Deep learning from HE slides to predict the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer

Background: The predictive value of adjuvant chemotherapy for early-stage hormone receptor-positive breast cancer has been only validated by a 21-gene expression assay. We hypothesized that deep-learning prediction from HE images, called Lunit-SCOPE, is a potential prognostic and predictive biomarker of adjuvant chemotherapy. Methods: : We retrospectively collected HE slides from 1153 de-identified breast cancer patients at the Samsung Medical Center (SMC) in order to develop a deep-learning algorithm called Lunit-SCOPE. The histological parameters from 255 patients, deciphered by Lunit-SCOPE, were trained to predict the recurrence score (RS) using the 21-gene assay from Oncotype DX. We validated the model’s performance using the recurrence survival of 898 patients and The Cancer Genome Atlas (TCGA) cohort, and examined related biological functions through RNA sequence data. Results: : The histologic parameter-based RS prediction model predicted the oncotype DX score (R 2 =0.96) and the recurrence survival analysis on the validation ( p <0.01) and TCGA cohort ( p <0.01), where the most important variables were the nuclear grade and the mitotic cells in the cancer epithelium. Of the 85 patients classified as the high-risk group, 72 patients who received adjuvant therapy had a significantly better survival ( p <0.01). The functions of the top 300 highly correlated genes with a predicted RS were enriched for cell cycle, nuclear division and cell division. Of the 21-genes from the Oncotype DX, the predicted RS had positive correlations with the proliferation category genes and was negatively correlated with the prognostic genes in the estrogen category. Conclusion: An integrative analysis using Lunit-SCOPE predicts a high risk of recurrence and those who would benefit from adjuvant chemotherapy for early-stage hormone-positive breast cancer.