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TP53 mutation analysis in gastric cancer and clinical outcomes of patients with metastatic disease treated with anti-angiogenic or standard chemotherapy regimens.

2020· article· en· W3030820321 on OpenAlexaff
Nicholas W. Fischer, Francesco Graziano, Irene Bagaloni, Maria Di Bartolomeo, Sara Lonardi, Bruno Vincenzi, Lorenzo Fornaro, Elena Ongaro, Giuseppe Aprile, Renato Bisonni, Michele Prisciandaro, Jean Gariépy, Fotios Loupakis, Donatella Sarti, Michela Del Prete, Vincenzo Catalano, Mauro Magnani, Annamaria Ruzzo

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineOxaliplatinCapecitabineInternal medicineOncologyChemotherapyCancerRamucirumabCisplatinPTENKRASCancer researchGastroenterologyColorectal cancerApoptosisPI3K/AKT/mTOR pathwayBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.040
GPT teacher head0.396
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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