Effects of immune architecture on response to adjuvant capecitabine in triple-negative breast cancer (FinXX trial).
Bibliographic record
Abstract
3142 Background: Recent studies have demonstrated a benefit of adjuvant capecitabine, particularly in triple negative breast cancer (TNBC) patients with residual disease after neoadjuvant chemotherapy. However, biomarkers to predict which patients are more likely to benefit from capecitabine are needed. Methods: The nanoString Breast Cancer 360 (BC360) and PanCancer Immunoncology (IO360) panels were used to quantify mRNA expression in TNBC samples in the FinXX trial. FinXX is a phase III trial which randomized high risk patients to receive either 3 cycles of docetaxel followed by 3 cycles of cyclophosphamide, epirubicin, and fluorouracil (Arm A: T+CEF) vs. 3 cycles of docetaxel plus capecitabine followed by 3 cycles of cyclophosphamide, epirubicin, and capecitabine (Arm B: TX+CEX). Gene signature scores were analyzed using prespecified algorithms developed by nanoString. Digital Spatial Profiling was carried out using GeoMX platform. Results: A total of 111 TNBC patients in FinXX trial were included (57 in Arm A and 54 in Arm B) with 10.2 years median follow up. There were 7 cancer- and immune-related gene signatures identified by BC360 and IO360 panels that were significantly associated with improved recurrent free survival favoring an addition of capecitabine. These include 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), PDL2 gene (HR 0.29, 95%CI 0.09-0.99, p 0.05), immunoproteasome (HR 0.34, 95%CI 0.13-0.89, p 0.02), exhausted CD8 (HR 0.29, 95%CI 0.09-0.97, p 0.04), and PD1 (HR 0.44, 95%CI 0.20-1.02, p 0.05). Conclusions: Analysis of RNA abundance signatures strongly suggests that there are important immune features that are associated with benefit from capecitabine in TNBC. However, analysis of RNA extracted from whole tumor sections lacks spatial discrimination. We anticipate that a more detailed, spatially-defined analysis of protein abundance, using the novel NanoString GeoMX platform, will provide more insights and define specific immune features associated with improved outcome. Additional results of GeoMX will be reported at the meeting. Clinical trial information: NCT00114816.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".