Signatures of mutational processes in human cancer

Signatures of mutational processes in human cancer

22 AUGUST 2013 | VOL 500 | 415 | A list of authors and their affiliations appears at the end of the paper
The study analyzed 4,938,362 mutations from 7,042 cancers to identify mutational signatures, which are imprints of the mutational processes that generate somatic mutations. Over 20 distinct signatures were identified, some common across multiple cancer types and others specific to a single type. These signatures are associated with factors such as patient age, known mutagenic exposures, and DNA maintenance defects. The presence of hypermutation in small genomic regions, known as "kataegis," was also observed in many cancer types. The findings highlight the diversity of mutational processes underlying cancer development and have implications for understanding cancer etiology, prevention, and therapy. The study used advanced sequencing technology to overcome previous limitations and developed an algorithm to extract mutational signatures from large datasets. The results provide insights into the mechanisms of cancer development and suggest potential targets for future research and therapeutic interventions.The study analyzed 4,938,362 mutations from 7,042 cancers to identify mutational signatures, which are imprints of the mutational processes that generate somatic mutations. Over 20 distinct signatures were identified, some common across multiple cancer types and others specific to a single type. These signatures are associated with factors such as patient age, known mutagenic exposures, and DNA maintenance defects. The presence of hypermutation in small genomic regions, known as "kataegis," was also observed in many cancer types. The findings highlight the diversity of mutational processes underlying cancer development and have implications for understanding cancer etiology, prevention, and therapy. The study used advanced sequencing technology to overcome previous limitations and developed an algorithm to extract mutational signatures from large datasets. The results provide insights into the mechanisms of cancer development and suggest potential targets for future research and therapeutic interventions.
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