50 Genes? 150 Genes? 500 Genes? Multi-Gene Cancer Panels – How Big Can We/Should We Go?

I first started in my current lab back in 2008. At that time, we did not have a separate section for testing solid tumors in our lab. The small amount of testing we did have were for three different types of sarcomas, and we still used a thermal cycler that didn’t have a heated lid, so we had to put mineral oil over the top of the reactions…

Fast forward eleven years and we now have a “bench” dedicated to solid tumor testing with next generation sequencing as a major part of this testing. We have been running our current solid tumor assay, a hotspot panel of fifty genes, for almost five years now and it has served us well. However, many of our oncologists have been starting to ask for more. We have begun the search for a larger panel to fulfill the needs of our oncologists and our patient population. As a smaller lab, we are somewhat limited in resources and are not quite ready to go completely custom, so we are left with kitted options from major vendors. As we research and evaluate these options, though, certain questions come to light. These panels have more than 150 genes and upwards of 500 genes in order to cover the most relevant genes in a number of different cancers. The areas tested in these genes are important for therapy and/or prognosis, but with the sheer number of bases we are looking at, we are bound to find many variants that do not have a known significance.

So, question one, how do the pathologists deal with trying to interpret the large number of variants of unknown significance (VUS’s)? Currently, with our very limited 50 gene panel, we may get one or two VUS’s, so it doesn’t take much time to assign significance and sign out the report. Our myeloid panel, which is a larger panel of 40 genes, some with full gene coverage, though, can sometimes result in reports with eight to ten VUS’s. These reports take a lot of time to research the potential impact each of these variants will have in the disease. I have seen reports from some of these large gene panels that have upwards of 25 or more VUS’s detected in a single specimen. How are these handled in the pathologists’ workflow? Can time be taken to investigate each of these, or are they just placed in a list in the report?

Question two, how do the oncologists feel when they receive a report with few, if any, variants with known significance, and many variants with unknown significance? Does this help at all, or make it more difficult and frustrating? I’d be interested if anyone has feedback in this area. In our internal tumor boards, when we review testing done at other locations, a great deal of time is spent trying to filter through the results to see how they can help point to the best possible treatment for the patient. If the variants do not point to therapy or clinical trials, those variants are not currently helpful.

Lastly, if and when we bring up a larger panel, do we keep running our smaller 50 gene panel? We believe the answer to this one is easy – yes. The amount of DNA needed for some of these larger panels is more than what we can get sometimes from the smaller biopsies. Also, insurance may not always cover the larger panels. The information we get from the 50 gene panel is still very useful and can point the oncologists to therapy options, as well as clinical trials, so we believe the smaller panel will still have a place in our lab.

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-Sharleen Rapp, BS, MB (ASCP)CM is a Molecular Diagnostics Coordinator in the Molecular Diagnostics Laboratory at Nebraska Medicine. 

The X-games of PCR

This is not your Mom’s PCR. These new kids on the block are making PCR extremely fast. PCR (Polymerase Chain Reaction) technology won the Nobel Prize for allowing molecular research to advance much more rapidly (for an interesting read on the quirky Laureate who gave up science to go surfing, read more here: Wikipedia ). It has become the most commonly used work horse of most molecular diagnostic assays, usually in the form of real-time PCR. It is used for a variety of purposes from detecting bacteria and viruses, identity testing for forensics and bone marrow engraftment, cancer mutation analysis, and even sequencing by synthesis used by Illumina for massively parallel sequencing.

This technique is still limited by requiring highly trained technologists to perform DNA extraction, time-consuming processing, and the time of real-time PCR itself. Overall, this process takes about a 5-8 hours. While this is much faster than in the past, it would be unacceptable for use in the point-of-care (POC).

But why would DNA testing need to be POC? The term sounds like an oxymoron in a field where many results have a 2-month turnaround time. There are certain circumstances where molecular testing would impact patient care. For instance, a doctor testing a patient in their office for a sexually transmitted infection would want to know if they have gonorrhea/ chlamydia so they could prescribe proper antibiotics. Similarly, POC molecular testing could be applied in a bioterrorism incident to test samples for an infectious agent. Or POC testing would benefit low-resource areas internationally where HIV testing could be used to manage anti-retroviral therapy in patients many miles from a laboratory.

