Unveiling Cerebrospinal Fluid Protein Biomarkers in Pediatric Acute Lymphoblastic Leukemia Using Proximity Extension Assay

Abstract

BackgroundAcute Lymphoblastic Leukemia (ALL) is a highly heterogeneous pediatric malignancy. Despite high survival rates, relapse and the involvement of central nervous system (CNS) remains a significant clinical challenge. Traditional clinical parameters often lack the precision required for early detection and risk stratification. This study utilizes high-throughput proteomics and machine learning (ML) to identify molecular signatures in cerebrospinal fluid (CSF) that characterize disease effect and treatment response. Methods82 CSF samples from 41 pediatric ALL patients at diagnosis (VD) and remission (VR) were analyzed. Proteomic profiling of 276 proteins was performed using Olinks Proximity Extension Assay. Differentially abundant proteins were identified (q-value< 0.05, |Log_2FC| > 0.5) using the Wilcoxon rank-sum test. Three ML algorithms - Random Forest, LASSO, and SVM-RFE - were integrated to select the differentially abundant proteins in VR and VD and between CNS involvement levels. To validate the data Pan-Cancer Atlas analysis was done using two different platforms. ResultsIn VR, we observed significant alterations in the expression of key proteins compared to VD, with ADGRG1 and KYNU showing a marked increase, while CCL17, CD5, CD27, CXCL9, CXCL11, FASLG, GZMA, and TNFRSF9 were significantly downregulated. Furthermore, our analysis identified distinct protein signatures associated with CNS involvement: CCL4, CTSC, CXCL10, CXCL9, and MMP7 were differentially abundant at the VD, whereas CAIX, CASP-8, HAGH, CXCL9, MMP7, MCP-2, and VWC2 at the VR. ConclusionIntegrating Olink proteomics with ML identified molecular signatures in ALL that have the potential to be further developed to a biomarker panel for monitoring treatment response and guiding personalized therapeutic strategies shifting the focus toward the Precision One Health approaches.