Demystifying Genetics
This is a podcast series called Demystifying Genetics where I, Dr Matt Burgess, Genetic Counsellor interview other genetic counsellors, people working in genetics or people affected by genetic conditions. We chat about human clinical genetics, genetic counselling, ethics, pyschosocial issues and more. To contact me, please reach out at matt at insightgenomica dot au
Sponsored by TrakGene
www.trakgene.com
Demystifying Genetics
Demystifying Genetics with Erica Spaeth
This episode dives into polygenic risk testing, exploring how genetic data can inform healthcare strategies. We discuss the complexities of communicating risk, the role of polygenic scores in identifying disease susceptibility, and the evolving landscape of personalized medicine.
• Introduction to polygenic risk testing and its significance
• Insights from Dr. Erica Spaeth on cancer biology and GeneType
• Engaging analogies to explain complex genetic concepts
• The challenges clinicians face when interpreting genetic data
• The accuracy and reliability of polygenic risk tests
• Discussion of economic impacts and public health implications
• Exploration of how polygenic risk informs prevention strategies
• The evolution of personalized medicine in genetics
• Future prospects for polygenic risk testing in clinical settings
Demystifying Genetics is sponsored by TrakGene
https://www.trakgene.com/
Hello and welcome to Demystifying Genetics.
Matt Burgess:I am your host, matt Burgess. I am a genetic counsellor in Melbourne, australia, and I recently finished a Doctor of Health Science program, where my research focused on gene therapy. My guest today is Dr Erica Spaeth. Erica has a PhD in cancer biology but currently works for a company called GeneType. Genetype is a polygenic risk test. One of the big areas of medicine at the moment is polygenic risk testing, and its uses and implications are only going to increase. In this podcast, erica and I discuss polygenic risk scores and testing. Enjoy, hello, erica, and welcome to the podcast.
Erica Spaeth:Thank you for having me.
Matt Burgess:Yeah, no, it was so good to sort of organize a time to meet with you. Now you have something very interesting on your LinkedIn and sorry, I should have been a bit more organized and opened it up before we started. But in your about section I love it At the end you say what your current clinical priority is and then you say what your current scientific obsession is and I was like, oh, like, oh wow, I love it so just to read from your profile, you say that your current clinical priority is making cancer risk reduction a priority in healthcare by identifying at-risk patients when they are still healthy and asymptomatic.
Matt Burgess:so I think a lot of us have that as a clinical priority. But then, as your current scientific obsession, you say integrating polygenic risk with other epidemiological factors and biomarkers to predict disease.
Erica Spaeth:Wow, yes, there's a lot. There isn't there yeah.
Matt Burgess:So I mean today I wanted to have a really good talk about polygenic risk score, so maybe we could start there. I know that you're involved with a company called GeneType that has a polygenic risk test on the market. Maybe first things first, when you're talking to people or explaining sort of polygenic risk like how do you explain it to a lay person?
Erica Spaeth:or do you ever have to explain? I do, I do so. Part of my job is communicating both to physicians, who've never heard of polygenic risk, as well as patients in the area, and so I've tried. I actually have quite a few different analogies I use. The one I've been using most recently is using a bag having people picture a bag of uncooked rice bag having people picture a bag of uncooked rice.
Erica Spaeth:So you spill one grain of rice on the floor. It's really hard to see, it's nearly impossible to find and it's just a cleaning nightmare until you accidentally step on it. And that's how you find it. And so that's how I sort of suggest the risk and polygenic risk. Looking at one single snip equates to that tiny grain of rice that is barely visible on the floor. But if I pour an entire bag of rice on the floor, suddenly I see a whole pile and it's not so hard to clean up, and that's sort of the combination of these snips in the polygenic risk. That can actually amount to something a little more quote-unquote, visible, if you will. So I think that's one of the most recent explanations I've been using. I've heard another scientist use a deck of cards in a poker game as an explanation. So everybody has their own hand, but every hand is a little bit different, and I like that one as well. Ah, yeah.
