Current and Emerging Role of Monoclonal Antibody-Based First-Line Treatment in Advanced Gastro-Esophageal and Gastric Cancer
Bibliographic record
Abstract
Gastric cancer is the fifth most common malignancy worldwide and one of the main causes of cancer-related death. While surgical treatment is the only curative option for early disease, many have inoperable or advanced disease at diagnosis. Treatment in this case would be a combination of chemotherapy and immunotherapy. Gastro-esophageal (GEJ) and gastric cancer (GC) genetic profiling with current molecular diagnostic techniques has significantly changed the therapeutic landscape in advanced cancers. The identification of key players in GEJ and GC survival and proliferation, such as human epidermal growth factor 2 (HER2), vascular endothelial growth factor (VEGF), and programmed cell death protein 1 (PD-1)/programmed cell death ligand-1 (PD-L1), has allowed for the individualization of advanced cancer treatment and significant improvement in overall survival and progression-free survival of patients. This review comprehensively examines the current and emerging role of monoclonal antibody-based first-line treatments in advanced GEJ and GC. We explore the impact of monoclonal antibodies targeting HER2, VEGF, PD-1/PD-L1, and Claudin 18.2 (CLDN18.2) on the first-line treatment landscape by talking about key clinical trials. This review emphasizes the importance of biomarker testing for optimal treatment selection and provides practical recommendations based on ASCO guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".