As part of our Faces of Science: Ambassadors for Equity project, we talk with graduate student Markia Smith about her research in cancer and genetics, understanding health disparities in historically marginalized populations, and why she chose these areas of study.

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ERIC: In one of our recent programs, we got a great question about the intersection of a scientist's work and their identity, and what factors influence a scientist to choose their field of study. Today on Pulsar, we're talking with a graduate student who is researching genetic and environmental factors affecting cancer, as well as the health disparities that arise because of them. My guest today is Markia Smith, a PhD candidate and biochemist at the University of North Carolina at Chapel Hill. Markia, thanks so much for joining me.

MARKIA: Thank you for having me. I'm really excited to share with you guys what I do for a living.

ERIC: So what does it mean to study the genetics of cancer?

MARKIA: When we look at the genetics of cancer, we're normally talking about tumor biology, and trying to figure out how your DNA differs. So what a lot of times they do is they can remove the tumor, and then we can sequence a tumor and see how that differs from the normal tissue that the person has, and what mutations it might have.

ERIC: So understanding how those differences might be related to factors like race can be really important for understanding cancer in general?

MARKIA: Yeah, so we can look at race. We can look at ancestry. What's really interesting about what my group does, is we don't just look at race as how it's black or white. We try to look at ancestry, ethnicity, race, and then also look at race as a social construct, so it's not a biological variable. But social things, such as socioeconomic status, and where you live, and different things like that can affect your tumor biology.

ERIC: So when we're looking into causes of cancer, it's not just genetics but also social and environmental factors that can play a part?

MARKIA: Exactly. So my thesis is based on breast cancer and liver cancer. And so I'm looking at the mutational differences between both types of cancer. And the idea is that, most of the time when we look at cancer, there's pan-cancer analyses that look across the different tumor types. But what I can compare between breast and liver cancer-- it allowed me to compare-- breast cancer is normally associated with more endogenous exposures, such as when women get their menses, and parity, and things like that. Whereas liver cancer is more associated with environmental exposures, so like aflatoxin, which is a grain that a lot of people eat, or based on your zip code and the things that are around you.

ERIC: Is it really hard to study cancer when there are so many different kinds of cancers, and each one is almost its own unique disease?

MARKIA: Yeah, that's the hard part in trying to figure out how we can find the similarities but then also find the differences between the two makes it really difficult. And so, right now, that's something that I'm kind of struggling with is how we can compare two things that are on the opposite ends of the spectrum but seem to have similar DNA repair mechanisms. And so by me looking at them and comparing them, we might be able to find some markers that are similar between the two, and lead to different therapeutic options. \

ERIC: So you have to be mindful to keep the big picture in mind, even as you focus on one type of cancer. 

MARKIA: Yeah, it is better to look individually. Most of our lab focuses on breast cancer, but there is a subset that looks at different types. And I think it really helps us become more well-rounded because we have an idea of how different things in the body may work, especially when it comes to looking at secondary cancers or mets.

ERIC: And your work takes all of these things into account, so tell us about your research.

MARKIA: My work specifically looks at mutational patterns of tumors. And so this basically just means how the mutations differ between the specific context that they're in. And so what I'm trying to do is see whether or not those mutations differ between Black women and non-Black women, for example.

But I'm also going to try to pull in and look at gene expression, and see how those also differ, and then basically try to-- it's like an integrated analysis, where I'm looking at all of these different things to see how they interconnect and interplay with each other. And then that'll give me an idea of how basically the whole tumor microenvironment plays a role, and then also looking at how race plays a role as a social construct, because then I'll try to include things like socioeconomic status, or when they got their period, or things like that.

ERIC: Can you give an example of the kinds of differences in cancer we see across different populations?

MARKIA: Black women are more likely to get more aggressive subtypes called basal breast cancer or triple-negative breast cancer. Our data set is unique in that, if you compare it to the Cancer Genome Atlas, which has only 17% Black-- not even that-- we have 50% Black, so that we're more empowered to look at these analyses and try to figure out how we can tease it apart.

ERIC: So you mentioned that data you look at, and it must be almost overwhelming to have so much of it. It's almost the opposite problem that scientists had a few decades ago when they said, we could solve this, understand that, if only we had more data. And now we have mountains of it. So what tools do you use to handle and make sense of these enormous amounts of data?

MARKIA: I do a lot of computational biology, so coding, programming, things like that. And I use R, Python, MATLAB, pretty much all of them, depending on what I need to do. We're lucky at UNC that we have a big cluster that we can use. So it allows us to house all the data on there, and then I can just do a lot of data wrangling.

ERIC: So this is computational biology. Can you give us an overview of how that's different from other ways of understanding biology and the human body?

