Speaker 0: Improve target therapies too.
Speaker 0: So if your cancer had that fusion, then they will
Speaker 0: be eligible for these drugs.
Speaker 0: So this is like the most recent from the and
Speaker 0: guidelines.
Speaker 0: If you have a lung adenocarcinoma now, really you should
Speaker 0: be tested for all of these different genomic markers or
Speaker 0: biomarkers.
Speaker 0: So EGFR mutations and these are all genes.
Speaker 0: Some are mutations in DNA, some are fusions.
Speaker 0: Like I mentioned that really every every lung cancer, the
Speaker 0: clinician wants this information now.
Speaker 0: And that's what that panel I just showed you is
Speaker 0: meant to do.
Speaker 0: The two of these are actually we have to use
Speaker 0: immunohistochemistry for.
Speaker 0: So it's a different part of the lab that that
Speaker 0: does those.
Speaker 0: So it's really kind of a a we have to
Speaker 0: do a lot of integration among our testing.
Speaker 0: And a lot of this comes through our our capacities
Speaker 0: team, our pathology software that then gets integrated into the
Speaker 0: EMR.
Speaker 0: But it just kind of shows you all the different
Speaker 0: things we need to be doing in our lab to
Speaker 0: make sure that our patients are getting the right drugs.
Speaker 0: And I will say some of these are very rare,
Speaker 0: like Interac fusions and lung cancer, like, you know, less
Speaker 0: than 2%.
Speaker 0: But if you have one, there are two FDA approved
Speaker 0: therapies for those, which I'll show you in the next
Speaker 0: slide that actually the patients can respond really well to
Speaker 0: those if they had those fusions.
Speaker 0: So this would be this is actually a control specimen,
Speaker 0: but this shows you ETB six and track fusion here.
Speaker 0: If a person's lung cancer, if they had that or
Speaker 0: another type of cancer, they would be eligible for the
Speaker 0: FDA approved therapy and tracked in our lower tract in
Speaker 0: it, which are in track inhibitors.
Speaker 0: So and these drugs are not cheap.
Speaker 0: So if you have you have to have proof that
Speaker 0: they have that change in order to get those drugs.
Speaker 0: And then we again, we can list different clinical trials
Speaker 0: and locations and so forth.
Speaker 0: But again, this is all just within a PDF report
Speaker 0: that gets put into the epic EMR.
Speaker 0: And then this is our newer assay we're finishing validation
Speaker 0: on.
Speaker 0: That's going to be it can be 500 genes.
Speaker 0: I think we're going to limit it to looking at
Speaker 0: 300 genes because it's just so much data.
Speaker 0: But this is going to cover even more.
Speaker 0: And better, more cancers, even better than the other one
Speaker 0: can cover.
Speaker 0: And this is having more for clinical trial enrollment.
Speaker 0: Or if a person has failed other therapies, has no
Speaker 0: other biomarkers for FDA approved therapies.
Speaker 0: Is there something else we can kind of grab a
Speaker 0: hold on to?
Speaker 0: So this is on the Illumina next take the X
Speaker 0: and just the direct means.
Speaker 0: It's an FDA approved instrument.
Speaker 0: But this test is not yet FDA approved.
Speaker 0: So it really doesn't matter if the instruments FDA approved
Speaker 0: because the FDA is not yet FDA approved.
Speaker 0: But we usually invest in the instruments that we can
Speaker 0: for preparation for further down the line in cases, assays
Speaker 0: become approved by the FDA.
Speaker 0: So as you can see, this will be an even
Speaker 0: larger asset that we're going to have to have a
Speaker 0: lot of data come from.
Speaker 0: That would be nice if you can make that discrete
Speaker 0: data in the medical record and searchable and usable than
Speaker 0: just within a PDF form.
Speaker 0: So right now, right now our current workflow is we
Speaker 0: sequence the cancer.
Speaker 0: We take the nucleic acids out, do all of our
Speaker 0: things.
Speaker 0: We do sequence it, and then we sign it out
Speaker 0: in a software called Genome Oncology, which is a company
Speaker 0: out of Cleveland, and we'll talk about them.
Speaker 0: They have an annotation software that we have pretty much
Speaker 0: used since we started doing the next gen sequencing in
Speaker 0: 2014.
Speaker 0: And they are kind of one of the first companies
Speaker 0: on the market with an annotation software that we felt
Speaker 0: comfortable we could use.
Speaker 0: So the molecular pathologist goes to the whole software, decides
Speaker 0: how they want to form the report, what's important and
Speaker 0: so forth, and then we sign it out in that
Speaker 0: software and then we have to pull in that PDF
Speaker 0: into the Copart system, which is our anatomic pathology system
Speaker 0: that we use for all of our biopsies.
Speaker 0: Plan out, surgical resection, sign out, pull that in, find
Speaker 0: out the attached PDF in our capacities, and then that's
Speaker 0: what goes into the epic pathology tab.
Speaker 0: So this is obviously not ideal for us.
Speaker 0: It's a lot of work for us to have to
Speaker 0: sign out, but then it also just shows up as
Speaker 0: a PDF under the PATH tab in an epic.
Speaker 0: And so we've been working over the last seven years
Speaker 0: with genome oncology.
