Association between continuous decrease of plasma VEGF-A levels and the efficacy of chemotherapy in combination with anti-programmed cell death 1 antibody in non-small cell lung cancer patients
Bibliographic record
Abstract
OBJECTIVES: Vascular endothelial growth factor-A (VEGF-A) plays important roles in tumor immune suppression and thus correlates with the efficacy of anti-programmed cell death-1/ligand 1 (anti-PD-1/PD-L1) antibodies. We aimed to determine the association between change in plasma VEGF-A levels and the efficacy of chemotherapy combined with anti-PD-1/PD-L1 antibodies (chemo-PD1) in non-small cell lung cancer (NSCLC) patients. METHODS: We included NSCLC patients treated with chemo-PD1. Plasma VEGF-A levels were measured at baseline (Pre) and days 7 (D7) and 14 (D14) after the initiation of chemo-PD1. Continuous VEGF-A decrease was determined by comparing Pre with the median value of maximum change rate of posttreatment VEGF-A as cutoff. Patients whose change rates of VEGF-A at both D7 and D14 were consistently lower than the cutoff value were classified into the VEGF-A decrease group, whereas those whose VEGF-A at D7 or D14 were higher than the cutoff level were classified into the VEGF-A no-decrease group. The primary outcome was progression-free survival (PFS). RESULTS: A total of 32 patients were evaluated. The median Pre VEGF-A levels was 49 (range, 13-257). The median change rate of VEGF-A at D7 and D14 was -25.6% (range, -77.5-376.9) and -42.3% (range, -100-138.5) respectively. The cutoff value of posttreatment VEGF-A change rate was -9.3%. The PFS was significantly longer in the VEGF-A decrease group than that in the VEGF-A no-decrease group (median, not reached vs 2.4 months; p = 0.017). CONCLUSIONS: Continuous decrease of plasma VEGF-A levels during treatment may be associated with the efficacy of chemo-PD1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".