Abstract P6-09-39: The role of quantitative estrogen receptor status in predicting breast tumor response to neoadjuvant chemotherapy
Bibliographic record
Abstract
Abstract Introduction: Patients with Estrogen Receptor negative breast cancer (BC) are known to have higher tumor response rates than ER positive patients when treated with neoadjuvant chemotherapy (NCT). Few studies have assessed ER status as a quantitative continuous measure in predicting tumor response in this setting. We aimed to study the association between quantitative ER status and tumor response at surgery in BC patients treated with NCT at our institution, and identify potential predictors of better survival outcomes. Methods: A retrospective review using a neoadjuvant BC database (The "Sunnybrook Biomatrix") identified 304 eligible patients that were included in the analyses. A univariate followed by a multivariable logistic regression analyses were conducted to assess the association between quantitative ER (expressed in percentage) and tumor response (good vs. poor response defined as < vs. ≥ 50% reduction in tumor size) while controlling for potential confounders. For the secondary outcome, the Kaplan Meier method was used to estimate the recurrence free survival (RFS) in this cohort. Predictors of RFS were identified using a cox proportional hazards model (CPH) to adjust for clinically relevant variables. A log-rank test was used to compare RFS between groups for any significant binary predictor. Results: The median follow up of all patients was 43.3 months (Q1-Q3: 28.7-61.1). Quantitative ER was inversely associated with tumor response in a multivariable logistic regression model (Odds Ratio 0.99 95%CI: 0.99-1.00, p=0.027). A cut-off of 60% seemed to best predict the association based on the c-statistic (c=0.67) and the receiver operating characteristic curve. However, quantitative ER was not associated with RFS; pathologic complete response (pCR) was shown to be an independent predictor of RFS in a CPH model (Hazard Ratio: 0.17, 95% CI: 0.07, 0.43, p=0.0002) in all patients, after controlling for potential confounders. At 5 years, 93% of patients with pCR and 72% of patients with residual tumor (no pCR) were recurrent-free respectively (log-rank test p=0.0012). Conclusion: This study suggests that BC patients with ER status < 60% are more likely to respond to NCT. Although ER status itself did not predict for relapse-free survival, patients with a pCR had better RFS, and this association was seen amongst all tumor phenotypes. The role of quantitative ER in predicting and maximizing tumour response to NCT (including optimizing pCR rate) needs to be better defined in prospective studies. Key words: Estrogen receptors, breast cancer, quantitative, tumor response, pathologic complete response. Citation Format: Raphael J, Trudeau M, Paramsothy T, Lee N, Gandhi S. The role of quantitative estrogen receptor status in predicting breast tumor response to neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-09-39.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".