Heidi Rehm: So I apologize in advance, I've had some Internet
connectivity issues, not to mention issues with pediatric members of my house.
But -- so I haven't been fully on all day, but I did -- I was asked to react in this
area, so I'll talk about three different topics that I was asked to talk about: actionability,
validation, and standards for lab reports, and just sort of go through them one by one.
Next slide, please.
Next slide.
Oh, thanks.
So I don't think I need to define actionability, but in my observation, outsider to eMERGE,
it seems to be a large focus to date on genotypes, either pharmacogenomics, GWAS, or PheWAS.
And one suggestion is, you know, and I think it's been talked about, is to expand the focus
to gene-phenotype relationships, not just relationships of genotypes.
And although I don't think I have very specific messages within the area of actionability,
I do think it's an opportunity for collaboration.
And I thought I'd mention a project that's ongoing.
Next slide, please.
So this has a few different arms to it.
One through the BabySeq Study, which is one of the newborn screening genome grants.
We've been doing an evidence-based review of gene-phenotype associations.
We're through 673 genes and have 3,000 more to go.
We've been systematically evaluating the evidence for the Gene Disease Association in a ranking
of -1 to 3, as well as categorizing a variety of other fields on each gene.
If you skip to the next slide, this project is being integrated into a larger effort that
we're doing collaboratively with two other labs.
It has been focused to a large extent on defining the medical exome and improving testing for
all genes that have known medical relevance.
And that's been led by Emory, CHOP, and my lab.
However, the evaluation of the evidence on variants, similar to what I just showed in
the prior slide, is being extended to a larger program called the ClinGen Resource, and on
the next slide, it defines the four different groups that are a part of that effort, really
creating centralized resources.
And one of those groups includes a grant by Jonathan Berg, who's really focused on actionability
as a component of this project and defining that for genes.
So on the next slide is just a slide that I actually took from one of his slide decks,
really identifying five key parameters that he has developed for evaluating medical actionability,
and each one of those is scored on a scale from zero to three, and then he's working
with Katrina Goddard's group, to really generate a streamlined evidence-based review of these
genes.
And so that's the built off the medical exome list for all of these genes, and we're doing
that collaboratively.
So I think that's an opportunity for collaboration among the many different consortia that maybe
working in this space.
And I'm happy to talk more about it later.
But if you go to the next slide, I'll switch over to the validation.
So I thought I would bring it to people's attention that we recently published guidelines
through the American College of Medical Genetics that addresses clinical laboratory standards
for next-generation sequencing.
And that includes how to validate a sequencing test on next-generation sequencing platforms,
involving both platform validation as well as individual test validation, and ongoing
quality assurance.
So, if you skip to the next slide, that covers many of the detailed aspects of this, and
I think gives reasonably good guidance.
I did want to point out, you know, a couple of specific recommendations that we made.
One is that when you validate a test, that you must cover all types of rare variants
that are being reported, as well as address homologous regions of which there are hundreds
of genes that have issues, either pseudogenes or other small regions of homology.
And so your validation has to address the types of things you will encounter in your
test and are planning to report.
That is distinct from common variants that are, you know, for instance, pharmacogenomics
variants, where you're specifically trying to report out on a particular known genotype.
And in that case, the validation would need to be variant specific, so we make that distinction.
There's also a lot of discussion about whether there's a need to orthogonally validate every
finding from next-generation sequencing, and we acknowledged in the recommendations that
that may not be necessary, although it's fairly standard today, but may not be necessary if
sufficient validation has been performed for -- as stated above.
And the quality metrics are high, both coverage and mapping quality, and, most importantly,
that the workflow has a low risk for sample swaps, so that you can ensure that the result
you got from your primary platform is, in fact, derived from the original specimen.
And so some labs put in other methods, SNP rays and things, to ensure the final results
came from the original specimens.
Next slide.
I also wanted to just point out a couple of things about variant calling.
A lot of people use traditional pipelines to perform alignment and variant calling,
such as TATK, that generate a variant call file.
However, when you're looking at specific genotypes, there are a number of groups that are taking
slightly different algorithms to analyze that data in a more accurate manner.
