Who to treat: a multi-assay signature approach for subgroup identification

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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.

Subgroup Identification: Application Example. Click to enlarge.

Subgroup Identification: Application Example. Click to enlarge.

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:

BSSI presented on approaches for subgroup identification at the Biomarker Summit 2014 in San Diego. Click to view our poster and presentation (PDFs).


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.