Listen In: Dr. Renee Brady

Cancer research is a scientific field that has been in existence for centuries, exploring the causes and looking for ways to prevent, diagnose, treat and cure cancer.

Studies are carried out to examine everything from what happens at the cellular level, to how patients respond in clinical trials, but while amazing strides have been made to improve patient outcomes, too many people still suffer and die from this disease.

So for May, which is National Cancer Research Month, we take the time to recognize the scientists who continue to make advances in the field by using all of the tools available to them, including mathematics?

Join us and Dr. Renee Brady to learn of the importance of equations in cancer research, in our episode “cancer by numbers.”

Dr. Brandon Blue: All right. Hello, everyone. We're glad you're joining us today for Cancer in our Community where we're having conversations about Black health equity.

Now this is actually our last episode for season two. So we hope you enjoy hearing from me and for the Spanish speakers among you, our conversations with my colleague Dr. Sarimar Agosto Salgado.

For today though, I remain your faithful host, Dr. Brandon Blue, and when I'm not podcasting, I'm an oncologist in malignant hematology at Moffitt Cancer Center.

Now, this recording is in honor of National Cancer Research Month. And to talk more of the subject, we're excited to have with us Dr. Renee Brady. How are you doing today, Renee?

Dr. Brady: I'm doing well. Thanks.

Dr. Blue: Wonderful, wonderful. So Renee had her bachelor's in science and mathematics from Florida A&M University. Go Rattlers! And her master's of science in applied mathematics at North Carolina State University and a Ph.D. in applied mathematics also from NC State.

After a postdoctoral position in the Integrated Mathematical Oncology Department at Moffitt, she now has her own group and position there as an Assistant Professor.

What a mouthful.

Dr. Brady: Right? [Renee laughs]

Dr. Blue: So I'd imagine, listening to your background, the audience might be very curious about your career path. As am I. So tell us, Renee, how did a mathematician like yourself end up working at a hospital like Moffitt doing cancer research?

Dr. Brady: So it's actually quite interesting. I didn't start off in biology for grad school.

I started off trying to avoid biology as much as possible. I started doing an initial project in operations research. And that's like trying to figure out like, controls and how to control a system. And I didn't really enjoy that.

And subsequently, I went on to start another project with an advisor who had space available, and she was working on cardiovascular dynamics. And working with her, again, trying to stay away from biology, but then having to get into it, I started to understand a little bit more about biology and how it could be integrated into math.

And from there, when I was graduating, she told me just to apply broadly. She was like, don't worry about, you know, what exactly you want to do, because I had no idea what I wanted to do. But she said just apply to anything that you qualify for and see what sticks. And I just knew that I didn't wanna be at a university firsthand because I didn't want to have to teach and do research. I wanted to focus on research.

So I applied to everywhere that I qualified for and being from Florida, Moffitt came up and I was like, well that seems ideal and I went down and did the interview and it was an amazing experience for me speaking to, at that time it was Dr. Heiko Enderling who was leading the group and I spoke with him and got an idea of the project that we were going to be working on and it was just fascinating to me that cancer and math could have a home together.

And so, you know, I took the position and I've been here ever since.

Dr. Blue: So did you like enjoy things like calculus and algebra.

Dr. Brady: Yes.

Dr. Blue: Wow. [Renee laughs] I didn't know people like you exist that really enjoy that stuff. Like you were like, give me more.

Dr. Brady: Yes. I did enjoy it.

Dr. Blue: All right. I was always the kid that like, "please don't give us homework, please." And you were the person who was like, "teacher, don't forget."

Dr. Brady: I wasn't that type of person. I tried not to be that one in class because you know, you get the looks, but I did really, really enjoy math. I remember my mom, she went back to school when I was maybe about six and she was doing like calculus and I was in class with her and learning like a negative times a negative was a positive and I was like, Oh, I know a fact.

I felt really smart and my brother is 10 years older than me and I would help him with his math homework. So I was like, math is the way to go. But when I actually started at FAM (Florida A&M), I started in business administration because they have a really good business program. But I was so bored. And so after my second year, I changed to actuarial science trying to stay with math. Trying to get into math but staying with business and went through the entire program really for actuarial science and did dual enrollment at Florida State for a minute. And when I finished with that, you know, I really could have just gone off to be an actuary and make a bunch of money.

Dr. Blue: Can you explain to the people what is an actuary?

Dr. Brady: So it's like risk management and insurance.

Dr. Blue: That was for me also, just so you know.

Dr. Brady: So it's crunching numbers, but it's a lot of like statistics and probability. And so that was interesting. And like I said, I was, that was my plan was to become an actuary and along the way you have to take a series of exams.

So in order to finish at FAM, we had to take at least one exam and pass it and no one was passing the exams. So a lot of people were transitioning over to just a math major. In my last year, I actually managed to pass my exam, so I was like, yes, I can be an actuary, this is the way to go.

But at the last minute, my advisors were all telling me that I need to go to grad school. And I was like, no, I don't really want to, I'm going to be an actuary. And one of my really good friends, we worked in the math lab together, and he came in one day with food, and he was like, are you hungry? And I was like, yeah. And he was like, well, you can't eat until you apply to grad school. [Renee laughs]

Dr. Blue: Alright, food does it every time.

Dr. Brady: So, food did it for me and I still talk to him to this day, he's one of my good friends. So, he went to Florida State for engineering and I ended up at NC State for applied math.

Dr. Blue: That is so fantastic. And you know, you never really know how the paths of life will just kind of lead you down things.

So that's fantastic. And I'm glad that if your friend is listening, so thankful that you brought her food that day because she's an asset to us here at Moffitt. So thank you so much for that.

Dr. Brady: Yes.

Dr. Blue: The things a hungry stomach will allow you to do.

Dr. Brady: Or a broke college student. [Both laugh].

Dr. Blue: So as I understand it, the Department of Integrated Mathematical Oncology is a very unique center that puts Moffitt at the forefront of the field of research.

Can you tell us about the history of that department and really what is it about and what makes it so exceptional?

Dr. Brady: The IMO department, it was started, I believe in 2008, by Drs. Sandy Anderson and Robert Gatenby. And they understood that mathematics is a very powerful tool, and that there might be a way that we could integrate it in this complex system of cancer. Really trying to understand and predict, specifically how a patient will do on this current line of treatment and subsequently. And I think they took it back to, or made it maybe akin to, like hurricane forecasting. Where you know the path of the hurricane based on historic data. And we can think of a patient as this hurricane, or the cancer within the patient that's evolving as a hurricane.

 One of the most unique things about the IMO department, being at a cancer center, is the fact that we have access to both the clinical and experimental side, and we integrate, which is why we're called integrated, we try to integrate with all of them in order to get their data put them into our mathematical models, make predictions, and then subsequently go back to them and say, this is how things should be done. And they can test those in clinical trials.

And so if we look at their past behaviors and their past trajectories from other patients, we might be able to develop a mathematical model and understand those dynamics a little bit better and then predict forward into the future. So I've done a little bit of that in like prostate cancer, where it's understanding how a patient is doing on one cycle of treatment, a cycle being, you know, when they're on treatment for a period of time and then off of treatment, that's a cycle of treatment.

And we can fit a mathematical model to that. understand the patient specific parameters or underlying dynamics, and then predict forward to say, okay, well, if they go on to their next cycle, their probability of resistance is 75%. So maybe don't put them on that next cycle of treatment, put them on a different line of treatment or a different type of treatment.

So, I think it was really just trying to predict and using our tools in a unique way. We're one of the few math departments within a cancer center, maybe the only. And so we're in a unique position where we're at like the forefront of this new area and it's really cool.

So for instance, we recently had the PSI trial that was done in head and neck cancer.

And with that trial, they had previously developed a mathematical model and shown that if a patient's volume to carrying capacity ratio is really high or low, they might need a different type of radiation therapy. And based on that, they went into the clinic and they said, okay, now we're going to recruit all of these patients and we're going to stratify them based on their proliferation saturation index, which is their volume to carrying capacity.

So they stratified the patients into hyper or standard fractionated therapy, and then treated them for a period of time, and then they tried to see who met the clinical endpoint, which was a 32 percent decline in their tumor volume. But they were able to get a higher proportion of patients meeting that clinical endpoint by just stratifying them based on their PSI rather than all giving them standard fractionated therapy.

Dr. Blue: And that was because of math?

Dr. Brady: Because of math.

Dr. Blue: And so it sounds like really the cool thing about it is that not only do you have the geniuses in math such as yourself, but you also have people who are experts in medical care, people who are experts in science, people are experts in biology, all kind of sitting at the same table trying to answer the same question.

