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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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Updated: Nov 22, 2025

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

A method for subtype analysis with somatic mutations.

Meiling Liu¹, Yang Liu², Michael C Wu¹

¹Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.

Bioinformatics (Oxford, England)

|January 8, 2021

Summary

This summary is machine-generated.

We developed a new method, Subtype Analysis with Somatic Mutations (SASOM), to analyze the link between cancer subtypes and somatic mutations. SASOM improves the identification of genetic pathways associated with specific cancer subtypes, aiding targeted therapy development.

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Published on: August 23, 2024

Area of Science:

Genomics
Cancer Biology
Statistical Genetics

Background:

Cancer is a heterogeneous disease with diverse subtypes.
Understanding cancer subtypes and genetic variations is crucial for targeted therapies.
Somatic mutations are key in tumor development but challenging to analyze due to low prevalence.

Purpose of the Study:

To develop a statistical approach for analyzing associations between cancer subtypes and somatic mutations.
To incorporate functional information of mutations into association analysis.
To provide a robust method for identifying significant genetic pathways linked to cancer subtypes.

Main Methods:

Proposed Subtype Analysis with Somatic Mutations (SASOM) approach.
Tested association between sets of somatic mutations (genetic pathways) and cancer subtypes.
Incorporated functional information of mutations.
Developed a Data-driven p-value Combination (DAPC) procedure for synthesizing statistical significance.

Main Results:

Simulation studies demonstrated SASOM's correct type I error and increased power compared to alternatives.
Applied SASOM to cutaneous melanoma data.
Identified a genetic pathway significantly associated with immune-related subtypes.

Conclusions:

SASOM is a powerful and robust method for analyzing cancer subtype associations with somatic mutations.
The approach aids in identifying clinically relevant genetic pathways.
Findings contribute to the development of targeted therapies for cancer patients.