Dissertation Diaries 2 Ep5 | Cancer cell dormancy, gender bias in science, and how does radiotherapy affect brain cancer?

[music] Welcome back to another episode of the UCL Caduceus Podcast. I'm Sarah. And I'm Madeline and today we're talking about student research. Need some dissertation inspiration or motivation? With graduation seasons upon Earth, we thought this would be the perfect opportunity to reflect on all the hard work and important research that students have done throughout the year. With most students culminating this work in a final dissertation project. Now, when we see dissertations, you may feel a sense of dread, think of sleepless nights or boredom in the library. We want to tell a different story. This year, UCL undergraduates have been conducting some fascinating and diverse research and to dispel the stereotypes and to get you excited and about maybe conducting your own research. We thought we would share a spotlight on these exciting projects. We hope this might make the world of dissertations seem less intimidating and more within your reach. Hopefully, by the end of the series, those of you who have to start your dissertations can feel inspired and take away some advice for your own research. In this episode, we're speaking to students from biological sciences. We've also included the timestamps in the description for each interview if there's a particular topic you're interested in. As well as our interviewees, contact details if you want to connect or find out more. Let's get into it. [MUSIC PLAYING] Hi, we've got Luca joining us today. So would you want to introduce yourself a bit and some of your dissertation in a minute or so? Yeah. Hi, guys. My name's Luca. I'm on the MRS Bio Sciences Computational Biology Stream. And you have my diss of the many focusing on cancer cell dormancy. And so when you usually think of cancer, you might usually think of cells that are dividing very rapidly and out of control. But we're starting to learn now that some cases of cancer resistance are coming from cancer cells that are actually doing the opposite. So cancer cells that have become dormant or quiescent are the benefit of the state of G0 arrest. And so my dissertation has been focusing on how we can better understand how these cells enter dormancy, maintain their dormancy, and then reawaken to start causing cancer again. And we've mainly doing that using computational techniques. So I've been analyzing publicly available single-cell RNA sequencing data sets and also some bulk RNA sequencing data sets that have a relapse data so we can understand how certain genes are related to relapsing earlier or relapsing later or surviving worse or better. Cool. How did you come up with that topic? Or did you just choose it from the list? Yes, so for all my master's program, we're expected to reach out to the supervisors on our own. So we're given a list of people we can maybe talk to but we're not really given anything specific. I found out that my supervisor through an old professor of mine from my undergrad that I also did at UCL. And he recommended her to me. And I was really interested in doing cancer. It's not something I've had the opportunity to do in my undergrad. And so when I heard that there's someone who does computational cancer biology specifically like the entire lab only does computational cancer biology. I was really excited to try and do that. So I went to have like a little informal interview with my supervisor, which is Dr. Maria Sacriere. I was really, really interested by dormancy just because I think it's so, it's just quite interesting and counterintuitive that cancer cells are doing the opposite of what we've kind of characterized and to do in, like, to avoid treatment and to, like, for metacetic tumors elsewhere in the body, which I thought was really interesting. It's not a very well defined state at all. Like we read it no much about it. And everything is kind of quite scattered. It's something we can, what we call experimental siloing, where dormancy presents differently based on the context. So there's different cancers have different markers associated with dormancy. Different treatments can cause different roots dormancy. So dormancy is like one kind of unified ish state. But there's probably hundreds, if not thousands of ways, that we can trigger cancer to end to dormancy day. So even if you've got a bunch of studies that have looked at cancer cell dormancy in different cancers or in different treatments and et cetera, the results they get are different. So we can't really bring them together and analyze them together very easily, which is like one of the big challenges that we've got, other than the main challenge that we talk about and the most cancer cell dormancy is, especially computationally speaking, is a ground truth problem. We have no, like, unified signature for a dormant cancer cell, whereas we might have other signatures for different types of cells in general, like we have gene signatures that we know are very common in CD40 cells or a few of your cells. We don't really have one for dormant cancer cells. So yeah, so it's quite hard thing to study, especially computationally. There's also the other problem with the cancer dormancy is non-genetic, which is a bit weird to think about because we're looking at RNA seamlessly, but what we mean by non-genetic is that it's not that they're gaining a mutation that makes them dormant. It's like a plastic transcriptional chain. We also think it's probably partly triggered by epigenetic changes as well. So there's lots of different aspects that are going into dormancy, because it's the hijacking