Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning.

The extent of tumor-infiltrating lymphocytes (TILs), along with immunomodulatory ligands, tumor-mutational burden and other biomarkers, has been demonstrated to be a marker of response to immune-checkpoint therapy in several cancers. Pathologists have therefore started to devise standardized visual approaches to quantify TILs for therapy prediction. However, despite successful standardization efforts visual TIL estimation is slow, with limited precision and lacks the ability to evaluate more complex properties such as TIL distribution patterns. Therefore, computational image analysis approaches are needed to provide standardized and efficient TIL quantification. Here, we discuss different automated TIL scoring approaches ranging from classical image segmentation, where cell boundaries are identified and the resulting objects classified according to shape properties, to machine learning-based approaches that directly classify cells without segmentation but rely on large amounts of training data. In contrast to conventional machine learning (ML) approaches that are often criticized for their "black-box" characteristics, we also discuss explainable machine learning. Such approaches render ML results interpretable and explain the computational decision-making process through high-resolution heatmaps that highlight TILs and cancer cells and therefore allow for quantification and plausibility checks in biomedical research and diagnostics.

Ovarian cancer emerging subtypes: role of oxidative stress and fibrosis in tumour development and response to treatment.

Epithelial ovarian cancer is a silent disease of usually late diagnosis and poor prognosis. Currently treatment options are limited and mainly consist of surgery followed by taxol- and platinum-based chemotherapy. Patient response to treatment is difficult to predict and there is a serious need for anticipating tumour response and orientating medical choices. In that aim, recent researches have focused on molecular aspects of ovarian tumours that could help patient stratification. We review here published discoveries in that field. We emphasize that signatures, defined by combining miRNA and transcriptomic data, enlighten important aspects of ovarian cancer biology and reliably stratify patients. The miR-200-dependent "Oxidative stress" and "Fibrosis" signatures are promising in patient stratification for defining oriented therapeutic strategies. Indeed, the "Stress" patients survive longer than the "Fibrosis" patients, who exhibit partial debulking and incomplete response to chemotherapy. Interestingly, these two subgroups might benefit from specifically targeted therapeutic approaches, as discussed here.