Akın Deniz Heper
Yale-NUS College
Singapore (1.3° N, 103.8° E)

featuring Anıl Korkut, Assistant Professor, Department of Bioinformatics & Computational Biology, University of Texas MD Anderson Cancer Center, Houston (29.7° N, 95.3° W)

Anıl Korkut is an assistant professor at the University of Texas MD Anderson Cancer Center. On a general level, his work concerns computational research in cancer; specifically, he runs the Network Pharmacology Lab and works to utilize network pharmacology and computational models to predict and avert resistance in cancer therapy. His research focuses on finding causal links, mostly on the proteomic level (i.e., using the protein networks of cancer cells), and developing tools using computer models to determine appropriate therapy approaches to various kinds of cancer.

My most important takeaway from my interview with Dr. Korkut was the significance of passion and excitement in science. He has dedicated his academic career path to the pursuit of science, beginning in high school with chemistry class, followed by a double major of chemistry and molecular biology and genetics in college, and then a PhD at Columbia University, where he realized he was going to work in biophysics. He told me he decided he wanted to focus on computational biology while he was dissecting larvae as a part of his first project in the US. I related to this deeply, though in the opposite way; I realized I did not want to work in computational biology when I did an internship in a genetic diagnosis center and found myself trying all I could to remain in the PCR (polymerase chain reaction) section and away from the bioinformatics lab. As Dr. Korkut pointed out, however, the future of science, and of biology specifically, is in the merging of computational and experimental methods. A part of Dr. Korkut’s work, accordingly, is in building computer models for cancer research and therapy. He believes that our growing understanding in cancer biology, coupled with patient profiling methods and computational models, could lead to cancer becoming a manageable disease in the next 5 to 10 years.

Dr. Korkut’s position at University of Texas also extends into the managerial side of education. He is critical of the way PhD programs prepare students for work environments because of the focus on academia and tenure positions despite the vast majority of PhD students moving onto the private sector—sometimes to positions unrelated to research altogether. He talked of a need to reform the way we approach science education, which spoke to my own experience. As much as I enjoy studying biology, I feel constrained to a handful of options in going forward with a science education, which does not reflect what options are actually available. The career pathways that lead from an education in science are numerous and growing each day, from pop-science journalism to research in the private sector, and yet the majority of science programs promote a direct path to academia.

Our discussion about the academic path for scientists touched on one of the largest problems in academic positions in science, which is the way success is measured. Currently, the profitability of a study, how many publications a researcher produces, and the impact factor of the published journal are used as metrics in determining the success of a scientist. The true value of a discovery, which may lead to a slew of other studies and discoveries, can be glossed over. One of the largest disadvantages of a career in science, Dr. Korkut said, is contending with this issue. It is also one of the downsides that has been turning me away from a future career in science. I do, however, find learning deeply enjoyable. I resonated with Dr. Korkut to a great extent when we talked about the most exciting part of his work and he told me about the feeling of knowing something that nobody else, ever, has learned before. That feeling, I believe, is what drives most people into an education in science and is one that I hope I will be able to achieve some day.

My interview with Dr. Korkut was very educational, and I learned a lot about the computational part of cancer research and the pleasures, as well as the pains, of working as a scientist. I found some of my fears about a future career in the field of science, and biology in particular, affirmed, as he emphasized the necessity of passion and commitment in his field. The dedication demanded by such a career is substantial; the rewards of knowledge, of discovery, and of invention, however, are unparalleled. Personally, this dynamic of great struggle and great reward has been the source of my doubts in furthering an education in science. The international nature of science, the capacity to collaborate with people from every corner of the world, and the opportunity to work for the single purpose of discovery has been pulling me back at each turn. For now, as I strive to find my passion, I will remember Dr. Korkut’s statement, “Science is not an afterthought.” In my career, I hope to make sure it never is.

Highlights from the interview

What made you say I’m going to get into science and biology?

That was really an early decision that goes all the way before starting primary school. It was almost obvious that I would choose something in science. We used to have a big encyclopedia in our old house that was called World of Science. My father used to read chapters from that book, and I was always fascinated.

