Abstract 6170: Risk stratification of smoldering multiple myeloma using fourier transform infrared spectroscopy of serum
Authors: Dingli David , Khonkhammy Darlyna , Gunnarson Caden , Schlauderaff Grant , Pham Dan Q. , Khammanivong Ali 
Published: March 2024

Background: Smoldering multiple myeloma (SMM) is a state where patients have more than 10% clonal plasma cells or a serum monoclonal protein &gt; 3g/dl but no evidence of end organ damage. Patients with SMM are at risk of developing active multiple myeloma (defined by the CRAB criteria). The best stratification system with respect to the risk of progression to active myeloma is the 20/2/20 (20% plasma cells, M protein &gt;2.0 g/dl, and involved to uninvolved immunoglobulin free light chain ratio &gt;20). The presence of ≥2 criteria predicts a 50 - 60% risk of progression within 2 years. Thus, many patients considered ‘high-risk’ may not progress in this time frame, and patients with ≤1 risk factor can progress to active myeloma. Therefore, improved risk stratification methods are needed to correctly identify patients at high risk of progression. We hypothesized that Fourier Transform InfraRed (FTIR) spectroscopic analysis of serum combined with artificial intelligence (AI) can correctly risk stratify patients with SMM.

Methods: We identified 192 SMM patients: 96 who progressed and 96 who did not progress within 2 years of diagnosis. We used stored sera from the time of diagnosis to perform FTIR to determine differences in the spectra of patients who did/did not progress and train an AI algorithm. A randomized 10-fold cross-validation analysis was performed using 172 samples (86 from each group) for training and the remaining 20 for testing to assess the model across 10 independent iterations. Subsequently, we blindly tested the trained model against an independent set of serum samples (N=24) from patients who progressed ≤3 years of diagnosis. Receiver operator characteristic (ROC) analysis was used to assess the FTIR AI algorithm output compared to the clinical outcome data.

Results: From the 96 patients who progressed, 48 had complete data for the 20/2/20 risk stratification. 11 patients (23%) had ≤1 of the criteria and so would have been considered at low likelihood to progress. Of the 96 who did not progress within 2 years, 49 had complete data for risk stratification. Of these, 27 (55.1%) met the high-risk criteria of the 20/2/20 classification and would have been predicted to progress within 2 years. FTIR/AI model correctly identified the patients at risk of progression (95% accuracy) with AUC of 0.96. Similarly, it was able to classify patients who did not progress within 2 years of diagnosis (94% accuracy) with an AUC of 0.96. FTIR/AI correctly predicted the patients at risk of progression within 2 years (90%) and 3 years (87.5%) in the blinded validation group with an AUC of 0.89 and 0.82, respectively.

Conclusion: Analysis of serum in patients with newly diagnosed SMM with FTIR is able to correctly identify those at risk of progression within 2 or 3 years of diagnosis with an accuracy of ~90% in a blinded external validation study. FTIR may, therefore, be used to assess SMM patient risk before committing to potentially toxic therapy.

Citation Format: David Dingli, Darlyna Khonkhammy, Caden Gunnarson, Grant Schlauderaff, Dan Q. Pham, Ali Khammanivong. Risk stratification of smoldering multiple myeloma using fourier transform infrared spectroscopy of serum [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6170.