Trial reportGut2022
Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial.
Trial report in Gut, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06490055 (Predicting the Efficacy of Paclitaxel Plus Ramucirumab in Advanced Gastric Cancer.), which is not on this map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Predicting the Efficacy of Paclitaxel Plus Ramucirumab in Advanced Gastric Cancer.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled it
- Paclitaxel Nanomedicines: Molecular Mechanisms of Drug Resistance, Tumor Microenvironment-Responsive Delivery, and Translational Challenges.International journal of molecular sciences · 2026Review
- Molecular Subtypes of Mixed Gastric Cancer Defined by Machine Learning for Predicting Prognosis and Treatment Response.Current medicinal chemistry · 2026Article
- A machine-learning powered liquid biopsy predicts response to paclitaxel plus ramucirumab in advanced gastric cancer: results from the prospective IVY trial.Molecular cancer · 2025Observational
- Development of a diagnostic model for ovarian cancer based on machine learning algorithms and functional analysis of key biomarker SOX17.Journal of ovarian research · 2025Article
- Utility of comprehensive genomic profiling combined with machine learning for prognostic stratification in stage II/III colorectal cancer after adjuvant chemotherapy.International journal of clinical oncology · 2025Article
- Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer.Journal of personalized medicine · 2025Review
- Predicting chemotherapy responsiveness in gastric cancer through machine learning analysis of genome, immune, and neutrophil signatures.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- The clinical application of artificial intelligence in cancer precision treatment.Journal of translational medicine · 2025Review
- Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.Current medicinal chemistry · 2025Review
- Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection.Computational and structural biotechnology journal · 2024Article
- Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms.Scientific reports · 2024Article
- RNA expression of 6 genes from metastatic mucosal gastric cancer serves as the global prognostic marker for gastric cancer with functional validation.British journal of cancer · 2024Article
- Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities.Life (Basel, Switzerland) · 2024Review
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- Article
- Artificial intelligence across oncology specialties: current applications and emerging tools.BMJ oncology · 2024Review
- Identification of intraoperative hypoxemia and hypoproteinemia as prognostic indicators in anastomotic leakage post-radical gastrectomy: an 8-year multicenter study utilizing machine learning techniques.Frontiers in oncology · 2024Article
- Recent advancement in targeted therapy and role of emerging technologies to treat cancer.Medical oncology (Northwood, London, England) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors at 16 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo date, there are no predictive biomarkers to guide selection of patients with gastric cancer (GC) who benefit from paclitaxel. Stomach cancer Adjuvant Multi-Institutional group Trial (SAMIT) was a 2×2 factorial randomised phase III study in which patients with GC were randomised to Pac-S-1 (paclitaxel +S-1), Pac-UFT (paclitaxel +UFT), S-1 alone or UFT alone after curative surgery.
designThe primary objective of this study was to identify a gene signature that predicts survival benefit from paclitaxel chemotherapy in GC patients. SAMIT GC samples were profiled using a customised 476 gene NanoString panel. A random forest machine-learning model was applied on the NanoString profiles to develop a gene signature. An independent cohort of metastatic patients with GC treated with paclitaxel and ramucirumab (Pac-Ram) served as an external validation cohort.
resultsFrom the SAMIT trial 499 samples were analysed in this study. From the Pac-S-1 training cohort, the random forest model generated a 19-gene signature assigning patients to two groups: Pac-Sensitive and Pac-Resistant. In the Pac-UFT validation cohort, Pac-Sensitive patients exhibited a significant improvement in disease free survival (DFS): 3-year DFS 66% vs 40% (HR 0.44, p=0.0029). There was no survival difference between Pac-Sensitive and Pac-Resistant in the UFT or S-1 alone arms, test of interaction p<0.001. In the external Pac-Ram validation cohort, the signature predicted benefit for Pac-Sensitive (median PFS 147 days vs 112 days, HR 0.48, p=0.022).
conclusionUsing machine-learning techniques on one of the largest GC trials (SAMIT), we identify a gene signature representing the first predictive biomarker for paclitaxel benefit. TRIAL REGISTRATION NUMBER: UMIN Clinical Trials Registry: C000000082 (SAMIT); ClinicalTrials.gov identifier, 02628951 (South Korean trial).
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.