Liyang Diao @ ROME Therapeutics | Data Science Hangout

Hey everybody, welcome to the Data Science Hangout. If we haven't met yet, I'm Rachel, I lead Customer Marketing at Posit. I'm excited to have you joining us today. The Hangout is our open space to hear what's going on in the world of data across all different industries, chat about data science leadership, and connect with others facing similar things as you.

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I love getting to use this time with everybody here to share a few other notes of upcoming events too, so just real quick. In the Hangouts, we've had a number of questions on best practices for management and stewardship of data. So I wanted to let you all know that we are having a special event in May on this topic with two previous featured leaders, so Jamie at Plymouth Rock Assurance and Dan at Biogen.

And then I also host a monthly data science workflow event, and it's the last Wednesday of every month. And so this month, Julia Silgay from our team is going to be joining us to share how to develop and deploy a machine learning model with Posit Team.

Veronica, I know you had something you wanted to share with people before we get started as well. I want to jump in. Hi, everyone. I just want to introduce myself. My name is Veronica, Veronica Clark, and I'm actually new to Posit. This is my one-month anniversary. But the great news I want to share, I'm actually a principal UX designer, and I want to leave it open if anybody ever wants to reach out and show me your workflow and how you're using Posit and areas of improvement, I am here for all of that. And it's very easy to reach out to me, it's just Veronica at Posit.co.

Well, thank you all again for joining us today. I am so excited to be joined by my co-host for the day, Leanne Dao, Director of Data Science at Roam Therapeutics. And Leanne, I'd love to have you kick us off by just introducing yourself and sharing a little bit about your role, but also something you like to do outside of work too.

Introducing Liyang and the dark genome

So my name is Leanne, I'm at Roam Therapeutics, and I started here in my current role about seven or eight months ago, so I'm still relatively new. So Roam Therapeutics is a small biotech company based in Boston. And what we are studying is what we call, well, what some people call the dark genome. So if you think about the human genome, we're all pretty familiar now with the genes that code proteins and have functions that are relatively well studied. But there has been, I think, an increasing appreciation for things that are not protein coding genes.

And I think that this is an area of study which is, it's challenging from like a computational perspective. You know, how do you quantify these kinds of elements which have special challenges which we can talk about? And then on top of that, how do you, you know, relate that to potential new therapies? And so I think it's a really exciting space. I'm really excited to be here.

My background, it's going all the way back to college, I guess. I studied mathematics, and I think I minored in Chinese language. So I spent some time in China, you know, just, I think I spent an entire semester just taking language and literature classes, which was really fun and totally different. I think that was my final semester in my senior year.

But before that, it was really just math all the time. And the idea that I had had when I was in college was to just pursue math. And I was really not interested in biology, to the chagrin of my mother who wanted me to become a doctor and study pre-med and go that track, you know, all the way to the end. I did try for, I think, a semester or a year, and I thought that biology was just, it's just a hot mess. No one knew what was going on.

And it was just very, it was not very satisfying compared to the kinds of problems that I was trying to solve in my math classes, which were clean and well-defined, and you can, you know, write a proof and it was either true or it wasn't true, right? In biology, you don't have those kinds of, it's not very binary.

And so I, you know, I really was very set on this all the way through to basically my senior year. And then in my senior year, I had to think about what to do next. And I wasn't ready to get a job. So I thought, well, I have to go to grad school then because I have no other skills. So I was applying to graduate school and I applied to a bunch of math programs, but then I also ended up applying to some bioinformatics programs, even though that wasn't something I had really studied before.

It was something that I sort of became interested in, in some of these summer undergraduate research programs. So I was a math major, but then I tried, you know, for a month or two to study a little bit of genomics. And with genomics, I thought that was something that was cleaner than biology, but was still applicable in the real world. And so that was, you know, it kind of piqued my interest and it felt like it was the best of both worlds.

Because towards the end of my undergraduate career, I was beginning to think that pure mathematics, which is what I was studying, it wasn't applied math, it was pure math, was becoming less satisfying because I just couldn't see how what I was studying day-to-day had any impact on, you know, what I saw day-to-day, you know, the lives that we were living. So that became less interesting to me.

But I applied to a couple of bioinformatics programs and, you know, I got into a few of them and I decided just to make the jump. You know, I had no idea really what I was getting myself into, but I was ready to pull the triggers, so I just went. And that's really how I ended up in computational biology slash bioinformatics.

I think it really makes use of, you know, some of my skill set from my math background, but also, you know, I've come to appreciate more and more that even though biology, I guess it's more that I appreciate the complexity of biology now more than I did before. I think before I was really turned off by it and kind of scared and I didn't know how to interpret anything. But I think over the years, I think that unknowingness has become more exciting rather than scary.

