P-191: The metabolic profile and prognostic model of multiple myeloma

Introduction: Multiple myeloma (MM) patients had improved overall survival rates due to considerable advances in the treatments. However, MM remains to be an incurable disease. There is a need for greater precision in clinical prognostic models. Furthermore, novel strategies are required to ameliorate the poor outcomes of relapsed and refractory patients. Metabolic alterations constitute a hallmark of cancer. Our previous studies revealed that imbalance in amino acids metabolism promotes MM progression. Glycine promotes MM progression through disrupting glutathione balance [1], whereas excessive serine impedes megakaryopoiesis and thrombopoiesis in MM [2]. Furthermore, kidney damage resulting from abnormal amino acid metabolism leads to accumulation of excessive urea, which is utilized by gut microbiota, ultimately resulting in nitrogen-recycling bacteria enrichment in MM [3]. The significance of metabolic alterations in prognostic prediction and enhancement of therapeutic effects in MM requires further investigation. Here, we performed a systematic analysis utilizing target metabolomics, with the aim of optimizing the management of MM patients.

Methods: The metabolomes of bone marrow and peripheral blood from 113 patients were included in the analysis, among which 20 had metabolomes of CD138 positive cells. Q300 was utilized for the detection of the metabolomes. Written consent was obtained from all patients. Clinical information encompassing demographics, clinical presentation, diagnosis, and OS time was collected.

Results: Given that malignant plasma cells reside in the bone marrow and absorb metabolites from the tumor microenvironment, our initial focus was on the metabolomes of bone marrow. Utilizing the overall survival time (OS) as a metric, four distinct metabolic clusters were identified in MM patients. Following a maximum follow-up period of 80 months, the median OS for clusters 1 to 4 is not reached, 50.1 months, 41.5 months, and 28.1 months respectively (p=0.011). The amino acids, specifically glycine, serine, and proline, were consistently present in each cluster, indicating their crucial role in MM. The significantly elevated metabolites in each cluster were marked in long chain fatty acid (LCFA), bile acid (BA) and carbohydrate, short chain fatty acid (SCFA), and bile acid (BA). Based on their metabolic characteristics, the four subtypes were categorized as LCFA, BA&Carbon, SCFA, and BA. Furthermore, the metabolic subtypes exhibited a strong correlation with clinical parameters such as monoclonal protein level, albumin, hemoglobin level, and IGH. These findings demonstrate that metabolic class designation serves as a novel predictor of survival in MM.

Conclusions: We present a metabolic prognostic model for MM patients. Several metabolic targets were identified based on the metabolic profiles. This study plays a significant role in advancing the current MM stratification systems and identifying promising therapeutic targets for MM.