title: Applying of hierarchical clustering to analysis of protein patterns in the human cancer-associated liver.
abstract: BACKGROUND
  There are two ways that statistical methods can learn from biomedical data. One way is to learn classifiers to identify diseases and to predict outcomes using the training dataset with established diagnosis for each sample. When the training dataset is not available the task can be to mine for presence of meaningful groups (clusters) of samples and to explore underlying data structure (unsupervised learning).
  
  
  RESULTS
  We investigated the proteomic profiles of the cytosolic fraction of human liver samples using two-dimensional electrophoresis (2DE). Samples were resected upon surgical treatment of hepatic metastases in colorectal cancer. Unsupervised hierarchical clustering of 2DE gel images (n = 18) revealed a pair of clusters, containing 11 and 7 samples. Previously we used the same specimens to measure biochemical profiles based on cytochrome P450-dependent enzymatic activities and also found that samples were clearly divided into two well-separated groups by cluster analysis. It turned out that groups by enzyme activity almost perfectly match to the groups identified from proteomic data. Of the 271 reproducible spots on our 2DE gels, we selected 15 to distinguish the human liver cytosolic clusters. Using MALDI-TOF peptide mass fingerprinting, we identified 12 proteins for the selected spots, including known cancer-associated species.
  
  
  CONCLUSIONS
  Our results highlight the importance of hierarchical cluster analysis of proteomic data, and showed concordance between results of biochemical and proteomic approaches. Grouping of the human liver samples and/or patients into differing clusters may provide insights into possible molecular mechanism of drug metabolism and creates a rationale for personalized treatment.
authors.affiliation: V. N. Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, Russia.
authors.forename: Natalia A
authors.initials: NA
authors.lastname: Petushkova
authors.affiliation: V. N. Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, Russia.
authors.forename: Mikhail A
authors.initials: MA
authors.lastname: Pyatnitskiy
authors.affiliation: Postgen Tech LLC, Moscow, Russia.
authors.forename: Vladislav A
authors.initials: VA
authors.lastname: Rudenko
authors.affiliation: V. N. Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, Russia.
authors.forename: Olesya V
authors.initials: OV
authors.lastname: Larina
authors.affiliation: V. N. Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, Russia.
authors.forename: Oxana P
authors.initials: OP
authors.lastname: Trifonova
authors.affiliation: V. N. Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, Russia.
authors.forename: Julya S
authors.initials: JS
authors.lastname: Kisrieva
authors.affiliation: V. N. Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, Russia.
authors.forename: Natalia F
authors.initials: NF
authors.lastname: Samenkova
authors.affiliation: V. N. Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, Russia.
authors.forename: Galina P
authors.initials: GP
authors.lastname: Kuznetsova
authors.affiliation: V. N. Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, Russia.
authors.forename: Irina I
authors.initials: II
authors.lastname: Karuzina
authors.affiliation: V. N. Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, Russia; Postgen Tech LLC, Moscow, Russia.
authors.forename: Andrey V
authors.initials: AV
authors.lastname: Lisitsa
mesh_terms: D016000:Cluster Analysis; D003600:Cytosol / Q000378:metabolism; D015180:Electrophoresis, Gel, Two-Dimensional; D006801:Humans; D008113:Liver Neoplasms / Q000378:metabolism*; D008862:Microsomes, Liver / Q000378:metabolism; D009363:Neoplasm Proteins / Q000378:metabolism*; D040901:Proteomics; D015203:Reproducibility of Results; D012984:Software; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
chemical_list: D009363:Neoplasm Proteins
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