Then I went to a special high school for people interested in science, and there I started doing a research project. My main interest in those years was in chemistry and a little bit biology. Later, I made a decision to choose physics. But then there was a new emerging field, molecular biology and genetics, that caught my interest in college. After having discussions with university professors, I decided to get into molecular biology and biophysics. But from the very early periods in my life, my main goal was doing science and contributing to science. It was a great excitement for me. I deliberately avoided going to engineering school; it was really about basic science for me. I was exposed to all that education in my home country, Turkey. Then I was lucky that there was a professor who came from Harvard to my department, and she suggested that I spend a summer, maybe a year, at Harvard doing research. So I came to the United States, and I started working for a project in neurodevelopment. My first project was dissecting fruit fly larvae to look at neuro-developmental patterns, and I quickly realized it wasn’t the right fit for me. I realized that I was interested more in computational science, and I was very lucky that I moved to another laboratory at Harvard doing more quantitative biology, structural biology, which I decided was a good fit for me. Later, I came to Columbia University for my PhD. At the time, it was obvious I was going to do something in biophysics, and from that point on all my career focused more on quantitative sciences, and then moving to more cancer biology, and after the postdoctoral period at Sloan Kettering, I came to MD Anderson.

Did you have a mentor or anybody you looked up to that led a path for you?

Yes, indeed. I can say I was extremely lucky in having very good mentors at all steps in my career. The first advice I would give to young people is you’ve got to find good mentors. It’s very important. And you need one at the early phase in your career, even if you become president of a university one day, or a Nobel Laureate, or CEO of a major company. You will need a mentor. I had a couple people in different departments or institutions, and my PhD advisor was a great role model for me. I was lucky in that sense. There’s no one recipe in choosing a good mentor. You first need to understand what you want to do in life, and then you have to find someone who achieved that, and in a way that would be compatible with your nature, and then you need to get mentorship. There’s not a single optimal mentorship recipe though.

What do you do in your current position?

There’s a technical and scientific aspect, there’s a managerial aspect, and there’s also a mental part to my position.

On the technical side, I am running a research laboratory with about six, seven people. Half of the laboratory is developing algorithms to analyze genomics data from cancer patients to predict how a given patient will respond to therapy and whether the person will be resistant to a conventional therapy and what can we do to nominate new and effective combination therapies. We also have an experimental lab where we do a lot of molecular profiling of cancer patients using imaging and other methods to validate our predictions from computational models. Basically, getting the right cancer models in the lab environment and testing the drug combinations that we predict that should work based on the computations. This is a highly important problem because we have new generation drugs that work reasonably well in cancer patients, but response is not durable, so our goal is to really find better drug combinations that will give more durable responses. Ultimately, we want to contribute to turning cancer into a manageable and curable disease, where you simply identify drug targets in a given patient at a given time—it’s a dynamic process—and come up with the right therapy at the right time so that the patient will live a healthy life. So that’s the technical part.

On the managerial side, I am basically running a laboratory, and that’s the most important part. We have a lot of young scientists and some more senior people as well. I’m blending these people to run projects in different research areas, which could be biostatistics or cell biology or physics. And my real goal is to (a) channel them to develop and solve scientific questions, (b) showing them how they can likely be more successful and also make sure that they have the right—enough—funding. A lot of time that I spend is writing research grants and research papers so that we get federal and private funding so that we can pay people’s salaries and also cover their research expenses; that means acquiring an enzyme or sending them to a conference. So I’m responsible for that too. That takes a lot of time. I also handle the collaborations. We have a network of collaborating research scientists. We act together, we write grants together. So I am coordinating all of these collaborations. That means people who are doing similar research but also complementary of course. I network with these people, we come together and we brainstorm. Some of them are at MD Anderson, and some of them are at other institutions, so I handle all of those partnerships.

And then I have a little bit of administrative roles that I try to minimize, of course. Sometimes it’s fun, like participating in recruitment of new faculty members. You have to read their papers and CVs and interview them. Or we recruit other postdoctoral researchers, so I participate in those committees, and I also participate in the university senate. Once a month we discuss the problems and new opportunities in the institution. And it can be anything, like something about data security to research funding or rules.

On the research side of things, I realize that you mostly focus on the proteome level in your research. What’s made you gain that focus?

That’s a very interesting question. It started that way, but it’s not exactly true anymore. In some sense, yes, I still think the proteomic level is the most important because it’s really reflecting how a given cancer cell will respond to some of the state-of-the-art therapies, and proteomic levels are critical for predicting whether a cell will be resistant or not. But then there is another level that is the genomic (DNA and mRNA) level that also matters. The genomic alteration that means mutations and copy number changes, mRNA expression and epigenetic changes, in some sense, define the infrastructure in the cell—determining the potential, the capabilities of the cell, but it’s the proteomic level that defines the refined plasticity. And the response to therapy. So, in fact, we need to understand both parts. If you can get away by simply understanding how the proteomic landscape is, you can still come up with therapies. But you usually need some genomic information to figure out how a cell may behave in response to therapy. I actually focus on both.

In my research, one part is team science. And that’s like the team projects such as TCGA, which recently ended. In that case, actually dozens of laboratories come together, and we do a lot of genomic and proteomic analyses of large patient cohorts using all sorts of data on protein activity, DNA mutations and mRNA expression. That involves a lot of computational analysis. In the lab, we use mostly proteomics, and right now we also look at image-based proteomics which gives us a lot of information about tumor microenvironment; that it is an emerging field. We understand now that the tumor microenvironment is very critical in determining and conferring resistance to therapy. Many new drug targets are indeed in the tumor microenvironment, but proteomic measurements for tumor microenvironment are challenging yet really fun to do, and also it’s very promising in terms of finding new drug targets. But it’s a mixture of things. There’s no one recipe. Sometimes you look at genomic levels and sometimes proteomes. 

And what can you tell me about perturbation biology?

Let’s first start from a system where there is no perturbation. We can make measurements on thousands of molecular identities, be it gene expression, protein levels, and all other things. And you will see a fairly rich landscape full of molecular associations that things melt together, go up or down. Most of these are associations and quantified as correlations, yet you know very little about the causal links in the cell or how cells will response when you intervene. So there are three levels, the associations, the causal links, and response to interventions on the system.

Perturbation biology gives us a tool to extract causal relations from such associations because you hit the system, you change one parameter, and then you see how other parameters changed together with that. And then you do a perturbation in the opposite direction, and you observe how your system changes and whether parameters change. And doing this in a repetitive, in a rich perturbation setting, the causal links emerge. And if you can indeed combine, integrate all those causal relations and put them into a systems framework such as a rich network model, then a new pattern emerges, and you can start predicting how the system will respond to previously untested, not applied perturbation. That gives us a chance to predict how we can design, let’s say a therapy, a new therapy that was not tested before. So, perturbation biology is helping us to form associations, molecular associations, and start to build these causal links and using those causal links at the higher level, in some sense, to predict how the system will respond. That is the perturbation biology. Actually, this approach is not unique to biology. In any science, to understand how a system will behave, you need to perturb the system and watch how it’s evolving.

And can I also ask you about network pharmacology? 

The network pharmacology—some of these terms are newly emerging. There are few leading groups like Peter Sorger group at Harvard Medical School, and Chris Sander’s group, again at Harvard Medical. Gordon Mills, who was at MD Anderson, now at OHSU, and Joe Gray at OHSU and couple other groups, that we coined these terms together. There’s, for example, a Laboratory of Systems Pharmacology at Harvard, and there is another group called Center for Spatial Systems Biomedicine at OHSU and that does a lot of perturbation biology. These are emerging themes in modern biology. Network pharmacology or systems pharmacology or quantitative pharmacology in this context can be seen as a subset of perturbation biology because it really focuses on how pharmacological agents can perturb the molecular networks within a cell. And those networks are indeed affected and rewired by extracellular or internal perturbations. The endpoints of these effects are the phenotypes such as cell death, survival or proliferation. All these entities are connected to each other. You apply the perturbation biology idea as you model the cell as a network and use pharmacological perturbations to interrogate the network, and that is what network pharmacology is. And you can actually do this using different small compounds such as clinical anti-cancer agents or new technologies such as CRISPR.

Are there any large misconceptions about what you do, both on the research level and on the managerial level?

This question could be answered in many different ways. I’m a relatively young scientist at this level. I think one thing that is misunderstood about science these days is success of a young scientist, or even a senior scientist, is measured by how much funding you bring or how many papers you write. And those are used as metrics by funding agencies or university administrations, et cetera. I think the success in science is very hard to measure…but the real focus should be more on the discovery, finding new things. Not, you know, how much money you bring to the center, or how many papers you wrote this year regardless of the content, or what is the impact factor of the journals, where you publish. These are artificial things that we create in the contemporary context. But a hundred years from now, none of this will matter. What will matter is the groundbreaking discoveries you make, and we need to think a little bit more on that. We sometimes lose the context, in that sense, for what is important. That’s one thing, I think, within universities we start to lose that, and sometimes there’s another misunderstanding from the society. People think that we are able to bring quick and definitive solutions, say, we should be able to cure cancer in a couple years, but in fact this is a very complicated problem, and there…bringing a cure just by doing a small research projects is not easy. We need a lot of team science to achieve this goal. And sometimes people expect simple recipes, which do not exist. Having said that, we are entering, I believe, a very exciting period. With all the understanding of the genomic landscape and the new tools we have, the ability to profile patients with multiple biopsies and profiling how tumors will respond to therapy, we will probably be able to turn cancer into a manageable and curable disease. I am very optimistic but that takes time. 

