A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.
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Date
2024-07-03ICR Author
Author
Hoang, D-T
Dinstag, G
Shulman, ED
Hermida, LC
Ben-Zvi, DS
Elis, E
Caley, K
Sammut, S-J
Sinha, S
Sinha, N
Dampier, CH
Stossel, C
Patil, T
Rajan, A
Lassoued, W
Strauss, J
Bailey, S
Allen, C
Redman, J
Beker, T
Jiang, P
Golan, T
Wilkinson, S
Sowalsky, AG
Pine, SR
Caldas, C
Gulley, JL
Aldape, K
Aharonov, R
Stone, EA
Ruppin, E
Type
Journal Article
Metadata
Show full item recordAbstract
Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets. ENLIGHT-DeepPT successfully predicts true responders in five independent patient cohorts involving four different treatments spanning six cancer types, with an overall odds ratio of 2.28 and a 39.5% increased response rate among predicted responders versus the baseline rate. Notably, its prediction accuracy, obtained without any training on the treatment data, is comparable to that achieved by directly predicting the response from the images, which requires specific training on the treatment evaluation cohorts.
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Subject
Science & Technology
Life Sciences & Biomedicine
Oncology
ARTIFICIAL-INTELLIGENCE
PROSTATE-CANCER
BIOPSIES
Research team
Cancer Dynamics
Language
eng
Date accepted
2024-06-06
License start date
2024-07-03
Citation
Nature Cancer, 2024,
Publisher
NATURE PORTFOLIO