Transl Res. 2023 Jul 25. pii: S1931-5244(23)00126-3. [Epub ahead of print]
Ovarian cancer (OV) is the most lethal gynecological malignancy and requires improved early detection methods and more effective intervention to achieve a better prognosis. The lack of sensitive and noninvasive biomarkers with clinical utility remains a challenge. Here, we conducted a genome-wide copy number variation (CNV) profiling analysis using low-coverage whole genome sequencing (LC-WGS) of plasma cfDNA in patients with non-malignant and malignant ovarian tumors, and identified 10 malignancy-specific and 12 late-stage-specific CNV markers from plasma cfDNA LC-WGS data. Concordance analysis indicated a significant correlation of identified CNV markers between CNV profiles of plasma cfDNA and tissue DNA (Pearson's r=0.64, p=0.006 for the TCGA cohort and r=0.51, p=0.04 for the Dariush cohort). By leveraging these specific CNV markers and machine learning algorithms, we developed robust predictive models showing excellent performance in distinguishing between malignant and non-malignant ovarian tumors with F1-scores of 0.90 and ranging from 0.75-0.99, and prediction accuracy of 0.89 and ranging from 0.66-0.98, respectively, as well as between early- and late-stage ovarian tumors with F1-scores of 0.84 and ranging from 0.61-1.00, and prediction accuracy of 0.82 and ranging from 0.63-0.96 in our institute cohort and other external validation cohorts. Furthermore, we also discovered and validated certain CNV features associated with survival outcomes and platinum-based chemotherapy response in multicenter cohorts. In conclusion, our study demonstrated the clinical utility of CNV profiling in plasma cfDNA using LC-WGS as a cost-effective and accessible liquid biopsy for OV.
Keywords: low-coverage whole-genome sequencing; noninvasive diagnosis; ovarian cancer; plasma cell-free DNA; progression monitoring