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Abstract

Background
The detection of ovarian carcinoma-derived somatic mutations in cervical samples and uterine lavages in several studies since 2013, has brought hope for the development of new biomarkers for early detection. High-grade serous ovarian carcinoma (HGSC) is strongly dominated by copy number alterations (CNAs). These CNAs are the consequence of underlying mutational processes in HGSC. We interrogated CNAs from low coverage whole-genome sequencing (WGS) data in HGSC tumors, plasma, endometrial biopsies, and cervical samples to explore if copy number signatures can be used as a biomarker for early detection of HGSC.
Methods
A total of 204 samples were included from 18 patients with HGSC, four BRCA mutation carriers and seven benign controls. Estimations of ploidy and cellularity, and thus calculation of absolute copy number, were optimized through a combination of the ACE, Rascal, and ichorCNA bioinformatic tools. Mixture modelling was used to subgroup the six fundamental copy number features and non-negative matrix factorization was used to generate the signatures and cluster the samples.
Results
We extracted six fundamental copy number features from 69 diagnostic and pre-diagnostic cervical samples from patients diagnosed with HGSC and generated six CN signatures. We found different distributions of features in benign samples compared to tumors and cervical samples from HGSC patients. We also observed different exposures to the six signatures in different patient groups.
Conclusions
Further understanding of the components and cell types contributing to each signature, and inclusion of more cervical samples into the approach, will hopefully identify a novel tumorigenic signature for early detection of HGSC in cervical samples.
Original languageEnglish
Publication statusPublished - 2022
Event“FUTURE PERSPECTIVES IN OVARIAN CANCER RESEARCH”
- Stockholm , Stockholm , Sweden
Duration: 2022 Nov 162022 Nov 17

Conference

Conference“FUTURE PERSPECTIVES IN OVARIAN CANCER RESEARCH”
Country/TerritorySweden
CityStockholm
Period2022/11/162022/11/17

Subject classification (UKÄ)

  • Cancer and Oncology

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