From Genomics to Precision Medicine in Multiple Myeloma: Building integrative models across data, biology, and clinical decision-making

ABSTRACT (ENG): Multiple myeloma is a plasma cell malignancy characterized by marked genetic and clinical heterogeneity, leading to variable responses to therapy and recurrent relapse. In this seminar, I will present a systems-level approach to understanding myeloma biology across risk groups, therapeutic modalities, and disease stages.We will discuss integrative multi-omics modeling strategies, including patient similarity networks and single-cell profiling, to refine risk stratification beyond conventional staging systems. Particular attention will be given to the role of chromosome 1q gain, clonal dynamics, and the tumor microenvironment in defining biologically and clinically high-risk disease.I will also describe how these insights translate into actionable strategies, including biomarkers of drug sensitivity and resistance, modeling of response to immunotherapies such as CAR T cells and bispecific antibodies, and computational platforms for precision treatment selection. Finally, I will introduce emerging AI-driven systems for automated evidence curation and knowledge integration to accelerate clinical decision-making in precision oncology.

ABSTRACT (ITA):  Il mieloma multiplo è una neoplasia delle plasmacellule caratterizzata da marcata eterogeneità genetica e clinica, che si traduce in risposte variabili alle terapie e frequenti recidive. In questo seminario presenterò un approccio sistemico allo studio della biologia del mieloma attraverso i diversi livelli di rischio, le modalità terapeutiche e le fasi di malattia.Verranno illustrate strategie di modellizzazione multi-omica integrata, tra cui patient similarity networks e profilazione a singola cellula, per affinare la stratificazione del rischio oltre i sistemi di stadiazione convenzionali. Particolare attenzione sarà dedicata al ruolo delle alterazioni del cromosoma 1q, alle dinamiche clonali e al microambiente tumorale nella definizione di malattia ad alto rischio dal punto di vista biologico e clinico.Infine, discuterò come queste conoscenze possano tradursi in strategie operative, inclusi biomarcatori di sensibilità e resistenza ai farmaci, modelli di risposta a immunoterapie come CAR T e anticorpi bispecifici, e piattaforme computazionali per la selezione terapeutica personalizzata. Presenterò inoltre sistemi emergenti basati su intelligenza artificiale per l’estrazione automatizzata delle evidenze scientifiche e l’integrazione della conoscenza a supporto delle decisioni cliniche in oncologia di precisione.

SHORT BIO:  Dr. Laganà is an Assistant Professor of Oncological Sciences (scienze oncologiche) and 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 his research is the development and application of computational methods for the integrative analysis of multi-omics data in order to:

• Investigate the role of coding and non-coding alterations in cancer pathogenesis and progression;
• Understand the clinical implications of intra-tumor heterogeneity and the tumor microenvironment;
• Improve patient risk stratification;
• Develop personalized therapy selection approaches guided by next-generation sequencing technologies.

Dr. Laganà’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 not fully understood, and treatment is generally administered empirically based on recurrence risk rather than specific genetic events. Dr. Laganà is leading the development of a precision medicine pipeline that has been successfully applied in a clinical trial to guide the treatment of patients with relapsed Multiple Myeloma. The goal of precision oncology is to design optimal treatment strategies tailored to the specific characteristics of each patient’s disease. However, the widespread heterogeneity among cancer patients and variability in drug response pose significant challenges in the design of effective personalized treatments. Dr. Laganà’s research addresses this critical challenge and aims to transform the current paradigm of empirical clinical practice. The systematic use of integrative genomics, systems biology approaches, big data analytics, and novel sequencing technologies will enable physicians to deliver more precise and effective personalized cancer therapies.