MétaCan
Menu
Back to cohort

Clinical predictors of long-term response to capecitabine in metastatic breast cancer (MBC).

2020· article· en· W3031809543 on OpenAlexaffabout
Deirdre Kelly, Sasha M. Lupichuk, Philippe L. Bédard, Karen King, David W. Cescon, Zachary Veitch

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsUniversity of TorontoSt. Michael's HospitalUniversity of AlbertaBaker Hughes (Canada)Princess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineCapecitabineInternal medicineMetastatic breast cancerBreast cancerCohortOncologyCancerDocetaxelProportional hazards modelStage (stratigraphy)Hormonal therapySurgery

Abstract

fetched live from OpenAlex

1088 Background: Select patients (pts) with metastatic breast cancer (MBC) treated with capecitabine (cape) experience long-term disease control. We aimed to evaluate the clinical and biological predictors of long-term response. Methods: Pts receiving cape monotherapy for breast cancer from 01/2006 to 01/2016 at Princess Margaret Cancer Centre in Toronto, Canada (N = 352) or Alberta, Canada (N = 798) were identified through central pharmacy records. A median time-to-progression (MTTP) 3-fold higher (19.3 months) than those seen in published studies for patients treated with cape monotherapy at 1000mg/m2 was applied to select for pts with long-term response. MBC pts meeting these criteria were identified through chart review, with collection of clinical, pathological, and survival outcomes in addition to oncologist assessed (o) best response (eg: oCR, oPR, oSD) by radiology report review. Descriptive statistics, Kaplan-Meier and Cox-regressions were applied. Results: Overall, 41 (4%) pts met long-term response criteria. Median age of the study cohort was 62 (range 40-80), with 39% metastatic at diagnosis and 76% post-menopausal. At initiation of cape, a majority of pts were HR positive (85%), with an LDH < ULN (56%), and had bone (83%) or visceral (63%) metastases. Only 1 (2%) patient was HER2+. In the metastatic setting, most patients were chemotherapy-naive (83%) and had received 0-1 lines of hormonal therapy (61%). Median treatment duration with cape was 2.2 years (range 1.7-5.7) with 37% of pts having ≥40 cycles (range 22-94). Visceral (49%), bone (34%), and lymph nodes (24%) were the most common sites of progression, with 41% requiring 1 dose reduction and 27% requiring 2 dose reductions of cape. Overall response rate was 56% (oCR = 2%, oPR = 54%) with 44% having oSD as best response. Median follow-up was 6.8 years (95%CI: 5.5-8.2). Median PFS was 2.3 years (2.0-2.6), with a median OS of 3.8 years (2.9-4.6). 5 patients (12%) remained alive at data-cutoff, and 1 (2%) remained on treatment after 94 cycles of cape. Conclusions: Clinical features associated with long-term response on cape include HR positive, HER2 negative postmenopausal patients with bone predominant metastasis who have received 0-2 lines of prior hormone therapy and 0-1 lines of chemotherapy in the metastatic setting. This is the largest reported analysis of MBC patients with prolonged responses to cape. In the absence of randomized controlled trials; real-world evidence could aid clinicians in the optimal patient selection for treatment with cape.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.182
GPT teacher head0.544
Teacher spread0.363 · 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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Clinical OncologySame topicCancer Treatment and PharmacologyFrench-language works237,207