Personalized medicine today is allowing doctors to become even more targeted with patient care. They are better to able predict how we will respond to a treatment or determine what diseases we may be at risk for. It also means – more effective treatments. Your own stem cells could be the main ingredient in your own personalized cure.

Manu Platt talks about his work in medicine and healthcare – specifically HIV and Sickle Cell and how personalized medicine fits into the diseases we face.

Album art, illustration
Host

Steve W. McLaughlin

College of Engineering
Dean, College of Engineering
Southern Company Chair
Professor, Electrical and Computer Engineering
Steve McLaughlin, headshot
Guest

Manu Platt

Wallace H. Coulter Department of Biomedical Engineering
Associate Professor
Diversity Director, STC on Emergent Behaviors of Integrated Cellular Systems (EBICS)
GRA Distinguished Scholar
Manu Platt, headshot

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[steam whistle]

[“Ramblin' Wreck from Georgia Tech”]

I'm Steve McLaughlin, dean of the Georgia Tech College of Engineering, and this is The Uncommon Engineer.

[“Ramblin' Wreck from Georgia Tech”]

Archive: We’re just absolutely pleased as punk to have you with us. Please say a few words.

[applause]

[big band music]

Warped voice: The hospitals, designed to serve thousands at a time, are equipped with the most modern devices.

Professor: System biology, predictive medicine, and a lot of computational biology.

Warped voice: Factors like this are the link between the research laboratory and the people.

[big band music echoing]

Steve McLaughlin: Better diagnoses, earlier interventions, more efficient drug therapies, and customized treatment plants. These are the promises of predictive medicine. By understanding the patient's unique genome, doctors are creating more effective treatments. In fact, your own stem cells could be the main ingredient for a cure.

Welcome to another episode of The Uncommon Engineer podcast. I'm Steve McLaughlin, dean of the Georgia Tech College of Engineering. The Uncommon Engineer discusses how Georgia Tech engineers make a difference in our world, in our daily lives, and in ways you might not expect.

Our guest today is Dr. Manu Platt. He's a professor in the Coulter School of Biomedical Engineering at Georgia Tech and Emory. He's going to talk to us today about his work in medicine and health care, specifically breast cancer and sickle cell, and how predictive medicine fits into the diseases we face.

Welcome to the program, Manu.

Manu Platt: Thanks for having me. Glad to be here.

Steve McLaughlin: Well, let's jump right in. Can you say a little bit about what's happening in your lab these days?

Manu Platt: Certainly, we've got so many exciting projects going on, at least I think so. Hopefully I can convince the audience.

A major focus we're looking at right now is breast cancer. We really think about that quite a bit. If two women have breast cancer, how come one person’s is more aggressive than the other’s? So we can give her and her doctor a little bit more information to decide on how radical the treatment should be for that person. And that's been a great thing. It brings into play system biology, predictive medicine, and a lot of computational biology that brings engineering and biology together.

We also do a lot of things working with our colleagues over at Emory University. As you know, I'm in the joint BME department. I work with my good buddy Mike Davis over there. We've been on some really interesting work in how if you can take these cardiac progenitor cells or these stem cells from children that have undergone surgery to repair, like, defects, that we can isolate these cells that they produce these amazing factors that we can then use on an adult heart to help that heart repair. So that's the biology part of the work.

And then I'm helping them look at, again, through computational modeling, what are some of the really interesting signals that are present in those little things that they're releasing that then we can then make a drug or make a combination that doesn't require us ultimately to use the stem cells from the pediatric hearts.

Other things that are going on. Sickle cell takes over my lab. It is a terrible disease that is still shortening life expectancy. Really high rate of people here in Georgia. And as you know, it predominantly affects African-Americans in the United States. And so we really care about why children with sickle cell disease are at risk of strokes, because when I was getting my PhD here long, long, long time ago, we were looking at adults getting strokes and these people 60, 70 years old, but in sickle cell, this genetic disease in children having strokes at the ages of 2 through 5 years old, which lots of mechanisms still need to be determined. And we use a great group using fluid dynamics, cell biology, and all these wonderful tools we learn here in biomedical engineering to address that problem.

Steve McLaughlin: You start a little bit about whether it's breast cancer for women, whether it's stem cells, and other things you talked about.

What are some of the common threads that you work on?

