Immunotherapy and Targeted Therapy for Advanced Gastroesophageal Cancer: ASCO Guideline
Bibliographic record
Abstract
PURPOSE: To develop recommendations involving targeted therapies for patients with advanced gastroesophageal cancer. METHODS: The American Society of Clinical Oncology convened an Expert Panel to conduct a systematic review of relevant studies and develop recommendations for clinical practice. RESULTS: Eighteen randomized controlled trials met the inclusion criteria for the systematic review. RECOMMENDATIONS: For human epidermal growth factor receptor 2 (HER2)-negative patients with gastric adenocarcinoma (AC) and programmed death-ligand 1 (PD-L1) combined positive score (CPS) ≥ 5, first-line therapy with nivolumab and chemotherapy (CT) is recommended. For HER2-negative patients with esophageal or gastroesophageal junction (GEJ) AC and PD-L1 CPS ≥ 5, first-line therapy with nivolumab and CT is recommended. First-line therapy with pembrolizumab and CT is recommended for HER2-negative patients with esophageal or GEJ AC and PD-L1 CPS ≥ 10. For patients with esophageal squamous cell carcinoma and PD-L1 tumor proportion score ≥ 1%, nivolumab plus CT, or nivolumab plus ipilimumab is recommended; for patients with esophageal squamous cell carcinoma and PD-L1 CPS ≥ 10, pembrolizumab plus CT is recommended. For patients with HER2-positive gastric or GEJ previously untreated, unresectable or metastatic AC, trastuzumab plus pembrolizumab is recommended, in combination with CT. For patients with advanced gastroesophageal or GEJ AC whose disease has progressed after first-line therapy, ramucirumab plus paclitaxel is recommended. For HER2-positive patients with gastric or GEJ AC who have progressed after first-line therapy, trastuzumab deruxtecan is recommended. In all cases, participation in a clinical trial is recommended as it is the panel's expectation that targeted treatment options for gastroesophageal cancer will continue to evolve.Additional information is available at www.asco.org/gastrointestinal-cancer-guidelines.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".