title: Tissue microarrays for rapid linking of molecular changes to clinical endpoints.
abstract: Advances in genomics and proteomics are dramatically increasing the need to evaluate large numbers of molecular targets for their diagnostic, predictive or prognostic value in clinical oncology. Conventional molecular pathology techniques are often tedious, time-consuming, and require a lot of tissue, thereby limiting both the number of tissues and the number of targets that can be evaluated. Here, we demonstrate the power of our recently described tissue microarray (TMA) technology in analyzing prognostic markers in a series of 553 breast carcinomas. Four independent TMAs were constructed by acquiring 0.6 mm biopsies from one central and from three peripheral regions of each of the formalin-fixed paraffin embedded tumors. Immunostaining of TMA sections and conventional "large" sections were performed for two well- established prognostic markers, estrogen receptor (ER) and progesterone receptor (PR), as well as for p53, another frequently examined protein for which the data on prognostic utility in breast cancer are less unequivocal. Compared with conventional large section analysis, a single sample from each tumor identified about 95% of the information for ER, 75 to 81% for PR, and 70 to 74% for p53. However, all 12 TMA analyses (three antibodies on four different arrays) yielded as significant or more significant associations with tumor-specific survival than large section analyses (p < 0.0015 for each of the 12 comparisons). A single sample from each tumor was sufficient to identify associations between molecular alterations and clinical outcome. It is concluded that, contrary to expectations, tissue heterogeneity did not negatively influence the predictive power of the TMA results. TMA technology will be of substantial value in rapidly translating genomic and proteomics information to clinical applications.
authors.affiliation: Institute of Pathology, University of Basel, Basel, Switzerland.
authors.forename: J
authors.initials: J
authors.lastname: Torhorst
authors.forename: C
authors.initials: C
authors.lastname: Bucher
authors.forename: J
authors.initials: J
authors.lastname: Kononen
authors.forename: P
authors.initials: P
authors.lastname: Haas
authors.forename: M
authors.initials: M
authors.lastname: Zuber
authors.forename: O R
authors.initials: OR
authors.lastname: Köchli
authors.forename: F
authors.initials: F
authors.lastname: Mross
authors.forename: H
authors.initials: H
authors.lastname: Dieterich
authors.forename: H
authors.initials: H
authors.lastname: Moch
authors.forename: M
authors.initials: M
authors.lastname: Mihatsch
authors.forename: O P
authors.initials: OP
authors.lastname: Kallioniemi
authors.forename: G
authors.initials: G
authors.lastname: Sauter
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000032:analysis*; D001943:Breast Neoplasms / Q000378:metabolism / Q000473:pathology*; D005260:Female; D006801:Humans; D007150:Immunohistochemistry; D008875:Middle Aged; D011379:Prognosis; D011960:Receptors, Estrogen / Q000032:analysis; D011980:Receptors, Progesterone / Q000032:analysis; D016019:Survival Analysis; D016159:Tumor Suppressor Protein p53 / Q000032:analysis
chemical_list: D015415:Biomarkers; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; D016159:Tumor Suppressor Protein p53