Zolbetuximab or Immunotherapy as the Initial Targeted Therapy in CLDN18.2-Positive, HER2-Negative Advanced Gastric Cancer: Weighing the Options
Bibliographic record
Abstract
Advanced gastric/gastroesophageal junction (G/GEJ) adenocarcinoma remains a common and deadly form of cancer. Advances in G/GEJ cancer treatment have improved survival outcomes with the claudin-18.2 (CLDN18.2)-targeted agent, zolbetuximab, and immune checkpoint inhibitors (ICIs) targeting the PD-1 receptor. This article offers an evidence-informed opinion on considerations when selecting between these first-line treatments for G/GEJ adenocarcinoma in patients with HER2-negative disease that expresses CLDN18.2 and/or PD-L1, including the reliability of biomarker scoring and interpretation, overall survival (OS) rates, toxicity profiles, and logistical practicalities. Evidence from Phase III trials for zolbetuximab and ICIs suggest similar OS benefits of 14-18 months compared to chemotherapy alone, but there appears to be a gradient of benefit for ICIs with increasing PD-L1 combined positive score (CPS). There is high inter-observer variability in CPS scoring, particularly at lower thresholds. Zolbetuximab is associated with high rates of nausea and vomiting during the initial infusion, whereas ICIs are associated with risk of later-onset immune-related toxicities that can be fatal in rare cases. In considering the available evidence, our opinion is that zolbetuximab is a reasonable option for initial targeted treatment in HER2-/CLDN18.2-positive advanced G/GEJ when PD-L1 CPS score is <10 based on the reliability of biomarker testing, comparable OS, and avoidance of potentially irreversible ICI-induced immune toxicity.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".