Abstract CT037: Nivolumab plus ipilimumab vs chemotherapy as first-line treatment for advanced gastric cancer/gastroesophageal junction cancer/esophageal adenocarcinoma: CheckMate 649 biomarker analyses
Bibliographic record
Abstract
Abstract Background: First-line nivolumab (NIVO) + chemotherapy (chemo) showed superior overall survival (OS) vs chemo, but NIVO + ipilimumab (IPI) vs chemo did not meet the prespecified OS boundary for significance in patients with advanced gastric cancer/gastroesophageal junction cancer/esophageal adenocarcinoma (GC/GEJC/EAC) in CheckMate 649. However, NIVO + IPI resulted in more durable responses and higher 24-month OS rates vs chemo. We present exploratory biomarker analyses of NIVO + IPI vs chemo. Methods: Tumor mutational burden (TMB) was derived from whole exome sequencing of baseline tumor tissue and matching blood. TMB-high (TMB-H) was defined as ≥ 199 mutations/exome. Gene expression signatures (GES), including 12-gene chemokine, 2-gene regulatory T cell (Treg), 15-gene fibroblast, and 5-gene endothelial, were assessed via RNA sequencing of baseline tumor tissue. GES subgroups were defined by signature score tertiles. Results: 813 patients were randomized to receive NIVO + IPI or chemo; 366 were evaluable for TMB (NIVO + IPI 45%, chemo 45%), and 402 were evaluable for GES (NIVO + IPI 49%, chemo 50%). OS HR for NIVO + IPI vs chemo was numerically lower in the TMB-H (6% of evaluable patients, HR 0.31) vs TMB-low (94% of evaluable patients, HR 0.87) subgroup (Table). Multiple GES subgroups showed OS benefit with NIVO + IPI vs chemo, including chemokine-high, Treg-high, fibroblast-low, and endothelial-low subgroups (Table). Additional analysis on microsatellite instability and GES in programmed death ligand-1 combined positive score subgroups will be presented. Conclusions: This exploratory analysis suggests that there are patient subgroups with GC/GEJC/EAC that may benefit from dual immune checkpoint blockade. Clinical utility of these biomarkers should be prospectively validated in future trials. Overall survival All randomized patients HR (95% CI) 0.89 (0.77-1.03) All evaluable HR (95% CI) High, n (%) HR (95% CI) Medium, n (%) HR (95% CI) Low, n (%) HR (95% CI) TMB (n = 366) 0.81 (0.65-1.00) 21 (6) 0.31 (0.10-0.95) - 345 (94) 0.87 (0.70-1.09) GES (n = 402) Chemokine 0.82 (0.67-1.01) 134 (33) 0.59 (0.40-0.86) 134 (33) 0.92 (0.65-1.32) 134 (33) 0.98 (0.68-1.40) Treg 134 (33) 0.59 (0.41-0.86) 134 (33) 0.91 (0.64-1.30) 134 (33) 1.07 (0.75-1.52) Fibroblast 134 (33) 0.98 (0.69-1.40) 134 (33) 0.94 (0.66-1.35) 134 (33) 0.63 (0.43-0.91) Endothelial 134 (33) 0.97 (0.68-1.38) 134 (33) 0.94 (0.65-1.34) 134 (33) 0.65 (0.45-0.94) OS HR data are unstratified for NIVO + IPI vs chemo. Citation Format: Yelena Y. Janjigian, Kohei Shitara, Jaffer Ajani, Markus Moehler, Jin Yao, Lin Shen, Marcelo Garrido, Carlos Gallardo, Lucjan Wyrwicz, Kensei Yamaguchi, Tomasz Skoczylas, Arinilda Bragagnoli, Tianshu Liu, Michael Schenker, Patricio Yañez, Ruben Kowalyszyn, Michalis Karamouzis, Thomas Zander, Kynan Feeney, Elena Elimova, Raheel Nathani, Ruslan Novosiadly, Ming Lei. Nivolumab plus ipilimumab vs chemotherapy as first-line treatment for advanced gastric cancer/gastroesophageal junction cancer/esophageal adenocarcinoma: CheckMate 649 biomarker analyses [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr CT037.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.004 | 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".