Dissecting the coordinated dynamics of genetic lesions and micro-environment in acute lymphoblastic leukemia
Acute lymphoblastic leukemia (ALL) is a malignant transformation and proliferation of lymphoid progenitor cells in the bone marrow, peripheral blood and extra-medullary sites. ALL typically harbor a low burden of somatic mutations, including inherited variants, chromosomal rearrangements, secondary genomic alterations, that can be enriched in minor clones or acquired after the initiation of therapy. Despite the high rate of response to induction therapy, only 30-40% of adult patients with ALL achieve long-term remission facing multiple recurrent relapse of the disease. The molecular basis of relapse is still poorly understood. Tumor cells evolve in concert with immune cells in the micro environment. Beside a role of genetic alterations, less is known about the impact of non-malignant immune cells on ALL relapse. The heterogeneity of ALL response to treatment relies not only on the intrinsic genetic properties of leukemic cells but also on the complex network of interactions between leukemic cells and surrounding non-neoplastic cells, i.e. T cells, monocytes / macrophages, mesenchymal cells and vascular endothelial cells. Micro environmental cells may establish a mutualistic cross talk with ALL cells both by secreting soluble factors and by direct cell-to-cell contact. Overall, during the last decade, the exhaustion and dysfunctional activity of T-cell compartment is emerging as a hallmark of ALL patients. In line with this notion, innovative immunotherapeutic approaches able to bypass the immune impairment have risen as a major breakthrough in the treatment of relapsed ALL patients. To date, few data are available to define the prognostic impact of T-cell exhaustion and monocytic dysfunctions in ALL patients during the course of disease. In this research line patients with acute lymphoblastic leukemia (ALL) will be categorized into specific genetic subsets using targeted RNA-seq tool and, concomitantly, bone marrow micro-environment will be dissected at transcriptional level using Single-Cell Analysis and Spatial Profiling. Then, we will conduct a feasibility study on 6 matched ALL samples (matched-cohort) collected at diagnosis, at complete remission and at relapse. A prospective collection of additional matched bone marrow samples of frozen cells, plasma samples and FFPE sections will be performed during the two years duration of the project to create a biologic archive necessary to develop a validation study on a larger cohort of ALL patients.
Optimizing the characterization of acute lymphoblastic leukemia by optical genome mapping
Despite the high rate of response to induction therapy, only 30-40% of adult patients with acute lymphoblastic leukemia (ALL) achieve long-term remission facing multiple recurrent relapses of the disease. ALL is driven by sentinel genetic alterations involving transcription factors, regulating lymphoid development, tumor suppressors, genes regulating cell cycle progression, or epigenetic modifiers. Molecular subtypes of ALL are increasingly becoming essential determinants for more accurate risk stratification, and treatment. Despite in recent years several new agents have been approved for the treatment of ALL, resulting in a tremendous improvement in long-term survival of patients and refinements in risk stratification have enabled escalation and de-escalation of therapy, a precise molecular characterization of some subtypes of ALL frequently remains undefined. Although the intensive development of sequencing in the last decade has made it possible to broaden the study of genomic changes, the characterization of the molecular events is still far from being fully defined. In fact, most genomic alterations that determine the biology of these hematological neoplasms are structural variants such as aneuploidy, chromosome rearrangements, DNA copy number changes which, by their nature, are difficult to explore by next-generation sequencing (NGS). Although chromosomal microarrays analysis (SNParrays) has greatly contributed to defining many unbalanced genomic aberrations and third-generation NGS, single-molecule NGS, looks very promising for studying balanced structural variants such as gene fusions, the characterization of such alterations is still very challenging. Optical genome mapping (OGM) is a technology for high-resolution reconstruction of the genome from single enzyme-labeled DNA molecules. OGM technology is able to produce high resolution maps of genome structure, resulting from the specific marking of each region of the genome (“barcode”) and allowing it to be uniquely identified. This technology enables the genome-wide detection of both number and structural anomalies, from the simplest to the most complex, including balanced rearrangements. In this research line, patients with ALL will be categorized into specific genetic subsets using targeted RNA-seq and all standard methods for the molecular characterization of the disease and concomitantly, the OGM will be used to deeply define the structural genome with three specific aims: 1) Optimizing the characterization of novel ALL subsets in adult settings and the refinement of risk stratification by implementing OGM in the diagnostic workflow of ALL at diagnosis, 2) Integrating the OGM in a diagnostic workflow for the assessment of Ph-like ALL subset, 3)Discovering novel gene fusions and cryptic alterations by applying OGM approach.
Analysis of low-frequency variants identified in hereditary cancer genes to recognize impact of constitutional mosaicism and patients’ management
This research line focuses on the analysis of low-frequency variants detected in hereditary cancer genes to clarify the clinical impact of constitutional mosaicism and improve patient management. The increasing use of high-sensitivity NGS panels has led to the frequent identification of variants with allele frequencies below the expected 50% for germline heterozygosity, raising diagnostic and interpretative challenges. Variants detected at low read frequencies may reflect technical artifacts, clonal hematopoiesis of indeterminate potential (CHIP), or true constitutional mosaicism arising from post-zygotic mutations.
The study aims to refine current diagnostic workflows by systematically evaluating pathogenic or likely pathogenic variants detected below 30% allele frequency. This includes confirmation with orthogonal methods, clinical and hematological review, exclusion of transplant-related chimerism, and analysis of DNA from multiple non-tumoral tissues of different embryonic origin. This integrated approach allows discrimination between somatic and constitutional mosaicism and provides insights into the developmental timing and heritability of mosaic variants.
The results have direct implications for cancer surveillance, preventive strategies, therapeutic decisions, and familial risk assessment. Additionally, large-scale data collection will help identify genes and mutation types more prone to mosaicism and clarify the contribution of mosaicism to tumor predisposition syndromes, particularly in breast and ovarian cancer.
Implementation of an Online Tool (iVar) for the Management of Gene Variants Identified in Patients Referred to the Modena Genomics Laboratory
This research line focuses on the development and clinical exploitation of a centralized genomic variant database derived from the diagnostic activity of the Diagnostic Hematology and Clinical Genomics Unit (EDGC). The Unit manages genomic diagnostics for hereditary cancer risk, selected rare diseases (including nephropathies, epidermolysis bullosa, and dyslipidemias), and hematological malignancies, and has collected tens of thousands of genomic variants from a large regional and national patient cohort. This extensive dataset represents a strategic resource for clinical practice, translational research, and bioinformatic analyses.
The project aims to leverage this knowledge base to improve genotype–phenotype correlations, support clinical interpretation and reclassification of variants, and enable personalized medicine, particularly in oncological and onco-hematological settings. A centralized and standardized database will also foster interoperability and data sharing among institutions, support population and epidemiological studies, and allow continuous updating of variant annotations through integration with external databases.
Specifically, the implementation will support the systematic re-annotation of genomic variants, the development of procedures to identify differences in risk factors, and the automated import of newly identified variants. It will also enable the reconstruction of the iVar software infrastructure on updated operating systems and the comprehensive update of the iVar stack to programming languages currently supported in biomedical applications, building on the already published tool “iVar, an Interpretation-Oriented Tool to Manage the Update and Revision of Variant Annotation and Classification”.
These activities will ensure a structured, automated, and sustainable management of genomic information generated by the project, indirectly but critically supporting the analysis of low-frequency variants. This is essential for the recognition of constitutional mosaicism in hereditary cancer genes, with direct implications for patient clinical management, surveillance strategies, and accurate risk assessment for family members, fully aligned with the scientific objectives of the project.