Multiple myeloma (MM) is the second most common hematological cancer in Spain and worldwide, with 3,500 new cases diagnosed and around 2,000 deaths in Spain by 2023. MM is a very heterogeneous monoclonal gammopathy characterized by the clonal proliferation of plasma cells (PCs) in the bone marrow. These PCs have uncontrolled growth and produce non-functional immunoglobulins, leading to destructive bone lesions, increased calcium levels, renal failure and anemia. Some therapies have improved the prognosis of the disease, but it remains an incurable cancer with a high relapse rate.

Disease onset and progression is highly dependent on genetic alterations, such as mutations, Structural Variants (SVs) or Copy Number Variants (CNVs). In this context, the exponential development of omics technologies and computational methods of analysis in recent years has enabled major advances in the study of numerous diseases, such as MM. For this reason, this project proposes a multidisciplinary and multi-omic approach to understand the genetic particularities of the disease by studying patients at the genomic and transcriptomic level. To achieve this, sequencing data obtained from the genetic material (DNA and RNA) of human bone marrow samples will be analyzed, with which the omic profiles of patients will be studied at both the global (Bulk) and Single-Cell levels, which may provide additional information on the clonality and particularities of the disease in each specific patient.

The main objective of the study is to understand the mechanisms that may explain the behavior of the disease in patients, relating their genomic and transcriptomic profiles with different clinical variables such as response to treatment or relapse. Likewise, by broadening our knowledge of MM, we aim to discover new markers that, after following validation, will allow us to provide personalized help to patients, being applied as a target for the development of new therapies and targeted drugs. It also contributes to the development of a tool that facilitates the analysis and study of omics data.

This project will advance precision medicine for patients with MM through the application of omics and computational techniques, including those based on artificial intelligence, with the interest of translating the results into clinical practice.