Biology subjects

Martelli, J.

Publications and source records attributed to Martelli, J..

3 recordsLinked to original sources

bioRxiv2026

Artificial Intelligence Applied to Epigenomic Data in Leukemia: A Systematic Review of Diagnostic, Prognostic and Monitoring Models

Background: Epigenetic deregulation is a defining feature of leukemia, and DNA methylation and chromatin accessibility profiles encode the cell of origin, lineage and clinical behaviour of the disease. Artificial intelligence (AI) and machine learning (ML) can extract predictive patterns from these high-dimensional data, but the published models have not been systematically appraised. We systematically reviewed AI models built on epigenomic data in leukemia for diagnosis, prognosis, relapse and treatment-response prediction, and measurable residual disease (MRD) detection. Methods: This review followed PRISMA 2020 and the SWiM guideline. PubMed was searched on 7 October 2026 with three structured queries, supplemented by a broader PubMed query, citation searching and web searching. Eligible studies were peer-reviewed primary studies that applied an ML or deep-learning algorithm to DNA methylation, chromatin-accessibility or histone-modification data from human leukemia samples and reported a quantitative diagnostic, prognostic, response or MRD outcome. Risk of bias and applicability were assessed with PROBAST, informed by PROBAST+AI. Owing to heterogeneity, results were synthesized narratively and in structured tables, without meta-analysis. Results: Of 68 unique database records and 32 reports from other sources, 30 studies (2010-2026; 22 since 2020) were included: 16 in acute myeloid leukemia, 4 in acute lymphoblastic leukemia, 2 spanning acute leukemia lineages, 3 in juvenile myelomonocytic leukemia and 5 in chronic lymphocytic leukemia; 10 were pediatric. DNA methylation was the input in 28 studies and chromatin accessibility in 2; no eligible study modelled histone marks. Tree ensembles (n = 9) and support vector machines (n = 5) were the most frequent algorithms. Diagnostic classifiers reported accuracy, concordance or F1 scores of 0.87-1.00, and two classifiers were applied to nanopore sequencing data, one returning acute leukemia subtype calls within about 2 hours. Prognostic epigenomic scores were associated with survival (reported hazard ratios 2.36-10.82), but discrimination fell on transport to independent cohorts (C-index 0.677 internally vs 0.529 in one external cohort). Fifteen studies validated models on an external epigenomic cohort, one prospectively. Overall risk of bias was high in 21 studies, unclear in 8 and low in 1, driven by the analysis domain (small samples, absent calibration, possible data leakage). Calibration was reported in 3 studies and code was available for 11. Conclusions: Epigenomic AI models classify leukemia subtypes with high reported accuracy and can deliver same-day diagnoses, and methylation-based scores add prognostic information. However, the evidence is dominated by retrospective, high-risk-of-bias development studies with limited external validation and almost no prospective or impact evaluation. Prospective, multi-center validation with calibrated, transparently reported models is required before clinical adoption.

cancer biology↗

bioRxiv2026

Single-Cell Sequencing in Pediatric Acute Leukemia: A Systematic Review of Biologic Insights and Translational Potential

Background: Relapse remains the principal cause of treatment failure in pediatric acute leukemia. Clinically validated minimal residual disease (MRD) assays - multiparameter flow cytometry, immunoglobulin/T-cell-receptor rearrangement sequencing, and targeted next-generation sequencing panels -- are highly sensitive but read out predefined features and do not reconstruct clonal architecture or link a cell's genotype to its functional state. We systematically reviewed what single-cell sequencing has revealed about clonal evolution, chemoresistance, the immune microenvironment, and post-therapeutic immune dynamics in this disease. Methods: This review followed PRISMA 2020. PubMed/MEDLINE, the Cochrane Library, and Google Scholar were searched to September 2026 for primary studies applying single-cell DNA, RNA, chromatin-accessibility, surface-protein, or immune-repertoire sequencing to pediatric acute leukemia samples or pediatric-derived models. Risk of bias in non-randomized studies was assessed with ROBINS-I. Because designs, platforms, and outcomes were heterogeneous, findings were synthesized narratively by biologic theme without meta-analysis, following the SWiM guideline. Results: Of 63 records identified, 10 studies were included (published 2016-2026; 6-159 patients or samples per study; up to ~700,000 cells profiled). Single-cell DNA sequencing traced minor NOTCH1-mutant diagnostic subclones into PTEN- or TP53-mutant relapse clones that bulk sequencing missed. Rare dormant, label-retaining cells and developmental-state-dependent drug sensitivity defined chemoresistant compartments. Remodeling of the bone marrow immune microenvironment identified patients with adverse outcome among those classified as MRD-low, and pre-infusion CAR T-cell type 2 functionality and post-transplant cytotoxic memory CD8+ T-cell subsets were associated with durable remission. Of the seven studies assessed with ROBINS-I, four were at moderate and three at serious overall risk of bias, driven chiefly by small, selected cohorts. Conclusions: Single-cell sequencing yields mechanistic insight that complements, rather than replaces, clinically validated MRD assays, and nominates candidate biomarkers for risk stratification, treatment selection, cell-therapy manufacturing, and post-transplant monitoring. The evidence remains preliminary; adequately powered, prospective, and harmonized studies are required before clinical adoption.

cancer biology↗

bioRxiv2026

Life history stage-dependent nuclear selection in chimeric fungi

A single hyphal compartment of the filamentous fungus Neurospora crassa may contain tens or hundreds of nuclei, sharing macromolecules with each other, and, via a continuous cytoplasm, with the nuclei in other compartments. Nuclear lineages acquire mutations with each mitosis, which, combined with the autonomous mitosis of nuclei, has fueled speculation that multilevel selection may occur, both upon the mycelium, and upon individual nuclear populations. Here, we combine experiments on fungal chimera formed from two auxotrophically and epitopically labeled nuclear populations, with specially created microscopy toolkit for extracting the proportions of the two nucleotypes to analyze the strength of nuclear-level selective forces at different stages in the fungal life history. We find strong nucleotype-selective forces during spore-germination and establishment of the mycelium, and no evidence of selection on nuclei inside a growing mycelium. The kinetics of mycelial initiation from individual spores therefore allow for the selection of nuclear compositions best adapted to the fungus environment, in accordance with the hypothesized function of unicellular life history stages for purging deleterious mutations.

evolutionary biology↗