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To select the optimal treatment for patients, clinical oncologists need to be skilled at critically evaluating data from clinical studies and interpreting these appropriately. Clinicians should also be proficient in the application of diagnostic tests, assessment of risk, and the estimation of prognosis. Equally, scientists involved in translational research should be aware of the problems and pitfalls in undertaking clinical studies. The practice of oncology is increasingly incorporating genomic assessment both to provide information on prognosis and to allow optimal choice of therapy. This chapter provides a critical overview of methods used in clinical research.


The field of cancer genomics is growing rapidly as a result of advances in DNA sequencing technologies (see Chap. 2, Sec. 2.2.10). This information is having an impact on cancer treatment, including intensive research into personalized cancer medicine, whereby attempts are made to match treatment with a molecular targeted agent to a mutation predicted to render the tumor sensitive (see Section 22.3.2). Genomic information is also adding to classical prognostic factors to produce more refined estimates of prognosis, and of prediction of response to different types of treatment (see Section 22.5).

Two main methods are available to study the cancer genome:

  1. Whole-genome sequencing (WGS) is the backbone technology that supports the in-depth sequencing of the entire human genome (Metzker, 2009).

  2. Targeted genome sequencing refers to strategies that enrich for DNA regions that are believed to be involved in tumor biology. This includes the whole exome or panels of genes recurrently altered in cancer (Robison, 2010).

Genomic analysis can provide in-depth information about a number of mutations in cancer. Not all these mutations have clinical significance. Therefore, a classification of mutations has been suggested (Sukhai et al, 2016). This classification provides information as to whether the identified mutation has been shown to be prognostic or predictive in the tumor site tested (class 1) or a different tumor site (class 2), or whether its significance is uncertain (classes 3-5). The classification of somatic alterations will continue to evolve as clinical data accumulate and as new therapies are developed.

High-throughput genotyping platforms such as microarrays (see Chap. 2, Sec 2.2.12) have been used successfully for genotyping clinical samples (Thomas et al, 2007). Microarrays allow for testing of large amounts of biological material through high-throughput miniaturized, multiplexed, and parallel processing and detection methods. The development of DNA microarray technology holds promise for improvements in the diagnosis, prognosis, and tailoring of treatment. The technical aspects of creating gene expression profiles are described in Chapter 2, Sections 2.2.12 and 2.5.2.

The ability to investigate the transcription of thousands of genes concurrently by using DNA microarrays poses a variety of analytical challenges. Microarray data sets are commonly ...

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