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as a routine test. Standards provide a reliable way to both monitor the sensitivity of mutation detection and to discern possible introduction of errors that potentially occur during the amplification and separation procedures (16-18). Mutations in the TP53 gene are the single most common genetic alterations observed in human cancers (19,20). For this reason, mutation detection of TP53 was selected as a candidate by NIST to create standards. These are expected to assist health care industries in the validation of new cancer detection assays independent of technology platform. The detection of mutations within this gene, like many others, is a difficult problem because mutations occur throughout the gene (19). In addition, the status of the TP53 gene in cancer has been linked to poor clinical outcome. TP53 mutations have been demonstrated as predictive indicators of recurrence and death in breast cancer and response to chemotherapy (21). Hence, TP53 genetic assays have the potential to become clinical diagnostic tools to track tumor progression and to determine therapy (22,23). Testing for TP53 mutations in Li-Fraumeni syndrome, although not common, is currently conducted in the United States by seven clinical laboratories (Genetests Laboratory Directory).

A panel of 12 plasmid clones has been developed by NIST that contains a 2.0-kb region of the TP53 gene spanning exons 5-9 (Fig. 1). These materials, as well as all NIST SRMs, will provide the clinical community with traceability between laboratory controls and a common reference material, thus providing interlaboratory conformity. Eleven of these clones contain a single mutation within the mutational hot spots of the TP53 gene (Table 2). The twelfth is wild type for this region of the gene. The eight most common single-base substitution mutations in human cancer are represented in this panel (clones 1-8), as well as three that proved difficult to detect (clones 9-11) by various scanning technologies (24-25). Each clone has been fully sequenced on both strands of the TP53 region.

To determine the effectiveness of this panel to serve as a SRM, we analyzed the single-point mutations by capillary electrophoresis-single-strand conformational polymorphism (CE-SSCP), denaturing gradient gel electrophoresis (DGGE), and denaturing high-performance liquid chromatography (DHPLC). The detection capabilities of these technologies were compared.

To this end, we have determined the accuracy of each method in detecting the mutations in our TP53 SRM panel in comparison to direct sequence analysis. CE-SSCP methods could detect 9 of the 11 mutations in the panel, a detection sensitivity of 82%. This is within the range of sensitivity reported by others (26). In this regard, CE-SSCP cannot be used to rule out the presence of a mutation, but remains a simple, inexpensive screening tool. DGGE is expected to detect mutations with a sensitivity of about 95% (27) and it successfully detected all of the mutations in the SRM panel. Although DGGE detected all of the mutations, unlike SSCP-CE it is not readily adaptable for high throughput. Similarly, DHPLC has been described as having a sensitivity of 95-100% (28) and it detected all of the mutations in the SRM panel. Once the appropriate assay conditions and column design are in place, high-throughput analysis is possible using DHPLC because of its quick run time. On the horizon is the use of microarrays for mutation detection (14,29). Their strength is in the detection of single-nucleotide substitutions (88% sensitivity) but are less useful for insertions and deletions (29).

Each of the three technologies has limitations in sensitivity to detect mutations that are not included in the SRM panel. This is an issue when screening disease genes that have many mutations (i.e., the TP53 gene). The SRM TP53 panel can accurately determine a method's sensitivity of detection of specific mutations and would be useful for the development of new mutation detection technologies. As a large area of the TP53 gene was used in the development of this panel of clones, position and sequence context effects can be evaluated through the use of different primer sets.

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