Manu Platt: My lab, we’re the Platt Lab for repair, regeneration, and remodeling, and really it's about tissue remodeling. So we really look at these enzymes that help change a tissue from its healthy tissue to its diseased state. So how can we stop that?

That's the first way.

And then of course for the regeneration, we're looking at the opposite way. How do you go from that diseased tissue to that healthy state? And we study these enzymes that actually do that remodeling. Now, the trick about these enzymes—they’re kind of tricky enzymes—have temporal natures. They're dynamic and that's where all the computation analysis is extremely helpful and helping us understand the dynamics that are changing that you can't test every step experimentally. So you fill in that gaps using the computational map.

Steve McLaughlin: Before we go into details, can you say what is predictive medicine for our listeners that don't—?

Manu Platt: When I try to explain it to others, what I think about is, again, how do you see in the future? What's going to happen for one person versus another person? Because we are all individuals and so the clinicians actually had to deal with end of one, which in scientific research we do the whole population. So what happens for this particular person? I want my clinician to think about me directly. So that is then taking particular markers or measures from me, my physiology, how I would respond. Of drugs that are on the market, they work for 80 percent of the population. 20 percent, they just don't work. It's not really clear why. They just don't work for 20 percent, and that 20 percent could be different for drug A than drug B than drug C.

So predictive medicine is not taking the risk of let's do the populations but let's sample you and find out what will exactly work for your course of treatment. And that, I think, should make the patient feel special. But then again, the other problem as we mentioned earlier, how do you predict the future? And as I always tell my students—I work with a breast cancer doctor, and I say, if I go in and ask him, hey, so can you just not treat this patient and we see what happens to see if I'm right? And he says yes? Right. Right. Luckily, he doesn't say yes. He says wait, stop.

So they still treat the patient as best as possible and then we try to predict the outcome along the way.

Each of us have a different proteolytic potential, we call it, where even in a non-diseased state, there's just something inherent about our genes, as we mentioned, something about the way we activate certain pathways, and that's where that personalized medicine comes into play. Something about you makes your cells be super active when they see this stimuli and mine might be very different.

But then if we get that breast cancer as a stimulus, how would our cells react to that case is what we're trying to predict. So that when that woman goes to the doctor, the doctor can say, “Listen, we've done this test. Looks like you have the more invasive phenotype. Maybe we should go for the more aggressive treatment,” or alternately say to another woman like, “We think we can keep a close watch on this and do something like local radiation. If you're interested in doing that, we can work with you. If you're not, you want the double mastectomy, we can do that also.”

But the trick about personalized medicine treatment is that's one part of the trick, but then we take that for predictive medicine and engineers are good, but we still can't really see the future yet, OK? Back to the Future hasn't quite happened yet. So how do you test predictive medicine in the future? And so working with the clinician here at DeKalb Medical Center in Atlanta. He would give us samples from his women undergoing double mastectomies. And by the way, he's a Georgia Tech alum. That's how we met.

And then we were testing our predictions with what were these women's initial diagnoses when they were diagnosed with cancer? And we actually did find a nice relationship.

Steve McLaughlin: I know that your lab is large. You have a lot of students, because we talked about computation, we've been talking about enzymes, you talked about biology, we talked about cancer. My guess is the skill set of the students in your labs and maybe even the director of the lab, you know, has a really…

Talk about the kinds of skills that are needed to be successful, because you're coming at so many of these problems holistically and from so many different angles. I mean, wow. What does it take to be in your lab?

Manu Platt: [laughter]

Curiosity, I think. Curiosity first. Curiosity and teamwork.

My lab is pretty sizable, I'd say. Yes. And it's also a really diverse lab. So that's why we can do so many projects. It's diverse in the people, their backgrounds, but also in their interests. So I have some people who are interested in really doing computational biology, right? And there are some great Georgia Tech undergrads that have been working with us to do some of that work, because we have some great coursework and again, engineers are really computationally minded. Then I've got those that want to do more of the biology side. So we do run a good wet lab, and we do work on the cellular level, but we also do preclinical models. Then I have students that are medically oriented that are interested in becoming medical doctors. So I have collaborators that are clinicians, and those students tend to favorite those projects. And so it's about letting people that are interested in the lab know all the different projects, what you might be interacting with if you are part of a project, and then kind of letting them sample the space a bit to find their niche.