Matt Burgess:Okay, I think as a genetic counselor in our training, a big part of our training is communication and how do we take complex scientific information and explain it in a way that makes sense? And yeah, I remember some of the lectures that I had with my fellow students and we were sort of talking about you know our genetic makeup. It's like you know blueprints to a house or you know like all of these sort of different analogies. I think in in clinic for me when I'm sort of trying to explain polygenic risk, I compare it to monogenic, um sort of genetic like. I think that even a lay person has like a good understanding of sort of you know genetic testing up until now it's sort of been monogenic, where you know we all have thousands of genes and we look at one gene and one big mistake that confers one big risk. But then polygenic risk it's sort of the opposite. You know like they're little like changes that by themselves probably don't sort of do too much, but it's when you sort of accumulate them together that you know something is significant.
Erica Spaeth:Exactly. And you know, building off of that, another comparison I've made kind of builds on that house analogy you've talked about and uses the monogenic. So I do have people picture a house and I might talk about BRCA1, some pathogenic variant, and I talk about a door as a BRCA1 variant. So everybody has two copies BRCA1, we say a front door and a back door and in normal people they function just right. In a pathogenic variant carrier maybe the front door looks identical but it's missing the doorknob. So the front door doesn't work, it doesn't function the way it should, but luckily they have a back door. So it's not a big deal initially.
Erica Spaeth:And then the polygenic risk. So that's sort of the monogenic piece of the house and the polygenic piece of the house, and the polygenic is more like a nick of paint on the house, maybe a roof tile missing a little, you know a rusty side corner of the house. So all these little issues on the house that aren't really visible. But over time and as you know, it rains and the sun comes down on your house and all these environmental factors start building up over time. If you don't maintain your house it could fall apart just from all these tiny little issues that you don't notice at the beginning.
Matt Burgess:So I tried to use that one.
Erica Spaeth:It's a little more complicated, takes a little more time to walk through.
Matt Burgess:I mean obviously with polygenic risk testing. There's a lot of research that's been going on. Have you been involved in any studies that looks at sort of communication of risk? Or you know the language or that sort of side of things?
Erica Spaeth:No, you know, I haven't been involved in any studies. Some of our collaborators that we're working with have spent a lot of time focusing on the communication of risk, but I haven't specifically focused on any studies that we've published. Now, from the commercial perspective, the company I work for does have clinical reports that we provide to both clinicians, and those reports are shared with the patients, and so there is a patient section. So, while I haven't been involved in studies, we've taken some evidence from quite a few of these studies that have been published and tried to combine them together into the reports that we've created, and then we take clinician feedback and patient feedback and try to modify the report. So our reports are somewhat changing in real time, with certain feedback that we get from clinicians who say you know, I really like this, but this point was really hard to convey to my patient, and so it's nice to get real time data from from users of a, of a test.
Matt Burgess:I just think that that is something really interesting in genetics.
Matt Burgess:You know, like genetics is obviously a part of medicine, part of pathology, and like, if you think of like another genetic test or another test like I know iron levels or cholesterol levels, you know the pathologist sort of just reports it and you know, maybe they'll add a comment saying you know, here are the guidelines, you can refer to that.
Matt Burgess:But you know, most of these sort of simple or straightforward tests that we see in pathology are pretty short, you know, like they fit onto one page. But then it's kind of like with genetic testing it's so complicated, it's like a real philosophical question, like, is the point of the test just to report the result? Or you know, is there responsibility from the lab to sort of explain what the test means? Or you know, and then I know, that there are studies where we just know that a lot of these clinicians do not look past the first page. So you know, you make this excellent report that is many pages long and it's very easy to follow and you put all of the information in there and you know it's well referenced. But you can't just put all of that on one page. Is that sort of like the conversations that you're having at GeneType. Is that what you mean?
