Abstract 5271: Correlative analysis of RNA biomarkers for adjuvant capecitabine benefit in the CIBOMA/2004-01phase III clinical trial of triple negative breast cancer patients
Bibliographic record
Abstract
Abstract Introduction: Predictive biomarkers for capecitabine benefit in triple negative breast cancer (TNBC) have been recently identified using samples from phase III clinical trials, including immunohistochemical (IHC) non-basal phenotype and RNA biomarkers related to angiogenesis, stroma and capecitabine activation genes. We aimed to validate these findings on the larger phase III CIBOMA clinical trial. Experimental Design: Tumor tissues from TNBC patients randomized to standard (neo)adjuvant chemotherapy with capecitabine vs. observation were analyzed using a 164 gene NanoString custom nCounter codeset. A prespecified statistical plan sought to verify the predictive capacity of PAM50 non-basal molecular subtype previously found by IHC, and tested the hypotheses that breast tumors with increased expression of (meta)genes for cytotoxic T cells, mast cells, endothelial cells, PDL2 and 38 individual genes benefit from adjuvant capecitabine for distant recurrence free survival (DRFS, primary endpoint) and overall survival. Exploratory analyses investigated (a) predictive capacity of categorical expression of biomarkers, and continuous expression of additional genes included in the codeset; (b) the prognostic capacity of continuous biomarker expression. Results: Of the 876 women enrolled in the CIBOMA trial, 658 (75%) were evaluable for analysis (337 with capecitabine and 321 without) with similar baseline characteristics relative to the intention-to-treat population. 553 (84%) cases were profiled as PAM50 basal-like while 105 (16%) were PAM50 non-basal. PAM50 non-basal subtype was the most significant predictor for capecitabine benefit (HRcapecitabine=0.19; 95%CI, 0.07-0.54; p=0<0.001) when compared to PAM50 basal-like (HRcapecitabine=0.9; 95%CI, 0.63-1.28; p=0.55) (p-interaction<0.001, adjusted p-value=0.01). Analysis of biological processes related to PAM50 non-basal subtype revealed its enrichment for mast cells, extracellular matrix, angiogenesis and features of the mesenchymal stem-like TNBC subtype. Multivariate analysis showed a significantly lower DRFS on the observation arm for the mast cell metagene (HRobservation=1.35; 95%CI, 1.12-1.62; p=0.002, adjusted p-value=0.006), particularly among PAM50 non-basal tumors (HRobservation=2.70; 95%CI, 0.99-7.35; p=0.01, p-interaction=0.08). Tumors above the median for genes involved in immune response (PDL2, CCR5), capecitabine metabolism (CES1) and angiogenesis (STC1) were significantly associated with favorable survival rates on the capecitabine arm (HRcapecitabine ranged between 0.51-0.60; p=0<0.05). Conclusions: In this prespecified correlative analysis of the CIBOMA trial, PAM50 non-basal status and the mast cell metagene identified early-stage TNBC patients most likely to benefit from adjuvant capecitabine. Citation Format: Karama Asleh, Ana Lluch, Angela Goytain, Carlos Barrios, Xue Q. Wang, Jesus Herranz, Dongxia Gao, Rosalia Caballero, Samuel Leung, Federico Rojo, Torsten O. Nielsen, Miguel Martin. Correlative analysis of RNA biomarkers for adjuvant capecitabine benefit in the CIBOMA/2004-01phase III clinical trial of triple negative breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5271.
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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 | 0.004 | 0.003 |
| 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".