Etiological links to multiple myeloma (MM) remain poorly understood, though emerging evidence suggests a significant hereditary component. This review integrates current literature on inherited factors contributing to MM risk, synthesizing both epidemiologic and genomic data. We examine familial clustering patterns, assess genome-wide association studies (GWAS) that reveal common genetic variants linked to MM, and explore rare, high-penetrance variants in key susceptibility genes. Additionally, we advocate for routine germline screening in high-risk MM populations, particularly those with a strong family history of cancer, a personal history of cancer, or early-onset disease. By elucidating the inherited influences on MM predisposition, this review seeks to inform future research and refine risk assessment strategies in this population.
The introduction of artificial intelligence (AI), and in particular machine learning (ML), has revolutionized biomedical research at the clinical level, a trend that also includes hematologic malignancies and myeloid neoplasia (MN). ML encompasses a wide range of applications such as enhanced diagnostics, outcome predictions, decision trees and clustering. Despite several reports in recent years and the achievement of promising results, none of the ML-based pipelines have been directly translated into clinical practice. ML offers the potential to help refine risk stratification and increase accuracy to correctly predict clinical outcomes and disease classification. One of the complications in the clinical utilization of ML is that a large percentage of hematologists have limited familiarity with these tools which can cause skepticism. Concerns have also been raised by patients that are worried about privacy issues, reliability of the outcomes, and loss of human interaction. In this review, we aim to pinpoint the main mechanisms and applications of ML, as well as application in MN and Myelodysplastic Syndrome, highlighting strengths and limitations, and addressing the potential promise in clinical implementation of ML-pipelines.
Immunocompetent murine models of multiple myeloma are critical for understanding the pathogenesis of multiple myeloma and for the development of novel immunotherapeutics. Different models are available in Balb/c and C57Bl strains, each with different advantages and disadvantages. The availability of many transplantable cell lines allows for the conduct of experiments with large cohorts of mice bearing identical tumors, while cell lines that grow in vitro can be used for genetic manipulations. The introduction of human CRBN into these models allows for the study of IMiDs and cereblon based PROTACs in mice. New genetically engineered models based on germinal center cell activation of Nsd2 or Ccnd1 together with constitutive NFkB are being developed to model some of the important genetic subtypes of human multiple myeloma.