Abstract P3-11-05: Predictive gene signatures of adjuvant capecitabine benefit in triple negative breast cancer in the FinXX trial
Bibliographic record
Abstract
Abstract Background: Recent studies have demonstrated a benefit of adjuvant capecitabine in early stage breast cancer, particularly in patients with residual disease after neoadjuvant chemotherapy. Subset analyses suggest that patients with triple negative breast cancer (TNBC) may be more likely to benefit with capecitabine in this setting. However, more precise biomarkers to predict which patients are most likely to benefit from capecitabine are needed. Methods: The NanoString Breast Cancer 360TM (BC360) panel was used to quantify mRNA expression in FFPE tissue samples from patients with TNBC in the FinXX trial. Gene signature scores were analyzed using prespecified algorithms developed by NanoString. 30 additional custom genes related to capecitabine metabolism and function were added. Patients in FinXX trial were randomized to receive either 3 cycles of docetaxel followed by 3 cycles of cyclophosphamide, epirubicin, and fluorouracil (T+CEF) or 3 cycles of docetaxel plus capecitabine followed by 3 cycles of cyclophosphamide, epirubicin, and capecitabine (TX+CEX). Cox proportional hazard ratio (HR) was used to determine the association of each gene signature with recurrence free survival (RFS). Results: A total of 111 samples from patients with TNBC in the FinXX trial were available for gene expression analysis. 57 patients were treated with T+CEF and 54 patients were treated with TX+CEX. The median age was 52 years and median follow up was 10.2 years. Consistent with the previous analysis of the FinXX trial, patients with TNBC had nonsignificant but favorable RFS with capecitabine (HR 0.60, 95% CI 0.27-1.3, p 0.2). Among 39 individual genes and metagene signatures generated with the BC360 panel, there were 4 gene signatures significantly associated with improved RFS favoring an addition of capecitabine with TX+CEX compared to T+CEF. These were the cytotoxic cell signature (HR 0.37, 95%CI 0.15-0.92, p 0.03), endothelial signature (HR 0.18, 95%CI 0.04-0.83, p 0.03), mast cell signature (HR 0.43, 95%CI 0.21-0.88, p 0.02), and PDL2 gene (HR 0.29, 95%CI 0.09-0.99, p 0.05). PAM50 intrinsic subtype was not predictive of capecitabine benefit within this TNBC subset. Moreover, we identified additional individual 12 genes that were significantly associated with capecitabine benefit (p-FDR<0.05). Among these genes, high expression of CES1, which encodes an enzyme that activates capecitabine, was significantly associated with improved RFS when treated with capecitabine (HR 0.71, 95%CI 0.32-1.61 with p-interaction=0.04). Conclusion: Our analyses demonstrated potential predictive individual genes and metagene signatures that may be used to identify TNBC patients who are more likely to benefit from adjuvant capecitabine. Interestingly, several gene signatures related to immune response and genes related to capecitabine activation were associated with improved outcome in TNBC patients treated with capecitabine in the FinXX trial. While these findings are compatible with basic science reports, future studies are needed to validate the significance of these gene signatures as predictive biomarkers for capecitabine benefit. Citation Format: Chumsri S, Asleh K, Brauer HA, Mashadi-Hossein A, Lauttia LS, Lindman H, Nielsen TO, Joensuu H, Thompson EA. Predictive gene signatures of adjuvant capecitabine benefit in triple negative breast cancer in the FinXX trial [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-11-05.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".