Deep learning for biomarker and outcome prediction in cancer

Machine learning in the form of deep learning (DL) has recently transformed how computer vision tasks are solved in numerous domains, including image-based medical diagnostics. DL-based methods have the potential to enable more precise quantitative characterisation of cancer tissue specimens routinely analysed in clinical pathology laboratories for diagnostic purposes. Computer-assisted tissue analysis within pathology is not restricted to the quantification and classification of specific tissue entities. DL allows to directly address clinically relevant questions related to the prediction of cancer outcome and efficacy of cancer treatment. This thesis focused on the following crucial research question: is it possible to predict cancer outcome, biomarker status, and treatment efficacy directly from the tissue morphology using DL without any special stains or molecular methods? To address this question, we utilised digitised hematoxylin-eosin-stained (H&E) tissue specimens from two common types of solid tumours – breast and colorectal cancer. Tissue specimens and corresponding clinical data were retrieved from retrospective patient series collected in Finland. First, a DL-based algorithm was developed to extract prognostic information for patients diagnosed with colorectal cancer, using digitised H&E images only. Computational analysis of tumour tissue samples with DL demonstrated a superhuman performance and surpassed a consensus of three expert pathologists in predicting five-year colorectal cancer-specific outcomes. Then, outcome prediction was studied in two independent breast cancer patient series. Particularly, generalisation of the trained algorithms to previously unseen patients from an independent series was examined on the large whole-slide tumour specimens. In breast cancer outcome prediction, we investigated a multitask learning approach by combining outcome and biomarker-supervised learning. Our experiments in breast and colorectal cancer show that tissue morphological features learned by the DL models supervised by patient outcome provided prognostic information independent of established prognostic factors such as histological grade, tumour size and lymph nodes status. Additionally, the accuracy of DL-based predictors was compared to other prognostic characteristics evaluated by pathologists in breast cancer, including mitotic count, nuclear pleomorphism, tubules formation, tumour necrosis and tumour-infiltrating lymphocytes. We further assessed if molecular biomarkers such as hormone receptor status and ERBB2 gene amplification can be predicted from H&E- stained tissue samples obtained at the time of diagnosis from patients with breast cancer and showed that molecular alterations are reflected in the basic tissue morphology and can be captured with DL. Finally, we studied how morphological features of breast cancer can be linked to molecularly targeted treatment response. The results showed that ERBB2-associated morphology extracted with DL correlated with the efficacy of adjuvant anti-ERBB2 treatment and can contribute to treatment-predictive information in breast cancer. Taken together, this thesis shows the potential utility of DL in tissue-based characterisation of cancer for prediction of cancer outcome, tumour molecular status and efficacy of molecularly targeted treatments. DL-based analysis of the basic tissue morphology can provide significant predictive information and be combined with clinicopathological and molecular data to improve the accuracy of cancer diagnostics.