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show how actively they are being used. The data are analyzed by advanced
statistical techniques to accurately detect deletions and additions. Many previ-
ously unknown additions and deletions have been found in human breast cancer
cells by this method. The technique helps to show how cells modify their own
genetic makeup and may allow cancer treatments to be tailored more precisely to
a patient’s disease.
Cancer Classifi cation Using Microarrays
Classifi cation of a cancer based on gene expression profi le is important for person-
alizing cancer therapy. In the process of expression profi ling, robotically printed
DNA microarrays are used to measure the expression of tens of thousands of genes
at a time; this creates a molecular profi le of the RNA in a tumor sample. A variety
of analytic techniques are used to classify cancers on the basis of their gene-
expression profi les. Pattern-recognition algorithms can be used to identify sub-
groups of tumors that have related gene-expression profi les. Statistical methods are
used to relate gene-expression data and clinical data. Determination of tumor
marker genes from gene expression data requires bioinformatic tools because
expression levels of many genes are not measurably affected by carcinogenic
changes in the cells. These molecular markers give valuable additional information
for tumor diagnosis/prognosis and will be important for the development of person-
alized therapy of cancer.
An example of the application of microarrays for gene expression is bladder
cancer, a common malignant disease characterized by frequent recurrences. The
stage of disease at diagnosis and the presence of surrounding carcinoma in situ are
important in determining the disease course of an affected individual. Clinically
relevant subclasses of bladder carcinoma have been identifi ed using expression
microarray analysis of well-characterized bladder tumors. Gene biomarker panels
provide new predictive information on disease progression in tumors compared
with conventional staging. Furthermore, gene expression profi les characterizing
each stage and subtype identify their biological properties, producing new potential
targets for therapy.
Global gene expression analysis using microarrays has been used to characterize
the molecular profi le of breast tumors. Gene expression variability at the mRNA
level can be caused by a number of different events, including novel signaling,
downstream activation of transcription enhancers or silencers, somatic mutation,
and genetic amplifi cation or deletion. The tyrosine kinase-type cell surface recep-
tor, ERBB2, is an oncogene located on chromosome 17q21.1 that is amplifi ed in
10–40 % of breast tumors. Phenylethanolamine N-methyltransferase (PNMT) is
coexpressed with ERBB2 in breast cancer biopsies and also mapped within the
same chromosomal location as the ERBB2 gene. Gene amplifi cation of ERBB2
and PNMT is signifi cantly correlated with increased mRNA gene expression.
10 Personalized Therapy of Cancer