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Oct 15, 2009 (Vol. 29, No. 18)

High-Resolution Melt Analysis Applications

Alternative to Probe-Based Genotyping Seeks to Overcome Drawbacks

  • Case Studies

    Perhaps the most challenging issue facing researchers seeking to identify the genetic factors involved in the proliferation of breast cancer is the polymorphic nature of genes commonly involved in expression of the disease. Kim de Leeneer, a Ph.D. student at the Center for Medical Genetics Ghent (CMGG) is researching the genetics of breast cancer, specifically the BRCA1 and BRCA2 genes.

    de Leeneer conducted her first HRM experiments in January 2007, screening 212 positive control samples for breast cancer. Until these experiments, the primary tools used at CMGG to screen for the genetic inheritance of breast cancer were denaturing gradient gel electrophoresis (DGGE) and direct sequencing of both large exons 11 of BRCA1 and BRCA2.

    Traditional sequencing experiments were conducted in parallel to compare results and verify accuracy of the new technique. All controls were recognized, so de Leeneer and colleagues began converting traditional assays to HRM analysis.

    Initially, all HRM analysis results were confirmed by sequencing: results demonstrated 100% sensitivity and 98.7% specificity of HRM analysis, with few false positives. As the researchers at CMGG developed confidence in HRM analysis they began processing sample assays in single replicates. Only aberrant melting curves get sequenced to confirm the presence of a genetic variant.

    As a prescreening tool, HRM analysis makes it easy to identify samples with genetic variants—and with a significant reduction in cost and time required over traditional methods. In de Leeneer’s lab, HRM has reduced, by approximately one-third, the costs and workloads compared to DGGE and direct sequencing.

    The main focus of Alessandro Martino’s studies in the department of biology at Pisa University is SNP-based pharmacogenetics for multiple myeloma. Currently, he is studying the rat gene expression that triggers repair responses in heart perfusion following heart failure. Martino’s experiments mainly center on membrane transporters, cytokines, and other pathways that potentially modulate drug response and survival rates after chemotherapy. Studies involve analyzing SNP mutations—primarily class I and II, but also some class IV, in MM patient blood samples.

    Until recently, the primary techniques used for SNP analysis were dual-labeled hydrolysis probe assays, but Martino has tested HRM analysis with the aim to replace probe-based screening methods. In initial HRM experiments, researchers used the same primer pairs and reagents as with dual-labeled hydrolysis probe assays.

    These early HRM experiments were run in parallel with probe-based assays, and Martino observed good correlation between the two. These studies demonstrated that melting temperature of the amplicon and primer pair specificity are influenced by the primer pairs used, so Martino began to develop primer pairs specifically for HRM studies.

    Replacing a self-made reagent mix with a commercial supermix further optimized experiments. The new reagent enabled amplification of targets that were previously problematic. HRM analysis also improved their results with allelic discrimination experiments over probe-based assays. Because allelic groups cluster using HRM-based methods, they can be identified via melting curves generated by the software.

    Currently, the laboratory conducts ~200 HRM experiments per week, although up to 200 experiments can be run in a single day. As more of the laboratory’s key instrumentation is replaced with HRM-compatible models, Martino believes that HRM has the potential to replace many probe-based assays.



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