Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles
This work addresses the challenge of combining morphological and molecular profiling in cancer research, offering a novel computational tool for tumor analysis.
The authors tackled the problem of integrating spatial and molecular features in cancer research by developing a machine learning approach that predicts molecular properties from breast cancer imaging data, enabling computational scoring of molecular markers and their spatial associations.
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.