 My name is Derek Casel, I'm a medical oncologist and director of medical oncology at Intermountain Healthcare. And my name is Lincoln Nadal, I'm also a medical oncologist and I am the director of Intermountain Precision Genomics for Intermountain Healthcare's Precision Medicine. And we're excited to talk today about this recent publication. Derek was the first author and I was the last author on this paper titled Precision Oncology, Advanced Cancer Patients, which sprues overall survival with lower weekly healthcare costs. And it was quite an effort, we learned a lot. And Derek, you want to maybe just give a little summary of what we found in that paper. So this was actually a continuation of a study we did earlier where we took a lot of patients with advanced cancer and we took a group of those patients and we treated them just as we would normally just kind of through the standard process. And the other group of the other half of the patients we were able to take treatment and do this next-gen sequencing on their cancers to determine what genetic mutations their tumors had. And in particular, Mastan with targeted drugs that were without appropriate for those patients. And so those were those are our treatment group that received and took a group of patients who had advanced cancer. And we were able to take a group of those patients and sequence their tumors and determine what particular genetic mutations were in those tumors. And Mastan with targeted drugs that were available. And most of those drugs were all of those. And we compared that group with a select group of historical dates that we had in our healthcare to compare them with those patients who had gotten standard therapies. Yeah. Yeah. So we were really looking at those two cohorts. The one cohort was a precision medicine or targeted treatment cohort based on next-gen sequencing. The other cohort was control cohort. And then we analyzed their overall survival and the overall healthcare costs associated. The damage we have as an integrated healthcare system is simply that we have ability to look at all the costs associated cancer care from the drugs that we use, but also the labs that we order and the infusions that people get and the ER visits and the hospital visits and all the sorts of things that you're able to measure and track. You know, one of the hardest parts of this, I remember doing this, was finding matches for patients in those two cohorts. So we matched patients in the precision medicine cohort and the control cohort according to age, gender, diagnosis, and number of previous treatments they had gotten for their cancer. And ultimately, we found 44 patients that had those matches, but it was hard to get there. It was hard to find the control cohort. Yeah, the difficult part was trying to match their lines of therapy that they had seen previously. But we thought that was a really important part of it. A lot of the arguments against precision medicine has been that we use it on patients who are going to succeed or are going to do well anyway. And so this we thought helped to kind of teach that back here. So these are patients who are heavily treated and whether they were in the treatment arm or the comparison arm, they were heavily treated with patients with lots of treatment that they were receiving. In fact, if I remember correctly, I should say that. In fact, the average patient had over three lines of treatment. That's right. They made in bulk cohorts on average had received three treatments. The other part of this study that I thought was hard and this came out in some of the reviews is that we didn't sequence, we didn't do genomics on patients in the control cohort. We were just looking at genomics in the patients who were in precision medicine or target treatment cohort, and we used those genomics to guide their therapy. And the potential criticism, of course, is that, well, maybe these genomic findings are not necessarily predictive of response to certain drugs, but simply prognostic of that outcome because of the biology of the disease. And some of the reviewers actually said, well, shouldn't we be matching specific genomics? So a patient in a treatment cohort, if they have a mutation in the gene called K-RAS, then maybe the patient in the control cohort should also have a mutation in the same K-RAS gene. Well, that's almost impossible to do. You'd have to have tens of thousands of patients. So we didn't look at it on a patient-by-patient basis once they were matched. We then looked at it on a cohort. So how did this entire cohort survive with precision medicine compared to this entire cohort was treated with standard therapy, what did their survival look like? And what did we see? Yeah, well, and to your point, there are obviously mutations in science and research when you're doing a study like this, especially in historical case controls, you do have some limitations to that. And so that's a criticism that's fair, but to your point as well, and they're matching up mutations and mutations as well, and you need to have patients to be able to do it. But this present thing we found is that taking that into consideration using precision medicine to treat these advanced cancer patients, we saw a doubling of overall survival. So rather than six months, we got 12 months. And that's big. Well, oncology, we've seen drugs approved on last week in proven overall survival. So seeing an advantage of a six month proven in overall survival in some of the advanced cancers that we do, especially invasions who are so heavily pretreated where we wouldn't predict that kind of survival. So that was pretty surprising to see a doubling of their overall survival. It was nice. It's good news for patients. But then when we looked at their old health care costs, that was also kind of surprising because it was actually a cost savings per week survival, which is great because again, one of the big criticisms against precision medicine has been the cost, the cost of sequencing the cost of these targeted drugs. But when you take into account the overall care of a patient and some of the things we looked at to be considered surrogates, I think, for improved overall quality of life. Because what we really saw was cost savings went into reduction in our business, reduction in hospitalizations. The ability to take some of these drugs by mouth at home. So patients are home about people that want to be around rather than at hospitals and clinics and stuff. We had to normalize the cost because some patients treat and cohort live so much longer that, of course, the cost more to keep those patients alive. So if you normalize health care costs to a week of survival, then we saw an almost $800 savings per patient per week of survival using precision medicine. I thought it would be a lot more expensive to use precision medicine. So I didn't see that coming. That