Proteomics Landscape and Machine Learning Predictor of Response to Splenectomy in Immune Thrombocytopenia

Introduction

Immune thrombocytopenia (ITP) is an auto-immune disease characterized by antibody-mediated platelet destruction. Splenectomy remains the effective curative treatment for patients with ITP who failure to respond to TPO-RAs and/or rituximab (Mageau et al, Am J Hematol. 2022). The effectiveness rate of splenectomy for ITP is reported to be roughly 80%, with a 5-year remission rate ranging from 50% to 75% (Chaturvedi et al, Blood. 2018). For subjects presenting with severely low platelet counts or active bleeding, splenectomy represents the primary approach for rapidly increasing platelet levels and potentially saving lives. To achieve personalized and precise treatment, the prediction efficacy on splenectomy remains an urgent problem. Here, we have characterized immune features of ITP patients with splenectomy, and developed a predictive model for assessing the response to splenectomy.

Methods

Bone marrow plasma samples before splenectomy were collected and analyzed by high-resolution liquid chromatography-tandem mass spectrometry. Weight protein co-expression network analysis (WPCNA) was applied to generate modules from all proteins and mapped them to clinical characteristics. We used least absolute shrinkage and selection operator (Lasso) regression, recursive feature elimination (RFE) for predictors selection. Next, multivariable logistic regression (LR), support vector machine (SVM), and decision tree algorithms were used to develop a predictive model.

Results

60 patients with ITP undergoing splenectomy treatment were enrolled into our study, and divided into three groups according to the response [patients with CR (n=30), NR (n=7), and Relapse (n=23)]. A pathway enrichment analysis of WPCNA modules by ClueGO revealed that megakaryocyte differentiation, humoral immune response mediated by circulating immunoglobulin, Fc receptor signaling pathway, and cGMP-PKG signaling pathway were correspondingly enriched in MEtan, MEturquoise, MEblue, and MEpink modules, which were associated with treatments with TPO-RA, IVIG, rhTPO, and post-operation platelet level, respectively. We then performed KEGG pathway enrichment analyses on the different expression proteins (DEPs) to deciphering the immune landscape among each group. For the relapse group, DEPs were primarily enriched in ribosome, spliceosome, neutrophil extracellular trap formation, and necroptosis pathways, comparing with the CR group. For the NR group, most DEPs were enriched in natural killer cell mediated cytotoxicity, NF-kappa B signaling, Fc gamma R-mediated phagocytosis pathways, comparing with the CR group.

The models were based on a multi-step predictor pipeline. Inside the pipeline, features were first filtered by lasso regression and RFE algorithm. We identified five proteomic features present in the bone marrow plasm that associated with relapsed to therapy, which included CD40, COL4A3, GALNT6, GSTM3, and UBE2A (Figure). Each ensemble consisted of three algorithms acting in parallel: LR, SVM and decision tree. The AUC values of LR, SVM, and decision tree were 0.962 (95%CI: 0.915-1), 0.957 (95%CI: 0.897-1), and 0.875 (95%CI: 0.782-0.967), respectively. The three algorithms were then tested for performance. The sensitivity, specificity, positive predictive value, and negative predictive value analysis revealed that LR model reached the best performance. Targeting CD40 represents a promising therapeutic approach for enhancing clinical outcomes in patients with ITP who have shown inadequate response following splenectomy.

Conclusion

Our analysis revealed that ITP patients with splenectomy showed distinct immune characteristics when achieving different response. Protein features extracted from bone marrow plasma can be selected as predictors of response to splenectomy.