bims-ovdlit Biomed News
on Ovarian cancer: early diagnosis, liquid biopsy and therapy
Issue of 2025–05–25
ten papers selected by
Lara Paracchini, Humanitas Research
  1. Genome-wide analyses of cell-free DNA for therapeutic monitoring of patients with pancreatic cancer.
  2. First Analysis of the Incidence of Serous Tubal Intraepithelial Carcinoma (STIC) in Germany - Evaluation of the Cancer Registry of the Federal State of Rhineland-Palatinate from 2016 to 2023.
  3. Detection of cancers three years prior to diagnosis using plasma cell-free DNA.
  4. A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data.
  5. Ovarian Cancer Screening: Recommendations and Future Prospects.
  6. Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma.
  7. An artificial intelligence-based model for prediction of clonal hematopoiesis variants in cell-free DNA samples.
  8. Whole genome sequencing improves tissue-of-origin diagnosis and treatment options for cancer of unknown primary.
  9. Distribution of age at natural menopause, age at menarche, menstrual cycle length, height and BMI in BRCA1 and BRCA2 pathogenic variant carriers and non-carriers: results from EMBRACE.
  10. Exploring potential methylation markers for ovarian cancer from cervical scraping samples.
  1. Sci Adv. 2025 May 23. 11(21): eads5002
    Genome-wide analyses of cell-free DNA for therapeutic monitoring of patients with pancreatic cancer.
    Carolyn Hruban, Daniel C Bruhm, Inna M Chen, Shashikant Koul, Akshaya V Annapragada, Nicholas A Vulpescu, Sarah Short, Susann Theile, Kavya Boyapati, Bahar Alipanahi, Zachary L Skidmore, Alessandro Leal, Stephen Cristiano, Vilmos Adleff, Julia S Johannsen, Robert B Scharpf, Zachariah H Foda, Jillian Phallen, Victor E Velculescu.
      Determining response to therapy for patients with pancreatic cancer can be challenging. We evaluated methods for assessing therapeutic response using cell-free DNA (cfDNA) in plasma from patients with metastatic pancreatic cancer in the CheckPAC trial (NCT02866383). Patients were evaluated before and after initiation of therapy using tumor-informed plasma whole-genome sequencing (WGMAF) and tumor-independent genome-wide cfDNA fragmentation profiles and repeat landscapes (ARTEMIS-DELFI). Using WGMAF, molecular responders had a median overall survival (OS) of 319 days compared to 126 days for nonresponders [hazard ratio (HR) = 0.29, 95% confidence interval (CI) = 0.11-0.79, P = 0.011]. For ARTEMIS-DELFI, patients with low scores after therapy initiation had longer median OS than patients with high scores (233 versus 172 days, HR = 0.12, 95% CI = 0.046-0.31, P < 0.0001). We validated ARTEMIS-DELFI in patients with pancreatic cancer in the PACTO trial (NCT02767557). These analyses suggest that noninvasive mutation and fragmentation-based cfDNA approaches can identify therapeutic response of individuals with pancreatic cancer.
    DOI:  https://doi.org/10.1126/sciadv.ads5002
  2. Geburtshilfe Frauenheilkd. 2025 May;85(5): 541-547
    First Analysis of the Incidence of Serous Tubal Intraepithelial Carcinoma (STIC) in Germany - Evaluation of the Cancer Registry of the Federal State of Rhineland-Palatinate from 2016 to 2023.
    Valerie Catherine Linz, Alina Breuer, Philipp Leppert, Nils Herm-Stapelberg, Katja Schwarzer, Annette Hasenburg.
       Introduction: Serous tubal intraepithelial carcinomas (STIC) are classified as precursor lesions of high-grade serous carcinomas (HGSC) in women. STIC are rare and their incidence, prognosis and therapy remain unclear. Since 2021, all cases of isolated STIC in Germany must be reported, which means that all STICs in the German federal state of the Rhineland-Palatine (RLP) are available for evaluation.
    Material and Methods: A systematic search of the pathology reports in the RLP cancer registry was carried out for the period 01/2016-12/2023 using keywords related to STIC, and the results of the search were evaluated.
