ArticleNature communications2025
Network-based clustering unveils interconnected landscapes of genomic and clinical features across myeloid malignancies.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Proteomic Remodeling in Fontan Circulation Associated with Fibrosis and Time-Dependent Immune-Metabolic Alteration.International journal of molecular sciences · 2026Article
- iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data.PLoS computational biology · 2026Article
- AI-guided integration of aging-related functional assessment to predict nonresponse to cardiac rehabilitation in heart failure.The journal of nutrition, health & aging · 2026Article
- Care demand networks in maternity care - an innovative approach exploring the complexity of care demands with routine data: Retrospective observational study.International journal of nursing studies advances · 2026Article
- Integrative Bioinformatics Reveals Novel Molecular Mechanisms and Therapeutic Targets in Acute Myeloid Leukaemia.Journal of cellular and molecular medicine · 2026Article
- Bayesian inference of fitness landscapes via tree-structured branching processes.Bioinformatics (Oxford, England) · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
Myeloid malignancies exhibit considerable heterogeneity with overlapping clinical and genetic features among subtypes. We present a data-driven approach that integrates mutational features and clinical covariates at diagnosis within networks of their probabilistic relationships, enabling the discovery of patient subgroups. A key strength is its ability to include presumed causal directions in the edges linking clinical and mutational features, and account for them aptly in the clustering. In a cohort of 1323 patients, we identify subgroups that outperform established risk classifications in prognostic accuracy. Our approach generalises well to unseen cohorts with classification based on our subgroups similarly offering advantages in predicting prognosis. Our findings suggest that mutational patterns are often shared across myeloid malignancies, with distinct subtypes potentially representing evolutionary stages en route to leukemia. With pancancer TCGA data, we observe that our modelling framework extends naturally to other cancer types while still offering improvements in subgroup discovery.
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