexpressionsniveaus der biologischen Probe zu bestimmen;

einen Eingabe-/Ausgabeschaltkreis, welcher mit dem mindestens einen Prozessor gekoppelt ist;

einen Speicherschaltkreis, welcher mit dem mindestens einen Prozessor gekoppelt und konfiguriert ist, um Daten, Parameter und/oder Klassifizierer zu speichern; und

einen Speicher, welcher mit dem Prozessor gekoppelt ist und einen computerlesbaren Programmcode umfasst, welcher in dem Speicher verkörpert ist, welcher, wenn er durch den mindestens einen Prozessor ausgeführt wird, den mindestens einen Prozessor dazu veranlasst, Schritte auszuführen, welche Folgendes umfassen:

Steuern/Durchführen einer Messung über den Probenanalyseschaltkreis von Genexpressionsniveaus einer vorbestimmten Menge an Genen (d. h., einer Signatur) in der biologische Probe; Normalisieren der Genexpressionswerte zum Erzeugen normalisierter Genexpressionswerte; Abrufen, aus dem Speicherschaltkreis, eines bakteriellen Klassifizierers für akute Atemwegsinfektion (ARI), eines viralen ARI-Klassifizierers und optionsweise eines Klassifizierers für nicht infektiöse Erkrankungen, wobei der bzw. die Klassifizierer vorbestimmte Gewichtungswerte (d. h., Koeffizienten) für jeden der Gene der vorbestimmten Menge an Genen umfassen, wobei der bakterielle Klassifizierer für akute Atemwegsinfektion (ARI) Expressionsniveaus von 5 bis 200 derjenigen Gene umfasst, welche als Bestandteil eines bakteriellen Klassifizierers in Tabelle 1, Tabelle 2, Tabelle 9, Tabelle 10 und/oder Tabelle 12 aufgelistet sind, wobei der virale ARI-Klassifizierer Expressionsniveaus von 5 bis 200 derjenigen Gene umfasst, welche als Bestandteil eines viralen Klassifizierers in Tabelle 1, Tabelle 2, Tabelle 9, Tabelle 10 und/oder Tabelle 12 aufgelistet sind, und der Klassifizierer für nicht infektiöse Erkrankungen Expressionsniveaus von 5 bis 200 derjenigen Gene umfasst, welche als Bestandteil eines Klassifizierers für nicht infektiöse Erkrankungen in Tabelle 1, Tabelle 2, Tabelle 9, Tabelle 10 und/oder Tabelle 12 aufgelistet sind;

Eingeben der normalisierten Genexpressionswerte in den bakteriellen Klassifizierer für akute Atemwegsinfektion (ARI), den viralen ARI-Klassifizierer und optionsweise den Klassifizierer für nicht infektiöse Erkrankungen;

Berechnen einer Ätiologie-Wahrscheinlichkeit für eine bakterielle ARI, eine virale ARI und optionsweise für eine nicht infektiöse Erkrankung, basierend auf dem bzw. den Klassifizierer(n), und

Steuern der Ausgabe über den Eingabe-/Ausgabeschaltkreis einer Bestimmung, ob die akute Atemwegserkrankung des Subjekts bakteriellen Ursprungs, viralen Ursprungs, nicht infektiösen Ursprungs oder eine Kombination davon ist,

wobei optionsweise das System computerlesbaren Code umfasst, um quantitative oder halbquantitative Detektion einer Genexpression in einen kumulativen Score oder eine kumulative Warscheinlichkeit der Ätiologie der ARI umzuwandeln,

wobei optionsweise das System eine Array-Plattform, eine Thermocycler-Plattform (beispielsweise Multiplex- und/oder Echtzeit-PCR-Plattform), eine Hybridisierungs- und multisignalcodierte (beispielsweise Fluoreszenz-) Detektorplattform, eine Nukleinsäure-Massenspektrometrieplattform, eine Nukleinsäure-Sequenzierungsplattform oder eine Kombination davon umfasst,

wobei optionsweise die vorbestimmte Menge an Genen 30 bis 200 Gene umfasst, welche in Tabelle 1, Tabelle 2, Tabelle 9, Tabelle 10 und/oder Tabelle 12 aufgelistet sind.


