US 2015O133469A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2015/0133469 A1 O'Garra et al. (43) Pub. Date: May 14, 2015

(54) EARLY DETECTION OF TUBERCULOSIS Related U.S. Application Data TREATMENT RESPONSE (60) Provisional application No. 61/610,121, filed on Mar. (71) Applicants: BAYLOR RESEARCH INSTITUTE, 13, 2012. Dallas, TX (US); MEDICAL RESEARCH COUNCIL, Swindon Publication Classification (GB); IMPERIAL COLLEGE (51) Int. Cl. HETICARE NHS TRUST, London CI2O I/68 (2006.01) A63L/33 (2006.01) (72) Inventors: Anne O'Garra, London (GB); Chloe st 7. :08: Bloom, London (GB); Matthew Paul A613 L/4965 (2006.015 Reddoch Berry, London (GB); Robert Wilkinson, Observatory (ZA); Jacques (52) U.S. Cl. F. Banchereau, Montclair, NJ (US); CPC ...... CI2O I/6883 (2013.01); A61 K3I/496 Damien Chaussabel, Bainbridge Island, (2013.01); A61 K3I/4965 (2013.01); A61 K WA (US); Maria Virginia Pascual, 31/455 (2013.01); A61K 31/133 (2013.01); Dallas, TX (US) C12O 2600/158 (2013.01); C12O 2600/I 18 (2013.01) (21) Appl. No.: 14/384,891 (57) ABSTRACT (22) PCT Filed: Mar. 13, 2013 The present invention includes methods for early detection of a treatment response in a patient Suspected of being infected (86). PCT No.: PCT/US2O13/030986 with Mycobacterium tuberculosis. Changes in the blood tran S371 (c)(1), scriptome are detectable within two weeks of the initiation of (2) Date: Sep. 12, 2014 antituberculosis therapy. Patent Application Publication May 14, 2015 Sheet 1 of 66 US 201S/O133469 A1

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EARLY DETECTION OF TUBERCULOSS 2010 May 29; 375(9729): 1920-37. Epub 2010 May 18, states TREATMENT RESPONSE that host or pathogen-specific tuberculosis biomarkers pro vide prognostic information, either for individual patients or CROSS-REFERENCE TO RELATED study cohorts. It is said that detection of Volatile organic APPLICATIONS compounds in the breath of patients with pulmonary tuber 0001. This application claims priority to, and incorporates culosis has been reported, but no study has reported changes by reference in its entirety, provisional patent application Ser. during treatment. It is stated that studies have examined levels of M. tuberculosis antigen 85 and 85B RNA in sputum during No. 61/610,121 filed Mar. 13, 2012, and which is also titled treatment, and the magnitude and duration of increases in this “Early Detection of Tuberculosis Treatment Response.” protein during the first week of treatment predicted relapse or TECHNICAL FIELD OF THE INVENTION failure in four of 42 patients. 0009 Berry et al., “An interferon-inducible neutrophil 0002 The present invention relates in general to the field driven blood transcriptional signature in human tuberculo of Mycobacterium tuberculosis infection, and more particu sis.” Nature 2010 Aug. 19:466(7309):973-7, reports identi larly, to methods for monitoring treatment response and fication of a whole-blood 393 transcript signature for active determining treatment effectiveness. tuberculosis in intermediate and high-burden settings, which is missing in the majority of individuals with latent tubercu STATEMENT OF FEDERALLY FUNDED losis, and missing from healthy controls. The signature cor RESEARCH related with radiological extent of disease and diminished 0003. None. after two months of treatment and reverted to that of healthy controls after completion of treatment. An 86-transcript sig BACKGROUND OF THE INVENTION nature discriminated between active TB and other inflamma tory and infectious diseases, and this signature was also 0004. Without limiting the scope of the invention, its back diminished after two months of treatment. The tuberculosis ground is described in connection with the treatment of signature was dominated by a neutrophil-driven interferon Mycobacterium tuberculosis infection. (IFN)-inducible gene profile, consisting of both IFN-gamma 0005 United States Patent Application Publication No. and type IIFN-alphabeta signaling. 2009/0104602, entitled “Diagnosis of Tuberculosis.” filed by 0010 Marchant et al., “Serological markers of disease Fernandez-Reyes et al. describes methods of diagnosing activity in tuberculosis and HIV infection.” Clin Exp Immu tuberculosis comprising: (i) providing expression data of two mol. 2000 October: 122(1):10-2, states that markers of disease or more markers in a Subject, wherein at least two of said activity are needed to evaluate disease progression and to markers are selected from transthyretin, neopterin, C-reactive monitor response to therapy. It is suggested that e.g., soluble protein (CRP), serum amyloid A (SAA), serum albumin, apoliopoprotein-A1 (Apo-A1), apolipoprotein-A2 (Apo tumour necrosis factor receptor type 1 (STNF-RI) and beta A2), hemoglobin beta, haptoglobin protein, DEP domain pro 2-macroglobulin, could be used as independent markers of tein, leucine-rich alpha-2-glycoprotein (A2GL) and hypo disease activity in TB and HIV infection, respectively. thetical protein DFKZp6671032; and (ii) comparing said 0011 Frahm et al., “Discriminating between latent and expression data to expression data of said marker from a active tuberculosis with multiple biomarker responses.” group of control Subjects, wherein said control Subjects com Tuberculosis (Edinb). 2011 May: 91(3):250-6. Epub 2011 prise patients Suffering from inflammatory conditions other Mar. 10, states that twenty-five biomarkers were evaluated than tuberculosis (TB), thereby determining whether or not and it was found that IL-15 and MCP-1 identified 83% of said test subject has TB. active and 88% of latent infections. The combination of IL-15 0006 United States Patent Application Publication No. and MCP-1 responses was accurate in distinguishing persons 2003/0138813, entitled “Method of diagnosis and disease with active tuberculosis from persons with latent tuberculosis risk assessment, filed by Engstrand et al., relates to methods in this study. of determining information about the likely clinical outcome of a microbiological infection in a patient and also to methods SUMMARY OF THE INVENTION of selecting a suitable therapeutic regimen for a patient with 0012 Part of the inventive subject matter that the present a microbiological infection. The application describes ana invention provides includes methods for early detection of a lyzing the virulence gene of Mycobacterium tuberculosis to treatment response in a patient Suspected of being infected determine the likely clinical outcome. with Mycobacterium tuberculosis. In some embodiments, 0007 Nahid et al., CDC/NIH Workshop Report, “Tuber changes in the blood transcriptome are detectable within two culosis biomarker and Surrogate endpoint research roadmap.” weeks or less of the initiation of antituberculosis therapy. Am J Respir Crit Care Med. 2011 Oct. 15: 184(8):972-9, 0013. In one aspect, a method is provided for evaluating states that Centers for Disease Control and Prevention and tuberculosis treatment response in a patient, the method com National Institutes of Health convened a multidisciplinary prising: measuring expression levels of genes in a biological meeting to discuss Surrogate markers of treatment response in sample from the patient to generate a first gene expression tuberculosis. It is said that, at a minimum, a biomarker of profile, wherein the biological sample is obtained at a first treatment response most useful for drug development would time point that is before or concurrent with commencement of need to: 1) correspond closely with treatment outcomes; 2) tuberculosis treatment; measuring expression levels of genes have a wide dynamic range that would allow analysis as a in a second biological sample from the patient to generate a continuous variable; and 3) provide this information from a second gene expression profile, wherein the second biologi limited number of early time points. cal sample is obtained after commencement of the treatment 0008 Wallis et al., “Biomarkers and diagnostics for tuber but at two weeks or less after commencement; and calculating culosis: progress, needs, and translation into practice. Lancet a temporal molecular response value by comparing the first US 2015/O 133469 A1 May 14, 2015 and second gene expression profiles. In a related aspect, a culosis in a patient Suspected of being infected with Myco method is provided for evaluating effectiveness of tuberculo bacterium tuberculosis, the method comprising: obtaining a sis treatment, the method comprising: measuring expression first gene expression dataset from a sample of the patient at a levels of genes in a biological sample from a tuberculosis first time point, wherein the first time point is before or patient to generate a first gene expression profile, wherein the simultaneous with a commencement of the treatment for biological sample is obtained at a first time point that is before tuberculosis; obtaining a second gene expression dataset or concurrent with commencement of tuberculosis treatment; from the sample of the patient at a second time point, wherein administering the treatment to the patient; measuring expres sion levels of genes in a second biological sample from the the second time point is less than 2 months after the com patient to generate a second gene expression profile, wherein mencement of the treatment for tuberculosis, wherein the first the second biological sample is obtained after commence gene expression dataset and the second gene expression ment of the treatment but at two weeks or less after com dataset comprises one or more genes; comparing the first mencement; and calculating a temporal molecular response gene expression dataset with the second gene expression value by comparing the first and second gene expression dataset; determining that treatment is effective if a significant change between the first gene expression dataset and the profiles. In some embodiments, a significant temporal second gene expression dataset is detected; or determining molecular response value is a biomarker for an effective treat that treatment is ineffective if no change or less than a sig ment. nificant change between the first gene expression dataset and 0014. In a further related aspect, a method is provided for the second gene expression dataset is detected. In one aspect, treating a patient with Mycobacterium tuberculosis infection, the gene expression data set comprises 2, 3, 4, 5, 6, 7, 8, 9, or the method comprising: measuring expression levels of genes 10 genes, between 10 and 19 genes, between 20 and 99 genes in a biological sample from the patient to generate a first gene or 100 or more genes of genes listed in table 1, 3, 4, 5, 6, 7, 8, expression profile, wherein the biological sample is obtained 9, 10, 11, or 12. In another aspect, the gene expression data set at a first time point that is before or concurrent with com comprises one or more genes selected from the group con mencement of treatment for the infection; administering a sisting of genes related to IFN Signaling selected from the treatment for the infection to the patient; measuring expres group consisting of IF135, IFIT1, IFIT3, IFITM1, OAS1, sion levels of genes in a second biological sample from the IRF1, JAK2, SOCS1, STAT1, STAT2, and TAP1, signifi patient to generate a second gene expression profile, wherein cantly changed upon two weeks after initiation of anti-TB the second biological sample is obtained after commence drug treatment. In another aspect, the gene expression data set ment of the treatment but at two weeks or less after com comprises one or more genes selected from the group con mencement; and calculating a temporal molecular response sisting of genes related to Tand B cell signaling selected from value by comparing the first and second gene expression the group consisting of CD40LG, CD79A, CD79A, CD79B, profiles. In an associated method, the treatment is continued if FAS, FCER1G, IL15, IL23A, IL1B, IL1RN, SLAMF1, the temporal molecular response value is significant. In a TLR2, TLR5, TLR8, TNFSF13B, TNFRSF13B, and CD86. further associated method, the treatment is discontinued if the In another aspect, the gene expression data set comprises one temporal molecular response value is not significant. or more genes selected from the group consisting of genes 0015. In some embodiments, the biological sample is related to a complement system selected from the group con blood. In addition, a gene expression profile may comprise sisting of C2, C1OB, C1OC, C4BPA, CD59, CR1, and SER RNA transcriptome expression data. Genes of a gene expres PING 1. In another aspect, the gene expression data set com sion profile may comprise 2, 3, 4, 5, 6, 7, 8, 9, or 10 genes, prises one or more genes selected from the group consisting between 11 and 20 genes, between 21 and 30 genes, between of genes having a role in pattern recognition selected from the 31 and 50 genes, between 51 and 75 genes, between 76 and group consisting of C5, C1OB, C1OC, CASP1, IFIH1, IL1B, 100 genes, between 101 and 200 genes, between 201 and 300 IRF7, NLRC4, OAS1, OAS2, OAS3, NOD2, TLR2, TLR5, genes, between 301 and 500 genes, between 501 and 750 TLR8, and C3AR1. In another aspect, the gene expression genes, or more than 751 genes. In addition, genes of a gene data set comprises one or more genes selected from the group expression profile may comprise genes selected from Table 1, consisting of genes related to JAK family kinases in IL-6 type 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 disclosed herein. cytokine signaling selected from the group consisting of 0016. In related embodiments of these methods, the sec MAPK14, OSM, SOCS1, SOCS3, and STAT1. In another ond time point is 13, 12, 11, 10,9,8,7,6, 5, 4, 3, or 2 days or aspect, the gene expression data set comprises one or more less, or 1 day or less, after commencement of treatment. In genes selected from the group consisting of genes having a other related embodiments, genes of the gene expression role in communication between innate and adaptive immune profile comprise 1, 2, 3, 4, 5, 6 or more different genes cells selected from the group consisting of CD86, CD40LG, selected from the group consisting of IF135, IFIT1, IFIT3, CXCL10, FCER1G, IL15, IL1B, IL1RN, TLR2, TLR5, IFITM1, IRF1, JAK2, SOCS1, STAT1, TAP1, CD40LG, TLR8, TNFRSF13B, and TNFSF13B. In another aspect, the CD79A, CD79B, FAS, FCER1G, IL15, IL1B, IL1RN, gene expression data set comprises one or more genes SLAMF1, TLR2, TLR5, TNFSF13B, C2, C1OB, C1OC, selected from the group consisting of genes related to den C4BPA, CD59, CR1, SERPING1, C5, CASP1, IFIH1, IL1B, dritic cell maturation selected from the group consisting of IRF7, NLRC4, NOD2, MAPK14, OSM, SOCS3, CD86, CD86, CD40LG, CREB5, FCER1G, FCGR1A, FCGR1B, CXCL10, FCER1G, TLR8, CD86, CREB5, FCGR1A, IL15, IL1B, IL1RN, IL23A, JAK2, MAPK14, STAT1, FCGR1B, IL15, IL23A, STAT2, CASP5, ITGAX, PLCG1, STAT2, and TLR2. In another aspect, the gene expression F2RL1, IL18R1, IL18RAP, IRAK3, NFAT5, PDGFA, data set comprises one or more genes selected from the group PLCG1, TRAF5, CD3E, FCGR1C, FCGR2C, FCGR3B, and consisting of genes related to TREM signaling selected from LCK. the group consisting of CASP1, CASP5, IL1B, ITGAX, 0017. In one embodiment, the present invention includes a JAK2, NOD2, PLCG1, TLR2, and TLR5. In another aspect, method to determine effectiveness of a treatment for tuber the gene expression data set comprises one or more genes US 2015/O 133469 A1 May 14, 2015

selected from the group consisting of genes related to a role of 0019. Another embodiment is a method of performing a macrophages, fibroblasts, and endothelial cells in rheumatoid clinical trial to evaluate the effectiveness of a candidate drug arthritis selected from the group consisting of C5. CREB5, believed to be useful in treating Mycobacterium tuberculosis, F2RL1, FCGR1A, IL15, IL18R1, IL18RAP, IL1B, IL1RN, the method comprising: (a) obtaining a biological sample IRAK3, JAK2, MAPK14, NFAT5, OSM, PDGFA, PLCG1, from a patient with a Mycobacterium tuberculosis infection; SOCS1, SOCS3, TLR2, TLR5, TNFSF13B, and TRAF5. In (b) from the patient sample determining a first gene expres another aspect, the gene expression data set comprises one or sion dataset from the sample of the patientata first time point, more genes selected from the group consisting of genes wherein the first time point is before or simultaneous with a related to systemic lupus erythematous signaling selected commencement of the treatment for Mycobacterium tuber culosis in one or more biological sample of the patient; (b) from the group consisting of C5, CD3E, CD40LG, CD79A, administering a candidate drug to the patient, and obtaining a CD79B, FCER1G, FCGR1A, FCGR1B, FCGR1C, second gene expression dataset from a second sample FCGR2C, FCGR3B, IL1B, IL1RN, LCK, NFAT5, PLCG1, obtained from the patient at a second time point, wherein the and TNFSF13B. In another aspect, the gene expression data second time point is less than 2 months after commencement set comprises one or more genes selected from the group of the treatment for Mycobacterium tuberculosis, wherein the consisting of IFI35, IFIT1, IFIT3, IFITM1, OAS1, IRF1, first gene expression dataset and the second gene expression JAK2, SOCS1, STAT1, STAT2, TAP1, CD40LG, CD79A, comprises one or more genes; comparing the first gene CD79A, CD79B, FAS, FCER1G, IL15, IL23A, IL1B, expression dataset with the second gene expression dataset IL1RN, SLAMF1, TLR2, TLR5, TLR8, TNFSF13B, following the treatment with the candidate drug; and deter TNFRSF13B, CD86, C2, C1OB, C1OC, C4BPA, CD59, mining that treatment is effective if a significant change CR1, SERPING1, C5, CASP1, IFIH1, IRF7, NLRC4, OAS2, between the first gene expression dataset and the second gene OAS3, NOD2, TLR2, TLR5, TLR8, C3AR1, MAPK14, expression dataset is detected or determining that treatment is OSM, SOCS3, STAT1, CD86, CD40LG, CXCL10, ineffective if less than a significant change between the first FCER1G, IL15, IL1RN, TLR2, TLR5, TLR8, TNFRSF13B, gene expression dataset and the second gene expression TNFSF13B, CD86, CD40LG, CREB5, FCER1G, FCGR1A, dataset is detected. FCGR1B, IL15, IL1RN, IL23A, JAK2, MAPK14, CASP5, ITGAX, JAK2, NOD2, PLCG1, TLR2, TLR5, CREB5, BRIEF DESCRIPTION OF THE DRAWINGS F2RL1, FCGR1A, IL15, IL18R1, IL18RAP, IL1RN, IRAK3, 0020. The patent or application file contains at least one JAK2, MAPK14, NFAT5, OSM, PDGFA, PLCG1, SOCS3, drawing executed in color. Copies of this patent or patent TLR2, TLR5, TNFSF13B, TRAF5, CD3E, CD40LG, application publication with color drawing(s) will be pro CD79A, FCER1G, FCGR1A, FCGR1B, FCGR1C, vided by the Office upon request and payment of the neces FCGR2C, FCGR3B, IL1RN, LCK, NFAT5, PLCG1, sary fee. TNFSF13B. 0021 For a more complete understanding of the features 0018. In another aspect, the second time point is between and advantages of the present invention, reference is now the start of treatment and two weeks after commencement of made to the detailed description of the invention along with treatment. In another aspect, the significant change between the accompanying figures and in which: FIGS. 1A through the first gene expression dataset and the second gene expres FIG.10E are further detailed in Example 1 herein; FIGS. 11A sion dataset comprises the Sum of transcripts that are greater through 18C are further detailed in Example 2 herein; and than 2-fold different between the first and second time points, FIGS. 19 through 21 are detailed in Example 3 herein. expressed as a percentage of the total number of transcripts in 0022 FIGS. 1A-1B illustrate numbers enrolled, assigned each of the gene signatures (Temporal Molecular Response to cohorts, and included in the analysis of 2011 cohorts. As Algorithm derived for this study). In another aspect the sec shown in FIG. 1A, a total of 67 active (29) and latent (38) TB ond time point is between 2 weeks and 2 months, showing a patients were enrolled into an Untreated South Africa 2011 significant change after the commencement of treatment. In Cohort. A total of 20 active TB patients were included in an another aspect, 2 months and 6 months after the commence Treated South Africa 2011 Cohort. Eleven were randomised ment of treatment. In another aspect, the change between the into an Active TB Training Set and nine into an Active TB Test first gene expression dataset and the second gene expression Set. As shown in FIG. 1B, a total of eight active TB patients dataset comprises is at least twofold change of expression were enrolled into a Treated UK 2011 Cohort. most significant as described by the Temporal Molecular 0023 FIGS. 2A-2F illustrate that a blood gene expression Response from initiation of treatment to after 2 weeks. In treatment response is readily detectable after only two weeks another aspect, the change between the first gene expression of treatment and independent of the pre-treatment signature. dataset and the second gene expression is observed in For FIG. 2A, a profile plot of all detectable transcripts between 10 and 100 percent of genes. In another aspect, the (16835) obtained without any filtering, in the treated active treatment comprises treatment with rifampin, pyrazinamide, TB patients in the South Africa 2011 cohort, including isoniazid ethambutol, or combinations thereof. In certain patients with missing time points, is presented. It can be seen aspects, the treatment comprises treatment with anti-myco that gene expression changes after just two weeks of treat bacterial drugs against drug-sensitive Mtb, including the ment. For FIG. 2B, 664 differentially expressed transcripts addition or Substitution of other anti-mycobacterial agents between untreated active and latent TB patients in the Such as levofloxacin, moxifloxacin, prothioniamide, ethiona Untreated South Africa 2011 cohort were obtained by two mide, cycloserine, amikacin, streptomycin, kanamycin, para fold change from the median and stringent statistical filtering amino salicylic acid, capreomycin, linezolid, TMC-205, or (Mann Whitney, Bonferroni p-0.01). The heatmap shows other similar drugs. In addition it could be applied to the dynamic change of gene expression in response to treatment monitoring of new drugs being tested for greater efficacy, and in the Treated South Africa 2011 cohort normalized to the also new drugs tested against MDR- and XDR-TB. median of all transcripts. FIG. 2C illustrates Ingenuity Path US 2015/O 133469 A1 May 14, 2015 way Analysis (IPA) of the 664 transcripts and shows the top 5B, a temporal molecular response shows significant changes significant pathways. FIG. 2D illustrates Interferon signaling in response at two weeks in the UK cohort (linear mixed pathway from the 664 list in IPA. FIG. 2E illustrates that models, bars represent mean & SD, *** p<0.001, *=p<0. weighted molecular distance to health (MDTH) of the 01, *=p-0.05). FIG.5C illustrates that a diminished response Treated South Africa 2011 cohort significantly diminishes can be seen in each patient by his or her temporal molecular over treatment time (Friedman, Dunns, bars represent median response with apparent different patient response rates. & IQR, ***=p<0.001, **=p<0.01, *=p<0.05). FIG.2F docu 0027 FIG. 6 illustrates that the changing transcriptional ments that temporal molecular response further shows sig response is independent of the magnitude of the untreated nificant and early changes in response to anti-TB treatment transcriptional response. Weighted molecular distance to (ANOVA repeated measures, Tukeys, bars represent mean & health (MDTH) during treatment has been shown to correlate SD). with radiological extent of active TB disease (Berry et al., 0024 FIGS. 3A-3F illustrate that a specific treatment Nature 2010; 466:973-977). FIG. 6 shows that MDTH of the response signature significantly diminishes at two weeks and 664-transcript signature does not significantly correlate with two months after initiation of treatment and after completion the temporal molecular treatment response at two weeks or of treatment. A specific TB treatment response signature was two months compared to pre-treatment (Pearson's correla derived from significantly differentially expressed genes tion, p<0.05), but does at six months and 12 months (Pear between untreated samples in the South Africa Active TB son's correlation, p<0.05). While the treatment response in Training Set and their corresponding six month samples using FIG.5 correlates with cure by MDTH and Temporal Molecu 391 transcripts. FIG. 3A shows a heatmap of South Africa lar Response, the treatment response rate cannot be predicted 2011 Active TB Training Set, normalised to the median of all by the magnitude of the transcriptional response as measured transcripts, shows transcripts differentiating over time in by the MDTH before treatment. response to treatment. FIG. 3B illustrates that a temporal (0028 FIGS. 7A-7B illustrate that individual patient’s molecular response further shows significant and early transcriptional responses occurred at a variable rate in an changes in response to TB treatment in the Active TB Train independently validated test set the 391 gene list, differen ing Set (Repeated measures, Tukeys, bars represent mean & tially expressed genes derived from comparing the untreated SD, ***=p<0.001, **=p<0.01, *=p<0.05). FIG. 3C displays expression profiles and their corresponding end-of-treatment a heatmap of South Africa 2011 Active TB Test Set, normal (six months) expression profiles in the South Africa 2011 ized to the median of all transcripts, and shows transcripts Active TB Training Set. FIG. 7A displays aheatmap of South differentiating over time in response to treatment. FIG. 3D Africa 2011 cohort Active TB Test Set and shows hierarchical illustrates that a temporal molecular response also shows in clustered transcripts normalised to the median of all tran the Active TB Test Set significant and early changes, signifi scripts, differentiating over time per individual. FIG. 7B illus cantly after two weeks of initiation of treatment, in response trates each patient’s temporal molecular response in the to TB treatment. The present inventors have developed this South Africa 2011 cohort Active TB Test Set. Temporal Molecular Response Algorithm for quantifying an (0029 FIGS. 8A-8C show that the Berry et al. (2010)393 active TB patients individual response to treatment; it facili transcript and 86-transcript TB signatures significantly tates, enables, and is of advantage for use in the clinical diminish in response to Successful treatment in the South setting and in drug development clinical trials. FIG.3E shows Africa 2011 Cohort. The 393-transcript and 86-transcript sig the IPA of the 391 transcripts showing the most significant natures were defined as described (Berry et al., Nature 2010; pathways. FIG. 3F illustrates a Venn diagram that shows 466:973-977) as differentiating active TB patients from latent many overlapping genes between the active TB 664-tran TB patients/healthy controls (393 signature), and differenti Script signature and the treatment-specific 391-transcript sig ating active TB patients from patients with other inflamma nature. tory and infectious diseases (86 signature). Both signatures 0025 FIGS. 4A-4B illustrate that each individual diminished in response to anti-TB treatment in the Treated patient’s transcriptional response (391 gene list) occurred at a South Africa 2011 cohort. FIG. 8A displays a heatmap that variable rate for the 391 gene list, which represents differen shows hierarchical clustering of the transcripts, normalized to tially expressed genes derived from comparing the untreated the median of all transcripts, with Samples grouped into time expression profiles and their corresponding end-of-treatment points. FIG. 8B displays a heatmap that shows hierarchical (six months) expression profiles in the South Africa 2011 clustering of the transcripts, normalized to the median of all Active TB Training Set. FIG. 4A displays aheatmap of South transcripts, with samples grouped per individual. FIG. 8C Africa 2011 cohort Active TB Training Set, normalized to the illustrates that temporal molecular response further shows median of all transcripts, and shows hierarchical clustered significant and early changes in response to anti-TB treatment transcripts differentiating over time per individual. FIG. 4B as early as two weeks after treatment initiation (ANOVA illustrates that each patient's temporal molecular response repeated measures, Tukeys, bars represent mean & SD, diminishes in the Active TB Training Set cohort but at differ ***=p<0.001, **=p<0.01, *=p<0.05). ent rates. 0030 FIG. 9 illustrates the numbers enrolled, assigned to 0026 FIGS.5A-5C illustrate that change in treatment spe cohorts, and included in the analysis of a South Africa 2009 cific signature (391 gene list) is validated in an independent cohort. A total of 51 active and latent TB patients were UK cohort. The 391 gene list is derived from the differentially enrolled into the South Africa 2009 Berry et alcohort (Nature expressed genes between the untreated and six month treated 2010; 466:973-977). Forty-four of these patients were samples in the Treated South Africa 2011 cohort. FIG. 5A included in the Untreated South Africa 2011 cohort, where displays a heatmap of the Treated UK 2011 Cohort, normal they were additionally sampled and monitored post-treat ized to the median of all transcripts, and shows diminution of ment. the treatment specific transcriptional signature in the UK 0031 FIGS. 10A-10E illustrate that a change in active TB cohort in response to successful anti-TB treatment. In FIG. transcriptional signatures derived by identical analysis from US 2015/O 133469 A1 May 14, 2015

