NGS Guidelines

 

·         We recommend that the likelihood, detectability, and severity of harm of potential errors should be determined at each step. Anticipated potential errors specific to the detection of somatic variants in tumor tissue by NGS should be addressed. Potential errors should be addressed through assay design, method validation, and/or quality controls.

·         Sample preparation:

·         Assess tumour cellularity

·         Another concurrent test (ex. flow, CBC for PB and BM)

·         Pathologist review (appropriately trained and certified)

·         Macrodissection or microdissection

·         Mutant allele fractions (including silent mutations) allow for more precise estimates

·         Stochastic bias is also a concern when working with small samples, as the number of genome equivalents present in the sample may be insufficient to consistently detect variants with low allele burden.

·         Increase input, multiple displacement amplification, single-molecule barcoding

·         Sensitivity control

·         Measure DNA yield - DNA yield is a potential source of error

·         Optimization of the entire extraction procedure is often necessary to minimize transfers and loss of material through multiple steps

·         DNA purity and integrity is a potential source of error

·         Deamination or depurination is a potential source of error

·         DNA obtained from older FFPE blocks (eg, >3 years) often shows evidence of deamination, which can significantly increase background noise in the final NGS reads, depending on the sequencing method used

·         Treatment with uracil N-glycolase can be helpful with such samples,37 but this may require increasing input DNA into the library step and should be validated thoroughly before being adopted routinely.

·         Consider Ung treatment, duplex reads

·         Confirm all positives with orthogonal method during validation

·         Contamination is a potential source of error

·         It is critical to avoid cross-contamination between samples

·         change scalpel blades between tissue dissections

·         wipe work surfaces frequently with bleach

·         ensure that samples are handled only one at a time.

·         Use a no template control during validation to detect contamination

·         Library preparation:

·         Optimize and monitor DNA library preparation to assess DNA purity and integrity

·         Hybrid capture NGS

·         Amplification-based NGS

·         Stochastic bias is a potential source of error

·         Amplification errors are a potential source of error

·         It is important to keep in mind the possible impact of amplification errors and content bias related to the library method used

·         Because potential sources of error can be addressed through assay design (in addition to method validation and quality controls), these should be considered early in the design phase of test development.

·         High-fidelity polymerase, duplex reads

·         Confirm all positives with orthogonal method during validation

·         Capture bias is a potential source of error

·         Optimize enrichment, long-range PCR

·         Define minimum coverage, back-fill with orthogonal method during validation

·         Primer bias and allele dropout are potential sources of error

·         Assess causes of false negatives, design overlapping regions

·         Bioinformatically flag homozygosity of rare variants

·         Sequencing Platform:

·         Recommend that laboratory directors consider the following during clinical NGS platform selection:

·         size of the panel (number of genes and the extent of gene coverage);

·         expected testing volume;

·         required test turnaround time;

·         availability of bioinformatics support;

·         provider’s degree of technological innovation,

·         platform flexibility, and scalability;

·         laboratory resources, technical expertise

·         manufacturer’s level of technical support

·         Illumina and Ion showed equal performance in detection of somatic variants in DNA derived from FFPE tumour samples using amplicon-based commercial panels (with the caveat associated with Ion sequencer’s ability to accurately detect homopolymer tracts)

·         Illumina:

·         Pros:

·          high versatility and scalability to perform a wide spectrum of assays from small and targeted panels to highly comprehensive

·         Cons:

·         Higher DNA and RNA input requirements (except Ion Torrent series)

·         Longer sequencing time (except Ion Torrent series)

·         Require more comprehensive bioinformatics support

·         Higher cost of instruments (except Ion Torrent)

·         Ion Torrent series may be the platform of choice for many institutions to run small gene panels (<50 genes) and on samples with limited amount of DNA or RNA (ie, biopsy specimens).

·         However, Ion Torrent series have increased error rate in homopolymer regions and have low scalability

·         Panel Design:

·         It is recommended to include only those genes that have sufficient scientific evidence for the disease diagnosis, prognostication, or treatment [eg, professional practice guidelines, published scientific literature, test registries (eg, National Center for Biotechnology Information Genetic Testing Registry, http://www.ncbi.nlm.nih.gov/gtr and Eurogen Tests, http://www.eurogentest.org/index.php?idZ160 , both last accessed January 8, 2016)].

·         The scientific evidence used to support NGS panel design should be documented in the validation protocol.

·         Panels designed for diagnosis and patient prognostication are usually tumor specific, tend to be smaller in size, and include only those genes that are directly implicated in the oncobiology of the tumor.

·         The size of the panel may affect sequencing reagent cost, depth of sequencing, laboratory productivity, and complexity of analytical and clinical interpretation.

·         Data Analysis:

·         The range of software tools and type of validation required depends on the assay design

·         Base calling:

·         Read alignment:

·         Variant identification:

·         Each of the 4 main classes of sequence variants (SNVs, indels, CNAs, and SVs) require a different computational approach for sensitive and specific identification

·         Published comparisons of various bioinformatics tools for SNV detection may be helpful

·         Indels:

·         Alignment of indel-containing sequence reads is technically challenging, and algorithms specifically designed for the task are required.

·         One such specialized approach is called “local realignment”

·         Probabilistic modeling based on mapped sequence reads can be used to identify indels that are up to 20 bp, but these methods do not provide an acceptable sensitivity for detection of larger indels, such as FLT3 internal tandem duplications that may exceed 300 bp in length

·         Split-read analysis approaches to indel detection use algorithms that can appropriately map the two ends of a read that is interrupted (or split) by insertion or deletion. These algorithms can also manage reads that have been trimmed (soft-clipped) because of misalignments caused by indels

·         CNAs:

·         Assuming deep enough sequencing coverage, the relative change in DNA content will be reflected in the number of reads mapping within the region of the CNA after normalization to the average read depth across the same sample

·         Analysis of allele frequency at commonly occurring SNVs can be a useful indicator of CNAs or loss of heterozygosity in NGS data

·         SVs:

·         The breakpoints for interchromosomal and intrachromosomal rearrangements are usually located in noncoding DNA sequences, introns of genes, often in highly repetitive regions, and therefore are difficult to both capture and to map to the reference genome.

·         In addition, SV breakpoints often contain superimposed sequence variation ranging from small indels to fragments from several chromosomes.

·         Discordant mate-pair methods (with analysis of associated soft-clipped reads) and split-read methods can be used to identify SVs, and often provide single base accuracy for the localization of the breakpoint, which is a significant advantage in that such precise localization of the breakpoint facilitates orthogonal validation by PCR

·         Multiple tools should be evaluated to determine which has optimal performance characteristics for the particular assay under consideration, because, depending on the design of capture probes and specific sequence of the target regions, different SV detection tools have large differences in sensitivity or specificity.

·         Detection of SVs using RNA (cDNA) as starting material uses different bioinformatics approaches, especially when it is performed using amplification-based sequencing.

·         fused transcripts are aligned to a gene reference of targeted chimeric fusion transcripts.

·         Be aware that many popular NGS analysis programs are designed for constitutional genome analysis with algorithms that may ignore SNVs with variant allel frequencies (VAFs) falling outside the expected range for homozygous and heterozygous variants.

·         Variant annotation:

 

References:

·         Jennings et al.  Guidelines for validation of next-generation sequencing-based oncology panels: a joint consensus recommendation of the Association for Molecular Pathology and College of American Pathologists.  J Mol Diag 2017;19(3):341-65. (currently at p. 348 heading “Optimization and Familiarization Process”)