RESUMEN
Smoldering multiple myeloma (SMM) is a precursor condition of multiple myeloma (MM) with significant heterogeneity in disease progression. Existing clinical models of progression risk do not fully capture this heterogeneity. Here we integrate 42 genetic alterations from 214 SMM patients using unsupervised binary matrix factorization (BMF) clustering and identify six distinct genetic subtypes. These subtypes are differentially associated with established MM-related RNA signatures, oncogenic and immune transcriptional profiles, and evolving clinical biomarkers. Three genetic subtypes are associated with increased risk of progression to active MM in both the primary and validation cohorts, indicating they can be used to better predict high and low-risk patients within the currently used clinical risk stratification models.
Asunto(s)
Mieloma Múltiple , Mieloma Múltiple Quiescente , Progresión de la Enfermedad , Humanos , Mieloma Múltiple/genética , Fenotipo , Riesgo , Factores de Riesgo , Mieloma Múltiple Quiescente/genéticaRESUMEN
Multiple myeloma (MM) is an incurable plasma cell malignancy characterized by clonal proliferation of plasma cells and a heterogenous genomic landscape. Copy number and structural changes due to chromosomal instability (CIN) are common features of MM. In this review, we describe how primary and secondary genetic events caused by CIN can contribute to increased instability across the genome of malignant plasma cells; with a focus on specific driver genomic events, and how they interfere with cell-cycle checkpoints, to prompt accelerated proliferation. We also provide insight into other forms of CIN, such as chromothripsis and chromoplexy. We evaluate how the tumor microenvironment can contribute to a further increase in chromosomal instability in myeloma cells. Lastly, we highlight the role of certain mutational signatures in leading to high mutation rate and genome instability in certain MM patients. We suggest that assessing CIN in MM and its precursors states may help improve predicting the risk of progression to symptomatic disease and relapse and identifying future therapeutic targets.
Asunto(s)
Predisposición Genética a la Enfermedad , Inestabilidad Genómica , Mieloma Múltiple/genética , Animales , Inestabilidad Cromosómica , Variaciones en el Número de Copia de ADN , Estudios de Asociación Genética , Humanos , Mieloma Múltiple/diagnóstico , Mieloma Múltiple/terapia , Mutación , Microambiente TumoralRESUMEN
PURPOSE: Smoldering multiple myeloma (SMM) is a precursor condition of multiple myeloma (MM) with a 10% annual risk of progression. Various prognostic models exist for risk stratification; however, those are based on solely clinical metrics. The discovery of genomic alterations that underlie disease progression to MM could improve current risk models. METHODS: We used next-generation sequencing to study 214 patients with SMM. We performed whole-exome sequencing on 166 tumors, including 5 with serial samples, and deep targeted sequencing on 48 tumors. RESULTS: We observed that most of the genetic alterations necessary for progression have already been acquired by the diagnosis of SMM. Particularly, we found that alterations of the mitogen-activated protein kinase pathway (KRAS and NRAS single nucleotide variants [SNVs]), the DNA repair pathway (deletion 17p, TP53, and ATM SNVs), and MYC (translocations or copy number variations) were all independent risk factors of progression after accounting for clinical risk staging. We validated these findings in an external SMM cohort by showing that patients who have any of these three features have a higher risk of progressing to MM. Moreover, APOBEC associated mutations were enriched in patients who progressed and were associated with a shorter time to progression in our cohort. CONCLUSION: SMM is a genetically mature entity whereby most driver genetic alterations have already occurred, which suggests the existence of a right-skewed model of genetic evolution from monoclonal gammopathy of undetermined significance to MM. We identified and externally validated genomic predictors of progression that could distinguish patients at high risk of progression to MM and, thus, improve on the precision of current clinical models.
Asunto(s)
Genómica/métodos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Mieloma Múltiple Quiescente/genética , Adulto , Anciano , Anciano de 80 o más Años , Progresión de la Enfermedad , Femenino , Humanos , Masculino , Persona de Mediana Edad , Factores de RiesgoRESUMEN
BACKGROUND: Waldenström macroglobulinemia (WM) is preceded by asymptomatic WM (AWM), for which the risk of progression to overt disease is not well defined. METHODS: We studied 439 patients with AWM, who were diagnosed and observed at Dana-Farber Cancer Institute between 1992 and 2014. RESULTS: During the 23-year study period, with a median follow-up of 7.8 years, 317 patients progressed to symptomatic WM (72%). Immunoglobulin M 4,500 mg/dL or greater, bone marrow lymphoplasmacytic infiltration 70% or greater, ß2-microglobulin 4.0 mg/dL or greater, and albumin 3.5 g/dL or less were all identified as independent predictors of disease progression. To assess progression risk in patients with AWM, we trained and cross-validated a proportional hazards model using bone marrow infiltration, immunoglobulin M, albumin, and beta-2 microglobulin values as continuous measures. The model divided the cohort into three distinct risk groups: a high-risk group with a median time to progression (TTP) of 1.8 years, an intermediate-risk group with a median TTP of 4.8 years, and a low-risk group with a median TTP of 9.3 years. We validated this model in two external cohorts, demonstrating robustness and generalizability. For clinical applicability, we made the model available as a Web page application ( www.awmrisk.com ). By combining two cohorts, we were powered to identify wild type MYD88 as an independent predictor of progression (hazard ratio, 2.7). CONCLUSION: This classification system is positioned to inform patient monitoring and care and, for the first time to our knowledge, to identify patients with high-risk AWM who may need closer follow-up or benefit from early intervention.