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Development of a clinically feasible molecular assay to predict recurrence of stage II colon cancer.

ABSTRACT: The 5-year survival rate for patients with Stage II colon cancer is approximately 75%. However, there is no clinical test available to identify the 25% of patients at high risk of recurrence. We have previously identified a 23-gene signature that predicts individual risk for recurrence. The present study tested this gene signature in an independent group of 123 Stage II patients, and the 23-gene signature was highly informative in identifying patients with distant recurrence in both univariate (hazard ratio [HR] 2.51) and multivariate analyses (HR, 2.40). The composition of this representative patient group also allowed us to refine the 23-gene signature to a 7-gene signature that exhibited a similar prognostic power in both univariate (HR, 2.77) and multivariate analyses (HR, 2.87). Furthermore, we developed this prognostic signature into a clinically feasible test with real-time quantitative PCR using standard fixed paraffin-embedded tumor tissues. When a 110-patient cohort was evaluated with the PCR assay, the 7-gene signature, demonstrated to be a strong prognostic factor in both univariate (HR, 6.89) and multivariate analyses (HR, 14.2). These results clearly show the prognostic value of the predefined gene signature for Stage II colon cancer patients. The ability to identify colon cancer patients with an unfavorable outcome may help patients at high risk for recurrence to seek more aggressive therapy.

SUBMITTER: Jiang Y

PROVIDER: S-EPMC2438204 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

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Development of a clinically feasible molecular assay to predict recurrence of stage II colon cancer.

Jiang Yuqiu Y Casey Graham G Lavery Ian C IC Zhang Yi Y Talantov Dmitri D Martin-McGreevy Michelle M Skacel Marek M Manilich Elena E Mazumder Abhijit A Atkins David D Delaney Conor P CP Wang Yixin Y

The Journal of molecular diagnostics : JMD 20080613 4

The 5-year survival rate for patients with Stage II colon cancer is approximately 75%. However, there is no clinical test available to identify the 25% of patients at high risk of recurrence. We have previously identified a 23-gene signature that predicts individual risk for recurrence. The present study tested this gene signature in an independent group of 123 Stage II patients, and the 23-gene signature was highly informative in identifying patients with distant recurrence in both univariate ( ...[more]

PMID: 18556775

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Project description:BackgroundBreast cancer (BC) grading plays a critical role in patient management despite the considerable inter- and intra-observer variability, highlighting the need for decision support tools to improve reproducibility and prognostic accuracy for use in clinical practice. The objective was to evaluate the ability of a digital artificial intelligence (AI) assay (PDxBr) to enrich BC grading and improve risk categorization for predicting recurrence.MethodsIn our population-based longitudinal clinical development and validation study, we enrolled 2075 patients from Mount Sinai Hospital with infiltrating ductal carcinoma of the breast. With 3:1 balanced training and validation cohorts, patients were retrospectively followed for a median of 6 years. The main outcome was to validate an automated BC phenotyping system combined with clinical features to produce a binomial risk score predicting BC recurrence at diagnosis.ResultsThe PDxBr training model (n = 1559 patients) had a C-index of 0.78 (95% CI, 0.76-0.81) versus clinical 0.71 (95% CI, 0.67-0.74) and image feature models 0.72 (95% CI, 0.70-0.74). A risk score of 58 (scale 0-100) stratified patients as low or high risk, hazard ratio (HR) 5.5 (95% CI 4.19-7.2, p < 0.001), with a sensitivity 0.71, specificity 0.77, NPV 0.95, and PPV 0.32 for predicting BC recurrence within 6 years. In the validation cohort (n = 516), the C-index was 0.75 (95% CI, 0.72-0.79) versus clinical 0.71 (95% CI 0.66-0.75) versus image feature models 0.67 (95% CI, 0.63-071). The validation cohort had an HR of 4.4 (95% CI 2.7-7.1, p < 0.001), sensitivity of 0.60, specificity 0.77, NPV 0.94, and PPV 0.24 for predicting BC recurrence within 6 years. PDxBr also improved Oncotype Recurrence Score (RS) performance: RS 31 cutoff, C-index of 0.36 (95% CI 0.26-0.45), sensitivity 37%, specificity 48%, HR 0.48, p = 0.04 versus Oncotype RS plus AI-grade C-index 0.72 (95% CI 0.67-0.79), sensitivity 78%, specificity 49%, HR 4.6, p < 0.001 versus Oncotype RS plus PDxBr, C-index 0.76 (95% CI 0.70-0.82), sensitivity 67%, specificity 80%, HR 6.1, p < 0.001.ConclusionsPDxBr is a digital BC test combining automated AI-BC prognostic grade with clinical-pathologic features to predict the risk of early-stage BC recurrence. With future validation studies, we anticipate the PDxBr model will enrich current gene expression assays and enhance treatment decision-making.

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Development of a clinically feasible molecular assay to predict recurrence of stage II colon cancer.

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Development of a clinically feasible molecular assay to predict recurrence of stage II colon cancer.

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