Next-generation sequencing in hematologic malignancies: what will be the dividends?

Abstract

The application of high-throughput, massively parallel sequencing technologies to hematologic malignancies over the past several years has provided novel insights into disease initiation, progression, and response to therapy. Here, we describe how these new DNA sequencing technologies have been applied to hematolymphoid malignancies. With further improvements in the sequencing and analysis methods as well as integration of the resulting data with clinical information, we expect these technologies will facilitate more precise and tailored treatment for patients with hematologic neoplasms.

Keywords: exome sequencing; genome sequencing; hematologic; high-throughput sequencing; massively parallel sequencing; next-generation sequencing; sequencing technologies.

Figure 1.

Sequencing cost per genome according to the National Human Genome Research Institute (NHGRI). This graph illustrates the production costs of sequencing a human genome since 2001. The marked reduction in cost first observed in early 2008 corresponds to the time when genome centers adopted next-generation sequencing technologies. Sequencing costs are compared with hypothetical data reflecting Moore’s law, a long-term trend in the computer industry whereby the number of transistors on a computer chip (and hence computing power) doubles approximately every 2 years. Technologies that keep pace with Moore’s law are considered to be improving rapidly, making it a common reference for comparison. The figure is used by courtesy of the National Human Genome Research Institute (Wetterstrand KA, DNA sequencing costs: data from the NHGRI Large-Scale Genome Sequencing Program; available at: http://www.genome.gov/sequencingcosts. Accessed 31 May 2012).

Figure 2.

Next-generation sequencing work flow. The work flow can generally be divided into at least three steps. (a) Up to billions of sequencing reads are generated in parallel using one of multiple different sequencing chemistries. (b) These sequence reads are then aligned to a reference genome in order to establish the genomic location of every read. (c) Variant calling algorithms are used to evaluate whether the number of reads and associated quality metrics provide support for the presence of a nucleotide change relative to the reference sequence at a specified confidence level. This illustration shows both a single-nucleotide variant (single-nucleotide polymorphism [SNP]) and a deletion. Parallel analysis of a matched normal specimen allows these variants to be interpreted as either germline, in which case variant reads are observed in tumor and normal reads, or somatic, in which case the variant reads are observed only in tumor reads. Other variant-calling algorithms can be used to detect copy-number changes or structural variants.