Predicting Which Cancer Cells May Become Drug-Resistant with Yogesh Goyal, PhD

Erin Spain, MS [00:00:00]: This is Breakthroughs, a podcast from Northwestern University Feinberg School of Medicine. I'm Erin Spain, host of the show. Cancer is a leading cause of death worldwide, and despite major advances in treatments, the fact remains that some cancer cells are resistant to drug therapy. Northwestern Medicine scientist Dr. Yogesh Goyal is investigating why this happens. He's an assistant professor of cell and developmental biology at Feinberg, and his lab is also part of the Center for Synthetic Biology at Northwestern. He joins me today to discuss his latest research on understanding drug resistance and cancer cells, which was recently accepted by the journal Nature. Welcome to the show. 

Yogesh Goyal, PhD [00:00:55]: Excited to be here. Thank you. 

Erin Spain, MS [00:00:57]: I want to delve into this latest research, but before we do that, let's talk a little bit about synthetic biology. Your lab sits jointly between the Center for Synthetic Biology and the Department of Cell and Developmental Biology. Explain synthetic biology and what's most exciting to you about the future of synthetic biology, specifically in cancer research. 

Yogesh Goyal, PhD [00:01:18]: I started my lab last year. I think one of the attractive features of, where I am is it's really sitting at the nexus of engineering and biology. And I think this is a really unique ecosystem where I'm trained as a classical engineer myself and mathematician and really being in medical school and with cell and developmental biology, it brings a new way of thinking which is synthetic biology. Where can we come up with engineering tools and techniques, which can be then used to manipulate cells in ways that the cells cannot do in their native environment? And that kind of formed a big part of this new research and it enables a lot of new things that are otherwise not possible with classical methods of doing molecular and cellular biology. So it's a very exciting time for synthetic biology itself. I always tell my students, you know, there's no better time doing biology than now because people from every discipline are constantly working together. New companies are coming up in synthetic biology in this interdisciplinary space. So it's a very exciting time and I'm very excited to be a part of it. 

Erin Spain, MS [00:02:14]: Well, you mentioned you joined the faculty last year in 2022. Share a little bit about your background, how you got into the field. You mentioned engineering as part of your background, and math. What drew you to Feinberg's program in particular? 

Yogesh Goyal, PhD[00:02:26]: So, I actually came to the US to do my PhD in mathematics or, or applied math and, you know, questions of which have nothing to do with biology. But when I moved to the U.S., I attended a couple of lectures where the professor was trying to combine ideas from biology and bring mathematics or applied mathematics into the equation. And I was just amazed by how complex biology is because until then I was constantly focusing on an industrial plant, which has a lot of moving parts or bridges, and thinking about mathematics of those things. And then I realized studying a cell or an embryo is basically like studying a plant, a chemical plant, or an industrial plant just at a much smaller scale and much more complex. But the beauty of this is, you know, we often have bridges failing. We often have buildings failing. We have plants failing. The process of development, which is how cells make decisions in development or in disease. It's so complex. So this really drew me to this idea of bringing my expertise in engineering and mathematics to biology. And, when I was applying for faculty, I think this was one of the very unique positions where my lab is within a synthetic biology center, which is on the 11th floor of this building, Simpson Querry, so it houses people from many different departments and it has nothing to do with one department alone. But it brings us all in the medical school context, which is important because I think it's been a long time, us engineers and people from not classically trained in biology, working on biological questions. I think it's high time that we do things which make a direct impact. And being in a medical school environment really brings us closer to these questions and stay in touch with these questions, which are really impactful and going to change the world. 

Erin Spain, MS [00:03:52]: Your lab is focused on problems, as you mentioned, in single cell systems biology and you investigate single cell variabilities in development and disease, including cancer and developmental disorders. Tell me more about this focus. 

