Figure to the comment on paper: DOI 10.1371/journal.pmed.1002786

Performance of the three transcriptomic signatures for discriminating between progressors and non-progressors on samples collected within 24 months of TB diagnosis in the test subsets or the full South African and Gambian sets of the GC6-74 cohort. Bar graphs representing area under the ROC curve (AUC) and 95% CI values (error bars) for the Zak16-gene CoR[3], Sweeney3[4] or Maertzdorf4[5] signatures, measured by qRT-PCR, to discriminate between progressors and non-progressors in the 3 test sets of the GC6-74 cohort on the left, as shown in Suliman et al., 2018[2], or on the full South African and Gambian sets, as shown for the Zak16-gene CoR in Zak et al., 2016[3]. The numbers in brackets below each cohort represent the number of progressors included in the cohort (non-progressors were matched ~4:1 to the progressors). The GC6-74 cohort test sets were comprised of 14 progressors and 83 controls from South Africa, 8 progressors and 56 controls from The Gambia and 12 progressors and 48 controls from Ethiopia. The GC6-74 full cohorts were comprised of 40 progressors and 159 controls from South Africa and 26 progressors and 104 controls from The Gambia. Scores for the Maertzdorf4 and Sweeney3 signatures for the full GC6-74 cohorts were based on cycle threshold data generated by qRT-PCR (Fluidigm, USA) using the classification models published for each signature [5,6]. In cases where the 95% CI crosses 0.5, the error bar is in red, indicating non-significant validation. Areas under the ROC curves show that Sweeney3 now validates in the complete South African cohort, while Maertzdorf4 validates in the complete Gambian cohort. References 1. Warsinske H, Vashisht R, Khatri P. Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signatures. Chaisson R, editor. PLoS Med. Public Library of Science; 2019;16: e1002786. doi:10.1371/journal.pmed.1002786 2. Suliman S, Thompson E, Sutherland J, Weiner Rd J, Ota MOC, Shankar S, et al. Four-gene Pan-African Blood Signature Predicts Progression to Tuberculosis. American journal of respiratory and critical care medicine. 2018;197: 1198–1208. doi:10.1164/rccm.201711-2340OC 3. Zak DE, Penn-Nicholson A, Scriba TJ, Thompson E, Suliman S, Amon LM, et al. A blood RNA signature for tuberculosis disease risk: a prospective cohort study. The Lancet. 2016;387: 2312–2322. doi:10.1016/S0140-6736(15)01316-1 4. Sweeney TE, Braviak L, Tato CM, Khatri P. Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis. Lancet Respir Med. 2016;4: 213–224. doi:10.1016/S2213-2600(16)00048-5 5. Maertzdorf J, McEwen G, Weiner J, Tian S, Lader E, Schriek U, et al. Concise gene signature for point-of-care classification of tuberculosis. EMBO molecular medicine. EMBO Press; 2016;8: 86–95. doi:10.15252/emmm.201505790 6. Warsinske HC, Rao AM, Moreira FMF, Santos PCP, Liu AB, Scott M, et al. Assessment of Validity of a Blood-Based 3-Gene Signature Score for Progression and Diagnosis of Tuberculosis, Disease Severity, and Treatment Response. JAMA Netw Open. 2018;1: e183779. doi:10.1001/jamanetworkopen.2018.3779 </p

Clinical Variables for the Adolescent Cohort Study in the Worcester region of South Africa

The studies described in the referenced papers collected clinical and demographic data from adolescents aged 12-18 years from high schools in the area of Worcester, South Africa. This data item contains the clinical and demographic data collected.clinical_data.csv, clinical_data.xlsx and clinical_data.rds contain the actual clinical data. They are identical except that the first is a CSV file, the second an XLSX file and the third an RDS file. The RDS file is useful because it can be read into R using `readRDS('clinical_data.rds')` and then retains the original variable types for each of the columns. clinical_data-column_descriptions.txt describes what each of the columns are and contain. </div

Parsimonious transcriptomic signatures: Statistical analysis plan

Statistical Analysis Plan: Evaluation of parsimonious host-blood tuberculosis transcriptomic signatures in HIV-infected and HIV-uninfected individuals: A sub-study of the CORTIS-01 and CORTIS-HR trials.Version: 1.0Date: 08 January 2020</div

H4:IC31 and BCG induced immune responses in a prevention of M. tuberculosis infection efficacy trial

Immunogenicity responses from the C040-404 clinical trial were measured by whole blood intracellular cytokine staining at baseline and 70 days after vaccination with H4:IC31 (subunit vaccine containing Ag85B and TB10.4), Bacille Calmette-Guerin (BCG, a live attenuated vaccine) or placebo (n=~30 per group) using flow cytometry.</div

CORTIS-HR: Statistical Analysis Plan

Statistical Analysis Plan: 11-gene Correlates of Risk (COR) Diagnostic and Predictive Performance Analysis in HIV-Infected Adults in the "Validation of Correlates of Risk of TB Disease in High Risk Populations (CORTIS-HR): A companion study of the CORTIS-01 Trial"Version: 1.0Date: 11 December 2019</div

PLOS Pathogens publication: A comparison of antigen-specific T cell responses induced by six novel tuberculosis vaccine candidates.

