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Impact & Major Publications

Over more than a decade of longitudinal data collection and analysis, the MMRF CoMMpass Study has generated foundational insights that have reshaped the understanding of multiple myeloma biology, risk stratification, immune dynamics, and therapeutic resistance.

CoMMpass-derived discoveries have directly influenced:

  • Clinical trial design and patient stratification
  • Identification of novel therapeutic targets
  • Development of genomic and immune-based risk classifiers
  • Translation of molecular findings into precision medicine strategies

CoMMpass Reports

Below we highlight selected case studies where CoMMpass findings have directly informed clinical or therapeutic approaches, followed by additional key publications that collectively define the scientific impact of the study.

Case Studies

Numerous CoMMpass-derived reports have significantly advanced the understanding of myeloma subtypes, therapeutic targets, immune states, and risk stratification—enabling incorporation into drug development pipelines and clinical trial strategies.

1. Defining High-Risk Multiple Myeloma

CoMMpass data enabled the development of an integrated high-risk classification that combines clinical, cytogenetic, and genomic features. This approach improves identification of patients with poor prognosis despite modern triplet or quadruplet therapies and forms the basis for ongoing high-risk–focused clinical trials.

International Myeloma Society / International Myeloma Working Group Consensus Recommendations on the Definition of High-Risk Multiple Myeloma
Avet-Loiseau H, Davies FE, Samur MK, et al.
Journal of Clinical Oncology. 2025 Aug 20;43(24):2739–2751.
DOI: 10.1200/JCO-24-01893

2. Disease Transitions and Immune Evolution

J&J led analyses of CoMMpass tumor genomics and single-cell immune profiling combined with tumor genomics revealed immune and tumor state transitions during progression from smoldering myeloma to multiple myeloma, linking immune phenotypes to clinical outcomes across rapid and slow progression subtypes.

Immunophenotypic changes in the tumor and tumor microenvironment during progression to multiple myeloma
Bergiers I, Köse MC, Skerget S, et al.
PLoS Genetics. 2025 Oct 7;21(10):e1011848.
DOI: 10.1371/journal.pgen.1011848

3. Predicting Resistance to Frontline Immunotherapy

Genomic and immune signatures derived from CoMMpass were shown to predict outcomes in newly diagnosed patients treated with immunotherapy-based regimens, providing early markers of resistance to frontline quad therapies.

Genomic and immune signatures predict clinical outcome in newly diagnosed multiple myeloma treated with immunotherapy regimens
Maura F, Boyle EM, Coffey D, et al.
Nature Cancer. 2023 Nov 9;4(12):1660–1674.
DOI: 10.1038/s43018-023-00657-1

4. Targeting High-Risk Genotypes

CoMMpass-informed genomic analyses identified high-risk disease subsets amenable to targeted immunotherapies, including optimized CAR-T strategies.

Targeting high-risk multiple myeloma genotypes with optimized anti-CD70 CAR-T cells
Kasap C, Izgutdina A, Patiño-Escobar B, et al.
bioRxiv. 2024 Feb 28.
DOI: 10.1101/2024.02.24.581875

5. TP53 Loss and Clinical Outcomes

Comprehensive genomic profiling revealed that multi-hit TP53 alterations confer the poorest survival outcomes in the era of novel therapies, refining prognostic stratification for high-risk disease.

Multi-hit TP53 confers the poorest survival in multiple myeloma in the era of novel therapies
Nesnadna R, Petrackova A, Minarik J, et al.
Molecular Medicine. 2025 Nov 29;32(1):3.
DOI: 10.1186/s10020-025-01392-2

6. Therapeutic Targets and Resistance Mechanisms

CoMMpass data revealed mechanisms of resistance that impact T-cell–redirecting therapies and identified novel surface targets for immunotherapy.

  • Plasma cell identity escape drives resistance to anti-BCMA T-cell–redirecting therapy in multiple myeloma
    Maura F, Freeman CL, et al.
    bioRxiv. 2025 Dec 11.

  • GPRC5D is a target for the immunotherapy of multiple myeloma with rationally designed CAR T cells
    Smith EL, Harrington K, et al.
    Science Translational Medicine. 2019 Mar 27;11(485).

  • Targeting CD74 in multiple myeloma with the antibody-drug conjugate STRO-001
    Abrahams CL, Li X, et al.
    Oncotarget. 2018 Dec 28;9(102):37700–37714.

7. Structural Variants in Myeloma

Large-scale analysis of structural variants revealed their critical role in disease progression and risk stratification.

Revealing the impact of structural variants in multiple myeloma
Rustad EH, Yellapantula VD, et al.
Blood Cancer Discovery. 2020 Sep 15;1(3):258–273.

8. Outcomes Linked to Precursor Disease

Transcriptional and clonal features associated with precursor disease states were shown to predict exceptionally favorable or adverse clinical outcomes.

  • Specific transcriptional profiles predict favorable prognosis in multiple myeloma
    Yan W, Qiu C, et al.
    Haematologica. 2025 Apr 24.

  • Genomic profiling to contextualize intervention in smoldering multiple myeloma
    Kazandjian D, Diamond B, et al.
    Clinical Cancer Research. 2024 Oct 1.

Additional Key Publications

Beyond these case studies, CoMMpass has produced a broad body of work that has fundamentally reshaped the understanding of multiple myeloma biology.

Key contributions include:

  • Identification of RNA-based molecular subtypes of myeloma
  • Discovery of actionable genetic alterations and novel high-risk categories
  • Development of NGS-based classifiers that outperform conventional cytogenetics
  • Mapping oncogenic dependencies and disease drivers
  • Comprehensive immune profiling and circulating tumor cell analyses

Selected Publications

  • Bolli N, et al. Genomic patterns of progression in multiple myeloma. Nature Genetics. 2018.
  • Walker BA, et al. Clonal evolution and heterogeneity in multiple myeloma. Leukemia. 2018.
  • Szalat R, et al. Genomic classification and outcomes in the MMRF CoMMpass study. Blood Cancer Discovery. 2021.
  • Skerget S, et al. Comprehensive molecular profiling of multiple myeloma identifies refined copy number and expression subtypes. Nature Genetics. 2024.
  • Pilcher J, et al. Immune Atlas of multiple myeloma. Nature Cancer. (reference forthcoming)
  • Garces JJ, et al. Elevated circulating tumor cells reflect high proliferation and genomic complexity. HemaSphere. 2025.
  • Walker BA, et al. Identification of novel mutational drivers reveals oncogene dependencies. Blood. 2018.
  • Maura F, et al. Genomic landscape and reconstruction of driver events in multiple myeloma. Nature Communications. 2019.