Prediction of tumour mutational burden of squamous cell carcinoma using histopathology images of surgical specimens

Immunotherapy is a novel anti-cancer treatment that shows significant improvements in outcomes for lung cancer patients. However, this treatment has the potential for substantial side effects in a minority of patients, and many lung cancer patients do not benefit from it. Programmed-death ligand-1 expression in tumour cells is currently the main biomarker used to identify those who might benefit but it is not very accurate. Tumour mutational burden (TMB) is a promising alternative, with lung cancers having more than 10 mutations/megabase being more likely to respond to immunotherapy. However, the cost and time it takes to obtain TMB makes it difficult to implement in the clinic. In this study, we used the deep learning technique of transfer learning with Alexnet to obtain a model that can estimate whether a cancer is highly mutated or not based on digitized hematoxylin and eosin histology slides that are routinely obtained from surgical resection of squamous cell carcinoma. The system was developed using images from 20 patients obtained through The Cancer Genome Atlas, five of which were reserved for validation. On this validation set, the system had an area under the receiver operator characteristic curve of 0.80, error rate of 24%, false negative rate of 26%, and false positive rate of 22%. This motivates additional work in this direction to build a system that can be used in the future to inform physicians as to which patients with squamous lung carcinoma would benefit from immunotherapy.