Sample Subset

Mmor allele freq 52.5%


figure 21.16. SNP: chip.

These methods may thus be complementary technologies for very high density SNP haplotyping.

Although high-density SNP haplotyping will ultimately be of value in oncology as a means to identify patient subpopulations with greater or lesser tolerance for certain cyto-toxic drugs and perhaps even disease susceptibility, the immediate value of SNPs, particularly as high-density microarrays, is their phenomenal ability to detect DNA copy loss and gain, and to map these genomic alterations to very precisely defined regions of chromosomes.48-50 In one respect, this can be considered a logical extension of cytogenetic analysis, SKY, CGH, and even array CGH, none of which approach the power of this technology to detect DNA loss and gain by as few as 1,000 bases within specific regions of the genome. This approach is of enormous potential value for identifying recurring genomic abnormalities in cancer that is currently beyond the limits of detection by current technology. Wider use of this technology for this purpose is inevitable. Initial publications, including use of laser-captured and amplified DNA, have already appeared.49 Many more are expected.

Bioinformatics in Medicine

An inevitable consequence of the use of large-scale genomic technologies in biology and medicine is the accumulation of vast amounts of data that must be reduced to useful knowledge. Each of the newer technologies discussed (e.g., mass spectrometry-based proteomics, microarray-based gene expression analysis, and SNP-based genotyping) of necessity create this challenge. This in turn has spawned an urgent need for those skilled in both biology and quantitative analyses. Unfortunately, the methods required for data management go far beyond data archiving and statistical analysis. Bioinfor-matics is the broadly defined discipline that has resulted from this need. Many different tools have and continue to be developed to meet this need. National Institutes of Health (NIH) sponsors NCBI, the National Center for Biotechnology Information, which both hosts genomic data and provides suitable browser and analytic tools. The HapMap project noted earlier offers similar tools for inspection and analysis of SNP data. Numerous commercial sources of software for analysis of gene expression data and linkage analysis are also available. The full scale of the endeavor is far beyond the scope of this chapter. It is fair to say, however, that as the technology advances rapidly, so does the need for analytical methods, but also the need for tools to integrate these data sets with one another and with other types of data that bear on the problem, such as clinical, biologic, and other molecular data. As a general comment, the steady intrusion of genomic methods on the practice of medicine, and especially in cancer, the most common genetic disease of all, will inevitably require different training for its practitioners in the future. At a minimum, the future oncologist, and allied disciplines, will likely need to concurrently deal with clinical, biologic, laboratory, genomic, and statistical data in the evaluation of a patient. Whether responsible for the primary analysis, or accepting or rejecting the result, this future oncologist will need to be conversant with at least a basic understanding of the quantitative analytical methods used for genomic studies.

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