Next-generation sequencing for cancer diagnostics: a practical perspective

Abstract

Next-generation sequencing (NGS) is arguably one of the most significant technological advances in the biological sciences of the last 30 years. The second generation sequencing platforms have advanced rapidly to the point that several genomes can now be sequenced simultaneously in a single instrument run in under two weeks. Targeted DNA enrichment methods allow even higher genome throughput at a reduced cost per sample. Medical research has embraced the technology and the cancer field is at the forefront of these efforts given the genetic aspects of the disease. World-wide efforts to catalogue mutations in multiple cancer types are underway and this is likely to lead to new discoveries that will be translated to new diagnostic, prognostic and therapeutic targets. NGS is now maturing to the point where it is being considered by many laboratories for routine diagnostic use. The sensitivity, speed and reduced cost per sample make it a highly attractive platform compared to other sequencing modalities. Moreover, as we identify more genetic determinants of cancer there is a greater need to adopt multi-gene assays that can quickly and reliably sequence complete genes from individual patient samples. Whilst widespread and routine use of whole genome sequencing is likely to be a few years away, there are immediate opportunities to implement NGS for clinical use. Here we review the technology, methods and applications that can be immediately considered and some of the challenges that lie ahead.

Figure 1.

(A) A typical pipeline schematic for variant detection with next-generation sequencing, showing the steps from raw reads to production of a final list of variants. (B) Box plots created by the tool FastQC showing the base qualities of reads for two samples. The box plot on the top shows a ‘bad’ result, where the quality deteriorates rapidly from the middle of the reads to the ends, while the box plot on the bottom shows a ‘good’ result with just a slight decrease in quality towards the ends of the reads. Trimming of the reads in the sample on the top is recommended as it would lead to more accurate variant detection. (C) The effect of using an ungapped (MAQ) versus a gapped aligner (BWA). With the ungapped aligner the presence of a deletion in the sample leads to misalignment of the parts of the read next to the deletion resulting in false positive single nucleotide variation (SNV) calls.