Deep Gaussian processes for weakly supervised learning: tumor mutational burden (TMB) prediction
Deep Gaussian process (DGP) model [5] a hierarchical composition of multiple Gaussian processes, can provide a more flexible prior distribution over functions than a single Gaussian process (GP) can. In this work, we propose a DGP based classification method for tumor mutational burden (TMB) prediction from histopathology whole slide images (WSIs) TMB (a quantitative measurement of the number of mutations in a patient's tumor) which can be fully assessed by next generation sequencing (NGS) technology, has been suggested as a biomarker to predict a patient's treatment response to immunotherapy in several cancer types, including lung and bladder cancer [3, 6] However, one key challenge is that not all patients would have adequate fresh tumor tissue samples or would be able to undergo biopsy to get tumor tissues. On the other hand, WSIs are widely available, as a standard diagnosis tool, for most cancer patients. We hypothesize that the morphological image features calculated from whole slide images could be used to predict TMB status.