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Comparative Lesions Analysis Through a Targeted Sequencing Approach

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Published on: November 5, 2019

Intra-clustering analysis reveals tissue-specific mutational patterns.

Stamatis Choudalakis¹, George A Kastis², Nikolaos Dikaios²

¹Mathematics Research Center, Academy of Athens, 4, Soranou Efesiou str., 11527 Athens, Greece; Medical School of Athens, National and Kapodistrian University of Athens, 75, Mikras Asias str., 11527 Athens, Greece.

Computer Methods and Programs in Biomedicine

|March 6, 2025

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This study introduces a novel intra-clustering analysis to identify rare cancer mutation patterns, revealing new insights into tumor heterogeneity and potential clinical markers. The method uncovered 42 new mutational patterns across 21 cancer types.

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Cancer mutational patterns are challenging to identify due to low mutation frequency and high inter-tumoral heterogeneity.
Existing methods struggle to uncover infrequent mutational signatures within diverse cancer types.

Purpose of the Study:

To develop and apply a novel intra-clustering analysis to uncover infrequent mutational patterns in cancer.
To address the challenge of inter-tumoral heterogeneity in cancer genomics.

Main Methods:

Constructed a network graph of 8303 patients and 198 genes using The Cancer Genome Atlas (TCGA) mutation data.
Employed two methodologies (modularity index and network dynamics) for patient-gene grouping.
Utilized a two-phase intra-clustering analysis with Fisher's exact test and multiple correspondence analysis (MCA) with DISCOVER, applying a 5% Benjamini-Hochberg false discovery rate.

Keywords:

Cancer Clustering analysis Graph theory Somatic point mutations

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Last Updated: May 24, 2025

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

Published on: July 11, 2019

A Next-generation Tissue Microarray ngTMA Protocol for Biomarker Studies

A Next-generation Tissue Microarray ngTMA Protocol for Biomarker Studies

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Main Results:

Analyzed 24 patient groups (2619 patients) across 21 cancer types, identifying 42 novel mutational patterns.
Identified AMER1 mutations as a potential differentiator between colon and rectal adenocarcinomas.
Highlighted RAC1's significant presence in head and neck squamous cell carcinoma.
Found EP300 mutations in head and neck squamous cell carcinoma to be independent of HPV status.
Demonstrated that mutational clusters can include patients with contrasting genetic alterations.

Conclusions:

The intra-clustering analysis successfully extracted statistically significant relationships within patient subgroups.
The method uncovered potentially clinically relevant connections and helped disentangle cancer mutational heterogeneity.
This approach offers a new way to identify subtle yet significant mutational patterns in cancer genomics.