  The advent of digital pathology presents opportunities for computer vision
for fast, accurate, and objective solutions for histopathological images and
aid in knowledge discovery. This work uses deep learning to predict genomic
biomarkers - TP53 mutation, PIK3CA mutation, ER status, PR status, HER2 status,
and intrinsic subtypes, from breast cancer histopathology images. Furthermore,
we attempt to understand the underlying morphology as to how these genomic
biomarkers manifest in images. Since gene sequencing is expensive, not always
available, or even feasible, predicting these biomarkers from images would help
in diagnosis, prognosis, and effective treatment planning. We outperform the
existing works with a minimum improvement of 0.02 and a maximum of 0.13 AUROC
scores across all tasks. We also gain insights that can serve as hypotheses for
further experimentations, including the presence of lymphocytes and
karyorrhexis. Moreover, our fully automated workflow can be extended to other
tasks across other cancer subtypes.
