Abbreviations: AAA-ATPase, ATPase associated with diverse cellular activities; BRCA1/2, breast cancer 1/2; PARP1, poly ADP-ribose polymerase-1; DBeQ, N 2 ,N 4 -dibenzylquinazoline-2,4-diamine; PBS, phosphate buffered saline; EGF, epidermal growth factor; TBS, tris-buffered saline; PERK, protein kinase R-like endoplasmic reticulum kinase; TCGA, the Cancer Genome Atlas; PVDF, polyvinylidene difluoride; IRE1a, inositol-requiring enzyme 1 alpha; CHOP, CCAAT/enhancer-binding protein homologous protein; ATF4, activating transcription factor 4; eIF2a, eukaryotic initiation factor 2 alpha; BiP, binding immunoglobulin protein; Grp78, glucose regulated protein 78; GADD34, growth arrest And DNA-damage-inducible 34.
FOXM1 transcription factor network is activated in over 84% of cases in high-grade serous ovarian cancer (HGSOC), and FOXM1 upregulates the expression of genes involved in the homologous recombination (HR) DNA damage and repair (DDR) pathway. However, the role of FOXM1 in PARP inhibitor response has not yet been studied. This study demonstrates that PARP inhibitor (PARPi), olaparib, induces the expression and nuclear localization of FOXM1. On the basis of ChIP-qPCR, olaparib enhances the binding of FOXM1 to genes involved in HR repair. FOXM1 knockdown by RNAi or inhibition by thiostrepton decreases FOXM1 expression, decreases the expression of HR repair genes, such as and, and enhances sensitivity to olaparib. Comet and PARP trapping assays revealed increases in DNA damage and PARP trapping in FOXM1-inhibited cells treated with olaparib. Finally, thiostrepton decreases the expression of BRCA1 in rucaparib-resistant cells and enhances sensitivity to rucaparib. Collectively, these results identify that FOXM1 plays an important role in the adaptive response induced by olaparib and FOXM1 inhibition by thiostrepton induces "BRCAness" and enhances sensitivity to PARP inhibitors. FOXM1 inhibition represents an effective strategy to overcome resistance to PARPi, and targeting FOXM1-mediated adaptive pathways may produce better therapeutic effects for PARP inhibitors. .
BackgroundAdvances in next-generation DNA sequencing technologies are now enabling detailed characterization of sequence variations in cancer genomes. With whole-genome sequencing, variations in coding and non-coding sequences can be discovered. But the cost associated with it is currently limiting its general use in research. Whole-exome sequencing is used to characterize sequence variations in coding regions, but the cost associated with capture reagents and biases in capture rate limit its full use in research. Additional limitations include uncertainty in assigning the functional significance of the mutations when these mutations are observed in the non-coding region or in genes that are not expressed in cancer tissue.ResultsWe investigated the feasibility of uncovering mutations from expressed genes using RNA sequencing datasets with a method called Variant Detection in RNA(VaDiR) that integrates 3 variant callers, namely: SNPiR, RVBoost, and MuTect2. The combination of all 3 methods, which we called Tier 1 variants, produced the highest precision with true positive mutations from RNA-seq that could be validated at the DNA level. We also found that the integration of Tier 1 variants with those called by MuTect2 and SNPiR produced the highest recall with acceptable precision. Finally, we observed a higher rate of mutation discovery in genes that are expressed at higher levels.ConclusionsOur method, VaDiR, provides a possibility of uncovering mutations from RNA sequencing datasets that could be useful in further functional analysis. In addition, our approach allows orthogonal validation of DNA-based mutation discovery by providing complementary sequence variation analysis from paired RNA/DNA sequencing datasets.
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