And then of course my part is kind of the Grand Puppeteer of all of it. And I'm just really loving the different projects that we're studying, because again, the thing that drives me and I hope drives my students is these are real problems that we want to solve for people. So it's not just an academic gesture, right? And by interacting—we also interact with the communities in which these problems address. So I also like people in my lab who care about doing the community outreach, working with the community-based organization, because I think that makes the problems real, hopefully motivating longer time in lab.

Steve McLaughlin: Well, I mean, I can. I think like a lot of people can relate to the need. The really critical need. There's a lot of patients out there, say, with sickle cell disease that are in need today, right now, for new and advanced therapies. I'm really curious about how close you get to those patients. You talked about reaching out to communities, but with our partnership with Emory, the hospitals right nearby. Georgia Tech doesn't have a hospital, but Emory does. I'm really curious about how you connect to patients and how… because there are too many patients today that need the work that you're doing.

So can you talk about how that fits into your research program?

Manu Platt: It's interesting. Again, I never—I did used to think that I wanted to be a doctor, but dealing with patients for me is just too much stress. I’m a very emotional person, so I really can't deal. But I find talking to the patients, number one, gives you insight on what they're actually feeling and what they are actually going through. So you mentioned we do have that partnership with Emory, so we work really closely with Emory's Department of Pediatrics, in particular their pediatric hematology oncology group, where there are several excellent researchers there and also clinicians that work on sickle cell disease as a problem. So that's been great to find out, because again, as an engineer you can come up with something, but if the clinician says there are barriers to getting it to the patient, then we have to kind of retool our design.

Steve McLaughlin: Don’t even start, right? I mean, to go down that path, because you're not going down the right path.

Manu Platt: And it's shocking when you're like, “This is clearly what we would identify as the key problem,” and they’re like, “Yeah, you can solve that, but we'll never be able to do that in patients.”

And you’re like, “Oh, OK.”

And then the other way that I access patients, particularly with sickle cell, is I work with the Sickle Cell Foundation of Georgia. And we've been doing that, actually since I started, where first we were actually… They would gather donors who would donate blood for some of our different projects. We got all of our approvals here through Georgia Tech IRB. And it was important to actually start to pay the donors, because a lot of people in sickle cell are on disability, because they have these pain crises that prevent them from working.

And so that was a relationship we established early on, where we would pay the foundation, plus we would pay the donors. And then we just started to be involved in multiple of their fundraising activities, because… I started in 2009, the economy took one of its tumbles, and their budget was cut, and so we pay a part—they have an Annual Sickle Cell Walk Run. So I've been having large Georgia Tech teams. Not only my lab, I recruit other undergrads, at the faculty/staff. We raise money, we try to have the largest team ever, and then we partner with them and some of their other things where they go to the capitol. They have a Sickle Cell Day at the capitol where they petition our state representatives, and it's always great. They kind of have this thing of, “Oh my, and we need the professor from Georgia Tech to talk to the state reps,” which I'm just a person. I feel like they have all the information and they have all the insight. But it's nice that we could bring whatever gravitas being a Georgia Tech professor brings to that. Again, I always like to bring my grad students and undergrads at those events, because they need to see what is the other face of this and these things are real and they are real people.

Steve McLaughlin: And that help as needed now.

Manu Platt: It's needed now. And that's really critical.

Steve McLaughlin: Not five years from now.

Manu Platt: Exactly.

Steve McLaughlin: So one of the things that you talked about. We had the chance to work on just a little bit around Project ENGAGES. So completely changing the topic a little bit. You talked about reaching out to the community, and so could you talk about Project ENGAGES and what's happening now and where do we hope it will go?

Manu Platt: Oh, I love to talk about Project ENGAGES. ENGAGES stands for “engaging the next generation at Georgia Tech in engineering and science.” It's a partnership with the Atlanta public school system where we right now have six partner schools. I think we're taking another one on this year. But really where we bring in juniors and seniors from Atlanta public schools who come and work in the Georgia Tech labs. We started off just in the bioengineering lab space, but we've blossomed out to mechanical, electrical, aerospace, biology, chemistry, and so we now, I think, on campus have about 30 students. There are also some working in GTRI labs so doing that really applied work.

