title: Whole genome expression analysis for biologic rational pathway modeling: application in cancer prognosis and therapy prediction.
abstract: Using semi-quantitative microarray technology, almost every one of the approximately 30 000 human genes can be analyzed simultaneously with a low rate of false-positives, a high specificity, and a high quantification accuracy. This is supported by data from comparative studies of microarrays and reverse-transcription PCR for established cancer genes including those for epidermal growth factor receptor (EGFR), human epidermal growth factor receptor-2 (HER2/ERBB2), estrogen receptor (ESR1), progesterone receptor (PGR), urokinase-type plasminogen activator (PLAU), and plasminogen activator inhibitor-1 (SERPINE1). As such, semi-quantitative expression data provide an almost completely comprehensive background of biological knowledge that can be applied to cancer diagnostics. In clinical terms, expression profiling may be able to provide significant information regarding (i) the identification of high-risk patients requiring aggressive chemotherapy; (ii) the pathway control of therapy predictive parameters (e.g. ESR1 and HER2); (iii) the discovery of targets for biologically rational therapeutics (e.g. capecitabine and trastuzumab); (iv) additional support for decisions about switching therapy; (v) target discovery; and (vi) the prediction of the course of new therapies in clinical trials. In conclusion, whole genome expression analysis might be able to determine important genes related to cancer progression and adjuvant chemotherapy resistance, especially in the context of new approaches involving primary systemic chemotherapy. In this review, we will survey the current progress in whole genome expression analyses for cancer prognosis and prediction. Special emphasis is given to the approach of combining biostatistical analysis of expression data with knowledge of biochemical and genetic pathways.
authors.affiliation: Institute for Tumor Biology, Hamburg, Germany.
authors.forename: D
authors.initials: D
authors.lastname: Kemming
authors.forename: U
authors.initials: U
authors.lastname: Vogt
authors.forename: N
authors.initials: N
authors.lastname: Tidow
authors.forename: C M
authors.initials: CM
authors.lastname: Schlotter
authors.forename: H
authors.initials: H
authors.lastname: Bürger
authors.forename: M W
authors.initials: MW
authors.lastname: Helms
authors.forename: E
authors.initials: E
authors.lastname: Korsching
authors.forename: A
authors.initials: A
authors.lastname: Granetzny
authors.forename: A
authors.initials: A
authors.lastname: Boseila
authors.forename: L
authors.initials: L
authors.lastname: Hillejan
authors.forename: A
authors.initials: A
authors.lastname: Marra
authors.forename: Y
authors.initials: Y
authors.lastname: Ergönenc
authors.forename: H
authors.initials: H
authors.lastname: Adigüzel
authors.forename: B
authors.initials: B
authors.lastname: Brandt
mesh_terms: D016000:Cluster Analysis; D020869:Gene Expression Profiling; D015894:Genome, Human; D006801:Humans; D008954:Models, Biological; D025202:Molecular Diagnostic Techniques; D009369:Neoplasms / Q000175:diagnosis* / Q000628:therapy; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis