Faculty, Staff and Student Publications
Publication Date
9-19-2023
Journal
Cell Reports Medicine
Abstract
We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models.
Keywords
Humans, Proteogenomics, Deep Learning, Neoplasms, Proteomics, Machine Learning, computational pathology, cancer proteogenomics, cancer imaging, CPTAC, molecular diagnostics
DOI
10.1016/j.xcrm.2023.101173
PMID
37582371
PMCID
PMC10518635
PubMedCentral® Posted Date
9-19-2023
PubMedCentral® Full Text Version
Post-print
Graphical Abstract
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons