ASK THE MASTERMINDS
ACMG CLASSIFICATION FOR RARE DISEASE
THURSDAY, JUNE 23, 2022
The American College of Medical Genomics (ACMG), in collaboration with the Association for Molecular Pathology (AMP), guides internationally accepted standards for the interpretation of genomic variants1. While some of the evidence is based on population or computational data, some of the criteria are based on peer-reviewed clinical and functional literature evidence. For rare diseases, this evidence can be difficult to find.
In this live webinar, we will demonstrate how the Mastermind® Genomic Search Engine can be used to efficiently score germline variants according to ACMG/AMP criteria, and describe how Mastermind creates certainty for rare disease clinicians.
You will learn:
- How to more quickly identify and prioritize publications by ACMG variant classification guidelines
- How a combination of increased sensitivity and specificity in literature search accelerates variant interpretation workflow, and results in fewer false negatives
- How finding a single paper can significantly impact a patient outcome
Medical Science Liaison
GARRETT: Hello, everyone, and welcome to today’s Ask the Masterminds, where we’ll be discussing ACMG classification for rare disease. My name is Garrett Sheets, and I’ll be your host. So, let’s get started! The Mastermind genomic search engine is the most comprehensive source of genomic evidence and can be used to quickly identify papers for patient diagnosis and treatment decisions. In today’s webinar, two of our experts will show you how they use Mastermind to efficiently score germline variants according to ACMG criteria, and most importantly, describe the certainty that this creates for rare disease clinicians for building patient care plans. Really exciting stuff today. Please note, today’s presentation will include Professional Edition features of Mastermind, so if you don’t already have a Mastermind account, you can create one today with the bit.ly link that you see on the screen and start with a free trial of Mastermind Pro, so do take advantage of that.
We have a lot of great info to share today, so I will go through a brief housekeeping item and then I’ll introduce our speakers. If you’re joining us live, feel free to drop questions down into the Q&A, and if we have time, we’ll get to those at the end. Also, please know this webinar is being recorded and will be emailed to you once we’re wrapped up. Now, let’s introduce our speakers! We’re joined today by Genomenon’s program manager Jeffrey Bissonnette. Hi, Jeffrey!
JEFFREY: Hi, Garrett!
GARRETT: Jeffrey is a certified genetic counselor who has previously worked in a cancer clinic setting counseling patients with hereditary cancer syndromes and also in a clinical laboratory where he was involved with variant interpretation and report writing. Lots of great experience! Here at Genomenon, Jeffrey manages our variant curation team.
We also have Lauren Chunn. Hi, Lauren! Lauren is our medical science liaison and genomics specialist, who has extensive background in clinical variant interpretation, and now collaborates with our pharma clients to deliver custom genomic data solutions. Jeffrey and Lauren, thank you both for being here to talk about this very interesting topic and to share your expertise! Lauren is going to get us started with a general overview of ACMG, and then Jeffrey’s going to join the conversation. Lauren, I will hand it off to you and you can take it away.
LAUREN: Great, thanks, Garrett! I’ll get started. We’re going to be talking about ACMG classification, particularly in the context of rare disease. For rare disease, it can be particularly difficult to identify variants and the evidence that would support those variants’ classification. You need the confidence that you won’t be missing the information that could materially impact the patient’s diagnosis. Mastermind provides that confidence through its increased sensitivity and comprehensiveness when it comes to organizing the genetic and clinical information that exists throughout the medical literature. Mastermind also has a number of key features which can accelerate ACMG classification, so not only can you be sure that you have the most complete evidence to support a patient’s diagnosis, but the process of variant interpretation is as efficient as possible.
Before I actually get into the details of Mastermind, I wanted to just quickly introduce ACMG classification. The ACMG standards and guidelines were originally published in 2015 by the American College of Medical Genetics and Association for Molecular Pathology, and since then, a number of modifications have been made to specific categories, as well as disease-specific modifications. However, we’ll primarily be talking about the original 2015 guidelines in our discussion today. The ACMG guidelines consider a variety of forms of evidence, including population data or population frequencies, clinical data, functional data, computational predictions of the variant’s effect upon the protein, as well as characteristics intrinsic to the gene and the disease mechanism. Mastermind particularly excels at organizing clinical and functional and mechanistic information. That’s what Jeffrey and I will be speaking to today. I’ll start with a description of how Mastermind can assist with organizing clinical and functional evidence, and then Jeffrey will join to talk about how to use Mastermind to apply gene level criteria within the ACMG guidelines.
Just to summarize the features that I alluded to earlier that can accelerate ACMG classification, we have ACMG filters, so we have a number of filters specific to certain categories or groups of categories that are useful for identifying and prioritizing clinical and functional studies; we also have a publication history diagram and articles list, which can be used to prioritize the most impactful publications for rapid review, depending on your specific search terms; we have a comprehensive and searchable variant list, which can be used to identify variants of the same or nearby residues very efficiently and comprehensively; and then we have a variant diagram, which can be used to identify clusters of variants by residue and/or the specific domain.
Literature evidence specifically is crucial for the application of clinical criteria and functional criteria within the ACMG guidelines. For clinical criteria, we’ll be looking for whether the variant has ever been found to segregate with disease, whether it’s been found in cases or controls, and how many, and whether it’s ever been found in a de novo case. Similarly, for functional criteria, we’ll be looking for functional studies and what the nature of those studies were, whether they determined there was a damaging effect or not. This clinical and functional evidence, particularly the functional evidence, often makes the difference between a variant being called a VUS, or of undetermined significance, and it being called pathogenic. The reason for that is that this functional evidence, assuming a damaging effect was confirmed, is considered strong evidence that the variant is pathogenic or disease causing. This is challenged by the fact that these studies can be particularly difficult to identify, as the variant is often not mentioned in the title or abstract, so it requires searching through tables and figures in the full text, which is something that Mastermind is particularly suited to do and to do very comprehensively. Oftentimes, when we find that Mastermind’s increased comprehensiveness resulted in a change in classification compared to historical classifications, it’s very frequently due to Mastermind’s ability to identify those functional studies as well as clinical studies, especially when there are multiple cases spread out across multiple studies.
I’ll start with a description of how Mastermind can assist with identifying and assessing these clinical studies. We have a number of ACMG filters that can be used for this purpose, whether we’re looking for individual case studies, case control studies, population studies, cohort studies, pedigrees, etc. I’ll go through a number of examples to illustrate this, starting with a use case that occurs where you need to identify whether the variant has ever been found in a de novo case. I’ll use the example of OTC p.R141Q. OTC is associated with the condition OTC deficiency. I’ll pull up this variant in the Mastermind interface. Before I actually get into the specifics of this example, I wanted to very quickly introduce all of the panels that exist in the user interface.
In the upper left here, we have the variant diagram, which has the residues of the protein on the x-axis and the citations per variant on the y-axis. We can see the variant we searched for is highlighted here, and we can see all of the variants that have been found by Mastermind surrounding it. We can scroll or pan to the left or right in this diagram, as well as zoom in and out to explore different areas of the protein. In the mid left we have the variant list. We see, again, our variant highlighted, and then you can also see variants that Mastermind has additionally identified in the OTC gene. There’s a column over here that shows you in how many articles both of those variants are mentioned together. There’s also PubMed data, which shows the title and abstract and metadata about the article. Then, in the top right, we have our publication history diagram, so that has the publication date on the x-axis and the citations per journal on the y-axis. More importantly, the bubble size correlates to the relevance of that particular article, depending on the search terms that we’ve used. In this case, we’ve searched for a gene and variant, so it’s taking into account how often those are mentioned within these papers and where, whereas if we added some additional keywords, the bubble size would change to take in account of those features. Below that, we have the article list, which is also prioritized by relevance, same as the bubble size in this diagram. Then, in the bottom right, we have the sentence fragments, which show us precisely how our gene or variant or other search term was matched within the paper.
Going back to this specific example, we’re again looking to identify whether this particular variant has ever been found in a de novo case. I can select filter categories, and I’ll be brought to our ACMG filters automatically. There’s a list of potential groups of keywords that I could apply, and there’s one specifically for de novo inheritance. There are three keywords in here, and I’ll enable all of those, and then submit with those filters. We’ve now reduced the number of articles to just 13, and if you look at the publication history diagram, there’s one standout paper that has a very large bubble. When you see something like that after applying filters, it generally means that you’ve really narrowed down the article list to that specific paper that you’re looking for that has the evidence you need. So we’ll click on that first paper and take a look at the sentence fragments.
We can see in the sentence fragments that the variant was mentioned, it’s right here, R141Q. Then, in the sentence below, we can see the word de novo, alongside what looks like patient data. In order to confirm, I’ll just open up the PDF of this particular paper. In table 1, I can see that the variant is mentioned, r141q, and it’s in a patient that has it as de novo. In that case, I can apply the PS2 or PM6 categories, depending on whether the parents were sequenced. So, just to summarize what we went through: We very quickly were able to reduce the article list using these filters, so I didn’t have to manually search through dozens of articles to identify this evidence. Using the filters, I was very quickly able to identify the very paper that had the evidence I was looking for, and it was right at the top of the list of articles.
Another use case for these filters as well is identifying whether the variant has ever been found to segregate with disease, so I’ll use the example of SCN1A p.R542Q. SCN1A is associated with autosomal dominant seizure disorders. Opening up this variant in Mastermind, I can see that there are 34 articles in our original search. I’m going to try to reduce that article list using filters again, but in this case, I’ll select pedigrees and case studies. There are a number of ways that you can filter clinical studies within here, If you’re looking for case control reports, for example, to apply PS4, you may select those specific keywords. If you’re looking for individual case studies, you may try these individual keywords here. For specific cases, you can also filter by genotype as well as inheritance pattern. We’re looking for segregation evidence, so I’ll scroll to the bottom and select all of these segregation-related keywords and submit.
We’ve now reduced to 29 articles, and again, there’s one standout paper here, as we saw in the last example. I’ll select that paper and then take a look at the sentence fragments again. We can see that the variant is mentioned several times within the paper, we’ve got all of these sentence fragments, and that segregation analysis was performed. I’ll again open up the PDF just to confirm that this is the case. In this particular paper, there is a pedigree displayed for this variant. You’ve got two affected siblings, one of which is heterozygous for the variants and one of which is negative for the variant. The unaffected parent is also heterozygous for the variant, so in this case, that’s evidence for a lack of segregation. You can apply the ps4 category, or strong evidence that the variant is benign. Again, I was very quickly able to pinpoint that specific paper that gave me the evidence I needed to apply those segregation categories.
Besides clinical evidence, Mastermind is also particularly useful for identifying functional studies, as I mentioned earlier. We have functional categories of our keywords, as well, that can be used to filter the results. I’ll use another example here, which is CPT2 p.D328G. CPT2 is associated with CPT II deficiency, which is a loss of enzyme activity. We’ll go into Mastermind again. For this particular variant, we have 20 articles to start with. For this particular search, I’ll go up to filter categories once more, and this time, I’ll select the functional keywords. There are two main ways to filter the results for functional keywords: you can search for in vivo studies — you may even select the keywords mice or mouse or murine, if you’re looking for a mouse model — and you can also filter for in vitro studies. In the beginning, I like to cast the widest net possible, so I’ll enable all of these keywords and submit to see how the results change. In this case, we reduce from 20 to 18 articles, so I’m going to try to apply some more specific keywords to see if we can reduce the article list even further and pinpoint the article of most interest. I’ll consider here the exact mechanism of disease, which I mentioned earlier is a loss of enzyme activity, so I will try to apply some filters specific to enzymatic assays or enzymatic function. There are two here that have matches, including enzyme assay and enzymatic assay, and then two more that do not have matches. I will select the two that have matches and then submit to see how the articles list changes. Now, we’ve reduced to just four articles, so substantially reduced the article list, and there’s one standout paper once again.