the different cohorts is still observed and is significant after els, bars represent mean & 95% confidence intervals, ***p<0. two weeks. The active TB transcriptional signatures were 001, **=p<0.01, *=p<0.05). Summary of demographics and shown for each cohort as unsupervised hierarchical clustering clinical data include, as in FIG. 11A, for a South Africa 2011 between the untreated active and latent TB samples, then by cohort: Of the 29 untreated active TB patients, 16 were also Ingenuity Pathway Analysis (IPA), then by forced grouping included in the previous Berry et al (2010) study, and, of the of the samples showing diminishing of the transcriptional 38 untreated latent TB patients, 17 were also included in the signature in response to treatment in a Treated South Africa previous Berry et al. (2010) study. For the study results of 2011 Cohort and lastly by the temporal molecular response. FIG. 12A-12C, all untreated samples were processed again 2011 cohorts were processed on different Illumina HT12 alongside all the other samples. The UK 2011 cohort is as BeadChip versions: V3 and V4. To compensate for this, the described in FIG. 11B. See also Supporting FIG.3 at doi:10. V3 probes were translated into V4 format; there are slightly 1371/journal.pone.0046191g001 (Bloom et al. 2012). fewer probes in V4 than V3. Transcripts were obtained by the 0034 FIGS. 13 A-13E. A blood transcriptional response is same approach and unsupervised clustering showed distinct detectable after two weeks of treatment. In FIG.13A, a profile clustering of the active and latent TB samples in all three of plot of a set of all detectable transcripts (15837), obtained the 2009 cohorts. IPA of the transcripts shows the most highly without any filtering, in the treated active TB patients in the significant pathways contains IFN-signaling in all three South Africa 2011 cohort is displayed. It can be seen that gene cohorts. FIG. 10A shows that for UK training set 2009, 565 expression changes after just two weeks of treatment. In the transcripts in Illumina HT-12 V3 BeadChip, translates to 540 heatmap of FIG. 13B, 664 differentially expressed tran transcripts in Illumina HT-12V4. FIG. 10B shows that for UK scripts, between untreated active and latent TB patients in the test set 2009, 224 transcripts in Illumina HT-12 V3 BeadChip, untreated South Africa 2011 cohort, were obtained by two translates to 214 transcripts in Illumina HT-12 V4 BeadChip. fold change from the median and stringent statistical filtering FIG. 10C shows that for South Africa cohort 2009, 711 tran (Mann Whitney, Bonferroni p-0.01). The heatmap shows the scripts in Illumina FIT-12 V3 BeadChip, translates to 684 dynamic change of gene expression in response to treatment transcripts in Illumina HT-12 V4 BeadChip. FIG. 10D dis in the treated South Africa 2011 cohort normalised to the plays a Venn diagram comparing the active TB transcriptional median of all transcripts. In previously presented FIG. 2C, signatures from each 2009 cohort. FIG. 10E displays a Venn Ingenuity Pathway Analysis (IPA) of the 664 transcripts diagram comparing: 1) all overlapping transcripts in 22 seg shows the top significant pathways. In FIG. 2D, an Interferon ments of the Venn diagram in FIG. 10D (344 transcripts in signaling pathway from the 664 list in IPA is shown. In FIG. Illumina HT-12 V3 BeadChip, translates to 332 transcripts in 13C, weighted molecular distance to health (MDTH) of a Illumina HT-12 V4); 2) the South Africa 2011 active TB treated South Africa 2011 cohort shows the signature signifi 664-transcript signature; and 3) the South Africa 2011 treat cantly diminishes over time (linear mixed models, bars rep ment specific 391-transcript signature. Regardless of how this resent median & IQR, ***=p<0.001, **=p-0.01, *=p.<0.05). host blood transcriptional signature was derived it was sig As shown in FIG. 13D, temporal molecular response further nificantly changed after two weeks post initiation of Success shows significant and early changes in response to anti-TB ful drug treatment. treatment (linear mixed models, bars represent mean & 95% 0032 FIGS. 11A-11B. South Africa: As illustrated in FIG. confidence intervals). See also FIG. 2 at doi:10.1371/journal. 11A, a total of 67 active and latent TB patients were enrolled pone.0046191g002 (Bloom et al. 2012). into an untreated South Africa 2011 Cohort. A total of 29 0035 FIGS. 14A-14B, Individual patient’s transcriptional active TB patients were included in a treated South Africa response occurred at a variable rate. For 320 gene list, differ 2011 Cohort. Fifteen were randomised into an Active TB entially expressed genes derived from comparing the Training Set and fourteen into an Active TB Test Set. UK: As untreated expression profiles and their corresponding end-of illustrated in FIG. 11B, a total of eight active TB patients were treatment (six months) expression profiles in the South Africa enrolled into the treated UK 2011 Cohort. See also FIG. 1 at 2011 Active TB Training Set are evidenced. Heatmap of FIG. doi:10.1371/journal.pone.0046191g001 (Bloom et al. 2012 14A is of South Africa 2011 cohort Active TB Test Set and “Detectable changes in the blood transcriptome are present shows hierarchical clustered transcripts normalised to the after two weeks of antituberculosis therapy. PLOS ONE median of all transcripts, differentiating over time per indi 7(10): e46191). vidual. Diagrams of FIG. 14B illustrate each patient’s tem 0033 FIGS. 12A-12C. Active TB signatures of Berry etal poral molecular response in the South Africa 2011 cohort (2010) also significantly diminish in response to Successful Active TB Test Set. See also Supporting FIG. 2 at doi:10. treatment. 393-transcript and 86-transcript signatures were 1371/journal.pone.0046191g001 (Bloom et al. 2012). defined as described (Reis-Filho and Pusztai 2011, Lancet 0036 FIG. 15. The changing transcriptional response is 378: 1812-1823) differentiating active TB patients from independent of the magnitude of the untreated transcriptional latent TB patients/healthy controls (393 signature), and dif signature. MDTH has been shown to correlate with radiologi ferentiating active TB patients from patients with other cal extent of active TB disease (see ref. 11 of Example 2). inflammatory and infectious diseases (86 signature). Both The magnitude of the patient’s temporal molecular response signatures diminished in response to anti-TB treatment in the during treatment, at both two weeks and two months, did not treated South Africa 2011 cohort. Heatmap of FIG. 12A dis correlate with the magnitude of their untreated transcriptional plays hierarchical clustering of the transcripts, normalised to signature, as evidenced measured by MDTH (linear regres the median of all transcripts, with Samples grouped into time sion r<0.25, p>0.01). However, the patient's temporal points. Heatmap of FIG.12B displays hierarchical clustering molecular response after treatment, at six months and 12 of the transcripts, normalised to the median of all transcripts, months, did significantly correlate with his or her untreated with samples grouped per individual. In FIG. 12C, temporal MDTH (linear regression r=0.32, p=0.003 and r=0.38, p=0. molecular response further shows significant and early 0004, respectively). See also Supporting FIG. 1 at doi:10. changes in response to anti-TB treatment (linear mixed mod 1371/journal.pone.0046191g001 (Bloom et al. 2012). US 2015/O 133469 A1 May 14, 2015

0037 FIGS. 16A-16F. Specific treatment response signa 0041 Table 1 lists genes present in the top significantly ture significantly diminishes at two weeks and onwards. A represented canonical pathways of Ingenuity Pathway Analy specific TB treatment response signature of 320 transcripts sis in the 664 transcript list from an Untreated South Africa was derived from significantly differentially expressed genes 2011 Cohort. between untreated samples in the South Africa Active TB 0042 Summaries of demographics and clinical data are Training Set and their corresponding six month samples. provided in Table 2A, a South African 2011 cohort, and Table Heatmap of FIG. 16A represents South Africa 2011 Active 2B, a UK 2011 cohort. TB Training Set, normalised to the median of all transcripts, 0043 Table 3 lists genes present in the top significantly and shows transcripts differentiating over time in response to represented canonical pathways of Ingenuity Pathway Analy sis in active TB transcriptional signatures of 2009 UK and treatment. FIG. 16B displays corresponding temporal South Africa cohorts. molecular response that further shows significant and early 0044) Tables 4 through 12 provide gene transcript lists for changes in response to TB treatment in the Active TB Train 224, 86, 393, 565, 664, 391, 1129, 711, and 320 genes, ing Set (linear mixed models, bars represent mean & 95% respectively. confidence intervals, ***=p<0.001, **=p<0.01, *=p-0.05). 0045 Related summaries of demographics and clinical Heatmap of FIG. 16C represents South Africa 2011 Active data are provided in Table 13A, a South African 2011 cohort, TB Test Set, normalised to the median of all transcripts, and and Table 13B, a UK 2011 cohort. shows transcripts differentiating over time in response to treatment. FIG. 16D displays corresponding temporal DETAILED DESCRIPTION OF THE INVENTION molecular response that also shows in the Active TB Test Set 0046 While the making and using of various embodi significant and early changes in response to TB treatment. ments of the present invention are discussed in detail below, it FIG.16E summarizes IPA of the 320 transcripts showing the should be appreciated that the present invention provides most significant pathways. FIG.16F is a Venn diagram show many applicable inventive concepts that can be embodied in a ing many overlapping genes between the active TB 664 wide variety of specific contexts. The specific embodiments transcript signature and the treatment specific 320-signature. discussed herein are merely illustrative of specific ways to See also FIG. 3 at doi:10.1371/journal.pone.0046191g003 make and use the invention and do not delimit the scope of the (Bloom et al. 2012). invention. 0038 FIGS. 17A-17B. Individual patient’s transcriptional 0047. To facilitate understanding of this invention, a num response occurred at a variable rate. FIGS. 17-17B concerns ber of terms are defined below. Terms defined herein have the 320 gene list and differentially expressed genes derived meanings as commonly understood by a person of ordinary skill in the areas relevant to the present invention. Terms such from comparing the untreated expression profiles and their as “a”, “an,” and “the are not intended to refer to only a corresponding end-of-treatment (six months) expression pro singular entity but include the general class of which a spe files in a South Africa 2011 Active TB Training Set. Heatmap cific example may be used for illustration. The terminology of FIG. 17A is of South Africa 2011 cohort Active TB Train herein is used to describe specific embodiments of the inven ing Set, normalised to the median of all transcripts, and shows tion, but their usage does not delimit the invention, except as hierarchical clustered transcripts differentiating over time per outlined in the claims. individual. For FIG. 17B, each patient’s temporal molecular 0048 Globally there are approximately nine million new response diminishes in the Active TB Training Set cohort. See active tuberculosis (TB) cases and 1.7 million deaths annu also FIG. 4 at doi:10.1371/journal.pone.0046191g004 ally. Effective anti-TB treatment monitoring is difficult as (Bloom et al. 2012). determining a treatment response by currently used methods takes at least two months. Inadequate treatment leads to wors 0039 FIGS. 18A-18C. Change in treatment specific sig ening disease, disease transmission, and drug resistance. Cur nature is validated in an independent UK cohort. FIGS. 18A rently, the best accepted method to predict treatment success 18C concern the 320 gene list and differentially expressed in pulmonary tuberculosis is the two-month sputum culture genes between the untreated and six-month treated samples in conversion. However, this method is of low sensitivity for the treated South Africa 2011 cohort. Heatmap of FIG. 18A is prediction of individual treatment response and is difficult to of the treated UK 2011 Cohort, normalised to the median of implement since many patients cannot produce sputum. In the all transcripts, and shows diminution of the treatment specific United States 30% of TB patients are treated empirically and transcriptional signature in the UK cohort in response to in South Africa 50% are treated with confirmation of diagno successful anti-TB treatment. For FIG. 18B, the temporal sis by culture. Currently, no recognized biomarkers of poor molecular response shows significant changes at two weeks adherence or inadequate treatment earlier than two months in the UK cohort (linear mixed models, bars represent mean exist. & 95% confidence intervals, ***=p-0.001, **=p<0.01, 0049. The present inventors determined if blood transcrip *=p <0.05). In FIG. 18C, a diminished response can be seen in tional signatures change in response to anti-TB treatment and each patient by their temporal molecular response. See also could act as biomarkers of a Successful response. FIG.5 at doi:10.1371/journal.pone.0046191g005 (Bloom et 0050. Surprisingly, transcriptional blood gene signatures al. 2012). {e.g.: a 664—(Table 8) (FIG. 2B); a 391—(Table 9) (e.g., 0040 FIGS. 19, 20, and 21 provide heatmaps and corre FIGS. 3A, 4A & 7A); an 86–(Table 5) (FIGS. 8A & 8B); a sponding temporal molecular response data for Patient ID 393 (Table 6) (FIGS. 8A & 8B); a 565 (Table 7) (FIG. 2208, Patient ID 2220, and Patient ID 2232, respectively, for 10A); a 224 (Table 4) (FIG. 10B); a 711–(Table 11) (FIG. 320, 86, and 393 transcript lists. FIGS. 19, 20, and 21 also 10C); or a 1129-transcript signature (Table 10) diminish provide summaries of clinical symptoms for Patient ID 2208, within two weeks after commencement of tuberculosis treat Patient ID 2220, and Patient ID 2232, respectively. ment; genes significantly altered in transcription include, e.g., US 2015/O 133469 A1 May 14, 2015 interferon-signaling genes including type I and type II IFN. significance value of p-0.05 between the first and second genes related to the innate immune pathways, genes related to time points temporal molecular response can be employed. complement, toll like receptors, a NOD like receptor gene, In other examples, the number of patients examined may, for and interleukin-1B. example, be eight and the p values0.001. When applying the 0051. As demonstrated herein, a change in whole blood temporal molecular response algorithm to all participants at host transcriptional signatures is significantly detectable as all the time points, the present inventors demonstrated that the early as two weeks or sooner after commencement of treat participants temporal molecular response at two weeks was ment for tuberculosis; this provides early biomarkers for statistically significant compared to their pre-treatment tem treatment monitoring. poral molecular response. In addition, at all time points after 0052. In short, blood transcriptional profiles of untreated two weeks, all participants’ response continued to improve active and latent TB patients in South Africa were analyzed, and the actual results showed that there was merely a 0.1% before, during (at two weeks and at two months), at the end of chance that this change occurred by chance. In some embodi (six months) and after (12 months) anti-TB treatment. The ments a significant change is determined for a single indi signature inactive TB patients as compared to latent individu vidual (independently from results from other individuals). In als (664 transcripts) was significantly diminished by two certain aspects, a percentage using the temporal molecular weeks after initiation of treatment and this significant response is determined. The percentage reflects the percent response was measured using a novel algorithm (termed age of genes that are changing over time relative to the tran “Temporal Molecular Responses') developed for this study. Scriptional signature being tested. This enhances the ability to A specific treatment response-transcriptional signature (391 monitor individual patients in hospitals/clinics. In certain transcripts) was derived and validated in two independent aspects, 19% or more correlates with a good treatment cohorts, to which two quantitative scoring algorithms were response and constitutes a significant change. In other applied to score the changes in the transcriptional response. aspects, any value above 10% correlates with a good treat The most significantly represented pathways were deter ment response and constitutes a significant change. In some mined using Ingenuity Pathway Analysis. The South African embodiment, an individual’s temporal molecular response active TB-transcriptional signature revealed more differen value of greater than 15% difference between the first and tially expressed genes than previously reported in UK second time point constitutes a significant change. cohorts. Interferon inducible genes were highly significantly 0054 As used herein, the term “array' refers to a solid elevated in all cohorts. The active TB-transcriptional signa Support or Substrate with one or more peptides or nucleic acid tures and the treatment specific transcriptional-signature sig probes attached to the Support. Arrays typically have one or nificantly diminished after two weeks of treatment and con more different nucleic acid or peptide probes that are coupled tinued to diminish significantly until six months. Significant to a surface of a substrate in different, known locations. These changes in the transcriptional signatures measured by blood arrays, also described as “microarrays' or “gene-chips' that tests were readily detectable just two weeks after treatment may have 10,000; 20,000, 30,000; or 40,000 different identi initiation. Therefore transcriptional responses provide a clini fiable genes based on the known genome, e.g., the human cal tool for monitoring an individual TB patient's response to genome. These pan-arrays are used to detect the entire “tran treatment. Scriptome' or transcriptional pool of genes that are expressed 0053 As used herein, a “significant change” between gene or found in a sample, e.g., nucleic acids that are expressed as expression datasets is indicative that treatment is effective; in RNA, mRNA and the like that may be subjected to RT and/or contrast, treatment is ineffective if less than a significant RT-PCR to made a complementary set of DNA replicons. change between the first gene expression dataset and the Arrays may be produced using mechanical synthesis meth second gene expression dataset is detected. A significant ods, light directed synthesis methods and the like that incor change can be determined by a person of ordinary skill in the porate a combination of non-lithographic and/or photolitho art upon viewing a clearly visible change in transcriptional graphic methods and solid phase synthesis methods. response using a heatmap or time-scaled profile plot of nor 0055 Various techniques for the synthesis of these nucleic malized intensity values or a simple time-scaled line graph of acid arrays have been described, e.g., fabricated on a Surface the transcriptional signature between the first and second time of virtually any shape or even a multiplicity of Surfaces. point. In some embodiments, the significant change can be Arrays may be peptides or nucleic acids on beads, gels, poly determined upon generating a simple time-scaled line graph meric Surfaces, fibers such as fiber optics, glass or any other (also called a profile plot) of normalized signal intensity appropriate Substrate. Arrays may be packaged in Such a values. See, for example, FIG. 2A. Further embodiments manner as to allow for diagnostics or other manipulation of an determine a significant change by employing heatmaps. This all-inclusive device, see for example, U.S. Pat. No. 6,955, is shown in FIGS. 2B, 3A, 3C, 4A, 5A, 7A, 8A, 8B, and 788, which is incorporated herein by reference in its entirety. 10A-C. The heatmaps are ordered either by time point or by 0056. As used herein, the term “disease' refers to a physi each participant and may also show normalized intensity ological state of an organism with any abnormal biological values. In yet further embodiments, the significant change is state of a cell. Disease includes, but is not limited to, an determined by line graphs showing molecular distance to interruption, cessation or disorder of cells, tissues, body func health (MDTH), as shown in FIG. 2E. In addition the signifi tions, systems or organs that may be inherent, inherited, cant change can be determined and described via a temporal caused by an infection, caused by abnormal cell function, molecular response algorithm, as provided for in FIGS. 2F, abnormal cell division and the like. A disease that leads to a 3B, 3D, 4B, 5B, 5C, 7B, 8C, 10A-C. To statistically demon “disease state' is generally detrimental to the biological sys strate and describe a significant change, a conventional level tem, that is, the host of the disease. With respect to the present of significance of 5% (only a 5% chance this change would invention, any biological state, such as an infection (e.g., occur by chance p-0.05) may be employed. As non-limiting viral, bacterial, fungal, helminthic, etc.), inflammation, auto example upon testing more than three patients, a statistical inflammation, autoimmunity, anaphylaxis, allergies, prema US 2015/O 133469 A1 May 14, 2015