Erica Spaeth:That's certainly one of them. So we do try to put the most important information for exactly those clinicians who don't want to flip through the rest of the pages try to put that up front. But I think what's more interesting is that clinicians have very different perspectives, so the feedback you get from one clinician will be very different and almost opposite to the next, so you can't please everybody. So, no matter how many studies and how much feedback you get, I have yet to see clinicians be on the same page in terms of what they would like and how they do communicate, because everybody has a different style to their communication and the way they want to engage with their patients on sort of the decision-making process that results from a test that might have polygenic risk, integrated or maybe even standing alone.
Matt Burgess:Yeah, it's interesting, like I don't know. As a genetic counseling student, I thought that a test result was gold. It was set in stone it was. You know you could not change it at all. Like this was just, it was a result that you just had to work with and then sort of in time, it's like I've seen people contact the lab and say I don't like this sentence, like reword the sentence and reissue the report. Or it's just funny to realize that, oh, okay, you know they are written by people and you know maybe there's some style or maybe there's a grammatical error or you know like, yeah, it's just interesting. It was an interesting observation for me to learn that yeah, it was kind of the opposite, it wasn't set in stone. And you know, in my sort of experience I've seen doctors kind of contradict each other, like they're kind of like you know, there wasn't enough information on here, and then you put all of the information and then they say oh, it was too long, and it's like well, how do you find that balance?
Erica Spaeth:yeah, oh, it's so difficult. And you know laboratories. From our accreditation standpoint as a clinical lab, there are a handful of very specific guidelines that have to be followed. So certain information has to go on the report. But there's quite a bit of flexibility within those guidelines of what needs to go on the report. So to your point, you know, after getting feedback, a lab might come back and say you know, we've gotten a lot of feedback here. We need to update this language and see if we can communicate this better to get across the same fact in a different manner.
Matt Burgess:Okay, so you work sort of closely with different genetic counsellors and sort of giving or talking about your test. Do you find that genetic counsellors sort of are all the same in their response to the polygenic risk, or do you find that you know some interpret it one way or some the other? Or what's your sort of experience being working with genetic counselors?
Erica Spaeth:Yeah, I think there are definitely some genetic counselors that are more comfortable and more open to the idea of polygenic risk. But I think it really stems from where their expertise and where their specialty is. Because you have genetic counselors that have trained in a certain area, they might be at a cancer hospital or in a pediatric ward. So depending on where they are, that really changes the patient population that they're dealing with. And polygenic risk is really a genetic component. That's an add-on, it's not, you know. So sorry, I'll step back.
Erica Spaeth:Monogenic disease is sort of straightforward, right. It's sort of a yes or no and it causes a disease and you sort of have an action plan Uh-huh, yes, exactly yes. There are always those caveats, but that's in the, in the perfect world. That's the very simple yes or no, straightforward black and white. And and then of course, we all know disease spectrums have this huge variability and that's where the polygenic risk comes in. And so if you have a genetic counselor that's used to dealing with such wide ranging variability, they really embrace the polygenic risk, because it almost helps explain some of that variability better than anything else has previously, previously, and so that's a really beneficial component. But then if a genetic counselor is in a certain setting where they don't need that variability. It's it's just, uh, it's outside of their comfort zone at that moment. So it's a different type of conversation yeah I know,
Matt Burgess:sort of like I reflect on my years as a genetic counsellor and I studied, you know, two decades ago now and, um, my course was a one-year graduate course that didn't have a strong research component at all. Um, it was very practical. I really feel like I almost did like an apprenticeship. It wasn't really. It didn't feel like a like a university degree, like we were seeing patients from the very start and you know, I think there were positives and negatives of that. But one of the things that I feel like you know the positive side was, as soon as I finished my course, I felt ready to go into clinic. I knew how to meet with patients and I felt like I was, you know, sort of a competent, new, newly graduated genetic counsellor.