I’ll take a look at that paper and see the results. We can see that there is a homozygous patient for this particular variant. We can also see that they did devise a CPT2 activity assay, so I’ll again open up the PDF to confirm what the results were. In this case, they confirmed that the CPT2 activity for this particular patient was less than one percent of the mean control value, so that’s evidence to apply PS, or strong evidence that the variant is pathogenic. Again, very quickly able to pinpoint this particular paper right at the top of the article list so that I don’t have to manually sift through dozens of papers to find the evidence I need to apply these very crucial categories. I just went through some examples of clinical and functional evidence and how to use Mastermind to assess that evidence very efficiently, particularly using the ACMG filters. However, Mastermind is also particularly useful for assessing and applying gene level criteria, so I’ll pass it off to Jeffrey now to describe that in some more detail.
JEFFREY: Thank you, Lauren. In this section, I will be discussing several use cases for ACMG criteria that assess evidence outside of the variant that’s being interpreted. Mastermind can aid in reviewing evidence for these criteria, as well. This includes criteria such as PS1 and PM5, which are applied if there are other variants at the same residue that are pathogenic; PM1, which can be applied for important functional domains and for hotspot regions; as well as PVS1, PP2, and BP1, which are applied for specific types of variants based on the gene’s mechanism of disease.
On this slide, I will show Mastermind’s filtering capabilities. Mastermind provides a list of all of the published variants in its variant list. One common question that is asked is whether this list can be filtered. As noted in this screenshot, by typing in N96 into the filter by: field, a user can see all of the variants that match this text. This includes variants at the 965 residue position as well as variants that are at the 96 position, all of which that match the filtered term. Additionally, one can see that there are missense variants on this list, as well as a frameshift variant and an in-frame deletion. To the right, one can see all of the articles that match to each of these variants. The total number of articles is very useful for allowing the user to prioritize reviewing those variants that are most likely to have evidence. In this use case, one can be able to utilize this evidence for applying PS1 or PM5 if there’s evidence of another variant that’s pathogenic at the same position, or additionally for hotspots, the PM1 classification criteria.
Let’s look now at an example. In this particular example, we’ll be assessing the missense variant E320K in the SLC19A3 gene. This particular gene is associated with autosomal recessive biotin-thiamine-responsive basal ganglia disease. If I go into Mastermind, what I can see here is that my variant is highlighted, and it’s been found in one article. Clicking on this article, I can see the full text matches, and reviewing this article in more detail, I’ll be able to identify within this article that this variant has been found in one affected patient. For rare disease, one affected patient can be very useful. However, it may not be sufficient evidence to classify the variant as pathogenic or likely pathogenic. Additionally, here on the variants list, we can see another missense variant at the same location, and we also see a nonsense variant at the 320 residue as well. However, one should note that this list is first sorted by the number of articles and then by the position. This may not be a comprehensive list of all of the variants at this 320 position.
In order to look at all of the variants at the E320 position, I’m going to type in E320 into the filter by. What I’ll find is that there’s an additional variant, E320Q, that has been seen in 17 articles. I’m able to then click on this, and I can review all of the articles for this particular variant and be able to identify that this has been seen in far more affected individuals, and can classify this variant as either pathogenic or likely pathogenic. This is useful evidence for my E320K variant in being able to then classify that with the PM5 classification criteria. This example shows how one can quickly filter this list to be able to look for evidence for PS1 or PM5.
On this slide, I will show how our variant diagram is able to provide useful information for functional domains as well as hotspot regions.This variant diagram is a unique representation of all of the published variants that occur within a single gene. This gives us a more gene-level view of this information. A user can use this diagram to view regions with multiple published variants within one region, as well as variants with a large amount of publications, as noted by the size of the bar. Additionally, one can utilize this information to find regions where there are very few published variants, such as in this region by 850 position of the residue. Below this is the protein domain diagram, and one can see all of the protein domains that occur based on residue position. By hovering over this, one can see the name of the domain, as well as the start and stop position based on the residues of that domain. This graphic representation is well-suited to provide the user with a quick reference for aiding in the application of PM1.
