Trans-aTTom: Breast Cancer Index for prediction of endocrine benefit and late distant recurrence (DR) in patients with HR+ breast cancer treated in the adjuvant tamoxifen—To offer more? (aTTom) trial.
Bibliographic record
Abstract
505 Background: The aTTom study is a prospective phase III trial that randomized 6953 HR+ women to stop or continue tamoxifen (TAM) for 5 more years after completing at least 4 years of prior TAM. Results at 9 years of median follow-up demonstrated fewer breast cancer recurrences (21% vs 25%; RR= 0.86 [95% CI 0.77-0.96]; P = 0.006) and reduced breast cancer mortality (13% vs 15%; HR= 0.91 [95% CI 0.80-1.04]; P = 0.18) but increased incidence of endometrial cancer with longer TAM use (P < 0.0001). The Breast Cancer Index (BCI) is a gene expression–based signature that stratifies patients based on the risk of overall (0-10y) and late (post-5y) DR and predicted the likelihood of benefit from extended endocrine therapy in MA.17. This Translational aTTom (Trans-aTTom) study is a large-scale validation of the predictive ability of BCI for extended endocrine therapy (EET) benefit. Methods: Patients treated in the aTTom trial with available primary tumor tissue were eligible for this multi-institutional prospective-retrospective study. Primary and secondary endpoints were recurrence-free interval (RFI) and disease-free interval (DFI), respectively. Statistical significance level for RFI was set at 0.0336 as per statistical plan. Kaplan-Meier and Cox proportional hazards regression analysis with time-varying coefficients were used to test the predictive activity of BCI by HoxB13/IL17BR (H/I) status (High vs Low). Likelihood ratio test based on Cox regression was used to evaluate treatment by biomarker interaction. Results: 2637 tumors were centrally assessed for ER, PR and HER2 status leading to 1822 HR+ patients analyzed (1018 N0, 583 N+). Initial results from patients with N+ disease at 12 years of median follow-up showed 287 (49%) were classified as H/I-High and 296 (51%) were classified as H/I-Low. H/I High patients showed a statistically significant benefit of 9.8% in RFI with 10y vs 5y of TAM (HR=0.35 [95% CI 0.15-0.85]; P=0.027), whereas H/I Low patients showed no benefit (-0.2% RFI; HR=1.07 [95% CI 0.69-1.65]; P=0.77). A statistically significant interaction between continuous BCI and treatment was demonstrated (P = 0.02). Conclusions: These data provide further validation and establish level 1B evidence for BCI as a predictive biomarker for preferential benefit from EET in HR+ breast cancer.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".