Matt Burgess:But one of the sort of disadvantages, I think, was I really lacked that sort of exposure to research, lacked that sort of exposure to research and sort of throughout my years, uh, you know I I've worked clinically but then sort of amongst that there's been different sort of research components and one thing that I really liked has been, um, being involved with clinical trials and, I guess, with polygenic risk scores. I've really seen that it was something that was very much research, or you know this is we can enroll you into a research study and then slowly over time, now in Australia and and in America, you can get a commercial polygenic risk test. Could you sort of talk about that process of, like you know, seeing a test to go from purely research into the clinical realm?
Erica Spaeth:Yeah, yeah, it's a really exciting space to be in to watch something go from the academic side. You do all this great research and then it just sits there unless a company comes in and picks it up. And pushing it into the real world is a lot harder. So I think that apologetic risk has certainly exploded over the last what 14 years or so. I think that one of the diseases that has quite a bit of data behind it is breast cancer.
Erica Spaeth:Whole genome sequencing and looking at larger arrays in big populations of adults with some kind of disease and we can talk about breast cancer, so adults with breast cancer compared to adults without breast cancer they were able to pick out these SNPs that were associated with those women who were developing breast cancer compared to those women who were not developing breast cancer, and that was a really exciting step and it's only possible with large data.
Erica Spaeth:The difference is, I mean, scientists have been doing this type of sort of epidemiological work for a very long time, but they've been doing it with basic risk factors like BMI or family history, so very visible, simple risk factors and having access to genetic data now in these groups enabled us to pick out these little SNPs that might actually have an association with disease, and so it's been really exciting to sort of follow that and the more cohorts that existed out there in smaller case control populations, where researchers were able to collect genetic information and then smart biostatisticians were able to sit there and actually do the math and pull out these relevant SNPs.
Erica Spaeth:It's really exciting to see what they pulled out and then, more importantly, to see they could reproduce it in other populations, and so that's slowly evolved over time and they've obviously done quite a few other diseases now, and so it's exciting to see how we're progressing in this area and now we have tests that are clinically available that utilize some of this data to really ultimately it's just stratifying the population a little bit better, and at this point that's as much as it can do.
Erica Spaeth:It's not. You know, the polygenic risk scores don't have a mechanism of action associated with it, so we don't know why they're working the way they do. We just know that there's an association of risk which is enough to tease apart that population and say you're on the higher end of the spectrum, you're on the lower end of the spectrum, and hopefully that information gives a little more insight for the clinician who actually has to communicate risk, as well as screening and risk reduction strategies, to that patient to try to decide how how to manage their care yeah, I mean the exciting, one of the exciting things in medicine at the moment is sort of how personalized it's becoming.
Matt Burgess:That you know the buzzword like precision medicine, like you, you know, back in the day when someone had one disease they were all treated the same way, whereas now we're able to sort of look at features of this particular person to try and treat the disease in a bit more sort of personalized way to get a better outcome. And I think one of the things that may surprise sort of lay people when we we talk about cancer you know breast cancer or bowel cancer, like it's like we talk about one, like breast cancer being one disease, but actually like there are many different types of breast cancer you know there are different grades there are. You know the way that they respond to hormones and you know, and all of that sort of feeds into, how the patient is actually treated and you know what we think the outcome will be. How does the polygenic risk score for something like breast cancer take into account that?
Erica Spaeth:you know the actual condition itself is quite complicated and complex yeah, that's a great question and we are just at the tip of the iceberg in sort of utilizing polygenic risk in a much more personalized way. So, even though we're looking at people's individual genetic signatures, if you will, for their polygenic risk, it's sort of an interesting discussion point that, yes, it's unique to the individual, but the polygenic risk scores are derived based on population Risk scores are derived based on population studies. So it's a funny combination of, yes, we're personalizing it to this patient, but we're looking at this patient in the context of the whole population and that's how we're assigning risk In the future. So where we are with breast cancer and this is very similar across all the diseases we're looking at we're clumping these diseases into giant categories where we know that there are subtypes of each of these diseases and we could really get granular. But the only way we can get granular is if we have more data can get granular is if we have more data. So we need much larger population studies to look at very specific tumor subtypes and then I do believe in the future we will have very specific polygenic risk scores to address that subtype and that would change the way we provide risk-reducing medication.
