Who to treat: a multi-assay signature approach for subgroup identification
The promise of personalized medicine is becoming a reality in the space of oncology with a noticeable shift in the past decade from targeting the largest possible population to targeting cancer subtypes with smaller patient populations or biomarker-defined subgroups of a patient population with enhanced response. Approximately 60% of US drug sales related to anti-cancer drugs are targeted therapies and it is estimated that a similar percentage of drugs in development have a biomarker component. As the emphasis on biomarker strategies in anti-cancer therapies is widespread and on the rise, it is critical that consideration is given to advancing analytics for the discovery and commercialization of biomarkers.
To date, much of the attention has been placed on the identification and usage of an individual biomarker to target the desired patient population; however, biomarker investigations in oncology generally gather information on multiple biomarkers from a broad range of biological assays, e.g., immunohistochemistry (IHC), protein immunoassays, copy number, circulating tumor cells (CTCs) and somatic mutations. The next generation of anti-cancer biomarkers will necessitate an understanding of the cascade of information captured across different types of biomarkers and the ability to integrate this information with a unified analytical framework.
A novel approach for treatment-specific subgroup identification has the ability to aggregate data across assay platforms and estimate patient specific multi-marker molecular signatures, which then serves as a surrogate marker for membership in the unobserved underlying treatment-specific subgroup or cancer subtype. This flexible strategy also provides for incorporation of both scientific and business factors, such as confining the search space to a subgroup size that is commercial viable, ultimately resulting in actionable information for use in empirical based decision making.
For More Info:
Li, L., Guennel, T., Marshall, S., & Cheung, L. W. (2014). A multi-marker molecular signature approach for treatment-specific subgroup identification with survival outcomes. The pharmacogenomics journal.