Biomarker analyses in REGARD gastric/GEJ carcinoma patients treated with VEGFR2-targeted antibody ramucirumab
Bibliographic record
Abstract
BACKGROUND: Angiogenesis inhibition is an important strategy for cancer treatment. Ramucirumab, a human IgG1 monoclonal antibody that targets VEGF receptor 2 (VEGFR2), inhibits VEGF-A, -C, -D binding and endothelial cell proliferation. To attempt to identify prognostic and predictive biomarkers, retrospective analyses were used to assess tumour (HER2, VEGFR2) and serum (VEGF-C and -D, and soluble (s) VEGFR1 and 3) biomarkers in phase 3 REGARD patients with metastatic gastric/gastroesophageal junction carcinoma. METHODS: A total of 152 out of 355 (43%) patients randomised to ramucirumab or placebo had ⩾1 evaluable biomarker result using VEGFR2 immunohistochemistry or HER2, immunohistochemistry or FISH, of blinded baseline tumour tissue samples. Serum samples (32 patients, 9%) were assayed for VEGF-C and -D, and sVEGFR1 and 3. RESULTS: None of the biomarkers tested were associated with ramucirumab efficacy at a level of statistical significance. High VEGFR2 endothelial expression was associated with a non-significant prognostic trend toward shorter progression-free survival (high vs low HR=1.65, 95% CI=0.84,3.23). Treatment with ramucirumab was associated with a trend toward improved survival in both high (HR=0.69, 95% CI=0.38, 1.22) and low (HR=0.73, 95% CI=0.42, 1.26) VEGFR2 subgroups. The benefit associated with ramucirumab did not appear to differ by tumoural HER2 expression. CONCLUSIONS: REGARD exploratory analyses did not identify a strong potentially predictive biomarker of ramucirumab efficacy; however, statistical power was limited.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".