Background: DNA somatic mutations of EGFR, KRAS, BRAF and PIK3CA in the epidermal growth factor receptor (EGFR) signaling pathway play critical roles in the response or resistance of tumors to targeted therapy with tyrosine kinase inhibitors (EGFR-TKIs). To provide a high-throughput (HTP) clinical testing service for detecting these mutations, we developed a novel platform, SurPlex®-xTAG70plex-EGFR liquidchip.
Methods: This platform was developed based on a universal 100-tag system. The procedures for multiplex PCR, allele specific primer extension (ASPE) and hybridization were optimized and standardized.
Results: A total of 70 alleles of somatic mutations of EGFR, KRAS, BRAF and PIK3CA can be detected simultaneously in one reaction from one formalin-fixed and paraffin-embedded (FFPE) slide within one day. Cross-reaction was < 8% between individual amplimers and 70 different ASPE primers. The sensitivity for detecting mutants in the wild-type DNA was 1%-5%. Seventy-three FFPE samples with somatic mutations were used to validate the 70plex. Seventy-one showed a complete match, while two were not detected.
Conclusions: A simple, accurate, sensitive HTP technology was developed and standardized for detecting simultaneously 70 different alleles of EGFR, KRAS, BRAF and PIK3CA gene mutations from FFPE tumor slides.
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http://dx.doi.org/10.1515/CCLM.2011.040 | DOI Listing |
J Egypt Natl Canc Inst
December 2024
Department of Clinical Pathology, Faculty of Veterinary Medicine, Cairo University, Giza, 12211, Egypt.
Background: Lung cancer is a form of cancer that is responsible for the largest incidence of deaths attributed to cancer worldwide. Non-small cell lung cancer (NSCLC) is the most prevalent of all the subtypes of the disease. Treatment with tyrosine kinase inhibitors (TKI) may help some people who have been diagnosed with non-small cell lung cancer.
View Article and Find Full Text PDFJ Card Fail
December 2024
Division of Cardiology, Duke University School of Medicine, Durham, NC; Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, North Carolina; Duke Center for Precision Health, Duke University School of Medicine, Durham NC. Electronic address:
STAR Protoc
December 2024
Princess Máxima Center for Pediatric Oncology, Utrecht 3584 CS, the Netherlands; Oncode Institute, Utrecht 3521 AL, the Netherlands. Electronic address:
The study of somatic mutations in single cells provides insights into aging and carcinogenesis, which is complicated by the dependency on whole-genome amplification (WGA). Here, we describe a detailed workflow starting from single-cell isolation to WGA by primary template-directed amplification (PTA), sequencing, quality control, and downstream analyses. A machine learning approach, the PTA Analysis Toolkit (PTATO), is used to filter the hundreds to thousands of artificial variants induced by WGA from true mutations at high sensitivity and accuracy.
View Article and Find Full Text PDFJ Steroid Biochem Mol Biol
December 2024
Department of Pathology, University of Michigan, Ann Arbor, MI, United States; Rogel Cancer Center, University of Michigan, Ann Arbor, MI, United States; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, United States.
Cushing syndrome represents a multitude of signs and symptoms associated with long-term and excessive exposure to glucocorticoids. Solitary cortisol-producing adenomas (CPAs) account for most cases of ACTH-independent Cushing syndrome (CS). Technological advances in next-generation sequencing have significantly increased our understanding about the genetic landscape of CPAs.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Interuniversity Institute of Bioinformatics (IB2), Université Libre de Bruxelles, Vrije Universiteit Brussel (ULB-VUB), Triomflaan, Brussels 1050, Belgium.
The mutations driving cancer are being increasingly exposed through tumor-specific genomic data. However, differentiating between cancer-causing driver mutations and random passenger mutations remains challenging. State-of-the-art homology-based predictors contain built-in biases and are often ill-suited to the intricacies of cancer biology.
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