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

Veerla, Srinivas LU orcid ; Martin de la Fuente, Laura LU ; Li, Minerva LU ; Tang, Guyuan ; Ebbesson, Anna LU ; D'Incalci, Maurizio ; Marchini, Sergio ; Mannarino, Laura ; Måsbäck, Anna LU and Malander, Susanne LU orcid , et al. (2025) SCRM 2025 - the 7th Swedish Cancer Research Meeting
Abstract
Background
The lack of effective screening methods for the detection of ovarian cancer leads to late diagnosis and poor outcomes. High-grade serous ovarian carcinoma (HGSC) is strongly dominated by copy number alterations (CNAs), resulting from underlying mutational processes. Several studies have reported the detection of ovarian carcinoma-derived somatic mutations in cervical samples, uterine lavages, and plasma, which has brought hope for the development of new biomarkers for early detection.
Methods
Using 212 cervical samples from 128 women (58 HGSC, 22 healthy BRCA1/2 mutation carriers and 48 women with benign gynecological conditions), we interrogated CNAs from low-coverage whole-genome sequencing (WGS) data from cervical... (More)
Background
The lack of effective screening methods for the detection of ovarian cancer leads to late diagnosis and poor outcomes. High-grade serous ovarian carcinoma (HGSC) is strongly dominated by copy number alterations (CNAs), resulting from underlying mutational processes. Several studies have reported the detection of ovarian carcinoma-derived somatic mutations in cervical samples, uterine lavages, and plasma, which has brought hope for the development of new biomarkers for early detection.
Methods
Using 212 cervical samples from 128 women (58 HGSC, 22 healthy BRCA1/2 mutation carriers and 48 women with benign gynecological conditions), we interrogated CNAs from low-coverage whole-genome sequencing (WGS) data from cervical samples collected at surgery and at multiple time points up to 94 months before surgery. A machine-learning-based classifier was used to identify patterns of CNAs and to construct the HCsig predictor.
Results
Using HCsig, 79% of HGSC patients had at least one positive cervical sample before surgery (up to 65 months); 91% for women with stage I-II disease (up to 27 months), and 78% for women with stage III-IV disease (up to 65 months). Notably, 90% of the BRCA1/2-mutated tumors were detectable in at least one pre-diagnostic cervical sample (up to 54 months), compared to 76% of the BRCA1/2 wildtype tumors (up to 51 months). Using an independent dataset of 172 samples for validation, HGSC was detected with a specificity of 94% and sensitivity of 76% (AUC=0.83); importantly, including high sensitivity for early-stage cancers.
Conclusions
Our findings show that HGSC-derived copy number features of HCsig can detect HGSC with high performance in archival cervical samples collected several years before the development of symptoms and eventual diagnosis. Secondary prevention based on earlier-stage detection using HCsig addresses the unmet need for HGSC screening and detection. (Less)
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organization
publishing date
type
Contribution to conference
publication status
published
subject
keywords
Early detection, Ovarian Cancer, copy number alterations (CNA), Machine Learning (ML), Liquid biopsy, BRCA1 and BRCA2 mutation carriers, Stage I/II
conference name
SCRM 2025 - the 7th Swedish Cancer Research Meeting
conference location
Malmö, Sweden
conference dates
2025-05-23 - 2025-05-23
language
English
LU publication?
yes
id
040e9ce7-54ef-445a-8eb0-e7d001f5107c
date added to LUP
2025-05-26 11:56:52
date last changed
2025-05-26 14:45:14
@misc{040e9ce7-54ef-445a-8eb0-e7d001f5107c,
  abstract     = {{Background<br/>The lack of effective screening methods for the detection of ovarian cancer leads to late diagnosis and poor outcomes. High-grade serous ovarian carcinoma (HGSC) is strongly dominated by copy number alterations (CNAs), resulting from underlying mutational processes. Several studies have reported the detection of ovarian carcinoma-derived somatic mutations in cervical samples, uterine lavages, and plasma, which has brought hope for the development of new biomarkers for early detection.<br/>Methods<br/>Using 212 cervical samples from 128 women (58 HGSC, 22 healthy BRCA1/2 mutation carriers and 48 women with benign gynecological conditions), we interrogated CNAs from low-coverage whole-genome sequencing (WGS) data from cervical samples collected at surgery and at multiple time points up to 94 months before surgery. A machine-learning-based classifier was used to identify patterns of CNAs and to construct the HCsig predictor.<br/>Results<br/>Using HCsig, 79% of HGSC patients had at least one positive cervical sample before surgery (up to 65 months); 91% for women with stage I-II disease (up to 27 months), and 78% for women with stage III-IV disease (up to 65 months). Notably, 90% of the BRCA1/2-mutated tumors were detectable in at least one pre-diagnostic cervical sample (up to 54 months), compared to 76% of the BRCA1/2 wildtype tumors (up to 51 months). Using an independent dataset of 172 samples for validation, HGSC was detected with a specificity of 94% and sensitivity of 76% (AUC=0.83); importantly, including high sensitivity for early-stage cancers.<br/>Conclusions<br/>Our findings show that HGSC-derived copy number features of HCsig can detect HGSC with high performance in archival cervical samples collected several years before the development of symptoms and eventual diagnosis. Secondary prevention based on earlier-stage detection using HCsig addresses the unmet need for HGSC screening and detection.}},
  author       = {{Veerla, Srinivas and Martin de la Fuente, Laura and Li, Minerva and Tang, Guyuan and Ebbesson, Anna and D'Incalci, Maurizio and Marchini, Sergio and Mannarino, Laura and Måsbäck, Anna and Malander, Susanne and Kannisto, Päivi and Hedenfalk, Ingrid}},
  keywords     = {{Early detection; Ovarian Cancer; copy number alterations (CNA); Machine Learning (ML); Liquid biopsy; BRCA1 and BRCA2 mutation carriers; Stage I/II}},
  language     = {{eng}},
  month        = {{05}},
  title        = {{Copy number signatures in cervical samples enable early detection of high-grade serous ovarian carcinoma}},
  year         = {{2025}},
}