Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

TL;DR

This paper introduces a machine learning approach that integrates morphological and molecular tumor profiling from breast cancer images, enabling detailed spatial and molecular analysis for cancer research.

Contribution

It presents a novel computational method that combines tissue morphology with molecular data to predict tumor molecular profiles from imaging data.

Findings

Successful prediction of molecular tumor features from imaging data

Integration of microanatomic and molecular profiling enhances cancer analysis

Provides a new tool for spatially-resolved molecular scoring

Abstract

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment.