Correction: Discovery and validation of a prognostic proteomic signature for tuberculosis progression: A prospective cohort study.
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ABSTRACT:
SUBMITTER: Penn-Nicholson A
PROVIDER: S-EPMC6638739 | biostudies-literature | 2019 Jul
REPOSITORIES: biostudies-literature
Publications
Correction: Discovery and validation of a prognostic proteomic signature for tuberculosis progression: A prospective cohort study.
Penn-Nicholson Adam A Hraha Thomas T Thompson Ethan G EG Sterling David D Mbandi Stanley Kimbung SK Wall Kirsten M KM Fisher Michelle M Suliman Sara S Shankar Smitha S Hanekom Willem A WA Janjic Nebojsa N Hatherill Mark M Kaufmann Stefan H E SHE Sutherland Jayne J Walzl Gerhard G De Groote Mary Ann MA Ochsner Urs U Zak Daniel E DE Scriba Thomas J TJ
PLoS medicine 20190718 7
[This corrects the article DOI: 10.1371/journal.pmed.1002781.]. ...[more]
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Project description:BackgroundA nonsputum blood test capable of predicting progression of healthy individuals to active tuberculosis (TB) before clinical symptoms manifest would allow targeted treatment to curb transmission. We aimed to develop a proteomic biomarker of risk of TB progression for ultimate translation into a point-of-care diagnostic.Methods and findingsProteomic TB risk signatures were discovered in a longitudinal cohort of 6,363 Mycobacterium tuberculosis-infected, HIV-negative South African adolescents aged 12-18 years (68% female) who participated in the Adolescent Cohort Study (ACS) between July 6, 2005 and April 23, 2007, through either active (every 6 months) or passive follow-up over 2 years. Forty-six individuals developed microbiologically confirmed TB disease within 2 years of follow-up and were selected as progressors; 106 nonprogressors, who remained healthy, were matched to progressors. Over 3,000 human proteins were quantified in plasma with a highly multiplexed proteomic assay (SOMAscan). Three hundred sixty-one proteins of differential abundance between progressors and nonprogressors were identified. A 5-protein signature, TB Risk Model 5 (TRM5), was discovered in the ACS training set and verified by blind prediction in the ACS test set. Poor performance on samples 13-24 months before TB diagnosis motivated discovery of a second 3-protein signature, 3-protein pair-ratio (3PR) developed using an orthogonal strategy on the full ACS subcohort. Prognostic performance of both signatures was validated in an independent cohort of 1,948 HIV-negative household TB contacts from The Gambia (aged 15-60 years, 66% female), longitudinally followed up for 2 years between March 5, 2007 and October 21, 2010, sampled at baseline, month 6, and month 18. Amongst these contacts, 34 individuals progressed to microbiologically confirmed TB disease and were included as progressors, and 115 nonprogressors were included as controls. Prognostic performance of the TRM5 signature in the ACS training set was excellent within 6 months of TB diagnosis (area under the receiver operating characteristic curve [AUC] 0.96 [95% confidence interval, 0.93-0.99]) and 6-12 months (AUC 0.76 [0.65-0.87]) before TB diagnosis. TRM5 validated with an AUC of 0.66 (0.56-0.75) within 1 year of TB diagnosis in the Gambian validation cohort. The 3PR signature yielded an AUC of 0.89 (0.84-0.95) within 6 months of TB diagnosis and 0.72 (0.64-0.81) 7-12 months before TB diagnosis in the entire South African discovery cohort and validated with an AUC of 0.65 (0.55-0.75) within 1 year of TB diagnosis in the Gambian validation cohort. Signature validation may have been limited by a systematic shift in signal magnitudes generated by differences between the validation assay when compared to the discovery assay. Further validation, especially in cohorts from non-African countries, is necessary to determine how generalizable signature performance is.ConclusionsBoth proteomic TB risk signatures predicted progression to incident TB within a year of diagnosis. To our knowledge, these are the first validated prognostic proteomic signatures. Neither meet the minimum criteria as defined in the WHO Target Product Profile for a progression test. More work is required to develop such a test for practical identification of individuals for investigation of incipient, subclinical, or active TB disease for appropriate treatment and care.
| S-EPMC6467365 | biostudies-literature
Project description:Sepsis remains a diagnostic challenge with no gold-standard test. Urine provides a readily available, non-invasive biofluid with significant diagnostic potential. Urinary gene expression has been previously used for diagnosis and prognosis of urological malignancies and transplant allograft rejections, but remains unutilized for sepsis diagnosis. In this study, the authors use urinary gene expression profiles to both diagnose sepsis and characterize its pathophysiology. By using differential expression augmented with machine learning ensembles, the authors identify a collection of cellular mRNA from 239 genes in patient urine which show exceptional power in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes the disrupted biological pathways in early sepsis and additionally reveals key molecular networks driving its pathogenesis. This study serves a pioneering step towards expanding the clinical potential of urinary molecular profiles for application to systemic diseases.
