How Prognostic and Predictive Biomarkers Are Transforming Our Understanding and Management of Advanced Gastric Cancer
Bibliographic record
Abstract
BACKGROUND: Gastric cancer (GC) is the second leading cause of cancer death worldwide. GC is a heterogeneous disease in terms of histology, anatomy, and epidemiology. There is also wide variability in how GC is treated in both the resectable and unresectable settings. Identification of prognostic and predictive biomarkers is critical to help direct and tailor therapy for this deadly disease. METHODS: A literature search was done using Medline and MeSH terms for GC and predictive biomarkers and prognostic biomarkers. The search was limited to human subjects and the English language. There was no limit on dates. Published data and unpublished abstracts with clinical relevance were included. RESULTS: Many potential prognostic and predictive biomarkers have been assessed for GC, some of which are becoming practice changing. This review is focused on clinically relevant biomarkers, including EGFR, HER2, various markers of angiogenesis, proto-oncogene MET, and the mammalian target of rapamycin. CONCLUSION: GC is a deadly and heterogeneous disease for which biomarkers are beginning to change our understanding of prognosis and management. The recognition of predictive biomarkers, such as HER2 and vascular endothelial growth factor, has been an exciting development in the management of GC, validating the use of targeted drugs trastuzumab and ramucirumab. MET is another potential predictive marker that may be targeted in GC with drugs such as rilotumumab, foretinib, and crizotinib. Further identification and validation of prognostic and predictive biomarkers has the potential transform how this deadly disease is managed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".