For PCR as a test to be useful at the POC setting, it would have to provide a result within 10-15 minutes and be performed as a waived test. Two recent examples the demonstrate how this is possible have been highlighted at recent conferences of the American Association of Clinical Chemistry, which I just got back from: Extreme PCR1 and Laser-PCR.2

Extreme PCR refers to a technique of rapidly cycling the temperature of PCR reactions. The reaction occurs in a thin slide that evenly distributes the reagents, temperature and is clear to permit easy reading of fluorescence measurements (Figure 1). DNA Polymerase enzyme and primers to amplify the target DNA are added at much higher concentrations than normal (20x).

Figure 1. Thin reaction chamber for ultra-fast PCR.

This flies in the face of traditional PCR chemistry dogma as specificity would plummet and normal DNA could be amplified instead of target DNA. This would create a false positive. However, let’s think about what is actually happening with non-specific reactions. Primers are designed to match one region of DNA, which is very unique within the whole genome. However, the genome is so large that some segment may look very similar and be different in just 1 or 2 of the 20 base pairs that a primer matches. A primer could bind to this alternate region but less efficiently. So, the binding would be weaker and take more time to occur.

Therefore, by speeding up the cycling time to just a few seconds, only the most specific interactions can take place and non-specific binding is offset (Figure 2)!

Figure 2. Fluorescence from a dye that fluoresces when bound to double stranded DNA, which is increasing here within seconds (high point represents when the reaction temperature cools and dsDNA anneals, then low points represent heating to high temperatures).

Laser PCR does not report the use of increased reagents like Extreme PCR (it may be proprietary), but they boast a very innovative method to quickly heat and cool PCR reactions. GNA Biosciences use gold nanoparticles with many DNA adapters attached (Watch the video below for a great visual explanation!).

These adapters are short sequences of DNA that bring the target DNA and primers together to amplify the target DNA sequence. Then as the name implies, a laser zaps the gold beads and heats them up in a very localized area that releases the DNA strands. The released DNA binds another gold particle, replicates, rinses, and repeats. The laser energy thus heats the gold in a small area that allows for quick heating and cooling within a matter of seconds.

These new PCR methods are very interesting and can have a big impact on changing how molecular pathology advances are brought to the patient. On a scientific note, I hope you found them as fascinating as I did!

References

  1. Myrick JT, Pryor RJ, Palais RA, Ison SJ, Sanford L, Dwight ZL, et al. Integrated extreme real-time PCR and high-speed melting analysis in 52 to 87 seconds. Clin Chem 2019;65:263–71.
  2. CLN Stat. A Celebration of Innovation. AACC’s first disruptive technology award to recognize three breakthrough diagnostics. https://www.aacc.org/publications/cln/cln-stat/2018/july/10/a-celebration-of-innovation
  3. G. Mike Makrigiorgos. Extreme PCR Meets High-Speed Melting: A Step Closer to Molecular Diagnostics “While You Wait” Clin Chem 2019.

-Jeff SoRelle, MD is a Chief Resident of Pathology at the University of Texas Southwestern Medical Center in Dallas, TX. His clinical research interests include understanding how the lab intersects with transgender healthcare and improving genetic variant interpretation.

Next Generation Sequencing: Types of Variants

We have reviewed from start to finish the next generation sequencing wet bench process, data review and troubleshooting.  I’d like to take a more in-depth look at the types of variants that can be detected by the targeted amplicon NGS panels that our lab performs:  single nucleotide variants, multi-allelic variants, multi-nucleotide variants, insertions (including duplications), deletions and complex indels.  In our lab, we review every significant variant and variant of unknown significance in IGV to confirm the call is made correctly in the variant caller due to the difficult nature of some of these variants.  I have included screenshots of the IGV windows of each of these types of variants, to show what we see when we review.

