Application and detection system of markers for tracing tumor tissue of unknown primary lesions

Through liquid biopsy technology, a copy number variation map analysis was carried out on plasma free DNA at the whole genome level, and a classification model was constructed, which solved the accuracy of tumor tissue traceability in unknown primary foci, and achieved efficient and accurate tumor tissue traceability detection, especially the accuracy of 17 tumor types reached 85.8%.

CN118308490BActive Publication Date: 2025-08-29NANJING SHIHE MEDICAL DEVICES CO LTD +1
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Patent Information

Application Number
CN202410730754.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-06-06
Publication Date
2025-08-29
Estimated Expiration
2044-06-06

AI Technical Summary

Technical Problem

It is difficult for the prior art to accurately trace tumor tissues with unknown primary foci, traditional treatment effect is poor, patients have poor prognosis, traditional detection methods have insufficient samples or inconsistencies, and ctDNA detection sensitivity is limited.

Method used

Through liquid biopsy technology, a genome-wide copy number variation map was analyzed for plasma free DNA, a classification model was constructed, and a CNV marker in 140 gene regions was used for tumor tissue traceability, and a H2O AutoML algorithm was used for model training and prediction.

Benefits of technology

It has achieved efficient and accurate traceability of 17 tumor types, with large number of sample verifications, many types of cancers covered, high detection performance, and accuracy of more than 85.8%.

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Abstract

The present invention relates to the use and detection system of markers for tracing the origin of tumor tissues of unknown primary lesions, and belongs to the field of tumor gene detection technology. This study used plasma cfDNA from 17 tumor types, including lung cancer, intestinal cancer, hepatobiliary cancer, gastric cancer, head and neck cancer, kidney cancer, breast cancer, pancreatic cancer, ovarian cancer, prostate cancer, thyroid cancer, cervical cancer, urothelial cancer, esophageal cancer, endometrial cancer, sarcoma, and melanoma, to study the differences in gene copy number variation in different tumor tissues, and screened out regions of gene copy number variation with significant differences. Then, using a method constructed by the H2O AutoML classifier, 140 optimal differential gene copy number variation regions were screened for the first time to establish a diagnostic prediction model for tracing the origin of tumor tissues of unknown primary lesions. The model is suitable for diagnosing and predicting the tissue tracing of tumors of unknown primary lesions, and is suitable for diagnosing the tissue origin of tumors of unknown primary lesions, and for diagnosing people with tumors of unknown primary lesions.
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