Distinguishing between Monoclonal Gammopathy of Undetermined Significance and Smoldering Multiple Myeloma with FTIR/ML of Serum

Monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), and active multiple myeloma (MM) represent a continuum across the spectrum of plasma cell disorders. While patients with MGUS can be observed, it has a risk of progression to MM, while patients with SMM have a higher risk of progression to MM. Determining the risk of progression is important for proper patient care and elimination of anxiety, especially for patients identified at very low risk of progression. In contrast, identifying patients at high risk may reduce morbidity by more active surveillance or the introduction of therapy. We hypothesized that Fourier Transform InfraRed Spectroscopic analysis of serum combined with machine learning (FTIR/ML) could be used to facilitate this decision-making and correctly stratify patients' risk. Methods: After Institutional Review Board approval, we identified 99 patients with MGUS who did not progress to SMM/MM or AL amyloidosis, 96 patients with SMM who did not progress to active MM over a 2-year interval from diagnosis, and another 96 SMM patients who progressed to MM within two years of diagnosis. The patients had been diagnosed and followed up at Mayo Clinic in Rochester. Serological molecular fingerprint analysis was then performed using surface- and chemical-enhanced infrared absorption FTIR spectroscopy combined with artificial neural network ML. A randomized 10-fold cross-validation analysis was performed using 262 samples for training and the remaining 29 samples for validation to assess the model across ten independent iterations. Receiver operator characteristic (ROC) analysis was used to assess the FTIR/ML algorithm output compared to the clinical outcome data. Results: The patients with both MGUS and SMM had a similar median age (64.4 years), but patients with SMM tended to have lower hemoglobin compared to MGUS (median 12.4 versus 13.8g/dl respectively). As expected, patients with SMM also had a higher serum M-spike (2.0g/dl versus 1.2) and a higher clonal plasma cell burden in the bone marrow (20% versus 9%). FTIR/ML was able to correctly classify sera into MGUS in 95.8% of samples and SMM in 92.9% of samples. This gave an average accuracy of 94.8%. We performed ROC analysis and determined an area under the curve of 98.7%. The F1 score/precision was 94.9%, with a recall of 94.8%. After establishing a suitable threshold, the ML algorithm was able to correctly classify MGUS in 96.8% and SMM in 97.2% of cases. Conclusion: A single serum test at the time of diagnosis can correctly distinguish between MGUS that will not progress to another plasma cell disorder from smoldering multiple myeloma with a high degree of accuracy. Given that the patients with MGUS that were chosen had not progressed to a plasma cell disorder and were correctly identified, this methodology offers an opportunity to provide reassurance and minimize the need for repeat testing and follow-up of such patients.