Single Nucleotide Variants (SNV)

The most common (and straight forward) type of variant is a single nucleotide variant – one base pair is changed to another, such as KRAS c.35G>A, p.G12D (shown below in reverse):

Multi-allelic Variants

A multi-allelic variant has more than one change as a single base pair (see below – NRAS c.35G>A, p.G12D, and c.35G>C, p.G12A – shown below in reverse).  This may be the rarest type of variant – in our lab, we have maybe seen this type in only a handful of cases over the last four years.  This could be an indication of several clones, or different variants occurring over a period of time. 

Multi-nucleotide Variants (MNV)

Multi-nucleotide variants are variants that include more than one nucleotide at a time and are adjacent.  A common example is BRAF p.V600K (see below – in reverse) that can occur in melanoma.  Two adjacent nucleotides are changed in the same allele.  These variants demonstrate one advantage NGS has over dideoxy (Sanger) sequencing.  In dideoxy sequencing, we can see the two base pair change, but we cannot be certain they are occurring on the same allele.  This is an important distinction because if they occurred on the same allele, they probably occurred at the same time, whereas, if they are on different alleles, they were probably two separate events.  It is important to know for nomenclature as well – if they are on the same allele, it is listed as one event, as shown below (c.1798_1799delGTinsAA, p.V600K) as opposed to two separate mutations (c.1798G>A, p.V600M and c.1799T>A, p.V600E).  As you can see in the IGV window below, both happen on one strand.

Insertions/Duplications

Insertions are an addition of nucleotides to the original sequence.  Duplications are a specific type of insertion where a region of the gene is copied and inserted right after the original copy.  These can be in-frame or frameshift.  If they are a replicate of three base pairs, the insertion will move the original sequence down, but the amino acids downstream will not be affected, so the frame stays the same.   If they are not a replicate of three base pairs, the frame will be changed, causing all of the downstream amino acids to be changed, so it causes a frameshift.   A common example of a frameshift insertion is the 4bp insertion in NPM1 (c.863_864insCTTG, p.W288fs) that occurs in AML.  In IGV, these are displayed by a purple hash that will show the sequence when you hover over it.

Deletions

Deletions, on the other hand, are when base pairs are deleted from the sequence.  These can be in-frame or frameshift, as well.   An example is the 52bp deletion (c.1099_1150del, p. L367fs) found in the CALR gene in cases of primary myelofibrosis or essential thrombocythemia.

Complex Indels

Lastly, NGS can detect complex indels.  These, again, are a type of variant that we could not distinguish for sure using dideoxy sequencing.  We would be able to detect the changes, but not whether or not they were occurring on the same strand, indicating the changes occurred at the same time.  The first example is a deletion followed by a single nucleotide change – since these both occur on the same strand, they most likely occurred together, so they are called one complex deletion/insertion event (KIT c. 1253_1256delACGAinsC, p. Y418_D419delinsS).  First the ACGA was deleted, then a C was inserted. 

The last example involves multiple nucleotides changes all in the same vicinity (IGV is in reverse for this specimen as well).  Using HGVS nomenclature as in all the previous examples, this would be named RUNX1 c.327_332delCAAGACinsTGGGGT, p.K110_T111delinsGV.

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-Sharleen Rapp, BS, MB (ASCP)CM is a Molecular Diagnostics Coordinator in the Molecular Diagnostics Laboratory at Nebraska Medicine. 

Genetic Test Results Change Faces

As a part of my Molecular Genetic Pathology fellowship, we experience a clinical component to training in addition to all of the laboratory training we receive. This last month, I rotated through Cancer Genetics, where genetic counselors discuss genetic testing with patients with a personal or family history of cancer. The counselors describe the process of genetic testing and help chose genetic tests to look at the patient’s risk for an inherited cancer syndrome.

Patients are looking forward to the certainty that will come from a genetic test, because it is the wave of the future and they think you can learn so many things from your genetics. The truth, however, can be much less clear. Up to 30% of people receive a Variant of Uncertain Significance (VUS) as their genetic test result. This rate increases as larger panels are test more genes.