Erica Spaeth:For example, one example is in the breast cancer world, where breast cancer is a prevalent enough cancer in women, that there are actually pretty big numbers. And so if you divide breast cancer just by their subtype, by hormone receptor, we can have estrogen receptor positive disease and estrogen receptor negative disease. So if we just look at those two subtypes, 80% to 85% of breast cancers are actually ER positive disease. So there are a lot more women in that category. But we see different polygenic risk signatures for those two subtypes. And that's really interesting because one of the risk-reducing medications available for adults that are healthy but at high risk are selective estrogen receptor modulators and aromatase inhibitors, which theoretically would work a lot better on estrogen receptor positive disease. So there is certainly an application to be able to look at subtypes.
Erica Spaeth:And another example is in the prostate cancer setting. So looking at a polygenic risk score, that in the future and there is data out there but I don't think it's as strong as we'd like it to be yet but to be able to tell the difference between an indolent and an aggressive prostate cancer, because there is that whole discussion about over-diagnosis and the indolent cancers. Yeah, we want to let them sit and we don't want to over-treat that person, but if we had a signature for an aggressive cancer, that's the one that we want to have more screening for and that we might want to do the biopsy for. So, again, it's a really exciting space. We're not there yet, but that's where all the research is pushing towards to try to fine-tune these polygenic risk scores to be even more helpful than they are now. God, it's just complex and complicated, isn't it?
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Matt Burgess:So one of the experiences that I've had when speaking to patients about polygenic risk testing is you know, one of the questions that I have been asked quite a bit is how accurate is the test? And it's such a straightforward question and I really understand what the person is asking, but it's a really difficult thing to answer. It's like you know, sort of you know. They're kind of saying you know, I think part of what they're asking is is it valid, is it reliable? What tell you? Know? What about the positive predictive value, the negative predictive value? But that's kind of the language that statistics has given us. But I, yeah, you may not have a strong background in statistics, but you sort of have an idea of what accuracy means. How do you speak to people about how accurate or what? How, if someone says how accurate is the test, how do you normally explain that?
Erica Spaeth:yes, this is such a difficult conversation to talk about, uh. So risk prediction is much harder to talk about, accuracy, as opposed to a diagnostic test where you're looking for a yes or no answer. So a diagnostic test where you're saying yes or no, you have that disease. Accuracy is a much easier statistical metric to provide some insight around Risk prediction. We're not saying yes or no at all, we're putting you on a spectrum and saying over the next few years, this is your chance of getting this disease, which means you also have this much chance of not getting that disease. So the result we're providing you is a guess. It's a good guess, but ultimately it's a guess. And so from a statistical standpoint and I'm not a biostatistician at all, so this is my again layman's interpretation from the smart biostatisticians that work on this very problem but what we do, there are different set of statistical metrics that we use as opposed to accuracy when we compare risk prediction, and so we look at things called area under the curve, which is looking at if you consider sort of people who develop the disease and people who don't develop the disease, how much they sort of overlap in their risk and how much can you sort of tease them apart. We also look at something called calibration of the model. So how of the result that you're getting if you go back in a historical data set and you apply that risk score? Did it match what you observed? So, is what you predicted close to what you observed? And a good model? It should be a one-to-one. What you predict is what you should observe. We also use something called net reclassification. That is a metric where we use a new model and compare it to an old model and say how did they do? Were we able to move people that actually had the disease? Were we able to move them into the right category and move people who never got the disease down to a lower risk category? And so there are these tools that we're able to use and ultimately, what it comes down to is we compare a new risk model with an old risk model and that's how we measure quote unquote accuracy.
