The impact of high-throughput genomics technologies on personalized medicine and novel cancer treatments will ultimately be determined by the ability to interpret the data accordingly. Therefore, the applicant's long-term objective is to translate genome-based knowledge, through integrative analysis of high- throughput genomic technologies, into the development of novel diagnostic, prognostic, and therapeutic strategies in cancer. As an initial step towards this endeavor, this proposal focuses on the central hypothesis that common carcinomas harbor driving recurrent gene fusion mutations that are hidden by non-specific aberrations. We believe that Next Generation Sequencing of cancer transcriptomes may elucidate these driving gene fusions and therefore established the following specific aims to accomplish this: (1) employ integrative analysis of genomics data to detect novel gene fusions in ETS negative prostate tumors, (2) further develop the bioinformatic approaches to prioritize casual chimeras and apply these methods in melanoma and pancreatic cancer, and (3) implement a sequencing based gene- and exon-level expression outlier profile analysis to assess the prevalence, and nominate novel, gene fusions in publicly available data collections. Overall, if the aims of this application are achieved we may reveal novel pathogenomic targets in cancer that can be translated into the development of a targeted therapy. Even the successful discovery of a single target, whether discovered directly from the proposed work or uncovered by another lab leveraging the methodologies developed as a result of this proposal, will have significant impact on how we treat cancer. The Michigan Center for Translational Pathology has in-depth experience using bioinformatics and genomics for studying gene expression, particularly gene fusions, yielding numerous biomarkers and therapeutic targets in prostate cancer and therefore serves as an ideal environment for supporting the applicant. This can be exemplified by the applicant's extensive preliminary data, which has led to multiple high impact first author manuscripts. Given Dr. Chinnaiyan's excellent record of mentoring biostatisticians and bioinformaticians as they transition into successful independent investigators, he will provide a similar career development plan for the current applicant. The first specific aim, to be completed during the Mentored phase, will focus on detecting gene fusions in prostate cancer and therefore offer critical integrative analysis, cancer biology, and translational research training. The focus of the independent phase will be placed on sequence analysis of chimera detection in newly established cancer transcriptome projects and RNA-Seq based outlier expression analysis to reveal novel therapeutic targets, which will have minimal overlap with, but complements, the mentor's research on prostate gene fusions. Overall, an NIH Pathway to Independence Award will be indispensable for the applicant to become an independent investigator in the much needed field of integrative cancer biology to continue translating genome-based discovering into widely adopting clinical applications.
Characterization of key genetic aberrations in cancers holds the key to the development of early diagnostic markers and effective therapeutic targets. Therefore this proposal seeks funding to detecting novel, recurrent gene fusions using Next Generation transcriptome sequencing of human tumors. Due to their cancer-specific expression, novel gene fusions serve as ideal therapeutic targets that could have a significant impact on how we classify and treat cancer which will ultimately lead to improved patient care.
| Collins, Colin C; Volik, Stanislav V; Lapuk, Anna V et al. (2012) Next generation sequencing of prostate cancer from a patient identifies a deficiency of methylthioadenosine phosphorylase, an exploitable tumor target. Mol Cancer Ther 11:775-83 |
| Iyer, Matthew K; Chinnaiyan, Arul M; Maher, Christopher A (2011) ChimeraScan: a tool for identifying chimeric transcription in sequencing data. Bioinformatics 27:2903-4 |
