Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Abstract

DNA mutations are the source of genetic variation within populations. The majority of mutations with observable effects are deleterious. In humans mutations in the germ line can cause genetic disease. In somatic cells multiple rounds of mutations and selection lead to cancer. The study of genetic variation has progressed rapidly since the completion of the draft sequence of the human genome. Recent advances in sequencing technology, most importantly the introduction of massively parallel sequencing (MPS), have resulted in more than a hundred-fold reduction in the time and cost required for sequencing nucleic acids. These improvements have greatly expanded the use of sequencing as a practical tool for mutation analysis. While in the past the high cost of sequencing limited mutation analysis to selectable markers or small forward mutation targets assumed to be representative for the genome overall, current platforms allow whole genome sequencing for less than $5,000. This has already given rise to direct estimates of germline mutation rates in multiple organisms including humans by comparing whole genome sequences between parents and offspring. Here we present a brief history of the field of mutation research, with a focus on classical tools for the measurement of mutation rates. We then review MPS, how it is currently applied and the new insight into human and animal mutation frequencies and spectra that has been obtained from whole genome sequencing. While great progress has been made, we note that the single most important limitation of current MPS approaches for mutation analysis is the inability to address low-abundance mutations that turn somatic tissues into mosaics of cells. Such mutations are at the basis of intra-tumor heterogeneity, with important implications for clinical diagnosis, and could also contribute to somatic diseases other than cancer, including aging. Some possible approaches to gain access to low-abundance mutations are discussed, with a brief overview of new sequencing platforms that are currently waiting in the wings to advance this exploding field even further.

Cell-free DNA (cfDNA): Clinical Significance and Utility in Cancer Shaped By Emerging Technologies

“…This problem is equivalent to an extreme case of identifying low-abundance mutations in tissue samples, which has also proven difficult for NGS methods. Guntry and Vijg noted that "the single most important limitation of current MPS approaches from mutation analysis is the inability to address low-abundance mutations that turn somatic tissues into mosaics of cells" (85). This argument was based on the comparison of the natural mutation rates in mammalian cells (e.g., 0.05 Â 10 À9 per base per cell division for human cells) versus well-established error rates of existing sequencing platforms, which was estimated in 2012 to be approximately 0.05%-1% and has not significantly changed since.…”

Detection of ultra-rare mutations by next-generation sequencing

“…Although, in theory, DNA subpopulations of any size should be detectable when deep sequencing a sufficient number of molecules, a practical limit of detection is imposed by errors introduced during sample preparation and sequencing (9). PCR amplification of heterogeneous mixtures can result in population skewing due to differential amplification (10,11), and polymerase mistakes generate point mutations resulting from base misincorporations and rearrangements due to template switching (10,12).…”

“…Combined with the additional errors that arise during cluster amplification, cycle sequencing, and image analysis, ∼1% of bases are incorrectly identified, depending on the specific platform and sequence context (1). This background level of artifactual heterogeneity establishes a limit below which the presence of true rare variants is obscured (9).…”

A high-fidelity method for genomic sequencing of single somatic cells reveals a very high mutational burden

“…Application of methods such as SCMDA will soon reveal the entire landscape of somatic mutations in aging tissues and organs of humans and experimental animals. 44 This brings us to the question as to how a stochastic process of somatic mutation accumulation can cause loss of cell fitness.…”