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Related Experiment Video

Updated: Nov 16, 2025

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Multiregion Sequence Analysis to Predict Intratumor Heterogeneity and Clonal Evolution.

Soyeon Ahn¹, Haiyan Huang^2,3

¹Division of Statistics, Medical Research Collaborating Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.

Methods in Molecular Biology (Clifton, N.J.)

|February 19, 2021

Summary

This summary is machine-generated.

Multiregion sequencing provides insights into tumor heterogeneity and evolution. This study details multiregion sequencing methods, analysis protocols, and tumor evolution models for DNA sequencing data.

Keywords:

Clonal evolution Clone ordering Heterogeneity Multiregional sequencing Phylogenetic tree

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Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Intratumor heterogeneity (ITH) and clonal evolution are key drivers of cancer progression and treatment resistance.
Understanding these processes is crucial for developing effective cancer therapies.

Purpose of the Study:

To provide a comprehensive overview of multiregion sequencing.
To discuss study design, sampling strategies, and tumor evolution models.
To present a protocol for analyzing DNA sequencing data from multiregion samples.

Main Methods:

Review of multiregion sequencing principles and applications.
Discussion of computational and statistical methods for analyzing ITH.
Outline of a standardized protocol for DNA sequencing data analysis.

Main Results:

Multiregion sequencing enables detailed mapping of intratumor genetic diversity.
Analysis reveals complex clonal architectures and evolutionary trajectories within tumors.
The proposed protocol facilitates robust identification of subclonal mutations.

Conclusions:

Multiregion sequencing is a powerful approach to study tumor evolution.
Standardized analysis protocols are essential for reproducible research in cancer genomics.
This work provides a framework for advancing the understanding of ITH.