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Using deep learning to identify bladder cancers with FGFR-activating mutations from histology images
Constantine S Velmahos1, Marcus Badgeley2, Ying-Chun Lo3
1University of Massachusetts Medical School, Worcester, MA, USA.
Cancer Medicine
|June 11, 2021
Summary
Tumor histology images can predict fibroblast growth factor receptor (FGFR) mutations in bladder cancer. This method uses tumor-infiltrating lymphocyte (TIL) percentage, identified by convolutional neural networks (CNNs), to screen patients for targeted therapies.
Area of Science:
- Oncology
- Computational Pathology
- Genomics
Background:
- The fibroblast growth factor receptor (FGFR) pathway is a key therapeutic target in bladder cancer.
- FGFR-targeted therapies benefit patients with specific FGFR mutations, typically identified via genetic sequencing.
- Genetic sequencing is not standard at diagnosis, while tumor histology is.
Purpose of the Study:
- To computationally extract imaging biomarkers from diagnostic tumor slides to predict FGFR alterations in bladder cancer.
- To assess the feasibility of using routine histology to identify patients eligible for FGFR-targeted therapies.
Main Methods:
- Analysis of genomic profiles and H&E-stained bladder cancer slides from The Cancer Genome Atlas (n=418).
- Utilized a convolutional neural network (CNN) to identify tumor-infiltrating lymphocytes (TIL).
- Employed logistic regression to predict FGFR activation status using TIL percentage.
Main Results:
- A CNN-based TIL percentage model accurately predicted any FGFR gene aberration (AUROC=0.76).
- A refined model specifically for FGFR2/FGFR3 mutations demonstrated high sensitivity and specificity (AUROC=0.86).
Conclusions:
- TIL percentage, a derived image biomarker from histology, can predict FGFR mutations in bladder cancer.
- Digital pathology methods like CNNs can complement genomic sequencing for early screening of targeted therapy candidates.
