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Morphological and molecular breast cancer profiling through explainable machine learning

Alexander Binder
Michael Bockmayr
Miriam Hägele
Stephan Wienert
Daniel Heim
Katharina Hellweg
Masaru Ishii
Albrecht Stenzinger
Andreas Hocke
Carsten Denkert
Klaus-Robert Müller
Frederick Klauschen

March 08 , 2021

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.