Importance: Multiple myeloma (MM), a blood cancer arising from malignant plasma cells in the bone marrow, is complex and time-consuming to diagnose, requiring several laboratory tests, which can delay treatment increasing progression risk.

Objective: To develop a diagnostic test based on untargeted mass spectrometry(MS)-based proteomics and machine learning (ML) to diagnose MM already at the first point of screening.

Design, Setting, and Participants: This is a prospective cohort study involving 2,501 samples (serum, peripheral plasma, bone marrow plasma) obtained from patients diagnosed with MM, monoclonal gammopathy of undetermined significance (MGUS), smoldering MM (SMM), patients referred for M-component screening, and healthy controls.

Methods: Proteomes were quantified by LC-MS and analyzed with supervised ML to identify diagnostic signatures. Performance was evaluated by cross-validation and external validation, with correlation to disease severity, survival, and progression assessed.

Results: The model achieved AUCs of 0.85 (CV-test), 0.91 (national validation cohort), and 0.79 (international validation cohort). The predicted disease score showed a continuous dis-tribution correlating with disease severity from controls to MGUS to SMM to MM. Correla-tion between the LC-MS-derived estimate of monoclonality and conventional M-spike quan-tification was 0.78 (p=2×10⁻⁶¹). Classification performance was 95% in healthy peripheral plasma and 95% (peripheral) and 83% (bone marrow) in newly diagnosed MM. We found no significant survival differences between upper and lower quartiles of the disease score in newly diagnosed MM.

Conclusions and Relevance: Combining proteomics with ML allows diagnostic support and severity assessment in MM and potentially other medical conditions.

Co-authors: None