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Context.--KRAS Mutation Test v2 is used for the qualitative detection and identification of 28 mutations in exons 2, 3, and 4 of the human KRAS gene. Objective.--To verify the performance of KRAS Mutation Test v2 and to evaluate its accuracy by correlation with a next-generation sequencing method on Illumina MiSeq. Design.--In this study, we used formalin-fixed, paraffin-embedded tissue and plasma specimens from non-small cell lung cancer, colorectal cancer, and pancreatic cancer patients. Results of specificity, precision, analytical sensitivity, and accuracy as compared with a MiSeq method are reported. Results.--The KRAS Mutation Test v2 demonstrated exquisite sensitivity and specificity and broad coverage of KRAS mutations. Precision was 100% (108 of 108) across all samples, operators, and instruments for formalin-fixed, paraffin-embedded tissue and 99.8% (615 of 616) for plasma. Analytical sensitivity was high with detection of 1% mutant sequence in formalin-fixed, paraffin-embedded tissue samples and as low as 25 mutant sequence copies/ mL for plasma samples. The test also showed high overall concordance for formalin-fixed, paraffin-embedded tumor tissue as well as for plasma specimens when compared with MiSeq sequencing results. Conclusions.--The KRAS Mutation Test v2 is a highly robust, reproducible, and sensitive test for the qualitative detection of 28 mutations in exons 2, 3, and 4 of the KRAS gene in both solid (tissue) and liquid (plasma) biopsies from colorectal cancer, non-small cell lung cancer, and pancreatic cancer, and is a convenient option for KRAS mutation testing. doi: 10.5858/arpa.2017-0471-OA, The era of personalized oncology medicine is about efforts to identify patients likely to benefit from targeted therapy. Aided by the recent advances in molecular biology, biomarkers hold the promise [...]

Leprosy is a chronic dermatologic and neurologic disease caused by the infectious agent Mycobacterium leprae and can lead to severe disabilities; >200,000 new cases are reported annually worldwide, according to [...]

Author(s): Elizabeth A Traxler [1, 2]; Yu Yao [1]; Yong-Dong Wang [3]; Kaitly J Woodard [1]; Ryo Kurita [4]; Yukio Nakamura [4, 5]; Jim R Hughes [6]; Ross C Hardison [...]

Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them--STK11, EGFR, FAT1, SETBP1, KRAS and TP53--can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at (https://github.com/ncoudray/DeepPATH). A convolutional neural network model using feature extraction and machine-learning techniques provides a tool for classification of lung cancer histopathology images and predicting mutational status of driver oncogenes, Author(s): Nicolas Coudray [sup.1] [sup.2] , Paolo Santiago Ocampo [sup.3] , Theodore Sakellaropoulos [sup.4] , Navneet Narula [sup.3] , Matija Snuderl [sup.3] , David Fenyö [sup.5] [sup.6] , Andre L. [...]

Author(s): Elaine R. Mardis 1,*, Marc Ladanyi 2,* Oncology has, arguably, led the development of precision medicine in recent decades, with genomic and molecular advances informing the development of targeted [...]

More than one-quarter of cells in ageing, sun-exposed skin carry mutations that are known to drive cancer--even though the skin continues to function normally. To understand how healthy cells can [...]