Copy number signatures in cervical samples enable early detection of high-grade serous ovarian carcinoma

BackgroundOvarian cancer is often diagnosed in advanced stages, resulting in poor outcomes. There is an unmet need for a sensitive and specific screening tool for early-stage detection of ovarian cancer. MethodsRecognizing that high-grade serous ovarian carcinoma (HGSC) is driven by copy number alterations (CNAs) and that tumor DNA can be detected in cervical samples, we analyzed CNAs from shallow whole genome sequencing of 212 cervical samples from 128 women with/without HGSC, including 29 germline BRCA1/2 mutation carriers. Using a machine-learning classifier, we developed High-grade serous ovarian cancer Cervical copy number signature (HCsig), a predictor for HGSC detection. ResultsHGSC-derived CNAs were detectable with HCsig in cervical samples collected several years before diagnosis. Importantly, HCsig correctly identified HGSC in 79% of archival cervical samples, including 91% stage I-II (0-27 months pre-diagnosis), and 77% stage III-IV (0-65 months pre-diagnosis). Among patients with HGSC who had multiple pre-diagnostic samples collected during the pre-symptomatic phase, 85% had at least one HCpositive cervical sample before surgery (up to 65 months before diagnosis). Detection rates were 90% and 76% for BRCA1/2-mutated and wildtype HGSC, respectively. Validation in 172 independent samples (0-98 months pre-diagnosis) showed 76% sensitivity and 94% specificity (AUC=0.83), including high sensitivity for early-stage cancers. ConclusionsWe show that applying the HCsig classifier to cervical samples, including from non-symptomatic women several years before diagnosis, holds promise for early-stage detection and secondary prevention of HGSC. Moreover, it may serve as a screening tool to aid decision-making regarding timing of risk-reducing surgery in high-risk populations.