Circulating miRNA panel for prediction of acute graft-versus-host disease in lymphoma patients undergoing matched unrelated hematopoietic stem cell transplantation

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Circulating miRNA panel for prediction of acute graft-versus-host disease in lymphoma patients undergoing matched unrelated hematopoietic stem cell transplantation Silvia Gimondi, Matteo Dugo, Antonio Vendramin, Anisa Bermema, Giulia Biancon, Alessandra Cavané, Paolo Corradini, Cristiana Carniti Experimental Hematology Volume 44, Issue 7, Pages 624-634.e1 (July 2016) DOI: 10.1016/j.exphem.2016.03.005 Copyright © 2016 ISEH - International Society for Experimental Hematology Terms and Conditions

Figure 1 Study design. Experimental Hematology 2016 44, 624-634.e1DOI: (10.1016/j.exphem.2016.03.005) Copyright © 2016 ISEH - International Society for Experimental Hematology Terms and Conditions

Figure 2 Circulating miRNAs in aGVHD patients are shifted toward lower expression levels. (A) The number of miRNAs expressed is significantly smaller in patients post-HSCT than in healthy donors. Moreover, the average number of miRNAs detected differs among the three biological groups. (B) Acute GVHD patients have a significantly smaller number of detected miRNAs compared with non-aGVHD patients and healthy donors. (C) Accordingly, the expression levels in the aGVHD group are lower, as indicated by higher values of cycle threshold (Ct), considering all 377 miRNAs on the card (mean Ct: aGVHD patients, 25.8; non-aGVHD patients, 24.9; healthy donors, 23.1). (D) These results were confirmed considering only miRNAs detected (Ct < 32) in all samples (mean Ct: aGVHD, 23.9; non-aGVHD, 22.0; healthy donors, 21). In (A–D), p values (t test) are reported. Experimental Hematology 2016 44, 624-634.e1DOI: (10.1016/j.exphem.2016.03.005) Copyright © 2016 ISEH - International Society for Experimental Hematology Terms and Conditions

Figure 3 MicroRNA expression profiles can distinguish aGVHD from non-aGVHD samples. Hierarchical clustering analysis performed with the 30 ratios differentially expressed is able to discriminate aGVHD from non-aGVHD samples as expected, although some of the aGVHD patients (aGVHD12, aGVHD8, aGVHD9, aGVHD4, aGVHD5) are grouped in the non-aGVHD cluster. Experimental Hematology 2016 44, 624-634.e1DOI: (10.1016/j.exphem.2016.03.005) Copyright © 2016 ISEH - International Society for Experimental Hematology Terms and Conditions

Figure 4 MicroRNA ratios and aGVHD prediction. SVM classification of samples based on the 30 differentially expressed ratios. Dots represent the decision values assigned by the SVM algorithm to each patient's miRNA profile at day +28 post-HSCT. The decision value represents the confidence that an SVM classifier has in classifying a given sample. A positive value assigns the sample to the non-aGVHD class, a negative value to the aGVHD class. Experimental Hematology 2016 44, 624-634.e1DOI: (10.1016/j.exphem.2016.03.005) Copyright © 2016 ISEH - International Society for Experimental Hematology Terms and Conditions

Figure 5 Pathway and network analysis of miR-194 and miR-518f target genes performed with the IPA software. Global canonical pathway analysis revealed that target genes of miR-194 and miR-518f are involved in pathways that could be related to aGVHD. (A) Data for canonical pathways with a p value < 0.01, calculated using Fisher's exact test, are shown. (B, C) IPA analysis of miRNA target mRNAs: (A) graphical presentation of “Infectious Disease, Cell Death and Survival, Cellular Growth and Proliferation” network (score 32, focus molecules 25); (C) Graphical presentation of “Cellular Development, Embryonic Development, Hematological System Development and Function” network (score 30, focus molecule 24). Experimental Hematology 2016 44, 624-634.e1DOI: (10.1016/j.exphem.2016.03.005) Copyright © 2016 ISEH - International Society for Experimental Hematology Terms and Conditions