of the normal process where a lot of our cells are acquiescent normally. It's perfectly normal for our cells to go acquiescent. Most of the time, our cells aren't dividing. So it comes from those problems where it's like, it's hijacking at evolutionary conserv modules. So how do we differentiate from just the normal acquiescence program and a harmful acquiescence program? So I guess way more data and, like, consistent data would be needed to be able to tell. Yeah, because it's just like it's like a very specific sub-issue, and it's only really appeared as you've gotten better at treating cancer. It's one of those things that we've not gotten an amazing at treating cancer, but there's lots of cancers now that we can treat fairly effectively. So the question is to kind of move from, or in the past, it would have been great. We can start treating, like, 50% of cancer that's now, why are the other 50% not responding to this treatment? And that's kind of what's brought that question of, like, "Oh, there might be some dormancy going on." I guess it would be nice to, like, then simulate this kind of stuff on organoids. That's another, like, because there's kind of two arms of, like, the cancer cell dormancy research, as people who are working in the more computational field, like me, there's also people doing quite a lot of work experimentally. And so they're getting much better at identifying what's going on mechanistically. The problem is that if you have a cell line of cancer cells, that's great, but because we know that there's lots of interactions going on with the micron environment, unless you start adding yellow cells in to make, like, an accurate representation of what they're having with the cancer in the body, it's kind of like, well, we don't really know what's going on, but obviously, getting those co-pilot experiments going is, like, extremely difficult. And it was one of those things where, like, I mean, it's very useful for us, because what's kind of happening now is you've got this kind of closed-loop of research, where computational biologists will take a data set and identify some new factors, and then that would usually kind of get passed off to an experimental group, and then they will kind of find something new, and then we'll usually pick that up, and it kind of does, like, a little loop of experimental, or going from computational to experimental. And there's something out. Our lab does quite a lot. There's quite a few labs we collaborate with, where they will work with them to generate a data set that we analyze, and then that further feeds back into their experiments, so they can, like, kind of, focus on things. The main benefit of computational biology is that we can kind of do a lot of, like, we can't necessarily find the exact answer, but we can narrow down, like, a list of hundreds of thousands into a list of, maybe 10 that we can test. There's been quite a lot of scrutiny in the last, like, for years, I would say, with people having to be more explicit about what they've done, with their code. It's quite nice, because if I see someone's done something interesting on you, I can, like, usually they should have their code uploaded somewhere, so I can go look at it, which is not quite another quite nice thing. I think definitely having more standardized stuff, even with computational stuff, there's so much stuff that's not standardized. Like, everyone's kind of got their own quirks of doing things. Like, even between Python and R, you can analyze single-cell sequencing data with either R or Python, but the way it happens is slightly different. It would be nice if we started to come into it with, like, more standardized stuff, especially in computation, I also experiment or work. Because then we can start probably doing more meta-analysis work, because it's kind of quite difficult to do at the moment. What was your favorite and least favorite aspect of the work you did throughout the year? I think my favorite part has been, like, the independence that I've been given to kind of get on with my work. I mean, I think it's definitely partly a function of the fact that I do computational biology, because my lab is my laptop. I can kind of do what I want. I don't know to worry about wasting, like, precious reagents or anything, or, like, being stressed about maybe doing something wrong, like, climbing on myself with something very quite a lot of independence to kind of do what you want with your data, which is really quite nice, because you get to basically explore. Even though I've received tons of amazing guidance and feedback from my supervisor and the post-docs in the lab, I've really felt that it's been me driving the project and, like, where I've wanted to go, which has been realized. It's been a really great experience. I've loved that. I think my least favorite has probably been dealing with, I guess, some of the more annoying parts of computational biology, like, having issues with your computer not working. Like, I had my computer almost died recently. That was really stressful. Oh, that was really bad. Like, my hard drive, essentially, I got up there. I was trying to back up my work, actually. I was like, you know what, I've got a lot of my entire pieces, like all my data are done here. I really should back this up. And then I got an error from software I was using. I was like, oh, what does that mean? And I checked and I was like, oh, your SSD is dying. I was like, right. Okay. So I had to, luckily, one of the post-docs in my lab, thank you, Chen, Klubben. And he helped me. Well, I ordered an SSD and he replaced it for me in my laptops. And everything's fine. But