Characterization of a RAD51C-silenced high-grade serous ovarian cancer model during development of PARP inhibitor resistance.

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.

Acquired RAD51C Promoter Methylation Loss Causes PARP Inhibitor Resistance in High-Grade Serous Ovarian Carcinoma.

In high-grade serous ovarian carcinoma (HGSC), deleterious mutations in DNA repair gene RAD51C are established drivers of defective homologous recombination and are emerging biomarkers of PARP inhibitor (PARPi) sensitivity. RAD51C promoter methylation (meRAD51C) is detected at similar frequencies to mutations, yet its effects on PARPi responses remain unresolved.In this study, three HGSC patient-derived xenograft (PDX) models with methylation at most or all examined CpG sites in the RAD51C promoter show responses to PARPi. Both complete and heterogeneous methylation patterns were associated with RAD51C gene silencing and homologous recombination deficiency (HRD). PDX models lost meRAD51C following treatment with PARPi rucaparib or niraparib, where a single unmethylated copy of RAD51C was sufficient to drive PARPi resistance. Genomic copy number profiling of one of the PDX models using SNP arrays revealed that this resistance was acquired independently in two genetically distinct lineages.In a cohort of 12 patients with RAD51C-methylated HGSC, various patterns of meRAD51C were associated with genomic "scarring," indicative of HRD history, but exhibited no clear correlations with clinical outcome. Differences in methylation stability under treatment pressure were also observed between patients, where one HGSC was found to maintain meRAD51C after six lines of therapy (four platinum-based), whereas another HGSC sample was found to have heterozygous meRAD51C and elevated RAD51C gene expression (relative to homozygous meRAD51C controls) after only neoadjuvant chemotherapy.As meRAD51C loss in a single gene copy was sufficient to cause PARPi resistance in PDX, methylation zygosity should be carefully assessed in previously treated patients when considering PARPi therapy. SIGNIFICANCE: Homozygous RAD51C methylation is a positive predictive biomarker for sensitivity to PARP inhibitors, whereas a single unmethylated gene copy is sufficient to confer resistance.

CDK2-Mediated Upregulation of TNFα as a Mechanism of Selective Cytotoxicity in Acute Leukemia.

Although inhibitors of the kinases CHK1, ATR, and WEE1 are undergoing clinical testing, it remains unclear how these three classes of agents kill susceptible cells and whether they utilize the same cytotoxic mechanism. Here we observed that CHK1 inhibition induces apoptosis in a subset of acute leukemia cell lines in vitro, including TP53-null acute myeloid leukemia (AML) and BCR/ABL-positive acute lymphoid leukemia (ALL), and inhibits leukemic colony formation in clinical AML samples ex vivo. In further studies, downregulation or inhibition of CHK1 triggered signaling in sensitive human acute leukemia cell lines that involved CDK2 activation followed by AP1-dependent TNF transactivation, TNFα production, and engagement of a TNFR1- and BID-dependent apoptotic pathway. AML lines that were intrinsically resistant to CHK1 inhibition exhibited high CHK1 expression and were sensitized by CHK1 downregulation. Signaling through this same CDK2-AP1-TNF cytotoxic pathway was also initiated by ATR or WEE1 inhibitors in vitro and during CHK1 inhibitor treatment of AML xenografts in vivo. Collectively, these observations not only identify new contributors to the antileukemic cell action of CHK1, ATR, and WEE1 inhibitors, but also delineate a previously undescribed pathway leading from aberrant CDK2 activation to death ligand-induced killing that can potentially be exploited for acute leukemia treatment. SIGNIFICANCE: This study demonstrates that replication checkpoint inhibitors can kill AML cells through a pathway involving AP1-mediated TNF gene activation and subsequent TP53-independent, TNFα-induced apoptosis, which can potentially be exploited clinically.

Gene expression differences between matched pairs of ovarian cancer patient tumors and patient-derived xenografts.

As patient derived xenograft (PDX) models are increasingly used for preclinical drug development, strategies to account for the nonhuman component of PDX RNA expression data are critical to its interpretation. A bioinformatics pipeline to separate donor tumor and mouse stroma transcriptome profiles was devised and tested. To examine the molecular fidelity of PDX versus donor tumors, we compared mRNA differences between paired PDX-donor tumors from nine ovarian cancer patients. 1,935 differentially expressed genes were identified between PDX and donor tumors. Over 90% (n = 1767) of these genes were down-regulated in PDX models and enriched in stroma-specific functions. Several protein kinases were also differentially expressed in PDX tumors, e.g. PDGFRA, PDGFRB and CSF1R. Upon in silico removal of these PDX-donor tumor differentially expressed genes, a stronger transcriptional resemblance between PDX-donor tumor pairs was seen (average correlation coefficient increases from 0.91 to 0.95). We devised and validated an effective bioinformatics strategy to separate mouse stroma expression from human tumor expression for PDX RNAseq. In addition, we showed most of the PDX-donor differentially expressed genes were implicated in stromal components. The molecular similarities and differences between PDX and donor tumors have implications in future therapeutic trial designs and treatment response evaluations using PDX models.

NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.

The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets.