Cancer is caused by the accumulation of genetic and epigenetic changes that result from the altered sequence or expression of cancer-related genes, such as oncogenes or tumor suppressor genes and of genes involved in cell cycle control, apoptosis, adhesion, DNA repair and angiogen­esis. Gene expression profiles provide a snapshot of cell functions and processes at the time of sample preparation. Until recently, diagnostic and prognostic assessment of diseased tissues and tumors relied heavily on indirect indicators. This does not take into account the alterations in individual gene expression. Microarray analysis provides invaluable information on disease pathology, progression, resistance to treatment, and response to cellular microenvironments.This ultimately leads to improved early diagnosis and innovative therapeutic approaches for cancer [36]. Microarray methods permit the analysis of copy number imbalances and gene amplification of DNA [37], and have recently been applied to the systematic analysis of expression at the protein level [38]. Many of the guiding principles of global analysis using microarrays are applicable for determining: i) molecular tumor classification; ii) drug sensitivity and iii) identification of tumor-specific molecular markers.
Molecular Tumor Classification. Improvements in tumor classification are central to the development of novel and individualized therapeutic approaches. Histo­logically indistinguishable tumors often show significant differences in clinical behavior, and subclassification of these tumors based on their molecular profiles may help explain why they respond so differently to treatment. In one analysis, a pool of nine cell lines was used to establish the expression profiles of a series of 15 ovarian cancer samples [36]. Microarray technology was applied to develop innovative classifications of leukemias, using micro­array analysis [39]. This strategy could distinguish between acute myeloid leukemia and acute lymphocytic leukemia without supervisory analysis. The expression profiles of some genes were used to classify the leukemia samples into two classes: acute myeloid leukemia and acute lymphocytic leukemia [39]. Large-scale RNA profiling helped to construct a molecular classification for 10 carcinomas (prostate, lung, ovary, colorectum, kidney, liver, pancreas, bladder/ureter and gastroesophagus) [40]. Gene expression pattern analysis has also been used to classify breast tumors at the molecular level [41,42], B-cell lymphoma [43], cutaneous melanoma [44] and lung adenocarcinoma [45,46]. Likewise, another study [47] has analyzed molecular profiles of 50 non neoplastic and neoplastic prostate samples, and established signature expression profiles of healthy prostates, benign prostatic neoplasia, localized prostate cancer and metastatic prostate cancer. These studies established the feasibility of combining expression profiles with classic morphologic and clinical methods of staging and grading cancer for better diagnosis and outcome prediction. Comprehensive combinatorial analysis of the gene expression patterns of thousands of genes in tumor cells, and comparison to the expression profile obtained with healthy cells, may provide insights concerning consistent changes in gene expression. These associate with tumor cellular dysfunction and any concomitant regulatory pathways.
Drug Sensitivity. Drug development protocols for new products are beginning to include genomic and proteomic microarray data obtained during preclinical stages of investigation. Such data provide greater insight into, and better prediction of, the performance characteristics of the product as it moves into clinical phases of development.
Despite considerable advances in cancer treatment, acquired resistance to chemotherapeutic drugs continues to be a major obstacle in patient treatment and overall outcome. Anticancer drug resistance is thought to occur through numerous mechanisms, and microarrays offer a new approach to studying the cellular pathways implicated in these mechanisms and in predicting drug sensitivity and unexpected side effects. Most array studies have been carried out using cancer cell lines that are rendered resistant to commonly used anticancer drugs. For example, the expression profiles of doxorubicin-induced and -resistant cancer cells have been monitored in an attempt to obtain molecular fingerprinting of anticancer drugs in cancer cells [48]. A subset of 1,400 genes has been analyzed so as to clarify the correlation between expression profiles and the drug mechanism of action of a panel of 118 anti­cancer drugs [49,50]. Obtaining further insights into the mechanism of action of anticancer drugs and the diverse pathways involved in drug resistance may be invaluable for design of more strategic treatments that are most appropriate for an individual tumor.
Identification of Tumor-Specific Molecular Markers. Several research groups have focused on identifying genes that show differential expression in healthy tissues or cell lines, and their tumor counterparts, to identify bio­markers for several solid tumors. They include ovarian carcinomas [51,52], oral cancer [53], melanoma [54], colorectal cancer [55], and prostate cancer [56]. In one study carried out on a cohort of 13 patients with ovarian carcinoma [51], a subset of genes was identified that show differential expression between healthy ovaries and ovarian tumors. Some of these genes, such as metallothionein 1G, that was found to be up-regulated in tumor samples, are implicated in resistance to the anticancer drug cis­platin, and might be an indicator of pretreatment resistance of these tumors to cisplatin. The osteopontin gene, that is secreted in the serum of patients with metastatic cancer, might be an excellent candidate for a biomarker of tumor progression in ovarian carcinoma [57]. Its concentrations has been found to be higher in a majority of patients with ovarian cancer than in healthy controls [58]. This study demonstrates the potential value of cDNA microarray analysis in identifying biomarker genes in cancer, and the feasibility of subsequently testing these genes at the protein level by conventional biochemical assays. An important challenge is to determine which of the expressed genes is biologically relevant to the tumor system being studied. Even when rigorous efforts are made to minimize the number of variables in a microarray study, there may be an unmanageable number of differentially expressed genes that will contribute excessive background values. Therefore, the combination of other approaches is necessary, for example, expression microarray analysis with cytogenetics such as spectral karyotyping or array comparative genomic hybridization (CGH) [37].This offers focusing on significantly smaller subsets of genes of direct relevance to tumor biology [59]. Recently, a combination of expression arrays and CGH array techniques has been used on breast cancer cell lines [60] to identify a limited number of genes that are both amplified and over­expressed.