A Deep Learning solution for triaging patient with cancer from their predicted mutational status using histopathological images

Abstract

The presence of mutation in cancer can be associated with a response to a targeted cancer therapy. Therefore it has become an important information while it helps giving a more specialized and more efficient treatment for each patient. Detection of mutation is routinely made by DNA-sequencing diagnostic tests. Recently, a new strategy of deep learning based method of prediction of mutation from histopathological images has shown promising results. However it is still unknown whether these methods can be useful as screening tests aside from sequencing methods for efficient population diagnosis. Here we developed our own deep learning based prediction pipeline for the detection of mutation in breast, lung, colon and ovarian cancer and find clinically relevant genes that have easy-to-detect mutational status, and especially TP53 which can be detected in these four tumors. We then propose 3 potential screening strategies and show the effectiveness of our deep learning pipeline for optimizing diagnosis in the patient population.