The KRAS (v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog) gene encodes a GTPase binding protein that functions as an intermediary in the RAS-MAPK pathway, one of several signaling cascades downstream of epidermal growth factor receptor (EGFR) activation (1). EGFR signaling pathways are important in the development and progression of several aggressive cancers, with KRAS gene mutations common in pancreatic, lung, and colorectal cancers (2). Activating KRAS mutations, most frequently described in codons 12 and 13, result in a constitutively activated form of KRAS causing EGFR pathway signaling independent of EGFR activation, thus making therapeutic agents that block EGFR, such as cetuximab or panitumumab, ineffective (3, 4). Consequently, anti-EGFR therapies are recommended only for patients with wild-type KRAS status (5, 6). Although not limited to, a typical analysis of KRAS mutational status would be performed on biopsies of tumor tissue that are processed into formaldehyde fixed and paraffin embedded (FFPE) blocks, from which sections are generated and gDNA extracted. As a consequence of the fixation process, the quality of gDNA from these samples is compromised and, additionally, the samples can be highly heterogeneous with a mixture of normal cells as well as both KRAS mutant and non-mutant tumor cells. Therefore, the methods used to determine the mutational status of the KRAS gene must be both sensitive and robust.

A number of platforms and techniques can be used to assess the mutation status of the KRAS gene with fluorescent dye terminator Sanger sequencing (FTSS) being widely acknowledged as the standard for direct detection of sequence variants. However, the software analysis and base-calling algorithms used to detect mixed bases, a heterozygous state at a particular nucleotide, were designed for typical resequencing mutation detection in which different alleles are expected as heterozygous mixtures of 50% and, as a result, do not facilitate sensitive detection of minor alleles from heterogeneous samples.

In this paper, we present a simple method for improving the analytic sensitivity of FTSS reactions. A standard cycle sequencing workflow is followed consisting of PCR amplification, followed by cycle sequencing using the BigDye Terminator v1.1 Cycle Sequencing Kit. The reaction is first analyzed by capillary electrophoresis (CE) and a standard sequencing file (.ab1) is generated. Following collection of the sequencing file, a fluorescently labeled size standard is added to the sequencing reaction. Then, the sequencing reaction is analyzed by CE as a fragment analysis reaction generating a standard fragment file (.fsa). The addition of the size standard allows for the sizing of all the fluorescently labeled sequencing reaction fragments. The sized fragments then can be further analyzed with genotyping software, which allows automated allele calling through the use of bins specifying the expected size and dye of a particular nucleotide fragment. The use of genotyping software allows the user greater control over how the raw data are analyzed including control over the peak detection algorithm, a functionality that is not possible with base-calling analysis of sequencing sample files (.ab1). To benchmark the improvements to CE FTSS, the following detection methods were examined: shifted termination assay (STA) (7), single-base extension (SBE) (8, 9), pyrosequencing (PS) (10), high-resolution melt (HRM) (11), and real-time PCR (qPCR) (12).

Method summary

This study describes a simple extension of the Sanger sequencing workflow for improving the detection of minor component alleles. The novelty of the approach is the addition of a size standard to the completed sequencing reaction and the collection of sequencing data as a fragment file for analysis using genotyping software. Sizing analysis facilitates detection by separating the minor component alleles in two dimensions (by size and dye color) from fragments corresponding to the major component allele. Further, genotyping software allows user control over peak detection, such as setting a peak height detection threshold so that minor component alleles can be readily distinguished from baseline noise.

Materials and methods

Samples

Sample Type 1: Various percentages (50%, 20%, 10%, 5%, 2%, 1%, and 0.5%) of gDNA extracted from KRAS mutant cancer cell-lines were mixed with gDNA extracted from a KRAS wild-type cancer cell line (HT-29). The KRAS mutant cancer cell-lines were: A549 (c.34G>A), MIA-PaCa-2 (c.34G>T), SW1116 (c.35G>C); SW480 (c.35G>T), and HCT116 (c.38G>A).