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Record W2088568685 · doi:10.4161/15384101.2014.948787

PARP inhibitors target ATM+p53-defective gastric cancer

2014· letter· en· W2088568685 on OpenAlexaboutno aff
Nicola J. Curtin

Bibliographic record

VenueCell Cycle · 2014
Typeletter
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsnot available
Fundersnot available
KeywordsDNA damageCancer researchCarcinogenesisGenome instabilityDNA repairSynthetic lethalityPoly ADP ribose polymeraseHomologous recombinationBiologyCancerCell cycle checkpointCell cycleDNAGenetics

Abstract

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Dysregulation of the DNA damage response (DDR) creates the genomic instability that promotes tumorigenesis. Depending on the type of DDR defect, it can have prognostic significance and confer sensitivity or resistance to different chemotherapeutic drugs. Exploiting defects in the DDR by inhibiting complementary DDR pathways is an exciting new paradigm in anticancer therapy. The prime example is the exquisite sensitivity of cells with defects in BRCA1 and BRCA2 to PARP inhibitors (PARPi).1 BRCA1 and BRCA2 are key components of homologous recombination DNA repair (HRR) while PARP plays a central role in DNA single strand (SS) break repair (SSBR). While loss of SSBR or HRR alone does not impact significantly on viability that disruption of both SSBR and HRR together is synthetically lethal. Several PARPi are in currently late-stage clinical evaluation, largely in patients with tumors harbouring known (BRCA mutations) or suspected defects in HRR. Since HRR is a multicomponent pathway and a defect in any one component can compromise the whole pathway the search is on for other determinants of sensitivity to PARPi in order to identify patients that may benefit from this novel tumor-specific therapeutic approach. ATM signals DNA double-strand breaks (DSBs) to cell cycle checkpoints via Chk2 and p53. ATM has been linked to HRR and knockdown of ATM is also synthetically lethal with PARPi.2 Following on from their studies in ATM-defective Mantle cell leukemia,3 and similar studies in ATM-defective (11q deletion) chronic lymphocytic leukemia,4 the group in Calgary now demonstrate the increased sensitivity of gastric carcinoma cells with low levels of ATM protein and p53 dysfunction to the PARPi, olaparib.5 They support this finding with a number of complementary approaches using unmatched cells with differing ATM and p53 status, ATM and p53 knockdown and the use of inhibitors, with converging results. Based on these data one might speculate that PARP, by promoting repair, reduces replication stress and replication-associated DSBs and that p53 and ATM prevent replication stress leading to cell death by activating cell cycle arrest and also promoting repair. Therefore PARPi will preferentially kill cells in which cell cycle checkpoint activation has been compromised by the inactivation of p53 and ATM (Fig. 1). Figure 1. Role of PARP ATM and p53 in cell viability Endogenous DNA damage is repaired by PARP to maintain viability. If PARP is inhibited replication stress and DSB ensue, triggering p53 and ATM to arrest the cell and promote repair of the DSB. In the absence/inhibition ... Since gastric cancer is the second most common cause of cancer deaths in the world and many harbour defects in ATM, associated with microsatellite instability,6 these findings suggest that PARPi therapy may benefit a substantial number of patients and that ATM levels may be used as a biomarker to stratify patients to receive a PARPi or conventional therapy. Olaparib, in combination with paclitaxel was recently shown to be of benefit in patients with gastric cancer, particularly those with low ATM levels determined by IHC ({type:clinical-trial,attrs:{text:NCT01063517,term_id:NCT01063517}}NCT01063517), demonstrating the feasibility of the approach.7 Interestingly, in the study by Kubota et al 5 levels of ATM protein in the cell line panel did not correlate with mutations in the ATM gene on the COSMIC database, and the olaparib sensitivity correlated with the protein level rather than the genomic data. This may have implications for clinical trials of molecularly targeted agents, such as PARPi, in which the patients are stratified on the basis of genomics rather than protein levels.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.169
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.276
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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