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

Soo Youn Cho,Chang Ho Ahn,Jeong Hoon Lee,Jeong Eon Lee,Chan-Young Ock,Kyunghyun Paeng,Inwan Yoo,Jai Min Ryu,Sang Yong Song,Eun Yoon Cho

doi:10.1038/s41598-021-96855-x

Abstract

We hypothesized that a deep-learning algorithm using HE images might be capable of predicting the benefits of adjuvant chemotherapy in cancer patients. HE slides were retrospectively collected from 1343 de-identified breast cancer patients at the Samsung Medical Center and used to develop the Lunit SCOPE algorithm. Lunit SCOPE was trained to predict the recurrence using the 21-gene assay (Oncotype DX) and histological parameters. The risk prediction model predicted the Oncotype DX score > 25 and the recurrence survival of the prognosis validation cohort and TCGA cohorts. The most important predictive variable was the mitotic cells in the cancer epithelium. Of the 363 patients who did not receive adjuvant therapy, 104 predicted high risk had a significantly lower survival rate. The top-300 genes highly correlated with the predicted risk were enriched for cell cycle, nuclear division, and cell division. From the Oncotype DX genes, the predicted risk was positively correlated with proliferation-associated genes and negatively correlated with prognostic genes from the estrogen category. An integrative analysis using Lunit SCOPE predicted the risk of cancer recurrence and the early-stage hormone receptor-positive breast cancer patients who would benefit from adjuvant chemotherapy.

Highlights

We hypothesized that a deep-learning algorithm using HE images might be capable of predicting the benefits of adjuvant chemotherapy in cancer patients
The panels decipher the histological parameters in the image divided into small patch images and return the aggregated count values corresponding to the tissue, structure, and cell from the whole slide images (WSIs)
We developed a deep learning (DL)-based HE image analyzer called Lunit SCOPE to identify and quantify various histological parameters from HE-stained WSIs

Summary

Introduction

We hypothesized that a deep-learning algorithm using HE images might be capable of predicting the benefits of adjuvant chemotherapy in cancer patients. An integrative analysis using Lunit SCOPE predicted the risk of cancer recurrence and the early-stage hormone receptor-positive breast cancer patients who would benefit from adjuvant chemotherapy. Several multigene assays, such as the 21-gene assay (Oncotype DX), PAM50, and Mammaprint, are used to stratify patients and guide ACTx according to the recurrence risk in HR-positive, and lymph node- negative breast cancer after extensive clinical v alidation[7,8]. As Lunit SCOPE accurately identifies the comprehensive features of HE slides, especially regarding mitotic count and the infiltration of immune cells or stromal cells, we hypothesized that histological parameters analyzed using Lunit SCOPE would predict the RS from the 21-gene assay, revealing potential prognostic and predictive biomarkers of ACTx in early stage hormone receptor-positive breast cancer

Methods

Results

Conclusion