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Abstract P3-13-04: Effect of age on tolerability and efficacy of eribulin and capecitabine in patients with metastatic breast cancer treated in study 301

2013· article· en· W2127276650 on OpenAlexaff
PA Kaufman, Louise Yelle, Javier Cortés, EA Perez, Ahmad Awada, J. Wanders, MS Olivo, Yifeng He, CE Dutcus, Chris Twelves

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineCapecitabineEribulinInternal medicineTolerabilityMetastatic breast cancerAdverse effectBreast cancerGastroenterologyPost-hoc analysisCancerProgression-free survivalIncidence (geometry)ToxicityOncologyOverall survival

Abstract

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Abstract Background: The Phase III trial (NCT00337103) compared eribulin (E) with capecitabine (C) in patients (pts) with metastatic breast cancer (MBC) in the 1st-, 2nd-, and 3rd-line setting. Median overall survival (OS) was 15.9 and 14.5 months (HR 0.88; 95% CI 0.77, 1.00; P = 0.056) and median progression-free survival (PFS) was 4.1 and 4.2 months (HR 1.08; 95% CI 0.93, 1.25; P = 0.30) for E and C, respectively. This analysis compares toxicity and efficacy of E and C in relation to age. Material and methods: In this post-hoc exploratory analysis, the effect of age on the incidence of adverse events (AEs), OS, PFS, and objective response rate (ORR) with E and C were analyzed for two age groups: ≤65 years (E, n = 468; C, n = 491) and >65 years (E, n = 86; C, n = 57). For OS and PFS, analyses were stratified by HER2 and geographic region. Results: With increasing age, the proportion of pts with worse performance status (PS ≥1: 54.5% vs 69.2% for ≤65 and >65 years, respectively), ER+ (47.4% vs 57.3%), and PgR+ MBC (41.3% vs 45.5%) increased, and the proportion with triple-negative MBC decreased (26.6% vs 20.3%). With both treatments, AEs were reported in a higher proportion of pts in the older age group, this becoming more apparent for grade 3+ AEs (E: 64.6% vs 70.2%, and C: 45.0% vs 54.4% for ≤65 and >65 respectively). With E, there was a trend for increased incidence of grade 3/4 neutropenia (45.0% vs 50.0%) and leukopenia (13.7% vs 22.6%) but, in contrast, decreased peripheral sensory neuropathy (3.9% vs 1.2%) with increasing age. For C, there was a trend for increased palmar-plantar erythrodysethesia syndrome (total: 44.4% vs 50.9%; grade 3/4: 14.1% vs 17.5%), and grade 3/4 fatigue (1.8% vs 7.0%) and diarrhea (4.7% vs 10.5%) with increasing age; emesis and nausea were similar for both age groups. Dose adjustments due to AEs with E were slightly higher in the older age group: withdrawals 7.4% vs 10.7%; dose reductions 31.1% vs 36.9%; and dose delays 30.9% vs 36.9%. With C, there was a trend for an increased incidence of withdrawals (9.2% vs 21.1%) and dose delays (34.2% vs 49.1%) due to AEs with increasing age: the incidence of dose reductions was slightly higher in the older age group (31.3% vs 36.8%). In an unadjusted analysis, a trend for improved OS with E vs C was observed in both subgroups (≤65 years: median 15.8 vs 14.5 months; HR 0.90; 95% CI 0.78, 1.04; P = 0.16, and >65 years: median 18.4 vs 14.1 months; HR 0.74; 95% CI 0.50, 1.12; P = 0.16). PFS and ORR for E and C were: median PFS: E, 4.0 and 5.4 months; C, 4.2 and 5.9 months; ORR: E, 10.9% and 11.6%; C, 11.6% and 10.5%, in the ≤65 and >65 groups respectively. Conclusions: This exploratory and unadjusted analysis suggests a trend for improved OS with E in both younger and older pts with MBC. With both treatments there was a suggestion that AEs were reported in a higher proportion of pts in the older age group, this becoming more apparent for grade 3+ events. Specifically, these data suggest an increased incidence of grade 3/4 diarrhea, dose delays, and study withdrawal due to AEs in pts treated with C, and potentially suggest that with E there may be less difference between the AE profile in younger vs older pts than with C. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P3-13-04.

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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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.410
Teacher spread0.370 · 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 designObservational
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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Citations3
Published2013
Admission routes1
Has abstractyes

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