not just, like, only that. It's like, when you work in computational biology, we're not sitting and coding in from scratch. A lot of the time, we'll be using packages within Python or R. That have been developed over the computational biology of the last, like, 10, 15, or so years. But because they're quite niche all the time, what can happen is you can clearly often stumble across edge cases, where even though the package just does amazing work, there'll be like something they haven't accounted for on the code. And you get an error that isn't really awful. And you're spending hours thinking, why is this not working? And the new rise, it was actually like a very slight error in the code that you can maybe have to go in and put a monkey patch, or like, you have to write an issue that, like, please help. Consolid fixes for me. And it can be very first, especially, like, sometimes, where you're thinking, oh, I must be doing something wrong. Like, you spend hours trying to figure out how to use, oh, and this is my fault, this is something else. But then also, that's kind of part of what we do as computational biologists. It's actually quite a really good experience, like, being forced to really think about, like, what's going on behind the scenes. What was your decision-making process when it comes to, like, staying in research and transitioning into masters? And I know that you heard it for a PhD check after. So, any advice to, let's say, like, second years or third years deciding on their career path? I would say think about what you enjoy about the science. I think if you've got something that's really interesting to you. or you just love research, I think it's definitely beneficial for you to consider taking a master's. I think if you really want to send research you should consider pursuing an Amara's because it gives you the opportunity to do like a year or you're actually doing a proper year of research. With an MSC generally you'll spend maybe the last four to six months of your master's doing your project whereas for me I started my project at the start of October and so I'll be held on a full year project and that really gives you the time to find out you kind of really learn the trials and tribulations of what goes on when you're doing research and how like you operate in a lab you would essentially be full-time in the lab with a couple modules on the side. So I think if you're in second and third year thinking oh I'm not quite sure if I want to do a PhD or not I would definitely not I wouldn't try to jump into a PhD I would definitely say take an Amara's or an MSC but I would definitely pitch towards an Amara's to find out what it's like to do like a proper full research project because the research project that we do at undergrad really aren't super tough and they're definitely hard for where you are at your career and with that do you have undergrad but it doesn't necessarily give you the full experience of what it's like to do independent research a lot of the time as an undergrad you'll be quite heavy directed what you're doing whereas when you get to masters there's definitely more of an expectation that you're pushing the research on your own and as a PhD you'll be expected to push the entire new field of research essentially on your own. It was so lovely talking to you thank you so much for all your viewing sites and good luck with your right up obviously finishing up everything. So I think I just want to start by if you just give a bit of background about yourself maybe what degree you study what is the title of your dissertation. Yeah so my name is Sam I do biological sciences in the cell and developmental biology stream. Yeah I did my third year dissertation with Bio19 which is the like lab research module. So in a minute how would you sum up your dissertation project. My dissertation is in this type of pediatric brain cancer called diffuse midline glioma. This cancer is very much known for being a very aggressive cancer with a very dire outcome so there's no real curative treatment for it yet and the standard of care is radiotherapy but it doesn't work very well and there's a very highly lab straight so my project was it was doing two things it was trying to model DMG in vitro and then also looking at treatment response mechanisms and relapse mechanisms. Okay nice so how did you were you given this topic did you come up with it yourself. So my project was very much in line with what was happening in the lab at the time so the direction was kind of decided beforehand but like what was modified kind of the scope of the project and like where to start my dissertation and where to end it and what kind of experiments to include within it. What would you say was your favorite aspects of your project that can be to do with your results you could be to do with working in a lab what was your maybe least favorite too as well. For me like in a general sense I really liked like being part of this project from the beginning to the end because like I was able to be part of like making the experiments, doing experiments, analyzing results and then interpreting them. I like that kind of continuity that came with it and like in a more technical sense I got to do a lot of like immunostaining. I really enjoyed that. That was a lot of fun. Least favorite is a lot easier to just say. I did a lot of image analysis so much image analysis. It was a bit tedious at times but it's just how it is. And how are your results were they in line with what you expected? Did you get a positive result? Did things go well? In terms of like the experimental results? Yeah like the outcomes. Yeah I mean