Deep learning integrates histopathology and proteogenomics at a pan-cancer level.
Cell Rep Med
; 4(9): 101173, 2023 09 19.
Article
in En
| MEDLINE
| ID: mdl-37582371
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Proteogenomics
/
Deep Learning
/
Neoplasms
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Cell Rep Med
Year:
2023
Document type:
Article
Affiliation country:
Estados Unidos
Publication country:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
/
UNITED STATES OF AMERICA
/
US
/
USA