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Related Experiment Video

Updated: Jun 17, 2026

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

Statistical method on nonrandom clustering with application to somatic mutations in cancer.

Jingjing Ye¹, Adam Pavlicek, Elizabeth A Lunney

¹Global Pre-Clinical Statistics, Pfizer Global Research and Development, San Diego, CA 92121, USA. Jingjing.Ye@pfizer.com

BMC Bioinformatics

|January 8, 2010

Summary

This study introduces a novel statistical method to identify activating driver mutations in cancer by detecting nonrandom clusters of amino acid changes in proteins. This approach aids in discovering new cancer targets and potential therapeutic interventions.

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Characterizing Mutational Load and Clonal Composition of Human Blood

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Published on: July 11, 2019

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

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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:

Human cancers arise from accumulated oncogenic and tumor suppressor mutations, alongside non-contributory passenger mutations.
Distinguishing driver from passenger mutations is crucial, yet identifying activating driver mutations for targeted therapy remains challenging.

Purpose of the Study:

To develop a statistical method for detecting activating driver mutations in cancer.
To specifically identify mutations amenable to pharmacological intervention.

Main Methods:

A novel statistical approach using order statistics to identify nonrandom clusters of amino acid mutations in protein sequences.
A probability model based on the uniform distribution of mutation locations and analysis of order statistic differences.

Main Results:

The method successfully identified known activating mutation clusters in KRAS, BRAF, PI3K, and beta-catenin using the COSMIC database.
Demonstrated capability to detect novel cancer targets and gain-of-function mutations in tumor suppressors.

Conclusions:

The proposed statistical method effectively identifies activating driver mutations by analyzing clusters of somatic amino acid mutations.
This tool aids in the discovery of new cancer targets and mutations for therapeutic development.