Speaker 0: It's kind of been baby steps to get to a
Speaker 0: point where hopefully we can at least even if it's
Speaker 0: not presented in a discrete format initially in Epic and
Speaker 0: it's still only in a PDF format, the discrete data
Speaker 0: is there in the background of the whole epic environment
Speaker 0: so that if people want to identify clinical trials better
Speaker 0: or therapies or do you know, research and so forth,
Speaker 0: that data will be there in a discrete format that
Speaker 0: can be more easily usable.
Speaker 0: So Genome Oncology, again, it's a company in Cleveland that
Speaker 0: started, you know, like I said, 2013 or 14, we
Speaker 0: were one of the first academic institutions to sign with
Speaker 0: them.
Speaker 0: And there are only a couple of a few handful
Speaker 0: of companies that were providing software for annotating all this
Speaker 0: cancer genomic data.
Speaker 0: One of the main reasons we like them, they seem
Speaker 0: very attentive to our needs, but then they also could
Speaker 0: do stuff within our firewall where a lot of other
Speaker 0: things were cloud based in our i.t.
Speaker 0: Department was not super thrilled with that.
Speaker 0: They're much more amenable to it now that back then
Speaker 0: not so much.
Speaker 0: So we could do everything within our firewall.
Speaker 0: And like I said, we're one of the key partners
Speaker 0: with them now.
Speaker 0: We've done a lot of kind of R&D with them.
Speaker 0: There's over 40 healthcare institutions using them.
Speaker 0: A lot of the Big Ten cancer centers are using
Speaker 0: them.
Speaker 0: A lot of MTC and designated are NCI designated providers.
Speaker 0: A lot of molecular labs use them, so they've kind
Speaker 0: of grown as a company and they keep reaching for
Speaker 0: new applications to help with this whole problem.
Speaker 0: One of the problems that we're trying to solve here
Speaker 0: that we're talking about.
Speaker 0: So they really kind of started out as a company
Speaker 0: helping us with annotation and reporting of our genetic data.
Speaker 0: But now they're moving into trying to help us do
Speaker 0: more of that data within the research setting with, you
Speaker 0: know, curated results with actionable trial work and so forth.
Speaker 0: And so these are some slides from them and they
Speaker 0: said I could use, but they're trying to do a
Speaker 0: lot more with incorporating this data into a point where
Speaker 0: it can be more usable for institutional trials and so
Speaker 0: forth.
Speaker 0: And we'll talk more about how this looks within our
Speaker 0: institution in the next couple of slides.
Speaker 0: So again, when we first started.
Speaker 0: So here's kind of the good side.
Speaker 0: And then here's our side within kind of the epic
Speaker 0: environment here that we have here at our hospital.
Speaker 0: And so when we first started with genome oncology, I'll
Speaker 0: call it GEO from here on out.
Speaker 0: We started with their pathology workbench, which was really just
Speaker 0: their software to help us form a report to go
Speaker 0: into the medical record on a person's cancer.
Speaker 0: So that's what we've been doing since about 2015.
Speaker 0: And then they developed something called a molecular tumor board
Speaker 0: app, which could draw in those genetic results.
Speaker 0: To help us, we have a molecular tumor board where
Speaker 0: we look at patients who have more rare kinds of
Speaker 0: biomarkers to see if they could be eligible for certain
Speaker 0: trials or off label therapies and so forth.
Speaker 0: And so that was something they developed.
Speaker 0: And then they have a third component here called Genomic
Speaker 0: Analytics, which is more of a research component to help
Speaker 0: one identify cohorts of patients with various gene mutations, various
Speaker 0: type of mutation, disease types and so forth.
Speaker 0: So these are the three things on there.
Speaker 0: And they they have kind of developed as apps.
Speaker 0: But what they have also developed is something called this
Speaker 0: precision oncology platform.
Speaker 0: And and this is a lot Scott Campbell has worked
Speaker 0: a lot on this, too.
Speaker 0: In the background, it's kind of more of the nitty
Speaker 0: gritty information technology aspects of this.
Speaker 0: But basically, this is a partnership between pathology, oncology and
Speaker 0: genome oncology.
Speaker 0: And information technology here where there's this geo connect system.
Speaker 0: And so all this genomic data that we can develop
Speaker 0: in our own laboratory gets put into this oncology platform.
Speaker 0: And as and that can kind of play off and
Speaker 0: connect with our epic environment that has the clinical data
Speaker 0: warehouse housing, all that discrete patient data.
Speaker 0: And then it can also interact with our clinical trial
Speaker 0: management system, which I think is called Encore.
Speaker 0: And then we can also we're also and I'll explain
Speaker 0: how we're doing this in a minute.
Speaker 0: Not everything we do on patients here in cancer is
Speaker 0: done in our lab.
Speaker 0: In my lab.
Speaker 0: These patients come in from all over.
Speaker 0: There are outside reference labs that can do larger scale
Speaker 0: testing, and so there might be outside lab reports with
Speaker 0: genomic information that isn't necessarily in our epic system.
Speaker 0: And so one thing we've worked with genome Oncology is
Speaker 0: getting that outside reference lab data in a more usable
Speaker 0: format into this precision oncology platform in a more discreet
Speaker 0: way so that it to not only our data from
Speaker 0: our lab but from other labs can also play off
Speaker 0: some of this epic data that's more discrete within our
Speaker 0: clinical data warehouse and, you know, using our clinical trial
Speaker 0: management system.
Speaker 0: And so the idea is here that the goal is
Speaker 0: to really have all of this data in a discreet
Speaker 0: form to allow our oncologists to be able to use
Speaker 0: it and trying to find better clinical trials, better therapies.
