In high-grade serous ovarian carcinoma (HGSC), deleterious mutations in DNA repair gene are established drivers of defective homologous recombination and are emerging biomarkers of PARP inhibitor (PARPi) sensitivity. promoter methylation (me) 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 promoter show responses to PARPi.
View Article and Find Full Text PDFAcquired PARP inhibitor (PARPi) resistance in - or -mutant ovarian cancer often results from secondary mutations that restore expression of functional protein. 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 promoter methylation, was selected for PARPi resistance by cyclical niraparib treatment .
View Article and Find Full Text PDFAs 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.
View Article and Find Full Text PDFThe 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.
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