Biology subjects

Kondrashova, O.

Publications and source records attributed to Kondrashova, O..

10 recordsLinked to original sources

bioRxiv2026

Genomic alterations enable BRCA1 methylation loss and promoter bypass to drive resistance in high-grade serous ovarian cancer

BRCA1 promoter methylation predicts sensitivity to PARP inhibitors in high-grade serous ovarian cancer, yet therapeutic resistance is common and mechanistically unresolved. Using long-read direct DNA sequencing of patient-derived xenografts and cell lines, we resolved BRCA1 methylation at single-molecule resolution with structural and transcriptomic analyses. We revealed two convergent PARP inhibitor and platinum resistance mechanisms, validated in patient tumors. First, focal, allele-specific loss of BRCA1 methylation arose through local cis-acting genomic alterations, instead of global epigenetic reprogramming. Engineered in cis sequence alterations near the methylated BRCA1 promoter were sufficient to induce methylation loss, restore homologous recombination, and confer resistance. Similar associations were observed across the genome, suggesting this mechanism extends beyond BRCA1. Second, BRCA1 expression was restored despite intact promoter methylation via structural variant-mediated promoter bypass or alternative transcription initiation. Together, these findings redefine BRCA1 methylation loss as a locus-restricted process and reveal multiple routes by which tumors escape PARP inhibitor therapy. Statement of SignificanceWe show that high-grade serous ovarian cancers can restore BRCA1 expression after therapy through multiple genomic mechanisms, including local methylation loss and promoter bypass, thereby re-establishing homologous recombination and driving PARP inhibitor resistance. These findings challenge reliance on BRCA1 methylation alone as a predictive biomarker and support rational combination therapies for more durable responses.

cancer biology↗

bioRxiv2025

Demethylating agents drive PARP inhibitor resistance in ovarian carcinomas with BRCA1 gene silencing

DNA methyltransferase 1 inhibitor (DNMT1i) therapy is a promising option for increasing immune response as part of combination cancer therapy. High-grade serous ovarian carcinoma (HGSOC) is a highly aggressive cancer with poor survival outcomes, where DNMT1i therapy is being increasingly explored. HGSOC with epigenetically silenced BRCA1 has been shown to respond to PARP inhibitor (PARPi) treatment - a core targeted therapy for HGSOC. However, loss of silencing of even a single BRCA1 allele causes PARPi and platinum chemotherapy resistance. We tested whether BRCA1 silencing was robust to DNMT1i therapy, or would be reversed, thus driving PARPi resistance. We previously generated two homozygously silenced BRCA1 HGSOC cell lines: WEHI-CS62 and an OVCAR8 derivative. DNMT1i treatment caused sustained BRCA1 promoter methylation loss, gene re-expression and PARPi resistance in both of these silenced BRCA1 lines, but not in mutated BRCA1/2 or RAD51C lines. Methylation arrays confirmed transient global CpG methylation losses following DNMT1i. CRISPR deletion of the re-expressed BRCA1 copy in WEHI-CS62 restored silencing and PARPi sensitivity. Furthermore, DNMT1i treatment of a silenced BRCA1 PDX caused heterogeneous BRCA1 promoter methylation loss. In summary, DNMT1 inhibitors caused sustained reduction of BRCA1 promoter methylation in HGSOC cells. This resulted in BRCA1 re-expression and PARP inhibitor resistance, presenting a significant risk for up to 17% of HGSOC patients with BRCA1 gene silencing who could benefit from PARP inhibitor therapy. We conclude that DNA demethylation therapy should be avoided for HGSOC patients with epigenetically silenced BRCA1.

cancer biology↗

bioRxiv2025

Explainable machine learning identifies features and thresholds predictive of immunotherapy response

BackgroundImmunotherapy has improved patient survival for multiple cancer types, including melanoma. While a variety of molecular features have been linked to response to immune checkpoint inhibitors (ICI) treatment, clinically established biomarkers, such as tumour mutation burden (TMB) and PD-L1 expression, have shown limitations in accurately categorising responders versus non-responders. Due to the complex nature of ICI response, which includes cancer intrinsic and extrinsic features within the tumour microenvironment (TME), using a single biomarker to predict response is insufficient, necessitating the need to identify accurate clinical and multi-omic molecular predictors. MethodsWe integrate clinical, DNA and RNA sequencing data from four datasets, comprising 138 melanoma patients treated with ICI, to develop machine learning models for predicting ICI response. The performance of each model was evaluated using an independent dataset of patients with cutaneous melanoma (n=53). Interactions between trained models and features were rationalised using the explainability method SHAP. ResultsThe most optimal model was the multi-omic random forest model, with AUC-ROC of 0.78 when predicting response in the independent test dataset. Using SHAP, we predicted thresholds for mutational signatures, neoantigen load, immune cell-type abundance and immune receptor LAG3 expression. The relationship between these influential features and their SHAP scores revealed numerical thresholds constituting good and poor patient response. ConclusionsThis approach highlights patient response to ICI is influenced by both cancer intrinsic and extrinsic features, as well as identifies candidate biomarkers that could inform the use of ICI and potentially assist in the selection of combination therapy in melanoma treatment.

