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

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. 

Data Analysis for NGS by Ion Torrent – Part One – How Did the Run Perform?

Here comes the fun part.  It’s taken a day for library prep, an overnight run for the clonal amplification; the next day includes loading the chip with the ISPs and then running the chip on the sequencer.  After the chip has run on the sequencer, the data is pushed from the sequencer (the PGM) to the server connected to the sequencer.  This aspect of NGS surprised me – the size of the files is amazing – for one 316 chip, the file that includes all of the raw data averages about 100GB.  To deal with this amount of data, the server attached to the sequencer is 12TB, and even still we have to have a procedure to deal with removing files off that sequencer to keep space for future runs.

Anyway, the raw data is pushed to the server and the data analysis begins.  The Torrent Suite Software first analyzes the ISP info, as shown in the graphic below.  It gives a “heat map” of the chip (the football shape) in which red means the wells in those areas were full with ISPs.  Yellow means there are fewer ISPs and blue means there are none.  So, you can see below, there is a small area of blue within the football shape – this area did not have any ISPs in it.  92% of the wells on this chip were filled, however, which is about the max a chip can be loaded.

dataana1

These ISPs are then broken down into categories.  First, how many of the wells had ISPs in them – here, 92.5% of the 6,337,389 wells contained ISPs.  Of those ISPs, 99.8% of them have product on them that can be sequenced (Live ISPs).  Of those Live ISPs, 0.4% of them contain control Test Fragments and 99.6% of them contain actual patient sample library amplicons.  The Test Fragments are spiked in prior to sequencing and act as a control to evaluate how the sequencing run performed.  Lastly, the ISPs that contain patient sample library amplicons are analyzed.  Those ISPs that contain more than one amplicon (say it has an amplicon of EGFR Exon 19 and another specimen’s amplicon of KRAS Exon 2) give mixed signals and cannot be analyzed, so they are thrown out of the data analysis and into a bin called “polyclonal”.  Low quality ISPs are also thrown out – anything that did not pass the thresholds for quality.  And lastly, ISPs that only contain adapter dimers are thrown out.  For a run of AmpliSeq Cancer Hotspot Panel v2 specimens, most of which come from FFPE specimens that are low quality to start with, a run that contains over 50% Final Library ISPs is actually a very good run, interestingly enough.  The 316v2 chips are rated to sequence 1 million reads (each ISP yields one read), and on this example run, over 3 million reads were sequenced, so this is a successful run.

After the ISPs are analyzed and the high quality ones are kept, the analysis goes on.  The Torrent Suite software then calls the bases based on the raw flow data.  These bases are then aligned to a reference, in our case hg19, a commonly used human genome reference.  Quality scores are assigned at this point.  A Phred-based quality score is used for NGS, shown in the table below.

dataana2.png

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Lastly, the reads are put into bins based on the barcode that was used for each patient specimen – remember the small part of the adapter that was added in library prep so that the specimens could be mixed together?  The software reads that adapter sequence then assigns each read based on those sequences.  The specimens should all have approximately the same number of reads since they were normalized to the same concentration at the end of library prep, but there may be some variability due to specimen quality, as you can see below.

dataana5

In next quarter’s post, we will dive into the individual specimen results!

 

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

Next Generation Sequencing – Ion Torrent Semiconductor Sequencing

We’ve finally made it to the sequencing step of the NGS workflow. This post we will discuss the technology and process behind the Ion Torrent sequencing step. Next time, we will review the Illumina sequencing process.

When we left off, the final product of the clonal amplification had been prepared – Ion Sphere Particles (ISPs) covered in single stranded amplicons (hopefully all of the same amplicon). Next, control Ion Sphere Particles are added to the mix, along with sequencing primer, which is complimentary to one of the adapter sequences added back in library preparation. The primer is annealed to each of the amplicons on every ISP. This mixture of control ISPs and specimen ISPs is then loaded onto the chip. The size of the chip is determined by the number of bases needing to be sequenced. There are three different types of chips for the Personal Genome Machine (PGM) – 314, 316, 318 – and five different types for their GeneStudio S5 system (510, 520, 530, 540, 550), offering enough coverage for a single sample of a hotspot panel, all the way up to enough coverage for a specimen of exome sequencing. Each of the chips contains a top layer covered in tiny wells. Each well is just large enough to fit a single ISP. The ISP solution is loaded onto the chip, then flowed over it by centrifuging it in different directions, in order to attempt to get as many ISPs into wells as possible. The chip is then ready for sequencing.

Each well of the chip can be thought as of the smallest pH meter in the world. So before sequencing can be started, the instrument must be prepped (initialized) so that all of the reagents added to the chip are in the correct pH range. On the PGM, this takes approximately an hour and requires some hands-on steps and high quality 18MΩ water. On the GeneStudio S5, the reagents are added and the initialization is begun and, as long as everything works correctly, doesn’t require any other hands on time.

