Evolution of multiple myeloma from a genomic perspective.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
환자: high risk of progressing into MM
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
Therefore, integrating genomic and transcriptomic data with immune profiling or other clinical features is essential for identifying patients with high risk of progressing into MM. Here, we highlight the complexity of myelomagenesis, and underline the importance of state-of-the-art approaches for improved disease prediction.
In this review, we explore the role of complex interactions between genomic evolution, environmental and genetic predispositions, and immune surveillance in disease progression from precursor conditio
APA
Maura F, Samur M, Munshi N (2026). Evolution of multiple myeloma from a genomic perspective.. Blood, 147(13), 1423-1432. https://doi.org/10.1182/blood.2024026313
MLA
Maura F, et al.. "Evolution of multiple myeloma from a genomic perspective.." Blood, vol. 147, no. 13, 2026, pp. 1423-1432.
PMID
41359792 ↗
Abstract 한글 요약
In this review, we explore the role of complex interactions between genomic evolution, environmental and genetic predispositions, and immune surveillance in disease progression from precursor conditions smoldering multiple myeloma and monoclonal gammopathy of undetermined significance to multiple myeloma (MM). MM has been described to be universally preceded by precursor states, often decades before it is even diagnosed. Genetic predisposition plays an important role in the initial transformation, and is driven by both germline variants and MM-specific loci influencing risk. The reported disparities in occurrence of precursor conditions and MM among racial groups highlights the role of predisposition and the need for broader cohort studies. Early genomic events, such as translocations and hyperdiploidy, are essential in precursor initiation. However, additional factors are usually needed to transform the precursor stages into symptomatic disease, such as positive selection of subclonal populations. This process is affected by aging and environmental factors, such as exposures to Agent Orange and agrochemicals. Therefore, integrating genomic and transcriptomic data with immune profiling or other clinical features is essential for identifying patients with high risk of progressing into MM. Here, we highlight the complexity of myelomagenesis, and underline the importance of state-of-the-art approaches for improved disease prediction.
🏷️ 키워드 / MeSH 📖 같은 키워드 OA만
🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반
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