And I think one of the novel aspects is that we actually pay these students, and we pay them greater than $10 an hour, because, you know, APS schools, the schools that we work with are Title I schools, so you know most of them are all free lunch, which is an indicator of socioeconomic status. We didn't want to select for students who were just able to just intern or work for free and who would not have to work. So we pay them a good wage so they don't have to do fast food or retail or anything else.

You know, I worked at Hardee's when I was in high school, which I still love Hardee’s food. I was there when they did fried chicken. That's a whole other story.

But no, most of these students are African-American students coming here. I used to think that many were just first-generation college students, which most of them are. But in talking to the principals, we now know that actually some of them are first-generation high school graduates, right?

And so you look at what does that do for that family when their student is coming to Georgia Tech, working at cutting-edge research. We don't give them cookie-cutter projects. They are working with a graduate student. They're working with a postdoc. They're working with the principal scientists at GTRI, and they are solving a new problem.

And as we've progressed, we're now in year six. What's been great about it: We have actually now touched—there's been 100 students that have come through the program in these six years. And in May we actually had our first college graduates. So out of the first five, we had three of them finish in May. Two more finish in December.

That's very exciting, because we also realized, as we were going on, it wasn't just about giving them these experiences so they could get into college. The motive had to change. OK, just getting the college is one step. Now we need to give them the skills that they will graduate from college, right? So that becomes a different level of preparation at the door.

And it's been great. And what I love about it more than anything—not only someone poured into me when I was in high school and had science programs for me, which I am appreciative of those professors who had those programs, because now I know the work. But it also, for me, changes the face of science at Georgia Tech, where there's all these brown faces in all of our biotech buildings that—they're comfortable, they're hanging out in the lounge, they’re in the labs in their lab coats, they're using our core facilities, and it's just this is what science looks like, right?

And so then I think it impacts the other students who are just around the other grad students who are, “Oh well, this is just what Georgia Tech scientists and engineers look like.”

And I think that's a powerful thing. I remember we also do professional development for the students. And one of my good friends was in town visiting. He started out as an economist working for Congressional Budget Office, but he started a blog that became a thing. Sold the blog. And so we always want them to speak to the students to give them inspiration of different career ideas.

And he was waiting for me, because he arrived way earlier than he should have. And I said, “What building are you in?”

He said, “I’m in the one in the lobby where there's like all the black kids walking around, all the black people walking around.”

[laughter]

Well, tell me the building because—he was there and that's what he thought that the buildings looked like here. And so I love that, because then when their parents come on campus and their uncles and aunts and their grandparents come for our big events, everybody feels like they belong here. And I think that's what I've always loved about Georgia Tech.

Steve McLaughlin: Well, I think the… You know, Atlanta Georgia Tech's in Atlanta and there are so many of those students who, for whatever reasons, you highlighted many of them: socioeconomic familial reasons, that they really don't have access to places like Georgia Tech. And Project ENGAGES is that access point. I think that's absolutely remarkable.

I think the second thing the second thing to highlight is that I've seen the projects that the students do. And it's not busy work. The students are really accomplishing a basic science, and so I think it's a reminder to us that all high school students, when tapped into, have that ability and skills right at that moment. It doesn't mean they have to get a bachelor's degree or a master's degree and PhD before they can come into our labs and be productive and that they have the skills today.

And so I'm curious about that experience. You know, what do you do to tap into the fact that they don't have bachelor's degrees and master's degrees and so on? How is it that you can tap into and really spark and ignite that success in a high school junior or senior?

Manu Platt: That's right. That is a really—it's evolved over time. And it's been an interesting part. That is how we are able to recruit new labs to it, right? So that becomes the hurdle of convincing a professor and a graduate student. If we bring in this high school student, they will be able to contribute.

And so one of the first things that I like to remind them is there's always a fresh perspective that they would bring to the problem. So I think those of us that have been studying fields for years and for decades or however long get entrenched in the dogma behind the field. This thing can never work like this because we've known and how we've learned.

And someone coming in with a fresh perspective can have insight that would never pop into the mind of someone that's entrenched in the dogma. And so I find that very useful, even in what I've seen in my own lab, the questions that they may ask at a lab meeting or at a journal club, that you see the whole room turn and be like, “Oh, that does seem a simpler way to go about it.”