After the initialization is complete, the chip is loaded onto the instrument. The sequencing run is started and runs according to the plan prepared before the run. Thermo Fisher’s Ion Torrent uses semiconductor sequencing technology. Nucleotides are flowed over the chip one at a time. If the nucleotide is incorporated, a hydrogen ion is released. This release of hydrogen decreases the pH of the liquid surrounding the ISP. This pH change is then detected by the sensing layer beneath the well, where it is converted to a voltage change and is picked up by the software and recorded as that nucleotide. Let’s say two nucleotides in a row are incorporated (two G’s complementary to two C’s) – double the hydrogen is released, which results in double the signal, so the software will record two G’s in a row. The benefit of this type of technology is that it is fast – it only takes 15 seconds for each nucleotide flow, so a 200bp fragment can be sequenced in less than 3 hours.

ion-torrent
Image courtesy of http://www.genomics.cn/en/

 

 

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

CMS’s National Coverage Determination for Next Generation Sequencing (NGS) – What Does This Mean for the Future of NGS Testing for Molecular Oncology?

I thought I’d take a break from the next generation sequencing (NGS) wet bench description this month to review news occurring in the world of reimbursement of testing of cancer specimens with next generation sequencing.  As a tech, I don’t deal with the nitty gritty of insurance reimbursement of our tests on a day to day basis, but this one caught my eye as it would have had a real impact on the NGS testing in our lab.  On November 30th, 2017, a proposal was released by the Centers for Medicare & Medicaid Services (CMS) to review the national coverage analysis tracking sheet for NGS for Medicare beneficiaries with advanced cancer.  In the original wording of the proposal, one thing it stated was that CMS should only reimburse NGS testing for advanced cancers when the testing was done with an FDA approved assay.  This caught me, as well as many others in the molecular community, by surprise.  The reason?  Currently, there are only a few FDA approved assays on the market; much of the testing occurring right now for oncology assays by NGS are lab-developed tests (LDTs), including the ones that we run in our lab.  Under the proposal’s language, these types of assays would not be reimbursed for Medicare patients (and where CMS reimburses, the major insurance companies follow), making it very difficult for us to continue the testing that we perform.

The process for a proposal such as this one includes posting the proposal, then allowing a period for public comments about the proposal.  Six weeks were given for people to post their comments online, during which, 315 comments were left.  These comments included praise to CMS for recognizing that NGS testing is increasingly useful for precision medicine, but also stressed the limitations of only allowing FDS approved assays to be used.  Some comments pointed out how clinicians and pathologists work together in the institutions performing the NGS assays in a way that would be impossible if forced to use an assay from an outside institution.  They also indicated how difficult it would be for all NGS testing to be performed by the very small number of FDA approved assays and how it is almost impossible for small academic institution labs to get FDA approval for assays due to the amount of money and time the approval process takes.

On March 16, 2018, the final decision memo was released with altered wording compared to the original and can be found here https://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?NCAId=290 and is also shown below:

“A.  Coverage

The Centers for Medicare & Medicaid Services (CMS) has determined that Next Generation Sequencing (NGS) as a diagnostic laboratory test is reasonable and necessary and covered nationally, when performed in a CLIA-certified laboratory, when ordered by a treating physician and when all of the following requirements are met:

  1. Patient has:
    1. either recurrent, relapsed, refractory, metastatic, or advanced stages III or IV cancer; and
    2. either not been previously tested using the same NGS test for the same primary diagnosis of cancer or repeat testing using the same NGS test only when a new primary cancer diagnosis is made by the treating physician; and
    3. decided to seek further cancer treatment (e.g., therapeutic chemotherapy).
  2. The diagnostic laboratory test using NGS must have:
    1. FDA approval or clearance as a companion in vitro diagnostic; and
    2. an FDA approved or cleared indication for use in that patient’s cancer; and
    3. results provided to the treating physician for management of the patient using a report template to specify treatment options.
  3. The diagnostic laboratory test using NGS must have:
    1. FDA approval or clearance as a companion in vitro diagnostic; and
    2. an FDA approved or cleared indication for use in that patient’s cancer; and
    3. results provided to the treating physician for management of the patient using a report template to specify treatment options.
    4. Other

Medicare Administrative Contractors (MACs) may determine coverage of other Next Generation Sequencing (NGS) as a diagnostic laboratory test for patients with cancer only when the test is performed in a CLIA-certified laboratory, ordered by a treating physician and the patient has:

  1. either recurrent, relapsed, refractory, metastatic, or advanced stages III or IV cancer; and
  2. either not been previously tested using the same NGS test for the same primary diagnosis of cancer or repeat testing using the same NGS test only when a new primary cancer diagnosis is made by the treating physician; and
  3. decided to seek further cancer treatment (e.g., therapeutic chemotherapy).

