 Hi my name is Herbert Levine. I'm a faculty member in physics and bioengineering at Northeastern University and I'm the director of the Boston branch of a National Science Foundation Center which devotes energy to applying ideas from physics and mathematics to problems in the living world including biology, including in this particular case some aspects of cancer progression and cancer therapy. The paper which I want to talk about at the most general level is a continuing part of our effort to understand the process called EMT. EMT stands for epithelial mesenchymal transition or sometimes transformation. It's a process that was originally discovered as noticed that some cells spontaneously transformed from being sedentary epithelial cells all attached together forming epithelial tissues to cells that were more mesenchymal which meant they were more motile they left their original tissue migrated throughout the developing body to other regions and then transformed themselves into final products once they got to their destination. It turned out that this process was not only operative in developmental systems but also seemed to play a major role in cancer metastasis and cancer the initial start of cancer is when up typically epithelial cells most cancers arise from epithelial tissues. Typically those cells begin to grow uncontrollably for whatever reason usually some type of genetic abnormality cells lose their self-control they start dividing and they start forming a tumor mass in some part of the body and we label different types of cancers by which part of the body that initial tumor takes place breast, pancreas, liver etc. Initial tumors as long as they stay in one part of the body are not particularly good for you but we have a variety of therapies which are quite effective for example surgery for example localized radiation therapy it's only when cancer metastasizes which is when it spreads throughout the body that you run the risk of actually dying from the disease because then you expose to the possibility of multiple organ failure throughout the body and no therapy that can then counteract that. In order to get from the primary tumor to the rest of the body cells that initially were epithelial as part of the cancer need to become motile they need to start being able to move and one of the ways they can do this is by undergoing this EMT transition. So the cost metastasis is the primary cause of death in most tumors there's been a huge emphasis in the last let's say decade or two in the cancer community on understanding what are the underlying molecular processes that enable cells to undergo this transition and of course the eventual hope is to develop therapies which would cause this to stop and the path to understanding this of course you know goes from looking at experiments in laboratories to experiments in animals such as mice and eventually just studying the process in humans. So if one goes back and looks at the experiment in laboratories one thing that's been noticed is that there are signals which can induce this transformation in cells so that sort of gives us a clue as to what could be happening in in patients but we've also observed a great deal of heterogeneity in the different cells that when you take a series of cells that look like they came from the same type of cell and apply these signals to them rather than getting uniform response you get a wide variety of responses this has been enabled by new technology in the biology world where one can measure details of single cell behavior rather than cell population behavior and one of the things that was noticed in this context of EMT was that if you applied an EMT signal to a population of cells you would notice that a significant proportion of the cells looked like they were resistant to those signals that you would apply what you would think would be a typical dose that would induce this transition but nonetheless those cells because of some uniqueness in their molecular you know makeup or their molecular dynamics somehow were being resistant to that so this paper attempted to address by means of mathematical physical modeling tied to experimental data analysis what could be possible causes of this mechanism what could be possible mechanisms that underlie this very interesting and somewhat surprising feature that's been seen experimentally in EMT so maybe you should explain what some of the possibilities were and how we sort of began to investigate that. Sure hi my name is Mohit Kumar Jali I'm an assistant professor at the Center for Biosystem Science and Engineering at Indian Institute of Science in Bangalore I'll follow up on what we talked about this paper so we started looking into this processes of EMT when I was a graduate student with Herbie a couple years ago and they're trying to ask the questions about how is the dynamics of this process looking like but because at that point of time our experimental cancer biology colleagues mostly had readings only at sort of day zero and day n when this process ends there was very little understanding of how this process exactly happens and that sort of took us by surprise given the importance of this process not only in metastasis but also in the years over the past years as we realized in terms of evading say even the immune system various different drugs out there changing the metabolism etc so EMT sort of happens to act as a central fulcrum through which cells seem to adapt to various different bottlenecks that they face during the process of metastasis so when we had earlier developed mathematical models for it our predictions were that EMT is not as binary a process as black and white as all of none kind of a process as people were talk practically thinking about at that point of time so we made these predictions that there can be multiple state stable states in which cells can maintain themselves for long durations of time and over the past couple of years those predictions have been experimentally tested validated such what we call hybrid cells have been seen in