2020-02-20 | GSE112098 | GEO
Project description:Sepsis remains a diagnostic challenge with no gold-standard test. Urine provides a readily available, non-invasive biofluid with significant diagnostic potential. Urinary gene expression has been previously used for diagnosis and prognosis of urological malignancies and transplant allograft rejections, but remains unutilized for sepsis diagnosis. In this study, the authors use urinary gene expression profiles to both diagnose sepsis and characterize its pathophysiology. By using differential expression augmented with machine learning ensembles, the authors identify a collection of cellular mRNA from 239 genes in patient urine which show exceptional power in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes the disrupted biological pathways in early sepsis and additionally reveals key molecular networks driving its pathogenesis. This study serves a pioneering step towards expanding the clinical potential of urinary molecular profiles for application to systemic diseases.
2020-02-20 | GSE112099 | GEO
Project description:Sample collection and identification of biomarker for personalized therapy in tuberculosis patients
2020-03-30 | GSE147689 | GEO
Project description:Identification of blood biomarkers that prospectively predict progression of Mycobacterium tuberculosis infection to tuberculosis disease might lead to interventions that combat the tuberculosis epidemic. We aimed to assess whether global gene expression measured in whole blood of healthy people allowed identification of prospective signatures of risk of active tuberculosis disease. RESULTS:Between July 6, 2005, and April 23, 2007, we enrolled 6363 from the ACS study and 4466 from independent South African and Gambian cohorts. 46 progressors and 107 matched controls were identified in the ACS cohort. A 16 gene signature of risk was identified. The signature predicted tuberculosis progression with a sensitivity of 66·1% (95% CI 63·2–68·9) and a specificity of 80·6% (79·2–82·0) in the 12 months preceding tuberculosis diagnosis. The risk signature was validated in an untouched group of adolescents (p=0·018 for RNA sequencing and p=0·0095 for qRT-PCR) and in the independent South African and Gambian cohorts (p values <0·0001 by qRT-PCR) with a sensitivity of 53·7% (42·6–64·3) and a specificity of 82·8% (76·7–86) in 12 months preceding tuberculosis. Interpretation: The whole blood tuberculosis risk signature prospectively identified people at risk of developing active tuberculosis, opening the possibility for targeted intervention to prevent the disease.
2016-03-22 | GSE79362 | GEO
Project description:OSCC is associated with substantial mortality and morbidity. In this study, we built on our previous molecular work to identify and validate a prognostic 13-gene signature that showed a higher ability than tumor stage in predicting survival for patients with HPV-negative oral squamous cell carcinoma (OSCC). We previously reported 131 genes to be associated with survival in OSCC patients. In this study, we applied L1/L2-penalized Cox regression models to the Affymetrix array data from 97 HPV-negative OSCC patients on the 131 gene to identify a model for prediction of OSCC-specific survival. We then tested the model in an independent dataset and compared to tumor stage.
| S-ECPF-GEOD-41613 | biostudies-other
Project description:Identification of blood biomarkers that prospectively predict progression of Mycobacterium tuberculosis infection to tuberculosis disease might lead to interventions that combat the tuberculosis epidemic. We aimed to assess whether global gene expression measured in whole blood of healthy people allowed identification of prospective signatures of risk of active tuberculosis disease. RESULTS:Between July 6, 2005, and April 23, 2007, we enrolled 6363 from the ACS study and 4466 from independent South African and Gambian cohorts. 46 progressors and 107 matched controls were identified in the ACS cohort. A 16 gene signature of risk was identified. The signature predicted tuberculosis progression with a sensitivity of 66·1% (95% CI 63·2â68·9) and a specificity of 80·6% (79·2â82·0) in the 12 months preceding tuberculosis diagnosis. The risk signature was validated in an untouched group of adolescents (p=0·018 for RNA sequencing and p=0·0095 for qRT-PCR) and in the independent South African and Gambian cohorts (p values <0·0001 by qRT-PCR) with a sensitivity of 53·7% (42·6â64·3) and a specificity of 82·8% (76·7â86) in 12 months preceding tuberculosis. Interpretation: The whole blood tuberculosis risk signature prospectively identified people at risk of developing active tuberculosis, opening the possibility for targeted intervention to prevent the disease. In this prospective cohort study, we followed up healthy, South African adolescents aged 12â18 years from the adolescent cohort study (ACS) who were infected with M tuberculosis for 2 years. We collected blood samples from study participants every 6 months and monitored the adolescents for progression to tuberculosis disease. A prospective signature of risk was derived from whole blood RNA sequencing data by comparing participants who developed active tuberculosis disease (progressors) with those who remained healthy (matched controls). After adaptation to multiplex qRT-PCR, the signature was used to predict tuberculosis disease in untouched adolescent samples and in samples from independent cohorts of South African and Gambian adult progressors and controls. Participants of the independent cohorts were household contacts of adults with active pulmonary tuberculosis disease.