You know, I think that's important because, when I was growing up, there used to be an old cartoon called transformers, you know, and the cool thing about transformers is that each one of them individually was great. But they used to combine to make this really super, like, transformer that kind of made a bigger impact.

And so it sounds like that's kind of what you guys are doing. I don't know if that cartoon's still around, but it sounds like as an individual, you know, each department is really doing fantastic things, but it sounds like now you're coming together to use these different people to really kind of make an even larger impact.

Dr. Brady: Exactly. That's a really good way of putting it. I've never thought about us as transformers, but the entire institute, I guess, Moffitt would be a big transformer when we all put each other together.

Dr. Blue: Transforming and making change, that's what we're doing.

Dr. Brady: Yeah, that's awesome.

Dr. Blue: Now, when you say predicting, the thing that comes to my mind are things like computers and AI and using like artificial intelligence to try to like predict some things.

Dr. Brady: Right.

Dr. Blue: Do you guys work with that? Like, are you also trying to use technology and AI to kind of figure out some of these tough equations?

Dr. Brady: So we're getting more now into like machine learning. That's becoming like the buzzword of the time. I personally don't do a lot of it. I just try to keep it as simple as possible.

There's something called differential equations. So we're looking at the change in something over time. So in my case, I'm looking at the change in PSA, prostate specific antigen, over time. And from that if it's a very simple model, you might have a couple of parameters that we can use to describe the dynamics.

And then I can analyze those parameters and then see how they're changing from one patient to the next or one cycle of treatment to the next and then make my prediction. I don't do a lot of machine learning. I try to stay away from it.

Dr. Blue: I will say that differential equations was a course when I was in college. And luckily they told us that I didn't have to take that one. That was the one that I was like, Ooh, thank God. So I'm glad that someone did sign up for that. So I appreciate that.

But let's talk about things that you are heavily involved in. From your background, it says that you're looking at minimally invasive biomarkers using mathematical modeling. And so you talked a little bit about prostate cancer and some of the things that you're doing there, but how exactly are you using this to help with cancer research?

I'm a little confused there. So what can you explain?

Dr. Brady: So by minimally invasive or even non invasive, I'm trying to focus on those measures that we can get from patients that aren't going to take a lot from them. I think especially in the area of like disparities research, which I have a strong passion for, is that a lot of patients from underrepresented backgrounds, they don't have access to the best type of care.

They can't afford to come in for a CT scan every four to six weeks. But if we can get something like a simple blood draw where, you know, they can go to their local lab and have a simple blood draw done, then we might be able to infer some of their more invasive dynamics just based on the blood draw.

Or non invasive, I'm thinking along the lines of like patient reported outcomes, so those are just surveys. Patients come into the clinic every day and they fill out a survey. But if they can do it on their phone or on a tablet at home and just say, How are you feeling on a scale from 1 to 10? How well did you sleep the night before? What is your level of fatigue today? If they can just fill those out and it doesn't take much from them, and again we can infer something about their overall, you know, how they're doing on the inside, that's what I'm trying to focus on.

Dr. Blue: But how do you decide what goes into your math equation? Like, there's so many different variables, you know? So, like, I don't know, let's just say the patient didn't sleep that night because, they were up watching Monday Night Football.

Dr. Brady: Right.

Dr. Blue: Or maybe it is from their cancer treatment.

I mean, we don't know that. Like, so how do you know what things are, like, important enough to put in a mathematical equation to answer some of those questions?

Dr. Brady: So a lot of that comes down to, like, the preliminary analysis. So we really need to see. Is there a signal there? Because not everything is a signal, right?

So going back to like patient reported outcomes, I have outcomes for, like this particular survey that I had, they asked maybe 28 different questions. Not every one of those is going to be a signal. They might say that their insomnia level is at a zero and then it spikes to a one. Does that mean something?

Or if they say that, is that a sign that they just didn't sleep well the night before? Or is that really to say that their cancer is progressing? So we really have to analyze all the data and see where is the signal. And then from there, then we know what to include in our equations.

But as I said before, I try to keep it as simple as possible, so I don't want to include the kitchen sink. I don't want to include so many details that we're kind of washing out the overall signal or finding something there that shouldn't really be there. So I try to be very specific or very careful on my initial analysis of when I'm trying to build a model, what are the correlates to the overall response?

What are the important measures that we should be taking into account?