was kind of surprising. So that was inclusive of all the cost associated with sequencing and part of the drugs and associated with that. So I think it's a really good look into the overall perspective of the cost of care of the patient. To your point, we had to normalize those costs. That's an important concept that I think that, for instance, it costs more money to treat people with dialysis than to let them die of kidney catheter. So that's always going to be kind of an argument. It's always going to be something that's just cost-assisted and kind of based out to make those conversations. So I think those are important things to move to the message that was the cost of health care patients. But nevertheless to see something that was thought to be, that thought was going to cost so much more money actually to resolve the cost savings than normalizing this big deal. Yeah. I mean, the extreme skeptic would say, well, you're saving money per week in the precision medicine cohort, but they live twice as long. So therefore the overall costs are higher. And we actually, that is true, but to your point, that's the entire principle of medicine that does cost money to keep people alive. If you were to walk out the door and be hit by a bus today, we would say, but it would also be a lot cheaper to take care of you than if you were to survive another 40. And that's, of course, an extreme example, but the extreme skeptics like that out there, I think that's, that's a challenge to help the overall costs of our issues. So I think the summary statement from this paper is that we saw in the precision medicine cohort a doubling of the overall survival actually overall savings in healthcare associated costs, excuse me, per week per patient savings in overall healthcare associated costs. And, you know, it was really fun to, that's kind of a weird word to use in this kind of study, but it was fun to do an analysis that included not only survival and genomics, but also costs. Honestly, we haven't seen very many papers out there that include not only survival, but the healthcare costs. And we've gotten a lot of comments from about that. And it really is because it's the unique situation that we live in as an in great healthcare system that has, you know, access to, you know, all the ER data and all the cost data and the amount to sort of generate that amount of information. So, so this unique place to be, you know, I think that, you know, obviously it's not a very good stage for me. It's one of the things that we are looking at now is really moving out precision medicine in the life care. So rather than waiting until somebody is really advanced and they fail a three or four treatment, would it make sense to do precision medicine or take this approach in some person? Yeah. The number one question that we seem to get when we talk about this study and our precision medicine in general is why don't we do this sooner? You know, as mentioned, we were, we were implementing these target therapies as like fourth line treatment missions. And why not do it first line? A lot of people even say why not do it in stage three or stage two cancers? I think it's hard to justify doing this in stage two or stage three cancers where we know there is an ability to cure. I think in those situations where you can cure an early stage cancer, we are obligated to pursue curative pathways. The patients that don't have a well described, well documented, proven curative pathway, then we can implement these strategies early. And I agree, that's where we're going in the future, right? That's one of our future times. That is, I don't think that it's far better to think of it that way. And I think the other thing that we learned by doing this in kind of our work in general and in our policy program is really, it helps to kind of outline the, kind of get you a roadmap of treatments for these patients. And so you always know what the next options would be. Another major future effort, this is becoming a current effort, is to take all of these genomic analysis capabilities, bioinformatics capabilities that we've been developing over the last five years and use them on some of our archival historical samples. We have in our possession in our healthcare five million, five million samples that have been collected and archived since 1975. So we're starting to pull some of those out and ask, you know, hypothesis driven questions about certain stages of disease and what were those patients outcomes like. And then do a genomic analysis and look for biomarkers that have gone domestic or that's the important part of it. It's coupled with the electronic number 12. So we know what the outcome of those patients are. We actually know what their family history is and what their, you know, tolerance of different therapies and their outcomes in regards to surgeries and those sorts of things. So I think that there's a real potential through that, through that project, through those projects, those efforts, through that in terms of asset. Oh yeah, through those assets we have at Interval Health Care to be really sort of answer some of those questions and be able to hopefully be a little bit more educated, it's smart and predictive about what drugs we should use in certain situations. We think that the answers to how we treat future patients are locked away in those samples. So any other future things we should talk about or earnings from this effort? I think, I mean, I think it tells us we have still a lot of work to do and a long ways to go. But I think it's certainly a start and I think it's just one that we're going forward to continue to answer some of these tough questions. Yeah, I mean I think we can glean from this that it looks like precision medicine may improve survival in the cost from shore and cost savings. I think it says something about the historical trials structure and that these large randomized phase three trials in precision medicine setting probably not feasible and we've got to be more innovating in our clinical trials. I think it says something about potential power of genomics in guiding treatment pathways for patients with cancer. That's exciting, that's good news. I mean, 40 years we've been trying to make progress. Since the war on cancer was declared in 1970, I've been trying to make progress and this is one of the advancing pillars in the oncology right now for how we can improve our industry. And we've seen, you know, it's always, it's always difficult to bring up anything that with a lot of patients that we have seen that we have been able to buy quality time. By no means do we think it's cured, you know, do we think we're getting closer? Yeah, do I think we are we closer to turning this into all the chronic disease? Yeah, I think we are. By no means are we there yet, but I think we're on the right path. Well, should we do some jumping jacks?