bioinformatics↗

bioRxiv2024

Generation of a PARPi-sensitive homozygous BRCA1-methylated OVCAR8 cell line using targeted CRISPR gene editing

Up to 17% of high grade serous ovarian carcinomas (HGSOC) harbour BRCA1 promoter methylation (meBRCA1), making them susceptible to treatment with targeted PARP inhibitor (PARPi) therapy. Unfortunately, meBRCA1 loss can be acquired following PARPi or platinum chemotherapy, resulting in BRCA1 re-expression and PARPi resistance. Our understanding of meBRCA1 stability in HGSOC is currently limited, in part due to a paucity of pre-clinical models with homozygous meBRCA1. Herein, we describe the generation of a several OVCAR8 cell line derivatives containing landing pad constructs, for future functional studies, and representing various BRCA1 states, including a homozygous meBRCA1 variant. Our PARPi resistant OVCAR8 has two methylated BRCA1 copies and one unmethylated copy, enabling BRCA1 expression. CRISPR-Cas9 gene editing was used to delete copies of the BRCA1 gene in landing pad-containing clones of this cell line (A6 and H4). We produced one variant with deletion of all BRCA1 copies (H4-53), and another with two copies deleted and only a single methylated gene copy remaining (A6-30 - validated further using nanopore long-read sequencing). These both lacked BRCA1 gene expression and were sensitive to PARPi treatment. The A6-30 line was transplanted into immunocompromised mice to generate a xenograft model that retained homozygous meBRCA1 and demonstrated some response to PARPi in vivo. Thus, using CRISPR gene editing we have created several novel isogenic HGSOC cell line models, including one with homozygous meBRCA1, that will support future studies of meBRCA1 stability and PARPi resistance.

cancer biology↗

bioRxiv2022

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

Trust and transparency are critical for deploying deep learning (DL) models into the clinic. DL application poses generalisation obstacles since training/development datasets often have different data distributions to clinical/production datasets that can lead to incorrect predictions with underestimated uncertainty. To investigate this pitfall, we benchmarked one pointwise and three approximate Bayesian DL models used to predict cancer of unknown primary with three independent RNA-seq datasets covering 10,968 samples across 57 primary cancer types. Our results highlight simple and scalable Bayesian DL significantly improves the generalisation of uncertainty estimation (e.g., p-value = 0.0013 for calibration). Moreover, we demonstrate Bayesian DL substantially improves accuracy under data distributional shifts when utilising uncertainty thresholding by designing a prototypical metric that evaluates the expected (accuracy) loss when deploying models from development to production, which we call the Area between Development and Production curve (ADP). In summary, Bayesian DL is a hopeful avenue of research for generalising uncertainty, which improves performance, transparency, and therefore safety of DL models for deployment in real-world.

bioinformatics↗

bioRxiv2021

Genomic analysis of patient-derived xenograft models reveals intra-tumor heterogeneity in endometrial cancer and can predict tumor growth inhibition with talazoparib

BackgroundEndometrial cancer (EC) is a major gynecological cancer with increasing incidence. It comprised of four molecular subtypes with differing etiology, prognoses, and response to chemotherapy. In the future, clinical trials testing new single agents or combination therapies will be targeted to the molecular subtype most likely to respond. Pre-clinical models that faithfully represent the molecular subtypes of EC are urgently needed, we sought to develop and characterize a panel of novel EC patient-derived xenograft (PDX) models. MethodsHere, we report whole exome or whole genome sequencing of 11 PDX models and the matched primary tumor. Analysis of multiple PDX lineages and passages was performed to study tumor heterogeneity across lineages and/or passages. Based on recent reports of frequent defects in the homologous recombination (HR) pathway in EC, we assessed mutational signatures and HR deficiency scores and correlated these with in vivo responses to the PARP inhibitor (PARPi) talazoparib in six PDXs representing the different molecular subtypes of EC. ResultsPDX models were successfully generated from all four molecular subtypes of EC and uterine carcinosarcomas, and they recapitulated morphology and the molecular landscape of primary tumors without major genomic drift. We also observed a wide range of inter-tumor and intra-tumor heterogeneity, well captured by different PDX lineages, which could lead to different treatment responses. An in vivo response to talazoparib was detected in two p53mut models consistent with stable disease, however both lacked the HR deficiency genomic signature. ConclusionsEC PDX models represent the four molecular subtypes of disease and can capture intra-tumoral heterogeneity of the original primary tumor. PDXs of the p53mut molecular subtype showed sensitivity to PARPi, however, deeper and more durable responses will likely require combination of PARPi with other agents.

genomics↗

bioRxiv2020

Characterization of a RAD51C-Silenced High Grade Serous Ovarian Cancer Model During PARP Inhibitor Resistance Development