 


Revendications

1. Procédé pour constituer des classificateurs de maladies respiratoires aiguës ou ARI pour une plate-forme, dans lequel les classificateurs comprennent un classificateur d'ARI bactériennes, un classificateur d'ARI virales et un classificateur de maladies non infectieuses pour la plate-forme, ledit procédé comprenant :

(a) la fourniture d'échantillons biologiques qui ont été obtenus à partir d'une pluralité de sujets qui sont connus comme souffrant d'une infection respiratoire aiguë bactérienne ;

(b) la fourniture d'échantillons biologiques qui ont été obtenus à partir d'une pluralité de sujets qui sont connus comme souffrant d'une infection respiratoire aiguë virale ;

(c) la fourniture d'échantillons biologiques qui ont été obtenus à partir d'une pluralité de sujets qui sont connus comme souffrant d'une maladie non infectieuse ;

(d) la mesure, sur ladite plate-forme, des niveaux d'expression de gènes d'une pluralité de gènes (par exemple tous les gènes exprimés ou la totalité du transcriptome exprimé ou un sous-jeu de ces gènes ou de ce transcriptome) dans chacun desdits échantillons biologiques qui sont issus des étapes (a), (b) et (c) ;

(e) la normalisation des niveaux d'expression de gènes qui sont obtenus au niveau de l'étape (d) pour générer des valeurs d'expression de gènes normalisées ; et

(f) la génération d'un classificateur d'ARI bactériennes, d'un classificateur d'ARI virales et d'un classificateur de maladies non infectieuses pour la plate-forme sur la base desdites valeurs d'expression de gènes normalisées pour ainsi constituer les classificateurs de maladies respiratoires aiguës pour la plate-forme, en option dans lequel ladite mesure comprend ou est précédée par une ou plusieurs étapes parmi : la purification des cellules qui sont issues dudit échantillon, la partition des cellules dudit échantillon et l'isolement de l'ARN dudit échantillon.


 
2. Procédé selon la revendication 1, dans lequel ladite mesure comprend une hybridation semi-quantitative par PCR et/ou par sonde d'acide nucléique, ou dans lequel ladite plate-forme comprend une plate-forme à réseau, une plate-forme à thermocycleurs (par exemple une plate-forme PCR multiplexe et/ou en temps réel), une plate-forme de détection d'hybridation et codée multi-signaux (par exemple la fluorescence), une plate-forme de spectrométrie de masse des acides nucléiques, une plate-forme de séquençage des acides nucléiques ou une combinaison de ces plateformes.
 
3. Procédé selon la revendication 1, dans lequel ladite génération comprend de façon itérative :

(i) l'attribution d'un poids à chaque valeur d'expression de gène normalisée, l'entrée du poids et de la valeur d'expression pour chaque gène à l'intérieur d'une équation de classificateur (par exemple un classificateur à régression linéaire) et la détermination d'un score de résultat pour chacun de la pluralité de sujets ; puis

(ii) la détermination de la précision de classification pour chaque résultat sur la pluralité de sujets ; et puis

(iii) le réglage du poids jusqu'à ce que la précision de classification soit optimisée, pour fournir ledit classificateur d'ARI bactériennes, ledit classificateur d'ARI virales et ledit classificateur de maladies non infectieuses pour la plate-forme ;

dans lequel les gènes qui présentent un poids non à zéro sont inclus dans le classificateur respectif ;

et en option, le téléchargement des composants de chaque classificateur (les gènes, les poids et/ou une valeur seuil d'étiologie) sur une ou plusieurs bases de données, en option dans lequel le classificateur est un classificateur à régression linéaire et ladite génération comprend la conversion d'un score dudit classificateur selon une probabilité.


 
4. Procédé selon l'une quelconque des revendications 1 à 3, comprenant en outre la validation dudit classificateur d'ARI par rapport à un jeu de données connu qui comprend au moins deux attributs cliniques pertinents.
 
5. Procédé selon l'une quelconque des revendications 1 à 4, dans lequel le classificateur d'ARI bactériennes comprend des niveaux d'expression de 5, 10, 20, 30 ou 50 à 80, 100, 150 ou 200 des gènes (qui peuvent être mesurés par exemple à l'aide de sondes d'oligonucléotides qui sont homologues auxdits gènes) qui sont listés en tant que partie d'un classificateur d'ARI bactériennes dans Tableau 1, Tableau 2, Tableau 9, Tableau 10 et/ou Tableau 12 ; ou

dans lequel le classificateur d'ARI virales comprend des niveaux d'expression de 5, 10, 20, 30 ou 50 à 80, 100, 150 ou 200 des gènes (qui peuvent être mesurés par exemple à l'aide de sondes d'oligonucléotides qui sont homologues auxdits gènes) qui sont listés en tant que partie d'un classificateur d'ARI virales dans Tableau 1, Tableau 2, Tableau 9, Tableau 10 et/ou Tableau 12 ; ou

dans lequel le classificateur de maladies non infectieuses comprend des niveaux d'expression de 5, 10, 20, 30 ou 50 à 80, 100, 150 ou 200 des gènes (qui peuvent être mesurés par exemple à l'aide de sondes d'oligonucléotides qui sont homologues auxdits gènes) qui sont listés en tant que partie d'un classificateur de maladies non infectieuses dans Tableau 1, Tableau 2, Tableau 9, Tableau 10 et/ou Tableau 12.