See Appendix D for the NCD manual language.”

 

In part B, it addresses those assays that are not FDA approved, but are run in a CLIA-certified laboratory.  This part was added in the final decision and makes it possible for non-FDA approved assays run in CLIA-certified laboratories to be reimbursed, dependent upon the local MACs.  While this is a huge improvement over the previous, there are still questions regarding some of the wording and we will have to see how this affects testing for our patients.  For example, in 1b, where it mentions repeat testing – some patients have multiple mutations that are followed over time for hematological malignancies – will this be considered repeat testing? It will remain to be seen.  Needless to say, I am happy to be able to continue doing my job.

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

When Rapid Blood Culture Identification Results Don’t Correlate, Part 2: Contamination

All laboratories are prone to contamination events. Blood products, analyzers, reagents, media, etc. all have the potential to be contaminated. If you are a molecular microbiologist, then you have to worry about not only bacterial, but also nucleic acid contamination. 

The Issue

The topic of my blog last month focused on discrepant results between blood culture and PCR. Traditional blood culture workflow involves correlating the Gram stain result to what grows in culture. Nowadays, many laboratories are also performing PCR on positive blood cultures. Because we know PCR is more sensitive, it may be easy for some to justify discrepancies. Let’s image that gram positive cocci in clusters were observed in the Gram stain, the PCR detected Staphylococcus and Enterococcus DNA, but only S. aureus grew in the culture. Where did the Enterococcus come from and where did it go? It was not observed in the Gram stain and it didn’t grow in cultures, so was it “real”? Possibly. It could be a contaminant or it could be real, just present in low numbers. It’s difficult to say without having to invest more effort.

When this type of situation occurs in my laboratory, three things happen. First, we review the data. For example, if the Gram stain is discrepant, then we review the Gram stain or perform an acridine orange stain (in the case of positive PCR, but negative culture). If it’s the PCR, then we would make sure that a result entry error did not occur, etc. Second, we add the comment, “clinical correlation needed”. We have found little value in going back to the blood culture bottle and trying to recover the missing organism because in most cases when we look hard enough, using selective agar and other strategies, we do find the organism from the PCR results buried among overgrowth. Therefore, our approach is to let the clinician know that they must use other clinical data to aid in their diagnosis. Third, we document all discrepant blood culture PCR results; which includes an automatic notification to the doctoral director.

Next, let’s imagine that two more blood cultures (from different patients) become positive all within a relatively short period of time from the first discrepant result noted above. gram negative bacilli are observed in one culture and the other displays gram positive bacilli. PCR detects Enterococcus DNA in both cases. What are the odds of that happening? Not good. Something strange is going on!

The Solution

A contamination investigation needs to immediately occur. The two likely sources of contamination are 1) the PCR assay or 2) the blood culture bottles. To determine whether the issue is due to amplicon or target contamination of the PCR assay, we need to identify which instruments reported the Enterococcus. Was it a single instrument or were different instruments involved? Our laboratory performs routine “swipe” tests of the environment as part of our quality control, which allows us to monitor contamination. Swipe tests may also be performed 1) after a known contamination event (i.e., spill due to cracked or leaky product) to ensure that decontamination was properly carried out, 2) to investigate increased positivity rates, or 3) follow up on unusual results, such as the scenario outlined above.

PCR may be performed on a random sampling of uninoculated bottles to determine whether the issue is due to contamination of the blood culture media. If the contamination is high density, this may be useful; however if it is low density, then all bottles you test may still be negative. If the contamination is due to bacterial DNA, then Gram stain or culture will not be useful, hence the need for PCR. It is important to note that the presence non-viable organisms and/or nucleic acids (at levels that can be detected by PCR) is a known limitation noted in the package insert of some blood culture media and PCR manufacturers. If contamination is suspected, then immediately file a report with the manufacturer. Be sure to document lot numbers and expiration dates so that they may alert other customers.
The Conclusion

Human error contributes to the majority of discordant laboratory results. However, errors in interpretation and result entry/clerical errors are only part of the problem. Contamination events only complicate matters. If the test volume is significant, then the number of discordant results should be quickly realized, especially if there truly is a contamination issue. It is important to have a process in place to help reconcile contamination events as quickly as possible as they have the potential to majorly impact operations and patient care.

 

References

  1. https://labmedicineblog.com/2018/02/20/when-rapid-blood-culture-identification-results-dont-correlate-part-1-clinical-correlation-needed/

 

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-Raquel Martinez, PhD, D(ABMM), was named an ASCP 40 Under Forty TOP FIVE honoree for 2017. She is one of two System Directors of Clinical and Molecular Microbiology at Geisinger Health System in Danville, Pennsylvania. Her research interests focus on infectious disease diagnostics, specifically rapid molecular technologies for the detection of bloodstream and respiratory virus infections, and antimicrobial resistance, with the overall goal to improve patient outcomes.