Establishing a Distinct Cytokine Signature for Multiple Myeloma Using Bone Marrow RNA and Peripheral Blood Cell-Free RNA (cfRNA)

Introduction: The combined effects of cytokines/chemokines and their receptors are believed to play a major role in determining the overall environment for plasma cell growth and the clinical course of multiple myeloma (MM). Cytokines also play a significant role in the immune response to the neoplastic plasma cells and are relevant to various therapeutic approaches. Numerous studies have evaluated various cytokines individually and correlated with clinical behavior. We used next generation sequencing (NGS) and RNA quantification along with machine learning algorithms to establish signatures based on the levels of cytokines/chemokines and their receptors that distinguish MM from other lymphoid neoplasms.

Methods: RNA was extracted from the bone marrow samples of patients with MM (N=409), chronic lymphocytic leukemia (CLL) (N=184), and bone marrow samples without any molecular evidence of abnormalities (N=430). RNA was also extracted from lymph nodes with diffuse large B-cell lymphoma (DLBCL) (N=287). cfRNA was extracted from the peripheral blood of 430 normal individuals, 23 patients with MM, 19 patients with DLBCL, and 16 patients with CLL. Tissue RNA and cfRNA were sequenced using a 1500-gene targeted RNA next generation sequencing (NGS) panel. Two-thirds of the tissue samples were used for training and building the models and one third was used for testing the models. In every model, Bayesian with 10-fold cross validation using leave-one-out was used to evaluate the sensitivity and specificity of each biomarker in distinguishing between two classes. The biomarkers were ranked, and random forest was used to develop algorithms selecting top-ranked biomarkers. Each model was confirmed by one-third of the tissue samples. Each model was then used to test if cfRNA samples showed the same results obtained from tissue samples.

We measured the RNA expression levels of 36 different cytokines/chemokines and their receptors in bone marrow (BM) from patients with MM, normal control, and patients with chronic lymphocytic leukemia (CLL). Lymph node biopsies were used for evaluating expression in patients with diffuse large B-cell lymphoma (DLBCL). We compared the signatures obtained from these tissues with those obtained from peripheral blood cfRNA.

Results: In comparing MM bone marrow samples with normal bone marrow, Bayesian statistics selected and ranked the various cytokine/chemokine and receptor biomarkers. The random forest algorithm showed that a signature of 10 biomarkers reliably distinguished MM from normal (AUC= 0.915, CI: 0.891-0.939). The top 10 biomarkers are: TGFBR2, TNFRSF10D, CXCR4, TNFRSF14, TNFRSF17, TNFRSF10B, TNFAIP3, TGFBR3, IL1RAP, and IL12RB2. The same algorithm and the same biomarkers quantified in peripheral blood cfRNA also distinguished MM from normal with AUC of 0.743 (CI: 0.678-0.809). Using the same approach, BM with MM was distinguishable from BM with CLL using a signature of 10 different biomarkers (TGFBR2, TNFRSF10B, CTLA4, TNFRSF14, IL21R, TNFRSF10D, TGFBR3, CXXC4, TNFRSF9, and TGFBI) (AUC = 0.978, CI 0.952-1.00). Similarly, cfRNA showed high accuracy in predicting MM from CLL using the same signature and algorithm (AUC= 0.829, CI: 0.689-0.968). Distinguishing BM with MM from LN with DLBCL, a signature of 10 biomarkers (IL21R, TNFRSF9, TNFRSF4, IL2RA, TNFRSF10B, TNFRSF6B, TGFBR2, CTLA4, TGFB3, and TNFAIP3) was adequate (AUC of 0.981, CI: 0961-1.00). However, these biomarkers when quantified in cfRNA failed to distinguish between MM and DLBCL (AUC: 0.542, CI: 0.365-0.720). The three signatures are specifically relying on the TNF pathway. They shared TNF and TNFAIP3 and their receptors (TNFRSF10B, TNFRSF10D, TNFRSF14, TNFRSF17, TNFRSF4, TNFRSF6B, TNFRSF9). Sorted multiple myeloma cells using CD138 antibodies showed that all TNF receptors are expressed at significantly (P<0.0001) higher level than in the flow-through cells (macrophages and myeloid cells). In contrast, flow-through cells showed significantly (P<0.0001) higher levels of TNF and TNFAIP3 than in multiple myeloma cells.

Conclusions: The findings suggest that the MM bone marrow microenvironment is unique and distinct from other lymphoid neoplasms. This uniqueness is mainly driven by the TNF pathway. The distinct cytokine signature of MM is commonly reflected and can be measured and monitored using cfRNA as possibly replacing the need for bone marrow samples.