Dr. Blue: Fantastic, like I said, I'm learning something new today. So, I appreciate you for sharing this knowledge, but, you know, one of the focuses of the podcast is when we talk about Black health equity.

So with using a lot of the mathematical modeling and some of the prediction modeling and things that you're doing. How is that helping the people? How is that helping the culture? How are you bridging that gap? What are you doing to help us?

Dr. Brady: One of my primary focuses right now because my prior work is in prostate cancer.

And I find that one of the large issues that we have is an imbalance in the data. So for instance, we might have a data set that has 200 patients. But out of the 200, only 10 percent are Black. So, in trying to understand overall dynamics and make predictions over time, if you're looking at a predominantly white data set and trying to make predictions for a Black patient, that's not beneficial, right?

So what I'm trying to do is to use my mathematical skills in order to understand that 10 percent of data and maybe upcycle it in a way where we can like sample or bootstrap from it and get a larger data set and understand the overall dynamics for this population and personalize treatment for that population of patients.

So it's a little difficult right now because of the imbalance and we're trying to figure out smart and innovative ways in order to get more information out of that. So I have a student right now who's doing a little bit of machine learning, even though I say I stay away from it. He's doing a little bit of machine learning where he's trying to generate synthetic patients based on just the 10 percent of patients that we have and the other 90%. He's trying to put in the 90% Plus the, the little bit of 10 percent and generate more patients that look like the 10 percent of patients that we have.

And then from there, we might be able to calibrate a model or fit a model and understand the dynamics of that extra data set. And from there, then we can make predictions and say, well, this is a Black patient. He needs to be on this type of treatment. This is a white patient he needs to be on this type of treatment.

Dr. Blue: You know, I wish though, cause the best case scenario is that of those 200 patients that you would have an equal representation of the amount of Black people who actually have prostate cancer. You say, all right, well, if we know that, you know, 20 percent of all prostate cancer patients are Black and 20 percent of the population that is in our data needs to be representative, you know, and so, you know, I know that there's a lot of things of why we aren't represented in a lot of science and a lot of research studies. A lot of it comes down to trust, a lot of it comes down to bias, a lot of it comes down to really a lot of different what we call social determinants of health that kind of unfortunately don't allow a lot of minorities to really kind of get involved in some of these research studies, but it sounds like you are able to use math as a way to potentially help this vulnerable group of people.

You know, I guess my question would be, let's just say, for example, if we're successful, if we're able to kind of bridge the trust, if people are listening to you and say: Hey, I trust Renee, you know, and they start coming, can you just explain to them? And I know you kind of touched on a little bit, but, but why that is important.

Like, what could you use if you say: Hey, I really have a large sample size of people who have this particular disease. What could we do with that? You know, you bring that back to the doctors, but what kind of information could we actually do with it for the person?

Dr. Brady: It would have a tremendous impact as far as, we would be able to help so much more than just the individual, but a larger population of patients. Because, If we can understand the fundamental difference, and there are, there's fundamental differences between races in different patients, and we need to get a better understanding of those dynamic differences such that we can personalize treatment for that patient group.

Treating one patient or one, one subset of patients with one type of treatment and then going to another subset of patients who are completely different might have detrimental effects for that other patient group, so we really need to hone in on those dynamics individually. We need to understand those dynamics a lot better. So I can upcycle or generate synthetic patients as much as I possibly can but it's really just, we're reproducing the ones that we currently have available. We really need to start to recruit more patients, gain more patient trust within the clinic and say: your data is important. You are fundamentally different from this other patient, this subset of patients and we need to understand your dynamics to help patients going forward.

Dr. Blue: You know, I think that's important because a lot of people don't really realize this, but when drugs get approved, a lot of the people who were in those studies that get the drug approved, they weren't Black, they weren't Hispanic. You know, they didn't look like unfortunately the people who need these drugs sometimes the most. And so, it's great to see what you're doing is really what we call personalized medicine, meaning that like we can't keep doing what we call cookie cutter medicine, meaning that we just give each person the same exact medicine.

Say no, we actually can see that one group of people actually may benefit from drug A, plus drug B maybe, you know, who knows, you know what I mean, instead of just giving everyone the same treatment. But I think also, like you said, we have to kind of reach the people and do what we call outreach. And, you know, this podcast is helped funded by the COEE is what we call here at Moffitt.

