Personalized Medicine and Precision Medicine

There are often new buzzwords flying around that everyone uses, but few actually understand what they mean. Personalized and precision medicine are two of these terms that are often used interchangeably. Every lab wants to say they are performing personalized medicine. And to be fair we really do all provide personalized medicine in some form. Almost all lab results are used to customize the treatment for patients. However these buzzwords are used to refer to tests that describe linking genetic, lifestyle, or environmental information with predicted response to treatment. Precision medicine may be the more accurate term to describe identifying effective treatment for the right patient at the right time based on genetic, lifestyle, or environmental information. The term personalized medicine may give the false impression that therapies were developed specifically for the patient, when really they are developed to target a specific genotype or phenotype.

One example of precision medicine being used clinically today is in oncology. Many cancer drugs now require an associated test to determine the presence or absence of a specific biomarker to determine which patients are likely respond to the therapy. The biomarker tests that are linked to a specific therapy are called companion diagnostics. Biomarkers analyzed can be a specific protein or gene such as programmed death ligand-1 (PD-L1) or epidermal growth factor receptor (EGFR) or they can be much broader such as tumor mutational burden (TMB) or immune signatures. Identifying biomarkers that determine which patients are likely to respond to therapy and only giving patients with the biomarker the drug increases response rates to the therapy and may decrease side effects. More than half of the clinical trials for cancer drugs in 2018 were linked to a specific biomarker. Linking drug selection with specific laboratory tests is causing an increased need for multidisciplinary collaboration among pathology, oncology, and the laboratory.

In our lab we perform precision medicine using PCR or NGS assays to analyze patient’s tumor for specific genes. Although we still perform single gene testing when ordered, most of our cases are analyzed by a NGS panel. NGS panel testing allows us to look at numerous biomarkers with one test. This decreases the cost, time and tissue utilized to determine the patient’s biomarker status. Our NGS panel analyzes 52 genes to look for mutations that would indicate a patient is likely to respond to a targeted therapy. Most of our oncology testing is done on lung, colon, and melanoma specimens, although the panel is validated for most solid tumors. The report that we issue the oncologist provides clear information on which therapies the patient is likely to respond to or likely to be resistant to based on their tumor’s genetic profile. We also include information in the report to match patients to clinical trials. Precision medicine utilizing panel NGS testing for predicted response to treatment is becoming standard of care for many solid tumors.  

-Tabetha Sundin, PhD, HCLD (ABB), MB (ASCP)CM,  has over 10 years of laboratory experience in clinical molecular diagnostics including oncology, genetics, and infectious diseases. She is the Scientific Director of Molecular Diagnostics and Serology at Sentara Healthcare. Dr. Sundin holds appointments as Adjunct Associate Professor at Old Dominion University and Assistant Professor at Eastern Virginia Medical School and is involved with numerous efforts to support the molecular diagnostics field. 

Hematopathology and Molecular Diagnostics Case Study: A 63 Year Old Man with Fatigue

The following case is an interesting overlap of Hematopathology and Molecular Diagnostics, and shows the utility of sequencing to detect a cancer before biopsy could.

A 63 year old gentleman presented to a heme/onc physician with six months of intractable anasarca, fatigue, and a recent mild thrombocytopenia (Table 1). They were otherwise in healthy condition. The physician initiated a lymphoma work-up that included a bone marrow biopsy. The tests were negative for M-protein.

Table 1. Summary of symptoms and relevant abnormal labs.

The bone marrow biopsy was somewhat limited, but the core contained multiple marrow elements. After a thorough review by a Hematopathologist, no evidence of dysplasia or other irregularities could be detected (Image 1). Flow cytometry detected no aberrant blast population. Cytogenetics detected 20del [16/20] and 5del [3/20]. These findings did not clearly indicate a specific diagnosis.

Image 1. 40x view of the bone marrow specimen at the initial presentation. No evidence of dysplasia was found.

As the clinical suspicion for a malignancy was high, the bone marrow specimen was sent for sequencing on a 1385-gene panel test. The test included tumor-normal matched DNA sequencing (“tumor” sample: bone marrow, normal: saliva), RNA whole transcriptome sequencing on the bone marrow, and Copy Number Variant (CNV) analysis. Tumor-normal matched sequencing helps rule out variants that are normal and present in the patient.

