title: Proteomics-based strategies to identify proteins relevant to chronic lymphocytic leukemia.
abstract: Chronic lymphocytic leukemia (CLL), a malignant B-cell disorder, is characterized by a heterogeneous clinical course. Two-dimensional nano liquid chromatography (2D-nano-LC) coupled with matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry (MALDI-TOF/TOF MS) (LC-MALDI) was used to perform qualitative and quantitative analysis on cellular extracts from 12 primary CLL samples. We identified 728 proteins and quantified 655 proteins using isobaric tag-labeled extracts. Four strategies were used to identify disease-related proteins. First, we integrated our CLL proteome with published gene expression data of normal B-cells and CLL cells to highlight proteins with preferential expression in the transcriptome of CLL. Second, as CLL's outcome is heterogeneous, our quantitative proteomic data were used to indicate heterogeneously expressed proteins. Third, we used the quantitative data to identify proteins with differential abundance in poor prognosis CLL samples. Fourth, hierarchical cluster analysis was applied to identify hidden patterns of protein expression. These strategies identified 63 proteins, and 4 were investigated in a CLL cohort (39 patients). Thyroid hormone receptor-associated protein 3, T-cell leukemia/lymphoma protein 1A, and S100A8 were associated with high-risk CLL. Myosin-9 exhibited reduced expression in CLL samples from high-risk patients. This study shows the usefulness of proteomic approaches, combined with transcriptomics, to identify disease-related proteins.
authors.affiliation: Institute of Cancer & Genetics, School of Medicine, Cardiff University , Heath Park, Cardiff, CF14 4XN, United Kingdom.
authors.forename: Suliman A
authors.initials: SA
authors.lastname: Alsagaby
authors.forename: Sanjay
authors.initials: S
authors.lastname: Khanna
authors.forename: Keith W
authors.initials: KW
authors.lastname: Hart
authors.forename: Guy
authors.initials: G
authors.lastname: Pratt
authors.forename: Christopher
authors.initials: C
authors.lastname: Fegan
authors.forename: Christopher
authors.initials: C
authors.lastname: Pepper
authors.forename: Ian A
authors.initials: IA
authors.lastname: Brewis
authors.forename: Paul
authors.initials: P
authors.lastname: Brennan
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000032:analysis / Q000097:blood; D040501:Calgranulin A / Q000378:metabolism; D016000:Cluster Analysis; D005500:Follow-Up Studies; D006801:Humans; D053208:Kaplan-Meier Estimate; D015451:Leukemia, Lymphocytic, Chronic, B-Cell / Q000097:blood* / Q000401:mortality / Q000473:pathology / Q000628:therapy; D015458:Leukemia, T-Cell / Q000378:metabolism; D046228:Microarray Analysis; D008875:Middle Aged; D020409:Molecular Motor Proteins / Q000378:metabolism; D018995:Myosin Heavy Chains / Q000378:metabolism; D011379:Prognosis; D011506:Proteins / Q000032:analysis* / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods; D053719:Tandem Mass Spectrometry / Q000379:methods
chemical_list: D014408:Biomarkers, Tumor; D040501:Calgranulin A; C103814:MYH9 protein, human; D020409:Molecular Motor Proteins; D011506:Proteins; D018995:Myosin Heavy Chains
keywords: CLL; S100A8; TCL-1; TR150; iTRAQ-based quantitative proteomics; myosin-9