Joint and individual analysis of breast cancer histologic images and genomic covariates

TL;DR

This paper introduces a novel framework combining CNN-based feature extraction with AJIVE to analyze and interpret the relationships between breast cancer histopathology images and genomic data, revealing meaningful connections.

Contribution

It develops a joint analysis method that integrates CNN features with AJIVE to explore similarities and differences between histopathology and genetic data in breast cancer.

Findings

Identified interpretable links between image features and genetic markers.

Developed methods to interpret CNN features in the context of statistical variation.

Bridged the gap between histopathology and genomics in breast cancer analysis.

Abstract

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics.