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Impact of different sequencing strategies of talazoparib and carboplatin combination upon efficacy and toxicity in BRCA-wild type and BRCA-mutant triple-negative breast cancer models.

2021· article· en· W3169161004 on OpenAlexafffund
Michèle Beniey, Alexia K. Cotte, Audrey Hubert, Nelly Béchir, Takrima Haque, Danh Tran‐Thanh, Saima Hassan

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de Montréal
FundersPfizer Canada
KeywordsCarboplatinMedicinePARP inhibitorOlaparibBRCA mutationConcomitantCancerCombination therapyPopulationInternal medicineOvarian cancerOncologyBreast cancerTriple-negative breast cancerVeliparibPharmacologyCancer researchChemotherapyBiologyPoly ADP ribose polymeraseCisplatin

Abstract

fetched live from OpenAlex

586 Background: PARP inhibitors (PARPi) such as talazoparib and olaparib, have demonstrated an improvement in progression-free survival (PFS) amongst metastatic HER2-negative breast cancer patients with germline mutations in BRCA1/2 (BRCA-MUT). Clinical trials have evaluated PARPi in combination with carboplatin, but with mixed results. Earlier trials studied the combination of carboplatin and a low-dose PARPi of low potency, veliparib. The concomitant combination of carboplatin and talazoparib, a higher-potency PARPi, was also evaluated in solid tumors, using a heavily pre-treated population. Here, we perform a comparative evaluation of different sequencing strategies of talazoparib and carboplatin to determine efficacy and toxicity in BRCA-MUT and BRCA wild-type (WT) TNBC models. Methods: We used three orthotopic xenograft models in NSG (NOD scid gamma) mice, with 7-14 mice in each treatment group: MDAMB231 (BRCA-WT), HCC1806 (BRCA-WT), and MX1 (BRCA-MUT). We treated mice with carboplatin (C) (35 mg/kg intraperitoneally) in combination with talazoparib (T) (0.33 mg/kg oral gavage) using 2 dosing strategies: a) concomitant administration of C + T; and b) T first, followed by C three days later, each compared to vehicle control. We evaluated primary tumor inhibition and hematologic toxicity. Kruskal-Wallis test and Dunn’s multiple comparison test was used to assess statistical significance. Results: Using the MDAMB231 xenograft, we found the T-first approach led to a 66.7% (P < 0.0001), and the concomitant approach resulted in a 51.4% decrease in primary tumor volume, (P = 0.08), in comparison to control. In HCC1806, the T-first approach resulted in a 62.0% decrease in tumor volume (P < 0.0001), whereas the concomitant combination showed a 54.4% decrease (P = 0.002). In MX1, the T-first and concomitant approaches resulted in 72.7% (P < 0.0001) and 81.4% (P < 0.0001) decrease in tumor volume, respectively. With regards to neutrophil counts, T-first approach decreased neutrophils by 66.2% and 43.0% in MDAMB231 and HCC1806 xenografts respectively, similar to the trend with concomitant T + C: 61.4% and 38.0%. In the MX1 cohort, the T-first approach resulted in a 66.2% decrease in neutrophils (P = 0.001), and the concomitant approach led to a 77.5% decrease in neutrophils (P = 0.006). Conclusions: Our results demonstrate that the talazoparib-first approach is effective in two BRCA-WT models with no statistically significant neutropenia. While the concomitant combination approach demonstrated greater tumor inhibition in the BRCA-MUT model, this was also associated with significant neutropenia. This is suggestive that sequencing of talazoparib and carboplatin may have differential effect in BRCA-WT and BRCA-MUT tumors and may play an important role in improving efficacy in BRCA-WT tumors.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.123
GPT teacher head0.468
Teacher spread0.345 · 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 designBench or experimental
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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Citations1
Published2021
Admission routes2
Has abstractyes

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Same venueJournal of Clinical OncologySame topicPARP inhibition in cancer therapyFrench-language works237,207