lignancy, malignancy, Surgical, transplantation, physiologi of identity and abundance of RNA, is also referred to hereinas cal, and the like that is associated with a disease or disorder is the transcriptome. Generally, a substantial fraction of all the considered to be a disease state. A pathological state is gen relative constituents of the entire set of RNA species in the erally the equivalent of a disease state. sample are measured. 0057 Disease states may also be categorized into different 0063 Regarding the “expression level.” the group com levels of disease state. As used herein, the level of a disease or parison for a given disease provides the list of differentially disease state is a measure reflecting the progression of a expressed transcripts. It was found that different diseases disease or disease state as well as the physiological response yield different subsets of gene transcripts. upon, during and after treatment. Generally, a disease or 0064. Using the present invention it is possible to deter disease state will progress through levels or stages, wherein mine the effectiveness of a treatment for tuberculosis at the the effects of the disease become increasingly severe. The gene-level; i.e., two diseases can have the same vector (iden level of a disease state may be impacted by the physiological tical proportion of differentially expressed transcripts, iden state of cells in the sample. tical “polarity'), but the gene composition of the vector can 0058 As used herein, the terms “therapy’ or “therapeutic still be disease-specific. Gene-level expression provides the regimen” refer to those medical steps taken to alleviate or distinct advantage of greatly increasing the resolution of the alter a disease state, e.g., a course of treatment intended to analysis. Furthermore, the present invention takes advantage reduce or eliminate the affects or symptoms of a disease using of composite transcriptional markers. pharmacological, Surgical, dietary and/or other techniques. A 0065. As used herein, the term “composite transcriptional therapeutic regimen may include a prescribed dosage of one markers' refers to the average expression values of multiple or more drugs or Surgery. Therapies will most often be ben genes (composite of transcripts) as compared to using indi eficial and reduce the disease state but in many instances the vidual genes as markers (and the composition of these mark effect ofatherapy will have non-desirable or side-effects. The ers can be disease-specific). The composite transcriptional effect of therapy will also be impacted by the physiological markers approach is unique because the user can develop State of the host, e.g., age, gender, genetics, weight, other multivariate microarray scores to assess disease severity in disease conditions, etc. patients with, e.g., tuberculosis (TB) or systemic lupus 0059. As used herein, the term “pharmacological state' or erythematosus (SLE), or to derive expression vectors dis “pharmacological status' refers to those samples from dis closed herein. It has been found that using the composite eased individuals that will be, are and/or were treated with transcriptional markers of the present invention the results one or more drugs, surgery and the like that may affect the found herein are reproducible across microarray platform, pharmacological state of one or more nucleic acids in a thereby providing greater reliability for regulatory approval. sample, e.g., newly transcribed, stabilized and/or destabilized 0.066 Gene expression monitoring systems for use with as a result of the pharmacological intervention. The pharma the present invention may include customized gene arrays cological state of a sample relates to changes in the biological with a limited and/or basic number of genes that are specific status before, during and/or after drug treatment and may and/or customized for the one or more target diseases. Unlike serve as a diagnostic or prognostic function, as taught herein. the general, pan-genome arrays that are in customary use, the Some changes following drug treatment or Surgery may be present invention provides for not only the use of these gen relevant to the disease state and/or may be unrelated side eral pan-arrays for retrospective gene and genome analysis effects of the therapy. Changes in the pharmacological state without the need to use a specific platform, but more impor are the likely results of the duration of therapy, types and tantly, it provides for the development of customized arrays doses of drugs prescribed, degree of compliance with a given that provide an optimal gene set for analysis without the need course of therapy, and/or un-prescribed drugs ingested. for the thousands of other, non-relevant genes. One distinct 0060. As used herein, the term “biological state' refers to advantage of the optimized arrays and gene sets of the present the state of the transcriptome (that is the entire collection of invention over the existing art is a reduction in the financial RNA transcripts) of the cellular sample isolated and purified costs (e.g., cost per assay, materials, equipment, time, person for the analysis of changes in expression. The biological state nel, training, etc.), and more importantly, the environmental reflects the physiological state of the cells in the blood sample cost of manufacturing pan-arrays where the vast majority of by measuring the abundance and/or activity of cellular con the data is irrelevant. By reducing the total number of genes stituents, characterizing according to morphological pheno for analysis, or eliminating genes for analysis, it is possible to, type or a combination of the methods for the detection of e.g., eliminate the need to manufacture thousands of expen transcripts. sive platinum masks for photolithography during the manu 0061. As used herein, the term “expression profile” refers facture of pan-genetic chips that provide vast amounts of to the relative abundance of RNA, DNA abundances or activ irrelevant data. Using the present invention it is possible to ity levels. The expression profile can be a measurement for completely avoid the need for microarrays if the limited probe example of the transcriptional state or the translational state set(s) of the present invention are used with, e.g., digital by any number of methods and using any of a number of optical chemistry arrays, ball bead arrays, multiplex PCR, gene-chips, gene arrays, beads, multiplex PCR, quantitative quantitative PCR, “RNA-seq for measuring mRNA levels PCR, run-on assays, Northern blot analysis, or using RNA using next-generation sequencing technologies, nanostring seq., nanostring, nanopore RNA sequencing etc. Apparatus type technologies or any other method, apparatus and system and system for the determination and/or analysis of gene for the determination and/or analysis of gene expression that expression that are readily commercially available. are readily commercially available. 0062. As used herein, the term “transcriptional state' of a 0067. The “molecular fingerprinting system” of the sample includes the identities and relative abundances of the present invention may be used to facilitate and conduct a RNA species, especially mRNAs present in the sample. The comparative analysis of expression in different cells or tis entire transcriptional state of a sample, that is the combination Sues, different Subpopulations of the same cells or tissues, US 2015/O 133469 A1 May 14, 2015

different physiological states of the same cells or tissue, dif Scriptional studies by taking the conceptualization of ferent developmental stages of the same cells or tissue, or microarray data past the level of individual genes or lists of different cell populations of the same tissue against other genes. diseases and/or normal cell controls. In some cases, the nor 0072 The present inventors have recognized that current mal or wild-type expression data may be from Samples ana microarray-based research is facing significant challenges lyzed at or about the same time or it may be expression data with the analysis of data that are notoriously “noisy.” that is, obtained or culled from existing gene array expression data data that are difficult to interpret and do not compare well bases, e.g., public databases such as the NCBI Gene Expres across laboratories and platforms. A widely accepted sion Omnibus database. approach for the analysis of microarray data begins with the 0068. As used herein, the term “differentially expressed identification of subsets of genes differentially expressed refers to the measurement of a cellular constituent (e.g., between study groups. Users may try Subsequently to “make nucleic acid, protein, enzymatic activity and the like) that sense' out of resulting gene lists using standard algorithms varies in two or more samples, e.g., between a disease sample and existing scientific knowledge and by validating in inde and a normal sample. The cellular constituent may be on or pendent sample sets and in different microarray analyses. off (present or absent), upregulated relative to a reference or downregulated relative to the reference. For use with gene Example 1 chips or gene-arrays, differential gene expression of nucleic acids, e.g., mRNA or other RNAs (miRNA, siRNA, hnRNA, 0073 Pulmonary tuberculosis (PTB) is a major and rRNA, tRNA, etc.) may be used to distinguish between cell increasing cause of morbidity and mortality worldwide types or nucleic acids. Most commonly, the measurement of caused by Mycobacterium tuberculosis (M. tuberculosis). the transcriptional state of a cell is accomplished by quanti However, the majority of individuals infected with M. tuber tative reverse transcriptase (RT) and/or quantitative reverse culosis remain asymptomatic, retaining the infection in a transcriptase-polymerase chain reaction (RT-PCR), genomic latent form, and it is thought that this latent state is maintained expression analysis, post-translational analysis, modifica by an active immune response. Blood is the pipeline of the tions to genomic DNA, translocations, in situ hybridization immune system, and as Such it is the ideal biologic material and the like. from which the health and immune status of an individual can 0069. The skilled artisan will appreciate readily that be established. samples may be obtained from a variety of sources including, 0074 Blood represents a reservoir and a migration com e.g., single cells, a collection of cells, tissue, cell culture and partment for cells of the innate and the adaptive immune the like. In certain cases, it may even be possible to isolate systems, including either neutrophils, dendritic cells and sufficient RNA from cells found in, e.g., urine, blood, saliva, monocytes, or B and T lymphocytes, respectively, which tissue or biopsy samples and the like. In certain circum during infection will have been exposed to infectious agents stances, enough cells and/or RNA may be obtained from: in the tissue. For this reason whole blood from infected indi mucosal Secretion, feces, tears, blood plasma, peritoneal viduals provides an accessible source of clinically relevant fluid, interstitial fluid, intradural, cerebrospinal fluid, sweator material where an unbiased molecular phenotype can be other bodily fluids. The nucleic acid source, e.g., from tissue obtained using gene expression microarrays as previously or cell sources, may include a tissue biopsy sample, one or described for the study of cancer in tissues (Alizadeh AA., more sorted cell populations, cell culture, cell clones, trans 2000: Golub, T R., 1999: Bittner, 2000), and autoimmunity formed cells, biopsies or a single cell. The tissue source may (Bennet, 2003: Baechler, EC, 2003; Burczynski, ME, 2005; include, e.g., brain, liver, heart, kidney, lung, spleen, retina, Chaussabel, D., 2005; Cobb, J.P., 2005: Kaizer, E. C., 2007: bone, neural, lymph node, endocrine gland, reproductive Allantaz, 2005; Allantaz, 2007), and inflammation (Thach, D organ, blood, nerve, vascular tissue, and olfactory epithelium. C., 2005) and infectious disease (Ramillo, Blood, 2007) in blood or tissue (Bleharski, J R et al., 2003). Microarray analy 0070 The present invention includes the following basic ses of gene expression in blood leucocytes have identified components, which may be used alone or in combination, diagnostic and prognostic gene expression signatures, which namely, one or more data mining algorithms, one a novel have led to a better understanding of mechanisms of disease algorithm specifically developed for this TB treatment moni onset and responses to treatment (Bennet, L 2003; Rubins, K toring, the Temporal Molecular Response; the characteriza H., 2004; Baechler, EC, 2003; Pascual, V., 2005; Allantaz, F., tion of blood leukocyte transcriptional gene sets; the use of 2007: Allantaz. F., 2007). These microarray approaches have aggregated gene transcripts in multivariate analyses for the been attempted for the study of active and latent TB but as yet molecular diagnostic/prognostic of human diseases; and/or have yielded small numbers of differentially expressed genes visualization of transcriptional gene set-level data and results. only (Jacobsen, M., Kaufmann, S.H., 2006; Mistry, R. Lukey, Using the present invention it is also possible to develop and PT, 2007), and in relatively small numbers of patients (Mis analyze composite transcriptional markers. The composite try, R., 2007), which may not be robust enough to distinguish transcriptional markers for individual patients in the absence between other inflammatory and infectious diseases. The of control sample analysis may be further aggregated into a present inventors recognize that a neutrophil driven blood reduced multivariate score. transcriptional signature in active TB patients was missing in 0071. An explosion in data acquisition rates has spurred the majority of latent TB individuals and in healthy controls. the development of mining tools and algorithms for the See, also (9). This signature of active TB was reflective of exploitation of microarray data and biomedical knowledge. lung radiographic disease and was diminished after two Approaches aimed at uncovering the function of transcrip months of treatment. The signature was dominated by inter tional systems constitute promising methods for the identifi feron-inducible genes, and at a modular level the active TB cation of robust molecular signatures of disease. Indeed, Such signature was distinct from other infectious or autoimmune analyses can transform the perception of large-scale tran diseases. US 2015/O 133469 A1 May 14, 2015

0075 To define an immune signature in TB, the blood of ERON-TB Gold In-Tube assay (Cellestis). South African TB patients before and after commencement of treatment and active TB patients were sampled before treatment and at two controls were analyzed; patients were selected using very weeks and two, six, and 12 months after treatment initiation. stringent clinical criteria. Response was assessed clinically. The UK 2011 TB patients 0076 Approximately one third of the world is infected were sampled before treatment and at two weeks and two, with the pathogen Mycobacterium tuberculosis (Mtb), the four and six months after treatment initiation. Chest X-rays cause of TB. While most remain asymptomatic, termed were performed before and during treatment. The 2009 latent, approximately 10% develop active TB during their cohorts were as previously described (9). lifetime (1). Over nine million new cases of active TB and 1.7 I0081 Expression Profiling: The following were per million deaths annually have been reported (2). Improved formed according to the manufacturers instructions. Blood diagnostics, more effective and shorter treatments than the was collected into Tempus tubes (Applied Biosystems/Am current minimum of six months, and improvements in treat bion). 2011 sample's RNA was isolated using MagMAX-96 ment monitoring are badly needed. Blood RNA. Isolation Kit (Applied Biosystems/Ambion), 0077 Active pulmonary TB diagnosis requires culture of globin reduced using GLOBINclear 96-well format kit (Ap Mtb, which may take up to six weeks (3). Conventional deter plied Biosystems/Ambion), biotinylated, amplified antisense mination of antibiotic sensitivities demands several more complementary RNA (cRNA) targets were prepared using weeks of culture. Mtb is isolated from sputum, which is often Illumina CustomPrep RNA amplification kit (Applied Bio difficult to obtain, or from lung washings using invasive and systems/Ambion). RNA integrity and yield were assessed expensive methods, which are prohibitive in developing using Agilent 2100 Bioanalyzer (Agilent Technologies) and countries. Due to insufficient samples and poor availability of NanoDrop 800 spectrophotometer (NanoDrop Products, culture, approximately 30% of patients in the USA and 50% Thermo Fisher Scientific), respectively. Labeled cRNA was of South African patients are treated empirically (2, 4). hybridized to Illumina Human HT-12 V4 BeadChip arrays Although the World Health Organization (WHO) endorsed (Illumina) and Scanned on an Illumina iScan. GenomeStudio Xpert MTB/RIF automated molecular test for Mtb results in (Illumina) was used for quality control and to generate signal rapid diagnosis, this test still requires sputum (5). After diag intensity values. 2009 sample's RNA was processed as pre nosis there are no available early biomarkers correlating with viously described (9). Using GeneSpring GX version 11.5 treatment success, resulting in significant delay in assessing (Agilent Technologies) raw data were analyzed by the fol treatment response. In poor responders this delay can result in lowing: background Subtraction, filtering by detection sig worsening disease and spread of drug resistant bacteria. Cur nificance (p<0.01), threshold set, log 2 transformed, per-chip rently sputum conversion to negative culture after two months normalised (75th percentile shift algorithm) and per-gene of treatment is the only accepted biomarker (6). However a normalised to median of latent TB samples. systematic review and meta-analysis to assess its accuracy to I0082 Statistical Analysis: GeneSpring 11.5 was used to predict an individual’s treatment failure revealed low sensi select transcripts with an expression fold change (active TB tivity and modest specificity (7). Chest X-rays are commonly signature: twofold expression from latent TB samples; treat used to assess response but are not universally available and ment specific signature: threefold expression in 8/11 training assessment is difficult to standardize (8). Lack of practicable set matched untreated and six-month treated samples). Sta treatment monitoring is concerning due to the development of tistical filtering was then applied using non-parametric tests multidrug resistant (MDR) and extensively drug resistant and multiple testing corrections (Benjamini Hochberg or (XDR) TB, mainly caused by non-adherence or inappropriate Bonferroni) (10, 11). The Treated South Africa 2011 cohort drug regimens, resulting in a detrimental impact on global TB was randomised into a training and test set (12). Derived treatment programs. signatures were then applied to the: Treated South Africa 0078. A whole blood transcriptional signature can distin 2011 cohort, Treated UK 2011 Cohort, and cohorts from the guish active TB from latent TB and other diseases, and be earlier Berry et al. (2010) study. Data was displayed in heat correlated with radiographic extent of disease (9). This active maps generated by hierarchical clustering (distance metric: TB blood signature diminished after two months of success Pearson’s uncentered with average linkage (13)) showing ful treatment and reverted to that of healthy individuals after either clustering of transcripts and samples, or just clustering completing treatment (9). Early blood biomarkers correlating of transcripts. with treatment response will allow monitoring of patients I0083 Molecular distance to health (MDTH) was deter without sputum, expedite knowledge of an individual’s treat mined as previously described (14). In one embodiment, the ment response and may permit stratification of patients Temporal Molecular Response was calculated from the sum requiring differing treatment regimens. Furthermore early of transcripts that were greater than twofold different between biomarkers can be instrumental in drug development. one time point and the baseline values, then expressed as a 0079 Certain embodiments of the present invention are percentage of the total number of transcripts in that signature. designed to establish that early changes in a blood transcrip MDTH and temporal molecular response were calculated in tional response can be observed during anti-TB treatment. Microsoft Excel 2010. Graphs, p-values and linear regression Furthermore, it adds to previous results by examining the were generated in GraphPad Prism version 5 for Windows transcriptional treatment response directly in a larger cohort except linear mixed models was performed in SASTM soft from a high-burden TB country, South Africa (2). ware (SAS Institute Inc., USA). Ingenuity Pathway Analysis 0080 METHODS: Study Population: Blood was col (Ingenuity Systems, Inc., Redwood, Calif.), identified signifi lected between May 2008-November 2011 in Ubuntu cant canonical pathways (Fisher's exact Benjamini Hochberg TB/HIV clinic, South Africa and Royal Free Hospital NHS p<0.05). Trust, London from patients (age->17 years) with Mtb culture I0084 Study Population and Inclusion Criteria: All partici positive active pulmonary TB (FIG. 1A: Table 2A,B). Latent pants in South Africa were recruited from the Ubuntu TB patients were asymptomatic with a positive QuantiF TB/HIV clinic in Khayelitsha, a large peri-urban African US 2015/O 133469 A1 May 14, 2015

