Citations

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. Citation Format: Wanxing Zhang, Chaitanya R. Acharya, Steven M. Foltz, David E. Avigan, Samir Parekh, Ravi Vij, Shaji Kunnathu Kumar, Taxiarchis Kourelis, Sagar Lonial, Hearn Cho, Ioannis S. Vlachos, Sacha Gnjatic, Li Ding, Manoj Bhasin, George Mulligan. CoMMpass Explorer: An interactive platform to explore clinical and genomic data from newly diagnosed multiple myeloma patients from the landmark CoMMpass observational study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5504.

Abstract The CoMMpass study (NCT01454297) is a prospective, longitudinal observational trial involving 1,141 newly diagnosed, treatment-naïve multiple myeloma (NDMM) patients. Bone marrow aspirates from the study participants were collected, separated into CD138 negative and CD138 positive fractions 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 these study participants. CE provides four main views: Overall Summary for clinical feature distribution and Kaplan-Meier curves; Mutational Profile for visualizing and comparing somatic mutations; Tumor Profile for differential expression and gene set enrichment; and Immune Microenvironment for comparing cell type abundance and cell cycle dynamics between cohorts using scRNA-seq data. 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. We validated CE by reproducing findings from two prior CoMMpass-based studies. In the first study by Simhal et al, we stratified patients using transcript-level expression by WEE1 into tertiles and confirmed that high WEE1 expression is associated with poorer progression-free survival across multiple genomic subgroups. In the second study by Manojlovic et al, we replicated mutation frequencies between African American and Caucasian patients by creating race-based cohorts. We further demonstrate CE's utility by identifying and comparing two NDMM cohorts of patients at baseline who have not received autologous stem cell transplant: cohort 1 includes patients that received up-front triplet therapy (N=342) and cohort 2 includes those who did not receive a triplet up-front (N=235). While survival differences between these two cohorts were not statistically significant (p>0.05), CE revealed elevated epithelial-mesenchymal transition (EMT) pathway activity in cohort 1. Immune profiling showed lower abundance of CD8+ T cells (p < 0.05) and NK cells (p = 0.01), but a higher abundance of B cells (p < 0.05) in cohort 1. Genomic data showed fewer EIF2AK3 mutations and higher transcript-level expression of EIF2AK3 in cohort 1. Notably, patients in cohort 1 with lower transcript-level expression of EIF2AK3 had better survival outcomescompared to patients in cohort 2. CE democratizes access to the CoMMpass multi-omic resource, allowing researchers to interrogate clinical and molecular heterogeneity with ease. By enabling hypothesis generation and replication of published results, CE serves as a valuable tool for identifying meaningful patterns in gene expression, mutation profiles, and immune contexture. It also highlights potential prognostic biomarkers, such as EIF2AK3 expression, that may inform treatment decisions in NDMM, especially in the context of triplet-based regimens.