Target Discovery and Validation Reviews and Protocols

Molecular Profiling of Breast Cancer 93
heterogeneity with respect to its microenvironment, including specifically the
types and numbers of infiltrating lymphocytes, adipocytes, and stromal and
endothelial cells. The cellular composition of tumors is a central determinant of
both the biological and clinical features of an individual’s disease.
Microarray technologies, applied to the study of DNA, RNA, and protein
profiles as well as to the genome-wide distribution of epigenetic changes such
as DNA methylation, can be used to portray a tumor’s detailed phenotype in its
unique context (see Chapters 1 and 4, this volume). Systematic and detailed
characterization of tumors on a genomic scale can be correlated with clinical
information and greatly enhance our understanding of the causes and progression
of cancer, ability to discover new molecular markers, and possibilities for
therapeutic intervention. Eventually, advances in tumor portraiture will lead to
improved and individualized treatments.
In recent years, the use of DNA microarrays in breast cancer research has led
to important discoveries: first, individual tumors arising in the same organ may
be grouped into distinct subclasses based on their gene expression patterns,
independent on stage and grade; and second, the biological relevance of such
classification is corroborated by significant prognostic impact (1).
2. Microarray Procedures and Handling of Data
Most published studies have used spotted cDNA arrays that were originally
introduced by Schena and colleagues in 1995 (2); however, commercially
manufactured oligonucleotide-based arrays are increasingly gaining market
also among academic laboratories. Most of the platforms allow the use of two
different fluorescent labels to distinguish, on the same spots, the abundance of
gene-specific transcripts from two different samples. The data presented herein
Fig. 1. (Opposite page) Strategy for a typical reference-based DNA microarray
experiment. (A) Tumor biopsies are obtained and immediately frozen for isolation of
high-quality RNA. In parallel, RNA from a reference sample, usually a collection of
different cell lines is isolated for use in a two-color hybridization protocol. (B) The two
samples are differentially labeled with fluorescent nucleotides (by convention Cy5 for
tumor RNA and Cy3 for reference RNA). (C) The two samples are combined and
allowed to hybridize to a DNA microarray in which each gene is represented as a distinct
spot. (D) A laser scanner is used to excite the hybridized array at the appropriate
wavelengths, and the relative abundance of the two transcripts is visualized in a pseudocolored
image by the ratio of the “red” to “green” fluorescence intensities at each spot.
The ratios are log transformed and placed in a table in which each row corresponds to
a gene and each column corresponds to a single hybridization experiment. (E) For
further data analysis and interpretation of multiple experiments, a wide range of statistical
methods exist, both supervised and unsupervised: average-linkage hierarchical
clustering; SAM, and machine learning algorithms. Adapted from ref. 1.