But what also comes about from that is we train the mentors really well. So we do a mentor orientation and a training, and it's now become a full-day retreat, because with the great evaluation we've done on the program just to make sure we're improving it, which has been great with CEISMC has been helping us to the evaluation. Is we realize that the graduate student, the postdoc mentors, are really the critical point to the program. They are the ones who interface with the students every day. And as you know, not only do the students work during the summer, they work during the academic school year. So they leave school early, come over here and work.

So our grad students are working with them every day, and grad students are under certain pressures on their own, and faculty are putting certain pressures on them. And so we realized they were kind of in this sandwich space of critical for the student success.

And so in our orientation what we stress them at those things that you said there. These students, I always tell them, “What’s the difference between a freshman at Georgia Tech and a high school senior? Just a little bit of time.” Right?

And then the other reminder is everyone, all of us in that lab, all of us had our first lab experience. So you can have all this book learning, but that first time you’re in lab, things are different. And so I said, that's just how they are.

But we actually prepare them with… We do a boot camp before they enter a lab. So we do a summer biotech and engineering boot camp, where we like run them through the paces for two weeks hardcore. And the students get a grey hair or two. OK, they don’t, they’re still young.

They'll be given the basics so that when we turn them over to the lab, the mentor doesn't have… It's not just a complete what do they know. So we let them know they have these basic skills. Now you can take them from there.

And it really becomes about trust. It's about establishing a close mentor-mentee bond. If you trust this student, and again, trust, I think discipline is a part of trust, because we do remind them this is employment for the students but also for the grad students. These people are capable. Have expectations. Hold the bar up and let them reach it.

And what we've seen over this time period, we've had our ENGAGES students be co-authors on publications. I mean, I think at least… Now, I think we have up to about seven students who've co-authored like primary research publications, before they start college. And so we're watching now to watch how many then enter into undergraduate labs when they've started their college career, because they've got a résumé that says I've been doing this for a year or two. I've got a leg up.

So I think it's just that trust, that openness, and being open to this diversity of ideas and respecting that they are full contributors to the sciences.

Steve McLaughlin: Where we always end, Manu, is asking the same question of all of our guests: What makes you an uncommon engineer?

Manu Platt: Well, I've never met another engineer like me.

What makes uncommon? I think what we do, again. We think about solving problems that affect real people. And we think about doing it with a diverse team, with diverse ideas, and working in diverse continents. And I've never met a problem that had a human component that we were scared to think about. And I love that we are raising a team of people that are like that as well.

And I think another thing that makes us uncommon is we value education and outreach and bringing new people into engineering. Not as just charity work but as a way to solve these problems. And I think we couldn't do it without the team that we that we've gathered.

Steve McLaughlin: That's so fantastic, because one of the things I kept saying over and over again: There's real people and there's real patients that need it today. And I think the public needs to know that there are people like you that are not just sitting in their ivory tower, you know, with their elbow patches. That there are plenty of us out there working with the community, working on real problems for people that need those solutions today.

So we're really lucky to have you here today. And we're really lucky to have you at Georgia Tech. Thanks, Manu.

Manu Platt: Thanks a lot.

Steve McLaughlin: For now, that's all for The Uncommon Engineer. I'm Steve McLaughlin. Thanks for listening.

[“Ramblin' Wreck from Georgia Tech”]

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[music]

Narrator: I’m Steve McLaughlin, dean of the Georgia Tech College of Engineering, and this is The Uncommon Engineer.

[radio modulating to different frequencies]

Radio: 1-800… We live in an age… Sounds incredibly complex. It sounds like… You need to have abilities that span… I'm really geeking out here…

[jazz, ragtime music]

Manu Platt: They’re efficacious in fixing the disease, but side effects stop them from making it through to FDA. And so that's again what we're trying to help understand, because these are fairly well-designed inhibitors. But just what are the other ways these things are working under normal physiology that it's hitting other organs in ways to cause problems? So that's why we need to know the levels of them and that's why we need to know how active they are. And again, I take that back to the computational modeling. So that's where we need to understand, in this system of these things working in a network, how they are working with and against each other, so that we can repurpose these well-designed drugs for those.

That's the aspect we care about, as far as treating them for the breast cancer. But we also look at them as functional biomarkers, because they are turned on in the cancer and they're off in the normal tissue.

