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Precision medicine attempts to individualize cancer therapy by matching tumor-specific genetic changes with effective targeted therapies. A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogeneity of clinical tumor samples. We compared the impact of admixture of non-cancerous cells and low somatic allele frequencies on the sensitivity and precision of 19 state-of-the-art SNV callers. We studied both whole exome and targeted gene panel data and up to 13 distinct parameter configurations for each tool. We found vast differences among callers. Based on our comprehensive analyses we recommend joint tumor-normal calling with MuTect, EBCall or Strelka for whole exome somatic variant calling, and HaplotypeCaller or FreeBayes for whole exome germline calling. For targeted gene panel data on a single tumor sample, LoFreqStar performed best. We further found that tumor impurity and admixture had a negative impact on precision, and in particular, sensitivity in whole exome experiments. At admixture levels of 60% to 90% sometimes seen in pathological biopsies, sensitivity dropped significantly, even when variants were originally present in the tumor at 100% allele frequency. Sensitivity to low-frequency SNVs improved with targeted panel data, but whole exome data allowed more efficient identification of germline variants. Effective somatic variant calling requires high-quality pathological samples with minimal admixture, a consciously selected sequencing strategy, and the appropriate variant calling tool with settings optimized for the chosen type of data.
This article was published in the following journal.
Name: PloS one
Somatic mutations promote the transformation of normal cells to cancer. Accurate identification of such mutations facilitates cancer diagnosis and treatment, but biological and technological noises, i...
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Clinical Data evaluation to document the performance and clinical outcomes of the Comprehensive Segmental Revision System.
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Comprehensive, methodical analysis of complex biological systems by monitoring responses to perturbations of biological processes. Large scale, computerized collection and analysis of the data are used to develop and test models of biological systems.
Information application based on a variety of coding methods to minimize the amount of data to be stored, retrieved, or transmitted. Data compression can be applied to various forms of data, such as images and signals. It is used to reduce costs and increase efficiency in the maintenance of large volumes of data.
Method of measuring performance against established standards of best practice.
Various units or machines that operate in combination or in conjunction with a computer but are not physically part of it. Peripheral devices typically display computer data, store data from the computer and return the data to the computer on demand, prepare data for human use, or acquire data from a source and convert it to a form usable by a computer. (Computer Dictionary, 4th ed.)
A rare but highly lethal childhood tumor found almost exclusively in infants. Histopathologically, it resembles RHABDOMYOSARCOMA but the tumor cells are not of myogenic origin. Although it arises primarily in the kidney, it may be found in other parts of the body. The rhabdoid cytomorphology is believed to be the expression of a very primitive malignant cell. (From Holland et al., Cancer Medicine, 3d ed, p2210)
Bladder Cancer Brain Cancer Breast Cancer Cancer Cervical Cancer Colorectal Head & Neck Cancers Hodgkin Lymphoma Leukemia Lung Cancer Melanoma Myeloma Ovarian Cancer Pancreatic Cancer ...
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