the clinic as well and more importantly it has been shown that not only these hybrid cells exist but the existence of these hybrid cells is actually paramount importance for the process of metastasis if you deplete these hybrid cells particularly then metastasis can decrease significantly what has some of the experimental studies have shown so now we wanted to ask the question if a cell is becoming mesenchymal and then it is supposed to revert back to being epithelial once it reaches a distant organ what you need in cells is their ability to come back the the diversity aspect of this entire process so we ask this question that is are these processes always reversible and one of the previous mathematical models that we have developed for this suggested that that's not necessarily always the case there comes a tipping point in between after which it is possible that cells do not come back even when you withdraw the signal for long durations of time and our enthusiasm was increased by the fact that there was experimental evidence both collected by our collaborators and others existing in the published literature which actually showed this is possible now in this particular paper published in Onko Target we wanted to take this question further and ask that is MET or when cells come back from mesenchymal to epithelial is that always reversible or in other words as her view is mentioning does every cell always undergo the transition so if you have a population of cells can you say that every single cell is going will undergo a transition at some point of time or there will always be cells which will just resist any change in their behavior. So here we looked at two specific mechanisms which so one of them relates to the epigenetic feedback which comes back to some of the chromatin remodeling that have that has been shown experimentally to be happening during this particular process and the other is the process of stochasticity noise asymmetry in distribution of molecules when a cell divides. So I'll explain these processes one by one. So what has been reported not only in EMT but in various other similar processes where cells transition from one state to one other that there are mechanisms at the chromatin level regulatory processes which somehow lock the cell in the new state and that is important because you need the other state to be stable as well. You don't need it to be completely metastable that it can be pushed out from that on the sort of work and IO power perturbation. So there are these mechanisms which happen which somehow change the regulatory network underlying and enabling this more or less irreversible kind of transition. So we developed a simple mathematical model to represent that dynamics and ask this question is this a process is this a mechanism through which some cells can continue to resist undergoing EMT throughout and we observe indeed that was the case. We wanted to look at also another process which is the asymmetry in cell division. So when a cell divides for instance in the EMT field we still do not completely understand that when a cell is undergoing EMT the same cell which is becoming from E2M or is the cell binding and giving rise to two daughter cells one of which stays epithelial and the other becomes mesenchymal or M and these the timescales at which these processes are studied at least in the laboratory systems over a week or so are timescales at which cells are continuously dividing. So we wanted to see what exactly is the effect at a cell population that might be happening due to cell division. Now every time a cell divides there can be some differences in the partitioning of molecules. You cannot expect both the daughter cells to get the exact number of molecules exact number of molecules for all different biochemical species in a cell which was there in the parent molecule. There is always going to be some noise there's always going to be some asymmetry. So we wanted to ask this question that given there is some bare minimum level of asymmetry can this be yet another mechanism through which some cells can resist undergoing this transition because when there is this asymmetry then some cells are likely to be more resistant what says some cells are likely to be less resistant. So even if you start from a homogeneous population you are actually creating more and more heterogeneity and you're sort of creating more and more subpopulations some of which are more likely as compared to the parent and some of which are less likely as compared to the parent to undergo these transitions. So these are two of the processes that we have identified which can contribute to this and fortunately there were recent studies at a single cell level also that showed that in a cell population this kind of phenomena are indeed seen and they did show at least correlative evidence that epigenetic players can be associated with that. There is no cause and effect that particular study has shown so far. So what our approach establishes is that potentially causatory link that those epigenetic changes are not just correlative but they maybe actually underline this completely different responses of the two subpopulations two or more subpopulations actually in the process. I wanted to say a little bit more about the general problem of trying to incorporate epigenetic effects into these regulatory networks. By regulatory networks we're usually sort of thinking back to when most of the work on those networks was being done in relatively simpler organisms bacteria, yeast, etc. And in those cases essentially most of what's interesting and happening dynamically to cells can be described by the interaction of transcription factors proteins that determine DNA expression and those are responsible for changing the cell state from one part to the other in the interaction of transcription factors thought of as a network of interacting chemical interactions has been a very fruitful way of creating models that can explain and predict data in those systems. But when we get to humans but more generally