Establishing Lymphoma Type-Specific Cytokine Signatures Using Tissue-Based RNA or Peripheral Blood Cell-Free RNA (cfRNA)
Authors: Albitar Maher , Zhang Hong , Agersborg Sally , Charifa Ahmad , Pecora Andrew L , Leslie Lori A. , Feldman Tatyana , Ip Andrew , Goy Andre 
Published: December 2024

Introduction: Cytokines and chemokines play important roles in lymphoma growth and response to therapy. They are also relevant for the clinical symptoms and various manifestations of the disease. Evaluating RNA levels of large numbers of cytokines/chemokines and their receptors in tissue is now possible using next generation sequencing (NGS). However, it is not known if the tumor microenvironment is reflected in peripheral blood cell-free RNA (cfRNA). Using NGS, we evaluated the RNA levels of 36 cytokines/chemokines and their receptors in tissue samples from patients with various types of lymphoid neoplasms and used machine learning to establish signatures that distinguish between various types of lymphoma. We also explored if these signatures are adequately reflected when peripheral blood cfRNA is tested instead of tissue RNA.
Methods: RNA was extracted from tissue samples with confirmed chronic lymphocytic leukemia (CLL) (N=184), diffuse large B-cell lymphoma (DLBCL) (N=287), mantle cell lymphoma (MCL) (N=74), T-cell lymphoma (N=276), and bone marrow samples with low level B-cell lymphoid neoplasm not otherwise classified (N=750). Peripheral blood cfRNA was extracted from 19 patients with DLBCL, 23 with mantle cell lymphoma, 39 with T-cell lymphoma, 16 with CLL, and 361 with low level B-cell lymphoid neoplasm not otherwise classified. Cellular RNA and cfRNA were sequenced using a 1500-gene panel. The expression levels of 36 cytokines/chemokines were used in this analysis. Using Bayesian statistics, we first evaluated and ranked the sensitivity and specificity of each biomarker individually with 10-fold cross validation using leave-one-out. Then, two-thirds of the tissue samples were used for training and building models and one-third was used for testing these models. Each model was then used to test if cfRNA samples showed the same results obtained from tissue samples.
Results: For differentiating MCL from DLBCL, random forest used a signature of 16 biomarkers to distinguish between the two diseases (AUC: 0.928, CI: 0.855-1.00). The top 16 biomarkers are: TGFBR2, IL21R, TGFBI, CXCR4, TNFAIP3, IL8, TGFB3, IL2, TNFRSF17, CTLA4, TNFRSF4, TNFRSF6B, IL2RA, TNFRSF10B, IL3RA, and CXXC4. The same random forest algorithm using these 16 biomarkers as measured in cfRNA was also able to reliably distinguish MCL from DLBCL (AUC of 0.789, CI: 0.652-0.927). Using the same approach to distinguish between CLL and DLBCL, only 6 biomarkers (TGFBR2, IL8, IL21R, CCL2, TNFRSF6B, TNFRSF11A) were needed (AUC: 0.988, CI 0.968-1.00) for tissue and for cfRNA (AUC: 0.887, CI: 0.7772-1.00). Bone marrow samples with B-lymphoid neoplasms not otherwise classified can be distinguished from CLL using 25 biomarkers as measured in cells (AUC of 0.907, CI: 0.874-0.940) and as measured in cfRNA (AUC of 0.920, CI: 0.879-0.961). These markers are CXXC4, TGFBR2, TNFRSF10B, CTLA4, TNFRSF14, TGFBR3, IFNG, TNFRSF10D, IL15, IL3RA, IL7R, IL3, TNFRSF6B, TGFB3, IL2, TNF, CXCR4, TNFRSF9, TNFAIP3, IL12RB2, CCL2, IL1RAP, IL1B, IL6, and IL8. DLBCL can be distinguished from T-cell lymphoma using only 6 biomarkers that were measured in tissue samples (TGFBR3, TNFAIP3, TNFRSF17, IL21R, IL1RAP and TGFBR2) (AUC:0.946, CI: 0.912-0.980). However, the same biomarkers when measured in cfRNA were not able to distinguish between DLBCL and T-cell lymphoma (AUC: 0.643, CI: 0.495-0.790). In contrast, distinguishing between CLL and T-cell lymphoma was very reliable using either tissue-based RNA quantification or cfRNA quantification (AUC: 0.914, CI: 0.870-0.959 for tissue RNA and AUC: 0.900, CI: 0.820-0.981 for cfRNA). This signature used the following 10 biomarkers: TGFB3, TNFRSF10B, TNFRSF6B, TGFBR2, CXXC4, TGFBI, CCL2, CTLA4, TNFRSF9, and TGFBR3.
Conclusions: Cytokine signatures for each type of lymphoma are unique and can distinguish one type of lymphoma from another. Targeting these signatures may improve outcomes and possibly ameliorate some of the clinical symptoms. Our data also shows that tissue cytokine signatures are reflected in peripheral blood cfRNA, which suggests that cfRNA can be used in lieu of tissue samples for the differential diagnosis and monitoring of treatment. However, cfRNA failed to reflect tissue in distinguishing between DLBCL and T-cell lymphoma. This is likely due to overlap between the two diseases in the generation of systemic response that is different from the localized tissue-based cytokine response.