Additionally, we can use Mastermind in order to apply gene level criteria, such as PVS1 for truncating variants, PP2 for variants that are missense variants that are commonly considered to be pathogenic, as well as BP1. When the mechanism of action within the gene is typically loss of function, this BP1 can be applied to missense variants. Lauren has already shown the filter categories for ACMG interpretation. However, in this particular diagram, by going to the gene mechanism section and under variants, one can choose missense. By choosing the missense filter, one can review information about missense variants within articles within that gene and determine whether they are commonly pathogenic and therefore PP2 could be applied, or whether they’re commonly benign and truncating variants are most commonly pathogenic and could utilize BP1.
Here, we’ll move to a slightly more complex example, which shows a number of the different capabilities of Mastermind. For this particular example, we’re looking at a nonsense variant that is in the last exon of the PALB2 gene. Truncating variants commonly can have PVS1 applied to them, which is an ACMG criteria for null variants. This is because truncating variants undergo something called nonsense-mediated decay, where the mRNA is decayed prior to a protein being produced. However, within the ACMG framework, there is a caveat that is applied for truncating variants at the three prime end of the gene. This is because nonsense-mediated decay typically doesn’t occur if the premature stop codon occurs in either the last exon or the second to last exon. Overall, what this notes is that for truncating variants that occur towards the end of the gene, like this one, further evidence may need to be obtained in order to be able to classify this as pathogenic or likely pathogenic. For the PALB2 gene, this is associated with the autosomal recessive condition Fanconi anemia. Heterozygous variants within PALB2 are associated with an increased risk of cancer.
Switching to Mastermind, what we see is that our variant, G1178X, is not published. One thing I’ll note here is the power of zero, which is essentially the idea that Mastermind has such a sensitive capability of identifying literature that if you identify that your variant is not published, that can give you confidence that there are no articles out there that mention your variant. However, just because this variant is not published doesn’t mean that Mastermind can’t aid in the classification. One of the things that might be important to look at for truncating variants is whether it might impact the domain, so I’m going to look at the domain here in the three prime end region to see if this variant is contained within that domain, and indeed, it is. This domain covers the region of 853 to 1186, and so our variant is contained within that domain. Additionally, because our variant is in that domain, it’s potentially possible that it disrupts that domain. One way to look for that is to go into the filter categories and to go under protein domains and to enable all. One thing that you’ll view here as we enable all the filters within this particular area of search is that none of these have any literature references noted. This is because our variant of interest doesn’t have any articles published. If you were to go into any of these filters, you would see a zero beside all of these.
I’m going to submit with the chosen filters, and again, what we’ll see comes up is that there’s no articles that match this search. However, additionally, what we’ll see is that if we look at the variant list here, we’ll see that there’s a total number of articles that have been referenced for each of these variants and the filtered numbers. So, this filter did work. It did go through, and it is looking for any protein domains within any of the articles for all of these variants. What I’m going to do next is I’m going to look at the sort here. If I sort this and click it twice, it will show me all of the variants in the three prime end most position of the gene. Scrolling down, I can see a missense variant at the G1178 position. However, our nonsense variant is not published. What I’m most interested in, though, is whether there are any other truncating variants that potentially disrupt this particular domain, so I’m going to look for variants around this that are truncating variants that may impact this domain. If I look downstream, I see one frameshift, I see another frameshift here, and I see a nonsense variant right here at the position 1183 that is very well published. It’s been published in total in 88 articles, and 47 articles for which domains are mentioned.
Clicking on this, it will now search based on the protein domain filter, as well as this particular variant. Opening the publication history, I can once again see a very large circle here, indicating a relevant article that is at the top of our article search. Clicking on this particular article, I can see all of the full text matches and the gene matches for PALB2. I can see here that there’s a dimension of the domain when it comes to this particular variant. Switching to this article, I see that this PALB2 variant results in the loss of the last three amino acids of PALB2, which disrupts hydrogen bonding for this particular domain. It also leads to severely reduced expression, as well as incomplete folding, and it’s degraded rapidly. Therefore, this is evidence that truncating variants within this portion of the protein are pathogenic or likely pathogenic. This information can very much help with classifying our unpublished variant in PALB2. Overall, I was able to quickly and easily utilize several different methods of searching for information that would help us to classify our variant of interest.