So the Community Outreach Engagement and Equity here at Moffitt. And I know that you do some work in the community in really helping with not only understanding, but help to maybe even build some of that trust. So can you maybe tell the listeners, what are some of the things that you do, you know, kind of, I hate to say on your time off, but also out in the community to get the word out about this.

Dr. Brady: So, I know, with me personally, anyone that I know that has cancer, I try to, I try to encourage them first to get into a clinical trial, because again, if we don't have the data available, we can't help this population of patients. So, like my stepmom the other day has breast cancer. I said, hey, get into a trial.

She's up in New York. I said, get into a trial as much as you can, get a second opinion, talk to other people. Outside of that, I think we also need to go back to like the younger generation. So if we can educate them as much as possible, they might be able to pass it on to their parents and their grandparents and such.

So I try to do a lot of outreach with like different high schools. Like I went to a predominantly Black high school. We never really had access to, you know, this type of research that we're doing, but I try to go back to them and say: Hey, this is the cool research. Look what I can do with math. But outside of that, here's what you need to know about cancer. And here's what you need to be telling your parents and your friends and your grandparents about cancer and tell them that they need to get involved in these types of things. They need to be involved in clinical trials.

They need to be going to different clinics outside of their primary care doctor. Go to a cancer center. If you have cancer, you need to be at a cancer center. Do these things so that you can get the best type of care, but also so that we have access to your data, that we might be able to use them in our mathematical models and in our simulating.

Dr. Blue: So you also hopefully tell the students that they should enjoy math and don't like not another math test. They should actually be excited and say, Hey, try to go as far as you can in math, because this is what math can do.

Dr. Brady: Absolutely. Right now we have the high school internship program that's been going on for about seven years now. And with that, we bring in high schoolers every summer to do research in our department. And they get to do like real research where we're not just giving them like, you know, a question that we don't care about, we're actually looking at like, how does this cancer evolve? What is the important biomarker that we need to be looking at?

And these are 16, 17, 18 year old students who have never done this type of work before, but they're being exposed to it. And I never had that type of exposure. I didn't understand that math could be used in this way, but we're hoping that we can teach the younger generation and show them like, math is really cool. Cancer is very complex, but you can make a difference.

So we're excited about those types of things where we can go out and tell them how great math is, but also that they can make a difference.

Dr. Blue: So outside of a take home message that math is cool because it sounds like it really is doing some fantastic things.

You know, we've talked a lot about things that people probably never heard of before listening to this episode today. So what would be kind of one major take home point that people could learn and say: Hey, I heard this message from Dr. Renee Brady at Moffitt. But what do you want them to know? What would be one thing that you would say: Hey, this would be fantastic for you to leave this conversation with?

Other than math is cool.

Dr. Brady: Other than math is cool. Get involved. It's more, more about education, and understanding that we can all make a difference at the end of the day. So whether you have cancer or not, you know, be involved with Miles for Moffitt, be involved with different outreach opportunities, tell a friend to tell a friend that this is like a group effort.

Cancer is affecting everyone at the end of the day, we always have a family member who has been affected by it. And so I think we just need to be encouraging everyone around us to get involved in these clinical trials. Get involved in being in the right place at the right time to get the proper type of care so that we can have access to your data and that we might be able to make better predictions.

Dr. Blue: Yeah, you know, I do want to just bring this up. When you say access to the data, I want the listeners to understand and really understand, what will be used with that data? Like when they say, all right, we did all this outreach, we brought all these folks to Moffitt. Moffitt's gonna use their data, but you know, sometimes people don't really understand what that means, you know what I mean?

Can you can you explain to them how we use their information?

Dr. Brady: It's really just to get a better understanding of overall dynamics. We're not, we're not stealing anyone's data.

We're not going to say, okay, well we have your data now and now we're going to put you on the worst type of care possible because I think that's a lot of the ignorance within our community. I feel bad saying that, but a lot of people just believe like they're going to steal my data and,

Dr. Blue: People don't know.

Dr. Brady: They don't know.

Dr. Blue: And they're scared.

Dr. Brady: They're scared.

Dr. Blue: Which again rightfully so though, you know what I mean?

Like I would say that you know unfortunately, there's been a group of people that really have gotten the cold shoulder a little bit, you know what I mean? And so now luckily things are changing and we're trying to greet people with open arms, but people still have a closed fist because they're like, hey, I've never been at the front of any line now you want me to kind of give information.

