Current and Emerging Treatment Options for HER2‑Positive Gastroesophageal Cancer
Bibliographic record
Abstract
Gastroesophageal cancer (GEC) is the fifth most common cancer and the second most common cause of cancer-related mortality, with 1.3 million annual deaths worldwide. The global incidence is increasing, particularly among younger patients. GEC can be classified into subtypes based on anatomic location, histology, molecular characteristics, or tumour biology and genomics. In approximately 20% of all GECs overexpression of HER2 is identified. The landscape of treatment options in this patient population is evolving rapidly. This review summarizes the progress of HER2-directed therapies for advanced disease and highlights future directions in targeting the disease. The epidermal growth factor receptor (EGFR) family of transmembrane tyrosine kinase receptors, EGFR/HER1, HER2/neu, HER3, and HER4, all have an extracellular ligand-binding domain, lipophilic transmembrane domain, and an intracellular domain with tyrosine kinase activity, binding to these receptors results in activation of downstream RAS/MAPK and PI3K/AKT pathways. In turn, this induces cell proliferation, differentiation, migration, and survival. The phase III Trastuzumab for Gastric Cancer (ToGA) trial reported the incidence of HER2-positive gastric cancer to be 22%. Therefore, targeting HER2 and its downstream signaling pathways holds important potential as a therapeutic strategy. Figure 1 illustrates potential targeting mechanisms that will be discussed in this review.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".