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Related Experiment Video

Updated: Dec 10, 2025

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Inferring clonal composition from multiple tumor biopsies.

Matteo Manica^1,2, Hyunjae Ryan Kim³, Roland Mathis¹

¹IBM Research-Zurich, 8803, Rüschlikon, Switzerland.

NPJ Systems Biology and Applications

|August 27, 2020

Summary

This summary is machine-generated.

Understanding tumor clonal evolution is key to cancer research. We developed Chimæra, an algorithm that accurately estimates mutation frequencies by accounting for copy number alterations (CNAs) using multiple tumor biopsies.

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Published on: December 28, 2015

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Published on: December 28, 2015

Area of Science:

Oncology
Genetics
Computational Biology

Background:

Tumor clonal evolution provides insights into cancer development and progression.
Estimating subclone frequencies from tumor biopsies is crucial but challenging due to genetic instability.
Copy number alterations (CNAs) can significantly confound mutation frequency estimates, hindering accurate tumor phylogeny reconstruction.

Purpose of the Study:

To develop a computational method that accurately estimates subclone frequencies in tumors, accounting for the confounding effects of CNAs.
To improve the reconstruction of tumor phylogenies and characterization of cancer subclones.

Main Methods:

Proposed an optimization algorithm named Chimæra.
Utilized profiles from multiple tumor biopsies per patient to account for CNAs.
Validated the algorithm using simulated data and real tumor profiles.

Main Results:

Chimæra provides more accurate mutation frequency estimates compared to existing methods.
The algorithm enables improved tumor phylogeny reconstructions.
Chimæra facilitates better characterization of cancer subclones, including their evolutionary trajectories.

Conclusions:

Accurate estimation of mutation frequencies requires accounting for CNAs.
Chimæra offers a robust solution for analyzing tumor evolution, even in the presence of high genetic instability.
The method has been successfully applied to study initiating mutations in liver cancer, clonal composition in Wilms' tumors, and drug resistance in prostate cancer.