I think a lot of people are going to say this but it was a mix of both some of my experiments went really well and we got results that aligned with what we expected and then some of them had more confusing results which were kind of like what is happening. But that makes for good discussion in a report so I think it was a good mix of both. Do you have any examples specifically? So one thing that went well was when we were looking at treatment response mechanisms, we were looking specifically for treatment-induced senescence in response to radiotherapy. So we radiated cancer models and looked for senescence and that was something that we did find. We found a senescence response which is great and gives us like a direction to go forward with. Something that went less well was that we were looking at modeling this cancer in vitro and as part of that we were looking at whether like a certain component of the model is compatible with the media that we were using and we found that it was sometimes and not sometimes. So we had to kind of figure out what to do with that. That also kind of leads into my next question. What do you think is the future of this research that you've done specifically? Well my project is like I think a bit different in the way that it was kind of constructed with the aim of doing like a bigger project with these results. So mine was kind of two separate pieces that was supposed to like come into a bigger project in the end. So for me it's like the goal is to create this complex model of this cancer in vitro and then look for treatment responses within that model. So I've kind of done preliminary work on both of those branches and then hopefully eventually we'll be able to combine them and do like the bigger in vitro model research. And how do you think this project has helped you in terms of any sort of future careers, future studies that you're looking to do? Oh I think it was a huge help just because this is like for one thing I definitely gained a lot of like practical skills in terms of lab work and like organizing experiments, organizing time, communicating science, all of these things are super important for future careers. And then I also think I just gained a better understanding of like what I like to do in lab and kind of what kind of projects I like to work on, what kind of areas of science I like to work on. And so that was a lot of help for deciding where I want to go from here. Okay nice. So what advice would you give someone that is maybe about to start a dissertation or about to start a lab project, something that you would have wanted to know at the beginning? Oh there's so much you could say for this. Well in a more practical sense definitely start reading as early as you can and start writing as early as you can because that stuff really helps both like to write later on so that you're not rushing everything at the end and then it also helps while you're doing experiments to kind of understand what you're seeing and understand where to go from here. In a more general sense I think a big like because you're in a lab environment I think it's a big help to just try and integrate yourself into the lab environment as much as possible. So just like go to the lab as much as you can like talk with as many people as you can like attend lab meetings whenever you can and just kind of make the most of the fact that you're in this lab environment. Yeah I think that helps a lot. Okay so maybe just to finish off do you have maybe like a fun fact it's about your dissertation topic? Well my dissertation is on a bit a bit of a depressing topic so I wouldn't say the fun fact but something interesting that I learned during this was my research was about cellulose in essence which is it's supposed to be a state of stable cells like all the rest so cells are supposed to permanently stop growing and that's something that we wanted to investigate within this cancer and I found out that like you would think it was a stable state of cells like all the rest but within cancer cells because they have so many mutations and especially mutations in like growth pathways they can often escape in essence and start growing again which is a very common way of relapse and like getting relapse I thought that was interesting. Okay nice well it sounded really interesting and it sounded like you enjoyed it as well. I do really like it yeah. Well so thank you so much for chatting with me. Of course. I'm talking to Valentina here who has just finished her final year doing biological sciences so would Would you like to try it? to sum up your dissertation in one minute? - Yeah, so I studied biological sciences but the biodiversity and conservation roots and my topic was on citizen science, which is basically involving citizens into science because when we mostly think of science, it's just scientists going into their lab with their lab codes on and doing stuff. But there has recently been movements to try to democratize science by involving the public into science so that the society as a whole can participate in science rather than leaving it to just a few handful of people. And more specifically, my dissertation focused on environmental citizen science, which is basically like, it's basically like Ebert. So I think some of you people that really like to record nature would be using Ebert or I naturalist. So basically like you take pictures of insects or birds and that would basically be recorded in apps. So that's a form of citizen science. And my dissertation looks into the gender gap of that citizen science. Yeah, so that was my dissertation topic. So what specific aspects to do look into