Dr. Brady: So the data that we get is completely anonymized. We don't have access to any of the PHI. So we wouldn't be able to go back and say you gave us your data on March 5th and now we're using this and we're making these predictions for you and this is how your cancer is going to evolve on a patient specific level yet.

So right now we're just using it in a completely anonymized way with a cohort of patients. And trying to understand the general dynamics and then say, okay, well, hypothetically now, if a patient was to come in and they would fall into group A or group B based on our modeling and our stratification, then we might be able to make a prediction for this patient.

But it's completely anonymized as to how we're getting it right now. We don't have access to any personalized information. We're just trying to do it on a cohort basis.

Dr. Blue: Got it. So you wouldn't necessarily say, all right, this is Brandon, who has this particular issue going on. You would say: Hey, this guy is an African American male who might have prostate cancer and this is how we learn from him.

Dr. Brady: Exactly.

Dr. Blue: So I just want people to understand and know that, you know, the reason why we're trying to do this is only to help. We know that a lot of the information that we have are typically from people of European descent. And we don't have a lot of information of people of African descent. So what we want to make sure is that as we give cancer care, as we give really any type of medical care, that we're giving it to the right group of people, the right medications, at the right time.

Who knows? There could be really medicines that would be good for the goose that's not good for the gander. But we don't know that if the goose and the gander are not included in research. And so, you know, I think this is just an important thing for people to understand is that we want you to come here, not only to receive best care, but also to be involved in the future.

Because the things that we learn now will affect your kids, will affect your grandkids, and really what we're trying to do is to make the world a better place.

Dr. Brady: Exactly.

Dr. Blue: So we, we thank all of the listeners who actually may have cancer or if the family member may have cancer because really that's how we answer these cancer related questions is by actually getting involved and trying to make sure that cancer care continues to get better and better each year.

So, I know that was a heavy science, a lot of math, you know, but before we go, I want people to know more about Renee.

Dr. Brady: Okay.

Dr. Blue: I know you're not always crunching numbers and not always behind a computer and using your calculator all day. Like, what's something that you do to kind of let your hair down and just enjoy yourself and have fun?

Dr. Brady: So, a lot of my time is taken up by my kids. I have three kids, a ten year old and two year old twins. So, they take up quite a bit of my time. But, I've actually picked up within like the past two years, woodworking, oddly enough. So I like to build things and I find that to be very therapeutic in a way. Like when I'm really stressed out, I'm like, I have to do something with my hands. I need to make something.

Dr. Blue: Now you don't get off that easy. Now we need to know what kind of things are you building Renee? Like you out here building like benches, you out here building cabinets, like what, what, give us an example of what, what do you build?

Dr. Brady: So I started small. I built like a shoe rack for my garage. And then the other day I got really outrageous and I built a Murphy bed.

You know the bed that comes out of the wall.

Dr. Blue: Wow.

Dr. Brady: Yeah, so that came out really good I was really excited about it, but I first started off with like accent walls like when I was pregnant with the twins I needed a wall in their bedroom, and I couldn't afford for someone to come out and do it, so I was like I'll do it myself and it came out gorgeous So that's what I like to do.

Dr. Blue: All right, this is fantastic.

So what I need, I need a jewelry box for my wife.

Dr. Brady: Okay.

Dr. Blue: So, I'm just putting that as a plug and since I'm putting that on the podcast, you have to say yes.

Dr. Brady: Okay. I got you.

Dr. Blue: All right. Yeah. That would be fantastic [Renee laughs]. All right. So not only is she great in math, but she's great in doing woodworks and doing anything else.

So when you talk about Dr. Renee Brady, you'll know she's multitalented.

Dr. Brady: Try to be. [Both laugh].

Dr. Blue: All right. Well, thank you so much for answering all of my questions today, Renee. It's been a pleasure to hear really all about the exciting things that you're doing and all the innovative work that you have done.

Dr. Brady: Thank you.

Dr. Blue: Thank you.

All right. That's a wrap folks. I hope you've enjoyed having me and all of our amazing guests on season two of Cancer in our Community podcast. Now remember, you can go back and listen to all of the episodes, including season one on Apple podcasts, Spotify, and wherever your favorite place might be to listen to podcasts.

While our work here is done for now, the work of the COEE still goes on. Now make sure that you check out our show notes, follow us on social media, and stay informed. Until next time, this is Dr. Brandon Blue, it's been a pleasure.