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Abstract P3-11-01: An updated systematic review and meta-analysis of the safety and efficacy of phosphoinositide 3-kinase inhibitors (PI3Ki) in advanced breast cancer

2020· article· en· W3013546657 on OpenAlexaff
Jacques Raphael, S. M. Qasim Hussaini, Kathleen I. Pritchard, Prakesh S. Shah, Phillip Blanchette, Ricardo Fernandes, Danielle Desautels

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsUniversity of TorontoUniversity of ManitobaMount Sinai HospitalSunnybrook Health Science CentreWestern University
Fundersnot available
KeywordsMedicineInternal medicineHazard ratioMeta-analysisOncologySubgroup analysisBreast cancerOdds ratioRandomized controlled trialCochrane LibraryConfidence intervalCancer

Abstract

fetched live from OpenAlex

Abstract Background In advanced breast cancer (ABC), the addition of pan-PI3Ki to the standard of care (SOC) treatment improves tumor response rate and progression free survival (PFS). Yet, further development of these drugs has been limited by their safety profile. Instead, selective PI3Ki were assessed and the Food and Drug Administration has recently approved the use of a selective PI3Ki based on a phase 3 randomized trial. Therefore, we updated our systematic review and meta-analysis to evaluate whether there is a difference in the efficacy and/or safety of pan versus selective PI3Ki in ABC. Methods The electronic databases Ovid PubMed, Cochrane Central Register of Controlled Trials and Embase, were searched for relevant publications reporting randomized controlled trials between January 2000 and June 2019. Pooled hazard ratios (HR) for progression-free survival (PFS), and pooled odds ratios (OR) for objective response rate (ORR), disease control rate (DCR) and toxicity were computed and weighted using Mantel-Haenszel method and generic inverse variance. Subgroup analyses compared patients with and without PI3K pathway activation. Results Seven studies comprising 3,597 patients were included (5 studies with pan-PI3Ki and 2 studies with selective PI3Ki). Pan and selective PI3Ki decreased the risk of progression by 21% and 32% compared to SOC: HR: 0.79, 95%CI 0.0.71-0.88 and 0.68, 95%CI 0.57-0.81 respectively, p for test for subgroup differences: 0.16. An improvement in ORR was more pronounced with the use of selective versus pan PI3Ki: OR 2.67, 95%CI 1.78-4.00 and OR 1.31, 95%CI 1.01-1.71 respectively, p for test for subgroup differences: 0.004 and DCR was only improved with selective PI3Ki: OR 1.85 95%CI 1.37-2.51. A statistically significant increase in the toxicity of any grade and of grade 3 and higher was observed with pan and selective PI3Ki: 1) for any grade toxicity: OR 5.51, 95%CI 3.05-9.96, and OR 3.77, 95%CI 2.16-6.58 respectively, p for test for subgroup differences 0.16; 2) for grade 3 or higher toxicity: OR 1.91, 95%CI 1.76-2.08 and OR 2.41, 95%CI 2.07-2.82 respectively; p for test for subgroup difference 0.01. In subgroup analyses, the PFS improvement was more pronounced in the PI3K mutant subgroup: HR 0.72, 95%CI 0.63-81 for the PI3K mutant subgroup and HR 0.83, 95%CI 0.73-0.96 for the PI3K wild-type subgroup, p for test for subgroup differences 0.11. Conclusion While selective PI3Ki have a better effect on ORR, DCR and PFS in patients with ABC compared to pan PI3Ki; their toxicity profile is still clinically substantial. Rigorous safety monitoring and early medical intervention are needed to reduce treatment discontinuation, optimize efficacy and render these drugs more clinically usable. Citation Format: Jacques Raphael, Syed Hussaini, Kathleen I Pritchard, Prakesh Shah, Phillip Blanchette, Ricardo Fernandes, Danielle Desautels. An updated systematic review and meta-analysis of the safety and efficacy of phosphoinositide 3-kinase inhibitors (PI3Ki) in advanced breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-11-01.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.071
GPT teacher head0.401
Teacher spread0.330 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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
Published2020
Admission routes1
Has abstractyes

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