Abstract
BackgroundGene 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.ResultsFocusing 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.ConclusionsThe tools presented here should be of general use for building and using predictive multivariate signatures in oncology and in other therapeutic areas.
Highlights
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
Experimental design for the AFLAME two-arm clinical trial The CRC data analyzed was generated by a phase 3 two-arm clinical trial called AFLA ME [9], conducted to test the efficacy of the anti-angiogenic, biologic drug aflibercept [10], in combination with standard-of-care chemotherapy (FOLFIRI panel [19]), for patients with metastatic colorectal cancer
For the analyses described here a subset of the data consisting of n = 209 gene expression profiles (68:141 placebo:aflibercept ratio), obtained after quality-control of samples for tumor content and quality of RNA-seq profiling was used
Summary
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. In the past several years prediction of the response and survival of patients undergoing anti-cancer therapies, using machine learning models based on gene expression profiling of tumor tissues, has been of great interest. An important distinction has been made between purely “prognostic” signatures, which predict outcome under a single treatment regimen (such as, for instance, breast cancer and a single type of hormone therapy), and “predictive” signatures, which are able to predict differential outcomes, i.e. between treatments involving different drug regimens The latter type of signature might be considered more important, because it provides a criterion for choosing one drug regimen over another, and for optimizing the treatment of patients in actual clinical settings. Gene expression profiling studies involving two-arm clinical trials have been rarer (e.g. [7, 8]), and the methodology for deriving predictive signatures less prominent
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