Driving innovation for rare skin cancers: utilizing common tumours and machine learning to predict immune checkpoint inhibitor response

Abstract

Metastatic Merkel cell carcinoma (MCC) and cutaneous squamous cell carcinoma (cSCC) are rare and both show impressive responses to immune checkpoint inhibitor treatment. However, at least 40% of patients do not respond to these expensive and potentially toxic drugs. Development of predictive biomarkers of response and rational, effective combination treatment strategies in these rare, often frail patient populations is challenging. This review discusses the pathophysiology and treatment of MCC and cSCC, with a particular focus on potential biomarkers of response to immunotherapy, and discusses how transfer learning using big data collected from patients with common tumours can be used in combination with deep phenotyping of rare tumours to develop predictive biomarkers and elucidate novel treatment targets.

Keywords: Immune checkpoint inhibitor; Machine learning; Merkel cell carcinoma; Squamous cell skin cancer.

Figure 1

Schematic overview of how data from common tumours can be used in combination with machine learning to predict immune checkpoint inhibitor responses in rare tumours. A big-data warehouse is constructed by pooling data from public repositories, clinical trials and biobanks. Data consist of clinicopathological, multi-omics and imaging data from common and rare tumours. By applying appropriate statistical inference on this big-data warehouse, clinicopathological, omics and imaging features can be selected that are strongly associated with immunological parameters potentially relevant to the cancer-immune setpoint. These selected features have the highest likelihood of contributing to the accuracy of a predictive model for response to immunotherapy. By using only these selected features as input parameters, the relatively small-scale cohorts of patients treated with immunotherapy can be used to train an accurate and non-overfitted predictive model, which will ultimately improve patient selection for this treatment.