Speaker 0: And then potentially you'll have this data that could be
Speaker 0: more mineable if you want to say start a clinical
Speaker 0: trial here and you need so many patients with this
Speaker 0: mutation or this gene mutation or whatever.
Speaker 0: And so this has kind of been something we've been
Speaker 0: working on since we started with genome oncology.
Speaker 0: And I think we're kind of in some of the
Speaker 0: final steps now.
Speaker 0: We still have a little bit of work.
Speaker 0: COVID set us back a couple of years, unfortunately, because
Speaker 0: my lab became a COVID lab for a couple of
Speaker 0: years.
Speaker 0: But now we're back to it.
Speaker 0: So I'll explain some of this in the next couple
Speaker 0: of slides, some more.
Speaker 0: And so this is a little bit complicated.
Speaker 0: But this is kind of the part I just talked
Speaker 0: about, where we have all these in the epic environment.
Speaker 0: We have many sources of discrete data that kind of
Speaker 0: play off and feed into this precision oncology platform.
Speaker 0: I mentioned that there's these outside vendors who have all
Speaker 0: of these.
Speaker 0: We have these outside reports that are either brought to
Speaker 0: us by patients or they get sent to the provider
Speaker 0: or to the case manager or maybe a genetic counselor
Speaker 0: even.
Speaker 0: And we have developed a way for those to be
Speaker 0: sent to our molecular lab, and then our lab will
Speaker 0: upload those images to genome oncology.
Speaker 0: And they have a natural language processor.
Speaker 0: And some of you might know more about this than
Speaker 0: I do.
Speaker 0: That can actually process through that scan document or that
Speaker 0: PDF and can actually pull out that data discretely to
Speaker 0: help feed it into the platform as more discrete data
Speaker 0: that we can use for the providers can use or
Speaker 0: we could even use it like, say, our molecular tumor
Speaker 0: board, for instance.
Speaker 0: And so we're taking data that's not, you know, kind
Speaker 0: of individual discrete data and making it so.
Speaker 0: And this, of course, has to was validated and so
Speaker 0: forth with GEO.
Speaker 0: But we've just started this process within the last half
Speaker 0: a year.
Speaker 0: And so the goal of this and this is the
Speaker 0: third step we're on right now is they have a
Speaker 0: new kind of app called Precision Decision.
Speaker 0: And the idea of this is when an oncologist is
Speaker 0: in an epic environment and they have a patient and
Speaker 0: they have all this genomic data that's come in from
Speaker 0: our lab or other labs that's in this new platform,
Speaker 0: they can single sign on.
Speaker 0: That's the goal any way out to this precision decision
Speaker 0: to help them look at more trials based on the
Speaker 0: data that's in this epic environment, either clinical trials or
Speaker 0: therapies and so forth that are more than what we
Speaker 0: can just do with our annotations here from our own
Speaker 0: lab.
Speaker 0: And so it's going to be an easier way for
Speaker 0: oncologists to kind of figure it out and learn more
Speaker 0: or identify better ways of treating their patients.
Speaker 0: Ideally.
Speaker 0: We're kind of in the last stages of this with
Speaker 0: it's, of course, overwhelmed and they have a lot going
Speaker 0: on.
Speaker 0: So we're still working on this SSL authentication, but this
Speaker 0: is kind of where we are.
Speaker 0: And so we're one of the first institutions that are
Speaker 0: doing this with Joe.
Speaker 0: And I will say we're not the only institutions trying
Speaker 0: to tackle this problem.
Speaker 0: Everyone has this problem right now.
Speaker 0: And so this is one way we've tried to really
Speaker 0: tackle it.
Speaker 0: So I hopefully will work in our clinicians will really
Speaker 0: like it when it finally goes live.
Speaker 0: And again, the goal is to be able to either
Speaker 0: identify a match or no match for the patients and
Speaker 0: maybe eventually update markers, update scenarios, treatments and so forth
Speaker 0: down the road where, you know, maybe they didn't match
Speaker 0: at one point in time, but further down the road
Speaker 0: something will happen or something will come up, a new
Speaker 0: trial, a new biomarker that down the road they might
Speaker 0: match to something else.
Speaker 0: And so that's kind of, you know, identifying clinical trial
Speaker 0: cohorts, cohorts is something we hope to do more of
Speaker 0: with within the setting.
Speaker 0: But if everything's just trapped in like a PDF document,
Speaker 0: you can't get at that data.
Speaker 0: And so that's that's one of the real problems.
Speaker 0: So that's kind of our first initiative that's mostly in
Speaker 0: the cancer setting, and that's what we're working on in
Speaker 0: that setting, which is of course, cancer.
Speaker 0: One of the first area that there was really a
Speaker 0: lot of genetic information being used, and that's the biggest
Speaker 0: growth for therapies and trials.
Speaker 0: And we, of course, have a major cancer center here
Speaker 0: too, so.
Speaker 0: So.
Speaker 0: So I talked about cancer.
Speaker 0: But the other problem is, is it's not just about
Speaker 0: cancer.
Speaker 0: There is lots of genetic testing that happens at our
Speaker 0: hospital or not at our hospital that gets sent out
Speaker 0: from our hospital because we aren't doing a lot of
Speaker 0: inherited testing here.