Figure 1. A set of genes and associated cancer types tested by a hereditary cancer genetic test. (Taken from Myriad MyRisk Gene Table.)

A VUS represents a variation in a person’s gene that doesn’t have enough information to say that it is benign or pathogenic. This gray zone is very uncomfortable and confusing for patients and providers alike. There are several cases where someone acted on a VUS as if it were a pathogenic variant and ended up having radical interventions like a bilateral mastectomy.

We know that as scientific and medical knowledge increases, our ability to reclassify these variants improves. For laboratories, this means periodic reanalysis of previously reported variants. If this process is not properly set up, it can be very laborious and extensive. Furthermore, not only was a timeline for variant reanalysis unknown, but also the likelihood of variants becoming upgraded or downgraded had not been described.

Two recent studies helped provide some answers to these questions. The first, published in JAMA, comes from the cancer genetic group I was working with, led by Dr. Theo Ross M.D. Ph.D., worked in conjunction with Myraid (Lab that first started testing the BRCA genes, and now tests many more) to determine how often variants were reclassified. Looking at 1.1 million individuals tested at Myriad, the average time to reclassification for a VUS was 1.2-1.9 years (Mersch J et al Jama 2018). Additionally, 90% of VUS were downgraded to benign/ likely benign representing 97% of patients with a VUS. This figure from the paper shows how the time to issuing a reclassification (amended report) has decreased (Figure 2).

Figure 2. The time to sending an amended report is shown by the year the report was first issued. From Mersch et al. JAMA 2018.

I worked on the second study, which looked at variant reclassification in childhood epilepsy genetic testing (SoRelle et al JAMA Peds 2019). The results, published in JAMA Pediatrics, also found most patients had a VUS reclassified to benign/likely benign. However, several clinically significant changes (reclassified to or from pathogenic/ likely pathogenic) occurred as well (Figure 3).

Figure 3. Patients with reclassification of gene variants from each category. Arrows that cross the red line represent an instance where a change in diagnosis would result from variant reclassification. Seven patients had both a pathogenic or likely pathogenic variant and VUS reclassified and are only represented once.

Furthermore, there was a linear relationship between the time the test was reported and the rate of variant reclassification (Figure 4). We found that 25% of patients with a VUS would experience a reclassification within 2 years.

Figure 4. Reclassification rate is plotted as the fraction of reclassified variants for each year testing was performed (VUS= black line, pathogenic or likely pathogenic= red line). Solid lines represent patients with a reclassified result and dotted lines are extrapolated slopes.

Overall, the conclusions of the two studies are somewhat similar:

  1. Most patients with a VUS experience a downgrade reclassification to likely benign or benign.
  2. Variant reclassification should be performed at least every 2 years
  3. Rates of reclassification may differ by disease type. Investigation by a similar study design should be performed in other genetic diseases.

References

  1. Mersch J, Brown NPirzadeh-Miller SMundt ECox HCBrown KAston MEsterling LManley SRoss T. Prevalence of variant reclassification following hereditary cancer genetic testing. JAMA. 2018;320:1266–1274.
  2. SoRelle JA, Thodeson DM, Arnold S, Gotway G, Park JY. Clinical Utility of Reinterpreting Previously Reported Genomic Epilepsy Test Results for Pediatric Patients. JAMA Pediatr. 2018 Nov 5:e182302.

-Jeff SoRelle, MD is a Molecular Genetic Pathology fellow at the University of Texas Southwestern Medical Center in Dallas, TX. His clinical research interests include understanding how the lab intersects with transgender healthcare and advancing quality in molecular diagnostics.

Blue Chips – Troubleshooting Ion Torrent Data

Ah, the blue chip – not much fun to see after spending a day preparing the libraries and running clonal amp overnight.  There are a couple possible explanations for a blue chip, and you can figure them out by looking at the metrics of the run. 

