Background: When a patient has multiple tumors in different organs, it is very important to identify whether the tumors are multiple cancers or metastasis from one tumor in order to establish an optimal treatment strategy. However, it is difficult to obtain an accurate diagnosis from conventional diagnostic strategies, including immunohistochemistry. We report two patients with multiple tumors in which a somatic mutation comparison using next-generation sequencing (NGS) was useful for the diagnosis of a metastatic tumor.
Case Presentations: Patient 1: A 64-year-old man was diagnosed with gastric and lung cancer. After radical chemoradiotherapy for lung cancer, gastrectomy was planned for gastric cancer. At gastrectomy, the patient underwent a multiple omics analysis for "Project HOPE". The gene mutational signature of the gastric tumor showed signature 4 of COSMIC mutational signature version 2, which was associated with smoking and has not been found in gastric cancer. To confirm that the gastric tumor was metastasis from lung cancer, we conducted a somatic mutation comparison of the two tumors with 409-gene panel sequencing, which revealed that 28 of 97 mutations in the lung tumor completely matched those of the gastric tumor. Based on these findings, the gastric tumor was diagnosed as metastasis from lung cancer. Patient 2: A 47-year-old woman underwent distal gastrectomy for gastric cancer. A colon tumor was detected 6 years after gastrectomy. The colon lesion was a submucosal tumor-like elevated tumor, and was suspected to be metastasis from gastric cancer. The patient underwent sigmoidectomy, and participated in "Project HOPE". The possibility of primary colon cancer could not be ruled out, and we conducted a somatic mutation comparison of the two tumors as we did with Patient 1. Panel sequencing revealed 11 mutations in the gastric tumors, 4 of which completely matched those of the colon tumor. The colon tumor was diagnosed as metastasis from gastric cancer.
Conclusion: We reported two patients with multiple tumors in which a somatic mutation comparison using NGS was useful for the diagnosis of a metastatic tumor.
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http://dx.doi.org/10.1186/s40792-022-01566-8 | DOI Listing |
Int J Mol Sci
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Department of Neuropediatrics, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, and Berlin Institute of Health, 13353 Berlin, Germany.
Epilepsy affects 50 million people worldwide and is drug-resistant in approximately one-third of cases. Even when a structural lesion is identified as the epileptogenic focus, understanding the underlying genetic causes is crucial to guide both counseling and treatment decisions. Both somatic and germline DNA variants may contribute to the lesion itself and/or influence the severity of symptoms.
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Division of Thoracic Surgery, Cantonal Hospital Lucerne, 6000 Lucerne, Switzerland.
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January 2025
Department of Respiratory Diseases, Qilu Hospital of Shandong University, No. 107, Culture West Road, Jinan, Shandong, China.
To integrate machine learning and multiomic data on lactylation-related genes (LRGs) for molecular typing and prognosis prediction in lung adenocarcinoma (LUAD). LRG mRNA and long non-coding RNA transcriptomes, epigenetic methylation data, and somatic mutation data from The Cancer Genome Atlas LUAD cohort were analyzed to identify lactylation cancer subtypes (CSs) using 10 multiomics ensemble clustering techniques. The findings were then validated using the GSE31210 and GSE13213 LUAD cohorts.
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Department of Computational Biology, Cornell University, Ithaca, 14853, NY, USA.
A major goal of cancer biology is to understand the mechanisms driven by somatically acquired mutations. Two distinct methodologies-one analyzing mutation clustering within protein sequences and 3D structures, the other leveraging protein-protein interaction network topology-offer complementary strengths. We present NetFlow3D, a unified, end-to-end 3D structurally-informed protein interaction network propagation framework that maps the multiscale mechanistic effects of mutations.
View Article and Find Full Text PDFMedicine (Baltimore)
January 2025
Department of Otolaryngology, Hangzhou Red Cross Hospital (Zhejiang Hospital of Integrated Traditional Chinese and Western Medicine), Hangzhou, Zhejiang, China.
T-helper 17 (Th17) cells significantly influence the onset and advancement of malignancies. This study endeavor focused on delineating molecular classifications and developing a prognostic signature grounded in Th17 cell differentiation-related genes (TCDRGs) using machine learning algorithms in head and neck squamous cell carcinoma (HNSCC). A consensus clustering approach was applied to The Cancer Genome Atlas-HNSCC cohort based on TCDRGs, followed by an examination of differential gene expression using the limma package.
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