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Deep-learning Integration of Histopathology and Proteogenomics at a Pan-cancer Level - Resubmission

泛癌水平上组织病理学和蛋白质基因组学的深度学习整合 - 重新提交

基本信息

批准号：
10606760
负责人：
Joshua Wang
金额：
$ 4万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-03-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10606760
关键词：
Address Antibodies Antigen Presentation Architecture Automobile Driving Biological Biomedical Research Biopsy Cancer Prognosis Cellular Morphology Clinic Clinical Collaborations Communities Complement Complete Blood Count Computing Methodologies DNA Sequence Alteration Data Data Set Decision Making Diagnosis Disease Disease-Free Survival Disparate Dreams Endometrial Carcinoma Genes Genetic Transcription Glioblastoma Hematology Histologic Histology Histopathology Image Immunohistochemistry Immunophenotyping Individual Intuition KRAS2 gene Knowledge Knowledge Discovery Learning Malignant Neoplasms Malignant neoplasm of lung Measurement Medical Methods Mitotic Modeling Molecular Molecular Profiling Morphology Multiomic Data Mutation National Cancer Institute Neural Network Simulation Nuclear Outcome PIK3CA gene Pathologic Pathologist Pathology Pathway interactions Patients Pattern Performance Process Prognosis Proteins Proteomics Regulatory Pathway Research Resolution Role Stains TP53 gene Tissues Training Trust Tumor Biology Tumor Pathology Tumor stage Urine Validation Visual Visualization Work ZFHX3 gene cancer therapy cancer type clinical practice clinical predictors convolutional neural network data integration deep learning deep learning model density diagnostic biomarker diagnostic tool diagnostic value differential expression illness length imaging platform immune cell infiltrate improved interest machine learning model machine learning prediction multiple omics novel personalized diagnostics predict clinical outcome predictive signature proteogenomics reconstitution tool transcriptomics tumor tumor heterogeneity tumorigenesis

项目摘要

PROJECT SUMMARY Discovering and understanding novel pathological features that correlate with proteogenomics is crucial in improving and streamlining cancer prognosis and treatment. Currently, biomedical research efforts to predict cancer outcomes rely on sequencing approaches not readily accessible in a clinical setting. Instead, clinicians frequently rely on histopathology images to visually assess for aberrant changes to tissue morphology. Subsequently, a tool to infer clinical and molecular signatures directly from histopathology images would harness the power of omics research with the feasibility of image-based diagnosis. Histopathology imaging data is still vastly underutilized in the quest to better understand tumor biology, largely because of inadequate tools for analysis and data integration. To identify pan-cancer hallmarks and conserved pathways of tumorigenesis, I therefore propose to develop multi-resolution deep convolutional neural network (CNN) models across 10 different cancer types and predict clinical annotations, histology outcomes, and critical mutations based on tumor histopathology images (Aim 1). Through our lab’s collaboration with the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC), we have access to multi-omics, clinical, and histopathologic data obtained from 1,602 patients. In addition, to understand the biological mechanisms driving morphology changes, I propose to develop computational methods that integrate transcriptomic and proteomic expression datasets with imaging to facilitate pathway-level knowledge discovery (Aim 2). Our proposal is the first to correlate expression perturbations with morphology patterns and identify enriched canonical pathways directly from histopathology images. Importantly, this proposal aims to connect scientific efforts of biomedical research with the diagnostic tools of clinicians to expand diagnostic power and improve clinical practice.

项目总结