Yogesh Goyal, PhD [00:04:06]: if you think about every process in biology, you can conceptualize it at different scales. So I will give you an example at the level of single cells, but you can think of the same thing in terms of species or populations. So, oftentimes interesting behaviors happen in very rare cells and rare populations. So extinction of species, right? You get mutations which make you unfit, or the other way around, you get mutations that becomes cancer. Every time these processes are initiated in a subset of cells. So cancer starts in a single cell and then it becomes a tumor. Quite often, I would say pretty much all the time, we infer what happened at time zero when the mutation happened from what is happening now. So a person gets cancer or resistance happens. So we only have endpoints. That is because it's hard for us to know what to predict for the future, right? So people are constantly inferring what drove a process from what happened much later. And I think that comes with a lot of challenges because oftentimes if someone gave you a picture of you now and gave you five options of five different babies at day five or something after you were born. There's no way you can guess who of those five babies you were. So it's very hard to go back in time and the bigger challenges, all these things are happening in individual cells. So when I say single cells, I don't mean that we study cells in isolation. What I mean by that is even if we are studying tissues or single cell, it has to happen at single cell resolution. Because if you combine all of them together, you will miss the exciting things that are happening in some cells but not others. So we are trying to think of these questions in various contexts, and cancer is very convenient, but also very clinically important because there's a very clear dichotomy, which is you either live or die. Whether it's in the face of a mutation, which starts the cancer. So, just to give you a quick tidbit, most times a lot of cancer mutations or mutations are constantly happening in our body, but the body has a system to fix those things for with various mechanisms. Sometimes it doesn't work, so that becomes cancerous. Same happens with drug resistance, where over the years especially last 20 years, I would say we have gotten so good at curing most of the cancer. And the word most is very important because most is not all. So pretty much many solid cancers and even liquid cancers, you get into a situation where you give therapy to patients, different kinds of therapies, and there are many different kinds of therapies. And after a few months, the patient is virtually tumor free. But a few months later it comes back stronger and now you cannot use those therapies to treat it. If I was using bulk information, which is not single cells, I would really miss the exact cell that survived because most cells are going to respond to therapy and die, but few cells will not. So we really have to catch those rare cells and that's where the challenge lies, because it's like needle in a haystack, but really in a translational context. 

Erin Spain, MS [00:06:40]: And that's been a problem that little has been known about the variability in these resistant outcomes. So tell me about the tools that you're using and the strategies that you're using to find this needle in the haystack. 

Yogesh Goyal, PhD [00:06:53]: That's a great question and it kind of segues into why synthetic biology. I think typically when people try to look at rare cells, you know, you can use imaging, you can use some biochemical methods. It quickly becomes a challenge when only one in a million cell is going to survive, or one in 2 million cells, or 5 million, even 10,000 cells. You know, you just cannot scale the process up. To get around this I developed a technology which we call a FateMap which is a synthetic biology tool where you can basically tag each cell of millions of cells at the same time with a unique barcode. It's almost like you go to a grocery store and each product has a QR code. And at the end when you're checking out the person just scans the QR  code and you get everything about the cell or about the product in that case. So in our case, we have built something like that where we barcode millions of cells and we give them drugs. And most of these millions of cells are going to die. So they're like useless information, but we need to track all of them. And then very few cells are going to make it past the drug treatment and they will become resistant. So at the end of the whole process, we can go back and ask which barcode survived, and let's scan their properties, and then we can sort of connect what was different about them before you added the drug to now. So synthetic biology plays a big role. And, you know, it's really simplifying the problem, but when you do these kind of sophisticated technologies like tagging millions of millions of cells and then sequencing them, comes with a lot of computational and mathematical challenges. So our work inherently involves combining ideas from many different fields. So what we showed just a single sentence conclusion was that whatever happens to a cell after months of treatment with anti-cancer therapies, I can predict its exact behavior several months before or just before you added the drug. So the whole idea of information flow, the whole information about what a cell is going to do after you add drug was stored before you added drug. So it's very predictive and that's what we showed in our paper. 

Erin Spain, MS [00:08:39]: That's right. And you're speaking about the paper that was accepted in the journal Nature. Tell me, what has reaction been like, especially in the cancer research world?  

Yogesh Goyal, PhD [00:08:47]: Yeah. So, typically when you think of cancer, early 1990s, sequencing was a big deal. Early 2000s, we spent a lot of time sequencing anything and everything. You know, whole genome sequencing was a big part of how we did research. So we were really obsessed with finding mutations that can explain different behaviors. And 30 years on or 20 years on, we are at a stage where mutations do explain a lot of things, but they do not explain everything. So a lot of metastasis, drug resistance, oftentimes you do not have clear mutational signatures. And we were really curious if, do you really need mutations to give rise to drug resistance or metastasis, or it's not a necessary condition may be sufficient, but not necessary. So, we did very well controlled experiments and our findings is that you do not need mutations to give rise to this resistance. And so clearly it's a paradigm shifting observation. A lot of classical clinicians and biologists and there's a hard time rationalizing these behaviors. But if you think about it, your body is made up of different cell types, which do not have necessarily driver mutations. Or mutations that can give rise to various effects, but they are very different from each other. So thinking in the context of cancer, just because we were so obsessed with sequencing, you know, it's almost like an inertia of looking for mutations and everything, but different cell types can have the same genome and behave very differently, which is what we are showing that even in homogeneous population, you can get many different ways by which cells become resistant and you do not need mutations for it. 

Erin Spain, MS [00:10:05]: This really does open up so many more doors for how we can study cancer. Tell me about that. You said this is a paradigm shift really. 