township in Cape Town which has over 1000 TB notifications eStudio (Illumina) was then used to perform quality control annually. During the period May 2008-August 2010 whole and generate signal intensity values. blood was collected from adult patients (age->17 years) with (0090 South African and UK 2009 sample's RNA was drug sensitive Mtb culture proven active pulmonary TB (FIG. isolated as previously described and hybridized to Illumina 1A). Due to the population’s high Mtb exposure, controls Human HT-12 V3 BeadChip arrays (Illumina) (9). Probes were considered as asymptomatic individuals with previous were translated from the HT-12 V3 BeadChip arrays to HT-12 exposure to Mtb (latent TB patients); exposure was evidenced V4 BeadChip arrays using GeneSpring GX version 11.5 by a positive QuantiFERON-TB Gold In-Tube (QFT). Par (Agilent Technologies) and translated to slightly fewer ticipants with latent TB were recruited from individuals self probes in V4. referring to the Voluntary testing clinic. All participants had 0091 Raw data were processed using GeneSpring GX negative HIV status. version 11.5 (Agilent Technologies), and the following was I0085 All participants in the 2009 UK Training and Test applied to all analysis. After background Subtraction each cohorts were selected as previously described (9). The UK probe was attributed a flag to denote its signal intensity detec 2011 Active TB Validation Cohort were all Mtb culture tion p-value. Flags were used to filter out probe sets that did proven adults (>17 years) recruited between August 2009 not result in a present call in at least 10% of the samples, November 2011 from the Royal Free Hospital, London (FIG. where the present lower cut off-0.99. Signal values were 1B). Clinical and demographic data was recorded for all then set to a threshold level of 1, log 2 transformed, and participants and stored in a database. per-chip normalised using 75th percentile shift algorithm. I0086. Follow Up Period: All 20 Treated 2011 South Africa Next per-gene normalisation was applied by dividing each active TB patients completed a full six months of treatment. messenger RNA transcript by the median intensity of the Each patient was sampled for venous blood at every time latent TB samples. All statistical analysis was performed after point: two weeks, two months, six months and 12 months this stage. after initiation of treatment (FIG. 1A). Patient’s response to 0092 All data collected and analyzed in the experiments anti-TB treatment was assessed clinically during the adhere to the Minimal Information About a Microarray 12-month period. All patients were discharged from the pro Experiment (MIAME) guidelines. gram as cured. 0093 Statistical Analysis: GeneSpring 11.5 was used to I0087. Eight Treated 2011 UK Active TB patients com select transcripts that displayed a degree of expression vari pleted a full six months of treatment, one patient completed ability. A filter was set to include only transcripts that had at nine months of treatment due to radiographic uncertainty of least twofold changes from the median intensity of all latent treatment Success. Each patient was sampled for venous TB samples and present in at least 10% of the samples. This blood at two weeks, two months, four months and six months approach was used to determine all the active TB-transcrip after initiation of treatment (FIG. 1B). Four patients had a tional signatures. To divide the South Africa 2011 cohort into sample at every time point, three patients had samples at two, a training and test set, a computer algorithm was used for four and six months, and four patients had samples at two randomization (12). For the specific treatment response sig weeks and six months. As part of their routine medical care all nature transcripts had to satisfy a threefold expression filter in patients had chest X-rays minimally at the beginning and end eight of the 11 training set matched untreated and six month of their treatment and were discharged from the program as treated samples. Selected transcripts were then filtered by cured. different levels of statistical stringency in GeneSpring 11.5. I0088. IFNY Release Assay Testing: The QFT Assay Non-parametric tests with multiple testing corrections were (Cellestis) was performed according to the manufacturers applied to all analyses (10,11). The active TB-transcriptional instructions. signatures were generated by Mann Whitney unpaired Ben I0089 Gene Expression Profiling: 3 ml of whole blood jamini Hochberg p<0.01 or Bonferroni p<0.01 (FIG. 2B). were collected into Tempus tubes (Applied Biosystems/Am The statistical filter used to generate the specific TB treatment bion) by standard phlebotomy, vigorously mixed immedi response-transcriptional signature was Mann Whitney paired ately after collection, and stored between -20 and -80° C. Benjamini Hochberg p<0.05. The 393 and 86 active TB sig before RNA extraction. South Africa and UK 2011 sample's natures were obtained as described previously (FIG. 8) (9). RNA was isolated using 1.5 ml whole blood and the Mag The transcript lists for each signature were then applied to the MAX-96 Blood RNA. Isolation Kit (Applied Biosystems/ cohorts they were derived from and/or to the following Ambion) according to the manufacturers instructions. 250 cohorts: South Africa 2011 active TB Training and Test Set, ug of isolated total RNA was globin reduced using the UK 2011 Cohort and the three cohorts from an earlier study. GLOBINclear 96-well format kit (Applied Biosystems/Am Visualization of the data was performed by heatmaps using bion) according to the manufacturers instructions. Total and hierarchical clustering where the correlation distance metric globin-reduced RNA integrity was assessed using an Agilent employed for the clustering was Pearson’s uncentered with 2100 Bioanalyzer (Agilent Technologies). RNA yield was average linkage (13). Heatmaps displayed either hierarchical assessed using a NanoDrop 800 spectrophotometer (Nano clustering of both transcripts and samples or hierarchical Drop Products. Thermo Fisher Scientific). Biotinylated, clustering of transcripts with forced grouping of samples. amplified antisense complementary RNA (cRNA) targets Visualization of common and different transcripts by Venn were then prepared from 200-250 ng of the globin-reduced diagrams was performed in GeneSpring 11.5. Translation of RNA using the Illumina CustomPrep RNA amplification kit probes/transcripts between V3 HT12 and V4 HT12 chip was (Applied Biosystems/Ambion). 750 ng of labeled cFNA was performed using the probe ID and Illumina specific probe id. hybridized overnight to Illumina Human HT-12V4 BeadChip Slightly fewer probes were translated from V3 to V4. arrays (Illumina), which contained more than 47,000 probes. (0094) Molecular distance to health (MDTH) was deter The arrays were washed, blocked, stained and Scanned on an mined for each time point, as previously described (14). Tem Illumina iScan, as per manufacturers instructions. Genom poral Molecular Response was determined per individual, for US 2015/O 133469 A1 May 14, 2015 each transcriptional signature, by calculating the sum of the these signatures were applied to the Treated South Africa transcripts that were greater than twofold up or down at a 2011 Cohort, a marked and rapid change in the transcriptional specific time point, e.g. two weeks, compared to the raw response was observed as early as two weeks, which then pre-treatment intensity values. For intensity values of Zero a continued through two and six months after treatment initia value of 10 to the power of -20 (10') was introduced. The tion (FIG. 2B). As previously reported, Ingenuity Pathway calculated number of altered transcripts was then expressed Analysis (IPA) of these blood transcriptional signatures dem as a percentage of the total number of transcripts in the tran onstrated a highly significant over-representation of Inter Scriptional signature. This calculation was then repeated for feron (IFN)-signaling genes including Type I and Type II IFN the rest of the time points. MDTH and temporal molecular (FIGS. 2C and D, p<0.001). response were calculated in Microsoft Excel 2010. GraphPad 0098. The Transcriptional Response Changes Signifi Prism version 5 for Windows was used to generate graphs, cantly at Two Weeks. Two Months & Six Months after Treat determine linear regression, and determine associated p-val ment Initiation: Since the South Africa untreated active TB ues using either Friedman and Dunn's multiple comparison signatures diminished in response to treatment, the present test for MDTH data or ANOVA repeated measures and inventors determined that there was a significant change in the Tukey's multiple comparison test for temporal molecular transcriptional signature during treatment. For this determi response data. Linear mixed models, fixed effects, was per nation, the molecular distance to health (MDTH) algorithm formed in SAS/STATR software (SAS Institute Inc., USA). was determined as this generates a quantitative score for the Statistical tests applied were dependent on the distribution of degree of transcriptional perturbation in a disease cohort rela the data as determined by D'Agostino and Pearson omnibus tive to the controls (14). The present inventors recognized that normality test. Pathway analyses were performed using Inge MDTH positively correlates with the severity of active pull nuity Pathway Analysis (Ingenuity Systems, Inc., Redwood, monary TB, as defined by the radiological extent of disease Calif.). Canonical pathways analysis identified the most sig (9). The present inventors found that the median MDTH nificantly represented pathways in the datasets (Fisher's associated with the 664 South African untreated active TB exact Benjamini Hochberg p-0.05). transcriptional signature altered significantly at two, six, and 0095 Results: Participants Demographics and Character twelve months, compared to the median pre-treatment istics: Participant numbers in the South Africa 2011 cohort MDTH (FIG. 2E). are described in FIG. 1; 29 active TB patients were recruited 0099] To expand on the treatment induced transcriptional and sampled for transcriptomic analysis; all active TB response, a metric was developed that allowed us to evaluate patients were treated for six months with quadruple antitu each individual’s change in gene expression relative to their bercular therapy (rifampin, pyrazinamide, isoniazid and own expression profile, rather than relative to a control group. ethambutol) for two months followed by rifampin and iso This temporal molecular response offers a potential advan niazid for four months. Of these, 20 were resampled after two tage in the clinical setting to allow separate assessment of weeks, and after two, six and 12 months after initiation of each patients outcome. For a given signature, the temporal treatment; blood from 38 latent individuals was sampled as molecular response was determined by measuring the tran asymptomatic controls. Demographics and clinical charac Scriptional perturbation between two time points, and teristics of the South Africa 2011 and UK 2011 cohorts are expressing this value as a percentage of the total number of reported in Tables 2A and 2B. Patient demographics and transcripts constituting the signature. The mean temporal clinical characteristics of the 2009 South Africa cohort and molecular response associated with the South Africa UK cohorts have been previously described (9). All treated untreated active TB 664 transcript signature altered rapidly active TB patients included in the study had drug sensitive and significantly as early as two weeks, and continued to alter treatment, took all treatment prescribed, showed Successful significantly at two months and six months (FIG. 2F). This clinical/radiological response to treatment, did not relapse finding demonstrates that the transcriptional signature not within one year and were discharged from the program as only changes rapidly and as early as two weeks, but also cured. continues to significantly change between two weeks and two 0096. A Change in Transcriptional Response is Readily months. Detectable after Two Weeks of Treatment: To determine 0100. The changing transcriptional response is indepen whether an active TB transcriptional signature in the blood of dent of the magnitude of the untreated transcriptional signa the 2011 South Africa cohort was perturbed upon treatment, ture: It may be predicted that individuals with more extensive gene expression profiles of only significantly detectable disease would respond to treatment differently from those genes without further filtering (detected p-0.01 from back with minimal disease. However the magnitude of the patients ground, 16,856 transcripts), were examined in the 20 active temporal molecular response during treatment, at both two TB patients before, during (two weeks and two months), at weeks and two months, did not correlate with the magnitude the end (six months), and after treatment (12 months). By of their pre-treatment transcriptional signature, as evidenced plotting the expression profiles of the 16856 transcripts along by MDTH (r2<0.1, non-significant) (FIG. 6). However, the a time scaled X-axis, a marked change was readily observed patient's temporal molecular response after treatment, at six after two weeks of anti-TB treatment (FIG. 2A). months and 12 months did significantly correlate with their 0097. An active TB 664-transcript signature was derived pre-treatment MDTH (r2=0.25, p=0.02 and r2=0.57, p=0.001 from differentially expressed genes in the active TB patients respectively) (FIG. 6). This finding, that the magnitude of the compared to the latent TB patients in the Untreated South pre-treatment signature does not appear to be able to predict Africa 2011 cohort (FIG. 2B). First, all transcripts were nor the patient’s response, further underscores the benefit of the malized to the median of the latent TB patients, then only temporal molecular response in offering continuous monitor transcripts with an equal or greater than twofold change from ing throughout treatment. the median were selected, before finally applying a stringent 0101. A specific TB treatment response signature signifi statistical filter (Bonferroni; FIG. 2B: 664 transcripts). When cantly diminishes at two weeks, two months and after US 2015/O 133469 A1 May 14, 2015 completion of treatment: The 664-transcript active TB signa ment (FIGS.5A and B). The changes in the blood transcrip ture significantly and rapidly changed in response to treat tional response could be clearly quantified in individual ment (FIGS. 2B, E and F). This signature was derived by patients as shown by the temporal molecular response (FIG. identification of differentially expressed genes between 5C). The significant transcriptional blood change correlated untreated active TB and latent TB patients. with Successful treatment of patients as assessed after six 0102. In addition, a transcriptional signature that specifi months by radiographic and clinical parameters. cally reflected the response of patients to clinically successful 0106 The significant change in transcriptional response at anti-TB treatment (comparing time points 0 and 6 months) two weeks occurs when applying active TB signatures was determined. To determine the treatment specific signa derived from other cohorts: thus far it was demonstrated that ture, a computer algorithm was used to randomize the South the 664-transcript active TB signature (FIG. 2), and the treat Africa 2011 cohort into two groups of patients (12) (FIG. 1). ment specific 391-transcript signature (FIG. 3) diminished This allowed us to derive the signature from one group of rapidly and significantly with treatment at two weeks. Then it patients (Active TB Training Set) and then validate findings in was sought to determine if active TB transcriptional signa another independent group of patients (Active TB Test Set). tures derived from other cohorts would also diminish in Upon analysis, 391 transcripts were found to be significantly response to treatment. Therefore active TB transcriptional differentially expressed between the untreated Active TB signatures derived from cohorts used in the earlier Berry et al. Training Set samples and their matched six-month treated (2010) study (9) were applied to all the time points of the samples. The 391-transcript treatment specific signature was Treated South Africa 2011 samples. shown to rapidly and significantly change at two weeks, two 0107 The established 393- and 86-transcript active TB and six months after treatment initiation in the Active TB signatures from the earlier Berry et al. (2010) study (9) Training set. This was validated in the Active TB Test Set obtained by comparing active TB patients to latent TB (FIG. 3A-E). In both cohorts the change in the temporal patients and healthy controls were applied first. The present molecular response was significant at two weeks post-treat inventors demonstrate that the 393 and 86-transcript active ment (FIGS.3B and D). Analysis of the 391 transcripts by IPA TB signatures significantly and rapidly diminished in the indicated the most significantly represented pathways were South Africa 2011 cohort, and this occurred as early again as related to the innate immune pathways, encompassing genes two weeks (FIG. 8A-C). Additionally active TB signatures related to complement, Toll-like receptors, a NOD like recep from the Berry et al (2010) cohorts were derived, only com tor gene and interleukin-1B, which were all significantly paring active TB patients to latent TB patients (removing altered with treatment (FIG.3E). Of the 391-transcript treat healthy controls), using the same analysis approach as was ment signature genes, 68% were also present in the 664 used to determine the 664-active TB signature in the transcript active TB signature (FIG.3F). Untreated South Africa 2011 cohort (FIG. 2A-C and FIG. 0103 Measuring an individual patient’s transcriptional 2E-F). The active TB transcriptional signatures thus obtained response to anti-TB treatment: Each patient’s discrete treat from the different cohorts (named 2009 UK Training set, ment response is shown in the heatmaps and by graphical 2009 UK Test set and 2009 South Africa set, FIG.9) showed form using the temporal molecular response in FIG. 4 and significant changes in the transcriptional response in the FIG. 7. All 20 patients in the active TB treated cohort had a Treated South Africa 2011 Cohort at two weeks, two months rapid and early positive temporal response after two weeks of and at the end of treatment (FIG. 10A-C). treatment. Interestingly, not all the individual transcriptional 0108. A core set of genes (344 transcripts) were found to responses were identical (FIG. 4A, FIG. 7A) as demonstrated be overlapping between these 2009 derived active TB tran by the quantitative scoring provided by the temporal molecu Scriptional signatures (FIG. 10D). The overlapping genes had lar responses (FIG. 4B, FIG. 7B). many genes in common with both the South Africa 2011 0104. By both the MDTH and temporal molecular derived active TB 664-transcript signature and the 2011 treat response, it was observed that none of the transcriptional ment specific 391-transcript signature (FIG. 10E). signatures revealed significant differences between two 0109 The whole blood active TB-transcriptional signa months and six months post treatment initiation (FIGS. 2E tures derived from a South African cohort, dominated by IFN and F: FIGS. 3B and D; and FIG. 8C). This could suggest that signaling and innate immune response genes, showed a the transcriptional response reaches a plateau at two months readily detectable change in response to clinically successful and would therefore be no different from latent TB patients. anti-TB treatment. Importantly the treatment-associated Therefore, each of the time points was compared: two, six, changes in the active TB-transcriptional signature and a spe and 12 months to the latent TB expression profiles. It was cific TB treatment response-transcriptional signature were determined that 151 transcripts were differentially expressed rapid and highly significant as early as two weeks after the between two months and latent TB (Mann Whitney paired initiation of therapy. The transcriptional response to treat Benjamini Hochberg p-0.01). However no genes were sig ment could be individually measured in each patient and was nificantly differentially expressed between six & 12 months, independent of the magnitude of their pre-treatment tran six months & latent TB, and 12 months & latent TB (Mann Scriptional signature. The significant and early change in the Whitney paired Benjamini Hochberg p-0.01). treatment specific transcriptional signature was then Vali 0105 Validation of early anti-TB treatment blood tran dated in a UK cohort. These findings demonstrate that blood Scriptional response: To determine whether the significant transcriptional signatures can be pragmatic as early Surrogate change in the 391-transcript treatment specific signature that markers of a successful treatment response, and can be used had been demonstrated in a South Africa cohort was also as biomarkers in both the clinical setting and in drug devel generalizable to patients in an intermediate burden setting, opment. In certain embodiments, the method is useful for the signature was tested in UK. As observed in the South improving stratification and monitoring of clinical treatment Africa cohort the signature was rapidly and significantly of active TB patients, testing novel therapies in to enhance diminished at two weeks onwards post commencing treat efficacy in treatment of drug-sensitive Mtb infection in clini US 2015/O 133469 A1 May 14, 2015

cal trials, and in the testing of novel drugs for use in the tein, IFN-Y and neopterin, all have shown poor sensitivity and potential treatment of MDR- and XDR-TB. specificity (26). Chest X-rays are commonly used in the clini 0110. An active TB transcriptional signature, originally cal setting as a marker of treatment response but they gener derived from a UK cohort, which distinguished active TB ally improve more slowly than the clinical response and lack patients from patients with other inflammatory and infectious specificity as interpretation can be confounded by previous diseases and which correlated with the radiographic extent of lung damage (24). Moreover, interpretation of chest X-ray disease, was demonstrated (9). The transcriptional response changes in response to treatment has not yet been standard in the high-burden TB country of South Africa was evaluated ised, and the facilities are not always available in developing before, during, and after anti-TB treatment. The study of TB countries (8). Therefore there is clearly a need for early and patients in both the UK and South Africa provided gene easily detectable biomarkers for treatment monitoring, expression profiles across diverse host populations, exposed capable of detecting drug resistance or poor treatment adher to different local environments and likely different Mtb ence and available for patients unable to produce sputum. In strains. The South African cohort contained participants from addition, such blood biomarkers of early anti-TB treatment Khayelitsha, a large peri-urban African township in Cape response would be vital in clinical trials to aid the evaluation Town, where 1.5% of the population develops active TB and development of more effective new and shorter treatment annually. It was found that South Africa active TB patients (all regimens. HIV uninfected) had more differentially expressed genes that 0113. In UK patients, active TB signatures, 393 and 86 those from the UK. This may be explained by a higher inci transcripts, diminished at two months of treatment (9). The dence of co-infection with other micro-organisms and present inventors now show a significant blood transcrip viruses, besides HIV, or a higher burden of Mtb infection due tional response to treatment occurs rapidly and as early as two to delayed diagnosis relative to countries like the UK, present weeks (FIGS. 2A, 2B, 2E, 2F, 3 A-D, 4A-B, 5A-C, 7A-B. in South Africa. For example, although the exact helminth 8A-C, 10A-C) or Sooner. This significant change in transcrip prevalence in adults is unknown, data from Surveys suggest tional response continued between two weeks and two between 70-100% of children are infected (15). Although the months and again between two months and six months. It was number of genes differed between the South Africa and UK demonstrated that this significant change occurred in all cohorts, the most significantly represented pathways, IFN active TB-transcriptional signatures derived from both South signaling and innate immune response pathways, were the Africa and UK cohorts. The use of this approach as an early same. Notably many of the genes contained within the innate biomarker correlating with treatment response strengthens its immune response pathways in all the cohorts, were also inter use as an adjunct diagnostic tool as well as an early treatment feron inducible genes (Table 1), including the complement biomarker. A significant response in the treatment specific genes, Toll-like receptor genes, and familiar IFN inducible transcriptional signature, derived from a clinically Success genes such as CXCL10 and OAS genes (16-20). fully treated active TB cohort, and validated in two other 0111 Studies in TB. Maertzdorf et al examined whole treated active TB cohorts, was demonstrated (FIGS. 3C-D blood gene expression of active TB and latent TB patients in and 5A-C). This treatment specific transcriptional signature cohorts from both South Africa and The Gambia (21, 22). also had many genes in common with the active TB transcrip Although some IFN inducible and innate response genes tional signatures. Therefore, this study will help guide future were significantly over expressed in the active TB patients, exploration for a highly specific Subset of genes that explicitly different microarray chips and analysis strategy were correlates with a patient’s mycobacterial response to anti-TB employed. treatment, therefore acting as a Surrogate marker of treatment 0112 While it is widely appreciated that the diagnosis of failure or success. TB has many difficulties, the present inventors recognize that 0114. Although no other studies have looked directly at TB treatment monitoring is a difficult challenge in trying to the transcriptional response to TB treatment, two other stud eradicate Mtb infection. So much so that in April 2010 the ies have observed some transcriptional changes, but only Centers for Disease Control and National Institutes of Health measured at two months or after treatment completion. Mis brought together experts in the field and research Scientists try et al. Studied whole blood gene expression in patients from with the sole purpose of addressing this problem (23). The South Africa, comparing patients with active TB, recurrent consequences of poor treatment monitoring, and therefore reactivation of TB, cured TB and latent TB (28). These study impending inadequate treatment, includes worsening of a methods were different from the methods employed herein patient’s disease, increasing potential for disease spread and because Mistry et al. did not measure the transcriptional most worryingly an escalation in drug resistant mycobacteria. profile during treatment but only after completion of treat Currently the two-month sputum culture conversion rate, ment. Mistry etal. showed that those who were cured from TB used to measure anti-TB treatment response, is the only biom displayed similar expression profiles to those with latent arker of successful TB treatment (6). However sputum culture infection. There was a small but non-significant increase in conversion is a time consuming test, since it takes several gene expression six months after stopping the anti-TB treat weeks to grow the bacilli and results can be compromised by ment in a small number of active TB patients (e.g., FIG. 4B). contamination. In fact, often the patients who have improved This may be explained by the continuous high exposure to are unable to expectorate sputum at two months but then are infections in South Africa, as anti-TB treatment consists of wrongly labeled as having a negative result (24). Furthermore antibiotics capable of treating a broad-spectrum of bacteria. although sputum conversion has efficacy as a Surrogate end Joosten et al. showed in a small cohort from The Gambia that point of treatment response in clinical trials evaluating new their active TB gene set, distinguishing active from latent TB, drugs, a systematic review and meta-analysis to assess its also diminished at two months of treatment (29). Embodi accuracy to predict an individual’s treatment failure revealed ments of the present invention, however, demonstrate the low sensitivity and modest specificity (7, 25). While other accomplishment of a novel, inexpensive, fast automated biomarkers have also been trialed, including C-reactive pro molecular method to measure blood gene expression profiles. US 2015/O 133469 A1 May 14, 2015