Speaker 0: Like I said, we used to the Munro Micro lab
Speaker 0: used to do a lot of just testing like panel
Speaker 0: based testing for, say, cardiomyopathies or collagen vascular diseases or
Speaker 0: hereditary cancer syndromes or what have you.
Speaker 0: But that got very expensive.
Speaker 0: And so there's a lot of companies that are doing
Speaker 0: that now.
Speaker 0: And so it's getting sent out.
Speaker 0: So one of the problems is that you have all
Speaker 0: these outside reports and like, where do they go and
Speaker 0: how do people identify these reports when they get sent
Speaker 0: out from these various places?
Speaker 0: So for those of you who use Epic and you're
Speaker 0: probably familiar with the Scary Media tab where I like
Speaker 0: to think of the scan, all scanned documents go to
Speaker 0: be sources of frustration to all the clinicians because everyone
Speaker 0: complains about this tab, but it's just kind of a
Speaker 0: tab where you don't have any internal data and you're
Speaker 0: looking at all that external external data from outside the
Speaker 0: lab values imaging.
Speaker 0: Maybe clinic notes or whatever from the outside.
Speaker 0: This is where it all goes, and it's all just
Speaker 0: scanned documents that come in through our hospital information system
Speaker 0: and they get scanned in the media tab, and it's
Speaker 0: really hard sometimes to find things.
Speaker 0: So there are multiple document types, you know, clinic notes,
Speaker 0: imaging lab, outside records, all these different document types.
Speaker 0: And sometimes they can scan in like 30 pages.
Speaker 0: And so what you're looking for is hidden in that
Speaker 0: scan of 30 pages.
Speaker 0: And you don't really you don't know to look for
Speaker 0: it there because it's hidden in all this other stuff.
Speaker 0: And they're not always adequately labeled or scanned individually.
Speaker 0: And genetic reports are really hard to find.
Speaker 0: So let's say someone sends out, you know, a test
Speaker 0: to another company, and that comes back to one of
Speaker 0: our providers for like a cardiomyopathy, you know, where does
Speaker 0: that report go?
Speaker 0: And usually something like that might get scanned in of
Speaker 0: media tab or, you know, a patient's coming from another
Speaker 0: hospital.
Speaker 0: They might have that that will get scanned in the
Speaker 0: media tab.
Speaker 0: And so what we've done is, as we've created, I
Speaker 0: worked with it to create a genetics document type.
Speaker 0: So anytime there's a report from the outside, that's a
Speaker 0: genetic report.
Speaker 0: It's supposed to get labeled as a genetic report when
Speaker 0: it sent to our hospital information system.
Speaker 0: And then when it gets scanned in and actually Monroe
Speaker 0: Meyer will scan is directly out of the media tab
Speaker 0: as a scan and report.
Speaker 0: It gets labeled as a genetics document type so that
Speaker 0: you can actually identify those genetic reports within the media
Speaker 0: tab instead of trying to go through lines and lines
Speaker 0: of other things.
Speaker 0: So it should make it more easily identifiable in the
Speaker 0: EMR.
Speaker 0: And so, again, this is not just for cancer.
Speaker 0: This is for any inherited disease report.
Speaker 0: It is really dependent on people recognizing that these are
Speaker 0: genetic reports.
Speaker 0: So these are the hospital information management or medical records.
Speaker 0: They understand they're supposed to do this, but they may
Speaker 0: not understand it's a genetic report or clinical recognition of
Speaker 0: that.
Speaker 0: And it's still not totally ideal because that data is
Speaker 0: still trapped in like in a scanned document, Like you
Speaker 0: can't pass that data out to be used in a
Speaker 0: certain setting.
Speaker 0: So if like like a Epileptologist wants to know if
Speaker 0: there's a certain variant in some gene that can cause
Speaker 0: epilepsy in kids.
Speaker 0: There's really no way of identifying those within these scanned
Speaker 0: documents in the media tab.
Speaker 0: So it's it's really not ideal, but that's kind of
Speaker 0: the setting we're doing in these bubbles I just described
Speaker 0: right now.
Speaker 0: So really, it would be great if we could have
Speaker 0: a way of better getting genetic data cancer or inherited
Speaker 0: or whatever into the epic EMR environment that allows for
Speaker 0: patients to identify and easily make it user friendly and
Speaker 0: also could be used down the road for some sort
Speaker 0: of research purpose or public health intervention, you know, finding
Speaker 0: cohorts of patients for whatever your purposes are.
Speaker 0: Oh, and this is just a picture of what the
Speaker 0: media tab looks like and our new genetic doc type.
Speaker 0: So if anyone is looking for genetic reports in the
Speaker 0: media tab, you can sort on this document type and
Speaker 0: it should come up with genetic results.
Speaker 0: It's not perfect.
Speaker 0: It's not retroactive or retrospective.
Speaker 0: It's only starting from like this last fall.
Speaker 0: So you might still have to search, but hopefully down
Speaker 0: the road it will make it a little bit easier.
Speaker 0: So that's where the last initiative comes in, which is
Speaker 0: called the Epic Genomics Module.
Speaker 0: So this is something that we are starting to look
Speaker 0: at here, potentially bringing in within the epic system that
Speaker 0: I think will help solve some of some of these
Speaker 0: problems, hopefully if it if we officially bring it in.
Speaker 0: And I will say these slides are from mainly Epic
Speaker 0: and they told me I could use them for this
Speaker 0: presentation just so you are aware.
