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Llaneza-Lago, Sergio, Fraser, William D. and Green, Darrell 
ORCID: https://orcid.org/0000-0002-0217-3322
(2025)
Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.
Briefings in Bioinformatics, 26 (1).
ISSN 1467-5463
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Abstract
Identification of cancer subtypes is a critical step for developing precision medicine. Most cancer subtyping is based on the analysis of RNA sequencing (RNA-seq) data from patient cohorts using unsupervised machine learning methods such as hierarchical cluster analysis, but these computational approaches disregard the heterogeneous composition of individual cancer samples. Here, we used a more sophisticated unsupervised Bayesian model termed latent process decomposition (LPD), which handles individual cancer sample heterogeneity and deconvolutes the structure of transcriptome data to provide clinically relevant information. The work was performed on the pediatric tumor osteosarcoma, which is a prototypical model for a rare and heterogeneous cancer. The LPD model detected three osteosarcoma subtypes. The subtype with the poorest prognosis was validated using independent patient datasets. This new stratification framework will be important for more accurate diagnostic labeling, expediting precision medicine, and improving clinical trial success. Our results emphasize the importance of using more sophisticated machine learning approaches (and for teaching deep learning and artificial intelligence) for RNA-seq data analysis, which may assist drug targeting and clinical management.
| Item Type: | Article |
|---|---|
| Additional Information: | Data availability statement: This study used publicly available data available for download from the original citations used in the text. Funding information: This work was funded by Children with Cancer UK [grant 21–343]. Additional support was received by research grants from the Norfolk Community Foundation through Sophie’s Sparkle Fund and the Peter Stebbings Memorial Charity plus donations made to the Childhood, Adolescent and Young Adult Cancer Research Programme at UEA. |
| Uncontrolled Keywords: | heterogeneity,latent process decomposition,osteosarcoma,precision medicine,rna-seq,information systems,molecular biology,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/1700/1710 |
| Faculty \ School: | Faculty of Medicine and Health Sciences > Norwich Medical School |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health Faculty of Medicine and Health Sciences > Research Groups > Musculoskeletal Medicine |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 04 Dec 2024 01:42 |
| Last Modified: | 17 Jan 2025 01:06 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/97892 |
| DOI: | 10.1093/bib/bbae665 |
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