2016-03-22 | E-GEOD-79362 | biostudies-arrayexpress
Project description:Tuberculosis (TB) is a transmissible disease listed as one of the 10 leading causes of death worldwide (10 million infected in 2019). A swift and precise diagnosis is essential to forestall its transmission, for which the discovery of effective diagnostic biomarkers is crucial. In this study, we aimed to discover molecular biomarkers for the early diagnosis of tuberculosis. Two independent cohorts comprising 29 and 34 subjects were assayed by proteomics, and 49 were included for metabolomic analysis. All subjects were arranged into three experimental groups—healthy controls (controls), latent TB infection (LTBI), and TB patients. LC-MS/MS blood serum protein and metabolite levels were submitted to univariate, multivariate, and ROC analysis. From the 149 proteins quantified in the discovery set, 25 were found to be differentially abundant between controls and TB patients. The AUC, specificity, and sensitivity, determined by ROC statistical analysis of the model composed of four of these proteins considering both proteomic sets, were 0.96, 93%, and 91%, respectively. The five metabolites (9-methyluric acid, indole-3-lactic acid, trans-3-indoleacrylic acid, hexanoylglycine, and N-acetyl-L-leucine) that better discriminate the control and TB patient groups (VIP > 1.75) from a total of 92 metabolites quantified in both ionization modes were submitted to ROC analysis. An AUC = 1 was determined, with all samples being correctly assigned to the respective experimental group. An integrated ROC analysis enrolling one protein and four metabolites was also performed for the common control and TB patients in the proteomic and metabolomic groups. This combined signature correctly assigned the 12 controls and 12 patients used only for prediction (AUC = 1, specificity = 100%, and sensitivity = 100%). This multiomics approach revealed a biomarker signature for tuberculosis diagnosis that could be potentially used for developing a point-of-care diagnosis clinical test.
| S-EPMC9694769 | biostudies-literature
Project description:OBJECTIVE:To date, The Cancer Genome Atlas (TCGA) has provided the most extensive molecular characterization of invasive cervical cancer (ICC). Analysis of reverse phase protein array (RPPA) data from TCGA samples showed that cervical cancers could be stratified into 3 clusters exhibiting significant differences in survival outcome: hormone, EMT, and PI3K/AKT. The goals of the current study were to: 1) validate the TCGA RPPA results in an independent cohort of ICC patients and 2) to develop and validate an algorithm encompassing a small antibody set for clinical utility. METHODS:Subjects consisted of 2 ICC patient cohorts with accompanying RPPA and clinical-pathologic data: 155 samples from TCGA (TCGA-155) and 61 additional, unique samples (MCW-61). Using data from 173 common RPPA antibodies, we replicated Silhouette clustering analysis in both ICC cohorts. Further, an index score for each patient was calculated from the survival-associated antibodies (SAAs) identified using Random survival forests (RSF) and the Cox proportional hazard regression model. Kaplan-Meier survival analysis and the log-rank test were performed to assess and compare cluster or risk group survival outcome. RESULTS:In addition to validating the prognostic ability of the proteomic clusters reported by TCGA, we developed an algorithm based on 22 unique antibodies (SAAs) that stratified women with ICC into low-, medium-, or high-risk survival groups. CONCLUSIONS:We provide a signature of 22 antibodies which accurately predicted survival outcome in 2 separate groups of ICC patients. Future studies examining these candidate biomarkers in additional ICC cohorts is warranted to fully determine their clinical potential.
| S-EPMC6825895 | biostudies-literature
Project description:Purpose: The primary objective of the current study was to validate biomarkers to identify the 10% to 27% of patients with stage I and 35% of patients with stage IIA squamous cell carcinoma of lung (SC) who are likely to recur following surgical resection, so that these patients may be offered enrollment in clinical trials evaluating directed ACT. A secondary objective was to identify patients with stage IIB SC who are unlikely to develop recurrences and might thereby be spared the potential significant toxicity and expense of ACT. Methods: Two-stage validation used independent core laboratories, objective quality control standards, locked test parameters, and large multi-institutional specimen/data sets. First stage validation confirmed a signature’s ability to stratify patient survival. Second stage validation determined which signature(s) optimally improved risk discrimination when added to baseline clinical predictors. Participants were prospectively enrolled on institutional (Cohort I) or cooperative group (Cohort II) biospecimen/data collection protocols. All cases underwent central review of clinical, pathologic and biospecimen parameters using objective criteria to determine final inclusion (Cohort I: n=249; Cohort II: n=234). Primary selection required that a signature significantly predict 3-years survival after surgery in Cohort I. Signatures meeting this criterion were further tested in Cohort II, comparing risk prediction using baseline risk factors alone versus in combination with the signature. Results: Male sex, advanced age, and higher stage were associated with shorter survival in Cohort I and established a baseline clinical model. Of three signatures validated in Cohort I, one signature was validated in Cohort II and statistically significantly enhanced prognosis relative to the baseline model (C-index difference 0.122; p<0.05). Conclusions: These results represent the first rigorous validation of a test appropriate to direct adjuvant treatment or clinical trials for patients with lung squamous cell carcinoma.
2020-08-28 | GSE157009 | GEO