Techniques such as this can be applied to early treatment version revealed low sensitivity and modest specificity for the blood transcriptomic of TB treatment response. Therefore prediction of treatment failure 7. Chest X-rays are com early treatment blood transcriptome analysis of TB treatment monly used to assess response but are not universally avail response at two weeks (or Sooner) has great potential for able and assessment is difficult to standardise 8. This lack of development as a pragmatic blood biomarker for clinical use. effective treatment monitoring can lead to the development 0115 A further problem in the management of TB is the and spread of multidrug resistant (MDR) and extensively extended length of treatment, requiring a minimum of six drug resistant (XDR) TB, which are mainly caused by non months, which has a negative impact on patient adherence adherence or inappropriate drug regimens, with a detrimental and treatment completion. Therefore the ability to stratify impact on global TB control. patients into groups that may require shorter lengths of treat 0119) To date transcriptional profiling has been used suc ment, particularly in resource limited settings, could be of cessfully in cancer classification, to identify prognostic biom value in improving patient compliance and reducing treat arkers 9, and to distinguish between inflammatory and ment related side effects. It is shown herein that transcrip infectious diseases 10. Moreover, a whole blood transcrip tional response of Some patients appeared to plateau before tional signature may be used to distinguish active TB from six months (FIGS. 4B, 5C and 7B), suggesting a tailored latent TB and other diseases, and it is correlated with radio treatment response for individual patients may be possible, graphic extent of disease 11. This active TB blood signature and that blood transcriptional signatures could help with this diminished in seven patients after two months of Successful stratification. treatment and reverted to that of healthy individuals after completing treatment 11. Earlier blood biomarkers corre Example 2 lating with treatment response would improve monitoring of 0116. This example provides further advances by the individual patient treatment responses without the need for present inventors in methods for monitoring changes in blood sputum production, which may permit stratification of transcriptional signatures in response to antituberculosis patients requiring differing treatment regimens. Additionally, treatment and details use of these changes as early biomarkers early biomarkers may aid in anti-TB drug development. of a successful response. In particular, significant changes in 0.120. The study detailed in this example was designed to the transcriptional signatures measured by blood tests were establish if early changes in blood transcriptional responses readily detectable just two weeks after treatment initiation, can be observed during standard anti-TB treatment. It adds to and transcriptional response to treatment is shown as being previous studies in part by examining the transcriptional readily measured in individual patients. These findings fur treatment response directly in a larger cohort from a high ther Support that blood transcriptional signatures are useful as burden TB country, South Africa 2. early Surrogate biomarkers of successful treatment response. I0121 Materials and Methods. All participants in South Unlike Example 1, use of a 320 gene (Table 12) transcrip Africa were recruited from the Ubuntu TB/HIV clinic in tional signature is prominently disclosed in Example 2. Khayelitsha, a large peri-urban African township in Cape 0117 More specifically, blood transcriptional profiles of Town which has over 1000 TB notifications annually. During untreated active tuberculosis patients in South Africa were the period May 2008-August 2010 whole blood was collected analysed before, during (two weeks and two months), at the from adult patients (age->17 years) with drug sensitive Mtb end of (six months) and after (12 months) antituberculosis culture proven active pulmonary TB (FIG. 11A). Due to the treatment, and compared to individuals with latent tubercu population’s high Mtb exposure, controls were considered as losis. An active-tuberculosis transcriptional signature and a asymptomatic individuals with previous exposure to Mtb (la specific treatment-response transcriptional signature were tent TB patients); exposure was evidenced by a positive derived. The specific treatment response transcriptional sig QuantiFERON-TB Gold In-Tube (QFT) (Cellestis). Partici nature was tested in two independent cohorts. Two quantita pants with latent TB were recruited from individuals self tive scoring algorithms were applied to measure the changes referring to the Voluntary testing clinic. All participants had in the transcriptional response. The most significantly repre negative HIV status. sented pathways were again determined using Ingenuity 0.122 The UK 2011 Active TBValidation Cohort were all Pathway Analysis. An active tuberculosis 664-transcript sig Mtb culture proven adults (>17 years) recruited between nature and a treatment specific 320-transcript signature sig August 2009-November 2011 from the Royal Free Hospital, nificantly diminished after two weeks of treatment in all London (FIG. 11B). All participants in an earlier 2009 study cohorts, and these continued to diminish until six months. The were selected as previously described 11. Clinical and transcriptional response to treatment could be individually demographic data was recorded for all participants and stored measured in each patient. in a database. 0118 Active pulmonary TB diagnosis requires culture of I0123 Ethics Statement. This study was approved by the Mtb, which may take up to six weeks 3. Although the World University of Cape Town Faculty of Health Sciences Human Health Organization (WHO) endorsed GeneXpert MTB/RIF Research Ethics Committee, Cape Town, South Africa (FHS automated molecular test for Mtb results in rapid diagnosis HREC 012/2007), and the Central London3 Research Ethics 4, this test still requires sputum which may be difficult to Committee (09/H0716/41). All participants gave informed obtain. Difficulties in obtaining sputum lead to approximately written consent. 30% of patients in the USA and 50% of South African patients (0.124. Follow Up Period. All 29 treated 2011 South Africa to be treated empirically 2,5. After diagnosis there are no active TB patients completed a full 6 months of treatment. available early biomarkers correlating with treatment suc Patients were sampled for venous blood at time points: pre cess, resulting in significant delay in assessing treatment treatment (29/29 patients), 2 weeks (25/29 patients), 2 response. Currently conversion to negative culture after two months (24/29 patients), 6 months (25/29 patients) and 12 months of treatment is the only accepted biomarker I6. How months (29/29 patients) after initiation of treatment (FIG. ever a systematic review and meta-analysis of sputum con 11A). Patient’s response to anti-TB treatment was assessed US 2015/O 133469 A1 May 14, 2015 clinically during the 12 month period. All patients were dis I0131 Data Analysis. GeneSpring 11.5 was used to select charged from the program as cured. transcripts that displayed a degree of expression variability. A 0125 Eight treated 2011 UK Active TB patients com filter was set to include only transcripts that had at least pleted a full six months of treatment, one patient completed twofold changes from the median and present in at least 10% nine months of treatment due to radiographic uncertainty of of the samples. To divide the South Africa 2011 cohort into a treatment Success. Each patient was sampled for venous training and test set, a computer algorithm was used for blood at two weeks, two months, four months and six months randomisation 12. For the specific treatment response sig after initiation of treatment (FIG. 11B). Four patients had a nature transcripts had to satisfy a threefold expression filter in sample at every time point, three patients had samples at two, 12 of the 15 training set matched untreated and six month four and six months, and four patients had samples at two treated samples. weeks and six months. As part of their routine medical care all (0132) Selected transcripts were then filtered by different patients had chest X-rays at the beginning and end of their levels of statistical stringency in GeneSpring 11.5. Non-para treatment. metric tests with multiple testing corrections were applied to 0126 IFNY, Release Assay Testing. The QFT Assay all analyses 13, 14. The active TB-transcriptional signa (Cellestis) was performed according to the manufacturers tures was generated by Mann Whitney unpaired Bonferroni instructions. p-0.01. FIG. 13A. The statistical filter used to generate the specific TB treatment response-transcriptional signature was 0127 Gene Expression Profiling. 3 ml of whole blood Mann Whitney paired Benjamini Hochberg p<0.01. The 393 were collected into Tempus tubes (Applied Biosystems/Am and 86 active TB signatures were obtained as described pre bion) by standard phlebotomy, vigorously mixed immedi viously 11 (see also FIG. 14A-14B). Visualisation of the ately after collection, and stored between -20 and -80° C. data was performed by heatmaps using hierarchical cluster before RNA extraction. South Africa and UK 2011 sample's ing where the correlation distance metric employed for the RNA was isolated using 1.5 ml whole blood and the Mag clustering was Pearson's uncentered with average linkage MAX-96 Blood RNA. Isolation Kit (Applied Biosystems/ 15. Heatmaps displayed either hierarchical clustering of Ambion) according to the manufacturers instructions. 250 ug of isolated total RNA was globin reduced using the both transcripts and samples or hierarchical clustering of GLOBINclear 96-well format kit (Applied Biosystems/Am transcripts with forced grouping of samples. Visualisation of bion) according to the manufacturers instructions. Total and common and different transcripts by Venn diagrams was per globin-reduced RNA integrity was assessed using an Agilent formed in GeneSpring 11.5. 2100 Bioanalyzer (Agilent Technologies). RNA yield was I0133 Molecular distance to health (MDTH) was deter assessed using a NanoDrop800 spectrophotometer (Nano mined for each time point, as previously described 16. The Drop Products. Thermo Fisher Scientific). Biotinylated, temporal molecular response was calculated for a particular amplified antisense complementary RNA (cRNA) targets gene list for each individual patient. The raw intensity tran were then prepared from 200-250 ng of the globin-reduced Script values in the gene list were consecutively compared at RNA using the Illumina CustomPrep RNA amplification kit each time point to the baseline (pre-treatment). The numbers (Applied Biosystems/Ambion). 750 ng of labeled cFNA was of transcripts that were at least two-fold up or two-fold down hybridized overnight to Illumina Human HT-12V4 BeadChip from the baseline were added together for each time point. arrays (Illumina), which contained more than 47,000 probes. This sum was then divided by the total number of transcripts The arrays were washed, blocked, stained and Scanned on an in the gene list to calculate a percentage score for each time Illumina iScan, as per manufacturers instructions. Genom point. This generated a percentage score of change at each eStudio (Illumina) was then used to perform quality control time point compared to the baseline, where the baseline and generate signal intensity values. always remains Zero (no change from itself). To allow for two-fold changes from Zero any baseline raw transcript inten 0128. The 393- and 86-transcript signatures were trans sity values of zero were converted to 10' (ten raised to the lated from the HT-12 V3 BeadChip arrays to HT-12V4 Bead power of minus twenty). MDTH and temporal molecular Chip arrays using GeneSpring GX version 11.5 (Agilent response were calculated in Microsoft Excel 2010. GraphPad Technologies) and translated to slightly fewer probes in V4 Prism version 5 for Windows was used to generate graphs and (FIG. 12A-12C). determine simple linear regression. Linear mixed models, 0129 Raw data were processed using GeneSpring GX fixed effects, were used to determine p-values associated with version 11.5 (Agilent Technologies) and the following was MDTH and temporal molecular response graphs, using SAS/ applied to all analyses. After background Subtraction each STATR software (SAS Institute Inc., USA). Pathway analy probe was attributed a flag to denote its signal intensity detec ses were performed using Ingenuity Pathway Analysis (Inge tion p-value. Flags were used to filter out probe sets that did nuity Systems, Inc., Redwood, Calif.). Canonical pathways not result in a present call in at least 10% of the samples, analysis identified the most significantly represented path where the present lower cut off-0.99. Signal values were ways in the datasets (Fisher's exact Benjamini Hochberg then set to a threshold level of 1, log 2 transformed, and p<0.05). per-chip normalised using 75" percentile shift algorithm. 0.134 Results. Participants Demographics and Character Next per-gene normalisation was applied by dividing each istics. Participant numbers in the 2011 cohorts are described messenger RNA transcript by the median intensity of the in FIG. 11A-11B: 29 South African and 8 UK active TB latent TB samples. All statistical analysis was performed after patients were recruited and sampled for transcriptomic analy this stage. sis. All treated active TB patients had fully sensitive Mtb, took 0130. The raw and normalised microarray data has been all treatment prescribed, showed successful clinical/radio deposited with the GEO (GSE40553). All data collected and logical response to standard therapy (rifampin, pyrazinamide, analysed in the experiments adhere to the Minimal Informa isoniazid and ethambutol for two months followed by tion About a Microarray Experiment (MIAME) guidelines. rifampin and isoniazid for four months), did not relapse US 2015/O 133469 A1 May 14, 2015 within one year and were discharged from the program as the transcriptional perturbation between two time points, and cured. The 29 South African patients were sampled at: pre expressing this value as a percentage of the total number of treatment (29/29 patients), two weeks (25/29 patients), two transcripts constituting the signature. The mean temporal months (24/29 patients), six months (25/29 patients) and 12 molecular response calculated for the active TB 664-tran months (29/29 patients) after initiation of treatment. Thirty Script signature revealed a statistically significant change in eight South African latent individuals were sampled as the transcriptional response at two weeks after treatment ini asymptomatic controls. Only five latent individuals were tiation (FIG. 13F). This continued to change between two aware of prolonged contact with another individual with weeks and two months, and between two weeks and six active TB. Participant characteristics are reported in Table months, after treatment initiation (FIG. 13F). The magnitude 13A and Table 13B. of the patient’s temporal molecular response during treatment 0135 A Change in Transcriptional Response is Readily (at two weeks and two months) did not correlate with the Detectable after Two Weeks of Treatment. To determine magnitude of their untreated transcriptional signature, as whether an active TB blood transcriptional signature was measured by MDTH (p<0.01) (FIG. 15). This suggests a perturbed upon treatment, gene expression profiles of signifi patient's untreated transcriptional signature is not predictive cantly detectable genes without further filtering (detected of the patient’s treatment response. p-0.01 from background, 15,837 transcripts) were examined 0.139. As a result, this active TB 664-transcript signature in the 29 active TB patients before, during (two weeks and (derived from untreated active and latent TB patients) signifi two months), at the end of (six months), and after treatment cantly and rapidly changed after two weeks of initiating treat (12 months). By plotting the expression profiles of the 15,837 ment (FIGS. 13B, 13E, and 13F). transcripts along a time scaled X-axis, a marked change was 0140. A Specific TB Treatment Response Signature Also readily observed after two weeks of anti-TB treatment (FIG. Significantly Diminishes at Two Weeks Post Treatment. 13A). Defining transcriptional signature that specifically reflected 0136. Next an active TB 664-transcript signature (as in the patients’ response to clinically successful anti-TB treat Table 8; see also Table S2 at at doi:10.1371/journal.pone. ment (comparing time points Zero and six months) was next 0046191g001 by Bloom et al. 2012) was derived from dif sought. To determine this treatment specific signature, a com ferentially expressed genes in the pre-treatment active TB puter algorithm was first used to randomise the South Africa patients compared to the latent TB patients in the South Africa 2011 cohort into two groups of patients 12 (FIG. 11A). This 2011 cohort. First, all transcripts were normalised to the allowed the signature from one group of patients (active TB median of the latent TB patients, then only transcripts with Training Set) to be derived and then the findings in another >twofold change from the median were selected, before independent group of patients (active TB Test Set) to be applying a statistical filter. When this signature was applied to validated. 320 transcripts (Table 12) were found to be signifi the South Africa 2011 Cohort, during and after treatment, a cantly differentially expressed between the pre-treatment marked and rapid change in the transcriptional response was active TB Training Set samples and their paired six-month observed as early as two weeks, which then continued treated samples (FIG.16A). The treatment specific 320-tran through two and six months, after treatment initiation (FIG. Script signature was shown to rapidly and significantly 13B). In agreement with the previous study, Ingenuity Path change at two weeks onwards after treatment initiation, in the way Analysis (IPA) of the active TB 664-transcript signature active TB Training set (FIGS. 16A and 16B). This was vali demonstrated a highly significant over-representation of dated in the active TB Test Set (FIGS. 16C and 16D). In both Interferon (IFN)-signaling genes including Type I and Type II cohorts the change in the temporal molecular response was IFN (FIG. 13C-D), p<0.001, Table 1: See also Table 1 at significant at two weeks post-treatment (FIGS. 16B and doi:10.1371/journal.pone.0046191.t001 (Bloom et al. 2012). 16D). Analysis of the 320 transcripts by IPA indicated the 0.137 The Transcriptional Response Changes Signifi most significantly represented pathways were related to the cantly at Two Weeks after Treatment Initiation. Since it was innate immune pathways, encompassing genes related to observed that the South Africa active TB 664-transcript sig complement and Toll-like receptors (FIG.16E).74% of genes nature diminished in response to treatment, determining if present in the treatment specific 320-transcript signature were this was a statistically significant change was desirable. To also contained the active TB 664-transcript signature (FIG. assess this, the previously described weighted molecular dis 16F). tance to health (MDTH) algorithm was employed as this 0141 Although by applying the temporal molecular generates a quantitative score for the degree of transcriptional response it was observed that the treatment specific 320 perturbation in a disease cohort relative to the controls 16. transcript signature changed significantly between two weeks Moreover, as already has been demonstrated. MDTH posi and six months post treatment initiation, this was no longer tively correlates with the severity of active pulmonary TB, as apparent between two months and six months post treatment defined by the radiological extent of disease 11. The median initiation (FIGS. 3B and D). This could suggest that the MDTH of the South African untreated active TB 664-tran transcriptional response reaches a plateau at two months and Script signature was found to have decreased significantly at therefore the two month gene expression profiles would not two weeks onwards, compared to the median pre-treatment be significantly different from the latent TB expression pro MDTH (FIG. 13E). files. To establish whether any significant changes occurred 0.138. The present inventors then developed a novel metric between two months and the latent TB patients, each of the that provides a quantitative measure of an individual’s tem time points was compared: two, six and 12 months to the poral change in gene expression. This temporal molecular latent TB profiles. It was determined that 96 transcripts were response offers a potential advantage in the clinical setting, significantly differentially expressed between two months allowing assessment of each patient’s expression change and latent TB (Mann Whitney paired Benjamini Hochberg without reference to a control group. For a given signature the p-0.01, data not shown). Ingenuity Pathway Analysis dem temporal molecular response was determined by measuring onstrated the top three significant pathways associated with US 2015/O 133469 A1 May 14, 2015

the 96 transcripts were role of NFAT in regulation of the for Disease Control and National Institutes of Health brought immune response, integrin signaling and primary immu together experts in the field and research scientists with the nodeficiency signaling (data not shown). However no genes sole purpose of addressing this problem 17. Poor treatment were significantly differentially expressed between six & 12 monitoring, and hence inadequate treatment, leads to wors months, six months & latent TB, and 12 months & latent TB ening of a patient’s disease, increasing the potential for dis (Mann Whitney paired Benjamini Hochberg p-0.01). ease spread and the risk of developing drug resistant myco 0142. Measuring an Individual Patient’s Transcriptional bacteria. Currently the two-month sputum culture conversion Response to Anti-TBTreatment. Each patient’s discrete treat is the only biomarker of successful TB treatment 6). How ment specific response (320 transcripts) is shown in the heat ever it is time consuming, taking several weeks to grow the maps of FIGS. 14A & 17A and using the temporal molecular bacilli and results can be compromised by contamination. responses in FIGS. 14B & 17B. All 29 patients in the active TB treated cohort had a rapid and early positive temporal Moreover patients who have clinically improved may be response after two weeks of treatment. Interestingly, not all unable to expectorate sputum at two months and potentially the individual transcriptional responses were identical (FIGS. incorrectly labeled as having a negative culture 18. Further 14A & 17A) as demonstrated by the quantitative scoring more, although sputum conversion is commonly used as a provided by the temporal molecular responses (FIGS. 14B & Surrogate end point for treatment response in clinical trials 17B). evaluating new drugs, a systematic review and meta-analysis 0143 To determine whether the significant change in the to assess its accuracy in predicting an individual’s treatment treatment specific 320-transcript signature that had been failure revealed low sensitivity and only modest specificity demonstrated in a South African cohort was also applicable to 7, 19. While other biomarkers have also been trialed, patients in an intermediate burden setting, the 320-transcript including C-reactive protein, IFN-Y and neopterin, all have signature was tested in a UK cohort. As observed in the South similarly shown poor sensitivity and specificity 20. Chest African cohort, the signature was rapidly and significantly X-rays are commonly used in the clinical setting as a marker diminished from two weeks post-treatment initiation (FIGS. of treatment response but they generally improve slower than 18A and 18B). The changes in the blood transcriptional the clinical response and lack specificity as interpretation can response could be clearly quantified in individual patients as be confounded by previous lung damage 18. Moreover shown by the temporal molecular response (FIG. 18C). The interpretation of radiographic changes in response to treat significant transcriptional blood change correlated with Suc ment has not yet been standardised, and the facilities are not cessful treatment of patients as assessed after six months by always available in developing countries 8. Therefore there radiographic and clinical parameters (data not shown). is clearly a need for early and easily detectable biomarkers for 0144. For additional validation that active-TB transcrip treatment monitoring, capable of potentially identifying poor tional signatures show significant changes as early as two responses due to drug resistance or lack of treatment adher weeks after treatment initiation, it was demonstrated that the ence, and available for patients unable to produce sputum. active TB signatures (393- and 86-transcript signatures) from an earlier study 11, also significantly diminished after two 0.147. In an earlier study, it was demonstrated in a small weeks treatment, in the South Africa 2011 treated cohort number of patients that blood transcriptional signatures in (FIG. 12A-12C). UK active TB patients diminished after two months of anti 0145 Discussion. As disclosed herein, a whole blood TB treatment 11. In study disclosed herein, a significant active-TB transcriptional signature was derived consisting of blood transcriptional response to anti-TB treatment has been 664 transcripts capable of distinguishing untreated South shown to occur rapidly, as early as two weeks (FIGS. 12A, African active TB patients from South African latent TB 12B, 12C, 13A, 13B, 13E, 13F, 14A, 14B, 16A, 16B, 16C, patients. It was demonstrated that this active-TB transcrip 16D, 17A, 17B. 18A, 18B, and 18C). This early transcrip tional signature significantly diminishes inactive TB patients tional response could be as a consequence of the observed after just two weeks of initiation of clinically successful anti rapid and high killing capacity of antimycobacterial antibiot TB treatment. In addition, it was demonstrated that a treat ics leading to a substantial reduction in mycobacterial load ment-specific transcriptional signature, consisting of 320 21, 22, 23. Although the signatures derived may not be transcripts, derived from comparing a cohort of South African completely specific for active TB, since clinically similar untreated active TB samples to their paired six-month end diseases such as sarcoidosis show common transcripts 24. of-treatment samples, also significantly diminishes after just demonstration of a response to antimycobacterial therapy as two weeks of anti-TB treatment. Furthermore the significant shown herein, could help resolve this overlap and so improve change in the treatment-specific signature was validated in diagnostic specificity. two more clinically successfully treated cohorts, from the 0.148. With the disclosure of this Example, it is shown that high TB-burden setting of South Africa and from the inter the whole blood active-TB transcriptional signature is domi mediate TB-burden setting of London, UK. Both the active nated by IFN signaling and innate immune response genes. TB and treatment-specific transcriptional signatures were These findings are in agreement with previous work 11, and dominated by IFN signaling and innate immune response with other gene expression studies in TB 25, 26. This genes. The transcriptional response to anti-TB treatment robust correlation occurring between different host popula could also be individually quantified for each patient. tions, likely different Mtb strains, diverse environments and Together, these findings suggest that blood transcriptional microarray analysis strategies indicates that blood transcrip signatures could be used as early Surrogate biomarkers of a tomics may be developed into robust novel diagnostic tools. Successful treatment response, in both the clinical setting and Furthermore, as demonstrated herein, the derived treatment in drug development. specific 320-transcript signature also had many genes in com 0146 TB treatment monitoring is a major challenge for mon with the active TB 664-transcript signature (FIG. 3F). attempts to eradicate Mtb infection. In April 2010 the Centers This overlap of genes may help in future development of gene US 2015/O 133469 A1 May 14, 2015 subsets that correlate with a patient’s response to anti-TB Examples was not changed. For a small sample of six patients treatment, acting as a Surrogate marker of treatment failure or having symptoms of TB, blood was sampled every day for the SCCCSS, first two weeks (depending on patient availability; i.e., miss 0149. Due to the ethical design of this study, active TB ing data points for various days are due to patient unavailabil patients who did not respond to TB treatment are not pre ity). Three of these patients were later confirmed as having sented. But this study has demonstrated a very important TB and as unambiguously meeting the previously-noted proof-of-principle that active TB patients who are success inclusion and exclusion criteria: Patient ID 2208; Patient ID fully treated have a dramatic measurable change in their 2220; and Patient ID 2232. blood gene expression profiles as early as two weeks. The use 0154 For RNA isolation and analysis, a few small meth of a commercially available whole genome microarray plat odology modifications were made to the methodologies of form together with broadly available bioinformatics analyses Example 2. First, sample RNA was isolated using 1 ml whole programmes should easily allow rapid validation in Subse blood and the PerfectPure RNA Blood Kit (Invitrogen/Ap quent TB treatment studies, including a comparison with plied BioSystems/Ambion) according to the manufacturers patients with MDR-TB and HIV/TB co-infected cohorts. instructions. Second, 0.7-2.2 Lug isolated total RNA was This study focused on TB patients who are not co-infected globin reduced using the GLOBINclear Human Kit (Invitro with HIV, as they represent the majority of patients infected gen/Applied BioSystems/Ambion) according to the manufac with Mtb. WHO 2010 reports that of the 1.4 million deaths, turers instructions. Third, raw data were processed using three-quarters were not known to be co-infected with HIV GeneSpring GX version 12 (Agilent Technologies). 2. 0.155. In addition to generating heatmaps and temporal 0150. No other studies appear to have specifically derived molecular response data from 320-transcript signatures for transcriptional signatures of response to TB treatment. How Patient ID 2208, Patient ID 2220, and Patient ID 2232, heat ever, two other studies have described relevant treatment maps and temporal molecular response data were generated related transcriptional differences. Mistry et al. found that for 393- and 86-transcript signatures for each of these patients patients who had completed a course of anti-TB treatment using the methodologies of Example 2. In detail, 393- and displayed similar expression profiles to a latent TB group, but 86-transcript signatures were translated from the HT-12 V3 Mistry etal. did not examine any patients during their anti-TB BeadChip arrays to HT-12 V4 BeadChip arrays using Gene treatment course, and Mistry et al. used custom arrays 27. Spring GX version 11.5 (Agilent Technologies) and trans making it therefore more difficult for others to validate. Joos lated to slightly fewer probes in V4' due to slight differences ten et al. showed in a small number of samples that their active in probe sets between Illumina Human HT-12 V3 and V4 TB gene set diminished after two months of anti-TB treat BeadChip versions. That is, data were also obtained for 380 ment; however they did not examine any patients at earlier list (i.e., Illumina Human HT-12 V4 BeadChip translation of timepoints 28. The early TB treatment blood transcriptional Illumina Human HT-12 V3 BeadChip 393 list) and 83 list signature disclosed herein has great potential for develop (i.e., Illumina Human HT-12 V4 BeadChip translation of ment in blood biomarkers for clinical use and could be mea Illumina Human HT-12 V3 BeadChip 86 list) treatment Sured in the field using a polymerase chain reaction assay, response profiles. similar to the WHO endorsed GeneXpert MTB/RIF test 0156 Results. Heatmaps and corresponding temporal already in use for TB diagnostics in both developing and molecular response data for Patient ID 2208, Patient ID 2220, developed countries. However a blood host biomarker, based and Patient 2232 are provided in FIGS. 19, 20, and 21, respec on the transcriptional signature of the study disclosed herein, tively, for 320, 86, and 393 transcript lists. For each of the would have advantages over the GeneXpert test since it would three patients, less temporal molecular response is shown for not require sputum. either the 86 or the 393 transcript lists than for the 320 tran 0151. A further problem in the management of TB is the script list. This is consistent with the 393 and 86 transcript extended length of treatment, requiring a minimum duration lists not being derived for treatment response. In contrast, of six months. However the treatment duration required for temporal molecular response for the 320 transcript list is maximum efficacy and preventing resistance, has not been more pronounced. For example, the “Day 3’ temporal fully established. The ability therefore to stratify patients into molecular response data points for Patient ID 2220 and groups requiring shorter or longer treatment durations, par Patient ID 2232, i.e., the data point differences from the “0” ticularly in resource limited settings, could be of value in baseline at “Day 3’ for these patients, is more pronounced for improving patient compliance and reducing treatment related the 320-transcript list than for either the 86- or 393-transcript side effects. We demonstrate here that some patient’s tran Scriptional response appeared to plateau before six months lists. (FIGS. 14A, 14B, 17A & 17B) suggesting blood transcrip 0157. In Summary, transcriptional signatures, measured in tional signatures may help develop personalized treatment easily accessible whole blood, showed a detectable response regimes. to anti-TB treatment, and this response was rapid and could be measured as early as two weeks (or, as preliminary data Example 3 from Example 3 show, very much sooner) after initiation of treatment—far more quickly, and more consistently, than in 0152. Use of a 320 gene transcriptional signature, as currently available tests. In addition, this early response to prominently disclosed in Example 2, was also prominently anti-TB treatment was demonstrated in both high- and inter used for tests disclosed in this Example 3. In particular, data mediate-burden settings. Transcriptional response could be from preliminary studies demonstrate that blood-derived measured for each individual TB patient, thus providing a transcriptional signatures are diminished between three to six potential clinical tool for single patient treatment monitoring. days after initiation of anti-TB treatment. Furthermore, this monitoring promises to aid in patient strati 0153. Materials and Methods. Recruitment methodology fication for treatment(s) with differing regimen lengths. (i.e., inclusion and exclusion criteria) as detailed in the above These findings provide compelling evidence for a biomarker US 2015/O 133469 A1 May 14, 2015 20

