  Current analysis of tumor proliferation, the most salient prognostic
biomarker for invasive breast cancer, is limited to subjective mitosis counting
by pathologists in localized regions of tissue images. This study presents the
first data-driven integrative approach to characterize the severity of tumor
growth and spread on a categorical and molecular level, utilizing multiple
biologically salient deep learning classifiers to develop a comprehensive
prognostic model. Our approach achieves pathologist-level performance on
three-class categorical tumor severity prediction. It additionally pioneers
prediction of molecular expression data from a tissue image, obtaining a
Spearman's rank correlation coefficient of 0.60 with ex vivo mean calculated
RNA expression. Furthermore, our framework is applied to identify over two
hundred unprecedented biomarkers critical to the accurate assessment of tumor
proliferation, validating our proposed integrative pipeline as the first to
holistically and objectively analyze histopathological images.
