Department of Hematology/Oncology, Loyola University Medical Center, Maywood, IL, 60153, USA.
Department of Cancer Biology, Oncology Institute, Cardinal Bernardin Cancer Center, Loyola University Medical Center, Maywood, IL, 60153, USA.
Ann Hematol. 2022 Jul;101(7):1407-1420. doi: 10.1007/s00277-022-04856-1. Epub 2022 May 18.
Our understanding of MM genomics has expanded rapidly in the past 5-10 years as a consequence of cytogenetic analyses obtained in routine clinical practice as well as the ability to perform whole-exome/genome sequencing and gene expression profiling on large patient data sets. This knowledge has offered new insights into disease biology and is increasingly defining high-risk genomic patterns. In this manuscript, we present a thorough review of our current knowledge of MM genomics. The epidemiology and biology of chromosomal abnormalities including both copy number abnormalities and chromosomal translocation are described in full with a focus on those most clinically impactful such as 1q amplification and del(17p) as well as certain chromosome 14 translocations. A review of our ever-expanding knowledge of genetic mutations derived from recent whole-genome/exome data sets is then reviewed including those that drive disease pathogenesis from precursor states as well as those that may impact clinical outcomes. We then transition and attempt to elucidate how both chromosomal abnormalities and gene mutations are evolving our understanding of disease risk. We conclude by offering our perspectives moving forward as to how we might apply whole-genome/exome-level data in addition to routine cytogenetic analyses to improve patient outcomes as well as further knowledge gaps that must be addressed.
在过去的 5-10 年中,由于在常规临床实践中获得的细胞遗传学分析以及对大型患者数据集进行全外显子/基因组测序和基因表达谱分析的能力,我们对 MM 基因组学的理解迅速扩展。这些知识为疾病生物学提供了新的见解,并越来越多地定义了高风险的基因组模式。在本文中,我们全面回顾了我们目前对 MM 基因组学的认识。充分描述了染色体异常的流行病学和生物学,包括拷贝数异常和染色体易位,并重点介绍了那些对临床影响最大的异常,如 1q 扩增和 del(17p)以及某些染色体 14 易位。然后,我们回顾了最近全基因组/外显子组数据集中获得的遗传突变的不断扩展的知识,包括那些从前驱状态驱动疾病发病机制的突变以及那些可能影响临床结果的突变。然后,我们过渡并试图阐明染色体异常和基因突变如何改变我们对疾病风险的理解。最后,我们提出了一些观点,即我们如何应用全基因组/外显子组水平的数据以及常规细胞遗传学分析来改善患者的预后,并进一步解决必须解决的知识差距。