Abstract
Delivering on the promise of personalized medicine has become a focus of the pharmaceutical industry as the era of the blockbuster drug is fading. Central to realizing this promise is the need for improved analytical strategies for effectively integrating information across various biological assays (for example, copy number variation and targeted protein expression) toward identification of a treatment-specific subgroup—identifying the right patients. We propose a novel combination of elastic net followed by a maximal χ2 and semiparametric bootstrap. The combined approaches are presented in a two-stage strategy that estimates patient-specific multi-marker molecular signatures (MMMS) to identify and directly test for a biomarker-driven subgroup with enhanced treatment effect. This flexible strategy provides for incorporation of business-specific needs, such as confining the search space to a subgroup size that is commercially viable, ultimately resulting in actionable information for use in empirically based decision making.
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Acknowledgements
We acknowledge and thank Stewart Fossceco, Lei Shen and the reviewers for their critical and in-depth review of the manuscript as well as Dilan C Paranagama for his contributions to the implementation of the methodology and simulation studies.
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This study results from the employment of all authors by BioStat Solutions, Inc. (BSSI) and Eli Lilly and Company.
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Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website
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Li, L., Guennel, T., Marshall, S. et al. A multi-marker molecular signature approach for treatment-specific subgroup identification with survival outcomes. Pharmacogenomics J 14, 439–445 (2014). https://doi.org/10.1038/tpj.2014.9
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DOI: https://doi.org/10.1038/tpj.2014.9