NGS can yield near-perfect fidelity for the detection of a mutation at a specific site, but at the same time, the error rates for any given sequencing read can be as high as 1%. This paradox reflects the fact that most sequencing errors are idiosyncratic, and, by simply resequencing the same region multiple times, developing a consensus results in such errors being lost. Thus, for normal, diploid genomes, sequencing is typically done to at least 30-fold coverage, meaning 30 reads for any given locus (referred to as 30× coverage). The coverage is influenced by copy number changes (e.g., aneuploidy or regions or gene deletion or amplification) or when there is admixture of normal cells within the tumor sample. To compensate for these copy number variations and normal cell contamination, typical cancer sequencing projects aim for a depth of coverage of at least 100×.

The US Food and Drug Administration (FDA) has issued guidelines[1] for test design, performance characteristics, run quality metrics, performance evaluation, variant annotation, and filtering. The guidance identifies six key aspects of test design: the indications for use statement, user needs for the tests, specimen type, the region of the genome being interrogated, performance needs, and components and methods. The guidance further identifies four key aspects of test performance: accuracy, precision, limit of detection, and analytical specificity. They also specify six test run quality metrics: coverage, specimen quality, DNA quality and processing, sequence generation base calling, mapping or assembly metrics, and variant calling metrics. Several minimum standards for test performance and quality metrics are suggested, including the following:

• a point estimate of 99.9% accuracy (e.g., positive predictive agreement, negative predictive agreement, and technical positive predictive value) with a lower bound of the 95% confidence interval of 99.0% for all variant types reported;

• reproducibility and repeatability of at least 95% of the lower bound of the 95% confidence interval;

• a minimum coverage (i.e., depth and completeness threshold) of 20× for targeted panels and 30× average coverage depth at 100% of bases targeted in the panel or 97% of bases for whole exome sequencing; • base calling with a base quality score of at least 30.

Germline Versus Somatic

Variants Mutations present in tumors but absent in the normal cells are referred to as somatic. Somatic mutations are major drivers of cancer behavior, but not all are causal. Indeed, the majority of somatic mutations observed in any individual tumor are most likely to be passenger mutations that play no functional role in the pathogenesis of the tumor but rather were expanded by an association with a driver mutation. The proportion of passengers to drivers differs dramatically from tumor type to tumor type. For example, tumors associated with tobacco (e.g., lung cancer) or sunlight exposure (e.g., melanoma) have very high mutation frequencies, with the majority of the observed mutations being “passengers.” In contrast, many hematologic malignancies (e.g., acute lymphoblastic leukemia [ALL]) have relatively low mutation rates, and some cancers such as infant leukemias have extra ordinarily low rates.

It is important to recognize the difference between germline variants and acquired somatic variants in the cancer genome. Germline variants are present in all cells of the body and may contribute to dis ease risk. Germline risk variants can be common (i.e., seen in ≥5% of the human population), or they can be a rare observation, which has given rise to the concept of “common disease common variant” that is associated with low penetrance alleles in contrast to rare variants associated with disease that are likely to be highly penetrant.

Point Mutations or Single Nucleotide Variants

The most common type of genetic variants is single-nucleotide variants, also known as point mutations. Although not as common as point mutations, small somatic insertions or deletions (indels) that consist of the loss or gain of one or a few nucleotides that results in translational frameshifts, generally yielding loss-of-function alleles, are also seen. In the human population, it is estimated that every individual will harbor 50 to 100 unique coding mutations, which implies, if we are to prevent the miss-assignment of “private” germ line variants as cancer-acquired somatic mutations, that we should compare the somatic genome of the tumor with its matched normal germline sequence.

Recently, computational models have been applied to large series of solid cancers, and signatures have been derived.[2] These signatures associated with mutations reflect their causative factors and have been termed mutographs. For example, G>T/C>A transversions are characteristic of tobacco-associated lung cancer, and C>T/G>A transitions are characteristic of ultraviolet radiation–associated skin cancers. The scientific rationale for mutographs is based on the preferential induction of a given nucleotide change within a 5′ and 3′ context, which is identified as a specific “signature.”[3] Considering six possible substitutions in pyrimidine context, and four possible bases each at the neighboring 5′ and 3′ positions, there are 96 possible combinations of substitutions in a trinucleotide context. In addition to point mutations, many other molecular events participate in shaping the pathogenesis of cancer; whole-genome sequencing (WGS) techniques can interrogate the full repertoire of SNVs, CNAs, and SVs, and these can also provide information on the mutational processes operative in the early pathogenesis of multiple myeloma (MM) (Fig.1).[4]

Fig1. THE COMPLEXITY OF MUTATIONAL SIGNATURES. From the initiating, self-propagating cell to the relapsed and refractory stage, patients will acquire mutations secondary to different events that can be either tumor specific (e.g., AID or APOBEC), related to treatment or exposures (e.g., melphalan, cisplatinum, or even chemical exposure), or simply related to the aging process (e.g., Clock mutations). Tumors will represent a combination of these different signatures that can be teased out bioinformatically, with some remaining unexplained, suggesting they could be used to seek for novel etiologies.

[4] Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458(7239):719–724. https://doi.org/10.1038/nature07943.