Daria Afenteva

Multi-omics analysis reveals the attenuation of the interferon pathway as a driver of chemo-refractory ovarian cancer

Ovarian high-grade serous carcinoma (HGSC) is one of the deadliest gynecological malignancies, with 10%-15% of patients exhibiting primary resistance to first-line chemotherapy. To characterize the molecular drivers of chemo-refractoriness, we perform multi-omics profiling of treatment-naive biopsies from patients with refractory HGSC enrolled in the DECIDER observational trial. We demonstrate that chemo-refractory HGSC is characterized by diminished interferon type I (IFN-I) and enhanced hypoxia pathway activity, and baseline IFN-I activity in chemo-naive cancer is an independent prognostic factor. Single-cell RNA sequencing and spatial protein profiling analyses corroborate the importance of elevated IFN-I activity in response to chemotherapy. Importantly, in vitro experiments demonstrate that high levels of IFN-I signaling increase cell chemosensitivity to platinum in a cell-autonomous manner. Together, these findings indicate that the IFN-I pathway activity in HGSC cancer cells predicts response to first-line chemotherapy in HGSC, proposing the stimulation of the IFN-I response as a therapeutic strategy. The study is registered at ClinicalTrials.gov (NCT04846933).

Decoding the Genomic and Functional Landscape of Emerging Subtypes in Ovarian Cancer

Abstract Ovarian high-grade serous carcinoma (HGSC) is characterized by pervasive genomic instability and high inter- and intra-tumor heterogeneity. Approximately half of HGSC tumors harbor homologous recombination deficiency (HRD), rendering them vulnerable to PARP inhibitors and platinum-based chemotherapy. In contrast, patients lacking HRD (HR-proficient, HRP) generally respond poorly to current therapies. To overcome heterogeneity and identify relevant HGSC subtypes, we characterized the genomic landscape of 640 tumors from 243 patients using whole-genome sequencing. Our chromosomal instability signature–based analysis characterized the structural variation landscape and revealed five HGSC subtypes, validated in an independent dataset. Two HRD subtypes, associated with BRCA1- or BRCA2-driven alterations, demonstrated favorable treatment responses. Strikingly, three HRP subtypes emerged, marked by unique structural alterations and gene expression patterns, tumor microenvironment interactions, and different chemotherapy responses. Notably, organoid experiments showed subtype-specific sensitivity to CHK1 inhibition, suggesting prexasertib as a potential targeted treatment for most currently untreatable HRP patients. Significance: These findings demonstrate that HGSC tumors can be divided into functionally and clinically distinct subtypes, offering new insights into the underlying biology of HGSC and providing a foundation to develop tailored therapeutic strategies for HRP tumors, which currently lack effective options.

Multi-layer Data to Improve Diagnosis, Predict Therapy Resistance and Suggest Targeted Therapies in HGSOC

Chemotherapy resistance is the greatest contributor to mortality in advanced cancers and severe challenges remain in finding effective treatment modalities to cancer patients with metastasized and relapsed disease. High-grade serous ovarian cancer (HGSOC) is typically diagnosed at a stage where the disease is already widely spread to the abdomen and current standard of practice treatment consists of surgery followed by platinum-taxane based chemotherapy and maintenance therapy. While 90% of HGSOC patients show no clinically detectable signs of cancer after surgery and chemotherapy, only 43% of the patients are alive five years after diagnosis because of chemoresistant cancer. This prospective, observational trial focuses on revealing major mechanisms causing chemoresistance in HGSOG patients and derive personalized treatment regimens for chemotherapy resistant HGSOC patients. The investigators recruit newly diagnosed advanced stage HGSOC patients who are then thoroughly followed during their cancer treatment. Longitudinal sampling includes digitalized H\&E stained histology slides mainly collected during routine diagnostics, fresh tumor \& ascites samples for next-generation sequencing/proteomics (WGS, RNA-seq, DNA-methylation, ATAC-seq, ChIP-seq, mass cytometry, etc.) and ex vivo experiments, plasma samples for circulating tumor DNA (ctDNA) analyses. Broad range of clinical parameters such as laboratory and radiologic parameters (e.g., FDG PET/CT), given cancer treatments and their outcomes are collected. Radiomic analyses are performed to PET/CT and CT scans. Long-term patient derived organoid lines are established from fresh tumor tissues. Actionable genomic alterations are searched. The general objective is to establish a clinically useful precision oncology approach based on multi-level data collected in longitudinal setting, and translate the most potent and validated discoveries into clinical use. DECIDER project will produce AI-powered diagnostic tools, cutting-edge software platforms for clinical decision-making, novel data analysis \& integration methods, and high-throughput ex vivo drug screening approaches.