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 Archival tumor samples, as are available in epidemiological and clinical settings where tumor blocks have been archived for 20 years or more, are rich sources of tumor material for a broad range of research questions.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, reliale, tailor-made, and publicly available tools for statistical and bioinformatic analysis. The identification of prostate cancer subtypes is a perfect case in point. A focus on more homogeneous groups may enhance understanding of underlying mechanisms of disease, and lead to more successful treatment and prevention through different strategies for each subtype. However, progress in this area has been hampered by the modest sample size and by the opportunistic designs common to mRNA profiling studies of fresh frozen (FF) tissues. The potential for discovery of novel prognostic subtypes through gene expression profiling of large cohorts of FFPE samples is a unique opportunity to advance the field of prostate cancer biomarkers. The investigative team bring 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 Heath 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 prostate cancer molecular subtypes in a large and well-annotated cohort.
Recent technologies are finally enabling the analysis of the activity of a large number of genes in tumor sampled that are preserved using a standard methodology, as are available in epidemiological and clinical settings where tumor blocks have been archived for 20 years or more. Analysis of this type of sample creates significant data analytic challenges: the goal of this proposal is to provide tools for identifying and solving technical problems with the data, and for processing the complex information generated by the analysis of these samples. These tools will be tested and immediately used to identify clinically relevant subtypes of prostate cancer sharing common biological origins, thus enhancing understanding of underlying disease mechanisms, and leading to improved treatment and prevention strategies.
| Cacciatore, Stefano; Zadra, Giorgia; Bango, Clyde et al. (2017) Metabolic Profiling in Formalin-Fixed and Paraffin-Embedded Prostate Cancer Tissues. Mol Cancer Res 15:439-447 |
| Tyekucheva, Svitlana; Bowden, Michaela; Bango, Clyde et al. (2017) Stromal and epithelial transcriptional map of initiation progression and metastatic potential of human prostate cancer. Nat Commun 8:420 |
