TP53 mutation analysis in gastric cancer and clinical outcomes of patients with metastatic disease treated with anti-angiogenic or standard chemotherapy regimens.
Bibliographic record
Abstract
e16571 Background: Current data support the angiogenic potential of cancer cells with mutant p53 and VEGF-A up-regulation in solid tumors. These findings have renewed interest in the p53 role as a predictive/prognostic factor in cancer therapy. We investigated TP53 mutations in gastric adenocarcinoma (GA) samples of metastatic patients (pts) who underwent anti-angiogenic Paclitaxel-Ramucirumab (PR) therapy. The analysis was also performed in a control group of pts who received first-line chemotherapy (CT) with platinum derivates and fluoropyrimidines. Methods: TP53 mutations were identified by next-generation sequencing in 110 GA primary tumors of two retrospective metastatic series including 48 pts who were treated with second-line PR and 62 pts who received first-line CT with Cisplatin or Oxaliplatin plus 5-Fluorouracil or Capecitabine. Detected TP53 mutations were classified for TP53 mutant-specific residual transcriptional activity scores ( TP53 RTAS) (Fischer NW et al JCI Insight 2018). TP53 RTAS results were used for stratifying pts in survival analyses. Primary end-point was overall survival (OS). Results: In the PR group, TP53 mutations were detected in 29 out of 48 tumor samples (60.4%) with 10 having TP53 RTAS 0%-to-<1%. In the CT group, TP53 mutations were found in 40 out of 62 tumor samples (64.5%) with 11 having TP53 RTAS 0%-to-<1%. In the PR group, the 10 cases with a TP53 mutation causing no residual or minimal activity ( TP53 RTAS 0%-to-<1%) showed better OS in comparison with pts in the remaining groups (wild-type and TP53 RTAS > 1%). This effect was retained in the multivariate model analysis (Hazard Ratio = 0.29, 95% confidence interval 0.17-0.85, p = 0.02). An opposite effect was seen in the CT group with the worst OS in carriers of TP53 RTAS 0%-to-<1% mutations (Hazard Ratio = 2.64, 95% confidence interval 1.17-5.95, p = 0.02). Notably, in the whole group of 110 pts, TP53 mutations (any type) occurred more frequently in the intestinal-type GA group (p = 0.02). Conclusions: Additional studies are warranted to explore the favorable role of TP53 mutations in cancer pts undergoing anti-angiogenic therapies. TP53 mutations frequently occur in GA and these findings would lead to novel tailored therapy strategies in this lethal disease.
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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) | 0.000 | 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 | 0.000 |
| 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".