Switching now here to a final example, and this example is in ATP7B, which is associated with Wilson’s disease. This information here shows that Mastermind found functional information that was of consequence for this variant. It’s been found in multiple cases. The computational algorithms predict this variant to be damaging, and as well, missense variants in this gene are typically pathogenic. All of this information can be utilized to classify this variant as likely pathogenic. Compared to historic classifications of this variant, which did not find the functional consequences, this particular variant was previously classified as a variant of uncertain significance. What this shows is that Mastermind is able to find the most relevant literature, find it in a very speedy manner so that you can conduct efficient searches, and then be able to provide the most accurate classification of a variant. I will now wrap up and give back the screen here to Garrett, who will be able to conduct the Q&A session.
GARRETT: Yes, Jeffrey and Lauren, outstanding conversation so far! It has definitely generated some questions from our viewers. At this point, we will move to the Q&A portion of our talk today. Looking at our questions, our first one: a viewer would like to know, do you give us access to documents behind paywalls?
LAUREN: So we do not provide access to PDFs behind paywalls, but our sentence fragments are independent of paywall status, so you’ll still be able to see sentence fragments regardless of whether the paper is open access or paywalled. If you use more filters, you’ll see more sentences for each of those papers as well.
GARRETT: Okay, awesome. Another question: Sometimes, I see false positives. Can you get rid of those?
JEFFREY: Yeah, thank you, Garrett, that’s an excellent question. When it comes to Mastermind, what we strive to do is provide the most sensitive information so that we can make sure that we’re returning all true positive matches, and the cost of sensitivity is specificity. That means that there will be false positives within the articles, but those can be quickly reviewed, and they can be identified, and then they can be reviewed from your search and your classification of that particular variant. The other thing that I’ll speak to about that is, once again, the idea of the power of zero. Given the fact that we do provide this very sensitive search, when we identify something, it’s most likely going to be real, but there’s going to be times where there’s false positives. But, if we identify nothing, then that suggests that there’s probably nothing out there as far as literature to identify for that variant.
GARRETT: Thank you, Jeffrey. Yes, the power of zero is a really incredible part of all of this. Our next question: can I get a list of all missense variants in the gene, and also, is there a way to filter for all missense variants to a single search?
LAUREN: Thanks, Garrett, that’s another great question. We do not provide a downloadable list of missense variants, but as Jeffrey showcased in his assessment of the gene-level criteria, you can use a missense filter to identify those missense variants. That’s under the genetic mechanism tab under the filter categories. If you apply that keyword, you’ll be able to see the missense variants within the gene in that prioritized variant list.
GARRETT: Awesome, awesome. Lots of great questions here. Another one is: is there a way to flag articles as those that I have reviewed, so I don’t review them again if I reinterpret this variant later?
JEFFREY: Well, that’s a terrific question. That is not currently a feature that is within Mastermind, but it’s a capability that we’re investigating trying to add in the future. We are enabling the export of all of the articles into a CSV file to be able to add them to your own internal tracking systems.
GARRETT: Okay, thank you. What else… Here’s one: sometimes, I find articles in ClinVar that are not in Mastermind. Can I add them?
LAUREN: Yes! Oftentimes, the articles that you would find in ClinVar that are not present in Mastermind are because they are at the gene level. If you’re performing a search for a variant and that particular paper that’s cited in ClinVar does not contain the variant, then Mastermind wouldn’t return that paper according to that search. However, you can identify those papers by searching just at the gene level, so we can find those as well. If you do, however, find an article that’s not in Mastermind and does contain the variant that you were searching for, we’d definitely welcome your input. We’d love to hear from you, so submit that via email to our support team at firstname.lastname@example.org and we’ll immediately cue that paper for indexing.
GARRETT: Yes, thank you for mentioning that. It’s good that, you know, we’re always trying to keep it updated, and yes, it’s great to submit that via email to our support team. Let’s see. Is there a way to save my search or share it with the teammates?
JEFFREY: That’s a terrific question. The URL that you have for a particular search is specific even to the article that you’re reviewing, so if you wish to send your search to someone else, you can simply copy that URL and send it to someone, and it’ll come up with the same search criteria and the same filters as those that you were applying when you did that search.
