Real-Time PCR Technology for Cancer

Background: Advances in the biological sciences and technology are providing molecular targets for diagnosing and treating cancer. Current classifications in surgical pathology for staging malignancies are based primarily on anatomic features (e.g., tumor-nodemetastasis) and histopathology (e.g., grade). Microarrays together with clustering algorithms are revealing a molecular diversity among cancers that promises to form a new taxonomy with prognostic and, more importantly, therapeutic significance. The challenge for pathology will be the development and implementation of these molecular classifications for routine clinical practice. Approach: This article discusses the benefits, challenges, and possibilities for solid-tumor profiling in the clinical laboratory with an emphasis on DNA-based PCR techniques.

Content: Molecular markers can be used to provide accurate prognosis and to predict response, resistance, or toxicity to therapy. The diversity of genomic alterations involved in malignancy necessitates a variety of assays for complete tumor profiling. Some new molecular classifications of tumors are based on gene expression, requiring a paradigm shift in specimen processing to preserve the integrity of RNA for analysis. More stable markers (i.e., DNA and protein) are readily handled in the clinical laboratory. Quantitative real-time PCR can determine gene duplications or deletions. Furthermore, melting curve analysis immediately after PCR can identify small mutations, down to single base changes. These techniques are becoming easier and faster and can be multiplexed. Real-time PCR methods are a favorable option for the analysis of cancer markers. Summary: There is a need to translate recent discoveries in oncology research into clinical practice. This requires objective, robust, and cost-effective molecular techniques for clinical trials and, eventually, routine use. Real-time PCR has attractive features for tumor profiling in the clinical laboratory.

The sequence for most of the human genome is now publicly available and can be applied to understand, characterize, and treat complex diseases such as cancer. The biological differences between tumors that account for variations in morphology and clinical behavior can be analyzed using gene expression microarrays (1–5 ), comparative genomic hybridization (CGH)1 (6–9), fluorescence in situ hybridization (10, 11), quantitative PCR (12 ), and mutation analysis (13, 14). Normal cell regulation can be disrupted by many factors, including viral infections (15, 16), DNA methylation (17 ), and sequence alterations (18, 19). Cancer develops when these insults affect the function of genes controlling cell division, cell repair, apoptosis, and angiogenesis. Current molecular techniques are providing the tools needed to investigate tumor biology and to discover the genetic and epigenetic causes of cancer.

Microarrays together with clustering analysis have allowed genome-wide expression patterns in biological systems to be deciphered and compared. Hierarchical clustering of microarray data groups together genes that are coordinately expressed under different conditions (20–22). Using microarrays, investigators have developed gene expression-based classifications for many malignancies, including lymphoma (4 ), leukemia (3 ), lung carcinoma (5 ), and both hereditary (23 ) and sporadic (1, 2) breast tumors. A unique signature can be found within the genetic programming of each tumor, revealing its molecular history. This allows tumor histology to be molecularly dissected based on the unique expression

profile of each cell type in the sample. For example, in breast tumors, the unique gene expression of mammary tumor cells can be distinguished from other cell types within the sample, such as lymphocytes and stromal cells (1, 24). In addition, molecular subtypes within a given histologic classification can often be identified. For example, there are two classes of B-cell chronic lymphocytic leukemia (3 ), two classes of diffuse large B-cell lymphoma (4 ), five classes of non-small cell lung tumors (including three types of adenocarcinoma) (5 ), and at least four molecular classes of invasive ductal cell breast carcinoma (1, 25). Finally, these molecular subtypes are clinically significant because they predict patient outcome and explain variability seen in the natural course of certain tumors with the same anatomic diagnosis (2–4, 25).

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