 
6. Procédé pour déterminer une étiologie d'une maladie respiratoire aiguë chez un sujet qui en souffre ou qui risque de la développer, sélectionnée parmi une maladie bactérienne, virale et/ou non infectieuse, comprenant :

(a) la fourniture d'un échantillon biologique qui a été obtenu à partir du sujet ;

(b) la mesure, sur une plate-forme, de niveaux d'expression de gènes d'un jeu prédéfini de gènes (à savoir une signature) dans ledit échantillon biologique ;

(c) la normalisation des niveaux d'expression de gènes pour générer des valeurs d'expression de gènes normalisées ;

(d) l'entrée des valeurs d'expression de gènes normalisées à l'intérieur d'un classificateur d'infections respiratoires aiguës (ARI) bactériennes, d'un classificateur d'ARI virales et en option, d'un classificateur de maladies non infectieuses, ledit/lesdits classificateur(s) comprenant des valeurs de pondération prédéfinies (à savoir des coefficients) pour chacun des gènes du jeu prédéfini de gènes pour la plate-forme, dans lequel le classificateur d'infections respiratoires aiguës (ARI) bactériennes comprend des niveaux d'expression de 5 à 200 des gènes qui sont listés en tant que partie d'un classificateur bactérien dans Tableau 1, Tableau 2, Tableau 9, Tableau 10 et/ou Tableau 12, le classificateur d'ARI virales comprenant des niveaux d'expression de 5 à 200 des gènes qui sont listés en tant que partie d'un classificateur viral dans Tableau 1, Tableau 2, Tableau 9, Tableau 10 et/ou Tableau 12, et le classificateur de maladies non infectieuses comprend des niveaux d'expression de 5 à 200 des gènes qui sont listés en tant que partie d'un classificateur de maladies non infectieuses dans Tableau 1, Tableau 2, Tableau 9, Tableau 10 et/ou Tableau 12, en option dans lequel ledit/lesdits classificateur(s) est/sont extrait(s) à partir d'une ou de plusieurs bases de données ; et

(e) le calcul d'une probabilité d'étiologie pour une ARI bactérienne, une ARI virale et en option, une maladie non infectieuse sur la base desdites valeurs d'expression de gènes normalisées et dudit/desdits classificateur(s),
pour ainsi déterminer si la maladie respiratoire aiguë chez le sujet est d'origine bactérienne, d'origine virale, d'origine non infectieuse ou relève d'une quelconque combinaison de ces origines, en option le procédé comprenant en outre :

(f) la comparaison de la probabilité à des seuils prédéfinis, à des valeurs de coupure prédéfinies ou à des plages prédéfinies de valeurs (par exemple un intervalle de confiance) qui indiquent une vraisemblance d'infection.


 
7. Procédé selon la revendication 6, dans lequel le sujet souffre de symptômes de maladie respiratoire aiguë et en option, dans lequel ledit sujet est suspecté de présenter une infection bactérienne ou une infection virale.
 
8. Procédé selon la revendication 6 ou la revendication 7, dans lequel, si l'échantillon n'indique pas une vraisemblance d'ARI bactérienne ou d'ARI virale, le procédé comprend en outre la répétition des étapes (d) et (e) en utilisant seulement le classificateur de maladies non infectieuses, pour déterminer si la maladie respiratoire aiguë chez le sujet est d'origine non infectieuse.
 
9. Procédé selon l'une quelconque des revendications 6 à 8, dans lequel le procédé comprend en outre la génération d'un rapport qui attribue au sujet un score qui indique la probabilité de l'étiologie de la maladie respiratoire aiguë.
 
10. Procédé selon l'une quelconque des revendications 6 à 9, dans lequel le jeu prédéfini de gènes comprend de 30 à 200 gènes qui sont listés dans Tableau 1, Tableau 2, Tableau 9, Tableau 10 et/ou Tableau 12.
 
11. Procédé selon l'une quelconque des revendications 6 à 10, dans lequel l'échantillon biologique est sélectionné parmi le groupe qui est constitué par du sang périphérique, une expectoration, un écouvillonnage du nasopharynx, un nettoyage du nasopharynx, un lavage broncho-alvéolaire, une aspiration endotrachéale et des combinaisons de ces fluides corporels et en option, dans lequel l'échantillon biologique est un échantillon de sang périphérique.
 
12. Procédé selon l'une quelconque des revendications 6 à 11, dans lequel un sujet qui est déterminé comme souffrant d'une maladie respiratoire aiguë, lorsque l'étiologie est déterminée comme comprenant une ARI bactérienne, est destiné à se voir administrer une thérapie antibactérienne.
 