    Results: 382 pathology reports were identified as relevant and screened. A total of seven patients with isolated STIC were reported to the RLP registry in the years 2020-2022. This corresponds to 0.014% of all reported cases of cancer in women in RLP in this period. Six patients had a diagnosis of isolated STIC, identified during risk-reducing salpingo-oophorectomy (RRSO). The mean patient age at the time of RRSO was 60.29 (± 7.09) years. RRSO was carried out on average 9.38 (± 6.75) years after a primary diagnosis of breast cancer/DCIS in five patients. No HGSC was reported for any of the patients with isolated STIC in the follow-up period until 01/2024. 43 synchronous STICs were reported for the period from 01/2016 to 12/2023.
    Conclusion: 2-3 diagnoses of isolated STIC were recorded annually in RLP in the years 2020-2022. To date, there have been no reports of HGSC in these patients. In the future, the systematic recording of STICs will be expanded to include the cancer registries of other federal states of Germany and it will be possible to obtain valid data on the incidence of STIC in Germany. The collected data will also provide the basic information for a national STIC registry.
    Keywords:  Rhineland-Palatinate; STIC; STIC registry; cancer registry; incidence; serous tubal intraepithelial carcinoma
    DOI:  https://doi.org/10.1055/a-2555-4602
  3. Cancer Discov. 2025 May 22.
    Detection of cancers three years prior to diagnosis using plasma cell-free DNA.
    Yuxuan Wang, Corinne E Joshu, Samuel D Curtis, Christopher Douville, Vernon A Burk, Meng Ru, Maria Popoli, Janine Ptak, Lisa Dobbyn, Natalie Silliman, Josef Coresh, Eric Boerwinkle, Anna Prizment, Chetan Bettegowda, Kenneth W Kinzler, Nickolas Papadopoulos, Elizabeth A Platz, Bert Vogelstein.
      To explore how early can cancers be detected prior to clinical signs or symptoms, we assessed prospectively collected serial plasma samples from the Atherosclerosis Risk in Communities (ARIC) study, including 26 participants diagnosed with cancer and 26 matched controls. At the index time point, eight of these 52 participants scored positively with a multicancer early detection (MCED) test. All eight participants were diagnosed with cancer within 4 months after blood collection. In six of these 8 participants, we were able to assess an earlier plasma sample collected 3.1 to 3.5 years prior to clinical diagnosis. In four of these six participants, the same mutations detected by the MCED test could be identified, but at 8.6 to 79-fold lower mutant allele fractions. These results demonstrate that it is possible to detect circulating tumor DNA more than three years prior to clinical diagnosis, and provide benchmark sensitivities required for this purpose.
    DOI:  https://doi.org/10.1158/2159-8290.CD-25-0375
  4. Genome Biol. 2025 May 23. 26(1): 141
    A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data.
    Haichao Wang, Paulius D Mennea, Yu Kiu Elkie Chan, Zhao Cheng, Maria C Neofytou, Arif Anwer Surani, Aadhitthya Vijayaraghavan, Emma-Jane Ditter, Richard Bowers, Matthew D Eldridge, Dmitry S Shcherbo, Christopher G Smith, Florian Markowetz, Wendy N Cooper, Tommy Kaplan, Nitzan Rosenfeld, Hui Zhao.
      Fragmentomics features of cell-free DNA represent promising non-invasive biomarkers for cancer diagnosis. A lack of systematic evaluation of biases in feature quantification hinders the adoption of such applications. We compare features derived from whole-genome sequencing of ten healthy donors using nine library kits and ten data-processing routes and validated in 1182 plasma samples from published studies. Our results clarify the variations from library preparation and feature quantification methods. We design the Trim Align Pipeline and cfDNAPro R package as unified interfaces for data pre-processing, feature extraction, and visualization to standardize multi-modal feature engineering and integration for machine learning.
    Keywords:  Cancer genomics; CfDNA; Feature extraction; Fragmentomics
    DOI:  https://doi.org/10.1186/s13059-025-03607-5
  5. Rofo. 2025 May 23.
    Ovarian Cancer Screening: Recommendations and Future Prospects.
    Selina Chiu, Helen Staley, Priya Jeevananthan, Sophie Mascarenhas, Christina Fotopoulou, Andrea Rockall.
