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HER2 status and prediction of extended endocrine benefit with breast cancer index (BCI) in HR+ patients in the adjuvant tamoxifen: To offer more? (aTTom) trial.

2020· article· en· W3031793799 on OpenAlexaff
John M.S. Bartlett, Dennis C. Sgroi, Kai Treuner, Yi Zhang, Tammy Piper, Ranelle Salunga, Ikhlaaq Ahmed, Lucy Doos, Sarah Thornber, Karen J. Taylor, Elena F. Brachtel, Sarah Pirrie, Catherine A. Schnabel, Daniel Rea

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsMedicineTamoxifenOncologyBreast cancerInternal medicineProportional hazards modelHazard ratioConfidence intervalCancer

Abstract

fetched live from OpenAlex

522 Background: BCI is a validated gene expression-based assay that stratifies patients based on risk of overall (0-10y) and late (post-5y) distant recurrence (DR) and predicts likelihood of benefit from extended endocrine therapy (EET). The Trans-aTTom study established Level1B validation for BCI (H/I) to predict benefit from EET.1 In this updated Trans-aTTom analysis including HER2 status, BCI (H/I) and prediction of endocrine benefit were further characterized. Methods: Centralized HER2 was determined for all cases according to current ASCO/CAP guidelines. Kaplan-Meier and Cox proportional hazards regression were conducted to assess primary and secondary endpoints of Recurrence-Free Interval (RFI) and Disease-Free Interval (DFI), respectively. A three-way interaction using likelihood ratio testing, which included treatment, BCI (H/I) and HER2, was performed to assess the effect of HER2 on BCI (H/I) prediction of EET benefit. Results: Of 789 N+ patients, 90% (N = 711) and 9% (N = 72) were HR+/HER2- and HR+/HER2+, respectively. In the HER2- subset, BCI (H/I)-High (48%) showed significant benefit from 10y vs. 5y of tamoxifen (9.4% RFI: HR = 0.35 [95% CI 0.15-0.81]; P = 0.047) while BCI (H/I)-Low patients did not (-2.1% RFI; HR = 1.15 [95% CI 0.78-1.69]; P = 0.491). For DFI, BCI (H/I)-High patients also showed significant benefit (10.3% DFI; HR = 0.41 [95% CI 0.18-0.91]; P = 0.047) while BCI (H/I)-Low patients did not (-1.7% DFI; HR = 1.10 [95% CI 0.75-1.62] P = 0.612). As demonstrated in the overall N+ cohort, significant interaction between BCI (H/I) and treatment was shown in the HER2- subset (RFI P = 0.045; DFI P = 0.044). Notably, three-way interaction evaluating BCI (H/I), treatment and HER2 status was not statistically significant (P = 0.85), indicating the ability of BCI (H/I) to predict benefit of EET activity was not significantly affected by HER2 status. Conclusions: In this updated Trans-aTTom analysis with HER2 data, BCI (H/I) showed similar predictive performance for EET response in the HER2- subset when compared to the overall N+ cohort. These data further support the clinical utility of BCI (H/I) as a predictive biomarker for informing EET benefit in HR+/HER2- and HR+/HER2+ disease. Clinical trial information: NCT00003678 . [Table: see text]

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.044
GPT teacher head0.383
Teacher spread0.338 · 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 designNon-randomized trial
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

Citations3
Published2020
Admission routes1
Has abstractyes

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