Computational modeling of multiple myeloma

Abstract:
Precision oncology seeks to optimize therapeutic strategies tailored specifically to the unique characteristics of an individual patient’s disease. However, the extensive inter- and intra-tumor heterogeneity observed in cancers such as multiple myeloma presents significant obstacles to effective personalized treatment approaches. This seminar highlights computational methodologies leveraging multi-omics data integration from bulk and single-cell sequencing to dissect tumor heterogeneity, identify high-risk disease features, and uncover novel biomarkers predicting therapeutic responses and resistance. Additionally, it explores innovative immunotherapeutic modalities, including CAR T cells and bispecific antibodies. Finally, it introduces emerging applications of generative artificial intelligence, particularly Retrieval-Augmented Generation (RAG) systems and Large Language Models (LLMs), as powerful tools for mining biomedical literature and accelerating the translation of complex biological insights into actionable precision medicine strategies.

Short Bio (English):

Dr. Laganà is an Assistant Professor in the Departments of Oncological Sciences and Genetics and Genomic Sciences at the Mount Sinai Hospital, New York. His research is centered on integrative cancer genomics, network biology, and precision medicine, with a particular emphasis on multiple myeloma. Over the past fifteen years, he has been actively involved in numerous projects focused on computational biology and cancer bioinformatics. Following graduate studies at the University of Catania, Italy, and post-doctoral training at the Ohio State University, his work initially concentrated on computational tools for microRNA function and RNA editing in cancer. Since joining Mount Sinai in 2014, he has broadened his expertise to tackle fundamental questions regarding the molecular genetics and biology of multiple myeloma, advancing our understanding and application of precision oncology.