  Molecular and genomic properties are critical in selecting cancer treatments
to target individual tumors, particularly for immunotherapy. However, the
methods to assess such properties are expensive, time-consuming, and often not
routinely performed. Applying machine learning to H&E images can provide a more
cost-effective screening method. Dozens of studies over the last few years have
demonstrated that a variety of molecular biomarkers can be predicted from H&E
alone using the advancements of deep learning: molecular alterations, genomic
subtypes, protein biomarkers, and even the presence of viruses. This article
reviews the diverse applications across cancer types and the methodology to
train and validate these models on whole slide images. From bottom-up to
pathologist-driven to hybrid approaches, the leading trends include a variety
of weakly supervised deep learning-based approaches, as well as mechanisms for
training strongly supervised models in select situations. While results of
these algorithms look promising, some challenges still persist, including small
training sets, rigorous validation, and model explainability. Biomarker
prediction models may yield a screening method to determine when to run
molecular tests or an alternative when molecular tests are not possible. They
also create new opportunities in quantifying intratumoral heterogeneity and
predicting patient outcomes.