Erica Spaeth:So now that I've gone through a lot of words, if I were.
Erica Spaeth:So, now that I've gone through a lot of words, if I were to explain accuracy to a lay person, I would say this risk model was compared to a standard clinical model that's used every day, that clinicians trust and use every day. So for some diseases it could be as much as asking about family history and as a genetic counselor you know how important family history is to your conversations of disease and that's a trusted risk factor. And for some diseases it's the only model you have to start off. It's your, you know, the number one question and we can assign a numerical value in that ability to predict risk based on that one risk factor. And so we're able to compare that to a new model which might be polygenic risk score or polygenic risk score compared with and any other risk model combined with it. And we say, side by side, model one is doing better than model two, and so if you're using model one in clinic, you're, and model two is doing better, which one should you use? And that's kind of how we communicate the accuracy question.
Matt Burgess:I think what's clear is that there are very smart people working behind the scenes to try and make these sort of tests the best that they can be. And I don't know if it's a bit naive to say and then it gets into sort of medical paternalism. But I don't know if it's a bit naive to say and then it gets into sort of medical paternalism. But I don't know. I just sort of have faith. You know, I see my GP and I think she's smart. You know, I know a lot about genetics but I don't know a lot about many other areas of medicine and I'm just going to have faith. You know, I'm going to trust what she. You know I'm gonna trust what she. You know. It kind of makes sense, yeah.
Erica Spaeth:So I think it's complicated it definitely is, and I mean I I think that's one of I don't know if it's in it I think it's an advantage, but it certainly makes things more complicated when you think about the electronic medical records that clinicians have a lot of.
Erica Spaeth:These systems have risk scores built into the background of them and I think that's becoming more and more popular and more and more utilized. And so these computer systems are actually taking algorithms, just like I talked about, and kind of spitting out predictions in the background already and the doctor saying okay, I see that you're a higher risk because of this. So to your point. I do think the future as we get, I mean medicine is getting more and more complicated the more personalized it gets, and no single clinician is going to be able to take all of the components and process them in their brain without a little bit of help from supporting software. And I think that's one of the areas where electronic medical records can sort of assist in some of these decision-making processes by pooling the data that the clinician gives it and calculating some of these numbers in the back end to help the clinician sort of inform the conversation that they're going to have with their patient.
Matt Burgess:Yeah, so one of the sponsors of this podcast is TractGene. I work at TractGene and TractGene is a healthcare technology company and one of the questions that we have is what risk models we're going to include. And you know, we have clients all over the world and some risk models are more popular in certain countries and, um, and then it sort of related to that is sort of you know, some people just want to put the the very minimum amount of data in, so then it's sort of like garbage in, garbage out, like the model is only as good as the information you put in. And oh, I it's just funny in this podcast how many times we said oh, it's, it's so complex. But I think this really is an area that is complicated and complex.
Erica Spaeth:It is. And you know you raise a really good point because the garbage in and garbage out is so important in risk models and it's one of the conversations I also have on a daily basis with clinicians, because the gene type the company I work for, the risk model that the company makes is more than just polygenic risk. It combines a lot of clinical risk factors. But the clinician has to spend the time putting in those risk factors and I communicate the garbage in, garbage out every day. But some clinicians don't want to spend the time. Garbage out every day, but some clinicians don't want to spend the time.
Erica Spaeth:And how do you? How do you communicate the value, saying look, the result you're going to get is not going to be good? We talk about accuracy. The accuracy diminishes greatly if you don't give the input originally requested. And so that is a big discussion point with clinicians and you know, again, clinicians don't have a lot of time. Genetic counselors are in a unique setting where you typically are able to block out a little bit more time with your patient to actually have a more robust conversation. But in you, but in primary care, where that doctor is seeing a patient for what? Six or seven minutes max. They don't have time to sit there and ask all these questions, so it's challenging.
