Method for monitoring esophageal squamous cell carcinoma treatment effect based on multi-omics dynamic ctDNA

By employing multi-omics analysis and machine learning models, this study addresses the shortcomings in assessing the response and prognosis of neoadjuvant immunotherapy for esophageal squamous cell carcinoma in existing technologies. It enables precise monitoring and prediction of the efficacy of treatment for esophageal squamous cell carcinoma, thereby improving the accuracy of treatment monitoring.

CN118448038BActive Publication Date: 2026-07-24TIANJIN UNIV
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Patent Information

Application Number
CN202410509225.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-04-26
Publication Date
2026-07-24
Estimated Expiration
2044-04-26

AI Technical Summary

Technical Problem

Current technologies are not yet able to effectively utilize ctDNA to assess the response and prognosis of patients with locally advanced esophageal squamous cell carcinoma to neoadjuvant immunotherapy, and there is a lack of accurate monitoring methods.

Method used

Through multi-omics analysis, including ctDNA targeted sequencing, cfMeDIP-seq, WGBS differential methylation region identification, TCGA 450K methylation chip differential methylation probe identification, and cfMeDIP-seq differential methylation region identification, a methylation risk score and immune index were constructed. Combined with a logistic regression model, the efficacy of treatment for esophageal squamous cell carcinoma was predicted.

Benefits of technology

It enabled accurate prediction and prognostic assessment of the response to neoadjuvant immunotherapy for esophageal squamous cell carcinoma, improved the monitoring accuracy of treatment effects, and achieved a classification model accuracy of 88%.

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Abstract

The application discloses a method for monitoring esophageal squamous cell carcinoma treatment effect based on multi-omics dynamic ctDNA, comprising the following steps: S1, analyzing ctDNA targeted sequencing data and cfMeDIP-seq data; S2, identifying WGBS differential methylation regions; S3, identifying TCGA450K methylation chip differential methylation probes; S4, identifying cfMeDIP-seq differential methylation regions; S5, methylation risk scoring; S6, defining a methylation immune index as an evaluation index for evaluating the prognosis of neoadjuvant immunotherapy; and S7, constructing a multi-omics mixed model by using a Logistic Regression method, so that a model risk score is obtained. The application integrates dynamic characteristics of genomics and epigenetics at multiple time points, constructs a machine learning model, predicts the response of esophageal squamous cell carcinoma neoadjuvant immunotherapy, and evaluates the primer, probe combination and classification model of esophageal cancer prognosis.
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