Biology subjects

Dobrovic, A.

Publications and source records attributed to Dobrovic, A..

8 recordsLinked to original sources

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↗

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↗

bioRxiv2021

Removing unwanted variation from large-scale cancer RNA-sequencing data

The accurate identification and effective removal of unwanted variation are essential to derive meaningful biological results from RNA-seq data, especially when the data come from large and complex studies. We have used The Cancer Genome Atlas (TCGA) RNA-seq data to show that library size, batch effects, and tumor purity are major sources of unwanted variation across all TCGA RNA-seq datasets and that existing gold standard approaches to normalizations fail to remove this unwanted variation. Additionally, we illustrate how different sources of unwanted variation can compromise downstream analyses, including gene co-expression, association between gene expression and survival outcomes, and cancer subtype identifications. Here, we propose the use of a novel strategy, pseudo-replicates of pseudo-samples (PRPS), to deploy the Removing Unwanted Variation III (RUV-III) method to remove different sources of unwanted variation from large and complex gene expression studies. Our approach requires at least one roughly known biologically homogenous subclass of samples shared across sources of unwanted variation. To create PRPS, we first need to identify the sources of unwanted variation, which we will call batches in the data. Then the gene expression measurements of biologically homogeneous sets of samples are averaged within batches, and the results called pseudo-samples. Pseudo-samples with the same biology and different batches are then defined to be pseudo-replicates and used in RUV-III as replicates. The variation between pseudo-samples of a set pseudo-replicates is mainly unwanted variation. We illustrate the value of our approach by comparing it to the TCGA normalizations on several TCGA RNA-seq datasets. RUV-III with PRPS can be used for any large genomics project involving multiple labs, technicians, or platforms.

bioinformatics↗

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↗

bioRxiv2018

A new normalization for the Nanostring nCounter gene expression assay

The Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency, and other factors. Most users adopt the normalization approach described in the nSolver analysis software, which involves background correction based on the observed values of negative control probes, a within-sample normalization using the observed values of positive control probes and normalization across samples using reference (housekeeping) genes. Here we present a new normalization method, Removing Unwanted Variation-III (RUV-III), which makes vital use of technical replicates and suitable control genes. We also propose an approach using pseudo-replicates when technical replicates are not available. The effectiveness of RUV-III is illustrated on four different data sets. We also offer suggestions on the design and analysis of studies involving this technology.

bioinformatics↗

bioRxiv2017

DNA methylation profiling of breast cancer cell lines along the epithelial mesenchymal spectrum - implications for the choice of circulating tumour DNA methylation markers

Epithelial-mesenchymal plasticity (EMP) is a dynamic process whereby epithelial carcinoma cells reversibly acquire morphological and invasive characteristics typical of mesenchymal cells, which facilitates metastasis. Understanding the methylation differences between epithelial and mesenchymal states may assist in the identification of optimal DNA methylation biomarkers for the blood-based monitoring of cancer. Methylation-sensitive high-resolution melting (MS-HRM) was used to examine the promoter methylation status of a panel of established and novel markers in a range of breast cancer cell lines spanning the epithelial-mesenchymal spectrum. Pyrosequencing was used to validate the MSHRM results. The results indicate an overall distinction in methylation between epithelial and mesenchymal phenotypes. The mesenchymal expression markers VIM, DKK3 and CRABP1 were methylated in the majority of epithelial breast cancer cell lines while methylation of the epithelial expression markers GRHL2, MIR200C and CDH1 was restricted to mesenchymal cell lines. We also examined EMP association of several methylation markers that have been used to assess minimal residual disease. Markers such as AKR1B1 and APC methylation proved to be selective for epithelial breast cell lines, however RASSF1A, RAR{beta}, TWIST1 and SFRP2 methylation was seen in both epithelial and mesenchymal cell lines, supporting their suitability for a multi-marker panel.

cancer biology↗

bioRxiv2017

GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly

The identification of genomic rearrangements, particularly in cancers, with high sensitivity and specificity using massively parallel sequencing remains a major challenge. Here, we describe the Genome Rearrangement IDentification Software Suite (GRIDSS), a high-speed structural variant (SV) caller that performs efficient genome-wide break-end assembly prior to variant calling using a novel positional de Bruijn graph assembler. By combining assembly, split read and read pair evidence using a probabilistic scoring, GRIDSS achieves high sensitivity and specificity on simulated, cell line and patient tumour data, recently winning SV sub-challenge #5 of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. On human cell line data, GRIDSS halves the false discovery rate compared to other recent methods. GRIDSS identifies non-template sequence insertions, micro-homologies and large imperfect homologies, and supports multi-sample analysis. GRIDSS is freely available at https://github.com/PapenfussLab/gridss.

bioinformatics↗