when we get to complex animals it turns out that just thinking about these networks as independent transcription factors that are operating in sort of an absolute context really doesn't seem to be the right paradigm for the field because the field has learned over the last 10 years that there are large scale changes in chromatin structure and accessibility of genes and how genes affect each other which sort of sets a context for how these transcription factor networks are going to behave. And we've identified various specialized transcription factors they're sometimes called pioneers which somehow set the stage for all the other transcription factors in the networks. And the field has been trying to understand how to incorporate these new insights into a next generation set of models. Our approach has been to take sort of very simplified versions of that to just get started to see if even simplified versions can begin to give us insights into what's going on. Other people have taken sort of more let's say detailed you know modeling perspective where they've tried to incorporate extremely large number of details about how these DNA reorganization chromatin chain processes actually work. And of course the problem with that is then you're subject to not knowing all the parameters not knowing which is relevant in which case not knowing all the couplings not even knowing all the players initially. So the field is still working very hard on trying to move forward with this sort of how epigenetics really affects cell transformations how it affects memory of what cells you were when you go through division. And this is a paper which in the general sense contributes to that effort even though of course its focus is on a specific transformation on the EMT process. I also want to mention in this regard that the aspect of the EMT process which we focused on which is the role of a specific molecule called GRHL2 is not arbitrary it's partially because GRHL2 is known to play a vital role in maintaining the chromatin structure relevant for epithelial like states. So GRHL2 can if it's enabled to be activated can very easily suppress EMT that's what we were looking for and therefore we focused on mechanisms that would control in an epigenetic way the ability of GRHL2 to accomplish that. So we have in mind a more general question but of course this particular paper takes a general question constructs a model relevant for a specific process and then tries to see to what extent it can explain data regarding EMT. So following up on that I think one of the overarching themes that this paper connects to is also growing literature on what one can call non-genetic heterogeneity or phenotypic heterogeneity in the context of cancer. So again the molecular oncology field has been largely dominated by thinking in terms of mutations and genetic changes which are irreversible, heritable, and sort of continue forever because they're encoded in the DNA, it's hardwired. What some of our previous work and extended by this particular work we have are highlighting mechanisms through which this heterogeneity can exist even when genetic background is completely identical or what is called an isogenic cells in that case. So be it the ability of cells to exist in multiple states, be it their ability to lock certain decisions or unlock certain decisions based on the chromatin rewiring, be it the noise or asymmetry in division of molecules, these are happening largely independently of the genetic background. The genetic background of course may be altering the rates of one versus the other and genetic and non-genetic are again perhaps not as clearly distinguishable as we are putting it now but nonetheless we want to highlight that there are other mechanisms which have been studied in plenty in sort of simpler microorganisms such as bacteria and yeast, there is literature on phenotypic heterogeneity or non-genetic heterogeneity for a long period of time but somehow the cancer community has not really caught up on that. So this is one more contribution at a conceptual or a climatic level that this paper tries to make. Yeah I want to add to that one of the directions for our future work is the role of this type of phenotypic heterogeneity specifically in the context of drug resistance because what's been observed in many different types of cancers is a drug that initially starts out creating a relatively good response shrinking the tumor, reducing the tumor load, the symptoms nonetheless fails relatively quickly as the tumor rebounds starts to grow again and of course eventually tumors can become really resistant to cancer therapy drugs usually by genetic mutations but they're sort of an intermediate timescale perhaps over the course of weeks or months when there hasn't been really enough time for a mutation to arise that specifically can account for why the drug is failing but nonetheless the drug begins to fail and what's believed to be the case is that the intrinsic heterogeneity in the tumor this phenotypic heterogeneity creates a variety of responses creates a sub-population of cells that are not really sensitive to the particular drug and of course if you kill other cells that are competing with them for resources or metabolic resources then the more tolerant sub-population will gain ascendancy and will take over the tumor population and your drug will stop to have a significant effect and then if you wait long enough this tolerant population which is still maybe growing at a slow rate or maybe just sort of static will eventually evolve a genetic change which will then completely defeat the drug making the drug no longer useful whatsoever so it's now believed that in many of these processes the pathway and route to drug resistance go this phenogeneity so studying phenotypic heterogeneity in a variety of contexts will offer insight into what can be done to combat it in the case of drug resistance and that's actually one of the future directions that we are looking at collaboratively with a number of cancer biology research groups