1. Construction and optimization of gene expression signatures for prediction of survival in two-arm clinical trials
- Author
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Marielle Chiron, Donald A. Bergstrom, Jack Pollard, Joachim Theilhaber, and Jennifer Dreymann
- Subjects
Male ,Oncology ,Multivariate statistics ,Triple Negative Breast Neoplasms ,Kaplan-Meier Estimate ,Biochemistry ,chemistry.chemical_compound ,0302 clinical medicine ,Structural Biology ,Medicine ,Multivariate cox models ,lcsh:QH301-705.5 ,Triple-negative breast cancer ,Metastatic TNBC ,Clinical Trials as Topic ,0303 health sciences ,Methodology Article ,Applied Mathematics ,Hazard ratio ,Middle Aged ,Prognosis ,Gene expression profiling ,Computer Science Applications ,Gene Expression Regulation, Neoplastic ,Predictive biomarker ,Patient Satisfaction ,030220 oncology & carcinogenesis ,lcsh:R858-859.7 ,Female ,Colorectal Neoplasms ,Algorithms ,medicine.medical_specialty ,Two-arm clinical trials ,Metastatic CRC ,Predictive signature ,lcsh:Computer applications to medicine. Medical informatics ,03 medical and health sciences ,Internal medicine ,Biomarkers, Tumor ,Confidence Intervals ,Humans ,Molecular Biology ,Proportional Hazards Models ,030304 developmental biology ,Receiver operating characteristic ,business.industry ,Proportional hazards model ,Patient Selection ,Clinical trial ,ROC Curve ,lcsh:Biology (General) ,chemistry ,Multivariate Analysis ,Iniparib ,Transcriptome ,Aflibercept ,business - Abstract
Background Gene expression signatures for the prediction of differential survival of patients undergoing anti-cancer therapies are of great interest because they can be used to prospectively stratify patients entering new clinical trials, or to determine optimal treatment for patients in more routine clinical settings. Unlike prognostic signatures however, predictive signatures require training set data from clinical studies with at least two treatment arms. As two-arm studies with gene expression profiling have been rarer than similar one-arm studies, the methodology for constructing and optimizing predictive signatures has been less prominently explored than for prognostic signatures. Results Focusing on two “use cases” of two-arm clinical trials, one for metastatic colorectal cancer (CRC) patients treated with the anti-angiogenic molecule aflibercept, and the other for triple negative breast cancer (TNBC) patients treated with the small molecule iniparib, we present derivation steps and quantitative and graphical tools for the construction and optimization of signatures for the prediction of progression-free survival based on cross-validated multivariate Cox models. This general methodology is organized around two more specific approaches which we have called subtype correlation (subC) and mechanism-of-action (MOA) modeling, each of which leverage a priori knowledge of molecular subtypes of tumors or drug MOA for a given indication. The tools and concepts presented here include the so-called differential log-hazard ratio, the survival scatter plot, the hazard ratio receiver operating characteristic, the area between curves and the patient selection matrix. In the CRC use case for instance, the resulting signature stratifies the patient population into “sensitive” and “relatively-resistant” groups achieving a more than two-fold difference in the aflibercept-to-control hazard ratios across signature-defined patient groups. Through cross-validation and resampling the probability of generalization of the signature to similar CRC data sets is predicted to be high. Conclusions The tools presented here should be of general use for building and using predictive multivariate signatures in oncology and in other therapeutic areas.
- Published
- 2020
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