Acquired PARP inhibitor (PARPi) resistance in BRCA1- or BRCA2-mutant ovarian cancer often results from secondary mutations that restore expression of functional protein. RAD51C is a less commonly studied ovarian cancer susceptibility gene whose promoter is sometimes methylated in the tumor, leading to homologous recombination deficiency and PARPi sensitivity. For this study, the PARPi-sensitive patient-derived xenograft PH039, which lacks demonstrable repair gene mutations but harbors RAD51C promoter methylation, was selected for PARPi resistance by repeated 21-day niraparib treatments in vivo. PH039 acquired PARPi resistance by the third cycle of treatment and demonstrated unimpeded growth during subsequent exposure to either niraparib or rucaparib. Transcriptional profiling throughout the time course of resistance development showed widespread pathway level changes along with a marked increase in RAD51C mRNA, which reflected loss of RAD51C promoter methylation. Analysis of RAD51C methylation in patient tumor samples from the ARIEL2 Part 1 clinical trial of rucaparib monotherapy likewise indicated that loss of RAD51C methylation prior to on-study biopsy was associated with limited response. Interestingly, the PARPi resistant PH039 model remained platinum sensitive. Collectively, these results not only indicate that PARPi treatment pressure can reverse RAD51C methylation and restore RAD51C expression, but also provide an important model for studying the clinical observation that PARPi and platinum sensitivity are sometimes dissociated.

cancer biology↗

bioRxiv2020

Acquired RAD51C promoter methylation loss causes PARP inhibitor resistance in high grade serous ovarian carcinoma

While loss of BRCA1 promoter methylation has been shown to cause PARP inhibitor (PARPi) resistance in high-grade serous ovarian carcinoma (HGSC), the impacts of RAD51C methylation (meRAD51C) remain unresolved. In this study, three PARPi-responsive HGSC patient-derived xenografts (PDX) with RAD51C gene silencing and homologous recombination deficiency were found to have either homogeneous or heterogeneous patterns of meRAD51C. PDX could lose meRAD51C following PARPi treatment (rucaparib/niraparib), where a single unmethylated RAD51C copy was sufficient to drive PARPi-resistance. Genomic profiling revealed this resistance was acquired independently in two distinct PDX lineages. Furthermore, we describe a patient sample where 1/3 RAD51C gene copies lost methylation following neoadjuvant chemotherapy. We show meRAD51C is a positive predictive biomarker for PARPi response and should be screened for routinely in patients. However, methylation loss in a single gene copy is sufficient to cause PARPi resistance and should be carefully assessed in previously treated patients considering PARPi therapy.

cancer biology↗

bioRxiv2020

Ovarian carcinosarcoma genomics and pre-clinical models highlight the N-MYC pathway as a key driver and susceptibility to EMT-targeting therapy

Ovarian carcinosarcoma (OCS) is an aggressive and rare tumour type with limited treatment options. OCS is hypothesised to develop via the combination theory from a single progenitor, resulting in carcinomatous and sarcomatous components, or alternatively via the conversion theory, with the sarcomatous component developing from the carcinomatous component through epithelial-to-mesenchymal transition (EMT). We show OCS from 18 women to be monoclonal through analysis of DNA variants from isolated carcinoma and sarcoma components. RNA sequencing indicated the carcinoma components were more mesenchymal when compared with pure ovarian carcinomas, supporting the conversion theory. We used pre-clinical OCS models to test the efficacy of microtubule-targeting drugs, including eribulin, which has been shown to reverse EMT characteristics. We demonstrated that microtubule inhibitors, vinorelbine and eribulin, were more effective than standard-of-care platinum-based chemotherapy. Eribulin reduced mesenchymal characteristics, N-MYC expression and cholesterol biosynthesis. Finally, eribulin induced a strong immune response, supporting immunotherapy combinations in the clinic.

cancer biology↗

bioRxiv2019

Inhibition of RNA Polymerase I Transcription Activates Targeted DNA Damage Response and Enhances the Efficacy of PARP Inhibitors in High-Grade Serous Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) accounts for the majority of ovarian cancer and has a dismal prognosis. PARP inhibitors (PARPi) have revolutionized disease management of patients with homologous recombination (HR) DNA repair-deficient HGSOC. However, acquired resistance to PARPi by complex mechanisms including HR restoration and stabilisation of replication forks is a major challenge in the clinic. Here, we demonstrate CX-5461, an inhibitor of RNA polymerase I transcription of ribosomal RNA genes (rDNA), induces replication stress at rDNA leading to activation of DNA damage response and DNA damage involving MRE11-dependent degradation of replication forks. CX-5461 cooperates with PARPi in exacerbating DNA damage and enhances synthetic lethal interactions of PARPi with HR deficiency in HGSOC-patient-derived xenograft (PDX) in vivo. We demonstrate CX-5461 has a different sensitivity spectrum to PARPi and destabilises replication forks irrespective of HR pathway status, overcoming two well-known mechanisms of resistance to PARPi. Importantly, CX-5461 exhibits single agent efficacy in PARPi-resistant HGSOC-PDX. Further, we identify CX-5461-sensitivity gene expression signatures in primary and relapsed HGSOC. Therefore, CX-5461 is a promising therapy alone and in combination therapy with PARPi in HR-deficient HGSOC. CX-5461 is also an exciting treatment option for patients with relapsed HGSOC tumors that have poor clinical outcome.

cancer biology↗