Single-cell profiling of tumour evolution in multiple myeloma — opportunities for precision medicine

不确定意义的单克隆抗体病 医学 多发性骨髓瘤 癌症的体细胞进化 恶性肿瘤 遗传异质性 疾病 精密医学 生物 个性化医疗 表型 生物信息学 计算生物学 内科学 病理 免疫学 单克隆 遗传学 单克隆抗体 基因 癌症 抗体
作者
Ankit K. Dutta,Jean-Baptiste Alberge,Romanos Sklavenitis‐Pistofidis,Elizabeth D. Lightbody,Gad Getz,Irene M. Ghobrial
出处
期刊:Nature Reviews Clinical Oncology [Nature Portfolio]
卷期号:19 (4): 223-236 被引量:116
标识
DOI:10.1038/s41571-021-00593-y
摘要

Multiple myeloma (MM) is a haematological malignancy of plasma cells characterized by substantial intraclonal genetic heterogeneity. Although therapeutic advances made in the past few years have led to improved outcomes and longer survival, MM remains largely incurable. Over the past decade, genomic analyses of patient samples have demonstrated that MM is not a single disease but rather a spectrum of haematological entities that all share similar clinical symptoms. Moreover, analyses of samples from monoclonal gammopathy of undetermined significance and smouldering MM have also shown the existence of genetic heterogeneity in precursor stages, in some cases remarkably similar to that of MM. This heterogeneity highlights the need for a greater dissection of underlying disease biology, especially the clonal diversity and molecular events underpinning MM at each stage to enable the stratification of individuals with a high risk of progression. Emerging single-cell sequencing technologies present a superlative solution to delineate the complexity of monoclonal gammopathy of undetermined significance, smouldering MM and MM. In this Review, we discuss how genomics has revealed novel insights into clonal evolution patterns of MM and provide examples from single-cell studies that are beginning to unravel the mutational and phenotypic characteristics of individual cells within the bone marrow tumour, immune microenvironment and peripheral blood. We also address future perspectives on clinical application, proposing that multi-omics single-cell profiling can guide early patient diagnosis, risk stratification and treatment strategies.
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