Matt Burgess:Yeah, and then I guess that raises the question of workforce. And can we employ other types that you know? Is it the best use of a physician's time to be filling out a requisition when genetic counselors are very good at that and you know we can interpret what someone's cholesterol or sugar level is? We can take someone's blood pressure, anyway. That's a whole different conversation. Is we can take someone's blood pressure anyway? That's a whole different conversation. Um, one of one of the other things I really wanted to talk about in relation to polygenic risk score, like one thing that I see is we get a result and you know the result kind of is significant, and either it changes someone's management, like so we say, okay, you're at high risk of developing, or you know you fall into the high risk category for developing breast cancer. That means that you meet criteria for this certain screen. You know whether it's.
Matt Burgess:MRI or ultrasound or a mammogram, and I see that that is actually sort of a different thing, because it's an intervention that the doctor does versus, okay, you're at increased risk of, I don't know, atrial fibrillation or type 2 diabetes, and the risk, you know, the recommendation is sort of behavior change, or you know, like the patient has to do something themselves. Um, what do you think about that like? Is there? Is that something that the companies think about as well, or is this just sort of like a little artifact that I've noticed in clinic?
Erica Spaeth:or no, that's a really good point and I it's certainly. It's certainly a point of discussion. So lifestyle changes are always easier said than done for any patient. I think every, every adult knows the healthy lifestyle habits they maybe should be engaged in. It's a lot harder to act on. So one thought is, with some of these diseases where there might not be some clinical guidelines and it is more lifestyle, does that help motivate? Does that give you one extra little motivation factor to you know? Maybe you're thinking about cutting back on the alcohol, maybe you have one less glass because you know you're at increased risk now from a new risk factor you never looked at before. So that's one argument to look at apologetic risk in one of these diseases that may not have some immediate clinical impact.
Erica Spaeth:The atrial fibrillation is an interesting one because the risk of atrial fibrillation is really age dependent. So the majority of adults in their 70s are at risk for AFib. So what's the point? And I think, from a clinical standpoint, one of the areas where I think there could be utility. Again, this is what we talk to clinicians about. But there is no, there's no guideline set in place. But if you had an adult in their early 40s that maybe had a high polygenic risk score for AFib, that would be something to lock away in the back of your brain in their chart. And if at some point in time you had to put them on a medication that you know is associated with increased risk of developing AFib, that's when you kind of pull out that information and maybe you can select a certain medication one direction or another. Or if an event happened, maybe AFib would pop into your mind a little bit sooner than if you hadn't had that.
Erica Spaeth:So I think there is a place for some of these polygenic risk scores without immediate clinical utility that might become useful in the future. But in that same breath you know there's so much work going around dementia and Alzheimer's polygenic risk scores, and that one you know. There there are certainly a few really expensive drugs out there, but I don't think you'd actually put someone on thousands and thousands of dollars of medication just because they're at increased risk based on a polygenic risk score. So what do you do with that information? You know there's besides the lifestyle, as you commented, there isn't anything to do besides trying to reduce their risk, and that's you know.
Erica Spaeth:We speak with quite a few doctors in the functional medicine space and there they focus a little bit more on lifestyle. Now I certainly don't have any expertise in this area, but I have heard clinicians talk a little bit more about certain supplements they might put people on again to try to influence their lifestyle changes. So I think there's a lot to be determined in the future about the utility of certain polygenic risk scores, and there are quite a few companies out there right now. You can get certain polygenic risk scores and there are quite a few companies out there right now. You can get you know a hundred polygenic risk scores, your risk of all sorts of different things and how you know it's interesting. So from a hobby genetics perspective I love it. From a clinical perspective it's not not doing much for for me weighing one way or another it's kind of like is there any difference between that and just reading a good horoscope?
