Edyta Biskup

Developing a DNA Methylation Signature to Differentiate High-Grade Serous Ovarian Carcinomas from Benign Ovarian Tumors

Epithelial ovarian cancer (EOC) represents a significant health challenge, with high-grade serous ovarian cancer (HGSOC) being the most common subtype. Early detection is hindered by nonspecific symptoms, leading to late-stage diagnoses and poor survival rates. Biomarkers are crucial for early diagnosis and personalized treatment OBJECTIVE: Our goal was to develop a robust statistical procedure to identify a set of differentially methylated probes (DMPs) that would allow differentiation between HGSOC and benign ovarian tumors. Using the Infinium EPIC Methylation array, we analyzed the methylation profiles of 48 ovarian samples diagnosed with HGSOC, borderline ovarian tumors, or benign ovarian disease. Through a multi-step statistical procedure combining univariate and multivariate logistic regression models, we aimed to identify CpG sites of interest. We discovered 21 DMPs and developed a predictive model validated in two independent cohorts. Our model, using a distance-to-centroid approach, accurately distinguished between benign and malignant disease. This model can potentially be used in other types of sample material. Moreover, the strategy of the model development and validation can also be used in other disease contexts for diagnostic purposes.

Validating reference-based algorithms to determine cell-type heterogeneity in ovarian cancer DNA methylation studies

AbstractInformation about cell composition in tissue samples is crucial for biomarker discovery and prognosis. Specifically, cancer tissue samples present challenges in deconvolution studies due to mutations and genetic rearrangements. Here, we optimized a robust, DNA methylation-based protocol, to be used for deconvolution of ovarian cancer samples. We compared several state-of-the-art methods (HEpiDISH, MethylCIBERSORT and ARIC) and validated the proposed protocol in an in-silico mixture and in an external dataset containing samples from ovarian cancer patients and controls. The deconvolution protocol we eventually implemented is based on MethylCIBERSORT. Comparing deconvolution methods, we paid close attention to the role of a reference panel. We postulate that a possibly high number of samples (in our case: 247) should be used when building a reference panel to ensure robustness and to compensate for biological and technical variation between samples. Subsequently, we tested the performance of the validated protocol in our own study cohort, consisting of 72 patients with malignant and benign ovarian disease as well as in five external cohorts. In conclusion, we refined and validated a reference-based algorithm to determine cell type composition of ovarian cancer tissue samples to be used in cancer biology studies in larger cohorts.

Comparative Performance of Methylation Array and Bisulfite Sequencing in Ovarian Tissue Samples and Cervical Swabs

Introduction DNA methylation has emerged as a promising tool for the early detection of ovarian cancer. Consequently, accurate and cost-effective methods for detecting DNA methylation are essential. Although the Infinium Methylation Array provides broad coverage, its high cost limits clinical utility. Bisulfite Sequencing (BS) represents a potential alternative for biomarker validation and diagnostic assay development, provided it can reliably reproduce array-based methylation profiles. This study aims to assess the concordance between BS and Infinium Methylation Array data in ovarian cancer tissues and cervical swabs. Methods DNA from 55 ovarian cancer tissues and 25 cervical swabs underwent bisulfite conversion and was analyzed using the Infinium Methylation Array and a custom BS panel. We compared the results, focusing on overall methylation levels, Spearman correlation between beta values, and Bland-Altman analysis. We also assessed whether sample clustering patterns by diagnosis were consistent across methods. Results Methylation profiles generated by bisulfite sequencing were consistent with those obtained using the Infinium Methylation Array. We observed strong sample-wise correlation between platforms, particularly in ovarian tissue samples. Agreement was slightly lower in cervical swabs, likely due to reduced DNA quality. Diagnostic clustering patterns were broadly preserved across both methods. Conclusion Our results show that BS can reliably replicate results from the Infinium Methylation Array and presents a cost-effective option for analyzing larger sample sets. Moreover, our work may serve as a best-practice guide, as it highlights key challenges associated with working with sequencing library preparation.