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Comprehensive analysis of point mutations in cancer

癌症点突变综合分析

基本信息

批准号：
10491092
负责人：
GAD A GETZ
金额：
$ 39.5万
依托单位：
BROAD INSTITUTE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-20 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10491092
关键词：
Address Aftercare Biological Biopsy Blood Cancer Biology Cancer Patient Cancerous Cells Clinical Clinical Data Code Competence Complex Computational Biology DNA DNA Methylation DNA Sequence Alteration Data Data Analyses Data Set Diagnosis Disease Epigenetic Process Event Evolution Future Genes Genetic Genome Genome Data Analysis Center Genome Data Analysis Network Genomic Data Commons Genomics Goals Heterogeneity Individual International Journals Knowledge Machine Learning Malignant Childhood Neoplasm Malignant Neoplasms Messenger RNA Methods Methylation Microsatellite Repeats Molecular Abnormality Mutation Mutation Analysis Normal Cell Nucleotides Oncogenes Outcome Paper Pathway interactions Patients Pattern Play Point Mutation Process Production Prognosis Publishing Quality Control RNA Regulatory Element Repetitive Sequence Research Resistance Role Sample Size Sampling Techniques The Cancer Genome Atlas Therapeutic Tissues Untranslated RNA Variant Work base cancer genome cancer genomics cancer subtypes cancer type cell free DNA cohort driver mutation epigenome flexibility genome analysis genome sequencing improved innovation insertion/deletion mutation interest neoantigens next generation novel personalized medicine precision medicine predictive modeling promoter rare cancer resistance mechanism single-cell RNA sequencing subclonal heterogeneity targeted treatment therapeutic target therapeutically effective tool translational oncology translational scientist tumor whole genome working group

项目摘要

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.

项目总结