AI Article Synopsis
- Tumours, especially in follicular lymphoma, show significant genetic and transcriptional diversity that influences cancer development and treatment strategies.
- The study introduces CaClust, an advanced model that combines various genomic data types to better understand tumor evolution and how genotypes translate into phenotypes.
- CaClust demonstrates improved performance over existing models and offers insights into mutations driving the disease, potential treatment targets, and confirms findings through single-cell analysis.
Article Abstract
Tumours exhibit high genotypic and transcriptional heterogeneity. Both affect cancer progression and treatment, but have been predominantly studied separately in follicular lymphoma. To comprehensively investigate the evolution and genotype-to-phenotype maps in follicular lymphoma, we introduce CaClust, a probabilistic graphical model integrating deep whole exome, single-cell RNA and B-cell receptor sequencing data to infer clone genotypes, cell-to-clone mapping, and single-cell genotyping. CaClust outperforms a state-of-the-art model on simulated and patient data. In-depth analyses of single cells from four samples showcase effects of driver mutations, follicular lymphoma evolution, possible therapeutic targets, and single-cell genotyping that agrees with an independent targeted resequencing experiment.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536712 | PMC |
| http://dx.doi.org/10.1186/s13059-024-03417-1 | DOI Listing |
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