Test Fragments

The test fragments serve as a control for the sequencing run.  They are spiked into the mixture of library ISPs before they are loaded on the chip.  These will allow you to figure out where the problem occurred if you encounter a blue chip.  If the Test Fragments are detected and are of sufficient quality, then this means the sequencing run worked and the problem most likely occurred before sequencing, during library prep or clonal amplification.  If the Test Fragments are not detected, then it could mean one of two things – one – the clonal amplification did not work for either the library or the Test Fragment ISPs, or – two – the sequencing run was somehow at fault.  Let’s take a look at both examples.

Troubleshooting a Blue Chip

In the event you see a blue chip, first, check to see what kinds of ISPs showed up after the analysis.  For the chip pictured above, there were ISPs that had product on them, as you can see in the Live category (6,475,553 ISPs or 95.3% of the ISPs, shown in the screenshot below).  This means clonal amp was successful for a small number of the library ISPs.  Next, there were also Test Fragments detected, at 433,392 ISPs or 6.7% of the total ISPs.  Scroll down to the bottom of the page, and you will see how the Test Fragments sequenced.  We like to see the Percent 50AQ17 and Percent 100AQ17 at least in the 80’s, but even still, you can see that these were detected and were sequenced.  Because of this, the sequencing run looks to be fine, so most likely the problem occurred before sequencing.  In this case, we believe the library prep did not yield the expected 100pM concentration, so the library pool was over-diluted prior to clonal amplification.  The library prep was repeated, and clonal amplification was run on the new pool of libraries, and the sequencing was successful.

In this next example, we have the other possibility.  This chip was blue as well (this is a 520 chip, instead of a 530, to explain the different sized pictures). 

First, there are only 2.7% Live ISPs, so even lower than the chip above.  But the even stranger thing was that there were 0.0% Test Fragments, and at the end of the analysis, there were absolutely no ISPs left to be analyzed, library or Test Fragment.  This was the only time we had ever seen a chip like this; generally, if we had blue chips, they were like the previous example.  We looked at our library pool quant and it was in the expected range, so we did not believe it was a library prep issue.  The sequencing initialization was successful and did not have any errors, so we did not believe it was a sequencing problem.  We repeated clonal amplification with the same library pool and had successful sequencing.  In speaking with our Field Application Scientist, it was decided it must have been a failure of one of the reagents of the clonal amplification – either a Taq was not present or something, so the clonal amplification never occurred, or something similar. 

Hopefully you will not experience too many of these blue chips, but if you do, I hope you are a little more prepared to troubleshoot!  Happy sequencing!

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-Sharleen Rapp, BS, MB (ASCP)CM is a Molecular Diagnostics Coordinator in the Molecular Diagnostics Laboratory at Nebraska Medicine. 

Genetic Results: Set in Stone or Written in Sand?

This month, I’m switching gears to another interest of mine: Molecular Pathology. I am currently in fellowship for Molecular Genetic Pathology which exposes me to unique, thought-provoking cases.  

Advances in genomic sequencing has allowed multiple genes to be analyzed in a single laboratory test. These so-called gene panels have increased diagnostic yield when compared to serial gene sequencing in syndromic and non-syndromic diseases with multiple genetic etiologies. However, interpretation of genetic information is complicated and evolving. This has led to wide variation in how results are reported. A genetic test result can either be positive (pathogenic or likely pathogenic), negative (benign or likely benign) or uncertain (variant of uncertain significance- VUS). A VUS may just be part of what makes each individual unique and doesn’t have enough evidence present to say that it is pathogenic or benign. Many results come back like this and can be frustrating for patients to hear and for genetic counselors and clinicians to explain.

Initial approaches to exclude benign variants through sequencing 100 “normal people” to determine the frequency of common variants in the population was fraught with bias. The “normal population” initially was constructed mostly of individuals with white European descent. Not surprisingly, lack of genetic diversity in control populations lead to errors in interpretation.

Fortunately, there are now several publicly available databases that exist to help determine whether gene variants are damaging. The first important piece comes from population sequencing efforts. These projects performed whole exome sequencing of hundreds or thousands of individuals to find variants that might be rarely expressed in a more genetically diverse population. If a variant occurs in a normal health population at a frequency >1%, then it likely doesn’t cause a severe congenital disease that would in turn prevent that genetic variant from being passed on.