Matt Burgess:so one last thing I just wanted to touch on before we finish up. Um was sort of uh, it's something that we mentioned a little bit um earlier in our conversation, but just sort of the commercial readiness of of a test.
Matt Burgess:uh, you know, like in the australian context, we have a public health care system.
Matt Burgess:We've got a federal government and state governments that put our tax money into health care and if there is a good test that benefits the population as a whole, then maybe it gets funded. I know in America it's very different from that, it's sort of almost the opposite. But when I was thinking of polygenic risk scores like, it sort of reminded me a little bit of, you know, the pharmacogenomic tests. And something that was interesting that I saw on LinkedIn the other day was a major genetic testing company in America that offers a pharmacogenomic test just had funding withdrawn from a major health insurer and it just made me think about, you know, there's lots of evidence that pharmacogenomic testing works, but maybe there wasn't, you know like there wasn't like a strong economic benefit. Do we think, like, what is the trend with polygenic risk testing? You know, like we sort of spoken about how it has moved from the research into clinical, but is there like a good case from a public health point of view that this is worth the money?
Erica Spaeth:I do think there I would focus probably on two diseases where I do think it's worth the money, and I might throw in three diseases, but I'll breast cancer. I think, hands down, it is worth the money. So two, two big reasons. One breast cancer has had clinical risk models since the late 1980s. So clinical guidelines have been developed around models, breast cancer risk models, and so, from a risk model perspective, adding a polygenic risk into a risk model in the same context that already has guidelines sitting around it. And if we just know that the model with polygenic risk is doing better, it's outperforming the one without polygenic risk, then I think it's a no-brainer, because there are 40 years worth of studies on the risk-reducing medication side. There are 20 years worth of studies showing the efficacy of risk reducing medication in women, stratified by their risk with a traditional model. So if the traditional model can now be outdone by a new model, I see that as a viable swap from a clinical standpoint without the need for much additional evidence. On top of that there have been quite a few. We call this from an economic standpoint a budget impact model. So work being done to show that applying a risk model that performs this well, you know whatever numerical value can actually save a health system X amount of money and those studies have been done because it does stage shift cancers, so cancers are detected at an earlier stage, which does save a large health care system money by avoiding these late stage cancers. So breast cancer, I definitely think there's an economic argument.
Erica Spaeth:Colorectal cancer is the other disease where I think there's an economic argument, especially in Australia, because everybody in Australia gets the fit kit mailed to them at age 50?. There are adults. A risk model incorporating just basic age, family history and polygenic risk can stratify the population well enough to pull out adults that should be getting screened earlier. And so getting someone a fit kit when they're 40 or getting them a colonoscopy when they're 40, when they can have polyps removed, that right there is preventing cancer from occurring and that's a lot better than waiting 10 years for their first fit kit only to know that they now have cancer. So colorectal cancer, the prevention aspect is there. Breast cancer prevention is a little harder. It's the risk reduction.
Erica Spaeth:Focus Colorectal cancer if you can get adults in for a colonoscopy and remove polyps, that is prevention. And then I think the third one is the prostate cancer. So if I were to add one. Psa screening has gone in and out of the guidelines for so many years and if you can flag the right type of adult to get PSA screening, it can be beneficial. So if you know an adult is at high risk, then yes, do PSA screening and monitor what it looks like over time, and I do think we could catch more cancers. So those are the three diseases that I do think there is sufficient clinical evidence, not polygenic risk alone. I would again it's polygenic risk combined with other risk factors into a model and that model is comparable to the quote-unquote model that's currently used clinically excellent.
Matt Burgess:I think that polygenic risk scores is definitely something that we're going to hear a lot more about in medicine, sort of going forward. Uh. I know that there was a a familial cancer conference in australia, uh, and it was like PRS was sort of the theme. So thank you so much for helping to break down these complex and complicated things to do with polygenic risk testing.
Erica Spaeth:It was a pleasure speaking with you.