And that could either be done through joint calling, where, in many cases, many data sets
are batched.
The problem with this is it's not a great approach for the rapid turnaround time that
happens in clinical labs.
So, in our group, we've implemented individual genotyping calling on the raw NGS data for
all of our raw pharmacogenomic variants and our complex common variants for complex traits.
And that has been more amenable to our clinical workflow, and it also ensures return when
there's a reference call and not just when the variant is there.
Next slide.
So, the last topic is related to standards for lab reports and EHR deposition.
And we did address a number of guidelines in the ACMG document as well, including all
of the, sort of, topics listed here: turnaround times, interpretations, incidental findings,
written report -- we give a number of sample reports for both NGS panels and exome -- and
address data reanalysis.
I will point out one specific recommendation related to the EHR, and that was that variants
that are going into the EHR today be restricted to those with analytic and clinical validity,
and that those variants be structured to allow for clinical decision support.
So the variants may be in structured form, and the interpretive part of it be -- could
be a PDF.
And we did not recommend today that an entire variant call file from an exome or genome
be deposited in the EHR until improved analytic validity.
Next slide.
This just shows an example of a sample report that shows the structured data, as well as
an overall result that we recommended be put onto individual reports.
Next slide.
And the other question that a lot of people talk about is how you return updated information
on variants that happen over time and inevitably.
And so we, in our -- in my lab, we specifically implemented a system called the GeneInsight
Clinic where this is a system that, in our instance, in integrated to the Partners Healthcare
EHR environment.
In other instances where it's not integrated, it's just web accessible.
But it serves as a repository for all of the clinics' genetic test reports, and all of
the data in their -- if you click the next slide, it'll show a bump out.
So when -- you can just click "next" -- when a variant classification changes, it will
show in the system that -- go back one -- the variant's original classification, as well
as a new classification, and physicians are sent in an email alert with a link deep into
the EHR record where they can find the information on this.
And they can click a hyperlink to the variant and it will show the updated interpretation
of that variant, based on connectivity with our last mutation database.
So that's the way that we have implemented that sort of ongoing updating of variants,
both rare and common, throughout our genetic test reports.
And this whole system has been the subject of an NIH grant that David Bates has led to
look at usability by clinicians using the system.
Next slide.
And I just wanted to show a couple of examples of our whole genome reports, where, to really
allow them to be usable to a primary care physician, we summarize all of the information
on a single page, shown here as monogenic disease risk, carrier status, pharmacogenomic
associations, and blood groups.
And then physicians can skip to the next pages -- next slide, please -- and they'll see more
details about each of the variants and the diseases being reported.
Next slide shows one of the pharmacogenomic associations, where they see the more detailed
information, as well as highlighted background population rate of the genotype that they
have compared to others.
Next slide, please.
Although this isn't being reported in our routine clinical service, for the MedSeq study,
we are also adding a cardiac risk supplement that gets into complex traits.
Next slide.
And it will zoom into the bottom section, where we are reporting genotypes from complex
traits, both contextual data as well as off to the right the polygenic relative risk and
percentile ranks compared to the general population.
And we've spent a lot of time trying to figure out how to return this type of data in a clinical
report.
So, next slide, please.
Lastly, you know, one of the biggest challenges we encounter is the interpretation of particularly
rare variants.
Many of you know that there is ACMG recommendations out listing 56 genes to return.
That has led to a number of groups that have looked at the rate of incidental findings
in cohorts.
Those rates have varied across studies, although I would say that many are around the 1 to
2 percent range for the 56 genes.
But some of the differences come to differences in how we all interpret variants, in the next
slide.
When we interpret variants in the genome, we routinely find many pathogenic variants,
97 percent of which we exclude for lack of evidence, as well as loss-of-function variants,
94 percent of which we exclude because there's insufficient evidence for a gene-disease association.
So that presents huge challenges in the interpretive process; it's very labor intensive.
Next slide.
I think that may be my last slide.
So I just wanted to acknowledge a number of the projects that are contributing to the
things that I talked about today.
And I will stop there.