Scholarlink

Background <p>Paclitaxel plus ramucirumab (PTX + RAM) is a widely used second-line treatment for advanced gastric cancer, yet no validated biomarkers exist to predict therapeutic response. Identifying non-invasive predictors could enable patient stratification and optimize outcomes.</p> Methods <p>We conducted a prospective observational multicenter study (IVY trial; NCT06490055) enrolling 115 patients with advanced gastric cancer treated with PTX + RAM. Serum was collected prior to the initiation of treatment. Small RNA sequencing identified differentially expressed exosomal microRNAs (exo-miRNAs) in patients with controlled disease versus those with progressive disease. Machine learning and logistic regression were employed to construct a predictive model, which was subsequently validated using quantitative real-time polymerase chain reaction (qRT-PCR) in the entire cohort.</p> Results <p>Ten candidate exo-miRNAs were initially discovered, and a five-miRNA panel (miR-10a-5p, miR-25-5p, miR-125a-5p, miR-139-5p, and miR-450a-5p) was selected via stepwise elimination. This 5-exo-miRNA model achieved high accuracy in distinguishing controlled disease patients from progressive disease patients (AUC = 0.84). When combined with body mass index (BMI), the composite model (EXEMPLAR) demonstrated enhanced predictive performance (AUC = 0.87). High-risk patients exhibited significantly shorter progression-free survival (PFS: median, 1.9 vs. 4.2 months, <i>p</i> = 0.019) and overall survival (OS: median, 1.1 vs. 1.7 years, <i>p</i> &lt; 0.001). Decision curve analysis confirmed the clinical benefit of the model. A nomogram was developed to facilitate personalized risk assessment.</p> Conclusions <p>This study identifies and validates a novel 5-exo-miRNA panel for predicting response to second-line PTX plus RAM therapy in gastric cancer. The combined exosomal signature and BMI risk model provides a clinically applicable, non-invasive tool for personalized treatment selection.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A machine-learning powered liquid biopsy predicts response to paclitaxel plus ramucirumab in advanced gastric cancer: results from the prospective IVY trial

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月22日

查看全文
  • Katsutoshi Shoda,
  • Caiming Xu,
  • Takeshi Nagasaka,
  • Daisuke Ichikawa,
  • Ajay Goel

摘要

Background

Paclitaxel plus ramucirumab (PTX + RAM) is a widely used second-line treatment for advanced gastric cancer, yet no validated biomarkers exist to predict therapeutic response. Identifying non-invasive predictors could enable patient stratification and optimize outcomes.

Methods

We conducted a prospective observational multicenter study (IVY trial; NCT06490055) enrolling 115 patients with advanced gastric cancer treated with PTX + RAM. Serum was collected prior to the initiation of treatment. Small RNA sequencing identified differentially expressed exosomal microRNAs (exo-miRNAs) in patients with controlled disease versus those with progressive disease. Machine learning and logistic regression were employed to construct a predictive model, which was subsequently validated using quantitative real-time polymerase chain reaction (qRT-PCR) in the entire cohort.

Results

Ten candidate exo-miRNAs were initially discovered, and a five-miRNA panel (miR-10a-5p, miR-25-5p, miR-125a-5p, miR-139-5p, and miR-450a-5p) was selected via stepwise elimination. This 5-exo-miRNA model achieved high accuracy in distinguishing controlled disease patients from progressive disease patients (AUC = 0.84). When combined with body mass index (BMI), the composite model (EXEMPLAR) demonstrated enhanced predictive performance (AUC = 0.87). High-risk patients exhibited significantly shorter progression-free survival (PFS: median, 1.9 vs. 4.2 months, p = 0.019) and overall survival (OS: median, 1.1 vs. 1.7 years, p < 0.001). Decision curve analysis confirmed the clinical benefit of the model. A nomogram was developed to facilitate personalized risk assessment.

Conclusions

This study identifies and validates a novel 5-exo-miRNA panel for predicting response to second-line PTX plus RAM therapy in gastric cancer. The combined exosomal signature and BMI risk model provides a clinically applicable, non-invasive tool for personalized treatment selection.