1648P Deep learning identifies oncogenic genetic alterations in BRAF and NTRK in H&E whole slide images from thyroid carcinomas

Abstract

Patient eligibility for matched oncogene-targeted therapies relies on detection by immunohistochemistry, Next Generation Sequencing (NGS) or PCR, but often is limited by clinical centers’ practice, lab capabilities and tissue-availability. Here, we tested the ability to predict oncogenic drivers (BRAF mutations and NTRK gene fusions) from thyroid tumors H&E whole slide images (WSIs) as a method to enrich for likely-positive patients that would merit NGS testing and benefit from targeted therapy. Using a set (N=833, of which BRAF+= 424, NTRK+= 43) of four cohorts (TCGA, Larotrectinib and Sorafenib trials and a biobank) of thyroid (mainly papillary and some anaplastic) carcinoma H&E stained digitized images we trained deep learning models to detect two known molecular oncogenic drivers: BRAF (mainly V600E) mutation (BRAF+) and NTRK gene fusions (NTRK+). We implemented an attention-based multiple instance learning (MIL) classifier and assessed its generalization in a leave-one-cohort-out paradigm. Different preanalytical variables (staining/section thickness/scanning) were evaluated. All slides and MIL heatmaps were reviewed by a pathologist to confirm areas of interest of the algorithm. Our MIL approach model detected both oncogenic drivers, with AUC up to 0.82 for BRAF+ and 0.90 for NTRK+ respectively. The model displayed highest attention in tumor areas (even in presence of immune infiltrate/normal glands), while being able to deal with various lab protocols in unseen cohorts. Our work shows that widely available H&E WSIs contain features that can predict the molecular status of BRAF+ and NTRK+, even when using a relatively small set of data (due to the rarity of NTRK+). Further refinement with additional datasets is ongoing. This deep learning method could offer a rapid and tissue-sparing alternative molecular diagnostic pathway supporting further detection and enrichment of NTRK+ patients. This would catalyze pathology workflows to bring additional efficiencies in testing of patients with the highest probability of having this rare molecular alteration, and provide them with opportunities to benefit from an NTRK targeted therapy.