生物
生殖系
基因组不稳定性
遗传学
基因组学
计算生物学
疾病
体细胞
遗传建筑学
基因组
全基因组关联研究
癌症的体细胞进化
种系突变
精密医学
外显子组
进化生物学
机制(生物学)
适应(眼睛)
表型
变色
免疫系统
生物信息学
克隆选择
拷贝数变化
DNA测序
基因
作者
María Carretero‐Fernández,Antonio José Cabrera-Serrano,Lucía Ruíz Durán,Mariam Ibáñez,Marco Bonilla,Francisco Mesa,Juan Francisco Gutiérrez‐Bautista,Rachid Chahboun,Fernando J. Reyes‐Zurita,Joaquín Martínez-Lopez,Rosa Collado,Juan Sáinz
标识
DOI:10.1016/j.tranon.2026.102796
摘要
Multiple myeloma (MM) is best understood as a dynamically evolving genomic ecosystem shaped by inherited susceptibility, early oncogenic events, and continuous selective pressures. We propose an evolutionary genomics framework integrating germline risk, disease initiation, clonal diversification, and therapeutic adaptation into a unified model of MM biology. Polygenic risk burden, rare predisposing variants, and alterations in DNA repair and telomere pathways create a permissive background that influences precursor states and immune interactions. Primary cytogenetic events, particularly immunoglobulin heavy chain (IgH) translocations and hyperdiploidy, establish biologically distinct founding clones and constrain subsequent evolutionary trajectories. Disease progression is driven by secondary chromosomal alterations, copy number changes, MYC activation, TP53 loss, and structural rearrangements, promoting genomic instability and transcriptional plasticity. Longitudinal studies reveal branching clonal architectures shaped by treatment-driven selection. Integrating germline and somatic landscapes within an evolution-aware precision framework may improve risk stratification, anticipate high-risk trajectories, and support adaptive strategies to achieve more durable disease control. While polygenic risk scores (PRS) provide insight into inherited susceptibility, they are not yet clinically actionable for risk stratification or screening in MM and currently remain research tools. This framework provides a clinically oriented basis for applying genomic biomarkers to risk stratification, treatment selection, and longitudinal monitoring.
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