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UCSC-Buck Genome Data Analysis Center for the Genomic Data Analysis Network v2.0

UCSC-Buck 基因组数据分析中心基因组数据分析网络 v2.0

基本信息

批准号：
10671031
负责人：
Christopher Benz
金额：
$ 30.66万
依托单位：
UNIVERSITY OF CALIFORNIA SANTA CRUZ
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：
2021-09-07 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10671031
关键词：
Address Atlases Automobile Driving Behavior Cancer Patient Cell Extracts Cells Characteristics Clinical Clinical Protocols Clinical Trials Clinical Trials Network Collaborations Collection Competence Complex Computing Methodologies Data Data Analyses Data Set Evolution Future Gene Expression Profile Genome Genome Data Analysis Center Genome Data Analysis Network Genomics Glean Goals Health Human Knowledge Machine Learning Maps Messenger RNA Mixed Neoplasm Modality Molecular Multiomic Data Outcome Pathway Analysis Pathway interactions Patient-Focused Outcomes Patients Pattern Play Prediction of Response to Therapy Predictive Value Publications Role Sampling Somatic Mutation The Cancer Genome Atlas Training Tumor Subtype Visualization Work cancer therapy cell type computerized tools data exploration gene expression database genomic data individual patient innovation mRNA sequencing machine learning classifier machine learning method machine learning prediction multiple omics neoplastic cell novel patient prognosis patient response predicting response pressure prognostic of survival prognostic value single cell mRNA sequencing treatment arm treatment response tumor tumor heterogeneity tumor microenvironment tumor progression working group

项目摘要

ABSTRACT Tumor heterogeneity -- the complex mix of tumor subclones, the cell-of-origin that first became transformed, the evolution of tumor subclones under selective pressures of the body and due to treatment, and the interplay of these cells with the tumor microenvironment (TME) -- contributes to the character, behavior, and mystery of tumors and is a key determinant of cancer progression and a patient’s response to therapy. Large-scale genomics projects like the Cancer Genome Atlas (TCGA) and the Genome Data Analysis Network (GDAN) have revealed important characteristics and patterns from a multi-omics overview of various tumor types. However, it remains a mystery on how to maximize the use of these data to choose the best course of treatment for an individual patient. The proposed GDAN will close this gap in knowledge by collecting clinical information and outcomes endpoints alongside the multiple omics platforms that will provide key linkages upon which to train supervised computational approaches. We propose to contribute our key competencies of pathway analysis, integrative machine-learning, mRNA-seq analysis, assessment of driving somatic mutations, and visualization of high-throughput datasets to serve the future GDAN analysis working groups (AWGs) to achieve these goals. We will collect and share widely a database of gene expression signatures that capture cell state information gleaned from the large collection of single-cell mRNA sequencing data such as from the Human Cell Atlas (Aim 1). In addition, we will contribute our existing, and novel extensions to, machine-learning approaches like AKIMATE to maximally use these signatures and others in combination with AWG-approved omics datasets as features to train accurate predictors of response for the GDAN’s studies like ALCHEMIST (Aim 2). Our proposal will adapt the TumorMap to benefit weekly analysis and bolster the exploration and publication of results. Specifically, we will work with the group to create new maps that show the TME and TIC comparisons of the patient samples separately to help elucidate new important subtypes implied by the collected data (Aim 3). As we have done for the past twelve years for TCGA and the GDAN, we propose to continue working closely with the consortium in these endeavors to significantly enrich our understanding of the molecular and cellular basis of tumor heterogeneity and its influence on cancer progression and treatment response.

摘要