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The prediction of microsatellite instability (MSI) using deep learning (DL) techniques could have significant benefits, including reducing cost and increasing MSI testing of colorectal cancer (CRC) patients. Nonetheless, batch effects or systematic biases are not well characterized in digital histology models and lead to overoptimistic estimates of model performance. Methods to not only palliate but to directly abrogate biases are needed. We present a multiple bias rejecting DL system based on adversarial networks for the prediction of MSI in CRC from tissue microarrays (TMAs), trained and validated in 1788 patients from EPICOLON and HGUA. The system consists of an end-to-end image preprocessing module that tile samples at multiple magnifications and a tissue classification module linked to the bias-rejecting MSI predictor. We detected three biases associated with the learned representations of a baseline model: the project of origin of samples, the patient’s spot and the TMA glass where each spot was placed. The system was trained to directly avoid learning the batch effects of those variables. The learned features from the bias-ablated model achieved maximum discriminative power with respect to the task and minimal statistical mean dependence with the biases. The impact of different magnifications, types of tissues and the model performance at tile vs patient level is analyzed. The AUC at tile level, and including all three selected tissues (tumor epithelium, mucin and lymphocytic regions) and 4 magnifications, was 0.87 ± 0.03 and increased to 0.9 ± 0.03 at patient level. To the best of our knowledge, this is the first work that incorporates a multiple bias ablation technique at the DL architecture in digital pathology, and the first using TMAs for the MSI prediction task.

D-amino acid oxidase (DAAO) catalyzes the oxidation of D-amino acids generating hydrogen peroxide, a potential producer of reactive oxygen species. In this study, we used a CLytA-DAAO chimera, both free and bound to magnetic nanoparticles, against colon carcinoma, pancreatic adenocarcinoma, and glioblastoma cell lines. We found that the enzyme induces cell death in most of the cell lines tested and its efficiency increases significantly when it is immobilized in nanoparticles. We also tested this enzyme therapy in non-tumor cells, and we found that there is not cell death induction, or it is significantly lower than in tumor cells. The mechanism triggering cell death is apparently a classical apoptosis pathway in the glioblastoma cell lines, while in colon and pancreatic carcinoma cell lines, CLytA-DAAO-induced cell death is a necrosis. Our results constitute a proof of concept that an enzymatic therapy, based on magnetic nanoparticles-delivering CLytA-DAAO, could constitute a useful therapy against cancer and besides it could be used as an enhancer of other treatments such as epigenetic therapy, radiotherapy, and treatments based on DNA repair.