PO.BCS01.12 · 生物信息与计算

CoMMpass Explorer: An interactive platform to explore clinical and genomic data from newly diagnosed multiple myeloma patients from the landmark CoMMpass observational study

海报缩略图:CoMMpass Explorer: An interactive platform to explore clinical and genomic data from newly diagnosed multiple myeloma patients from the landmark CoMMpass observational study
编号 5504 展板 9 🕑 4/21 02:00–05:00 📍 Section 4 主讲 Steven Foltz, BS;MS;PhD
分会场 New Software Tools for Data Analysis
📄 查看 PDF ⬇ 下载 PDF 🔒 需登录后查看 / 下载(免费注册) 🔗 AACR 官方页面

作者与单位 Authors & Affiliations

Wanxing Zhang1, Chaitanya R. Acharya1, Steven M. Foltz1, David E. Avigan2, Samir Parekh3, Ravi Vij4, Shaji Kunnathu Kumar5, Taxiarchis Kourelis5, Sagar Lonial6, Hearn Cho1, Ioannis S. Vlachos2, Sacha Gnjatic3, Li Ding4, Manoj Bhasin7, George Mulligan1

1Multiple Myeloma Research Foundation, Norwalk, CT,2Beth Israel Deaconess Medical Center, Boston, MA,3Icahn School of Medicine at Mount Sinai, New York, NY,4Washington University School of Medicine in St. Louis, St. Louis, MO,5Mayo Clinic, Rochester, MN,6Emory Winship Cancer Institute, Atlanta, GA,7Emory School of Medicine, Atlanta, GA

摘要 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.

🔍 在海报库中搜索更多海报 →