Genomic Data Integration in Chronic Lymphocytic Leukemia

We described a novel method to elucidate how these mutations affect gene expression by finding small‐scale signatures to predict the IgVH, NOTCH1 and SF3B1 mutations. We subsequently defined the biological pathways and correlation networks that are involved in the disease development with the potential goal of identifying new druggable targets. MethodsWe modeled a microarray data set consisting of 48807 probes derived from 163 samples. Using Fisher's ratio and Fold change combined with feature elimination allowed us to identify the minimum number of genes with the highest predictive mutation power and subsequently applied network and pathway analyses of these genes to identify their biological roles. ResultsThe mutational status of the patients was accurately predicted (94 to 99%) using small‐scale gene signatures: 13 genes for IgVH, 60 for NOTCH1, and 22 for SF3B1. LPL plays an important role in the case of the IgVH mutation, while MSI2, LTK, TFEC and CNTAP2 in the NOTCH1 mutation, and RPL32 and PLAGL1 in the SF3B1 mutation. Four high discriminatory genes (IGHG1, MYBL1, NRIP1 and RGS1) are common to these three mutations. IL‐4‐mediated signaling events pathway seems to be involved as a common mechanism and suggests an important role of the immune response mechanisms and antigen presentation.
Source: The Journal of Gene Medicine - Category: Genetics & Stem Cells Authors: Tags: RESEARCH ARTICLE Source Type: research