ABSTRACT Multiple myeloma (MM) is recognized as a malignancy shaped by its complex tumor microenvironment (TME), which fuels disease progression and therapeutic resistance. Recent advances in single‐cell omics, spatial transcriptomics, mass cytometry, and advanced imaging have enabled high‐resolution mapping of tumor and immune cell interactions within their native context, also revealing spatial heterogeneity that influences clinical outcomes. These tools, complemented by scalable computational frameworks and artificial intelligence, provide cost‐effective alternatives to dissect immune landscapes and derive prognostic biomarkers from both bulk and single‐cell data. However, technical complexity, resource demands, and the need for robust standardization limit their immediate clinical application. On the other hand, machine learning techniques enhance integration and predictive power of existing datasets, supporting the development of personalized, immune‐informed therapeutic strategies. This review highlights recent advances, discusses the strengths and limitations of emerging technologies with a particular focus on their integration to decipher TME biology and pave the way toward precision medicine in MM.
Gramegna et al. (Sun,) studied this question.