title: Genomic data integration in chronic lymphocytic leukemia.
abstract: BACKGROUND
  B-cell chronic lymphocytic leukemia (CLL) is a heterogeneous disease and the most common adult leukemia in western countries. IgVH mutational status distinguishes two major types of CLL, each associated with a different prognosis and survival. Sequencing identified NOTCH1 and SF3B1 as the two main recurrent mutations. We described a novel method to clarify how these mutations affect gene expression by finding small-scale signatures that predict the IgVH, NOTCH1 and SF3B1 mutations. We subsequently defined the biological pathways and correlation networks involved in disease development, with the potential goal of identifying new drugable targets.
  
  
  METHODS
  We modeled a microarray dataset consisting of 48807 probes derived from 163 samples. The use of 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, we applied network and pathway analyses of these genes to identify their biological roles.
  
  
  RESULTS
  The mutational status of the patients was accurately predicted (94-99%) 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, whereas MSI2, LTK, TFEC and CNTAP2 are involved in the NOTCH1 mutation, and RPL32 and PLAGL1 are involved in the SF3B1 mutation. Four high discriminatory genes (IGHG1, MYBL1, NRIP1 and RGS1) are common to these three mutations. The IL-4-mediated signaling events pathway appears to be involved as a common mechanism and suggests an important role of the immune response mechanisms and antigen presentation.
  
  
  CONCLUSIONS
  This retrospective analysis served to provide a deeper understanding of the effects of the different mutations in CLL disease progression, with the expectation that these findings will be clinically applied in the near future to the development of new drugs.
authors.affiliation: Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, Oviedo, Asturias, Spain.
authors.forename: Juan Luis
authors.initials: JL
authors.lastname: Fernández-Martínez
authors.affiliation: Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, Oviedo, Asturias, Spain.|Artificial Intelligence Center, University of Oviedo, Oviedo, Asturias, Spain.
authors.forename: Enrique J
authors.initials: EJ
authors.lastname: deAndrés-Galiana
authors.affiliation: Biomodels LLC, Watertown, MA, USA.
authors.forename: Stephen T
authors.initials: ST
authors.lastname: Sonis
mesh_terms: D014408:Biomarkers, Tumor; D019295:Computational Biology / Q000379:methods; D030561:Databases, Nucleic Acid; D053263:Gene Regulatory Networks; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease; D023281:Genomics / Q000379:methods; D006801:Humans; D007143:Immunoglobulin Heavy Chains / Q000235:genetics; D015451:Leukemia, Lymphocytic, Chronic, B-Cell / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism / Q000401:mortality; D008954:Models, Biological; D009154:Mutation; D020411:Oligonucleotide Array Sequence Analysis; D010750:Phosphoproteins / Q000235:genetics; D025341:Principal Component Analysis; D011379:Prognosis; D000072260:RNA Splicing Factors / Q000235:genetics; D051880:Receptors, Notch / Q000235:genetics; D015203:Reproducibility of Results; D012189:Retrospective Studies; D015398:Signal Transduction
chemical_list: D014408:Biomarkers, Tumor; D007143:Immunoglobulin Heavy Chains; D010750:Phosphoproteins; D000072260:RNA Splicing Factors; D051880:Receptors, Notch; C112491:SF3B1 protein, human
keywords: cancer; gene expression; hematologic; leukemia; mathematical modeling; oncology