      Ovarian cancer remains a significant cause of mortality among women, largely due to challenges in early detection. Current screening strategies, including transvaginal ultrasound and CA125 testing, have limited sensitivity and specificity, particularly in asymptomatic women or those with early-stage disease. The European Society of Gynaecological Oncology, the European Society for Medical Oncology, the European Society of Pathology, and other health organizations currently do not recommend routine population-based screening for ovarian cancer due to the high rates of false-positives and the absence of a reliable early detection method.This review examines existing ovarian cancer screening guidelines and explores recent advances in diagnostic technologies including radiomics, artificial intelligence, point-of-care testing, and novel detection methods.Emerging technologies show promise with respect to improving ovarian cancer detection by enhancing sensitivity and specificity compared to traditional methods. Artificial intelligence and radiomics have potential for revolutionizing ovarian cancer screening by identifying subtle diagnostic patterns, while liquid biopsy-based approaches and cell-free DNA profiling enable tumor-specific biomarker detection. Minimally invasive methods, such as intrauterine lavage and salivary diagnostics, provide avenues for population-wide applicability. However, large-scale validation is required to establish these techniques as effective and reliable screening options. · Current ovarian cancer screening methods lack sensitivity and specificity for early-stage detection.. · Emerging technologies like artificial intelligence, radiomics, and liquid biopsy offer improved diagnostic accuracy.. · Large-scale clinical validation is required, particularly for baseline-risk populations.. · Chiu S, Staley H, Jeevananthan P et al. Ovarian Cancer Screening: Recommendations and Future Prospects. Rofo 2025; DOI 10.1055/a-2589-5696.
    DOI:  https://doi.org/10.1055/a-2589-5696
  6. J Ovarian Res. 2025 May 19. 18(1): 103
    Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma.
    Hugo Swenson, Ella Ittner, Lucas Werner, Elisabeth Werner Rönnerman, Claudia Mateoiu, Anikó Kovács, Pernilla Dahm-Kähler, Ghassan M Saed, Szilárd Nemes, Per Karlsson, Toshima Z Parris, Khalil Helou.
       BACKGROUND: Epithelial ovarian cancer (EOC) is a deadly and heterogenous disease comprising five major histotypes: clear cell carcinoma (CCC), endometrioid carcinoma (EC), low- and high-grade serous carcinoma (LGSC, HGSC), and mucinous carcinoma (MC). Despite this heterogeneity, EOC is often treated as a homogenous disease, and reliable screening tests are lacking. Although progress has been made, there is a pressing need for biomarkers to refine patient stratification, guide treatment, and improve outcomes. Here, we elucidated the relationship between DNA methylation and gene expression patterns in EOC to identify histotype-specific biomarkers.
    METHODS: Differential DNA methylation and gene expression analyses were performed for 86 early-stage EOC samples after histopathological reclassification stratified by histotype. The correlation between DNA methylation and gene expression was examined, and histotype-specific biomarkers were identified. Hierarchical clustering and predictive machine learning modeling were employed to assess the performance of the histotype-specific biomarkers using four external cohorts.
    RESULTS: EOC histotypes exhibited distinct epigenetic, transcriptional, and functional profiles, with candidate histotype-specific biomarkers such as CTSE and VCAN effectively distinguishing CCC, HGSC, and MC on the transcriptional level. Gene expression for the candidate biomarkers was found to be reproducible across external cohorts, with histotype-specific differences remaining homogenous.
    CONCLUSIONS: This study identified promising histotype-specific biomarkers for EOC using integrative transcriptomic and epigenomic analysis. Furthermore, these findings indicate that additional stratification or potential reclassification of the EC histotype is warranted in future studies.
    Keywords:  Bioinformatics; DNA methylation; Gene expression; Machine learning; Ovarian cancer
    DOI:  https://doi.org/10.1186/s13048-025-01676-5
  7. NPJ Precis Oncol. 2025 May 20. 9(1): 147
    An artificial intelligence-based model for prediction of clonal hematopoiesis variants in cell-free DNA samples.
    Gustavo Arango-Argoty, Marzieh Haghighi, Gerald J Sun, Elizabeth Y Choe, Aleksandra Markovets, J Carl Barrett, Zhongwu Lai, Etai Jacob.
