Abstract S1-01: Analysis of molecular prognostic factors associated with tumor immune and stromal microenvironment in BEATRICE, an open-label phase 3 trial in early triple-negative breast cancer (eTNBC)
Bibliographic record
Abstract
Abstract Background: TNBC is a mutationally complex heterogeneous breast cancer subtype. In BEATRICE, adding bevacizumab to standard adjuvant chemotherapy for eTNBC improved neither invasive disease-free survival (IDFS; primary endpoint) nor overall survival (OS) [Cameron 2013; Bell SABCS 2014]. We explored prognostic effects of tumor-associated immune and stromal gene signatures. Methods: Gene expression (RNA) was assessed in pretreatment archival tumor tissue using an 800-gene nanostring platform. Given the low event rates and lack of bevacizumab effect in BEATRICE, treatment arms were pooled. The biomarker-evaluable population (BEP; all patients with an evaluable biomarker sample and ≥1 postbaseline efficacy assessment) was dichotomized using median gene expression level as the cutoff. Prognostic associations between IDFS/OS and prespecified candidate gene sets/de novo identified clusters were assessed using univariate Cox proportional hazards models. Results: Baseline characteristics and efficacy were similar in the BEP (988/2591 randomized pts; 38%) and the overall study population. In hierarchical cluster analysis based exclusively on immune gene expression, immune genes were enriched in 33% of samples, intermediate in 38%, and weak in 28%. Further characterization suggested differential prognostic value of distinct immune and stromal cell gene sets (Table). A significant prognostic effect for IDFS and OS was seen for CD8 effector T cell (Teff) and regulatory T cell (Treg) gene signatures, but not for the Teff:Treg ratio. A less pronounced positive prognostic effect was seen for other gene sets representing immune cells, including macrophages, CD4 T cells, and B cells (data not shown). Activated T helper (Th)-1 cell-derived chemokines and negative immune modulators of T cell activity (eg PD-L1) were highly prognostic for IDFS and OS. Both the cytokine IL-8 and ESM1 (target of VEGF-A pathway activation) were associated with worse IDFS and OS. No association was seen between outcome and markers for classic microvasculature (CD31, CD34), cancer-associated fibroblasts (FAP, BGN, DCN), VEGF-A, or VEGF-C. IDFSOSGene signatureHR (95% CI)Interaction p-valueHR (95% CI)Interaction p-valueTeff0.40 (0.28-0.57)7.2x10-70.29 (0.17-0.49)4.2x10-6Treg0.38 (0.26-0.54)1.6x10-70.23 (0.13-0.40)2.9x10-7Teff:Treg ratio0.80 (0.58-1.12)0.20.89 (0.57-1.39)0.6Th10.45 (0.31-0.64)8.1x10-60.43 (0.27-0.70)5.8x10-4PD-L10.42 (0.29-0.60)1.8x10-60.24 (0.14-0.41)3.4x10-7IL-81.48 (1.06-2.08)0.0221.89 (1.18-3.01)0.0076ESM11.73 (1.23-2.43)0.00172.22 (1.38-3.58)0.001 Conclusions: These molecular gene signature analyses in eTNBC confirm that markers of cytotoxic CD8 T cells are associated with good prognosis. This is the first report of a positive prognostic effect of regulatory T cell markers, immune checkpoint modulators, and macrophage-associated markers in the adjuvant TNBC setting. High VEGF-A activity, but not its expression, was associated with worse prognosis. The strong prognostic effect of immune checkpoint modulators suggests equilibrium between cytotoxic T cells and their inhibitors in eTNBC, supporting further exploration of immune checkpoint inhibitors in this therapeutic context. Citation Format: Molinero L, Yu J, Li C, Deurloo R, Dent RA, Bell R, Brown J, Parmar M, Toi M, Suter T, Steger G, Pivot X, Mackey J, Jackisch C, Hall P, Hegde P, Bais C, Cameron D. Analysis of molecular prognostic factors associated with tumor immune and stromal microenvironment in BEATRICE, an open-label phase 3 trial in early triple-negative breast cancer (eTNBC). [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr S1-01.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".