Yogesh Goyal, PhD [00:10:13]: Yes. So, there's good and bad news, right? So bad news I think is any cell can become resistant if the situations are right. But the good news is, when people study resistance, and this is not always true, but I would say this is a really good representation of how people think about it. When you get resistant tumors, you look for mutations in it. Then you say, okay, these mutations may have happened before you added drug, and that caused a resistance to happen. This is like a classical way of thinking of Darwinian evolution except mutations could still play a role. So Darwin did not comment on mutations or not mutations. All Darwin said was that there's a random chance that you will have a change which can result in different outcomes. And we think that random change could be because of nonmutational burdens. What that means is instead of focusing on designing therapies or treatments based on endpoint measurements, we can start thinking about new ways of cell state definitions. What defines a cell and can we catch them before you added drug? Not necessarily focusing on a mutation, but there's this word that is used in cancer a lot these days. And in fact, it's one of the grand challenges that people have identified. It's called cancer cell plasticity. Which means that a cancer cell can become many different cell types without necessarily mutations. So this gives us a new opportunity, therapeutic opportunity, to target these transitions before you added drug rather than trying to figure out after the resistance has happened, what led to resistance because mutations are permanent, right? So you get a mutation, there's a very low chance it'll revert. For all intents and purposes, it's a permanent change, whereas nonmutational changes are temporary. So we have an opportunity to push cells, for lack of a better word, like a little bit more abstract, but to push cells into an outside of these states. So if we are able to somehow find what cells are going to become resistant, then we can ask what was different about them compared to other cells. And it's not permanent. So maybe we can devise new methods to sell some cells out of the state. Just when we are adding therapy with, different, you know, orthogonal interventions, or we can say that prolong the off state where you never enter the on state So it really creates new ways of thinking about therapeutic opportunities, which I'm very excited to see how it evolves. 

Erin Spain, MS [00:12:17]: How long do you think it will be until people can start using this theory in clinical trials or studies like that? 

Yogesh Goyal, PhD [00:12:25]: We are not too far away because just when our paper is coming out this past year for different systems like metastasis, liquid tumors, our work was on melanoma primarily. But many different cancers and many different cancer initiation, cancer metastasis, people are finding similar behaviors from many different labs across the world, from Australia, from MIT, from UCSF. So people are reporting something very similar, conceptually similar in disperate biological context. So I think because of these, also the new synthetic biology techniques of barcoding cells is coming forward. I think the challenge for the field would be to identify whether there are common states or common things we can target across cancer. So each cancer or each context has its own target that you could target. So I think if once we address that challenge, and part of it we are trying to study in our lab, in our own lab where we are moving beyond melanoma and try to compare pan cancer behaviors of this kind and see if we can find unifying features or even contrasting. It's still a very nebulous field, calling something plastic. It's a very nebulous way of thinking about it. So I would say the next few years would be to formalize the definitions and understand what is common across cancers. I came back from a conference and I think people are thinking about clinical trials on some of the therapies which could target the state. That's I think, again, largely because our work is a lot more basic science than translational science. But seeing that this behavior is being reported across several different contexts, and it was not possible before. So why did we not find it before? It was not possible before because we were technologically limited. So that's where technology plays a big role in opening new questions, new ideas. I think we are on a path which is exponential. So I do think it'll happen sooner than not. 

Erin Spain, MS [00:14:02]: This is so exciting because as you mentioned, you're just getting started here. You've just established your lab and you are making all of these connections and relationships across the entire university at Northwestern, you're very interdisciplinary. You mentioned that you worked with melanoma. Tell me about some of the relationships that you've formed with other faculty and labs here, even on the clinical side at Northwestern. 

Yogesh Goyal, PhD [00:14:23]: I think one thing that I really enjoyed: I've always stayed on the East Coast before this, and I just loved how welcoming all the faculty members were. So, when I moved here, we had a joint meeting with Kathy Green's lab, whose lab is really a basic biology lab, studying melanoma initiation and formation and exchanging ideas with her. We also have some ongoing collaborations. It's been really nice, you know, again, I'm not trained as a clinician or anything. But having access to so many faculty here at Northwestern who work on clinical aspects. So for example, I had a chance to meet with Jeff Sosman who's a melanoma doctor here at Northwestern. And then as I'd mentioned before, we are moving into other cancers as well. So, Dr. Munshi is a pancreatic cancer oncologist who has been very helpful in fact, he has written a couple of mentor letters for me and just asking him questions about how people think about these things, has been absolutely brilliant. Because I think sometimes as a basic scientist or someone who's so far away from translation, sometimes maybe we we can make fine tweaks, which can make something very big or more impactful like choosing the drug that we want to look at these responses. And so, these people have been very helpful. And the Robert H. Lurie Comprehensive Cancer Center at Northwestern University, I should say, has been an even, equal partner in their support for my research. They have involved me in giving talks here and forming connections. And it is one of the best in the country so I'm so excited that we could work with them and think together. 

