Abstract S4-06: HER2 status as predictive marker for AI vs Tam benefit: A TRANS-AIOG meta-analysis of 12129 patients from ATAC, BIG 1-98 and TEAM with centrally determined HER2
Bibliographic record
Abstract
Abstract There is now significant evidence emerging from the pivotal trials of AIs versus Tamoxifen (AIOG) demonstrating the value of meta-analysis of key clinical questions. The "Trans-AIOG" group has been tasked with the exploration of key molecular/biomarker questions that are pertinent to meta-analyses of biomarkers (past/present/future) in AIOG trials. HER2 has been long proposed as a marker of endocrine "resistance". Data from three trials, before the era of HER-directed therapy, suggest a potential role for HER2 to select patients for treatment with upfront AIs. However the individual trials lack power to test treatment-by-HER2 interaction due to sample size and low HER2+ve rates. A meta-analysis of the predictive value of HER2 status, specifically within the first 3 years of endocrine therapy, has the potential to inform patient selection for upfront or sequential strategies with AIs. The pre-existing standardization of methodology for HER2 (IHC/FISH) facilitates analysis of existing data from BIG-1-98, TEAM and ATAC for this key marker. Analysis plan: Following a prospectively-designed analysis plan, patient-level data from 3 randomized phase III trials (ATAC, BIG 1-98, TEAM) comparing AIs to tamoxifen during the first 2-3 years of adjuvant treatment were collected at the CRCTU (Birmingham UK), accounting for both the established time-dependency of relapse in HER2+ve, anti-endocrine treated patients and to address the clinical question of "upfront" vs "sequential" strategies for AIs. For each trial, covariate-adjusted Cox models estimated HER2-by-treatment (AI vs Tam) interaction on distant recurrence-free interval-censored at 2-2.75 years follow-up. A meta-analysis of the HER2-by-treatment interaction terms and of treatment effects according to HER2 status was performed. Results: 12129 patients with centrally-confirmed ER and HER2 status, 1092 (9%) HER2+ve, with 473 (4%; 111 among HER2+ve) distant recurrences were analyzed. The meta-analysis estimated a pooled HER2-by-treatment interaction of 1.61 (95% CI 1.01,2.57), reflecting treatment effect hazard ratio(AI/Tam) of HR=1.13 (0.75,1.71) among HER2+ve and HR=0.70 (0.56,0.87) among HER2-ve. There was heterogeneity among interaction terms (I-squared=59%, p=.09) that resulted from treatment effect heterogeneity among HER2+ve subgroup (I2=71%, p=.03), not the HER2-ve subgroup (I2=0%). The results for disease-free survival were similar. Conclusion: An individual patient data meta-analysis across 3 trials (ATAC, BIG 1-98, TEAM) conducted prior to standard use of HER2-directed adjuvant therapy demonstrated a marginally-significant interaction between HER2 status and treatment with AIs vs Tamoxifen in the 2-2.75 years prior to potential "switching" between Tamoxifen and AIs. Patients with HER2-ve cancers experienced improved outcomes when treated with AIs vs Tamoxifen whilst patients with HER+ve cancers fared no better, or slightly worse, during AI treatment. However, the small number of HER2+ve cancers and events even in this meta-analysis may explain a large degree of heterogeneity in the treatment effects within the HER2+ve subgroups across the 3 trials. Other causes, perhaps related to subtle differences between AIs, cannot be excluded. Citation Format: Bartlett JMS, Ahmed I, Regan MM, Sestak I, Mallon EA, Dell'Orto P, Thürlimann BJK, Seynaeve C, Putter H, Brookes CL, Forbes JF, Colleoni MA, Bayani J, van de Velde CJH, Viale G, Cuzick J, Dowsett M, Rea DW, On Behalf of the Translational Aromatase Inhibitor Overview Group (Trans-AIOG). HER2 status as predictive marker for AI vs Tam benefit: A TRANS-AIOG meta-analysis of 12129 patients from ATAC, BIG 1-98 and TEAM with centrally determined HER2. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr S4-06.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.053 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".