Bibliographic Details
| Title: |
Exploring Intratumor Heterogeneity in Cancer: A Comparative Evaluation of Clustering Methods. |
| Authors: |
Ribeiro PH; Federal Institute of São Paulo, Barretos, Brazil., Simao ADS; University of São Paulo, São Carlos, Brazil. |
| Source: |
Journal of computational biology : a journal of computational molecular cell biology [J Comput Biol] 2026 Aug; Vol. 33 (8), pp. 787-803. Date of Electronic Publication: 2026 May 12. |
| Publication Type: |
Journal Article; Comparative Study |
| Language: |
English |
| Journal Info: |
Publisher: Mary Ann Liebert, Inc Country of Publication: United States NLM ID: 9433358 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1557-8666 (Electronic) Linking ISSN: 10665277 NLM ISO Abbreviation: J Comput Biol Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: New York, NY : Mary Ann Liebert, Inc., c1994- |
| MeSH Terms: |
Neoplasms*/genetics , Computational Biology*/methods , Genetic Heterogeneity*, Clustering Algorithms ; Humans ; Algorithms ; Cluster Analysis ; Mutation ; Bayes Theorem ; Gene Frequency |
| Abstract: |
Intratumor heterogeneity (ITH) impacts cancer progression, and its characterization is crucial. Clustering algorithms applied to the variant allele frequency (VAF) of mutations can facilitate the exploratory analysis of ITH. This study comparatively evaluated six clustering algorithms to characterize ITH by clustering mutations based on their VAFs. We utilized data from The Cancer Genome Atlas to analyze three cancer types by examining the distribution of clusters in the results from various methods and four internal validation metrics. The results indicated that the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Variational Bayesian Gaussian Mixture Model methods identified an insufficient number of clusters in most tumor samples. The Hierarchical DBSCAN (HDBSCAN) and Ordering Points to Identify the Clustering Structure (OPTICS) algorithms exhibited greater variability in the number of clusters, while Affinity Propagation (AP) showed controlled behavior, and Mean-Shift demonstrated greater consistency. The Mean-Shift and AP methods were consistently superior in the validation metrics, in contrast to HDBSCAN and OPTICS, which had inferior performance. We conclude that Mean-Shift and AP are promising and accessible alternatives for the initial exploratory analysis of ITH by VAFs. A computational pipeline is provided on the Google Colab platform to facilitate future studies. |
| Competing Interests: |
AUTHOR DISCLOSURE STATEMENTThe authors declare no conflict of interest. |
| Contributed Indexing: |
Keywords: cancer genomics; clustering algorithms; genetic mutations; intratumor heterogeneity |
| Entry Date(s): |
Date Created: 20260512 Date Completed: 20260617 Latest Revision: 20260617 |
| Update Code: |
20260617 |
| DOI: |
10.1177/15578666261449274 |
| PMID: |
42117677 |
| Database: |
MEDLINE |