Comparative mutational landscape analysis of patient-derived tumour xenografts.

Comparative mutational landscape analysis of patient-derived tumour xenografts.

Br J Cancer. 2017 Jan 24;:

Authors: Brait M, Izumchenko E, Kagohara LT, Long S, Wysocki PT, Faherty B, Fertig EJ, Khor TO, Bruckheimer E, Baia G, Ciznadija D, Sloma I, Ben-Zvi I, Paz K, Sidransky D

Abstract
BACKGROUND: Screening of patients for cancer-driving mutations is now used for cancer prognosis, remission scoring and treatment selection. Although recently emerged targeted next-generation sequencing-based approaches offer promising diagnostic capabilities, there are still limitations. There is a pressing clinical need for a well-validated, rapid, cost-effective mutation profiling system in patient specimens. Given their speed and cost-effectiveness, quantitative PCR mutation detection techniques are well suited for the clinical environment. The qBiomarker mutation PCR array has high sensitivity and shorter turnaround times compared with other methods. However, a direct comparison with existing viable alternatives are required to assess its true potential and limitations.
METHODS: In this study, we evaluated a panel of 117 patient-derived tumour xenografts by the qBiomarker array and compared with other methods for mutation detection, including Ion AmpliSeq sequencing, whole-exome sequencing and droplet digital PCR.
RESULTS: Our broad analysis demonstrates that the qBiomarker's performance is on par with that of other labour-intensive and expensive methods of cancer mutation detection of frequently altered cancer-associated genes, and provides a foundation for supporting its consideration as an option for molecular diagnostics.
CONCLUSIONS: This large-scale direct comparison and validation of currently available mutation detection approaches is extremely relevant for the current scenario of precision medicine and will lead to informed choice of screening methodologies, especially in lower budget conditions or time frame limitations.British Journal of Cancer advance online publication, 24 January 2017; doi:10.1038/bjc.2016.450 www.bjcancer.com.

PMID: 28118322 [PubMed - as supplied by publisher]

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