Multi-Modal Data Integration Reveals Functionally Credible Predictive Biomarkers in Ovarian Cancer

Background Precision oncology aims to tailor treatment according to tumor-specific molecular alterations, but the success of aberration-guided therapies has been limited in clinical trials. Here, we develop an integrated whole-genome and transcriptome workflow to systematically distinguish functionally credible, predictive driver aberrations from non-functional alterations across all classes of genomic events. Methods We applied the integrated omics workflow to 335 patients with ovarian high-grade serous cancer (HGSC) enrolled in the observational DECIDER trial. Tumor samples were collected from multiple cancer sites as a part of the standard cancer care. DNA and RNA were extracted together from snap-frozen tumor samples and sent to whole-genome and transcriptome sequencing. Sequencing data were processed with the Anduril 2 pipeline for detection and validation of short somatic changes and with the HMW toolkit and the nf-core/rnafusion pipeline for assessment of structural changes. Aberration-specific drug sensitivity was tested in patient-derived organoids with a drug screen combining targeted agents and chemotherapy. Results Using an agnostic integrated omics analysis, we identified clinically relevant ESCAT Tier II–III alterations in more than 40% of the patients, even though 60% of all nominally pathogenic variants proved to be false positives. Credible aberrations were predominantly clonal, detected across anatomical sites, and preserved from diagnosis to relapse, indicating early establishment during tumor evolution. The most recurrent actionable event was NF1 deficiency, which was associated with a robust transcriptional footprint and marked sensitivity to KRAS- and MEK-inhibition in patient-derived organoids. Notably, integrated DNA-RNA analysis enabled discrimination of treatment-guiding aberrations from false-positive findings that would otherwise misinform treatment selection and confound clinical trial outcomes. Conclusions Our findings provide a strategy for more reliable biomarker detection in precision oncology, inform biomarker-guided clinical trial design, and reveal unexploited therapeutic vulnerabilities in HGSC. Trial registration ClinicalTrials.gov: NCT04846933. Multi-layer Data to Improve Diagnosis, Predict Therapy Resistance and Suggest Targeted Therapies in HGSOC (DECIDER)