13. Procédé selon l'une quelconque des revendications 6 à 11, dans lequel un sujet qui est déterminé comme souffrant d'une maladie respiratoire aiguë, lorsque l'étiologie est déterminée comme comprenant une ARI virale, est destiné à se voir administrer une thérapie antivirale.
 
14. Procédé selon l'une quelconque des revendications 6 à 11, comprenant en outre la surveillance d'une réponse à un vaccin ou à un médicament chez le sujet.
 
15. Procédé selon la revendication 14, dans lequel le médicament est un médicament antibactérien ou un médicament antiviral.
 
16. Système pour déterminer une étiologie d'une maladie respiratoire aiguë chez un sujet qui est sélectionnée parmi une maladie bactérienne, virale et/ou non infectieuse, comprenant :

au moins un processeur ;

un circuit d'entrée d'échantillon qui est configuré pour recevoir un échantillon biologique qui est issu du sujet ;

un circuit d'analyse d'échantillon qui est couplé à l'au moins un processeur et qui est configuré pour déterminer des niveaux d'expression de gènes de l'échantillon biologique ;

un circuit d'entrée/de sortie qui est couplé à l'au moins un processeur ;

un circuit de stockage qui est couplé à l'au moins un processeur et qui est configuré pour stocker des données, des paramètres et/ou des classificateurs ; et

une mémoire qui est couplée au processeur et qui comprend un code de programme qui peut être lu par un ordinateur, qui est intégré dans la mémoire et qui, lorsqu'il est exécuté par l'au moins un processeur, a pour effet que l'au moins un processeur réalise les opérations comprenant :

la commande/la réalisation d'une mesure, via le circuit d'analyse d'échantillon, de niveaux d'expression de gènes d'un jeu prédéfini de gènes (à savoir une signature) dans ledit échantillon biologique ;

la normalisation des niveaux d'expression de gènes pour générer des valeurs d'expression de gènes normalisées ;

l'extraction, à partir du circuit de stockage, d'un classificateur d'infections respiratoires aiguës (ARI) bactériennes, d'un classificateur d'ARI virales et en option, d'un classificateur de maladies non infectieuses, ledit/lesdits classificateur(s) comprenant des valeurs de pondération prédéfinies (à savoir des coefficients) pour chacun des gènes du jeu prédéfini de gènes, dans lequel le classificateur d'infections respiratoires aiguës (ARI) bactériennes comprend des niveaux d'expression de 5 à 200 des gènes qui sont listés en tant que partie d'un classificateur bactérien dans Tableau 1, Tableau 2, Tableau 9, Tableau 10 et/ou Tableau 12, le classificateur d'ARI virales comprenant des niveaux d'expression de 5 à 200 des gènes qui sont listés en tant que partie d'un classificateur viral dans Tableau 1, Tableau 2, Tableau 9, Tableau 10 et/ou Tableau 12, et le classificateur de maladies non infectieuses comprend des niveaux d'expression de 5 à 200 des gènes qui sont listés en tant que partie d'un classificateur de maladies non infectieuses dans Tableau 1, Tableau 2, Tableau 9, Tableau 10 et/ou Tableau 12 ;

l'entrée des valeurs d'expression de gènes normalisées dans le classificateur d'infections respiratoires aiguës (ARI) bactériennes, le classificateur d'ARI virales et en option, le classificateur de maladies non infectieuses ;

le calcul d'une probabilité d'étiologie pour une ARI bactérienne, une ARI virale et en option, une maladie non infectieuse sur la base dudit/desdits classificateur(s) ; et

la commande de la sortie, via le circuit d'entrée/de sortie, d'une détermination de si la maladie respiratoire aiguë chez le sujet est d'origine bactérienne, d'origine virale, d'origine non infectieuse ou est une combinaison de ces origines ;

en option, dans lequel ledit système comprend un code qui peut être lu par un ordinateur pour transformer une détection quantitative ou semi-quantitative d'expression de gènes en un score cumulatif ou une probabilité cumulative de l'étiologie de l'ARI ;

en option, dans lequel ledit système comprend une plate-forme à réseau, une plate-forme à thermocycleurs (par exemple une plate-forme PCR multiplexe et/ou en temps réelle), une plate-forme de détection d'hybridation et codée multi-signaux (par exemple la fluorescence), une plate-forme de spectrométrie de masse des acides nucléiques, une plate-forme de séquençage des acides nucléiques ou une combinaison de ces plateformes ;

en option, dans lequel le jeu prédéfini de gènes comprend de 30 à 200 gènes qui sont listés dans Tableau 1, Tableau 2, Tableau 9, Tableau 10 et/ou Tableau 12.


 




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Cited references

REFERENCES CITED IN THE DESCRIPTION



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Patent documents cited in the description




Non-patent literature cited in the description