ToPCaP

Development of Free, Open-Source, FFPE-Specific Analytic Tools

Development of Free, Open-Source, FFPE-Specific Analytic Tools

The standard method used to preserve tissue morphology for pathological diagnosis and sample archiving of tumors is formalin-fixed, paraffin-embedded (FFPE). Archival tumor samples, as are available in epidemiological and clinical settings where tumor blocks have been archived for 20 years or more, are rich sources of tumor material for a broad range of research questions. As FFPE samples are utilized for virtually all routine pathology tests, they provide information on the gene expression of large patient populations with long-term clinical follow-up. Opening the vast archives of FFPE tissues to high-throughput expression profiling is critical to the development of clinically relevant biomarkers and to the genomic study of cancer subtypes as they relate to lifestyle and environmental factors.

Along with these promises for both population and clinical research, come significant technical and data analytic challenges. These are born out of the degradation and cross-binding of RNA, intrinsic in the FFPE methodology. All existing and foreseeable technologies for expression measurement will entail sources of variation unique to FFPE. Our ability to fully exploit information in archival samples depends critically on the availability of principled, reliable, tailor-made, and publicly available tools for statistical and bioinformatic analysis.

The investigative team brings together in-depth experience of statistical methods for both cancer epidemiology and genomic data analysis, with expertise in prostate cancer epidemiology and pathology, and access to a unique cohort of men with prostate cancer who participated in two US prospective studies: the Physicians Health Study (PHS) and the Health Professionals Follow-up Study (HPFS). Their goal in this proposal is to use their complementary and well-integrated expertise to develop free open source FFPE-specific analytic tools, validate them theoretically and empirically, and use them to investigate prognostic prostate cancer molecular subtypes in a large and well-annotated cohort.

Svitlana Tyekucheva is the PI for this grant.