Successful in assessing early anti-TB treatment response. preceding the term. For example, "A, B, C, or combinations This biomarker of early treatment response would allow rapid thereof is intended to include at least one of A, B, C, AB, detection of both inadequate treatment regimens and poor AC, BC, or ABC, and if order is important in a particular treatment compliance, and therefore shows particular useful ness for reducing the spread of TB as brought about through context, also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB. the generation and spread of drug resistant Mtb. Continuing with this example, expressly included are combi 0158. It is contemplated that any embodiment discussed in nations that contain repeats of one or more item or term, Such this specification can be implemented with respect to any as BB, AAA, MB, BBC, AAABCCCC, CBBAAA, method, kit, reagent, or composition of the invention, and CABABB, and so forth. The skilled artisan will understand Vice versa. Furthermore, compositions of the invention can be that typically there is no limit on the number of items or terms used to achieve methods of the invention. in any combination, unless otherwise apparent from the con text. 0159. It will be understood that particular embodiments 0164. As used herein, words of approximation Such as, described herein are shown by way of illustration and not as without limitation, “about”, “substantial” or “substantially limitations of the invention. The principal features of this refers to a condition that when so modified is understood to invention can be employed in various embodiments without not necessarily be absolute or perfect but would be considered departing from the scope of the invention. Those skilled in the close enough to those of ordinary skill in the art to warrant art will recognize, or be able to ascertain using no more than designating the condition as being present. The extent to routine experimentation, numerous equivalents to the specific which the description may vary will depend on how great a procedures described herein. Such equivalents are considered change can be instituted and still have one of ordinary skilled to be within the scope of this invention and are covered by the in the art recognize the modified feature as still having the claims. required characteristics and capabilities of the unmodified 0160 All publications and patent applications mentioned feature. In general, but Subject to the preceding discussion, a in the specification are indicative of the level of skill of those numerical value herein that is modified by a word of approxi skilled in the art to which this invention pertains. All publi mation such as “about may vary from the stated value by at cations and patent applications are herein incorporated by least +1, 2, 3, 4, 5, 6, 7, 10, 12 or 15%. reference in the entirety of each to the same extent as if each 0.165 All of the compositions and/or methods disclosed individual publication or patent application was specifically and claimed herein can be made and executed without undue and individually indicated to be incorporated by reference in experimentation in light of the present disclosure. While the its entirety. compositions and methods of this invention have been 0161 The use of the word “a” or “an' when used in con described interms of preferred embodiments, it will be appar junction with the term “comprising in the claims and/or the ent to those of skill in the art that variations may be applied to specification may mean “one.” but it is also consistent with the compositions and/or methods and in the steps or in the the meaning of"one or more.” “at least one and “one or more sequence of steps of the method described herein without than one.” The use of the term 'or' in the claims is used to departing from the concept, spirit and scope of the invention. All Such similar Substitutes and modifications apparent to mean “and/or unless explicitly indicated to refer to alterna those skilled in the art are deemed to be within the spirit, tives only or the alternatives are mutually exclusive, although Scope and concept of the invention as defined by the appended the disclosure supports a definition that refers to only alter claims. natives and “and/or.” Throughout this application, the teem (0166 TABLES “about is used to indicate that a value includes the inherent variation of error for the device, the method being employed TABLE 1 to determine the value, or the variation that exists among the Genes Present in the Top Significantly Represented study Subjects. Ingenuity Pathways (Untreated South Africa 2011 Cohort) (Ingenuity 0162. As used in this specification and claim(s), the words Pathway Analysis of 664 Transcripts) “comprising (and any form of comprising, Such as "com Altered T&B Cell Role of PRRS in prise' and "comprises”), “having (and any form of having, IFN Signaling in Recognition of such as “have and “has'), “including (and any form of Signaling Rheumatoid Arthritis Viruses and Bacteria including, such as “includes” and “include’) or “containing Fold Fold Fold (and any form of containing, Such as “contains' and “con Gene Change Gene Change Gene Change tain’) are inclusive or open-ended and do not exclude addi IFI35 2.32 CD4OLG -3.09 CS 2.45 tional, unrecited elements or method steps. As used herein, IFIT3 5.81 CD79A –5.09 C1QB 24.63 the phrase “consisting essentially of limits the scope of a IFITM1 2.39 CD79B –2.15 C1QC 4.66 IRF1 2.26 FAS 2.43 CASP1 2.11 claim to the specified materials or steps and those that do not JAK2 2.38 FCER1G 2.05 IFIH1 2.39 materially affect the basic and novel characteristic(s) of the SOCS1 3.33 IL15 S.S3 IL1B 2.29 claimed invention. As used herein, the phrase "consisting of STAT1 3.40 IL1B 2.29 IRF7 2.25 TAP1 2.42 IL1RN 2.06 NLRC4 3.17 excludes any element, step, or ingredient not specified in the SLAMF1 -2.84 NOD2 2.50 claim except for, e.g., impurities ordinarily associated with TLR2 3.12 TLR2 3.12 the element or limitation. TLRS 3.07 TLRS 3.07 TNFSF13B 2.36 0163 The term “or combinations thereof as used herein refers to all permutations and combinations of the listed items US 2015/O 133469 A1 May 14, 2015 21

TABLE 1-continued TABLE 3 Genes Present in the Top Significantly Represented Genes Present in the Top Significantly Represented Ingenuity Pathways (Untreated South Africa 2011 Cohort) (Ingenuity Pathway Analysis of 664 Transcripts) Ingenuity Pathway (UK & South Africa 2009 Cohorts). Role of Macrophages, SOUTH AFRICA 2009 (711) TREM1 Fibroblasts, Endothelial Cells SLE Signaling in Rheumatoid Arthritis Signaling IFN Signaling Complement System Fold Fold Fold Symbol Fold Change Symbol Fold Change Gene Change Gene Change Gene Change IFI35 2.514 C2 2.127 CASP1 2.11 CS 2.45 CS 2.45 CASPS 8.58 CREBS 3.70 CD3E -2.20 IFIT1 2.143 C1QB 6.215 IL1B 2.29 F2RL1 2.27 CD4OLG -3.09 IFIT3 4.149 C1QC 7.875 ITGAX 4.97 FCGR1A 10.37 CD79A -5.09 IFITM1 2.155 C4BPA 2.675 JAK2 2.38 IL15 5.53 CD79B -2.15 OAS1 2.476 CD59 2.535 NOD2 2.50 IL18R1 2.41. FCER1G 2.05 SOCS1 4.038 CR1 2.443 PLCG1 -2.02 IL18RAP 2.32 FCGR1A 10.37 STAT1 3.063 SERPING1 10.744 TLR2 3.12 IL1B 2.29 FCGR1B 12.33 TAP1 2.2OS TLRS 3.07 IL1RN 2.06 FCGR1C 9.66 IRAK3 2.OS FCGR2C S.O.3 JAK2 2.38 FCGR3B 2.13 Altered T & B Role of Pattern Recognition MAPK14 S.04 IL1B 2.29 Cell Signaling in Receptors in Recognition of NFAT5 2.56 IL1RN 2.06 Rheumatoid Arthritis Bacteria and Viruses OSM 2.76 LCK -2.22 PDGFA 2.16 NFATS 2.56 Symbol Fold Change Symbol Fold Change PLCG1 -2.02 PLCG1 -2.02 SOCS1 3.33 TNFSF13B 2.36 CD79A -4.048 C1QB 6.215 SOCS3 4.06 CD79B -2.100 C1QC 7.875 TLR2 3.12 FAS 2.096 CASP1 2.024 TLRS 3.07 TNFSF13B 2.36 FCER1G 2.026 IL1B 2.052 TRAFS -2.12 IL15 2.609 IRF7 2021 IL1B 2.052 NLRC4 2.039 IL23A -2.253 OAS1 2.476 TLRS 2.885 OAS3 2.578 TABLE 2A TNFRSF13B -2.693 TLRS 2.885 TNFSF13B 2.115 South Africa Cohort Role of JAK Family Communication Between Untreated Untreated Treated Kinases in IL-6 Type Innate & Adaptive Active TB Latent TB Active TB Cytokine Signaling Immune Cells Cohort Cohort Cohort

Total 29 38 2O Symbol Fold Change Symbol Fold Change Gender (total males) 19 17 12 Ethnicity 29 Black 38 Black 20 Black MAPK14 2.591 African African African OSM 2.514 Age (ave. & range, yrs) 34 (21-65) 22 (18-44) 36 (19-65) SOCS1 4.038 Previous TB (total no.) 6 O 3 Productive cough (total no.) 29 O 2O SOCS3 3.147 Smear +ve (total no.) 28 NA 19 STAT1 3.063 Night Sweats (total no.) 25 O 22 Dendritic Cell Maturation Weight loss (total no.) 28 O 19 Chest X-ray performed 1 (1/1 abnormal) Not done O Symbol Fold Change (total no.) UK TRAINING 2009 (565) TABLE 2B IFN Signaling Complement System UK 2011 Cohort Symbol Fold Change Symbol Fold Change Treated Active TB Cohort IFI35 2.685 Total 8 IFIT1 3.071 Gender (total males) 3 Ethnicity 2 Black African, 3 Indian Subcont., IFIT3 5.317 2 SE Asian, 1 Caucasian IFITM1 2014 Age (ave. & range, yrs) 33 (19-67) JAK2 2.424 Previous TB (total no.) O OAS1 3.454 Productive cough (total no.) 6 SOCS1 2.960 Smear +ve (total no.) 4 STAT1 2.900 Night Sweats (total no.) 4 Weight loss (total no.) 4 STAT2 2.079 Chest X-ray performed (total no.) 8 (7/8 abnormal) TAP1 2.291 US 2015/O 133469 A1 May 14, 2015 22

TABLE 3-continued TABLE 3-continued Genes Present in the Top Significantly Represented Genes Present in the Top Significantly Represented Ingenuity Pathway (UK & South Africa 2009 Cohorts). Ingenuity Pathway (UK & South Africa 2009 Cohorts). Altered T & B Role of Pattern Recognition TLR8 2.1.89 Cell Signaling in Receptors in Recognition of TNFRSF13B -2.714 Rheumatoid Arthritis Bacteria and Viruses TNFSF13B 2.025 Symbol Fold Change Symbol Fold Change Dendritic Cell Maturation CD86 -2.077 C1QB 9.514 CD4OLG -2.021 C3AR1 2.06S Symbol Fold Change CD79A -2.051 CASP1 2.146 FAS 2.185 IFIH1 2.027 CD86 -2.077 FCER1G 2.OO2 IL1B 2.037 CD4OLG -2.021 IL15 2.599 IRF7 2.438 CREBS 2.530 IL1B 2.037 NLRC4 2.276 FCER1G 2.OO2 IL1RN 2.297 OAS1 3.454 FCGR1A 6840 IL23A -2.964 OAS2 3.335 FCGR1B 7.042 TLR2 2.OOS OAS3 3.246 IL15 2.599 TLRS 2.346 TLR2 2.OOS IL1B 2.037 TLR8 2.1.89 TLRS 2.346 IL1RN 2.297 TNFRSF13B -2.714 TLR8 2.1.89 IL23A -2.964 TNFSF13B 2.O2S JAK2 2.424 MAPK14 3.174 Role of JAK Family Communication Between STAT1 2.900 Kinases in IL-6 Type Innate & Adaptive STAT2 2.079 Cytokine Signaling Immune Cells TLR2 2.OOS Symbol Fold Change Symbol Fold Change UK TEST 2009 (224) CD86 -2.077 IFN Signaling Complement System CD4OLG -2.021 CXCL10 3.407 Symbol Fold Change Symbol Fold Change FCER1G 2002 IL15 2.599 IFIT3 2.899 C2 2.419 IL1B 2.037 OAS1 2.257 C1QB S.O45 IL1RN 2.297 SOCS1 2.296 C1QC 3.630 TLR2 2.OOS STAT1 2.185 SERPING1 5.462 TLRS 2.346

TABLE 4 List of the 224 genes ProbeID Symbol Entrez Gene ID Definition 2710O20 BXO93329 Soares parathyroid tumor NbHPA Homo sapiens cDNA clone IMAGp998A124183; IMAGE: 1648403, mRNA sequence 7SSO392 GLUL 2752 Homo sapiens glutamate-ammonia ligase (glutamine synthetase) (GLUL), transcript variant 3, mRNA. 2190349 KCNJ15 3772 Homo sapiens potassium inwardly-rectifying channel, Subfamily J, member 15 (KCNJ15), transcript variant 3, mRNA. 412O243 PSG9 5678 Homo sapiens pregnancy specific beta-1-glycoprotein 9 (PSG9), mRNA. 5700725 EPSTI1 94240 Homo sapiens epithelial stromal interaction 1 (breast) (EPSTI1), transcript variant 2, mRNA. 670594 C2 717 Homo sapiens complement component 2 (C2), mRNA. 2O7O646 GPR84 S3831 Homo sapiens G protein-coupled receptor 84 (GPR84), mRNA. 130181 ANKRD22 118932 Homo sapiens ankyrin repeat domain 22 (ANKRD22), mRNA. S86007S CAMP 820 Homo sapiens cathelicidin antimicrobial peptide (CAMP), mRNA. 648O246 LOC649.095 64909S PREDICTED: Homo sapiens hypothetical LOC649095 (LOC649095), mRNA. 4290368 PSTPIP2 90SO Homo sapiens proline-serine-threonine phosphatase interacting protein 2 (PSTPIP2), mRNA. 2360348 CMPK2 1296O7 Homo sapiens cytidine monophosphate (UMP-CMP) kinase 2, mitochondrial (CMPK2), nuclear gene encoding mitochondrial protein, mRNA. 26301.9S VAMPS 10791 Homo sapiens vesicle-associated membrane protein 5 (myobrevin) (VAMP5), mRNA. US 2015/O 133469 A1 May 14, 2015 23

TABLE 4-continued List of the 224 genes Probe) Symbol Entrez Gene ID Definition 2370228 CCDC65 85.478 Homo sapiens coiled-coil domain containing 65 (CCDC65), mRNA. 662O209 2210 Homo sapiens Fc fragment of IgG, high affinity Ib, receptor (CD64) (FCGR1B), transcript variant 2, mRNA. 130609 FCGBP 8857 Homo sapiens Fc fragment of IgG binding protein (FCGBP), mRNA. 1510364 GBP5 115362 Homo sapiens guanylate binding protein 5 (GBP5), mRNA. 6370768 ETV 7 51513 Homo sapiens ets variant 7 (ETV7), mRNA. 344009S PSG3 5671 Homo sapiens pregnancy specific beta-1-glycoprotein 3 (PSG3), mRNA. 40SOO39 2214 Homo sapiens Fc fragment of IgG, low affinity IIIa, receptor (CD16a) (FCGR3A), mRNA. 70730 GAS6 2621 Homo sapiens growth arrest-specific 6 (GAS6), mRNA. 1470.091 IL15 3600 Homo sapiens interleukin 15 (IL15), transcript variant 1, mRNA. 2490315 Homo sapiens cDNA: FLJ23098 fis, clone LNGO7440 6040577 SLAMF8 S6833 Homo sapiens SLAM family member 8 (SLAMF8), mRNA. 276.0500 CD38 952 Homo sapiens CD38 molecule (CD38), mRNA. 268O161 GGTL3 2686 Homo sapiens gamma-glutamyltransferase-like 3 (GGTL3), transcript variant 2, mRNA. 2030309 SERPING1 710 Homo sapiens serpin peptidase inhibitor, clade G (C1 inhibitor), member 1 (SERPING1), transcript variant 2, mRNA. 3170273 FER1L3 26509 Homo sapiensfer-1-like 3, myoferlin (C. elegans) (FER1 L3), transcript variant2, mRNA. ZNFS4O 163255 Homo sapiens Zinc finger protein 540 (ZNF540), mRNA. GNLY 10578 Homo sapiens granulysin (GNLY), transcript variant 519, mRNA. FLJ40504 28.4085 Homo sapiens hypothetical protein FLJ40504 (FLJ40504), mRNA. SO132 ALS2CR16 130O29 PREDICTED: Homo sapiens amyotrophic lateral sclerosis 2 (juvenile) chromosome region, candidate 16 (ALS2CR16), mRNA. 1110O39 CEP68 23177 Homo sapiens centrosomal protein 68 kDa (CEP68), mRNA. 5270403 Homo sapiens cDNA FLJ20012 fis, clone ADKAO3438 716O164 MATK 4145 Homo sapiens megakaryocyte-associated tyrosine kinase (MATK), transcript variant 1, mRNA. GBP1 2633 Homo sapiens guanylate binding protein 1, interferon inducible, 67 kDa (GBP1), mRNA. 2190148 GBP1 2633 Homo sapiens guanylate binding protein 1, interferon inducible, 67 kDa (GBP1), mRNA. 150730 ANKRD36 375248 Homo sapiens ankyrin repeat domain 36 (ANKRD36), mRNA. 240053 GCH1 2643 Homo sapiens GTP cyclohydrolase 1 (GCH1), transcript variant 4, mRNA. 380O398 26270 Homo sapiens F-box protein 6 (FBXO6), mRNA. 650753 3899 Homo sapiens AF4/FMR2 family, member 3 (AFF3), transcript variant 2, mRNA. 622O332 SLC6A12 6539 Homo sapiens solute carrier family 6 (neurotransmitter transporter, betaine/GABA), member 12 (SLC6A12), mRNA. 489.0270 4061 Homo sapiens lymphocyte antigen 6 complex, locus E (LY6E), mRNA. 2760019 Homo sapiens cDNA FLJ46319 fis, clone TESTI4O4242O 940356 IL15RA Homo sapiens interleukin 15 receptor, alpha (IL15RA), transcript variant 1, mRNA. 387,0458 BRDG1 26228 Homo sapiens BCR downstream signaling 1 (BRDG1), mRNA. 4730059 BATF2 116O71 Homo sapiens basic leucine Zipper transcription factor, ATF-like 2 (BATF2), mRNA. EIF2AK3 945.1 Homo sapiens eukaryotic translation initiation factor 2 alpha kinase 3 (EIF2AK3), mRNA. 2070114 CLEC4D 338339 Homo sapiens C-type lectin domain family 4, member D (CLEC4D), mRNA. 7510647 CACNA1E 777 Homo sapiens calcium channel, voltage-dependent, R type, alpha 1E subunit (CACNA1E), mRNA. US 2015/O 133469 A1 May 14, 2015 24

