Myeloid neoplasms (MN), including acute myeloid leukaemia (AML), myeloproliferative neoplasms (MPN), and myelodysplastic syndromes (MDS), are complex blood cancers driven by genetic, immune, and environmental factors. While immune dysregulation and inflammation are known to influence disease progression and treatment response, the role of the gut microbiome in these processes remains poorly understood. Emerging evidence suggests that gut microbes communicate with the immune system and bone marrow, forming a gut–immunome–bone marrow (BM) axis that may shape disease outcomes and therapeutic efficacy (1,2).
This PhD project pioneers an integrative systems biology approach to unravel the mechanistic links between the gut microbiome, immune regulation, and bone marrow function in MN. It will combine multi-omics data—including gut metagenomics, bone marrow single-cell RNA sequencing (scRNA-seq), and genomic profiling—with artificial intelligence (AI) methods such as graph neural networks (GNNs) and genome-scale biological network modelling (3). This framework will not only enhance precision in disease stratification but also reveal causal mechanisms underlying immune–microbial interactions, informing the development of personalized immunotherapies and infection-prevention strategies.
The project aims to define the gut–immunome–BM axis and its clinical relevance in MN. Specific objectives include: (i) identifying microbiome and immune signatures linked to disease genotype and phenotype (4); (ii) mapping bone marrow immune heterogeneity at single-cell resolution; (iii) integrating multi-omics data into an AI-ready predictive model (5,6); and (iv) developing a decision-support platform for precision stratification and clinical outcomes.
1. Tentori et al, Immune-monitoring of myelodysplastic neoplasms: Recommendations from the i4MDS consortium. Hemasphere. 2024 May 15;8(5):e64. doi: 10.1002/hem3.64.
2. Bersanelli et al, Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes. J Clinical Oncology 2021 Apr 10;39(11):1223-1233. doi: 10.1200/JCO.20.01659.
3. Shi, M., Méar, L., Karlsson, M. et al. A resource for whole-body gene expression map of human tissues based on integration of single cell and bulk transcriptomics. Genome Biol 26, 152 (2025). https://doi.org/10.1186/s13059-025-03616-4
4. Lee et al, Global compositional and functional states of the human gut microbiome in health and disease. 2024, doi: 10.1101/gr.278637.123. Genome Res. 2024. 34: 967-978
5. Bidkhori et al, MIGRENE: The Toolbox for Microbial and Individualized GEMs, Reactobiome and Community Network Modelling. Metabolites 2024, 14(3), 132; https://doi.org/10.3390/metabo14030132
6. Rodriguez-Mier, P., Garrido-Rodriguez, M., Gabor, A. et al. Unifying multi-sample network inference from prior knowledge and omics data with CORNETO. Nat Mach Intell 7, 1168–1186 (2025). https://doi.org/10.1038/s42256-025-01069-9