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多维尺度分析技术揭示多发性骨髓瘤患者中存在 gain1q 和 loss13q 共发生现象,这些患者具有特定的基因组、转录组和不良临床特征。

Multi-dimensional scaling techniques unveiled gain1q&loss13q co-occurrence in Multiple Myeloma patients with specific genomic, transcriptional and adverse clinical features.

机构信息

IRCCS Azienda Ospedaliero-Universitaria di Bologna-Istituto di Ematologia "Seràgnoli", Bologna, Italy.

DIMEC-Department of Medical and Surgical Science, University of Bologna, Bologna, Italy.

出版信息

Nat Commun. 2024 Feb 20;15(1):1551. doi: 10.1038/s41467-024-45000-z.

Abstract

The complexity of Multiple Myeloma (MM) is driven by several genomic aberrations, interacting with disease-related and/or -unrelated factors and conditioning patients' clinical outcome. Patient's prognosis is hardly predictable, as commonly employed MM risk models do not precisely partition high- from low-risk patients, preventing the reliable recognition of early relapsing/refractory patients. By a dimensionality reduction approach, here we dissect the genomic landscape of a large cohort of newly diagnosed MM patients, modelling all the possible interactions between any MM chromosomal alterations. We highlight the presence of a distinguished cluster of patients in the low-dimensionality space, with unfavorable clinical behavior, whose biology was driven by the co-occurrence of chromosomes 1q CN gain and 13 CN loss. Presence or absence of these alterations define MM patients overexpressing either CCND2 or CCND1, fostering the implementation of biology-based patients' classification models to describe the different MM clinical behaviors.

摘要

多发性骨髓瘤(MM)的复杂性是由几种基因组异常引起的,这些异常与疾病相关和/或不相关的因素相互作用,影响患者的临床结果。由于常用的 MM 风险模型不能准确地区分高危和低危患者,患者的预后几乎无法预测,从而无法可靠地识别早期复发/难治性患者。通过降维方法,我们在这里剖析了一大群新诊断 MM 患者的基因组图谱,构建了所有 MM 染色体改变之间可能存在的相互作用模型。我们发现在低维空间中存在一个显著的患者簇,其临床行为不良,其生物学特征是 1q 染色体 CN 增益和 13 号染色体 CN 缺失的同时发生。这些改变的存在或不存在将 MM 患者分为 CCND2 或 CCND1 过表达的患者亚群,有助于实施基于生物学的患者分类模型来描述不同的 MM 临床行为。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4d1/10879136/fb914f2edd41/41467_2024_45000_Fig1_HTML.jpg

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