      Circulating tumor DNA is a critical biomarker in cancer diagnostics, but its accurate interpretation requires careful consideration of clonal hematopoiesis (CH), which can contribute to variants in cell-free DNA and potentially obscure true tumor-derived signals. Accurate detection of somatic variants of CH origin in plasma samples remains challenging in the absence of matched white blood cells sequencing. Here we present an open-source machine learning framework (MetaCH) which classifies variants in cfDNA from plasma-only samples as CH or tumor origin, surpassing state-of-the-art classification rates.
    DOI:  https://doi.org/10.1038/s41698-025-00921-w
  8. Nat Commun. 2025 May 20. 16(1): 4422
    Whole genome sequencing improves tissue-of-origin diagnosis and treatment options for cancer of unknown primary.
    Richard J Rebello, Atara Posner, Ruining Dong, Owen W J Prall, Tharani Sivakumaran, Camilla B Mitchell, Aidan Flynn, Alex Caneborg, Catherine Mitchell, Sehrish Kanwal, Clare Fedele, Samantha Webb, Krista Fisher, Hui-Li Wong, Shiva Balachander, Wenying Zhu, Shannon Nicolson, Voula Dimitriadis, Nicholas Wilcken, Anna DeFazio, Bo Gao, Madhu Singh, Ian M Collins, Christopher Steer, Mark Warren, Narayan Karanth, Huiling Xu, Andrew Fellowes, Rodney J Hicks, Kym Pham Stewart, Charles Shale, Peter Priestley, Sarah-Jane Dawson, Joseph H A Vissers, Stephen B Fox, Penelope Schofield, David Bowtell, Oliver Hofmann, Sean M Grimmond, Linda Mileshkin, Richard W Tothill.
      Genomics can inform both tissue-of-origin (TOO) and precision treatments for patients with cancer of unknown primary (CUP). Here, we use whole genome and transcriptome sequencing (WGTS) for 72 patients and show diagnostic superiority of WGTS over panel testing (386-523 genes) in 71 paired cases. WGTS detects all reportable DNA features found by panel as well as additional mutations of diagnostic or therapeutic relevance in 76% of cases. Curated WGTS features and a CUP prediction algorithm (CUPPA) trained on WGTS data of known cancer types informs TOO in 71% of cases otherwise undiagnosed by clinicopathology review. WGTS informs treatments for 79% of patients, compared to 59% by panel testing. Finally, WGS of cell-free DNA (cfDNA) from patients with a high cfDNA tumour fraction (>7%), enables high-likelihood CUPPA predictions in 41% of cases. WGTS is therefore superior to panel testing, broadens treatment options, and is feasible using routine pathology samples and cfDNA.
    DOI:  https://doi.org/10.1038/s41467-025-59661-x
  9. Breast Cancer Res. 2025 May 21. 27(1): 87
    Distribution of age at natural menopause, age at menarche, menstrual cycle length, height and BMI in BRCA1 and BRCA2 pathogenic variant carriers and non-carriers: results from EMBRACE.
    Nasim Mavaddat, Debra Frost, Emily Zhao, Daniel R Barnes, Munaza Ahmed, Julian Barwell, Angela F Brady, Paul Brennan, Hector Conti, Jackie Cook, Harriet Copeland, Rosemarie Davidson, Alan Donaldson, Emma Douglas, David Gallagher, Rachel Hart, Louise Izatt, Zoe Kemp, Fiona Lalloo, Zosia Miedzybrodzka, Patrick J Morrison, Jennie E Murray, Alex Murray, Hannah Musgrave, Claire Searle, Lucy Side, Katie Snape, Vishakha Tripathi, Lisa Walker, Stephanie Archer, D Gareth Evans, Marc Tischkowitz, Antonis C Antoniou, Douglas F Easton.
       BACKGROUND: Carriers of germline pathogenic variants (PVs) in the BRCA1 and BRCA2 genes are at higher risk of developing breast and ovarian cancer than the general population. It is unclear if these PVs influence other breast or ovarian cancer risk factors, including age at menopause (ANM), age at menarche (AAM), menstrual cycle length, BMI or height. There is a biological rationale for associations between BRCA1 and BRCA2 PVs and reproductive traits, for example involving DNA damage and repair mechanisms. The evidence for or against such associations is limited.