Erin Spain, MS [00:15:43]: I wanna shift gears a little bit. And you mentioned so many different disciplines. You've mentioned engineering. You've mentioned the clinical aspect of the medical school, mathematics. You are also very interested in art, and in fact, you co-authored a magazine article called "Art as a Toolkit for Data Science." Tell me about that. What value do you think art brings to the research that you're doing? 

Yogesh Goyal, PhD [00:16:06]: I got interested in art and science about five, six years back. I think the biggest lesson is that if you look at the history of art and science, I think them being in very different silos is very recent. I think an artist and a scientist have a lot of things in common, and as I become more mature as a scientist, I think the biggest takeaway I have from bringing the two together is embracing the uncertainty. I think medical schools across the country are also incorporating art in their curriculum because they want their doctors to be empathetic, and it's a part of their training. But not just empathetic, but embracing you don't have one single answer to questions. And I think that's a part of doing biology. Biology is so complex and I think as a STEM major or someone, especially mathematics, right, you get so absorbed into finding one unique answer, and I think appreciating the nuances that biology brings to your life. That has been a big learning lesson for me. And, I think it has created also an atmosphere in the lab itself. And I'm not just talking about art, but interdisciplinarity in general. So I've been very conscious in creating a lab atmosphere where we have people from many different disciplines. So right now, the lab makeup is so diverse that we have I think couple of biologists, one computer scientist, one clinician scientist, one mathematician. A few engineers. One of them is an artist as well. She made a lab doodle, which we'll put on the lab itself. So you really create this atmosphere where people begin to realize that no question is wrong and languages could be different, but we are all unified by curiosity and drive to bring something new and learn something new all the time. So that's been very special building up a lab, which is so diverse. 

Erin Spain, MS [00:17:34]: And tell me, what can we expect from this diverse lab that you've created? What other projects are you working on that we may be hearing about in the future? 

Yogesh Goyal, PhD [00:17:41]: One thing that I always say when I give talks is one of the legacies, or the way I think of my lab to leave an imprint is we have no boundaries. I really want people to come to lab and tell me I'm very excited about this topic. Can I work in your lab on this? And the answer should always be yes. So, when I started my lab, my postdoc work was a lot on cancer resistance and I had this brilliant student rotate in the lab, but then she was like, I want to join your lab, but I want to work on viruses. I'm like, I'm not trained as a biologist, but if you think you know, we can learn together, let's do it. So she's now in the lab for a year now and she's beginning to ask very clear questions. And I have learned a lot during this period about, you know, when people get viral infections in many cases it doesn't infect all the cells, only a subset of cells. So the questions are still unified by single cell biology, but going into something very fearlessly and the students have this drive to be fearless.  It's really pleasantly surprised me in terms of seeing when a student starts versus when they make a progress and you start seeing that they can completely think on their own and they surprise you in amazing ways. And I think that has been quite rewarding for me. I'm very excited in the next few years to see my students become even more mature and scientifically mature. The other area that I'm expanding in the lab quite a bit is thinking about can we make synthetic human embryos in the future? So starting with, you know, human stem cells and giving them different signals and can we make them go through various processes that human development goes through. So they're very different. You can see they're like from very different areas, but they're all unified by the idea of thinking about how do cells make decisions and can we find this decision making at single cell level. You know, almost like do cells a free will or it's all random. I'm very excited to bring quantitative methods to these questions because, even this last idea of building synthetic human embryos, looking at the future, I think we'll be asking a lot of ethical questions about what animals we can use, what animals we cannot use, and even for human embryos, how far can we go? But one bright light is we can start thinking about how can we use systems, which are from cells, how can we use in vitro systems to study human diseases? Because many of the developmental disorders, the first instance when things go wrong, happen very early. And we want to study these first instances, not after the fact. It's all ties to drug resistance as well. We want to catch it when it happens first, not much later. And these in vitro models, or models in a dish, a lot more amenable to quantitative biology. You can spawn a lot of them at once. And the technical and ethical challenges of studying a human embryo, which is impossible, right? These can circumvent moving forward. 

Erin Spain, MS [00:20:08]: Well, thank you so much Dr. Goyal. We're so happy to have you on the podcast and to learn more about your lab. Thank you for your time today. 

Yogesh Goyal, PhD [00:20:16]: Very exciting. Thank you. 

Erin Spain, MS [00:20:17]: Thanks for listening, and be sure to subscribe to this show on Apple Podcasts or wherever you listen to podcasts. And rate and review us. Also for medical professionals, this episode of Breakthroughs is available for CME credit. Go to our website, feinberg.northwestern.edu and search CME.