Jonathan Rodiger1*, Craig Barnes2*, Jessica Schulman1*, Edgar Sioson3, Rita Mormando1*, Alex Acic3, Gavriel Matt3, Edward Malinowski2, Sara Volk de Garcia2, Andrew Prokhorenkov2, Arianna Haynes1, Jordan Miller4, Brendan Coli4, Steven Foltz1, Chaitanya Acharya1, Michael Andreini1, Je Knight2, Michael Fitzsimons2, George Mulligan1**, Robert L Grossman2**, Xin Zhou3**, Alexander M Gout1**. 1Multiple Myeloma Research Foundation, Norwalk, United States of America, 2Center for Translational Data Science, The University of Chicago, Chicago, United States of America, 3St. Jude Children’s Research Hospital, Memphis, United States of America, 4Amazon Web Services, Seattle, United States of America * Equal Contribution **Corresponding Author Abstract Large-scale clinico-genomic datasets are critical for understanding disease biology and informing therapeutic development; however, their translational impact is constrained by data fragmentation, limited accessibility, and lack of interoperable analytical infrastructure. These challenges are particularly acute in diseases such as multiple myeloma, where longitudinal, multi-modal molecular datasets remain limited and di icult to systematically interrogate. Here, we present the MMRF Myeloma Data Commons (virtuallab.themmrf.org), a cloud native, disease-focused ecosystem enabling scalable access, analysis, visualization, and download of longitudinal clinical and multi-omic datasets. The platform integrates whole genome and exome sequencing, bulk and single-cell transcriptomic, and methylation datasets with harmonized longitudinal clinical and outcome-linked annotations. Built on the open-source Gen3 framework and deployed on AWS, the platform incorporates graph-based data modeling, secure, governed access controls, and scalable governed data access across >1,500 patients, including the MMRF CoMMpass longitudinal study. Central to the ecosystem is cohort-centric exploration, enabling dynamic cohort definition using integrated clinical, demographic, and molecular attributes. These capabilities are delivered through tightly integrated analytical interfaces, including cohort-building, clinical dashboards, Kaplan–Meier survival analysis, and genomic, transcriptomic, and methylation visualization tools. These interfaces support integrated visualization of somatic variation, structural variants, gene fusions, copy number alterations, gene expression, and single-cell transcriptomic data. Importantly, this enables real-time systematic interrogation of associations between molecular features, therapeutic response, and clinical outcomes, supporting biomarker discovery, translational hypothesis generation, retrospective analyses of therapeutic response, and reproducible multi-omic investigation within a collaborative research environment. The MMRF Myeloma Data Commons establishes a scalable framework for collaborative, multi-institutional precision hematology research and a reusable model for future disease focused multi-omic ecosystems.