TABLE 4-continued List of the 224 genes Probe) Symbol Entrez Gene ID Definition NCAPH2 29781 Homo sapiens non-SMC condensin II complex, Subunit H2 (NCAPH2), transcript variant 2, mRNA. 2970286 CEACAM1 634 Homo sapiens carcinoembryonic antigen-related cell adhesion molecule 1 (biliary glycoprotein) (CEACAM1), transcript variant 2, mRNA. 5700753 CEACAM1 634 Homo sapiens carcinoembryonic antigen-related cell adhesion molecule 1 (biliary glycoprotein) (CEACAM1), transcript variant 2, mRNA. 124O270 LMTK2 22853 Homo sapiens lemur tyrosine kinase 2 (LMTK2), mRNA. 1580435 TGM2 7052 Homo sapiens transglutaminase 2 (C polypeptide, protein-glutamine-gamma-glutamyltransferase) (TGM2), transcript variant 1, mRNA. 342O593 4001 Homo sapiens lamin B1 (LMNB1), mRNA. 478O128 467 Homo sapiens activating transcription factor 3 (ATF3), transcript variant 4, mRNA. 2640O2S HP 3240 Homo sapiens haptoglobin (HP), mRNA. 6330471 BLK 640 Homo sapiens B lymphoid tyrosine kinase (BLK), mRNA. 29.0661 CLN8 2055 Homo sapiens ceroid-lipofuscinosis, neuronal 8 (epilepsy, progressive with mental retardation) (CLN8), mRNA. 3180731 MGST1 4257 Homo sapiens microsomal glutathione S-transferase 1 (MGST1), transcript variant 1b, mRNA. 481 0079 KIAA1632 57724. Homo sapiens KIAA1632 (KIAA1632), mRNA. 604O653 TRIM6 117854 Homo sapiens tripartite motif-containing 6 (TRIM6), transcript variant 2, mRNA. 3610681 OR2A9P 4.41295 Homo sapiens olfactory receptor, family 2, Subfamily A, member 9 pseudogene (OR2A9P), non-coding RNA. PHOSPHO2 493.911 Homo sapiens phosphatase, orphan 2 (PHOSPHO2), mRNA. 130274 LCK 3932 Homo sapiens lymphocyte-specific protein tyrosine kinase (LCK), transcript variant 2, mRNA. ANKDD1A 34.8094 Homo sapiens ankyrin repeat and death domain containing 1A (ANKDD1A), mRNA. 6760681 PREDICTED: Homo sapiens similar to T-cell receptor alpha chain V region CTL-L17 precursor (LOC650761), mRNA. 2690477 LOC648710 64.871 O PREDICTED: Homo sapiens similar to leukocyte immunoglobulin-like receptor, subfamily B (with TM and ITIM domains), member 3 (LOC648710), mRNA. 1OSO215 KCNJ15 3772 Homo sapiens potassium inwardly-rectifying channel, subfamily J, member 15 (KCNJ15), transcript variant 1, mRNA. 1240O37 Homo sapiens DKFZP564O0823 protein (DKFZP564O0823), mRNA. 620403 LOC4007S9 4.00759 Homo sapiens similar to Interferon-induced guanylate binding protein 1 (GTP-binding protein 1) (Guanine nucleotide-binding protein 1) (HuGBP-1) (LOC400759) on chromosome 1. 780079 LOC654,053 PREDICTED: Homo sapiens similar to hypothetical LOC389634 (LOC654053), mRNA. 1110592 EBF1 1879 Homo sapiens early B-cell factor 1 (EBF1), mRNA. 318O392 MARCO 8685 Homo sapiens macrophage receptor with collagenous structure (MARCO), mRNA. 9560 Homo sapiens chemokine (C-C motif) ligand 4-like 1 (CCL4L1), mRNA. 130753 AIM2 9447 Homo sapiens absent in melanoma 2 (AIM2), mRNA. 126O270 AIM2 9447 Homo sapiens absent in melanoma 2 (AIM2), mRNA. 4929 Homo sapiens nuclear receptor Subfamily 4, group A, member 2 (NR4A2), transcript variant 1, mRNA. S390427 NAIP 4671 Homo sapiens NLR family, apoptosis inhibitory protein (NAIP), transcript variant 1, mRNA. S340240 NAIP 4671 Homo sapiens NLR family, apoptosis inhibitory protein (NAIP), transcript variant 1, mRNA. MMP28 79148 Homo sapiens matrix metallopeptidase 28 (MMP28), transcript variant 3, mRNA. 2750475 2.7065 Homo sapiens DNA segment on chromosome 4 (unique) 234 expressed sequence (D4S234E), transcript variant 2, mRNA. 461 O136 GPATCH4 Homo sapiens G patch domain containing 4 (GPATCH4), transcript variant 2, mRNA. US 2015/O 133469 A1 May 14, 2015 25

TABLE 4-continued List of the 224 genes Probe) Symbol Entrez Gene ID Definition 101.0360 GCH1 2643 Homo sapiens GTP cyclohydrolase 1 (GCH1), transcript variant 3, mRNA. 4.010270 LOC44O731 PREDICTED: Homo sapiens hypothetical LOC440731, transcript variant 2 (LOC440731), mRNA. 3850435 AGPAT3 56894 Homo sapiens 1-acylglycerol-3-phosphate O acyltransferase 3 (AGPAT3), mRNA. 522O524 51440 Homo sapiens hippocalcin like 4 (HPCAL4), mRNA. S910019 713 Homo sapiens complement component 1, q subcomponent, B chain (C1OB), mRNA. 4640309 EST391381 MAGE resequences, MAGP Homo sapiens cDNA, mRNA sequence 2710709 2210 Homo sapiens Fc fragment of IgG, high affinity Ib, receptor (CD64) (FCGR1B), transcript variant 1, mRNA. 1260482 GZMK 3003 Homo sapiens granzyme K (granzyme 3; tryptase II) (GZMK), mRNA. 3930368 INCA Homo sapiens inhibitory caspase recruitment domain (CARD) protein (INCA), mRNA. GPER 2852 Homo sapiens G protein-coupled estrogen receptor 1 (GPER), transcript variant 3, mRNA. 36O132 10184 Homo sapiens lipoma HMGIC fusion partner-like 2 (LHFPL2), mRNA. 3990435 BX282075 NIH MGC 120 Homo sapiens cDNA clone IMAGp998M201.1561; IMAGE: 5223355, mRNA Sequence FAM102A 3.99665 Homo sapiens family with sequence similarity 102, member A (FAM102A), transcript variant 1, mRNA. 461 O280 MEIS3P1 4213 Homo sapiens Meis homeobox 3 pseudogene 1 (MEIS3P1), non-coding RNA. 638O136 NFATC2IP 84901 PREDICTED: Homo sapiens nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 2 interacting protein, transcript variant 3 (NFATC2IP), mRNA. 344O630 ESPN 83715 Homo sapiens espin (ESPN), mRNA. 2710093 ZNF74 7625 Homo sapiens Zinc finger protein 74 (ZNF74), transcript variant 1, mRNA. 642O594 TIFA 926.10 Homo sapiens TRAF-interacting protein with forkhead associated domain (TIFA), mRNA. 1070367 C19Crf59 199675 Homo sapiens chromosome 19 open reading frame 59 (C19orf59), mRNA. 38.90609 PLSCR1 5359 Homo sapiens phospholipid scramblase 1 (PLSCR1), mRNA. 4150692 PARP14 S4625 Homo sapiens poly (ADP-ribose) polymerase family, member 14 (PARP14), mRNA. 780035 SYTL2 S4843 Homo sapiens synaptotagmin-like 2 (SYTL2), transcript varianta, mRNA. 99.0239 KCNE3 Homo sapiens potassium voltage-gated channel, Isk related family, member 3 (KCNE3), mRNA. 10360 OAS3 4940 Homo sapiens 2'-5'-oligoadenylate synthetase 3, 100 kDa (OAS3), mRNA. 99.0768 OAS3 4940 Homo sapiens 2'-5'-oligoadenylate synthetase 3, 100 kDa (OAS3), mRNA. 3190521 931 Homo sapiens membrane-spanning 4-domains, subfamily A, member 1 (MS4A1), transcript variant 1, mRNA. CEACAM1 634 Homo sapiens carcinoembryonic antigen-related cell adhesion molecule 1 (biliary glycoprotein) (CEACAM1), transcript variant 1, mRNA. 1940373 ZNF831 128611 Homo sapiens Zinc finger protein 831 (ZNF831), mRNA. 6380411 IKZF3 22806 Homo sapiens IKAROS family Zinc finger 3 (Aiolos) (IKZF3), transcript variant 1, mRNA. S80433 DENND1A Homo sapiens DENN/MADD domain containing 1A (DENND1A), transcript variant 1, mRNA. 331 O376 TIMM10 26519 Homo sapiens translocase of inner mitochondrial membrane 10 homolog (yeast) (TIMM10), nuclear gene encoding mitochondrial protein, mRNA. S340170 SNORD21 6083 Homo sapiens small nucleolar RNA, C/D box 21 (SNORD21), non-coding RNA. 7200035 DLEC1 9940 Homo sapiens deleted in lung and esophageal cancer 1 (DLEC1), transcript variant DLEC1-S3, mRNA. 7330671 APOBEC3A 20O315 Homo sapiens apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3A (APOBEC3A), mRNA. US 2015/O 133469 A1 May 14, 2015 26

TABLE 4-continued List of the 224 genes Probe) Symbol Entrez Gene ID Definition 3360343 RSAD2 91543 Homo sapiens radical S-adenosyl methionine domain containing 2 (RSAD2), mRNA. 5310437 MYL9 10398 Homo sapiens myosin, light chain 9, regulatory (MYL9), transcript variant 1, mRNA. S340468 3674 Homo sapiens integrin, alpha2b (platelet glycoprotein IIb of IIb, IIIa complex, antigen CD41B) (ITGA2B), mRNA. 1070487 KLRC3 38.23 Homo sapiens killer cell lectin-like receptor subfamily C, member 3 (KLRC3), transcript variant 1, mRNA. 1010246 IFI6 2537 Homo sapiens interferon, alpha-inducible protein 6 (IFI6), transcript variant 2, mRNA. 1090390 OAS1 4938 Homo sapiens 2',5'-oligoadenylate synthetase 1, 40/46 kDa (OAS1), transcript variant 3, mRNA. OAS1 4938 Homo sapiens 2',5'-oligoadenylate synthetase 1, 40/46 kDa (OAS1), transcript variant 3, mRNA. 990131 GUCY1A3 2982 Homo sapiens guanylate cyclase 1, soluble, alpha 3 (GUCY1A3), mRNA. S910632 SMARCD3 6604 Homo sapiens SWI/SNF related, matrix associated, actin dependent regulator of chromatin, Subfamily d, member 3 (SMARCD3), transcript variant 2, mRNA. 6770497 PLAG1 S324 Homo sapiens pleiomorphic adenoma gene 1 (PLAG1), mRNA. ZNF 365 22891 Homo sapiens Zinc finger protein 365 (ZNF365), transcript variant A, mRNA. 182O114 LILRB4 11006 Homo sapiens leukocyte immunoglobulin-like receptor, subfamily B (with TM and ITIM domains), member 4 (LILRB4), transcript variant 2, mRNA. 1240358 RAB20 55647 Homo sapiens RAB20, member RAS oncogene family (RAB20), mRNA. 19903OO SOCS1 8651 Homo sapiens suppressor of cytokine signaling 1 (SOCS1), mRNA. 6180491 ADAM28 10863 Homo sapiens ADAM metallopeptidase domain 28 (ADAM28), transcript variant 3, mRNA. 630315 DHRS9 10170 Homo sapiens dehydrogenase/reductase (SDR family) member 9 (DHRS9), transcript variant 1, mRNA. 6840600 GLT2SD2 23127 Homo sapiens glycosyltransferase 25 domain containing 2 (GLT25D2), mRNA. LOC642852 642852 PREDICTED: Homo sapiens hypothetical LOC642852 (LOC642852), mRNA. 676O286 RST24587 Athersys RAGE Library Homo sapiens cDNA, mRNA sequence 313O220 TMEM158 25907 Homo sapiens transmembrane protein 158 (TMEM158), mRNA. GTPBP3 847OS Homo sapiens GTP binding protein 3 (mitochondrial) (GTPBP3), nuclear gene encoding mitochondrial protein, transcript variant IV, mRNA. 329O292 LAP3 51056 Homo sapiens leucine aminopeptidase 3 (LAP3), mRNA. 4850592 P2RY14 9934 Homo sapiens purinergic receptor P2Y, G-protein coupled, 14 (P2RY14), transcript variant 2, mRNA. 1660SO4 ZNF181 3393.18 Homo sapiens Zinc finger protein 181 (ZNF181), mRNA. 651O170 FIT3 3437 Homo sapiens interferon-induced protein with tetratricopeptide repeats 3 (IFIT3), mRNA. FIT3 3437 Homo sapiens interferon-induced protein with tetratricopeptide repeats 3 (IFIT3), mRNA. 1980671 55.188 Homo sapiens resistance to inhibitors of cholinesterase 8 homolog B (C. elegans) (RIC8B), mRNA. 236O164 KLRC2 3822 Homo sapiens killer cell lectin-like receptor subfamily C, member 2 (KLRC2), mRNA. 41SO270 ANKRD22 118932 Homo sapiens ankyrin repeat domain 22 (ANKRD22), mRNA. 642OOO8 PROS1 5627 Homo sapiens protein S (alpha) (PROS1), mRNA. 6650735 Homo sapiens T cell receptor alpha locus, mRNA (cDNA clone MGC: 23964 IMAGE: 4687209), complete cols 761.0113 116369 Homo sapiens solute carrier family 26, member 8 (SLC26A8), transcript variant 2, mRNA. S360626 116369 Homo sapiens solute carrier family 26, member 8 (SLC26A8), transcript variant 2, mRNA. 6580626 CXorf57 55086 Homo sapiens chromosome X open reading frame 57 (CXorf57), mRNA. US 2015/O 133469 A1 May 14, 2015 27

TABLE 4-continued List of the 224 genes Probe) Symbol Entrez Gene ID Definition TNFRSF13B 23495 Homo sapiens tumor necrosis factor receptor superfamily, member 13B (TNFRSF13B), mRNA. D EFA1 1667 Homo sapiens defensin, alpha 1 (DEFA1), mRNA. DNASE1L3 1776 Homo sapiens deoxyribonuclease I-like 3 (DNASE1L3), mRNA. 642O121 tX34g11.x1 NCI CGAP Lu24 Homo sapiens cDNA clone IMAGE: 22715243, mRNA sequence DUSP2 1844 Homo sapiens dual specificity phosphatase 2 (DUSP2), mRNA. 698O133 HUMGSOOO4661 Human adult (K. Okubo) Homo sapiens cDNA3, mRNA sequence 57OOO86 641OS Homo sapiens centromere protein K (CENPK), mRNA. 659.0646 441168 Homo sapiens family with sequence similarity 26, member F (FAM26F), mRNA. 84824 Homo sapiens Fc receptor-like A (FCRLA), mRNA. 21 Homo sapiens ATP-binding cassette, Sub-family A (ABC1), member 3 (ABCA3), mRNA. 115004 Homo sapiens chromosome 6 open reading frame 150 (C60rf150), mRNA. 4900239 CD274 29.126 Homo sapiens CD274 molecule (CD274), mRNA. 66SO242 IFITM3 10410 Homo sapiens interferon induced transmembrane protein 3 (1-8 U) (IFITM3), mRNA. 182O750 STAT1 6772 Homo sapiens signal transducer and activator of transcription 1, 91 kDa (STAT1), transcript variant alpha, mRNA. MYST4 23522 Homo sapiens MYST histone acetyltransferase (monocytic leukemia) 4 (MYST4), mRNA. 721 0356 CR992.331 RZPD no. 9016 Homo sapiens cDNA clone RZPDp9016AO1415, mRNA sequence 686.0437 SLC25A26 115286 Homo sapiens solute carrier family 25, member 26 (SLC25A26), nuclear gene encoding mitochondrial protein, mRNA. 3870338 10964 Homo sapiens interferon-induced protein 44-like (IFI44L), mRNA. 23.40577 AQP10 89872 Homo sapiens aquaporin 10 (AQP10), mRNA. 3290411 KIAA1804 84.451 Homo sapiens mixed lineage kinase 4 (KIA A1804), mRNA. 7510537 SCO2 9997 Homo sapiens SCO cytochrome oxidase deficient homolog 2 (yeast) (SCO2), nuclear gene encoding mitochondrial protein, mRNA. 393O128 BX110640 Soares testis NHT Homo sapiens cDNA clone IMAGp998BO94156, mRNA sequence 74001S2 P2RY1O 27334 Homo sapiens purinergic receptor P2Y, G-protein coupled, 10 (P2RY10), transcript variant 1, mRNA. 2970747 DEFA3 1668 Homo sapiens defensin, alpha 3, neutrophil-specific (DEFA3), mRNA. 6760593 OSBPL10 114884 Homo sapiens oxysterol binding protein-like 10 (OSBPL10), mRNA. 31404.87 BMX 660 Homo sapiens BMX non-receptor tyrosine kinase (BMX), mRNA. S20086 FCGR1A 2209 Homo sapiens Fc fragment of IgG, high affinity Ia, receptor (CD64) (FCGR1A), mRNA. 2570300 IFI44 10561 Homo sapiens interferon-induced protein 44 (IFI44), mRNA. 628.0543 OASL 8638 Homo sapiens 2'-5'-oligoadenylate synthetase-like (OASL), transcript variant 1, mRNA. 2450707 SIGLEC16 400709 Homo sapiens sialic acid binding Ig-like lectin 16 (gene pseudogene) (SIGLEC16), non-coding RNA. 55.70039 LOCA28744 728744 PREDICTED: Homo sapiens hypothetical LOC728744 (LOC728744), mRNA. 371 OO68 WARS 7453 Homo sapiens tryptophanyl-tRNA synthetase (WARS), transcript variant 2, mRNA. 31.80528 MMP9 4318 Homo sapiens matrix metallopeptidase 9 (gelatinase B, 92 kDa gelatinase, 92 kDa type IV collagenase) (MMP9), mRNA. LOC647606 647606 PREDICTED: Homo sapiens hypothetical protein LOC647606 (LOC647606), mRNA. SMARCD3 6604 Homo sapiens SWI/SNF related, matrix associated, actin dependent regulator of chromatin, Subfamily d, member 3 (SMARCD3), transcript variant 1, mRNA. 6860717 HOXC4 3221 Homo sapiens homeobox C4 (HOXC4), transcript variant 1 mRNA. US 2015/O 133469 A1 May 14, 2015 28

TABLE 4-continued List of the 224 genes Probe) Symbol Entrez Gene ID Definition 3800576 ASGR2 433 Homo sapiens asialoglycoprotein receptor 2 (ASGR2), transcript variant 3, mRNA. GM2A 2760 Homo sapiens GM2 ganglioside activator (GM2A), mRNA. 1980S 24 GBP4. 115361 Homo sapiens guanylate binding protein 4 (GBP4), mRNA. 4560S61 BACH2 60468 Homo sapiens BTB and CNC homology 1, basic leucine zipper transcription factor 2 (BACH2), mRNA. 61105.75 FAIM 55179 Homo sapiens Fas apoptotic inhibitory molecule (FAIM), transcript variant 1, mRNA. 290437 HRK 8739 Homo sapiens harakiri, BCL2 interacting protein (contains only BH3 domain) (HRK), mRNA. 56701OO TCN2 6948 Homo sapiens transcobalamin II; macrocytic anemia (TCN2), mRNA. 1440341 C1QC 71.4 Homo sapiens complement component 1, q subcomponent, C chain (C1OC), mRNA. 270240 SLC26A8. 116369 Homo sapiens solute carrier family 26, member 8 (SLC26A8), transcript variant 1, mRNA. 486O224 WARS 7453 Homo sapiens tryptophanyl-tRNA synthetase (WARS), transcript variant 1, mRNA. BX116726 NCI CGAP Pr28 Homo sapiens cDNA clone IMAGp998J065569, mRNA sequence 422O187 DYSF 8291 Homo sapiens dysferlin, limb girdle muscular dystrophy 2B (autosomal recessive) (DYSF), mRNA. 2340441 SLC22A18AS 5003 Homo sapiens solute carrier family 22 (organic cation transporter), member 18 antisense (SLC22A18AS), mRNA. IFIT3 3437 Homo sapiens interferon-induced protein with tetratricopeptide repeats 3 (IFIT3), mRNA. MPO 4353 Homo sapiens myeloperoxidase (MPO), nuclear gene encoding mitochondrial protein, mRNA. 4O10184 C8orf13 83648 Homo sapiens chromosome 8 open reading frame 13 (C8orf13), mRNA. 1410221 S100A12 6283 Homo sapiens S100 calcium binding protein A12 (S100A12), mRNA. 6270553 CXCL10 3627 Homo sapiens chemokine (C-X-C motif) ligand 10 (CXCL10), mRNA. 41204.24 COCH 1690 Homo sapiens coagulation factor Chomolog, cochlin (Limulus polyphemus) (COCH), mRNA. 14701.76 SYT17 51760 Homo sapiens synaptotagmin XVII (SYT17), mRNA. 6290612 TMEM107 84314 Homo sapiens transmembrane protein 107 (TMEM107), transcript variant 1, mRNA. RORA 6095 Homo sapiens RAR-related orphan receptor A (RORA), transcript variant 3, mRNA. 46.70458 Sep-04 5414 Homo sapiens septin 4 (SEPT4), transcript variant 2, mRNA. 62OO156 BX107738 Soares testis NHT Homo sapiens cDNA clone IMAGp998F181825, mRNA sequence 1450427 RTP4 64-108 Homo sapiens receptor (chemosensory) transporter protein 4 (RTP4), mRNA. 421 OO39 ABCB1 S243 Homo sapiens ATP-binding cassette, Sub-family B (MDR/TAP), member 1 (ABCB1), mRNA. 3780047 GBP6 1633S1 Homo sapiens guanylate binding protein family, member 6 (GBP6), mRNA. 73804.00 GBP6 1633S1 Homo sapiens guanylate binding protein family, member 6 (GBP6), mRNA. S690463 79703 Homo sapiens chromosome 11 open reading frame 80 (C11orf&O), mRNA. 31.80474 DKFZp686H1820 r1 686 (synonym: hlcc3) Homo sapiens cDNA clone DKFZp686H1820 5, mRNA Sequence 4O60008 CAMK2N1 55450 Homo sapiens calcium calmodulin-dependent protein kinase II inhibitor 1 (CAMK2N1), mRNA. S490403 CD1E 913 Homo sapiens CD1e molecule (CD1E), transcript variant 5, mRNA. 7160468 DHRS9 10170 Homo sapiens dehydrogenase/reductase (SDR family) member 9 (DHRS9), transcript variant 1, mRNA. 5570343 TWISTNB 221830 Homo sapiens TWIST neighbor (TWISTNB), mRNA. SO324 ITPRIPL1 150771 Homo sapiens inositol 1,4,5-triphosphate receptor interacting protein-like 1 (ITPRIPL1), transcript variant 2, mRNA. US 2015/O 133469 A1 May 14, 2015 29

TABLE 4-continued

List of the 224 genes

ProbeID Symbol Entrez Gene ID Definition

214O242 TNFAIP6 7130 Homo sapiens tumor necrosis factor, alpha-induced protein 6 (TNFAIP6), mRNA.

