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Clin Cancer Res. 2012 Nov 1;18(21):6065-73. doi: 10.1158/1078-0432.CCR-12-1206. Epub 2012 Aug 27.

Developing and validating continuous genomic signatures in randomized clinical trials for predictive medicine.

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  • 1Department of Data Science, The Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan.



It is highly challenging to develop reliable diagnostic tests to predict patients' responsiveness to anticancer treatments on clinical endpoints before commencing the definitive phase III randomized trial. Development and validation of genomic signatures in the randomized trial can be a promising solution. Such signatures are required to predict quantitatively the underlying heterogeneity in the magnitude of treatment effects.


We propose a framework for developing and validating genomic signatures in randomized trials. Codevelopment of predictive and prognostic signatures can allow prediction of patient-level survival curves as basic diagnostic tools for treating individual patients.


We applied our framework to gene-expression microarray data from a large-scale randomized trial to determine whether the addition of thalidomide improves survival for patients with multiple myeloma. The results indicated that approximately half of the patients were responsive to thalidomide, and the average improvement in survival for the responsive patients was statistically significant. Cross-validated patient-level survival curves were developed to predict survival distributions of individual future patients as a function of whether or not they are treated with thalidomide and with regard to their baseline prognostic and predictive signature indices.


The proposed framework represents an important step toward reliable predictive medicine. It provides an internally validated mechanism for using randomized clinical trials to assess treatment efficacy for a patient population in a manner that takes into consideration the heterogeneity in patients' responsiveness to treatment. It also provides cross-validated patient-level survival curves that can be used for selecting treatments for future patients.

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