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Neoadjuvant PARP inhibitors in patients with early HER2-negative breast cancer harboring BRCA 1/2 germline mutations: A systematic review and meta-analysis.

2023· review· en· W4379329045 on OpenAlexaff
Maria Inez Dacoregio, Maysa Vilbert, Carlos Stecca, Isabella Michelon, Caio Castro, Eitan Amir

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineCarboplatinInternal medicineOncologyBreast cancerBRCA mutationMeta-analysisPARP inhibitorRandomized controlled trialCancerChemotherapyCisplatinBiologyGeneticsPoly ADP ribose polymerasePolymerase

Abstract

fetched live from OpenAlex

e12611 Background: Poly (ADP-ribose) polymerase inhibitors (PARPi) are approved for the treatment of germline BRCA mutated (gBRCAm) breast cancer (BC) patients in the adjuvant and metastatic settings. Several studies have explored the role of PARPi in the neoadjuvant setting both with and without chemotherapy. Here, we explore the efficacy and safety of neoadjuvant PARPi in early HER2-negative BC patients. Methods: We searched PubMed, Scopus, and the Cochrane Library databases for randomized (RCT) and non-randomized clinical trials (non-RCT) that reported the proportion of patients with pathological complete response (pCR) after neoadjuvant PARPi and safety. Statistical analysis was performed using R software. Data were pooled using the random effects model. Results: Analysis included 3 RCTs and 4 non-RCTs, with 942 HER-2 negative breast cancer patients, 293 harboring a gBRCA 1/2 mutation. In a pooled analysis, gBRCAm carriers achieved a significantly higher rate of pCR than BRCA wild-type patients (56% versus 37%, p=0.04) with neoadjuvant PARPi. There was no difference in pCR when PARPi were combined with carboplatin (+/- chemotherapy) (40% versus 44%, p=0.69). Among RCTs (n=457) patients received PARPi in combination with either Paclitaxel plus Carboplatin (PCb) (85%) or with Paclitaxel alone (P) (15%). PARPi were associated with a higher pCR which approached, but did not meet statistical significance (51% vs 41%; Odds Ratio [OR]: 1.30, 95%CI 0.97 to 1.74, p=0.08). gBRCAm was associated with higher pCR compared to wild type in RCTs analysis (60% versus 48%; OR: 1.64, 95%CI 1.01 to 2.68, p=0.05). The most common adverse events (AEs) were cytopenias, nausea, fatigue, alopecia, and dizziness. Hematological toxicities were the most frequent serious AEs, including anemia (15%), neutropenia (22%), and thrombocytopenia (3%). The combination of PARPi and carboplatin was associated with higher hematological AEs (78% versus 46%; P=0.002). Conclusions: The addition of PARPi in the neoadjuvant setting improves pCR in early-stage HER2-negative breast cancer and germline BRCA 1/2 mutations, but not for unselected patients. Combination of PARPi with carboplatin does not improve pCR and leads to substantially worse toxicity. Further studies are warranted to identify the best neoadjuvant PARPi schema.[Table: see text]

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0140.023
Bibliometrics0.0040.006
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.249
GPT teacher head0.526
Teacher spread0.278 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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