PO.BCS01.12 · 生物信息与计算
CoMMpass Explorer:一个用于探索里程碑式CoMMpass观察性研究中新诊断多发性骨髓瘤患者临床与基因组数据的交互式平台
CoMMpass Explorer: An interactive platform to explore clinical and genomic data from newly diagnosed multiple myeloma patients from the landmark CoMMpass observational study
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
CoMMpass研究(NCT01454297)是一项前瞻性、纵向观察性研究,纳入了1,141例新诊断多发性骨髓瘤(NDMM)患者。收集研究参与者的骨髓穿刺样本,用于对肿瘤(采用bulk RNA测序和全基因组测序)进行全面分子表征,并采用3'单细胞RNA测序(scRNA-seq)表征肿瘤免疫微环境。为支持对这一丰富多组学数据集的探索,我们开发了CoMMpass Explorer(CE),一个直观的交互式平台,可实现对CoMMpass临床和基因组数据的实时分析。
CE的核心功能是队列构建,用户可按临床、基因组和生存数据要素对患者进行筛选和分层,以创建用于分析的自定义队列。CE提供五个主要视图:总体概览(Overall Summary),用于临床特征分布、Kaplan-Meier曲线和多变量Cox比例风险模型;突变谱(Mutational Profile),用于可视化和比较体细胞突变;肿瘤谱(Tumor Profile),用于bulk差异表达和基因集富集;免疫微环境(Immune Microenvironment),用于利用scRNA-seq数据比较队列间的细胞类型丰度和细胞周期动态;以及伪bulk(Pseudo-Bulk),按细胞类型聚合单细胞表达,从而利用DESeq2实现队列层面的转录程序比较。
CE重现了先前的CoMMpass研究结果,包括WEE1表达与较差的无进展生存相关(Simhal等人),以及非裔美国人和欧裔美国人患者之间不同的突变频率(Manojlovic等人)。
我们进一步通过审视2025年共识基因组分期(CGS)风险分类系统(Avet-Loiseau等人)展示了CE的实用性。我们比较了CGS高危(HR,n=249)与CGS标危(SR,n=573)患者。与SR患者相比,HR患者的无进展生存、总生存和至二线治疗时间均显著更差。单变量模型再现了定义CGS风险的临床特征中的预期差异。bulk肿瘤RNA-seq和ssGSEA凸显了HR组中更具增殖性和基因组不稳定的转录程序,血管生成和细胞周期相关通路上调。在免疫微环境中,单核细胞在SR组中更为丰富。浆细胞的伪bulk RNA-seq显示,ANXA1、NSD2、IGF1R、MAF及相关基因在HR组中过表达。
CE使CoMMpass多组学资源的获取变得普及,使研究人员能够探究临床和分子异质性。通过支持假设生成和已发表结果的复现,CE成为识别预后生物标志物以及基因表达、突变谱和免疫微环境中有意义模式的宝贵工具。
查看英文原文 English abstract
The CoMMpass study (NCT01454297) is a prospective, longitudinal observational study involving 1,141 newly diagnosed multiple myeloma (NDMM) patients. Bone marrow aspirates from the study participants were collected for comprehensive molecular characterization of tumor (using bulk RNA sequencing and whole genome sequencing) and tumor immune microenvironment using 3' single-cell RNA sequencing (scRNA-seq). To support exploration of this rich multi-omic dataset, we developed CoMMpass Explorer (CE), an intuitive interactive platform that enables real-time analysis of clinical and genomic data from CoMMpass.
A central feature of CE is cohort building, where users can filter and stratify patients by clinical, genomic, and survival data elements to create custom cohorts for analysis. CE provides five main views: Overall Summary for clinical feature distribution, Kaplan-Meier curves, and multivariate Cox proportional hazards models; Mutational Profile for visualizing and comparing somatic mutations; Tumor Profile for bulk differential expression and gene set enrichment; Immune Microenvironment for comparing cell type abundance and cell cycle dynamics between cohorts using scRNA-seq data; and Pseudo-Bulk, which aggregates single-cell expression by cell type to enable cohort-level comparisons of transcriptional programs using DEseq2.
CE reproduces prior CoMMpass studies, including WEE1 expression being associated with poorer progression-free survival (Simhal, et al.) and different mutation frequencies between African American and European American patients (Manojlovic, et al.).
We further demonstrate CE's utility by interrogating the 2025 Consensus Genomic Staging (CGS) risk classification system (Avet-Loiseau, et al). We compared CGS high-risk (HR, n=249) vs. CGS standard-risk (SR, n=573) patients. HR patients have significantly worse progression-free survival, overall survival, and time to second-line therapy compared with SR patients. Univariate models recapitulated expected differences in the clinical features that define CGS risk. Bulk tumor RNA-seq and ssGSEA highlighted a more proliferative and genomically unstable transcriptional program in HR group, with upregulation of angiogenesis and cell-cycle-related pathways. In the immune microenvironment, monocytes were more abundant in the SR group. Pseudo-bulk RNA-seq of plasma cells showed overexpression of ANXA1, NSD2, IGF1R, MAF and related genes in HR.
CE democratizes access to the CoMMpass multi-omic resource, allowing researchers to interrogate clinical and molecular heterogeneity. By enabling hypothesis generation and replication of published results, CE serves as a valuable tool for identifying prognostic biomarkers and meaningful patterns in gene expression, mutation profiles, and the immune microenvironment.
利益披露 Disclosure
W. Zhang, None..
C. R. Acharya, None..
S. M. Foltz, None..
D. E. Avigan, None..
S. Parekh, None..
R. Vij, None..
S. K. Kumar, None..
T. Kourelis, None..
S. Lonial, None..
H. Cho, None..
I. S. Vlachos, None..
S. Gnjatic, None..
L. Ding, None..
M. Bhasin, None..
G. Mulligan, None.