In this webinar, Dr. Mark Kiel used real-world examples to demonstrate how a Comprehensive Genomic Landscape for a disease, pathway or gene set has empowered Pharmaceutical researchers and translational teams to understand genetic and rare diseases at the molecular level.
- How a Comprehensive Genomic Landscape delivered a 6-fold increase in identifying pathogenic drivers in just one Parkinson’s gene.
- How Genomic Landscapes have been used to segregate clinical trials using a comprehensive list of genes and pathogenic variants as genomic biomarkers.
- How to accelerate the cumbersome process of identifying genomic biomarkers for Companion Diagnostic (CDx) development, backed up with clinical evidence from the scientific literature.
About the Webinar
Drug targets with human genetic evidence of disease association are twice as likely to lead to approval (King et al. 2019). But navigating the millions of genetic data points to comprehensively identify genomic drivers of a target indication or drug pathway is daunting.
Understanding the molecular drivers of disease accelerates drug development at each stage of the process. It…
- Informs downstream research and discovery,
- Guides biomarker selection for clinical trial segregation criteria, and
- Provides documented evidence for CDx validation.
There is a proven process to assemble essential genomic insight into Neurodegenerative and other rare and inherited diseases to drive better target selection and biomarker identification.
For missense mutations, how are these being reported out in the clinical report – eg TARDPP?
There’s a couple of ways I can imagine that question being answered. A technical way to answer that would be in the variant nomenclature – given that this is a highly technical process up front, the variant nomenclature can be modified to fit any downstream needs that the client that we’re working with has so that the data that we produce doesn’t need to be transformed in any way but can be immediately actionable and intercalated into the downstream workflow.
So, that’s one way. The standard way that we produce the variant nomenclature is according to industry standard HGBS nomenclature but we’re flexible and able to modify the nomenclature as befits any given solution. The other way to answer that question would be, how do you particularly deal with missense variants, which by their nature don’t necessarily lend themselves to a clear understanding of what they’re doing to protein coding consequence. And so I’ll fall back on my slide where I discussed the Comprehensive Genomic Landscapes production, is that we use the ACMG and AMP criteria and assemble the clinical and functional evidence necessary to properly interpret the pathogenicity of variants, whether they’re nonsense or frameshift or deletion mutations or point mutations that lead to protein coding consequence such as missense variants or otherwise noncoding variants such as splice, or even intronic variants. We follow the industry standard approach to interpreting all of those different types of variants.
Can you suggest genetic biomarkers (also including mRNA) based on drug used and target indication?
This is a great question and allows me to clarify two things. I’ve been focusing here on the genetic variants for protein coding consequence but in the armamentarium of Genomenon to produce these data sets we can take a step back and asses information at the gene level and that necessitates looking at the different mechanisms of disease causation at the hand of the different genes and that includes differential expression. So if you’re talking about differential expression of different genes and how those differential expressions can lead to disease, that is something that can be produced in a Comprehensive Genomic Landscape at a gene level, yes.
The other thing to say to build on that is where I answered the first question by saying that we follow industry guidelines for interpretation of variants, ACMG or AMP, as well as genes. In the case of constitutional disease that would be ClinGen guidelines as a standard framework.
Nevertheless, we layer on top of that curation and annotation any custom insight that the client is particularly interested in. So I didn’t go into too much detail but hopefully in those examples that I showcased there were particular points of interest for the clients that they were specifically looking for based on their previous research and their preexisting knowledge of the gene and the disease. Things such as the clinical frameworks used to assess severity of disease – that is a curative activity and annotation that we can provide in the production of genomic landscapes.
Another example would be assembling a patient-specific laboratory value database if the disease is such that there are clinical laboratory biomarkers that allow you to track the level of severity of the disease. We’ve had engagements where we’ve produced such a database by exhaustively curating all such case studies and case series that report out on those studies.
A final example to really drive home the fact that we can do custom curation is in exhaustively reviewing all of the literature for a given disease to pull out prevalent studies as the prevalence in different variants and different genes are found in different subpopulations of disease based on ethnicity or based on disease subtypes. In conversation with a client, it really depends on what the focus is, and what the nature of the disease or the gene, or the gene pathway is, as to what custom annotations we layer on top of those standard frameworks.
What about new papers? How can we be sure this data is up to date?
That is a great question, particularly in the context of neurodegenerative disease, research is expanding exponentially. I mentioned the commoditization of genomic sequencing and how much more straightforward it is to perform these sequencing studies on a large scale, research is not slowing it’s only getting faster, and all of that information becomes less and less tractable to extract and understand manually and we appreciate that. With all of these genomic landscapes that I’ve described, the work is evergreen and continually updated both for previous curated variants for which there’s new papers about those variants – that is updated – in addition to any new variants that weren’t previously described that are newly published in any one of those papers that come out from quarter to quarter. The data in these genomic landscapes is kept up to date and the curation component is preserved across those updates so that you’re assured a sensitive, comprehensive and highly valuable up to date data set.
In the example of ATP7B, how many of the 145 variants with functional evidence were in the 248 ClinVar reported variants?
The short answer is I don’t know, I don’t remember that exactly. I do know though that we did look at that quite specifically, I just don’t know the result. I will say most of the ClinVar variants do not have reference citations and even if they do, they do have a characterization of those functional studies. So I don’t just want the audience members to perseverate on the number of variants that are accumulated, which is uniformly more and in most cases is much more than what is available in ClinVar, Genomenon’s Comprehensive Genomic Landscapes can double or multiply by ten the number of variants that are seen in ClinVar. Beyond that sheer number there’s exhaustive annotation of all of those variants as well and clarity, which is not provided by any of the references or any of the information in ClinVar at all.
What do you think of the cases with not much known genetics related to diseases? Do you think your platform or genetic biomarkers would still help target ID and patient stratification for such cases?
That is a great question. I’ll say that Genomenon’s data is at heart an association database. A lot of the focus of my talk, and certainly of our software in the hands of clinical users, is on variant information for known disease entities and clarity on published evidence for a given variant to make clinical grade calls or support some of the activities in Pharma that I described but a necessary preamble to assembling any of that data in Mastermind is an understanding of all of these associations at multiple levels. To answer the question directly, is if it exists in the literature directly or indirectly, no matter how rare, if there’s one reference that talks about this association we have it, we understand it, and we have tools that can automate the assembly of that information, followed up efficient and custom targeted review of that information.
As a specific example, one group that we’re working with doesn’t know how their drug works. They know that it works and they know that it’s extremely safe, but they don’t know how it works and they don’t know because it’s not directly published in a single reference. The work that we’re engaged in with them is in unweaving that understanding from disparate publications that have information that touch on what the mechanism may be but where the specific action of their drug will only become clear with a comprehensive view of all of those pieces of evidence brought together and assessed through the productions of a Comprehensive Genomic Landscape. So that’s a fairly long way to say, ‘yes, we have that information’.
What about looking at gene x gene interactions?
The gene level information that we have comes along with it, as I mentioned Mastermind being an association database, gene/gene associations. Gene/gene networks at the protein level, the protein interactome, the pathway participants if they’re not directly interacting with each other but participate in a common pathway, how proteins may affect the expression of other genes, all of that information is latent in our association database and can be uncovered simply by asking the question in conversation with a client and as I said, patterning to look for things to uncover when we organize and manually curate all of that information.
What does the data look like? How is it delivered?
That’s a great question and I have a slide that I loathe to put in the main presentation because it’s pretty busy but I want to clarify that this data is fully downloadable and integratabtle into your systems downstream and the format is modifiable so that you can fit any one of those downstream use cases. This is an example of one of the proteins that I talked about for Parkinson’s, the GBA gene, each row represents one of those dots, or one of those variants, each column is a specific datum or piece of information where I want to emphasize we have a very tiered approach to presenting this evidence. One of those columns that you see in the middle there is the provisional call, so it’s the top-line information from our curation so you can start to sort at a high level what this information means. That is the provisional ACMG call where you can see here they’re pathogenic variants. Then the next columns over are the numbers of articles that we investigated, technically and manually, to come to that conclusion and in many cases in many hundreds of papers that we looked at. A further level of summarization of the evidence is which of those variants have functional studies, in this case for ACMG it’s PS3 category of evidence, which of those variants have clinical grade evidence, and one example it would be PS4 or strong evidence of pathogenicity because that variants segregates with disease in a number of different patients as well as the population frequency data and the in silico predictive models.
A further tier of evidence is if you want to drill down into the specific information from any one of those assertions, categorized and then top-lined, you can see the reference from which those assertions came within their category of evidence. In the case that I described functional studies on the far right and the middle-bottom the clinical studies, with their PMID indicating references and a sentence que – a quote from the author – that makes the case for each of those different assertions followed by on the left any of the custom annotations that were requested, be they were specific assays that were used in the functional studies, other biomarkers that are associated with the protein consequence, different diseases where that variant is mentioned in that context or different ethnicities, or really anything that’s of interest to the group that we’re talking to based on their needs and based on the disease in addition to any of those baseline data points, such as population frequency at the upper right there, or the different ways a variant is described in the literature or the SIFT and PolyPhen type of in silico predictive models.
Could you please clarify if there is the possibility of Genomenon to resolve pathogenicity in case of well known BRCA1/2 mutations?
Yes – Genomenon has data for BRCA1 and BRCA2 mutations and can resolve pathogenicity based on published evidence and information in relevant databases.