Abstract GS4-09: Correlative studies of the breast cancer index (HOXB13/IL17BR) and ER, PR, AR, AR/ER ratio and Ki67 for prediction of extended endocrine benefit: A trans-aTTom study
Bibliographic record
Abstract
Abstract Background: Several biomarkers such as estrogen receptor (ER), progesterone receptor (PR), androgen receptor (AR) and Ki67 have been implicated in the pathogenesis and/or as prognostic biomarkers of breast cancer, and are utilized to determine treatment. Given the heterogeneity of response to endocrine therapy, however, predictive biomarkers are critical to better individualize patient care. Previous results from the Trans-aTTom study demonstrated that the Breast Cancer Index HOXB13/IL17BR [BCI (H/I)] biomarker significantly predicts extended endocrine benefit from 10 vs 5y of tamoxifen. In this correlative study, the predictive activity of BCI (H/I) was compared with ER, PR, AR and Ki67 protein expression in node positive patients treated in the aTTom trial. Methods: Patients with available tumor tissue and biomarker analyses were included. ER, PR, AR and Ki67 were centrally assessed by immunohistochemistry (IHC) utilizing tissue microarrays. BCI gene expression analysis by RT-PCR was performed blinded to clinical outcome. Multivariate Cox models adjusting for age, tumor size, tumor grade and HER2 status were used to assess the significance of the interaction between treatment and each biomarker as continuous variables. 17-year risk of recurrence, as a function of each continuous biomarker, was estimated from Cox models in each of the 2 treatment arms. Results: Analysis of 789 HR+, N+ patients showed a weak negative correlation between BCI (H/I) and ER, PR, and AR expression whereas Ki67 and the AR/ER ratio showed no correlation (ER, cor=−0.18; PR, cor=−0.25; AR, cor=−0.14; Ki67, cor=0.04; AR/ER ratio, cor=0.02). The interaction between BCI (H/I) and extended tamoxifen treatment was significant (p=0.014). In addition, analysis of risk of recurrence as a function of continuous BCI (H/I) demonstrated that the magnitude in the reduction in recurrence risk with extended tamoxifen correlated with increasing H/I levels. In contrast, interaction P values were nonsignificant (ER, p=0.829; PR, p=0.659; AR, p=0.783; Ki67, p=0.865; AR/ER ratio, p=0.835) and the magnitude of endocrine benefit did not correlate with expression levels of any of other biomarkers. Conclusion: Results from this post-hoc analysis of the Trans-aTTom study demonstrated that whereas BCI(H/I) is a significant predictive biomarker of endocrine response, analysis of ER, PR, AR, Ki67 and AR/ER expression showed no interaction with treatment, and lacked the ability to predict benefit of extended tamoxifen in HR+ early stage breast cancer. These results add to the growing body of evidence that BCI (H/I) is distinct in its ability to predict benefit from therapy and interrogates distinct tumor biology that is not captured by other traditional biomarkers. Citation Format: 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 W Rea, John MS Bartlett. Correlative studies of the breast cancer index (HOXB13/IL17BR) and ER, PR, AR, AR/ER ratio and Ki67 for prediction of extended endocrine benefit: A trans-aTTom study [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr GS4-09.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".