Dataset Information

Somatic mutations in plasma cell-free DNA are diagnostic markers for esophageal squamous cell carcinoma recurrence.

ABSTRACT:

Objectives

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignancies owing to the high frequency of tumor recurrence. The identification of markers for early ESCC diagnosis and prediction of recurrence is expected to improve the long-term prognosis. Therefore, we searched for associations between tumor recurrence and cell-free DNA (cfDNA) mutations in blood plasma, which contains genetic markers for various cancer types.

Experimental design

Genomic DNA from tumors and cfDNA from plasma were obtained from 13 patients undergoing treatment for newly diagnosed ESCC. Next-generation sequencing of cfDNA in plasma was performed to identify mutations in 53 cancer-related genes, in which recurrent mutations were previously detected in ESCC. cfDNA mutational profiles were compared before and after tumor resection in four patients. Furthermore, somatic mutations in serial plasma samples were monitored after treatment in four patients.

Results

We identified multiple concordant somatic mutations in cfDNA and primary tumor samples from 10 patients (83.3%) and in cfDNA and metastatic tumor samples from one patient (100%). Furthermore, the allele frequency of the concordant mutations in cfDNA changed concomitantly with tumor burden and increased approximately 6 months earlier than the detection of tumor recurrences by imaging tests in two patients. Conventional biomarkers, such as SCC and p53-Ab, did not reflect tumor recurrences.

Conclusions

The present multigene panel, which enabled the diagnosis of tumor recurrence with greater accuracy than did using standard tumor markers or imaging methods, is expected to greatly facilitate standard, postoperative follow-up monitoring in ESCC.

SUBMITTER: Ueda M

PROVIDER: S-EPMC5308726 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Publications

Somatic mutations in plasma cell-free DNA are diagnostic markers for esophageal squamous cell carcinoma recurrence.

Ueda Masami M Iguchi Tomohiro T Masuda Takaaki T Nakahara Yujiro Y Hirata Hidenari H Uchi Ryutaro R Niida Atsushi A Momose Kota K Sakimura Shotaro S Chiba Kenichi K Eguchi Hidetoshi H Ito Shuhei S Sugimachi Keishi K Yamasaki Makoto M Suzuki Yutaka Y Miyano Satoru S Doki Yuichiro Y Mori Masaki M Mimori Koshi K

Oncotarget 20160901 38

<h4>Objectives</h4>Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignancies owing to the high frequency of tumor recurrence. The identification of markers for early ESCC diagnosis and prediction of recurrence is expected to improve the long-term prognosis. Therefore, we searched for associations between tumor recurrence and cell-free DNA (cfDNA) mutations in blood plasma, which contains genetic markers for various cancer types.<h4>Experimental design</h4>Genomic DNA f ...[more]

PMID: 27556701

Similar Datasets

Project description:The aim of this study was to develop a diagnostic strategy for esophageal squamous cell carcinoma (ESCC) that combines plasma metabolomics with machine learning algorithms. Plasma-based untargeted metabolomics analysis was performed with samples derived from 88 ESCC patients and 52 healthy controls. The dataset was split into a training set and a test set. After identification of differential metabolites in training set, single-metabolite-based receiver operating characteristic (ROC) curves and multiple-metabolite-based machine learning models were used to distinguish between ESCC patients and healthy controls. Kaplan-Meier survival analysis and Cox proportional hazards regression analysis were performed to investigate the prognostic significance of the plasma metabolites. Finally, twelve differential plasma metabolites (six up-regulated and six down-regulated) were annotated. The predictive performance of the six most prevalent diagnostic metabolites through the diagnostic models in the test set were as follows: arachidonic acid (accuracy: 0.887), sebacic acid (accuracy: 0.867), indoxyl sulfate (accuracy: 0.850), phosphatidylcholine (PC) (14:0/0:0) (accuracy: 0.825), deoxycholic acid (accuracy: 0.773), and trimethylamine N-oxide (accuracy: 0.653). The prediction accuracies of the machine learning models in the test set were partial least-square (accuracy: 0.947), random forest (accuracy: 0.947), gradient boosting machine (accuracy: 0.960), and support vector machine (accuracy: 0.980). Additionally, survival analysis demonstrated that acetoacetic acid was an unfavorable prognostic factor (hazard ratio (HR): 1.752), while PC (14:0/0:0) (HR: 0.577) was a favorable prognostic factor for ESCC. This study devised an innovative strategy for ESCC diagnosis by combining plasma metabolomics with machine learning algorithms and revealed its potential to become a novel screening test for ESCC. Highlights • Six most prevalent diagnostic plasma metabolites were identified in ESCC.• Plasma-metabolite-based machine learning models (PLS, RF, GBM, and SVM) for ESCC diagnosis.• Acetoacetic acid was an unfavorable prognostic factor, while PC (14:0/0:0) was a favorable prognostic factor for ESCC.

Dataset Information

Somatic mutations in plasma cell-free DNA are diagnostic markers for esophageal squamous cell carcinoma recurrence.

Objectives

Experimental design

Results

Conclusions

Publications

Somatic mutations in plasma cell-free DNA are diagnostic markers for esophageal squamous cell carcinoma recurrence.

Similar Datasets

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