Alessandro is an Assistant Professor of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. His main research interests are in the fields of integrative cancer genomics, cancer network biology and precision oncology.

The aim of Alessandro's research is the development and application of computational methods for integrative analysis of multi-omics data to:

• Investigate the role of coding and non-coding alterations on cancer pathogenesis and progression;

• Understand the clinical implications of intra-tumor heterogeneity and of the tumor microenvironment;

• Inform patient risk stratification;

• Develop personalized therapy selection approaches guided by next-generation sequencing technologies.

Alessandro's current research focuses on Multiple Myeloma, a genetically complex and heterogeneous malignancy of bone marrow plasma cells affecting more than 30,000 patients each year in the United States. The causal drivers of Myeloma pathogenesis are still unclear, and treatment is administered empirically on the basis of recurrence risk rather than genetic events.

Alessandro is leading the development of a precision medicine pipeline that has been successfully applied in a clinical trial to guide treatment of relapsed Multiple Myeloma patients. The goal of precision oncology is to devise optimal treatment strategies tailored to the characteristics of the individual patient’s disease. However, the widespread heterogeneity of cancer patients and drug responses pose a significant challenge in the design of effective personalized treatments. Alessandro's research addresses this extremely important challenge and aims to transform the current paradigm of empirical clinical application. The systematic use of integrative genomics, systems biology approaches, big-data analytics and novel sequencing technologies will enable physicians to provide more precise and effective personalized treatment of cancer.

Precision oncology aims to tailor treatment decisions to the molecular characteristics of each patient’s tumor. Achieving this goal requires integrating genomic data with rapidly evolving biomedical knowledge, including evidence linking genes, variants, therapies, and clinical outcomes. However, relevant information is distributed across heterogeneous sources such as scientific publications, curated knowledge bases, and clinical datasets, creating challenges for timely interpretation and clinical decision support. We will discuss emerging artificial intelligence approaches for integrating these diverse data streams within precision oncology workflows. Within the broader context of a framework for AI-enhanced decision support, we will focus on methods for automated extraction and structuring of genomic evidence from biomedical literature. Using the OncoCITE system as a case study, participants will explore how large language models can assist in identifying gene–variant–therapy relationships from scientific papers and how such information can complement curated knowledge resources. Guided exercises will illustrate both the opportunities and the limitations of AI-driven evidence synthesis for clinical applications.