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Karnitz, L. M.

Publications and source records attributed to Karnitz, L. M..

2 recordsLinked to original sources

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

DAGBagM: Learning directed acyclic graphs of mixed variables with an application to identify prognostic protein biomarkers in ovarian cancer

MotivationDirected gene/protein regulatory networks inferred by applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of clinical outcomes. However, there remain unsolved challenges in DAG learning to jointly model clinical outcome variables, which often take binary values, and biomarker measurements, which usually are continuous variables. Therefore, in this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models for continuous and binary variables, DAGBagM allows for either type of nodes to be parents or children nodes in the learned graph. DAGBagM also employs a bootstrap aggregating strategy to reduce false positives and achieve better estimation accuracy. Moreover, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges for DAG reconstruction. ResultsAs shown by simulation studies, DAGBagM performs better in identifying edges between continuous and binary nodes, as compared to commonly used strategies of either treating binary variables as continuous or discretizing continuous variables. Moreover, DAGBagM outperforms several popular DAG structure learning algorithms including the score-based hill climbing (HC) algorithm, constraint-based PC-algorithm (PC-alg), and the hybrid method max-min hill climbing (MMHC) even for constructing DAG with only continuous nodes. The HC implementation in the R package DAGBagM is much faster than that in a widely used DAG learning R package bnlearn. When applying DAGBagM to proteomics datasets from ovarian cancer studies, we identify potential prognostic protein biomarkers in ovarian cancer. Availability and implementationDAGBagM is made available as a github repository https://github.com/jie108/dagbagM.

systems biology