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Updated: May 5, 2026

Establishment of a Human Multiple Myeloma Xenograft Model in the Chicken to Study Tumor Growth, Invasion and Angiogenesis
Published on: May 1, 2015
How Laboratory Innovations Are Shaping the Future of Multiple Myeloma Care
Joana Caetano1,2, Ana Marta Pires3, Carlos Costa4
1Hemato-Oncology Unit, Fundação Champalimaud, 1400-038 Lisbon, Portugal.
None:
Multiple myeloma is a complex hematologic malignancy characterized by significant biological heterogeneity, a relapsing-remission clinical course, and a continuously evolving therapeutic landscape. Accurate and timely laboratory assessment is central to disease management, supporting diagnosis, risk stratification, evaluation of treatment response, and long-term monitoring. Despite major advances in therapy, a critical need remains for laboratory tools that can detect disease with greater sensitivity, capture spatial and clonal tumor heterogeneity, and reflect the true depth of treatment response beyond conventional serological and bone marrow-based criteria. Recent laboratory innovations have the potential to transform myeloma care by enabling earlier detection, more accurate prognostication, and personalized therapeutic strategies. This review focuses specifically on innovative laboratory technologies for the diagnosis of multiple myeloma and the evaluation of treatment response. Within this scope, we examine the current diagnostic approaches and the role of high-throughput technologies for measurable residual disease assessment. We explore the emerging role of liquid biopsy approaches, including circulating tumor cells, cell-free DNA/RNA, and mass spectrometry for ultrasensitive detection of monoclonal proteins. We further discuss novel molecular biomarkers and the integration of artificial intelligence and machine learning tools to enhance data interpretation. The innovations reviewed here represent a shift in the contribution of laboratory medicine to myeloma care, offering a more precise, less invasive, and biologically informative framework for targeted and adaptive clinical decisions.
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