Biology subjects

Pagani, I. S.

Publications and source records attributed to Pagani, I. S..

2 recordsLinked to original sources

bioRxiv2025

Statistical inference of the cellular origin of chronic myeloid leukemia using a discrete-parameter ABC-PMC framework

Chronic myeloid leukemia (CML) arises from the BCR::ABL1 fusion gene, but the exact stage of cellular differentiation at which the first leukemic cell emerges remains uncertain. We develop a stochastic 27-compartment model of hematopoiesis (blood cell development) using a continuous-time multitype branching process to capture the dynamics of both healthy and cancer cells. To infer the origin of CML, we develop a discrete-parameter Approximate Bayesian Computation - Population Monte Carlo (ABC-PMC) algorithm, tailored to estimate the posterior distribution for the stage of differentiation at which the first cancer cell appeared. Applied to patient data, our method consistently identifies the stem cell compartment as the most likely source of CML. These findings improve understanding of disease initiation and demonstrate the power of discrete-parameter ABC-PMC for statistical inference in complex biological systems. Author summaryChronic myeloid leukemia is a blood cancer that begins when a genetic change called the BCR::ABL1 fusion gene appears in one cell. Although this disease has been widely studied, some questions remain, particularly about the exact stage of blood cell development at which the first cancer cell arises. In our study, we build a stochastic model based on a biological hematopoiesis model that represents how blood cells grow and mature through many stages, from stem cells to fully developed white blood cells. Using this mathematical model, we develop a statistical approach that can infer from patient data where in this hierarchy the disease most likely began. When we apply the method to clinical data from patients with chronic myeloid leukemia, it consistently points to the stem cell stage as the most probable origin. By linking biological data with mathematical modelling, our work offers new insight into how this cancer starts and shows how quantitative approaches can help answer questions that are difficult to test experimentally.

systems biology↗

bioRxiv2025

Mitochondrial DNA Mutations Determine Favourable Molecular Responses to Targeted Kinase Inhibitor Therapy and Impair Oxidative Phosphorylation

Somatic mutations in mitochondrial DNA (mtDNA) are not typically considered key oncogenic drivers of cancer, primarily because of a high synonymous to non-synonymous variant ratio. Here, we surveyed 248 matched diagnosis and remission samples from patients with chronic myeloid leukemia (CML) and found a 75% had mitochondrial mutations with a median number of 2 mutations per patient. mtDNA mutations were predominantly non-synonymous, enriched in the D-loop control region, and likely originated from replication and transcriptional errors. Functionally, mtDNA mutations were associated with reduced oxidative phosphorylation (OXPHOS), as measured by Seahorse analyser. This metabolic vulnerability could be phenocopied by treatment with the complex I inhibitor IACS-10759 in combination with the targeted tyrosine kinase inhibitor (TKI) imatinib, which significantly reduced the colony-forming potential of TKI resistant leukemic stem/progenitor cells (LSPCs). Strikingly, we show that mtDNA mutations were associated with increased sensitivity to imatinib therapy in the clinic. Patients with [≥]3 mutations and patients with mutations in the D-loop showed significantly higher cumulative incidence of major molecular response at 24 months (90% vs. 68%, p = 0.004, and 89% vs 68%, p = 0.004 respectively). Single-cell RNA sequencing further revealed enrichment in non-synonymous mtDNA variants in LSPCs from TKI-sensitive patients, while TKI-resistant cells exhibited upregulated gene signatures related to glycerolipid and phospholipid metabolism and mitochondrial biogenesis. Together, our findings demonstrate that mtDNA mutations are key determinants of sensitivity to targeted therapy, rather than oncogenic drivers of leukemogenesis. Mechanistically, non-synonymous mtDNA mutations appear to restrict mitochondrial metabolic plasticity, with widespread implications for precision oncology.

cancer biology↗