Abstract P2-11-10: Validation of the Breast Cancer Index (BCI) prognostic models optimized for late distant recurrence in postmenopausal women with early-stage HR+ breast cancer in the TEAM trial
Bibliographic record
Abstract
Abstract Background: Women with HR+ breast cancer experience a persistent risk of distant recurrence (DR) even after completion of 5 years of adjuvant endocrine therapy, with more than 50% of DR occurring after 5 years (late DR). The prognostic genomic signatures currently being used in the clinic were not developed or optimized specifically for late DR. We have previously shown that the Breast Cancer Index (BCI) and BCIN+ prognostic models were significantly prognostic for risk of overall (0-10y) and late (5-10y) distant recurrence (DR) in N0 and N1 HR+ patients in the Tamoxifen and Exemestane Adjuvant Multinational (TEAM) trial. Here, the prognostic performance of the BCI and BCIN+ models with alternative cut-points optimized for late DR were evaluated in patients from the TEAM trial, who were free from DR for at least 5 years. Methods: BCI testing was performed blinded to clinical outcome. The pre-specified alternative cut-points 4.4 and 1.8 for BCI and BCIN+ models were determined previously from Trans-aTTom and IDEAL studies, respectively (ESMO 2021). Kaplan-Meier analysis and log-rank test were used to evaluate the prognostic significance of BCI/BCIN+ risk groups based on DR. Univariate and multivariate Cox models were used to estimate hazard ratios (HRs) and the associated 95% confidence intervals (CIs). Results: 1285 HR+ N0 (median age 69.2, 54.2% T1, 92.5% G2-3, 21.3% chemotherapy) and 1762 N1 (median age 68.5, 49.7% T1, 80.8% G2-3, 42.6% chemotherapy) patients who remained free from DR at 5 years post randomization were included in the current analysis. For N0 patients, BCI identified 439 (34%) and 846 (66%) patients as low and high-risk with late 10-year DR rates of 3.8% (95% CI: 1.5-6.0%) and 9.1% (95% CI: 6.8-11.4%), respectively (HR: 2.6, 95% CI: 1.4-5.0; p=0.0025). For N1 patients, BCIN+ identified 287 (16%) and 1475 (84%) patients as low and high-risk with late 10-year DR rates of 3.4% (95% CI: 1.2-5.5%) and 12.3% (95% CI: 10.4-14.2%), respectively (HR: 3.5, 95% CI: 1.8-6.9; p< 0.0001). Similar results were observed in the HER2- patients. Notably, BCI/BCIN+ remained a statistically significant prognostic factor in the multivariate analysis after controlling for age, tumor size, grade, treatment. (Table). Conclusions: Compared to the original BCI/BCIN+ models, the optimized BCI and BCIN+ models showed improved prognostic performance for identifying low-risk patients with a very low risk of late DR (< 4%), for both N0 and N1 patients. These results provide further validation of BCI clinical utility as an aid in the decision-making for extended endocrine therapies for HR+ breast cancer, particularly in patients with N1 disease that may be spared extended endocrine treatment. Table Citation Format: John MS Bartlett, Keying Xu, Jenna Wong, Gregory R. Pond, Yi Zhang, Melanie Spears, Ranelle Salunga, Elizabeth Mallon, Karen J. Taylor, Annette Hasenburg, Christos Markopoulos, Luc Dirix, Caroline Seynaeve, Cornelis J.H. van de Velde, Daniel Rea, Catherine A. Schnabel, Kai Treuner, Jane Bayani. Validation of the Breast Cancer Index (BCI) prognostic models optimized for late distant recurrence in postmenopausal women with early-stage HR+ breast cancer in the TEAM trial [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-11-10.
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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 | 0.016 | 0.013 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".