Next-generation sequencing to guide cancer therapy.

Jeffrey Gagan,Eliezer M Van Allen

doi:10.1186/s13073-015-0203-x

Abstract

As a result of multiple technological and practical advances, high-throughput sequencing, known more commonly as “next-generation” sequencing (NGS), can now be incorporated into standard clinical practice. Whereas early protocols relied on samples that were harvested outside of typical clinical pathology workflows, standard formalin-fixed, paraffin-embedded specimens can more regularly be used as starting materials for NGS. Furthermore, protocols for the analysis and interpretation of NGS data, as well as knowledge bases, are being amassed, allowing clinicians to act more easily on genomic information at the point of care for patients. In parallel, new therapies that target somatically mutated genes identified through clinical NGS are gaining US Food and Drug Administration (FDA) approval, and novel clinical trial designs are emerging in which genetic identifiers are given equal weight to histology. For clinical oncology providers, understanding the potential and the limitations of DNA sequencing will be crucial for providing genomically driven care in this era of precision medicine.

Highlights

Many biological discoveries about cancer have been the product of a reductionist approach, which focuses on modeling phenomena with as few major actors and interactions as possible [1, 2]
Next-generation sequencing (NGS), known as massively parallel sequencing, represents an effective way to capture a large amount of genomic information about a cancer
Clinical NGS case study As an example that demonstrates the utility of clinical NGS, we look at the fictional scenario of a patient who presents with a newly diagnosed lung adenocarcinoma (Fig. 5)

Summary

Introduction

Many biological discoveries about cancer have been the product of a reductionist approach, which focuses on modeling phenomena with as few major actors and interactions as possible [1, 2]. Early methods to explore the genomic foundations of different cancers involved targeted exploration of specific variants and genes in a low-throughput fashion [3].

Results

Conclusion