Unknown

Dataset Information

0

Predictors and Modulators of Synthetic Lethality: An Update on PARP Inhibitors and Personalized Medicine.


ABSTRACT: Poly(ADP-ribose) polymerase (PARP) inhibitors have proven to be successful agents in inducing synthetic lethality in several malignancies. Several PARP inhibitors have reached clinical trial testing for treatment in different cancers, and, recently, Olaparib (AZD2281) has gained both United States Food and Drug Administration (USFDA) and the European Commission (EC) approval for use in BRCA-mutated advanced ovarian cancer treatment. The need to identify biomarkers, their interactions in DNA damage repair pathways, and their potential utility in identifying patients who are candidates for PARP inhibitor treatment is well recognized. In this review, we detail many of the biomarkers that have been investigated for their ability to predict both PARP inhibitor sensitivity and resistance in preclinical studies as well as the results of several clinical trials that have tested the safety and efficacy of different PARP inhibitor agents in BRCA and non-BRCA-mutated cancers.

SUBMITTER: Murata S 

PROVIDER: S-EPMC5013223 | biostudies-other | 2016

REPOSITORIES: biostudies-other

altmetric image

Publications

Predictors and Modulators of Synthetic Lethality: An Update on PARP Inhibitors and Personalized Medicine.

Murata Stephen S   Zhang Catherine C   Finch Nathan N   Zhang Kevin K   Campo Loredana L   Breuer Eun-Kyoung EK  

BioMed research international 20160824


Poly(ADP-ribose) polymerase (PARP) inhibitors have proven to be successful agents in inducing synthetic lethality in several malignancies. Several PARP inhibitors have reached clinical trial testing for treatment in different cancers, and, recently, Olaparib (AZD2281) has gained both United States Food and Drug Administration (USFDA) and the European Commission (EC) approval for use in BRCA-mutated advanced ovarian cancer treatment. The need to identify biomarkers, their interactions in DNA dama  ...[more]

Similar Datasets

| S-EPMC6175050 | biostudies-literature
| S-EPMC8211653 | biostudies-literature
| S-EPMC5927601 | biostudies-literature
| S-EPMC11369084 | biostudies-literature
| S-EPMC5576028 | biostudies-literature
| S-EPMC6082654 | biostudies-literature
| S-EPMC8238121 | biostudies-literature
2024-04-12 | GSE220223 | GEO
| S-EPMC6752209 | biostudies-literature
| S-EPMC5315641 | biostudies-literature