Mutation scanning using MUT-MAP, a high-throughput, microfluidic chip-based, multi-analyte panel.

Abstract

Targeted anticancer therapies rely on the identification of patient subgroups most likely to respond to treatment. Predictive biomarkers play a key role in patient selection, while diagnostic and prognostic biomarkers expand our understanding of tumor biology, suggest treatment combinations, and facilitate discovery of novel drug targets. We have developed a high-throughput microfluidics method for mutation detection (MUT-MAP, mutation multi-analyte panel) based on TaqMan or allele-specific PCR (AS-PCR) assays. We analyzed a set of 71 mutations across six genes of therapeutic interest. The six-gene mutation panel was designed to detect the most common mutations in the EGFR, KRAS, PIK3CA, NRAS, BRAF, and AKT1 oncogenes. The DNA was preamplified using custom-designed primer sets before the TaqMan/AS-PCR assays were carried out using the Biomark microfluidics system (Fluidigm; South San Francisco, CA). A cross-reactivity analysis enabled the generation of a robust automated mutation-calling algorithm which was then validated in a series of 51 cell lines and 33 FFPE clinical samples. All detected mutations were confirmed by other means. Sample input titrations confirmed the assay sensitivity with as little as 2 ng gDNA, and demonstrated excellent inter- and intra-chip reproducibility. Parallel analysis of 92 clinical trial samples was carried out using 2-100 ng genomic DNA (gDNA), allowing the simultaneous detection of multiple mutations. DNA prepared from both fresh frozen and formalin-fixed, paraffin-embedded (FFPE) samples were used, and the analysis was routinely completed in 2-3 days: traditional assays require 0.5-1 µg high-quality DNA, and take significantly longer to analyze. This assay can detect a wide range of mutations in therapeutically relevant genes from very small amounts of sample DNA. As such, the mutation assay developed is a valuable tool for high-throughput biomarker discovery and validation in personalized medicine and cancer drug development.