Development of AI-based models of risk of cancer recurrence and for other types of clinical and molecular patient stratifications

The the risk of cancer recurrence, the response od cancer cells to therapies and other important clinical facrors depend largely on the biological features of cancer  that can be modeled using multi-omics analysis. machine learning methods hold high power for turning clinical and molecular features of cancer into valuable prognostic models. We are working f.i. on the development of a model of disease recurrence in Pediatric Burkitt Lymphoma, in T- cell acute lymphoblastic leukemia and in Hepatocellular carcinoma. 

RNA-based therapies

In Large Granular Lymphocytic Leukemia (LGLL) we are developing a miRNA-based therapy to reverse neutropenia, the main clinical manifestation of this chronic malignancy. We are directly involved in the testing of miRNA restoration effects in vitro and in ex vivo samples to monitor the efficacy and specificity of the therapy for LGLL, using different methodologies including transcriptomic and proteomic analyses. Moreover, we are collaborating the the development of an effcient and specific RNA-delivery platform. This project is conducted in the frame of the Spoke 6 (RNA Drug Development) of the EU PNRR National (Italian) center for RNA and gene therapy

Single-cell and spatial transcriptomics studies in cancer

Single-cell and spatial transcriptomics techniques can capture cancer features at an unprecedented resolution, allowing the study of cancer tissue composition and organization. We are leveraging these methods to study haematopoiesis in LGLL and similar conditions and in Hepatocellular carcinoma.

Bioinformatics methods for circRNA charachterization 

Several project are ongoing aiming at the characterizing circular RNAs expression and role in normal hematopoiesis and in hematologic malignancies. Our goals are to disclose aberrantly expressed or fusion circRNAs of malignant cells, clarify their involvement in disease mechanisms and their usefulness ad biomarkers. The projects entails a combination of multi-omic profiling, bioinformatics analysis, and functional experiments.

Study of circRNA role in haematologic malignancies 

Several project are ongoing aiming at the characterizing circular RNAs expression and role in normal hematopoiesis and in hematologic malignancies. Our goals are to disclose aberrantly expressed or fusion circRNAs of malignant cells, clarify their involvement in disease mechanisms and their usefulness ad biomarkers. The projects entails a combination of multi-omic profiling, bioinformatics analysis, and functional experiments.

Analysis of genome sequencing data to study cancer genomics 

We develop informatics tools for analysis of genome sequencing data obtained from solid and blood cancers to prioritize variants, identify mutations driving cancer development and progression. Moreover, we use advanced systems genetics approaches to identify significantly mutated pathways and biological functions involved in cancer dynamics. 

We explore and reconstruct clonal evolution in leukemias tracking the mutational profile changes to disclose the biological features responsible for the relapse or the chemotherapy resistance in order to help the refinement of clinical treatments.