GARRETT: Okay, very good. Our next question, we have lots of questions here, do you rank journals according to impact factor?
LAUREN: That’s a great question. The impact factor of the journal is actually considered as part of the relevance score calculation. When I was showcasing that publication history diagram with the size of the bubbles being the relevance of the article, the impact factor is part of that calculation. In addition, in the articles list, it’s automatically sorted by relevance, but there’s also a drop down where you can change that to be the impact factor, if that’s your preferred way of filtering through the articles.
GARRETT: Okay, very interesting. Very good. Another question: how can we see the Mastermind classification?
JEFFREY: Well, that’s a terrific question. Mastermind offers classified variants in a disease-specific approach. When available, these classifications will appear at the top of the page in a banner that’s going to include the classification and the disease that it’s being referenced to. In addition, it’s going to have information about the reason why it was classified that way, including being seen in multiple cases, de novo evidence, segregation, functional data, etc. However, not all of the millions of variants in Mastermind are yet classified, so we index all of the articles in PubMed and offer those to any user to be able to support your classification. However, currently, we don’t have all of the variants classified, but that’s something that we are working on.
GARRETT: Very good, very good. I think it’s important to mention, we index those articles and offer them to their team to evaluate. I think that’s really important. Awesome. Our next question: is it possible to link to PubMed of the university?
JEFFREY: That’s a good question. I think, as far as linking to PubMed, we do have the option for all of the articles to link out to PubMed. If you are within Mastermind and you wish to open PubMed, there’s a one-click button in order to be able to do that. I think that’s what that question is referencing.
GARRETT: Okay. Let’s see, our next question: if an article is retracted, does Mastermind indicate that, or excuse me, does Mastermind indicate that in the snapshot view at the bottom left?
LAUREN: We do offer the updated article in Mastermind. We update Mastermind on a weekly basis. There’s usually 10k plus new articles a week, so we’ll offer the newly updated article.
GARRETT: Okay. So, back to the PubMed question. I know that there is a link to PubMed, but is there an internal one, so that it is accessible through the university?
JEFFREY: Yeah, our link to PubMed is specific to go out to PubMed. We don’t currently offer individual specific links to universities at this time, but that’s something that we could certainly look into for the future.
GARRETT: This was an awesome discussion! Jeffrey and Lauren, thank you for
sharing your expertise on this topic. I don’t know if any of you have any closing thoughts?
JEFFREY: As far as closing thoughts, I think the biggest take-home point for today is the fact that Mastermind is really set up, whether it’s the filters, whether it’s the sort capabilities, whether it’s the additional searches that one can do, to really power an individual’s searches for evidence that’s going to aid in the classification. Really, the power of Mastermind brings home the speed, the improved results, and then the ability to find additional evidence that’s going to classify a variant as pathogenic or likely pathogenic. I hope that’s really what we’ve been able to show here today.
LAUREN: Yeah, and I’d agree with Jeffrey. I think, especially for rare disease, the increased sensitivity and the efficiency that Mastermind provides is really crucial. That evidence can be particularly difficult to identify because the literature is so vast, and Mastermind can really just pinpoint right down to the evidence that you need to classify the variant according to clinical standards.
GARRETT: Awesome. Thank you both very much, and at this point, we will wrap up the webinar. Jeffrey and Lauren, thanks again for sharing your insights around ACMG classification! I know I learned a lot. Thanks to everyone watching! As a reminder, you will receive a recording of our discussion later today. Also, if you don’t yet have a Mastermind account, you can sign up at the link below that you see on the screen to start with a free trial of the Professional Edition. If you’re currently using Basic, talk to us about upgrading to get access to the features that Lauren and Jeffrey shared today. As always, and as Lauren and Jeffrey mentioned, if you have any questions, don’t hesitate to reach out to us at email@example.com. Finally, we have an exciting announcement! Be sure to put July 21st on your calendar — that will be our upcoming webinar with Lurie Children’s Hospital, where we will be exploring how Mastermind is supporting clinical decisions for them there. You won’t want to miss that one, that’s going to be an excellent conversation. With that, we’re wrapped up. Thanks so much, everyone, and have a fantastic rest of your day! Bye, now.