TABLE 5 List of the 86 genes Probe) Symbol Entrez Gene ID Definition RBCK1 10616 Homo sapiens RanBP-type and C3HC4-type zinc finger containing 1 (RBCK1), transcript variant 1, mRNA. THEM2 55856 Homo sapiens thioesterase Superfamily member 2 (THEM2), mRNA. WDR33 55339 Homo sapiens WD repeat domain 33 (WDR33), transcript variant 3, mRNA. 3801 O2 TMEMS1 55092 Homo sapiens transmembrane protein 51 (TMEM51), mRNA. 4860438 DMXL2 23312 Homo sapiens Dmix-like 2 (DMXL2), mRNA. 638O338 POLB S423 Homo sapiens polymerase (DNA directed), beta (POLB), mRNA. 3170273 FER 26509 Homo sapiens fer-1-like 3, myoferlin (C. elegans) (FER1 L3), transcript variant 2, mRNA. 610598 CHI3 1117 Homo sapiens chitinase 3-like 2 (CHI3L2), transcript variant 1, mRNA. 756O706 09 286333 PREDICTED: Homo sapiens chromosome 9 open reading frame 109 (C9orf109), mRNA. 4O40450 VCP Homo sapiens valosin containing protein (p97) p47 complex interacting protein 1 (VCPIP1), mRNA. 5270403 Homo sapiens cDNA FLJ20012 fis, clone ADKAO3438 S900S64 SLC Homo sapiens solute carrier family 16, member 6 (monocarboxylic acid transporter 7) (SLC16A6), mRNA. 1570669 LACTB 114294 Homo sapiens lactamase, beta (LACTB), nuclear gene encoding mitochondrial protein, transcript variant 1, mRNA. 1510O88 ATP 483 PREDICTED: Homo sapiens ATPase, Na+/K+ transporting, beta 3 polypeptide, transcript variant 2 (ATP1B3), mRNA. CAPS 828 Homo sapiens calcyphosine (CAPS), transcript variant 2, mRNA. S900692 GSTK1 373.156 Homo sapiens glutathione S-transferase kappa 1 (GSTK1), mRNA. MCL 417O Homo sapiens myeloid cell leukemia sequence 1 (BCL2 related) (MCL1), transcript variant 1, mRNA. 2360537 BIB 4793 Homo sapiens nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, beta (NFKBIB), transcript variant 1 mRNA. 1510538 23136 Homo sapiens erythrocyte membrane protein band 4.1-like 3 (EPB41L3), mRNA. 240392 Homo sapiens cDNA FLJ34585 fis, clone KIDNE2008758 5220070 HLA-F 3134 Homo sapiens major histocompatibility complex, class I, F (HLA-F), transcript variant 1, mRNA. 2570328 28.951 Homo sapiens tribbles homolog 2 (Drosophila) (TRIB2), mRNA. 6550703 WSB 2 SS884 Homo sapiens WD repeat and SOCS box-containing 2 (WSB2), mRNA. 450468 MRPL44 6508O Homo sapiens mitochondrial ribosomal protein L44 (MRPL44), nuclear gene encoding mitochondrial protein, mRNA. 429.0209 TMC 6 11322 Homo sapiens transmembrane channel-like 6 (TMC6), mRNA. 276O730 LOC644086 644086 PREDICTED: Homo sapiens hypothetical protein LOC644086, transcript variant 1 (LOC644086), mRNA. 2490240 PTPRE 5791 Homo sapiens protein tyrosine phosphatase, receptor type, E (PTPRE), transcript variant 2, mRNA. 177O152 64231 Homo sapiens membrane-spanning 4-domains, subfamily A, member 6A (MS4A6A), transcript variant 3, mRNA. NAT1 PREDICTED: Homo sapiens N-acetyltransferase 1 (arylamine N-acetyltransferase) (NAT1), mRNA. 1101.61 231.67 Homo sapiens EFR3 homolog A (S. cerevisiae) (EFR3A), mRNA. 4590228 GLRX 2745 Homo sapiens glutaredoxin (thioltransferase) (GLRX), mRNA. US 2015/O 133469 A1 May 14, 2015 30

TABLE 5-continued List of the 86 genes Probe) Symbol Entrez Gene ID Definition 481 0079 KIAA1632 57724. Homo sapiens KIAA1632 (KIAA1632), mRNA. 4890592 PDE7A 5150 Homo sapiens phosphodiesterase 7A (PDE7A), transcript variant 2 mRNA. ATP6VOE1 8992 Homo sapiens ATPase, H+ transporting, lysosomal 9 kDa, VO subunite1 (ATP6VOE1), mRNA. 232O110 ATP6VOE1 8992 Homo sapiens ATPase, H+ transporting, lysosomal 9 kDa, VO subunite1 (ATP6VOE1), mRNA. 61 OO136 CASP4 837 Homo sapiens caspase 4, apoptosis-related cysteine peptidase (CASP4), transcript variant gamma, mRNA. 1660300 PRCP 5547 Homo sapiens prolylcarboxypeptidase (angiotensinase C) (PRCP), transcript variant 2, mRNA. 11 OO64 RNASEL 6041 Homo sapiens ribonuclease L. (2',5'-oligoisoadenylate synthetase-dependent) (RNASEL), mRNA. 29OOS24 CCR2 1231 Homo sapiens chemokine (C-C motif) receptor 2 (CCR2), transcript variant B, mRNA. 7200753 TLR7 S1284 Homo sapiens toll-like receptor 7 (TLR7), mRNA. 2060600 RBMS1 5937 Homo sapiens RNA binding motif, single stranded interacting protein 1 (RBMS1), transcript variant 3, mRNA. PSMB10 S699 Homo sapiens proteasome (prosome, macropain) subunit, beta type, 10 (PSMB10), mRNA. 770538 LYSMD2 Homo sapiens LysM, putative peptidoglycan-binding, domain containing 2 (LYSMD2), mRNA. 6580717 ZMYND15 84225 Homo sapiens Zinc finger, MYND-type containing 15 (ZMYND15), mRNA. 1230278 LOC440348 440348 Homo sapiens similar to nuclear pore complex interacting protein (LOC440348), mRNA. PSMA4 5685 Homo sapiens proteasome (prosome, macropain) subunit, alpha type, 4 (PSMA4), mRNA. 342.0543 PSMA4 5685 Homo sapiens proteasome (prosome, macropain) subunit, alpha type, 4 (PSMA4), mRNA. 3710332 PSMB3 S691 Homo sapiens proteasome (prosome, macropain) subunit, beta type, 3 (PSMB3), mRNA. 31.80554 PIGU 128869 Homo sapiens phosphatidylinositol glycan anchor biosynthesis, class U (PIGU), mRNA. 1990041 55073 Homo sapiens leucine rich repeat containing 37, member A4 (pseudogene) (LRRC37A4), non-coding RNA. XM 934274 XM 94.1652XM 94.5358 XM 94.5361 XM 94.5364 630204 RAC1 5879 Homo sapiens ras-related C3 botulinum toxin substrate 1 (rho family, Small GTP binding protein Rac1) (RAC1), transcript variant Rac1, mRNA. POMP 51371 Homo sapiens proteasome maturation protein (POMP), mRNA. 23.70358 TYROBP 7305 Homo sapiens TYRO protein tyrosine kinase binding protein (TYROBP), transcript variant 1, mRNA. 29.0022 Sep-01 1731 PREDICTED: Homo sapiens septin 1, transcript variant 3 (SEPT1), mRNA. 6840577 KPNB1 3837 Homo sapiens karyopherin (importin) beta 1 (KPNB1), mRNA. USP47 55031 Homo sapiens ubiquitin specific peptidase 47 (USP47), mRNA. 10101.9S DBI 1622 Homo sapiens diazepam binding inhibitor (GABA receptor modulator, acyl-Coenzyme A binding protein) (DBI), transcript variant 2, mRNA. LOC2847.01 284,701 PREDICTED: Homo sapiens hypothetical protein LOC284701, transcript variant 2 (LOC284701), mRNA. LOC39.1811 39.1811 PREDICTED: Homo sapiens similar to DNA polymerase delta subunit 2 (DNA polymerase delta subunit p50) (LOC391811), mRNA. 4760537 C20orf24 55969 Homo sapiens chromosome 20 open reading frame 24 (C20orf24), transcript variant 2, mRNA. ATG3 64,422 Homo sapiens ATG3 autophagy related 3 homolog (S. cerevisiae) (ATG3), mRNA. AGMAT 79814 Homo sapiens agmatine ureohydrolase (agmatinase) (AGMAT), mRNA. S390246 CCRT 1236 Homo sapiens chemokine (C-C motif) receptor 7 (CCR7), mRNA. 212O224 ANKRD13A Homo sapiens ankyrin repeat domain 13A (ANKRD13A), mRNA. 3390221 EIF4E3 317649 Homo sapiens eukaryotic translation initiation factor 4E family member 3 (EIF4E3), mRNA. 9057 Homo sapiens Solute carrier family 7 (cationic amino acid transporter, y+ system), member 6 (SLC7A6), transcript variant 2 mRNA. US 2015/O 133469 A1 May 14, 2015 31

TABLE 5-continued List of the 86 genes Probe) Symbol Entrez Gene ID Definition USP15 9958 Homo sapiens ubiquitin specific peptidase 15 (USP15), mRNA. 60403.38 LOC149448 149448 PREDICTED: Homo sapiens hypothetical protein LOC149448 (LOC149448), mRNA. C20orf24 55969 Homo sapiens chromosome 20 open reading frame 24 (C20orf24), transcript variant 1, mRNA. 54.0075 NPC2 10577 Homo sapiens Niemann-Pick disease, type C2 (NPC2), mRNA. 2OOO110 BRSK1 84.446 Homo sapiens BR serine/threonine kinase 1 (BRSK1), mRNA. 361028O C9Crf127 51754 Homo sapiens chromosome 9 open reading frame 127 (C9orf127), mRNA. 1090291 DEPDC5 9681 Homo sapiens DEP domain containing 5 (DEPDC5), transcript variant 1, mRNA. 4560047 CD74 972 Homo sapiens CD74 molecule, major histocompatibility complex, class II invariant chain (CD74), transcript variant 1, mRNA. MTHFD2 10797 Homo sapiens methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 2, methenyltetrahydrofolate cyclohydrolase (MTHFD2), nuclear gene encoding mitochondrial protein, transcript variant 2, mRNA. 870632 Homo sapiens clone 23700 mRNA sequence S82O646 CALCOCO2 10241 Homo sapiens calcium binding and coiled-coil domain 2 (CALCOCO2), mRNA. 123 OO68 PPP1R3D 5509 Homo sapiens protein phosphatase 1, regulatory (inhibitor) subunit 3D (PPP1R3D), mRNA. 2O1895 Homo sapiens chromosome 4 open reading frame 34 (C4orf34), mRNA. 5700110 qwó7g08.x1 NCI CGAP Ov33 Homo sapiens cDNA clone IMAGE: 1996.1903, mRNA sequence TNFRSF14 8764 Homo sapiens tumor necrosis factor receptor Superfamily, member 14 (herpesvirus entry mediator) (TNFRSF14), mRNA. FCRL3 115352 Homo sapiens Fc receptor-like 3 (FCRL3), transcript variant 2, mRNA. 732O348 CLEC1A 51267 Homo sapiens C-type lectin domain family 1, member A (CLEC1A), mRNA. 63701.33 SEC23B 10483 Homo sapiens Sec23 homolog B (S. cerevisiae) (SEC23B), transcript variant 2, mRNA. 276O112 P2RY5 101.61 Homo sapiens purinergic receptor P2Y, G-protein coupled, 5 (P2RY5), mRNA. 4490079 DHRS9 10170 Homo sapiens dehydrogenase/reductase (SDR family) member 9 (DHRS9), transcript variant 1, mRNA.

TABLE 6 List of the 393 genes Probe) Symbol Entrez Gene ID Definition 2190349 3772 Homo sapiens potassium inwardly-rectifying channel, subfamily J, member 15 (KCNJ15), transcript variant 3, mRNA. 557O139 QPCT 25797 Homo sapiens glutaminyl-peptide cyclotransferase (QPCT), mRNA. 5700725 EPSTI1 94240 Homo sapiens epithelial stromal interaction 1 (breast) (EPSTI1), transcript variant 2, mRNA. 5220538 WDFY3 Homo sapiens WD repeat and FYVE domain containing 3 (WDFY3), transcript variant2, mRNA. GPR84 S3831 Homo sapiens G protein-coupled receptor 84 (GPR84), mRNA. HPSE 10855 Homo sapiens heparanase (HPSE), mRNA. FCER1G 22O7 Homo sapiens Fc fragment of IgE, high affinity I, receptor for; gamma polypeptide (FCER1G), mRNA. 130181 ANKRD22 118932 Homo sapiens ankyrin repeat domain 22 (ANKRD22), mRNA. LILRAS 3S3514 Homo sapiens leukocyte immunoglobulin-like receptor, subfamily A (with TM domain), member 5 (LILRA5), transcript variant 3, mRNA. US 2015/O 133469 A1 May 14, 2015 32

TABLE 6-continued List of the 393 genes Probe) Symbol Entrez Gene ID Definition 160370 TPM2 71.69 Homo sapiens tropomyosin 2 (beta) (TPM2), transcript variant 2, mRNA. 42903.68 PSTPIP2 90SO Homo sapiens proline-serine-threonine phosphatase interacting protein 2 (PSTPIP2), mRNA. 2360348 CMPK2 1296O7 Homo sapiens cytidine monophosphate (UMP-CMP) kinase 2, mitochondrial (CMPK2), nuclear gene encoding mitochondrial protein, mRNA. 263O195 VAMP5 10791 Homo sapiens vesicle-associated membrane protein 5 (myobrevin) (VAMP5), mRNA. 662O209 FCGR1B 2210 Homo sapiens Fc fragment of IgG, high affinity Ib, receptor (CD64) (FCGR1B), transcript variant 2, mRNA. 130609 FCGBP 8857 Homo sapiens Fc fragment of IgG binding protein (FCGBP), mRNA. 721 0035 SNORD13 692084 Homo sapiens small nucleolar RNA, C/D box 13 (SNORD13), non-coding RNA. 2004.8 440689 Homo sapiens histone cluster 2, H2bf (HIST2H2BF), mRNA. 399 OO10 AGENCOURT 7914287 NIH MGC 71 Homo sapiens cDNA clone IMAGE: 61565955, mRNA sequence 1510364 GBP5 115362 Homo sapiens guanylate binding protein 5 (GBP5), mRNA. 1340241 C5 727 Homo sapiens complement component 5 (C5), mRNA. 6702O2 SESTD1 91.404 Homo sapiens SEC14 and spectrin domains 1 (SESTD1), mRNA. 6370768 ETV 7 51513 Homo sapiens ets variant 7 (ETV7), mRNA. 716O274 TXNDC12 S1060 Homo sapiens thioredoxin domain containing 12 (endoplasmic reticulum) (TXNDC12), mRNA. 64OO148 SELM 14O606 Homo sapiens selenoprotein M (SELM), mRNA. 31.20440 full-length cDNA clone CSODIO56YK21 of Placenta Cot 25-normalized of Homo sapiens (human) 6100474 EVI2A 2123 Homo sapiens ecotropic viral integration site 2A (EVI2A), transcript variant 1, mRNA. 428O632 GAS6 2621 Homo sapiens growth arrest-specific 6 (GAS6), mRNA. 70730 GAS6 2621 Homo sapiens growth arrest-specific 6 (GAS6), mRNA. 1470.091 IL15 3600 Homo sapiens interleukin 15 (IL15), transcript variant 1, mRNA. 2490315 Homo sapiens cDNA: FLJ23098 fis, clone LNGO7440 110639 MAPK14 1432 Homo sapiens mitogen-activated protein kinase 14 (MAPK14), transcript variant 2, mRNA. SOSOO41 GPR109B 8843 Homo sapiens G protein-coupled receptor 109B (GPR109B), mRNA. 656O156 DUSP3 1845 Homo sapiens dual specificity phosphatase 3 (vaccinia virus phosphatase VH1-related) (DUSP3), mRNA. 3801 O2 TMEMS1 55092 Homo sapiens transmembrane protein 51 (TMEM51), mRNA. 3520327 LOC642161 642161 PREDICTED: Homo sapiens similar to T-cell receptor beta chain V region CTL-L17 precursor (LOC642161), mRNA. 276.0500 CD38 952 Homo sapiens CD38 molecule (CD38), mRNA. 7570324 ID3 3.399 Homo sapiens inhibitor of DNA binding 3, dominant negative helix-loop-helix protein (ID3), mRNA. 3940356 CR1 1378 Homo sapiens complement component (3b.4b) receptor 1 (Knops blood group) (CR1), transcript variant S, mRNA. 337.0053 Homo sapiens T cell receptor beta variable 21-1, mRNA (cDNA clone MGC: 46491 IMAGE: 5225843), complete cols 2030309 SERPING1 710 Homo sapiens serpin peptidase inhibitor, clade G (C1 inhibitor), member 1 (SERPING1), transcript variant 2, mRNA. 6770707 SQRDL S8472 Homo sapiens Sulfide quinone reductase-like (yeast) (SQRDL), mRNA. 3170273 FER1L3 26509 Homo sapiensfer-1-like 3, myoferlin (C. elegans) (FER1 L3), transcript variant2, mRNA. 694O164 LBH 81606 PREDICTED: Homo sapiens hypothetical protein DKFZp566J091 (LBH), mRNA. 423O112 LBH 81606 PREDICTED: Homo sapiens hypothetical protein DKFZp566J091 (LBH), mRNA. S87OOO8 Human mRNA for T-cell specific protein 7400743 KIAA1618 57714 Homo sapiens KIAA1618 (KIAA1618), mRNA. 110338 XRN1 54464 Homo sapiens 5'-3' exoribonuclease 1 (XRN1), transcript variant 2, mRNA. 412O561 LIMK2 3985 Homo sapiens LIM domain kinase 2 (LIMK2), transcript variant 1 mRNA. 4200719 LOCA30092 730092 Homo sapiens RRN3 RNA polymerase I transcription factor homolog (S. cerevisiae) pseudogene (LOC730092) on chromosome 16. US 2015/O 133469 A1 May 14, 2015 33

TABLE 6-continued List of the 393 genes Probe) Symbol Entrez Gene ID Definition 268OOSO SIRPG 55423 Homo sapiens signal-regulatory protein gamma (SIRPG), transcript variant 2, mRNA. 7560520 Homo sapiens cDNA FLJ41813 fis, clone NT2RI2011450 5270403 Homo sapiens cDNA FLJ20012 fis, clone ADKAO3438 IFITM1 8519 Homo sapiens interferon induced transmembrane protein 1 (9-27) (IFITM1), mRNA. 383 0228 GPR109A 338442 Homo sapiens G protein-coupled receptor 109A (GPR109A), mRNA. 70431 LOC6SO799 650799 PREDICTED: Homo sapiens similar to Ig lambda chain V-I region BL2 precursor (LOC650799), mRNA. IFI35 3430 Homo sapiens interferon-induced protein 35 (IFI35), mRNA. IRF7 3665 Homo sapiens interferon regulatory factor 7 (IRF7), transcript variant b, mRNA. 257O112 ABLIM1 3983 Homo sapiens actin binding LIM protein 1 (ABLIM1), transcript variant 4, mRNA. 5310445 KREMEN1 83999 Homo sapienskringle containing transmembrane protein 1 (KREMEN1), transcript variant 4, mRNA. 33903O1 KREMEN1 83999 Homo sapienskringle containing transmembrane protein 1 (KREMEN1), transcript variant 4, mRNA. GBP1 2633 Homo sapiens guanylate bin ing protein 1, interferon inducible, 67 kDa (GBP1), mRNA. 2190148 GBP1 2633 Homo sapiens guanylate bin ing protein 1, interferon inducible, 67 kDa (GBP1), mRNA. 3870594 IFI16 3428 Homo sapiens interferon, gamma-inducible protein 16 (IFI16), mRNA. 240053 GCH1 2643 Homo sapiens GTP cyclohy rolase 1 (GCH1), transcript variant 4, mRNA. 383O349 IL7R 3575 Homo sapiens interleukin 7 receptor (IL7R), mRNA. 1260424 ADAM7 8756 Homo sapiens ADAM metal opeptidase domain 7 (ADAM7), mRNA. 26270 Homo sapiens F-box protein 6 (FBXO6), mRNA. 114294 Homo sapiens lactamase, be a (LACTB), nuclear gene encoding mitochondrial protein, transcript variant 2, mRNA. 5550279 ENTPD1 953 Homo sapiens ectonucleoside triphosphate diphosphohydrolase 1 (ENTPD1), transcript variant 2, mRNA. 1430711 KLF12 11278 Homo sapiens Kruppel-like actor 12 (KLF12), mRNA. 6770017 KLF12 11278 Homo sapiens Kruppel-like actor 12 (KLF12), mRNA. 1570129 TRAFD1 10906 Homo sapiens TRAF-type Zinc finger domain containing 1 (TRAFD1), mRNA. 6200577 CREBS 9586 Homo sapiens cAMP responsive element binding protein 5 (CREB5), transcript variant , mRNA. 4730059 BATF2 116O71 Homo sapiens basic leucine Zipper transcription factor, ATF-like 2 (BATF2), mRNA. 1510538 EPB41L3 23136 Homo sapiens erythrocyte membrane protein band 4.1-like 3 (EPB41L3), mRNA. 2070114 CLEC4D 338339 Homo sapiens C-type lectin domain family 4, member D (CLEC4D), mRNA. 7510647 CACNA1E 777 Homo sapiens calcium channel, voltage-dependent, R type, alpha 1E subunit (CACNA1E), mRNA. 2570328 TRIB2 28.951 Homo sapiens tribbles homolog 2 (Drosophila) (TRIB2), mRNA. 730528 PLAUR 5329 Homo sapiens plasminogen activator, urokinase receptor (PLAUR), transcript variant 1, mRNA. 2970286 CEACAM1 634 Homo sapiens carcinoembryonic antigen-related cell adhesion molecule 1 (biliary glycoprotein) (CEACAM1), transcript variant 2, mRNA. 5700753 CEACAM1 634 Homo sapiens carcinoembryonic antigen-related cell adhesion molecule 1 (biliary glycoprotein) (CEACAM1), transcript variant 2, mRNA. 5287 Homo sapiens phosphoinositide-3-kinase, class 2, beta polypeptide (PIK3C2B), mRNA. 394O477 6891 Homo sapiens transporter 2, ATP-binding cassette, Sub family B (MDR/TAP) (TAP2), transcript variant 1, mRNA. 346.0053 57568 Homo sapiens signal-induced proliferation-associated 1 like 2(SIPA1 L2), mRNA. 1510O26 FLVCR2 SS640 Homo sapiens feline leukemia virus Subgroup C cellular receptor family, member 2 (FLVCR2), mRNA. LMNB1 4001 Homo sapiens lamin B1 (LMNB1), mRNA. PSMB9 S698 Homo sapiens proteasome (prosome, macropain) subunit, beta type, 9 (large multifunctional peptidase 2) (PSMB9), transcript variant 1, mRNA.