So we started off thinking, “Oh, well, this will be great. We can use these as an early diagnostic,” but these metadata analyses have come out about diagnosing breast cancer early. It's not really reducing deaths due to breast cancer. So what's happening is women are undergoing these mastectomies and just saying, “Let's just do a prophylactic mastectomy, remove the breast, so I'm safe,” but it's not necessarily so. Some women, again, don't… The way the studies are showing, they would not have advanced to an aggressive form. So maybe they could have had a more breast conservative therapy, because a mastectomy is a major surgery. And then most women get a reconstructive surgery afterwards. So those are two major surgeries women are undergoing and maybe not all needed it.

And so that's where we try to predict which women will have the more aggressive form of the breast cancer. And so using these functional biomarkers, we know that white blood cells, they can enter into that are circulating in our blood. They will leave the blood and enter into that developing tumor and help it advance. So we've been developing ways to profile the white blood cells, because that would be a minimally invasive test and every time you go to the doctor you expect they're going to take blood and then profiling those to look at if that person were to develop the tumor, how aggressive with that tumor be?

Steve McLaughlin: And so to oversimplify, to kind of track what the white blood cells are doing, where they're going as an indicator of…

Manu Platt: As an indicator of how they would help that cancer metastasize and break out of its natural or out of its primary location.

Steve McLaughlin: I see. And so it sounds like another one of the common threads throughout, in addition to computation, are the enzymes and you know the progression from healthy to diseased tissue and diseased tissue to healthy tissue. For folks that aren't that familiar with it, can you talk about the role that enzymes play in that? And then how it relates to your research.

Manu Platt: Oh, sure. So enzymes are proteins, right? Our cells make these wonderful proteins and we study a particular class; they're called proteases. So they're enzymes that destroy or degrade other proteins, and that is, again, all of the tissues and all the structures that we have in our bodies, some cell had to make it, right? So because a cell made it, it’s usually made of a protein, lipid, something like that. And so the proteases that we study are turned up in the diseased state, then the cell spits them out, and then they start to degrade the tissue around them.

And the way I always look at physiology: It's a good thing at first, because our bodies are always trying to keep us healthy and keep things going. So it starts off as doing a good thing to kind of change the structure so the organ can survive, so other things can come in, and then just because of our positive feedback or we keep doing the bad part that keeps the disease going, that positive feedback gets out of control. And that's what we need to stop it from working.

Steve McLaughlin: And so is that—that kind of positive feedback that's needed to promote healthy progression. When that feedback goes wrong, what's the cause of that feedback? Are these genetic mutations that typically come in? What are the things that cause—

Manu Platt: It's usually humans. [laughter] We are the problem. No, not all the time. So when I studied the genetic diseases, the problem is the genetics, right? It is—there's a system that's off-kilter and our bodies have reached one level of balance or homeostasis, but as that disease continues and the body continues to try to manage that disease, it has to then push other systems into a new steady state—I'm told all my engineering talks are—into a new steady state, but it has to keep reaching these new steady states to kind of keep things going.

So that's the big case with sickle cell. Like, that mutation causes these bad red blood cells that are stiff and sticky. They continue to move all throughout the body, continue to damage tissue. We can't stop them from damaging tissue, but we can stop some of the damage that happens by turning down some of these programs.

Now, breast cancer is a really interesting thing also in that the cancer has now taken a route where it's going to survive for itself; whereas, normally the cells are like, “I want to do my part but I want the whole body to be successful,” the cancer cells take on a moment where they want to survive for themselves and they're doing all the things needed to survive for themselves, which then the rest of our system starts trying to accommodate that perturbation, which continues to take it out of whack.

So that's what we use the computational modeling, to predict how far will this go and what is the way we can cut it off at the pass before it gets past a certain place? And that's also where the genetics come in. How come that person A is really doing it a lot differently than person B is, and that means that the drugs may not work the same for A and for B, and the clinicians are going to have to make that decision. Not me. I don't want to be a medical doctor. It's too much pressure.

But where that took us internationally was working with Rudy Gleason, who's in mechanical engineering and biomedical engineering. He had a relation with Ethiopia for years. When I was doing our HIV work in South Africa, he was an HIV work in Ethiopia. And so we said, well, we should connect.

And in Ethiopia, I was there doing HIV work. A student of mine was there for two months with Rudy working at Addis Ababa University. There were a lot of problems with me being in Ethiopia, with keeping my luggage at the airport. That's another story.