The Exome Association Consortium (ExAC)1, which has been rolled into the larger gnomAD (genome aggregation database) database now contains sequencing information on 120,000 individuals (Figure 1). The smaller ESP (Exome Sequencing Project) was a project by the NHLBI division of NIH and sequenced several patients with different cardiovascular and pulmonary diseases.

Figure 1. Number and percent of various ethnicities present in 4 major population sequencing projects.

While there is ethnic diversity present in this database, the 1000 genomes project2 furthered efforts by searching all over the world to get genetic information from around 100 ethnically and geographically distinct sub-populations (Figure 2).

Figure 2. Geographic map of populations sequenced by the 1000 Genomes Project.

With use of these databases, we can effectively rule out rare polymorphisms as benign when they are expressed in several healthy individuals and especially when expressed in the homozygous state in a healthy individual. Before, it was common for a person of an ethnic minority to have different variants compared to predominantly European cohorts. In many cases, this led to uncertain test results.

One way to deal with these VUSs is for a lab to periodically review their test results in light of new knowledge. Although the CAP has a checklist3 item that requires a lab to have a policy about reassessing variants and actions taken. However, this item doesn’t require a lab to communicate the results with a physician and doesn’t specify how often to reanalyze variants. Before last year, there weren’t even any studies that indicated how often variant reanalysis should occur. Variant reanalysis had only been studied in a limited context of whole exome sequencing for rare diseases to improve the diagnostic yield4. However, this did not address the issue of frequent VUSs to determine how often they were downgraded to benign or upgraded to pathogenic.

One example of how reclassification can occur is illustrated in the case of a young African American boy who had epilepsy and received a genomic test that covered a panel of genes known to be involved in epilepsy in 2014. Two heterozygous VUS were reported back for EFHC1 (EFHC1 c.229C>A p. P77T and EFHC1 c.662G>A p. R221H), which causes an autosomal dominant epilepsy syndrome when one allele is damaged. However, this variant could later be reclassified as benign by looking at population databases. The ExAC database showed an allele frequency of 2.5% in African Americans and the 1000 Genomes database showed an 8.8% frequency in the GWD subpopulation (Gambian Western Divisions).

This case demonstrates the importance of reanalyzing genetic test results as medical knowledge continues to evolve. Recently studies looking at reclassification rates of epilepsy5 and inherited cancer syndromes6 have been published in JAMA journals and demonstrate that reclassification of variants is common. It is thus important for laboratories to periodically review previously reported variants to provide optimal quality results and patient care. I will elaborate on this further in the next blog post.

References:

  1. Lek M, Karczewski KJ, Minikel EV, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536:285-291.
  2. The 1000 Genomes Project Consortium, Auton A, Brooks LD, et al. A global reference for human genetic variation. Nature. 2015;526:68-74.
  3. Sequence Variants – Interpretation and Reporting, MOL.36155. 2015 College of American Pathologists (CAP) Laboratory Accreditation Program Checklist.
  4. Costain G, Jobling R, Walker S. Periodic reanalysis of whole-genome sequencing data enhances the diagnostic advantage over standard clinical genetic testing. Eur J Hu Gen. 2018.
  5. SoRelle JA, Thodeson DM, Arnold S, Gotway G, Park JY. Clinical Utility of Reinterpreting Previously Reported Genomic Epilepsy Test Results for Pediatric Patients. JAMA Pediatr. 2018 Nov 5:e182302.  
  6. Mersch J, Brown N, Pirzadeh-Miller, Mundt E, Cox HC, Brown K, Aston M, Esterling L, Manley S, Ross T. Prevalence of Variant Reclassification Following Hereditary Cancer Genetic Testing. JAMA. 2018 Sep 25;320(12):1266-1274.