Speaker 0: So the idea is, you know, and I've kind of
Speaker 0: already talked about this.
Speaker 0: You have laboratory testing.
Speaker 0: It's really good if you can get discrete variants or
Speaker 0: genetic information into the epic system to be more easily
Speaker 0: usable.
Speaker 0: So they've developed this platform.
Speaker 0: So I mentioned there's in-house testing.
Speaker 0: And right now, a lot of that's going.
Speaker 0: Some of it's going in discreetly, but the cancer says
Speaker 0: mainly in PDF form.
Speaker 0: But then a lot of stuff is being sent out
Speaker 0: to other labs.
Speaker 0: And so what Epic has done is they've started working
Speaker 0: with some of these big labs who do a lot
Speaker 0: of genetic testing.
Speaker 0: So like Foundation and Tempus do a lot of cancer
Speaker 0: testing, like somatic testing.
Speaker 0: Myriad exact.
Speaker 0: And BTI is a new company.
Speaker 0: I can tell you that because that was on one
Speaker 0: of their side, they just didn't put it in is
Speaker 0: another big germline testing company.
Speaker 0: They have are all starting to work with EPIC to
Speaker 0: build interfaces to get the testing done at that company.
Speaker 0: That data can be brought in discretely into the epic
Speaker 0: environment.
Speaker 0: Which would be really helpful.
Speaker 0: And I'll show you kind of what some of these
Speaker 0: again, these are from Abbott, these slides.
Speaker 0: Some of this looks like.
Speaker 0: So, for instance, this is obviously a fake patient, a
Speaker 0: patient who's had their testing done somewhere.
Speaker 0: And if you look in the lab tab, it comes
Speaker 0: in as it almost looks like it was done here.
Speaker 0: It gives you a genetic sequencing and it gives you
Speaker 0: the data in a more discreet format.
Speaker 0: And so this all becomes discrete data that can be
Speaker 0: used within like the Slicer dicer synch system we'll talk
Speaker 0: about a little bit at the end so that you
Speaker 0: have that searchable if you ever needed to be able
Speaker 0: to search it.
Speaker 0: So it can tell you if it's the germline or
Speaker 0: the somatic mutation.
Speaker 0: In this case, this patient had a rock, a mutation
Speaker 0: which is a cancer predisposition to breast cancer, but it's
Speaker 0: not a PDF anymore.
Speaker 0: This is actual like all of our other lab tests
Speaker 0: within the epic environment.
Speaker 0: And then you can actually set like criteria and rules
Speaker 0: and alerts.
Speaker 0: So like in the case of this patient, if they
Speaker 0: had a blocking mutation, an alert might pop up that
Speaker 0: this is a candidate for genetic counseling, that they have
Speaker 0: abnormal germline results and consider that as a genetic referral.
Speaker 0: So you can set various alerts for different whatever you
Speaker 0: decide you want for various genetic markers.
Speaker 0: You can also group markers based on like maybe a
Speaker 0: biomarker group.
Speaker 0: So, for instance, immunotherapies like pembrolizumab, these are things that
Speaker 0: they treat cancers with to try and allow the immune
Speaker 0: system to better attack the tumor.
Speaker 0: There are different biomarkers like tumor mutation burden, microsatellite instability,
Speaker 0: mismatch, repair status, PD-L1 expression.
Speaker 0: Each of these things can help determine whether a person
Speaker 0: might respond to that drug or not.
Speaker 0: So if you have them all in a panel here,
Speaker 0: you can kind of get an overview of like, well,
Speaker 0: my patient respond to this drug.
Speaker 0: Well, well, or will they not?
Speaker 0: So you could group based on biomarkers if that that
Speaker 0: were helpful or maybe in breast cancer hormone therapy biomarkers
Speaker 0: those sorts of things.
Speaker 0: You can more easily search the chart for genomic results,
Speaker 0: which would be great for the clinicians in your town
Speaker 0: or pathologist when you're trying to identify this patient as
Speaker 0: they have.
Speaker 0: They had the testing and what was identified.
Speaker 0: And so it can give you various genomic indicators and
Speaker 0: that they might be associated with, say, in this case,
Speaker 0: webcam deletion would be associated or mutation would be associated
Speaker 0: potentially with Lynch syndrome.
Speaker 0: And then if you had a rapport with both germline
Speaker 0: and somatic mutations, again, breakdown of all the different information
Speaker 0: in a more discreet manner.
Speaker 0: And then the other thing that is really nice is
Speaker 0: if you know, a patient is being seen here and
Speaker 0: they were seen in another epic institution with an EMR
Speaker 0: that's an epic, they have this happy together genomics where
Speaker 0: we could theoretically go pull in their results or identify
Speaker 0: their results that they had ordered some other institution and
Speaker 0: review that in our setting.
Speaker 0: And so I think there's a lot more potential here
Speaker 0: down the road, maybe not just clinically speaking, but who
Speaker 0: knows in the research setting as well.
Speaker 0: But that would be really nice.
Speaker 0: It would.
Speaker 0: It's only a, you know, limited to epic institutions and
Speaker 0: that everyone has epic at their as their EMR.
Speaker 0: But lots of people do or lots of institutions do
Speaker 0: like children's has epic, for instance and so it could
Speaker 0: be very useful in that setting also.
Speaker 0: And then, you know, the goal down the road would
Speaker 0: be not only just be able to see the variants
Speaker 0: and have the data there discreetly, but, you know, use
Speaker 0: it for patient education.