Tuberculosis (TB) causes more deaths than any other single infectious disease, and a new, improved vaccine is needed to control the epidemic. Many new TB vaccine candidates are in clinical development, but only one or two can be advanced to expensive efficacy trials. In this study, we compared magnitude and functional attributes of memory T cell responses induced in recently conducted clinical trials by six TB vaccine candidates, as well as BCG. The results suggest that these vaccines induced CD4 and CD8 T cellresponses with similar functional attributes, but that one vaccine, M72/AS01 E , induced the largest responses. This finding may indicate a lack of diversity in T cell responses induced by different TB vaccine candidates. A repertoire of vaccine candidates that induces more diverse immune response characteristics may increase the chances of finding a protective vaccine against TB.</div

Megapool

This dataset (Megapool) contains background subtracted frequencies of CD4 and CD8 T cells producing a combination of IFNγ, IL-2 and TNF in response to stimulation by Megapool for Mtb-infected but healthy individuals (Lindestam Arlehamn, 2016). See referenced paper for details. See Column names - Megapool.docx for meanings of column names in Megapool.xlsx.<br

Vaccines

The dataset (Vaccines) contains background subtracted frequencies of CD4 and CD8 T cells producing a combination of IFNγ, IL-2, TNF and IL-17 in response to stimulation by vaccine-specific antigens for the vaccines included in the paper "A head-to-head comparison of specific T cell responses induced by six novel tuberculosis vaccine candidates". See paper for details. See Column names - Vaccines.docx for meanings of column names in the dataset (Vaccines)

A comparison of antigen-specific T cell responses induced by six novel tuberculosis vaccine candidates (BioRxiv preprint)

Pre-print submitted to BioRxiv.Abstract: Eradication of tuberculosis (TB), the world's leading cause of death due to infectious disease, requires a highly efficacious TB vaccine. Many TB vaccine candidates are in pre-clinical and clinical development but only a few can be advanced to large - scale efficacy trials due to limited global resources. We aimed to perform a statistically rigorous comparison of the antigen - specific T cell responses induced by six novel TB vaccine candidates and the only licensed TB vaccine, Bacillus Calmette - Guérin (BCG). We propose that the antigen - specific immune response induced by such vaccines provides an objective, data - driven basis for prioritisation of vaccine candidates for efficacy testing. We analyze d frequencies of antigen - specific CD4 and CD8 T cells expressing IFN γ, IL - 2, TNF and/or IL - 17 from adolescents or adults, with or without Mycobacterium tuberculosis ( M.tb ) infection, who received MVA85A, A ERAS - 402, H1:IC31, H56:IC31, M72/AS01 E, ID93 + GLA - SE or BCG. Two key response characteristics were analyzed, namely response magnitude and cytokine co - expression profile of the memory T cell response that persisted above the pre-vaccination response to the final study visit in each trial. All vaccines preferentially induced antigen - specific CD4 T cell responses expressing Th1 cytokines; levels of IL - 17 - expressing cells were low or not detected. In M.tb - uninfected and - infected individuals, M72/AS01 E induced higher memory Th1 cytokine - expressing CD4 T cell response s than other novel vaccine candidates. Cytokine co - expression profile s of memory CD4 T cells induced by different novel vaccine candidates were alike. Our study suggests that the T cell response feature which most differentiated between the TB vaccine candidates was response magnitude, whilst functional profiles suggested a lack of response diversity. Since M72/AS01 E induced the highest memory CD4 T cell response it demonstrated the best vaccine take. In the absence of immunological correlates of protection the likelihood of finding a protective vaccine by empirical testing of candidates may be increased by the addition of candidates that induce distinct immune characteristics. <br

Statistical Analysis Plan

Statistical analysis plan for the MTBVAC 201 clinical trial. A phase Ib, randomised, double-blind, dose-escalation clinical trial of the safety, reactogenicity and immunogenicity of MTBVAC compared to BCG Vaccine SSI, in newborns living in a tuberculosis endemic region with a safety arm in adults