Scientists examine rates of secondary mutations in biomarker genes

Jun 29 2015

Many tests for predictive biomarkers in tumors focus on selected known mutations or regions (e.g. hotspot panels). In a new study, scientists at Molecular Health examined the rates of secondary mutations in known biomarker genes and their potential impact on the diagnostic reliability of specific single-nucleotide variant (SNV) biomarker testing. They systematically searched cancer cases for both, presence or absence of known predictive SNVs and high-impact mutations (HIMs) in the same gene.

"Current standards for predictive biomarker testing, look primarily for the existence of specific gene mutations, either as part of the assay method (e.g. hotspot testing) or as part of the downstream sequence analysis process (e.g. gene panel testing). However, this practice ignores the potential negating effects of other functional variants within the biomarker gene. By characterizing both the existence and rates of such events, this study highlights the potential diagnostic shortcomings of current testing standards and emphasizes the need for integrated analysis of all aberrations identified within biomarker genes," explains David Jackson, Ph. D. and Chief Innovation Officer at Molecular Health.

Sonia Vivas, Ph. D. and scientist at Molecular Health explains one of the key findings: "In contrast to hotspot sequencing and SNP arrays, NGS-based whole gene sequencing enables general assessment of biomarker genes. It consequently may allow more precise cancer diagnostics and may benefit treatment decisions." Ms Vivas presented the results today at the WIN Symposium (Worldwide Innovative Networking in personalized cancer medicine - http://www.winsymposium.org) in Paris.

Sonia Vivas and her co-authors Francesca Diella, MSc. and Alexander Zien, Ph.D. (all Molecular Health) showed that high impact mutations (HIMs) could invalidate conclusions based on the presence or absence of standard SNV biomarkers: it is clinically important to consider them in biomarker-driven treatment decisions. Ms Vivas added: "Our results may explain why patients with endorsed treatment biomarkers fail to achieve the predicted clinical response and support the need for more holistic approaches to the analysis of predictive biomarkers."

Example breast cancer

TP53 is a well-known tumor suppressor gene, where several germline variants are known to predispose for several cancers. Somatic mutations in this gene are related to a poor prognosis. The study showed a correlation between additional mutations and outcome. It turned out that analysis of known biomarkers is not enough to find the correlation.

SOURCE Molecular Health GmbH