Somatic mutations were determined as those that were present in the “tumor” sample and not in the matched normal sample. The somatic variants found are listed below with their variant allele frequency (VAF) in parenthesis. Recall that a VAF of 40% means that a mutation is present in the heterozygous state in 80% of cells.

  • IDH2 (p.R140Q, 46%)
  • SRSF2 (p.P95T, 51%)
  • CBL (p.R499*, 47%)
  • KRAS (p.K117N, 12%)
Figure 1. View of IGV, which displays the NGS reads for IDH1 along with the variant allele highlighted in red. The color of the bars indicates the direction of the reads (forward in red and reverse in blue). This reflects the allele frequency of approximately 50%.

The mutations in these genes are commonly found in myeloid cancers including myselodysplastic syndrome. Activating mutation in IDH2 (isocitrate dehydrogenase 2) increase the production of the oncometabolite 2-HG, which alters methylation in cells taking them to an undiffereitiated state. SRSF2 (Serine And Arginine Rich Splicing Factor 2) is a part of the spliceosome complex, which regulates how sister chromatids separate from each other. Failures in the proper function of the complex creates genomic instability. CBL (Casitas B-lineage Lymphoma) is a negative regulator of multiple signaling pathways, and loss of function mutations (as seen here) lead to increased growth signals through several tyrosine kinase receptors. KRAS (Kirsten RAt Sarcoma virus) is an upstream mediator of the RAS pathway, which acquires mutations that lead to constitutive activation and sends growth signals to cells causing them to proliferate.

Furthermore the CNV analysis also found the heterozygous loss of chromosome 20 as reported in cytogenetics. CNV analysis did not detect chromosome 5 deletion, as it was below the limit of detection (20% for CNV analysis).

Figure 2. This plot shows the normalized read frequency of genes across each of the chromosomes is shown here. The drop at chromosome 20 is shown in a pale brown color on the right side of the graph. This is consistent with the cytogenetic findings. The loss of 5q isn’t seen as it is below the limit of detection of 30%.

These mutations are all individually common in MDS, but the co-occurance of each gives very strong evidence that MDS is the diagnosis (Figure 3). There have also been studies that provide prognostic implications for several of the genetic mutations present. Some mutations like SRSF2 or CBL at high VAF (>10%) indicate a poor prognosis, but mutations in IDH2 or TP53 at any frequency have not only a high chance of progression, but also a faster time to onset of disease. Another non-genetic risk factor for developing MDS is an elevated RDW, which we saw in our patient.

Figure 3. From Becker et al 2016.

All of these high-risk factors together led us to push for a diagnosis of MDS based off of molecular findings, and the patient was started on treatment with Azacitadine. Our assessment was confirmed 3 months later when, the patient’s follow up bone marrow biopsy showed significant progression with megakaryocytic and erythroid dysplasia and hyperplasia and reticulin fibrosis MF2 (Image 2). Aberrant blasts were detected (1-2%), but not elevated. This demonstrates how molecular findings predicted and predated the patient’s rapid progression to morphologic disease.

Image 2. Dysplastic, hyperplastic megakaryocytes and erythroid lineage.

In summary, multiple molecular mutations indicative of MDS were found in a symptomatic patient’s unremarkable bone marrow biopsy months before a rapid progression to MDS.

References

  1. Steensma DP, Bejar R, Jaiswal S et al. Blood 2015;126(1):9-16.
  2. Sellar RS, Jaiswal S, and Ebert BL. Predicting progression to AML. Nature Medicine 2018; 24:904-6.
  3. Abelson S, Collord G et al. Prediction of acute myeloid leukemia risk in healthy individuals. Nature 2018; 559:400-404.
  4. Desai P, Mencia-Trinchant N, Savenkov O et al. Nature Medicine 2018; 24:1015-23.
  5. Becker PM. Clonal Hematopoiesis: The Seeds of Leukemia or Innocuous Bystander? Blood.2016 13(1)

-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.

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.

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