Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.

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 osteosarcom

SUBMITTER: Llaneza-Lago S 

PROVIDER: S-EPMC11658815 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature