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Abstract PS10-11: Associations with response to poly(ADP-ribose) polymerase (PARP) inhibitors in patients with BRCA mutated metastatic breast cancer: Results of a meta-regression analysis

2021· article· en· W3130563236 on OpenAlexaff
Alexandra Desnoyers, Brooke E. Wilson, Michelle B. Nadler, Eitan Amir

Bibliographic record

VenueCancer Research · 2021
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineBRCA mutationOncologyInternal medicineBreast cancerMetastatic breast cancerChemotherapyCancerMeta-analysis

Abstract

fetched live from OpenAlex

Abstract Background: PARP inhibitors (PARPi), when given as single agents, have modest antitumor activity in patients with advanced breast cancer and mutation in BRCA1 or BRCA2. It is unclear whether some subgroups derive greater benefit from treatment.Methods: Two electronic databases (MEDLINE, CENTRAL) and one registry (Clinicaltrials.gov) were searched from inception to June 2020 to identify trials of PARPi in patients with metastatic breast cancer. Objective response rate (ORR) and disease control rate (DCR) to PARPi were extracted and pooled in a meta-analysis using the Mantel Haenszel random effects model. Analyses were performed for patients with a BRCA mutation exclusively. Meta-regression explored the influence of patient and tumor characteristics and previous chemotherapy on ORR and DCR as reported in individual studies. Analysis comprised of a linear regression weighted by individual study sample size using the weighted least squares (mixed effect) method. Quantitative significance was defined using methods described by Burnand et al. Results: Twenty-two studies comprising 1627 patients were identified and among these 1466 (90%) patients had a germline (n=1451) or a somatic (n=15) BRCA mutation and were included in the analysis. In 7 of these studies (32%; n=680 patients), the PARPi was given in combination with a platinum-based chemotherapy.; 54% of breast cancers were triple-negative. 81% of patients had received at least 1 prior line of chemotherapy in the metastatic setting and 28% were previously exposed to a platinum-based chemotherapy in the metastatic setting. Pooled ORR was 46%; 66% when combined with platinum vs 36% with PARPi alone (OR 3.44, 2.77-4.29, p0.001). Meta-regression results are shown in the Table. Previous chemotherapy in the metastatic setting, especially platinum-based chemotherapy, was associated with highly significantly lower ORR as defined by by Burnand et al. Age and hormone receptor status were not associated with response. Quantitatively similar results were observed for DCR. Conclusion: PARPi therapy is associated with lesser response in patients with prior chemotherapy exposure, especially platinum-based treatment. There was no association between ORR and hormone receptor status or age. Dependent variableVariableCoefficients BêtaSignifianceORR BRCA1/2n = 146645.67 %Age-0.320.34Previous chemotherapy in metastatic setting-0.700.004Previous platinum in metastatic setting-0.620.02Platinum refractory-0.390.22Hormone receptor positive0.190.48Triple negative-0.120.66DCR BRCA1/2n = 126071.47 %Age-0.240.53Previous chemotherapy in metastatic setting-0.740.006Previous platinum-0.420.21Platinum refractory-0.070.83Hormone receptor positive-0.120.69Triple negative0.230.47 Citation Format: Alexandra Desnoyers, Brooke E. Wilson, Michelle B. Nadler, Eitan Amir. Associations with response to poly(ADP-ribose) polymerase (PARP) inhibitors in patients with BRCA mutated metastatic breast cancer: Results of a meta-regression analysis [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS10-11.

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)0.020
metaresearch head score (Gemma)0.029
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: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.029
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0150.068
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.003
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 head0.068
GPT teacher head0.410
Teacher spread0.343 · 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
GenreEmpirical

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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Citations0
Published2021
Admission routes1
Has abstractyes

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