Speaker 0: You can you can build in best practices practice advisories
Speaker 0: for clinicians.
Speaker 0: So you say for former genome pharmaco genomic markers, you
Speaker 0: know, this patient has, you know, some sort of a
Speaker 0: clopidogrel variant that makes them respond less to clopidogrel so
Speaker 0: they might have a higher risk of acute coronary syndrome.
Speaker 0: Therefore, maybe you want to give them more drug or
Speaker 0: something like that.
Speaker 0: So you can have advisories, you could have education for
Speaker 0: your providers based on biomarkers.
Speaker 0: Registries could be built and so forth.
Speaker 0: So there's a lot more power to this if you
Speaker 0: have lots more genomic data coming in and a discreet
Speaker 0: way.
Speaker 0: And then I mentioned slicer dicer.
Speaker 0: That's a way of kind of cutting through all the
Speaker 0: data in Epic on patients.
Speaker 0: And so in this case, it's kind of hard to
Speaker 0: see.
Speaker 0: But trying to look for, you know, patients who have
Speaker 0: lung cancer and have an EGFR mutation and maybe have
Speaker 0: been treated with a certain drug, trying to figure out,
Speaker 0: you know, how are they responding.
Speaker 0: You can group and look for that data, you know,
Speaker 0: for so many days out in your epic environment and
Speaker 0: find those patients if you wanted to start a clinical
Speaker 0: trial.
Speaker 0: It can also alert you if a patient might be
Speaker 0: eligible for a trial if you set those alerts up.
Speaker 0: For instance, this patient meets the initial screening criteria for
Speaker 0: a study of patients with locally advanced or metastatic tumors
Speaker 0: with Bracco mutation.
Speaker 0: You know, do you want to respond to the study
Speaker 0: or not?
Speaker 0: So you can build all these different options in And
Speaker 0: I think some of this is still kind of in
Speaker 0: the building phase, but these are all things that are
Speaker 0: going to hopefully be possible with this module should we
Speaker 0: choose to incorporate it here.
Speaker 0: And it would solve a lot of problems.
Speaker 0: So that initiative is not definitively happening yet, but hopefully
Speaker 0: in the future will be something we can bring in
Speaker 0: that would help us out with, again, the problem of
Speaker 0: making data more easily usable here.
Speaker 0: So just some future thoughts in the last few minutes.
Speaker 0: So I think personally that academic medical centers will be
Speaker 0: sequencing the exams of all consenting patients within the next
Speaker 0: 5 to 10 years.
Speaker 0: And I think also that cancer at the large institutions
Speaker 0: where we have cancer centers, all the cancers will be
Speaker 0: comprehensively profiled here.
Speaker 0: I think that's happening quite a bit anyway, at least
Speaker 0: in the metastatic setting.
Speaker 0: And this may be done in-house, or it may be
Speaker 0: that an outside labs, and I think that depends on
Speaker 0: the facility, but I think that's going to be happening.
Speaker 0: And I think we need to keep working on finding
Speaker 0: ways to handle all this data within our EMR.
Speaker 0: Because when I've kind of gone through for decision support,
Speaker 0: preventative medicine, familial risk, clinical trial and all manage to
Speaker 0: put all those things in here, research.
Speaker 0: And if we can get shared data among centers, of
Speaker 0: course that's going to be even more powerful.
Speaker 0: But at least among institutions, maybe with Epic.
Speaker 0: So this is what I you know, I think this
Speaker 0: is what we have to think about long term and
Speaker 0: in terms of data in a clinical setting.
Speaker 0: And I also will just put a plug in.
Speaker 0: We have a precision medicine initiative here that I started
Speaker 0: about a year and a half ago with Hal Thurmont,
Speaker 0: who's kind of my hospital partner.
Speaker 0: And we have a group of people from kind of
Speaker 0: all different backgrounds, you know, cancer, cardiology, neurology and so
Speaker 0: forth.
Speaker 0: And one of the big low hanging fruit that have
Speaker 0: come out of this group are two of these initiatives,
Speaker 0: the media tab.
Speaker 0: And then looking at this Epic Genomics, because the clinicians
Speaker 0: were all like, we can't find genetic data, we don't
Speaker 0: know how to use genetic data, we can't cohort data
Speaker 0: on patients to do studies.
Speaker 0: We need to make it easier to find genetic data
Speaker 0: and use it in our system.
Speaker 0: And so this initiative that's been kind of one of
Speaker 0: our first things that we've done and we hope to
Speaker 0: have other things that we can do in the future.
Speaker 0: And if anyone has ideas, please send them to me
Speaker 0: or our thoughts.
Speaker 0: But there is this initiative here that we have.
Speaker 0: So this has been one of the positives that's come
Speaker 0: out of that, I think.
Speaker 0: So in conclusion, genomic data, of course, is a powerful
Speaker 0: tool.
Speaker 0: I think.
Speaker 0: I don't have to convince you all of that, but
Speaker 0: it's a lot of data, and data can come from
Speaker 0: all different places.
Speaker 0: In house outside labs, patients can bring it in.
Speaker 0: Different tests and platforms are being used.
Speaker 0: It's complex data.
Speaker 0: If it's coming from sequencers.
Speaker 0: Current laboratory information systems in our EMR have a hard
Speaker 0: time handling this data.
Speaker 0: And so I think people have seen a problem and
Speaker 0: everyone's trying to solve this problem.
