Malignant abdominal fluid (ascites) frequently develops in women with advanced high-grade serous ovarian cancer (HGSOC) and is associated with drug resistance and a poor prognosis 1 . To comprehensively characterize the HGSOC ascites ecosystem, we used single-cell RNA-seq (scRNA-seq) to profile ~11,000 cells from 22 ascites specimens from 11 HGSOC patients. We found significant inter-patient variability in the composition and functional programs of ascites cells, including immunomodulatory fibroblast sub-populations and dichotomous macrophage populations. We find that the previously described "immunoreactive" and "mesenchymal" subtypes of HGSOC, which have prognostic implications, reflect the abundance of immune infiltrates and fibroblasts rather than distinct subsets of malignant cells 2 . Malignant cell variability was partly explained by heterogeneous copy number alterations (CNA) patterns or expression of a stemness program. Malignant cells shared expression of inflammatory programs that were largely recapitulated in scRNA-seq of ~35,000 cells from additionally collected samples, including three ascites, two primary HGSOC tumors and three patient-ascites-derived xenograft models. Inhibition of the JAK/STAT-pathway, which was expressed in both malignant cells and CAFs, had potent anti-tumor activity in primary short-term cultures and PDX models. Our work contributes to resolving the HSGOC landscape 3-5 and provides a resource for the development of novel therapeutic approaches.

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.