  Determining early-stage prognostic markers and stratifying patients for
effective treatment are two key challenges for improving outcomes for melanoma
patients. Previous studies have used tumour transcriptome data to stratify
patients into immune subgroups, which were associated with differential
melanoma specific survival and potential treatment strategies. However,
acquiring transcriptome data is a time-consuming and costly process. Moreover,
it is not routinely used in the current clinical workflow. Here we attempt to
overcome this by developing deep learning models to classify gigapixel H&E
stained pathology slides, which are well established in clinical workflows,
into these immune subgroups. Previous subtyping approaches have employed
supervised learning which requires fully annotated data, or have only examined
single genetic mutations in melanoma patients. We leverage a multiple-instance
learning approach, which only requires slide-level labels and uses an attention
mechanism to highlight regions of high importance to the classification.
Moreover, we show that pathology-specific self-supervised models generate
better representations compared to pathology-agnostic models for improving our
model performance, achieving a mean AUC of 0.76 for classifying histopathology
images as high or low immune subgroups. We anticipate that this method may
allow us to find new biomarkers of high importance and could act as a tool for
clinicians to infer the immune landscape of tumours and stratify patients,
without needing to carry out additional expensive genetic tests.