MARKIA: This approach more so focuses on big data, how you mentioned how we have all this data to look at. And so there's a couple of different ways people may look at it. They may use machine learning techniques, programming, cancer genomics in the brain. There's different genomics for that as well. And there's also the Computational Medicine aspect, where you can do precision medicine and try to look at a bunch of different markers and figure out what hits you get.

ERIC: And what do you see as the ultimate goal of this area of research? What do you hope it will accomplish in the next decade or two?

MARKIA: So I'm hoping that it helps us reduce health disparities, because the idea would be that we would gain information about potential, maybe, tumor markers and therapeutics that we can use to target a subset of patients. I'm looking at Black women, so particularly Black women, or in the case of liver cancer, Asian Americans, or Asians in general. So the idea would be that, in the future, we could use the knowledge that we've gained here and try to apply it to other cancer types as well to figure out why there's disparities there, but also try to reduce the disparities that we see in liver and breast cancer, and then how we can apply that to the community initiatives and public health initiatives to improve awareness.

ERIC: Well, accomplishing all that would be really awesome. Now the question that inspired this episode was, how do scientists choose their research? So what was it about this specific problem that drove you to work on finding a solution? And how did you end up finding this method of research?

MARKIA: So it's funny because my first research experience was in high school. And I actually was working on HIV and trying to characterize HIV 1 cascade, which is basically just this protein that's involved in HIV. And that's what originally we were trying to figure out is how we can target this. And then I was like, OK, I really like this, but it was a biochemistry lab, and it was a little boring. It was hands on, but it wasn't-- sometimes you need the gratification of seeing and being able to interact with the patients, because I get to interact with patient advocates and just see the work and how it translates. And so I was like, well, I don't want to be in a lab that's just bench work. And so I did some more experience in undergrad, where I worked looking at breast cancer morphology, and dormancy, and things like that.
But it was biomedical engineering, and so I was finally getting closer to what I really wanted to do. I did a post-bacc at Baylor College of Medicine. I finally got to be immersed in a more clinical setting, and so getting to interact more with the tumor boards, which are meetings where they talk about different patient cases, and then how the clinicians are going to solve the problems. And it helped us translate our work and what we were doing in the lab. And so I really enjoyed doing that at the bench. But then once I got to grad school, I was like, I don't want to be at the bench anymore. And so then I did a rotation in computational biology, and I loved it. And I loved the interactions that I got to have with other people, especially because we work with so many different labs. And it doesn't feel as isolated sometimes as the bench can be.
So then I was like, I've always been really passionate about health equity. And so then I was like, well, I like cancer, and I like health disparities, and so I'll stick to this. And then I don't have to go to the lab.

ERIC: So really pretty iterative, almost like engineering your career to figure out, I like this area, but not that part of it, and getting closer and closer until you find something that you're passionate about doing every day.

MARKIA: Yeah, and I asked my mentors. I'm always like, so how did you guys know? And they're like, we still don't know. And so that kind of helps me figure out, OK, am I on the right path? Is it OK to be doing all this exploration? And I usually tell my mentees to do the same thing because you get the opportunity to rotate before you join a lab in grad school. The first rotation, I did something I've never done in my life. And then the second one is when I finally got into computational biology. And then the last one was mouse work, and I was like, OK, I definitely don't want to do this. So I was like, OK, we're just crossing things off the list, slowly but surely. And I'm sure once I graduate, then I'll be like, oh, I want to actually move towards this or something. I just feel like it's always changing, or evolving, I should say.

ERIC: And something else that we haven't talked about yet that you're passionate about is diversity in science. Can you talk about why that's so important to work towards?

MARKIA: It's so important, mainly because when you're working on more diverse research teams, there's things that people who are not as diverse don't think about. Particularly, a great example I always make is that, in breast cancer, they didn't start looking at race until about 10 years ago. And it's like, to me, as someone who's a person of color, I would have immediately been like, well, it seems like we may want to look at other factors outside of specific types of breast cancer, things like that. And so having these diverse populations definitely helps on the research side of things, in figuring out what is exactly going on because they might have different experiences that might aid in you figuring out what the problem is or what is potentially driving differences.

ERIC: And to wrap up, what do you enjoy most about your work right now?

MARKIA: The thing that I love the most is connecting with people. I learn something new every day. I connect with people who do what I love, and so they understand my passion. And sometimes that can be hard, coming from different environments. But I definitely love connecting with different people and learning new things.

ERIC: That's awesome. Well Markia, thanks so much for joining me and telling us about your career and research today.

MARKIA: Thank you so much for having me.

ERIC: For more on this subject, visit the Museum of Science, and stop by our brand new exhibit - Faces of Science, Ambassadors for Equity, on Level 2 of the Blue Wing. Until next time, keep asking questions.

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