Driver mutations

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Kin-Driver: a database of driver mutations in protein kinases

Somatic mutations in protein kinases (PKs) are frequent driver events in many human tumors, while germ-line mutations are associated with hereditary diseases. Here we present Kin-driver, the first database that compiles driver mutations in PKs with experimental evidence demonstrating their functional role. Kin-driver is a manual expert-curated database that pays special attention to activating ...

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Synonymous Mutations Frequently Act as Driver Mutations in Human Cancers

Synonymous mutations change the sequence of a gene without directly altering the sequence of the encoded protein. Here, we present evidence that these "silent" mutations frequently contribute to human cancer. Selection on synonymous mutations in oncogenes is cancer-type specific, and although the functional consequences of cancer-associated synonymous mutations may be diverse, they recurrently ...

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Molecular Epidemiology of Lung Cancer Driver Mutations

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Abstract Purpose: The molecular epidemiology of most EGFR and KRAS mutations in lung cancer remains unclear. Experimental Design: We genotyped 3026 lung adenocarcinomas for the major EGFR (exon 19 deletions and L858R) and KRAS (G12, G13) mutations and examined correlations with demographic, cli...

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A Landscape of Driver Mutations in Melanoma

Despite recent insights into melanoma genetics, systematic surveys for driver mutations are challenged by an abundance of passenger mutations caused by carcinogenic UV light exposure. We developed a permutation-based framework to address this challenge, employing mutation data from intronic sequences to control for passenger mutational load on a per gene basis. Analysis of large-scale melanoma ...

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Driver and passenger mutations in cancer.

Next-generation sequencing has allowed identification of millions of somatic mutations and epigenetic changes in cancer cells. A key challenge in interpreting cancer genomes and epigenomes is distinguishing which genetic and epigenetic changes are drivers of cancer development. Frequency-based and function-based approaches have been developed to identify candidate drivers; we discuss the advant...

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ژورنال

عنوان ژورنال: Cancer Biology & Therapy

سال: 2010

ISSN: 1538-4047,1555-8576

DOI: 10.4161/cbt.10.6.13128