Diagnosis of Multiple Cancer and Monitoring of Minimal Residual Tumors After Treatment Using Blood and High-Sensitivity Genetic Analysis Techniques

This is a combined prospective and retrospective observational study aiming to validate a highly sensitive and specific blood-based method for the early diagnosis and post-treatment monitoring of multiple cancers. The study leverages a newly developed sequencing method to improve the detection of circulating tumor DNA (ctDNA) in blood, focusing on enhancing sensitivity and specificity in clinical applications.

The study targets patients with ovarian, lung, pancreatic, colorectal, esophageal, breast, kidney, bladder, and gastric cancer, as well as healthy controls with asymptomatic gallstones, benign polyps, or individuals undergoing routine medical screening. Blood samples will be analyzed for cell-free DNA (cfDNA), RNA, and protein profiles. A key objective is to determine how much the newly developed method increases the sensitivity and specificity of ctDNA detection, especially in early-stage cancers and minimal residual disease (MRD) after treatment.

The method evaluates the variant allele frequency (VAF) of ctDNA to detect residual disease and track tumor dynamics. Serial blood sampling will be conducted before and after surgery or chemotherapy and during follow-up outpatient visits in cancer patients, while one-time sampling will be done for controls. Additionally, tissue biopsies collected during surgery will be used to analyze concordance between tumor-specific mutations and those found in ctDNA.

Primary outcome measures include quantitative differences in ctDNA or RNA levels between cancer and control groups. Secondary outcomes assess the clinical correlation between changes in ctDNA VAF and patient outcomes such as recurrence and survival. Statistical tools including ROC curve analysis, Cox regression, and log-rank tests will be used to quantify performance.

This study seeks to establish a clinically robust, non-invasive diagnostic tool that enables earlier detection and more precise treatment decisions, while potentially reducing physical, psychological, and socioeconomic burdens related to cancer care.