GDANII_DLBCL cohort analysis and cloud costs - PROJECT SUMMARY Precision medicine in cancer, a disease of the genome, relies on a deep and comprehensive understanding of the genetic mutations and abnormalities that accumulate in normal cells and drive transformation to cancer. The Getz and Rheinbay Labs have expertise in the discovery and characterization of point mutations through rigorous cancer genome analysis. In this proposal, we aim to create a Genome Data Analysis Center (GDAC) focused on employing our existing tools to robustly and comprehensively characterize point mutations (single-nucleotide variations and small indels) across the entire cancer genome to address scientific questions related to biological underpinnings of cancer that arise in each project we are assigned. We also have the flexibility to adapt our tools as deemed necessary by the unique needs of each project. Specifically, we plan to integrate and characterize mutations, mutational signatures, and other data types to comprehensively discover cancer drivers in coding and non-coding regions of the genome, including the often ignored more difficult-to-analyze regions of the genome. We will do this by incorporating methods to determine DNA methylation signatures as well as by interrogating the epigenome in both coding and non-coding regions of the genome. We further plan to advance our ability to determine trajectories of tumor evolution and heterogeneity by adapting our PhylogicNDT suite of tools to analyze the evolution, subclonal heterogeneity, and timing and order of mutational events from multiple samples (e.g., samples acquired longitudinally or spatially) from the same patient, or even from cell-free DNA (cfDNA) from non-invasive blood biopsy. In the interest of advancing the GDC’s goal of improving personalized medicine, we teamed with expert clinicians and translational scientists, Dr. Keith Flaherty and Dr. Kirsten Kübler, that will interpret our findings, associate them with clinical data and direct them towards clinical impact. They will also enhance our tools for identifying the tissue- and cell-of-origin of cancers to not only better understand the underlying mechanisms of transformation in a particular cancer type or subtype but also provide more effective therapeutic targets. Moreover, our final Aim is to perform patient-specific analysis to improve and enable precision medicine, especially in patients whose tumors do not have any identified actionable driver events. Here, we will employ machine learning techniques to build predictive models of therapeutic vulnerabilities. Overall, we offer primary competencies in DNA point mutation characterization, analysis of cfDNA, and determination of mutational signatures to the GDAN. We also bring added value with secondary competencies in methylation analysis (in the context of mutational signatures), mRNA analysis, single-cell RNA sequencing, and pathway/integrative data analysis. Bringing our extensive expertise to the various newly assembled Analysis Working Groups and collaborating with other GDACs within the GDAN can help to answer outstanding questions in cancer with the ultimate goal of improving diagnosis, prognosis, and treatment for every cancer patient.