That is a very hopeful thought, to be honest. What do you think is one of the biggest disadvantages in your field? Do you have moments where you feel like “I should have been an engineer” or maybe that was a better idea? Or nothing like that?

Let me put it this way. With all these advances in medical sciences, antibiotics, heart medications, et cetera, still a healthy person lives around eighty to ninety years at most. Sometimes it may take 20 to 25 years, a substantial fraction of one’s life, to finish a project and make a real high impact discovery. In my field, sometimes it really takes a lot of time to see how your hard work pays back. So during the process when you do something, you fail, do again, fail again, do, fail, do, fail. There are points when you say, well, I could finish another school, find a good job, can become a manager when I’m 30, and just live and wait for my retirement. In science, there is a long period when you are kind of in the dark. So in those times you sometimes say, well I could have a different life. But now when I look back, I can see the future a little bit better and become more optimistic. I see a lot of young people suffering in the same way. Indeed, a lot of my friends had to quit at some point because I think, like in many fields in the world now, the competition is harsh and return of hard work is not clear.

What reconciles all of this in your job? What makes you say, I’m very glad I’m still doing this?

There are times—let’s say once in every couple years, and it’s getting more frequent as things get more established—there are points that you learn and know a certain thing that others don’t. Learning is always fun. For example, you read an article or open a Wikipedia page, you learn something, you find it interesting, and you enjoy it if you like scholarly things. Now imagine that you learn something that you know no one else knows. And you’re the very first person, and then you know that you’re going to start announcing it, and people will find it very interesting and will just enjoy that, and one day it could even turn into something useful for other people. I think that’s the most fun part. And second, you see other young people coming and trying to learn from you, and you train them, and they also pass the stages you had passed 10 years ago or so. Seeing that is also really fun. 

So going back to changes in your field, what do you think the future holds for cancer research at large and specifically in your case?

There are similarities to other fields. There will be more automation and more predictive algorithms, and things will change, just like driverless cars versus today’s cars. There will be similar changes in science too, more automation, et cetera. But I think the most important thing is the boundary between experimental and computational work will disappear, and we will completely be integrated with robotic devices, but the algorithm development will be extremely important. We will have a brain machine interface to make new discoveries, and things will get very fast in that sense. The data sets will become very large but, as a colleague of mine just said, the big data in biology—I’m sure you know the term “big data”—is not really big today. Compared to click data or other marketing data, et cetera, our data sets are limited. This is going to change quite rapidly. And a lot of other big changes will follow. Another thing is we will, of course, have better profiling methods from patients. That actually is one of the factors that will change the volume of data, but that also means we will be able to predict the immediate molecular changes in a given patient after therapy, and that will enable us to come up with better therapies especially drug combinations. And I think we are going to change people’s lives. There’s definitely a shift from more theoretical, or basic science in biology to more applied and predictive projects, so I’m expecting, also, a commercial explosion in that sense. There will be a lot of biotech companies and, in some sense, more flow of private funding into biology. There are good parts of this, and also there are some challenges, of course, because we have to keep quality high. You cannot create bubbles in life sciences because this is not a bunch of software; biotechnology affects people’s health and lives. So that’s going to be important; we just cannot sell snake oil. There is that danger, of course, if there are tens of thousands of new companies emerging. We have to be careful.

What advice do you have for interested students? Just in research in general and specifically in computational and cancer biology?

I will give the same advice to both. If you really like it, and if you have an excitement, do it. There’s no question. Science is not an afterthought. It’s not like a second choice that you just had wanted to do that, that didn’t happen, let’s be a scientist. Or I don’t know what to do, let’s do a PhD. Don’t do that. You won’t be happy. There are cases that people really become more interested in time, but if you have a passion for science and learning new things, don’t give up just because there are challenges. But also if it seems like the only option for you, or you just don’t know what to do, don’t do it. So this is for people who really like it. Also for people who want to do cancer biology—I think this is the right time, and there are so many good programs, I believe there will be a lot of opportunities in the next 5 to 10 years for people in this field. Landscape is quite promising, I can tell you that.

Is there anything else you wanted to mention that came up during the interview and I didn’t ask?

One thing I want to say is there are really three things in life that matter, and that’s really special, and I think it’s science, arts, and sports. Science is one of the few things that is truly international, or global. I mean, it’s not even international. It doesn’t really care about national versus international or intranational, so in that sense these are truly universal values. If you want to do something special in your life, I think science, art, and sports are really great things. Even if you don’t become an expert or a professional in one of these fields, I would recommend every young person should enjoy at least one of these things. That makes your life much better.

Interview excerpts have been lightly edited for clarity and readability and approved by the interviewee.