-Jeff SoRelle, MD is a Molecular Genetic Pathology fellow at the University of Texas Southwestern Medical Center in Dallas, TX. His clinical research interests include understanding how the lab intersects with transgender healthcare and advancing quality in molecular diagnostics.

This work was produced with the guidance and support of:

Dr. Jason Park, MD, PhD, Associate Professor of Pathology, UT Southwestern Medical Center

Dr. Drew Thodeson, MD, Child Neurologist and Pediatric Epileptologist

Evaluating and Analyzing Next Generation Sequencing Specimen Results

Welcome back – in my previous blog we discussed how a run is evaluated on the Ion Torrent instrument. This quarter’s blog will review the individual specimen results from that run.

First off, we take a look at how many reads per specimen have been sequenced and how those reads performed over the areas that are targeted. For the AmpliSeq Cancer Hotspot Panel v2 that we run, there are a total of 207 amplicons that are created and sequenced. To assess the depth of coverage over these amplicons, we need to think about the biology of the tumor cells and the limit of detection of the assay. We feel confident that we can detect 5% variant allele frequency for single nucleotide changes, and 10% variant allele frequency for insertions or deletions. In order to be confident that we are not missing variants, we require the specimen has a tumor percentage greater than 20%. This is because, for a given tumor, it can be assumed that if it is mutated, it will be only heterozygous – only one of the two alleles will have the variant. This automatically halves the possible allele frequencies from any given tissue. If a colon specimen that we are given to test has a tumor percentage of 40%, it can be assumed that any variant will have a variant allele frequency of no more than 20%. Because of this then, we also require the amplicons that are sequenced to have at least 500x coverage – they need to be sequenced at least 500 times so that if we have a 5% mutation, we will see it in 25 of the reads and we can feel confident this is an actual change, as opposed to background noise.

Next, we look at the On Target percentage and Uniformity percentage (over 95% for each is expected). The On Target value tells us what fraction of the amplicons actually cover the 207 amplicons that are in the panel. Uniformity informs us of how even the number of reads is over all the 207 amplicons – were they all equally represented or were there a subset of these that had more coverage than the others? This information can actually lead us to further testing – if there is a subset of amplicons that have more coverage than the rest, and it they are all from one gene, this may indicate gene amplification. In these cases, the clinician is alerted and additional testing can confirm the amplification.

All of this coverage information is provided by one of the “plugins” we run after the basecalling and alignment are finished:

The most useful (and interesting!) information is gathered from the variant calling plugin. This plugin compares the specimen sequences with the reference sequences and reports the differences – the “variants”. Many of the variants that are detected are single nucleotide polymorphisms (variants that are detected in greater than 1% of the population). They could also be known artifacts of the sequencing itself. These are all analyzed and categorized in the validation of the assay and then can be filtered out when analyzing clinical data. After filtering out the known SNPs and artifacts, the somatic changes can then be evaluated. Generally, the panel will detect 15-20 variants, but after filtering only 1-4 variants will be somatic changes. Each change that is detected is reviewed using a program called IGV, shown below. We compare the sequence to confirm that what the plugin is reporting looks correct in the actual reads from the sequencer. See screenshots below of a subset of variants called, then filtered, and analyzed in IGV. While the plugin is exceptionally good at variant calling, no program is perfect and visualizing the data is still necessary to confirm there is not anything else going on in the area that is sequenced. The fastq file from the run is also run through a secondary software to compare results. The variants for each specimen are assessed for variant allele frequency, coverage and quality in both software.

VariantCaller Output

Filtered Calls: White cells means SNP, Blue cells mean possible somatic call

IGV Output for KRAS and STK11 calls:


Lastly, the results are brought into yet another software to be reported. This software will allow the pathologists to assign significance to the variants. It will also pull in any treatment information linked to the variants and then allow the pathologist to pick any applicable clinical trials in order to assist the clinician as much as possible. In future blogs we will take a look at cases like this to see interesting findings of oncology cases.

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-Sharleen Rapp, BS, MB (ASCP)CM is a Molecular Diagnostics Coordinator in the Molecular Diagnostics Laboratory at Nebraska Medicine.