    METHODS: We used data on 3,046 BRCA1 and 3,264 BRCA2 PV carriers, and 2,857 non-carrier female relatives of PV carriers from the Epidemiological Study of Familial Breast Cancer (EMBRACE). Associations between ANM and PV carrier status was evaluated using linear regression models allowing for censoring. AAM, menstrual cycle length, BMI, and height in carriers and non-carriers were compared using linear and multinomial logistic regression. Analyses were adjusted for potential confounders, and weighted analyses carried out to account for non-random sampling with respect to cancer status.
    RESULTS: No statistically significant difference in ANM between carriers and non-carriers was observed in analyses accounting for censoring. Linear regression effect sizes for ANM were -0.002 (95%CI: -0.401, 0.397) and -0.172 (95%CI: -0.531, 0.188), for BRCA1 and BRCA2 PV carriers respectively, compared with non-carrier women. The distributions of AAM, menstrual cycle length and BMI were similar between PV carriers and non-carriers, but BRCA1 PV carriers were slightly taller on average than non-carriers (0.5 cm difference, p = 0.003).
    CONCLUSION: Information on the distribution of cancer risk factors in PV carriers is needed for incorporating these factors into multifactorial cancer risk prediction algorithms. Contrary to previous reports, we found no evidence that BRCA1 or BRCA2 PV are associated with hormonal or anthropometric factors, except for a weak association with height. We highlight methodological considerations and data limitations inherent in studies aiming to address this question.
    Keywords:  BRCA1; BRCA2; Body mass index; Cancer; Height; Menarche; Menopause
    DOI:  https://doi.org/10.1186/s13058-025-02030-9
  10. Hum Genomics. 2025 May 17. 19(1): 56
    Exploring potential methylation markers for ovarian cancer from cervical scraping samples.
    Ju-Yin Lien, Lu Ann Hii, Po-Hsuan Su, Lin-Yu Chen, Kuo-Chang Wen, Hung-Cheng Lai, Yu-Chao Wang.
       BACKGROUND: Ovarian cancer has the highest mortality rate among gynecological cancers, making early detection crucial, as the five-year survival rate drops from 92% with early-stage diagnosis compared to 31% with late-stage diagnosis. Current diagnostic methods such as histopathological examination and detection of cancer antigen 125 and human epididymis protein 4 biomarkers are either invasive or lack specificity and sensitivity. However, the Papanicolaou (Pap) test, which is widely used for cervical cancer screening, shows the potential for detecting ovarian cancer by identifying tumor DNA in cervical scrapings. Since aberrant DNA methylation patterns are linked to cancer progression, DNA methylation offers a promising avenue for early diagnosis. Therefore, this study aimed to develop a methylation-based machine-learning model to stratify patients with ovarian cancer from the cervical scraping samples collected via Pap test.
    RESULTS: Cervical scrapings were collected by gynecologists using conventional Pap smears. In total, 160 samples were collected: 95 normal, 37 benign, and 28 malignant. Methylation data were generated using the Illumina Infinium MethylationEPIC BeadChip array, which contains approximately 850,000 CpG loci. Methylation data were initially divided into training and testing sets in a 3:1 ratio comprising 120 and 40 samples, respectively. A two-step methylation-based model was trained using the training data for classification: a principal component analysis (PCA) model, consisting of 30 features, to classify samples as normal or tumor; then a gradient boosting model, containing 16 features, to further stratify tumor samples as benign or malignant. The two-step model achieved an accuracy of 0.88 and an F1-score of 0.86 on the testing data. Furthermore, an over-representation analysis was conducted to explore the functions associated with genes mapped from differentially methylated positions (DMPs) in comparisons between normal and tumor samples, as well as between benign and malignant samples. These results suggest that DMPs may be associated with olfactory transduction when comparing normal versus tumor samples, and immune regulation when comparing benign and malignant samples.
    CONCLUSIONS: Our two-step model shows promise for predicting ovarian cancer and suggests that cervical scrapings may be a viable alternative for sample collection during screening.
    Keywords:  Biomarker; Cancer screening; Epigenetics; Machine learning; Methylation; Ovarian cancer; Papanicolaou test (Pap test)
    DOI:  https://doi.org/10.1186/s40246-025-00763-4
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