Postdoctoral Fellow

Alessia Buratin

Profile

I hold a Master’s degree in Statistics and a Ph.D. in Biosciences from the University of Padova, where I developed advanced skills in bioinformatics. My research focuses on molecular biology, specifically on hematologic malignancies, by exploring complex molecular interactions using RNA sequencing data. As a statistician, I improve the downstream analysis of RNA-seq, transitioning from bulk to single-cell technologies. As a triathlete, I bring the same determination and motivation to my professional endeavors, driving my talents and skills to achieve objectives.

Project description

Single Cell Multi Omics and Deep Learning Approach to reverse immunosuppression in Multiple Myeloma

Despite the advances in treatment in the last decade, Multiple Myeloma (MM) remains an incurable disease, and almost all patients relapse. Yet, it is increasingly evident that treatment strategies solely based on targeting the intrinsic properties of myeloma cells are insufficient. Lately, approaches that redirect the cells of the otherwise suppressed immune system to take control of myeloma have emerged. One strategy to harness both innate and antigen-specific immunity against MM is drug repurposing. The premise behind this strategy is that small molecules, initially designed for one indication, may actually have beneficial effects across other diseases. With the present proposal we pursued to develop novel immunotherapies which may reverse the tumor-promoting effects of MM immunosuppression through repurposing of ‘old’ drugs. To achieve that, we propose a comprehensive characterization of the immune single-cell (sc) states associated with MM patients who display distinct patterns of disease progression after first-line therapy over time. Recent advances in sc technologies enable the profiling of multiple modalities that characterize different genetic and epigenetic sequencing information in a cell simultaneously. In turn, sc multi-omics analysis provides us a unique opportunity to understand the contribution of immune cells to tumor progression at the highest resolution. Altogether, the analysis of clinically-relevant time points will permit the reconstruction of immune cell dynamics in different categories of patients, enabling the identification of pathways associated with prolonged disease-free survival. Ultimately, this research will not only advance our knowledge of MM immunopathogenesis, but will also provide tools for the identification of novel molecular targets, providing an unprecedented opportunity for drug repurposing in the novel and attractive frame of precision medicine in immuno-oncology.

Keywords: Immunology, Artificial Intelligence, Cell Biology, Data Science and Technology, Bioinformatics