But on the fourth day going back to the airport, when I was like, “I'm never coming back here,” the professor we worked with there was saying, “Hey, I know you do breast cancer work. You know there's this problem of young women getting breast cancer in Ethiopia?”

And I was like, “What do you mean?”

He said, “Women in their early 20s are showing up with Stage 3 and 4 breast cancer.”

And you know I have heart. That's why I want to fix these diseases.

And I said, “Wait, wait. I've never heard about this. What are you talking about?”

And that was five years ago, sparked a program that we need to come along and help, because now it is, what about Ethiopians or descendants from Ethiopia, what about their tumors make them so aggressive? And it dovetails nicely with my predictive medicine in that now we almost have a positive control for aggressive forms of tumors.

So we're profiling them to match that with our computational predictive model, but then also at the same time doing some genomic screening and trying to figure out what makes theirs so aggressive.

But where that becomes so interesting is we're training up grad students in Addis Ababa University, right? Because they have access to the samples and they are the ones who are there chronically. You all asked me to teach over here, so I have to be over here a couple of times a year. And just training the grad students on the simple test.

And what I've learned a lot about engineering through my training is what do engineers do? They make it better, faster, cheaper, and in trying to develop something for a low-resource country, that's the critical point. So we've got our technology that's simple enough, transportable. We've taught them how to use it. And now they're collecting samples and we'll be visiting back and forth monitoring.

Steve McLaughlin: So in a purely research setting or in a clinical setting?

Manu Platt: Both. So they are doing it in a research lab, but they go to the O.R. to get the samples. So they're getting patient match, normal, and tumor breast tissue. And the surgeon calls them in, they go and get it, and then they come back, and then they can do the processing and lab.

Steve McLaughlin: And then does the surgeon take your outputs and pass along for treatment options?

Manu Platt: So we haven't yet for Ethiopia. They also—this is where that low-resource country becomes another issue.

In the United States, most women are diagnosed Stage 1 or 2 breast cancer. And we have these great medicines and therapies, particularly for some of the identified breast cancer subtypes. In Ethiopia—as told to me by them so I'm not trying to speak for them—they can't get the antibodies and some of the reagents used to subtype the breast cancer. So they are unable to be able to say you have ER+ breast cancer or HER2+ breast cancer, which we have therapies to target those. And so that's why we have to, number one, do the epidemiology as well, to kind of capture the disease so that we can disseminate that and it will be recognized more as a problem. So then we can help bring in some, I don’t know, NGOs to help motivate how do you get those drugs to these countries to help these people.

So that's where it takes it out of the research domain and makes us have conversations with.

Steve McLaughlin: Yeah, but without the research, you would never even be having this conversation with the NGOs. And as you build, as you build the data, as you build your experience, as you build the “here's what we can do in a resource-constrained country,” we can't do that yet, but you have the evidence that those treatments would be successful or at least this advanced predictive ability would improve.

Again, it's kind of where we started from the beginning there. There are patients that need this. Not in six months, not in five years. There are patients that need it now.

Manu Platt: Exactly. And that's where—I take it back to, like you said, the computational modeling and doing the more data-driven models. With our biomarkers and the things we are able to measure there, if we're able to use the right statistical model that correlates that strongly with one of the sub-phenotypes that we can identify here in the US, then maybe we can motivate.

“Well, all signs look like it is most likely this type of cancer. Maybe you should go ahead and give the drug to this patient and we can move forward.”

So it's about—I love it when my students go there, because again, all the things that we have in our lab here are not there, and so they have to think differently about what is a workable solution. Some of the obstacles that get put in their way are not things that would be over here and they're like, “So how do we get over this one?”

And I do always think there's a way that technology can work to get around some of those things.

So the way we do the predictive medicine, our strategy. When I was a postdoc, I worked with Doug Laffenburger, who was one of these computational model leaders in the field, and he developed this cue signal response paradigm, where if you treat with the cue, you measure a number of signals, and you match that to a response using matrix algebra or just using math. Right?

And so what happens there is, by measuring these signals, we are able to train a model that kind of becomes like the decoder. So then anytime you put in new signals, it'll help you tell you what that response would be based on that decoder matrix. So that's why if we get enough patients to help train this model where it’s very reliable, now we can predict a number of different outcomes for patients measured in all kinds of different ways.

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