Citations

Abstract 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, leading to homologous recombination (HR) deficiency and PARPi sensitivity. For this study, the PARPi-sensitive patient-derived ovarian cancer xenograft PH039, which lacks HR gene mutations but harbors RAD51C promoter methylation, was selected for PARPi resistance by cyclical niraparib treatment in vivo. PH039 acquired PARPi resistance by the third treatment cycle and grew through subsequent treatment with either niraparib or rucaparib. Transcriptional profiling throughout the 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 ovarian cancer samples from the ARIEL2 Part 1 clinical trial of rucaparib monotherapy likewise indicated an association between loss of RAD51C methylation prior to on-study biopsy and 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 a model for studying the clinical observation that PARPi and platinum sensitivity are sometimes dissociated.

Abstract PARP inhibitor (PARPi) resistance in high-grade serous ovarian carcinoma (HGSOC) can be acquired as a result of restored homologous recombination (HR) due to secondary or reversion mutations in HR genes, such as BRCA1, BRCA2, and RAD51C, or due to loss of BRCA1 promoter methylation (meBRCA1). We have demonstrated that homozygous meBRCA1 can be lost or reverted to heterozygous methylation following treatment with platinum-based chemotherapy, resulting in HR-competent PARPi-resistant tumors. RAD51C promoter methylation (meRAD51C) is detected in approximately 2% of HGSOC cases and, as with meBRCA1, is associated with gene silencing and HR deficiency. We are exploring PARPi response in meRAD51C preclinical models to determine clinical relevance. Two patient-derived xenograft (PDX) models of HGSOC with RAD51C gene silencing caused by meRAD51C have distinct meRAD51C profiles (measured by methylation-specific high-resolution melt analysis and targeted bisulfite next-generation sequencing), and different responses to PARPi treatment pressure. PDX PH039 loses methylation and regains RAD51C expression after only 2 cycles of PARPi retreatment (niraparib), resulting in PARPi-refractory tumors by cycle 3-4. Illumina EPIC methylation array analysis of PH039 revealed increasing global methylation losses following each round of PARPi treatment. Lack of meRAD51C stability and rapid development of PARPi resistance in PH039 may be due to the high degree of meRAD51C heterogeneity within the tumor favoring selection of pre-existing HR-competent clones under PARPi pressure. In contrast, PDX 183 has a relatively homogeneous and stable meRAD51C profile. We have multiple examples of using unique PDX models to demonstrate various important features of PARPi resistance, including loss of promoter methylation for BRCA1 vs. RAD51C. Thus, meRAD51C confers response to PARPi in HGSOC, but PARPi treatment pressure can cause loss of methylation and drug resistance in some tumors. The contrasting PARPi responses of these PDX provide a platform for the study of meRAD51C stability in vivo and may present therapeutic opportunities to improve meRAD51C durability and PARPi responses in patients. Citation Format: Kasenija Nesic, Rachel M Hurley, Cordelia McGehee, Olga Kondrashova, Maria I. Harrell, Giada V. Zapparoli, Ashan Musafer, Ming E. Wong, John Weroha, Xiaonan Hou, Hu Li, Vivian Negron, Kevin Peterson, Paula Schneider, Elizabeth M. Swisher, Melissa Southey, Alexander Dobrovic, Matthew Wakefield, Scott H. Kaufmann, Clare L. Scott. DNA repair gene promoter methylation patterns adapt and influence PARP inhibitor response [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr IA02.

Abstract BACKGROUND Accurate evaluation of unclassified sequence variants in cancer predisposition genes is essential for clinical management and depends on a multifactorial analysis of clinical, genetic, pathologic, and bioinformatic variables and assays of transcript length and abundance. The integrity of assay data in turn relies on appropriate assay design, interpretation, and reporting. METHODS We conducted a multicenter investigation to compare mRNA splicing assay protocols used by members of the ENIGMA (Evidence-Based Network for the Interpretation of Germline Mutant Alleles) consortium. We compared similarities and differences in results derived from analysis of a panel of breast cancer 1, early onset (BRCA1) and breast cancer 2, early onset (BRCA2) gene variants known to alter splicing (BRCA1: c.135-1G>T, c.591C>T, c.594-2A>C, c.671-2A>G, and c.5467+5G>C and BRCA2: c.426-12_8delGTTTT, c.7988A>T, c.8632+1G>A, and c.9501+3A>T). Differences in protocols were then assessed to determine which elements were critical in reliable assay design. RESULTS PCR primer design strategies, PCR conditions, and product detection methods, combined with a prior knowledge of expected alternative transcripts, were the key factors for accurate splicing assay results. For example, because of the position of primers and PCR extension times, several isoforms associated with BRCA1, c.594-2A>C and c.671-2A>G, were not detected by many sites. Variation was most evident for the detection of low-abundance transcripts (e.g., BRCA2 c.8632+1G>A Δ19,20 and BRCA1 c.135-1G>T Δ5q and Δ3). Detection of low-abundance transcripts was sometimes addressed by using more analytically sensitive detection methods (e.g., BRCA2 c.426-12_8delGTTTT ins18bp). CONCLUSIONS We provide recommendations for best practice and raise key issues to consider when designing mRNA assays for evaluation of unclassified sequence variants.