Speaker 0: We need to find a way to integrate it discretely
Speaker 0: so we can have improved efficiency for our clinicians, more
Speaker 0: easily accessible data for research.
Speaker 0: And then more important are most importantly, improved patient care,
Speaker 0: like now and in the future.
Speaker 0: And so there's more to come.
Speaker 0: But that's all I have, and I'm happy to answer
Speaker 0: questions as best I can.
Speaker 0: The group.
Speaker 1: Thank you very much for presenting to us.
Speaker 1: That was fascinating.
Speaker 1: Anybody who wants to put questions in the chat and
Speaker 1: who doesn't want to unmute their mike, please do so
Speaker 1: I'm happy to read them.
Speaker 1: Anyone else?
Speaker 1: Please feel free to join in.
Speaker 1: I unmuted.
Speaker 1: Hi, Alison.
Speaker 1: Wonderful talk this hour.
Speaker 0: Thank you.
Speaker 0: Hi.
Speaker 0: How are.
Speaker 1: I?
Speaker 1: I think certainly, you know, I agree with you that
Speaker 1: within five years we'll be sequencing a lot.
Speaker 1: And, you know, given the rapid pace of cost reduction
Speaker 1: and information and maybe even sooner.
Speaker 1: So is there a working group or anything as part
Speaker 1: of the Precision Medicine Initiative to plan for this?
Speaker 1: Or are we hoping, you know, the commercial providers, the
Speaker 1: Lumina or somebody will provide a way to deal with
Speaker 1: all this data in a format that can be analyzed?
Speaker 1: All right.
Speaker 1: So instead of these panels that you mentioned, exome sequencing
Speaker 1: or even whole genome sequencing.
Speaker 0: Yeah.
Speaker 0: So, yes, we have been entertaining ideas and thoughts and
Speaker 0: I'm many on the many on the group are pushing
Speaker 0: like this is something we have to be doing.
Speaker 0: It's it's right now it's about the cost, especially with
Speaker 0: everything else.
Speaker 1: But again, if it's not the money, it is the
Speaker 1: money.
Speaker 0: It is money.
Speaker 0: I mean, you know, Illumina just launched a new sequencer
Speaker 0: that's like $1.25 million, but it's supposed to bring down
Speaker 0: the cost of an exon to, I want to say,
Speaker 0: like, less than $200, which is.
Speaker 0: But then you have the whole, like, cost of like
Speaker 0: annotating, you know.
Speaker 1: Yeah.
Speaker 1: The cost comes out through the data.
Speaker 0: Yeah.
Speaker 0: You need big groups of people because you're not just
Speaker 0: going to put in these variants for patients who you
Speaker 0: know.
Speaker 0: And so that's where the other big cost is.
Speaker 0: But yes, there there's a lot of discussion about how,
Speaker 0: you know, personally it would be nice to be doing
Speaker 0: it here if we could, but that's a big ask.
Speaker 0: And it's hard to find people to work in the
Speaker 0: labs in the clinical setting.
Speaker 1: But in the interim, that patients are even harder or
Speaker 1: as hard, you know, getting those people that are skilled
Speaker 1: in the analysis.
Speaker 0: And I think partnering with maybe a company down the
Speaker 0: road would be a to do the actual sequencing in
Speaker 0: part at least.
Speaker 1: Right.
Speaker 0: Now.
Speaker 0: There's lots of different models.
Speaker 0: But yes, it's on the radar for sure.
Speaker 1: Now, you mentioned the Big Ten collaborations we've done cancer.
Speaker 1: Is there anything there?
Speaker 1: I mean, you know, because there's a group that'd be
Speaker 1: pretty powerful.
Speaker 0: I don't know.
Speaker 0: I mean, I'm all I can keep up with the
Speaker 0: cancer.
Speaker 0: There's like, the cancer consortium know, but I don't know
Speaker 0: if there's other consortia that are not cancer among Big
Speaker 0: Ten.
Speaker 1: Right.
Speaker 0: Maybe.
Speaker 0: You know, I don't I don't know.
Speaker 0: I only see the.
Speaker 1: I'm interested in looking into it because the better we're
Speaker 1: prepared, the more exciting.
Speaker 1: Wonderful talk.
Speaker 0: Thank you.
Speaker 0: Thanks.
Speaker 1: Allison.
Speaker 1: John Wendell Harris, thank you very much.
Speaker 1: One of the things as you were presenting that I
Speaker 1: was thinking about is we have been working with what's
Speaker 1: called a fire accelerator called Codex.
Speaker 1: And I know Scott Campbell has been doing some work
Speaker 1: with them, but they specifically have two areas that I
Speaker 1: think might be worth looking at.
Speaker 1: So they have both an oncology accelerator as well as
Speaker 1: a genomics accelerator.
Speaker 1: And the reason I think about it is from this
Speaker 1: group, the Center for Intelligent Health Care is actually a
Speaker 1: governmental member of that organization.
Speaker 1: And so we have access to all the work they're
Speaker 1: doing, and I think they're doing some interesting initiatives that
Speaker 1: that.
Speaker 1: Would really help bring the digital data forward so that
Speaker 1: we move away because that's one of our fundamental principles
Speaker 1: is moving from documents to data.
Speaker 1: And so Melissa and I can do a little bit
Speaker 1: of exploration, and if it's okay, we might have some
Speaker 1: people from Codex give you a call and kind of
Speaker 1: talk about what they're doing because the University of Nebraska
Speaker 1: is a member.
