Abstract LB-225: RNA molecular signatures as predictive biomarkers of response to monotherapy pembrolizumab in patients with metastatic triple-negative breast cancer: KEYNOTE-086
Bibliographic record
Abstract
Abstract Background: Response to anti-programmed death 1/programmed death ligand 1 (PD-L1) therapy is associated with tumor expression of PD-L1 and an 18-gene T-cell-inflamed gene expression profile (GEP) across several tumor types. The association and utility of the GEP, as calculated using baseline RNA-seq data as a predictor of pembrolizumab response, was evaluated in patients with triple-negative breast cancer (TNBC) enrolled in the KEYNOTE-086 trial (NCT02447003). Additionally, a 37-gene tissue -resident memory (TRM) T-cell signature was evaluated and compared with the GEP. Methods: In the phase II KEYNOTE-086 study, patients with previously treated, metastatic TNBC (independent of PD-L1 status; cohort A, n=170) and treatment-naive, PD-L1-positive (combined positive score ≥1) TNBC (cohort B, n=84) were treated with pembrolizumab monotherapy. Using RNA-seq data, the GEP and TRM signature scores were calculated prospectively, merged with clinical outcome data, evaluated for their level of correlation with each other, and tested for their association with pembrolizumab response (best overall response [BOR], progression-free survival [PFS], and overall survival [OS]) in cohorts A and B after adjusting for Eastern Cooperative Oncology Group performance status. The independent predictive value of the TRM was also assessed after adjusting for the explanatory value of the GEP. Results: RNA-seq data from both baseline tumor specimens and clinical data were available for 154/254 pembrolizumab-treated patients in KEYNOTE-086 (12 [7.8%] were considered responders). The GEP and TRM signature scores were highly correlated (Spearman correlation, 0.89; Kendall’s tau, 0.72), suggesting that they measure linked immune phenomena in the tumor microenvironment (TME). The GEP showed a statistically significant association with clinical outcome (BOR AUROC, 0.76 [95% CI, 0.65-0.86], P=0.004; PFS, P<0.001; OS, P<0.001). A similar result was found for the TRM signature and clinical outcome (BOR AUROC, 0.76 [95% CI, 0.64-0.88], P=0.003; PFS, P<0.001; OS, P<0.001). Testing of TRM in models that adjusted for the explanatory value of the GEP showed no evidence of additional predictive value for the TRM signature beyond the GEP. Conclusions: Using RNA-seq-based data, we confirmed that inflammatory state signatures measuring the TME are associated with response to pembrolizumab in TNBC. Both signatures evaluated (GEP and TRM) were significantly associated with clinical outcome but were highly correlated with each other and did not show independent explanatory value. Results confirmed that there may be multiple ways to measure the inflammatory state of the TME, but understanding their relative clinical utility and potential use in conjunction with PD-L1 via immunohistochemistry will require larger, randomized studies. Citation Format: Sherene Loi, Peter Schmid, Javier Cortés, David W. Cescon, Eric P. Winer, Deborah Toppmeyer, Hope S. Rugo, Michelino De Laurentiis, Rita Nanda, Hiroji Iwata, Ahmad Awada, Antoinette Tan, Chunsheng Zhang, Andrey Loboda, Andrew Albright, Razvan Cristescu, Maureen Lane, Anran Wang, Jared Lunceford, Gursel Aktan, Vassiliki Karantza, Sylvia Adams. RNA molecular signatures as predictive biomarkers of response to monotherapy pembrolizumab in patients with metastatic triple-negative breast cancer: KEYNOTE-086 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-225.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".