any interesting findings? I looked into whether, I mean, there has been some research about gender gaps in environmental citizen science or just citizen science in general. But I feel like a lot of the studies were focused on the demographics alone. So it was like, oh, is there actually gender gap or some kind of ethnicity gap? And the studies tended to show that there was, but for gender it wasn't really clear, which I thought it was interesting. But I did an approach it was there gap or not, rather more like, so what's the structural aspects that might create these situations? And in that process, I found out that the structure is indeed basically the cause of these gaps that my arise in the projects, in different projects. And it's really important to basically recognize who is participating and important to widen opportunities because a lot of women that participate in citizen science, they tend to be given different roles compared to men. And it's really important to just recognize them and appreciate them because without them, it's really difficult for these projects to be run. When you said women participate in citizen science, they were often given different roles, like what kind of roles are they given? - So it doesn't mean that they tend to prefer taking up these roles, but most of the times, I guess these subconscious biases, like people tend to give them. So for example, when I was talking to several volunteers, they tend to say things like, oh yeah, like talking to people and about the project. These are like something that women do better, you know? And men are like, oh yeah, like they are more, like you know, like trains batting or bird watching these are like men's stuff. I mean, they themselves also participate, but they, I think I feel like some of them had those kinds of subconscious thoughts, which I believe that these are not true. - Yeah, did you look into kind of how we can help dismantle these stereotypes or biases? - Yeah, so the thing that I suggested that we could do in my dissertation was that it was like recognition basically. So like we did not recognize that these things were going on, I guess at least in academia and the citizen science community. So it's important to diversify these roles and because if there isn't like an official structure, it's really easy for a woman to just be sort of forced into these positions and just make sure that they really want to do it. Instead of just like, oh, like you're good at it, just do it and recognize these roles. And a lot of the times these, the volunteers didn't, like they, although they invested a lot of their time in volunteering, their efforts were recognized because there wasn't like an official task that was recognized for them. For example, like they would just do it additionally. And that sort of made it obscure them, obscure these tasks, like behind the scene tasks. So making it clear that they did these and recognizing their contributions, I think that's really important. Yeah. Let's switch up the topic a little bit. What piece of advice would you like to share with someone who is about to start their dissertation or who is deciding on the topic? Right, so I personally did an investigative research. So there's an option to do the research or like a summer paper. Is that right? Yeah, I think so. Yeah, but I chose to do the research, which means it does give you like 15 more credits, but it's really not enough time. So I think I feel like it's really important that you time-enage. I know it's really boring of you to hear this again, but I think it's really important to time-enage. And especially when it comes to ethics applications, I really highly recommend that you look into ethics applications because I was like a really special case where I worked with another department professor. So if you are participating in a project that your professor is already working on or like your supervisor is already working on, that should be fine because you're going under this project at this project would have already sort of gotten the approval for ethics, but if you're doing like a completely new project in your own, or if you're working with another professor from another department, I really highly recommend that you look into this before you start your project because there was like a little bit of an issue for me with my project because the methods involved interviews with volunteers. And you also need to do some ethics application work and that can take time, like up to two months, I'd say, because I mean, if you do it like well on one go, the application, it would be approved, but that also takes time. But if that gets rejected and then you need to revise it, that takes time on itself. So I suggest talking to your supervisor about this, like being really clear like, oh, I don't need an ethics application, like how does it work in your department or do I not need to do that? That would be really great. - Right, yeah, that's a really useful tip to keep in mind. And that's it, thank you so much for your time and sharing your destination. - That was very interesting. - Bye-bye. - Bye-bye. (upbeat music) - We hope we've shared some lie on undergraduate dissertations and made them feel less scary, especially for students who'll be starting their final year this September. Hopefully, this will also give you some inspiration, even for coming up with your own title or for personal research into these really cool topics. Don't forget to subscribe and check out the description for more info. Lastly, a very big congratulations from the Kinesa's team to all the final year students who are graduating. Congrats. - Congrats.