title: Messina: a novel analysis tool to identify biologically relevant molecules in disease.
abstract: BACKGROUND
  Morphologically similar cancers display heterogeneous patterns of molecular aberrations and follow substantially different clinical courses. This diversity has become the basis for the definition of molecular phenotypes, with significant implications for therapy. Microarray or proteomic expression profiling is conventionally employed to identify disease-associated genes, however, traditional approaches for the analysis of profiling experiments may miss molecular aberrations which define biologically relevant subtypes.
  
  
  RESULTS
  Here we present Messina, a method that can identify those genes that only sometimes show aberrant expression in cancer. We demonstrate with simulated data that Messina is highly sensitive and specific when used to identify genes which are aberrantly expressed in only a proportion of cancers, and compare Messina to contemporary analysis techniques. We illustrate Messina by using it to detect the aberrant expression of a gene that may play an important role in pancreatic cancer.
  
  
  CONCLUSIONS
  Messina allows the detection of genes with profiles typical of markers of molecular subtype, and complements existing methods to assist the identification of such markers. Messina is applicable to any global expression profiling data, and to allow its easy application has been packaged into a freely-available stand-alone software package.
authors.affiliation: Cancer Research Program, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia.
authors.forename: Mark
authors.initials: M
authors.lastname: Pinese
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Scarlett
authors.forename: James G
authors.initials: JG
authors.lastname: Kench
authors.forename: Emily K
authors.initials: EK
authors.lastname: Colvin
authors.forename: Davendra
authors.initials: D
authors.lastname: Segara
authors.forename: Susan M
authors.initials: SM
authors.lastname: Henshall
authors.forename: Robert L
authors.initials: RL
authors.lastname: Sutherland
authors.forename: Andrew V
authors.initials: AV
authors.lastname: Biankin
mesh_terms: D000465:Algorithms; D002630:Chemotactic Factors / Q000378:metabolism; D030541:Databases, Genetic; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D005819:Genetic Markers; D005821:Genetic Techniques / Q000706:statistics & numerical data*; D006801:Humans; D007150:Immunohistochemistry; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D010190:Pancreatic Neoplasms / Q000235:genetics; D040081:Protein Array Analysis / Q000706:statistics & numerical data; D009418:S100 Proteins / Q000378:metabolism; D012680:Sensitivity and Specificity; D012984:Software
chemical_list: D002630:Chemotactic Factors; D005819:Genetic Markers; D009418:S100 Proteins; C098997:S100A2 protein, human