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Cancers Originate from Somatic Mutations in a Single Cell

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

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Comparative Lesions Analysis Through a Targeted Sequencing Approach

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Published on: November 5, 2019

Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level

William Poole¹, Kalle Leinonen¹, Ilya Shmulevich¹

¹Institute for Systems Biology, Seattle, WA, United States of America.

Plos Computational Biology

|February 8, 2017

Summary

This summary is machine-generated.

Researchers developed a new algorithm to identify cancer mutation clusters across various scales within genes. This method reveals functional and clinical impacts, aiding in drug target discovery and personalized cancer treatments.

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Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

Published on: July 11, 2019

Area of Science:

Genomics
Cancer Biology
Bioinformatics

Background:

Somatic mutations in cancer are not uniformly distributed within genes.
Current mutation identification methods often focus on either whole genes or single amino acids, potentially missing crucial patterns.

Purpose of the Study:

To develop and apply a novel multiscale mutation clustering algorithm to identify variable-length mutation clusters in cancer genes.
To investigate the functional and clinical significance of these identified mutation clusters.

Main Methods:

A multiscale mutation clustering algorithm was developed.
The algorithm was applied to mutation data from 539 genes across 23 cancer types from The Cancer Genome Atlas (TCGA).
Statistical associations were made between mutation clusters, gene expression, and drug response data.

Main Results:

1295 mutation clusters were identified across a wide range of scales within 539 cancer genes.
These clusters frequently overlap with functional protein features like domains and phosphorylation sites.
Multiple clusters within single genes (e.g., PTEN, FUBP1, CDH1) showed distinct functional associations.

Conclusions:

The multiscale mutation clustering approach effectively identifies functionally relevant mutation hotspots in cancer genes.
This methodology offers potential for identifying novel drug targets, understanding cancer biology, and advancing personalized cancer therapies.
The algorithm and identified clusters are publicly available for further research and application.