A validated algorithm for sensitive and cost-effective mutation detection in clinical cancer specimens.

Abstract

e12009 Background: Patients with EGFR-mutant lung cancer experience tumor shrinkage and prolonged survival when treated with EGFR inhibitors. Furthermore, drugs targeting additional mutational activated proto-oncogenes are currently being evaluated clinically. Thus, validated approaches for sensitive and accurate mutation detection in a clinical setting are urgently needed but have, so far, been lacking. Methods: Here, we performed a careful and systematic validation of two sequencing approaches (namely, Sanger sequencing and pyrosequencing) against the gold standard, massively parallel sequencing. Results: Prospective mutational annotation of clinical lung tumor samples as a function of tumor content revealed previously recognized limitations of Sanger sequencing but also indicated that pyrosequencing is a reliable and sensitive approach to mutation detection in the setting of limited tumor content. Importantly, pyrosequencing is highly cost effective and dramatically extends the fraction of tumors that can be analyzed without the need for costly massively parallel sequencing. Furthermore, we identified severe limitations of histopathological estimates of tumor content, thus supporting the application of sensitive mutation detection approaches as apposed to microdissection of cancer specimens. Conclusions: In summary, we have established an analytical algorithm for mutation detection in an everyday clinical setting, taking into account quality and type of tumor specimens, tumor content as well as sensitivity, feasibility and costs of appropriate sequencing assays. Furthermore, we suggest thorough evaluation of all mutation detection approaches against massively parallel sequencing as a prerequisite for any clinical implementation. Author Disclosure Employment or Leadership Position Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Roche, Sequenom Boehringer Ingelheim, Infinity, Roche AstraZeneca