Vascular Endothelial Growth Factor Pathway Polymorphisms as Prognostic and Pharmacogenetic Factors in Cancer: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Angiogenesis is an important host process that interacts with cancer cells to promote growth, invasion, and metastasis. Numerous therapeutic agents targeting the VEGF pathway have been developed. Host variability in VEGF pathway can influence angiogenesis-dependent signaling, altering sensitivity to antiangiogenic drugs and prognosis. A systematic review and meta-analysis was conducted (May 1990-July 2011). Eligible studies involved cancer patients and compared polymorphisms in the VEGF pathway [VEGF and molecules directly interacting with VEGF: KDR, FLT1, FGF, FGF2, FGFR, NRP1, endostatin (encoded by COL18A1)], and reported one of the following outcomes: overall survival, progression-free survival, time to recurrence, disease-free survival, response rate, or drug toxicity. We identified 48 cancer studies assessing prognosis and 12 cancer studies exploring pharmacogenetics of anti-VEGF therapy across various VEGF pathway polymorphisms. There was marked inter- and intradisease site heterogeneity in the effect of polymorphisms on both outcome and response to therapy. Meta-analyses of 5 VEGF polymorphisms (+936C>T, -460T>C, +405G>C, -1154G>A, and -2578C>A) identified a significant prognostic relationship: VEGF +405G>C variants showed a highly statistically significant improvement in overall survival [HR, 0.74; 95% confidence interval, 0.60-0.91; P = 0.004]. Variants (heterozygotes and/or homozygotes) of VEGF +405G>C were significantly associated with improved survival in a meta-analysis of multiple cancer sites.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".