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Record W2170476788 · doi:10.1158/1078-0432.ccr-10-0939

Poly(ADP-Ribose) Polymerase Inhibition: “Targeted” Therapy for Triple-Negative Breast Cancer

2010· review· en· W2170476788 on OpenAlexaff
Carey K. Anders, Eric P. Winer, James M. Ford, Rebecca Dent, Daniel P. Silver, George W. Sledge, Lisa A. Carey

Bibliographic record

VenueClinical Cancer Research · 2010
Typereview
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsSunnybrook Health Science Centre
FundersNational Cancer Institute
KeywordsBreast cancerTriple-negative breast cancerPARP inhibitorCancer researchPoly ADP ribose polymeraseCancerSynthetic lethalityMedicineDNA repairTargeted therapyPolymeraseBiologyInternal medicineDNAGenetics

Abstract

fetched live from OpenAlex

In contrast to endocrine-sensitive and human epidermal growth factor receptor 2 (HER2)-positive breast cancer, novel agents capable of treating advanced triple-negative breast cancer (TNBC) are lacking. Poly(ADP-ribose) polymerase (PARP) inhibitors are emerging as one of the most promising "targeted" therapeutics to treat TNBC, with the intended "target" being DNA repair. PARPs are a family of enzymes involved in multiple cellular processes, including DNA repair. TNBC shares multiple clinico-pathologic features with BRCA-mutated breast cancers, which harbor dysfunctional DNA repair mechanisms. Investigators hypothesized that PARP inhibition, in conjunction with the loss of DNA repair via BRCA-dependent mechanisms, would result in synthetic lethality and augmented cell death. This hypothesis has borne out in both preclinical models and in clinical trials testing PARP inhibitors in both BRCA-deficient and triple-negative breast cancer. The focus of this review includes an overview of the preclinical rationale for evaluating PARP inhibitors in TNBC, the presumed mechanism of action of this novel therapeutic class, promising results from several influential clinical trials of PARP inhibition in advanced breast cancer (both TNBC and BRCA deficient), proposed mechanisms of acquired resistance to PARP inhibitors, and, finally, concludes with current challenges and future directions for the development of PARP inhibitors in the treatment of breast cancer.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.914
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0090.000

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 head0.405
GPT teacher head0.610
Teacher spread0.205 · 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
GenreReview

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

Citations154
Published2010
Admission routes1
Has abstractyes

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