Speaker 1: So there's no cost to Howard or you or anybody
Speaker 1: else except your time in participating.
Speaker 0: Okay.
Speaker 0: No, that.
Speaker 0: Yeah.
Speaker 0: And I can ask.
Speaker 0: Forgot about it too.
Speaker 0: Yeah.
Speaker 0: But now that.
Speaker 0: Yeah, I'll.
Speaker 0: I'll look into that.
Speaker 0: Thank you.
Speaker 1: Alisyn.
Speaker 1: This was Chris while on I am a pediatrician in
Speaker 1: the department of Pedes.
Speaker 1: Do you know what the output is from these machines?
Speaker 1: Do you know the the code that comes out from
Speaker 1: the back end of these machines?
Speaker 1: And have you thought about rather than integrating into Epic,
Speaker 1: bring your epic data into a neutral server and do
Speaker 1: the joins there.
Speaker 0: Well, So the data that comes out, the sequence, there's
Speaker 0: there's about four or five steps there, like the raw
Speaker 0: data from like the chemical or whatever fluorescent data or
Speaker 0: whatever you want to call it.
Speaker 0: Again, it gets slowly narrowed down to what's called a
Speaker 0: band, which is an alignment where you're aligning to like
Speaker 0: a reference genome.
Speaker 0: And then the very call file is where you actually
Speaker 0: call the variant, at least for the anger sequencers.
Speaker 0: And so it's the those bam and the vocal files
Speaker 0: that get fed into like genome oncology, for instance.
Speaker 0: You know, Epic has, you know, know how many of
Speaker 0: you've heard of Beaker, which is kind of their laboratory
Speaker 0: system that I think we're looking at both from the
Speaker 0: anatomic and clinical path side.
Speaker 0: And I don't know enough about it to know whether
Speaker 0: or not it could ingest those file formats.
Speaker 0: I'm assuming it could get that more discrete with an
Speaker 0: epic also.
Speaker 0: But that's a whole large undertaking that we we aren't
Speaker 0: there yet.
Speaker 0: So I think there are ways to take I mean,
Speaker 0: you can take this data, there's all these different companies,
Speaker 0: just take these two file formats and do what they
Speaker 0: want.
Speaker 0: You know, you can do a lot with them.
Speaker 0: I think the opposite direction, I think, is what you're
Speaker 0: asking would be really hard, especially with Epic.
Speaker 0: We wouldn't be able to handle that data.
Speaker 0: I think it's probably easier going into Epic versus the
Speaker 0: other direction.
Speaker 0: So.
Speaker 0: But I think I think eventually we will probably have
Speaker 0: the V.A. system and.
Speaker 0: That will that will kind of probably change how things
Speaker 0: are done down the road.
Speaker 0: And but you don't need that for this epic genomics
Speaker 0: module.
Speaker 0: We did ask that questions.
Speaker 1: So it's me again, just thinking DEA and the like
Speaker 1: you mentioned reference genomes.
Speaker 1: How universal are those for the different patient populations and
Speaker 1: derivations that we have in our area in the West?
Speaker 0: Not as well.
Speaker 0: People are working a lot on trying to make them
Speaker 0: more diverse and it's getting better.
Speaker 0: Even I know this because, you know, we'll get variance
Speaker 0: and they look like a rare variant, but then you'll
Speaker 0: go into it and you'll say, well, you know, it's
Speaker 0: in a certain population of people with that.
Speaker 0: And that population is probably a normal polymorphism, but because
Speaker 0: it dilute it out with all the other races and
Speaker 0: ethnicities, it looks like a rare variant.
Speaker 0: So I think the people or the whatever databases out
Speaker 0: there have worked really hard and I h and getting
Speaker 0: more reference genomes from various populations there.
Speaker 0: So it's getting better, but it's not perfect.
Speaker 0: And it's yeah, it's a major focus I think for
Speaker 0: Yeah.
Speaker 1: And just yeah.
Speaker 1: First of all for Yeah.
Speaker 1: But also as you mentioned, oh, here's a variant, what
Speaker 1: is it turns out to be normal in a population.
Speaker 1: Yeah.
Speaker 1: I guess that part of a thousand genomes but yeah.
Speaker 0: So I don't even know how many genome director now
Speaker 0: but, but yeah, it's important because you know, we might
Speaker 0: call something a view us and a patient which isn't
Speaker 0: fair to that patient just because they're underrepresented race when
Speaker 0: really it means nothing, but then they have to worry
Speaker 0: about it or their clinician has to worry about it.
Speaker 0: Whereas if it were a variant in the Caucasian person
Speaker 0: and that 10% of the population would be have been.
Speaker 0: I mean, this is a real problem.
Speaker 1: Yes.
Speaker 1: Mm hmm.
Speaker 1: Now.
Speaker 1: Well, again, Alison, thank you very much.
Speaker 0: Thank you.
Speaker 0: Let me know if you have questions.
Speaker 0: Give me a email, holler what have you.
Speaker 1: Perfect.
Speaker 0: All right.
Speaker 0: And, John, we can talk later.
Speaker 1: Thank you.
Speaker 0: Okay.
Speaker 0: Bye.
Speaker 1: Get off your program.
Speaker 0: Okay.
Speaker 0: Sounds good.
Speaker 0: Thanks.
Speaker 0: Bye bye.