But I think over the years, I think that unknowingness has become more exciting rather than scary.

Thank you so much for that intro into your journey. What about something that you like to do outside of work too? Yeah, that has really changed a lot over the years. You know, now I have two little kids. They're both in daycare, so they're not school-aged yet. And I spend most of my day sitting in front of a computer, you know, doing this. So what I like to do outside of work is really to get outside, to get moving. So I run. I've been running for maybe like 20 plus years now.

It's really, I'm not fast. I don't run to race. I run to be outside. I run for the feeling of running. You know, I think one of my best graduate school friends was also a big runner. And I think that she put it really well when she described why she enjoyed running. You know, she really treated it almost as like a meditation activity. We used to go for these long runs and you just get all of the stuff that you were thinking about that was percolating in your head, you know, all day or all night, you get it out and you get the endorphins instead. And it really helps to clear my mind, especially when I'm like really stuck on a problem and I'm feeling really frustrated.

The dark genome explained

So I think probably everyone has learned in school this central dogma of, you know, we have our DNA. It encodes, you know, genes which get transcribed into RNA and then RNA gets turned into protein and that the protein is the stuff that does stuff in our body. And I think that that has been the dogma for many, many years. But the the percentage of our genome that encodes these protein coding genes is actually really small. So most of our DNA is not coding for that.

And I think that we're, you know, we're still sort of learning what else is there. So these non protein coding genes, I think, for a long time have been described as junk DNA. But we called it junk because we didn't know what it was doing. And so at Rome, you know, we're very specifically focused on this class of elements called repetitive elements. So, you know, we have like one copy of gene A, maybe two copies of gene B. But then there are these elements where we have like hundreds of thousands of copies of basically the same element.

It's like a gene. It may or may not encode protein. And some of these elements have the ability to lift themselves up and make new copies in our genome. It's very cool. But we don't really understand like the full implications of what they're doing. And so we think, you know, especially in the last, I don't know, maybe five or 10, 10 years, people have been starting to appreciate that they're playing a role in potentially cancer, in autoimmune disease, in inflammation. And so at Rome, we're, you know, thinking about these elements and not only what roles they play, but, you know, in which human diseases could they be causal, or maybe a biomarker of disease, and where we can potentially impact human health for the better.

Yeah, so the sequencing technology itself, we're primarily using the same sequencing technology. So, you know, there's short-read sequencing and long-read sequencing, there are kinds of pros and cons of both for looking at these repetitive elements. I think the challenge is, I mean, in part the technology, but really what we are focused on is downstream of that in the quantification algorithms, and the algorithms to detect where new elements are being inserted, which weren't there previously. So a lot of this is algorithmic development.

Research pipeline and computational discovery

Yeah, so it depends on, I guess, how far along the asset is, you know, so we have, you know, assets that are approaching the clinic very soon. And then we have assets that are like early, early development, right. And so depending on where we are, in terms of how far along clinically, I think it changes, you know, what kind of research we do. So for our lead program, our PT 2950, we're going after autoimmune disease, and specifically cutaneous lupus.

And so, you know, a lot of research has already been done there. And we've done like, for example, a lot of in vivo and in vitro studies, and some ex vivo studies, which are really interesting, where you actually, so this is they're like a CRO that does this, where they take these skin samples, which have been excised from patients undergoing like cosmetic surgery. And then they can, you know, grow, not necessarily grow, but like keep them alive in a in a dish. And you can treat them with different things with like, with UV or your compounds or whatever. And then you can sort of see what changes in terms of their gene expression, you can even profile, like with imaging technology, like how the skin changes over that period of time, it's really cool.

For like early discovery things, when we're looking for new indications and new targets, that's where I think you have to do, you have to kind of take a step back, because you don't know exactly what you're looking for there, right. And so I think that in part is driven by computational discovery. So we've processed, I don't know how many, like 10s of 1000s of samples, maybe 100,000 samples at this point, of public data, all across different kinds of indications, all across your, you know, your your gold standard data sets, like TCGA, GTEx, and you know, those kinds of data sets, and a bunch of different indications from studies in public databases like GEO. And then there, we look for, you know, any indication of activity of the targets that we're interested in, and also genes which are related to activity.

You know, that's, I think that's one of the fun slash frustrating parts of being in a small company. Like Rome is small. I think in total, now we have like 30, 30 something people, and the computational group is me and three scientists. And so you can imagine the amount of work that it takes to develop and then maintain the pipeline, and then do the analysis, and then like continually trying to improve that pipeline. It's a lot of work, and we get stretched pretty thin. So prioritization is, is really important. And your priorities are going to change from day to day.

Prioritization and alignment in a small company

You know, I think that you really need to have a high level understanding of what the timelines are for the company and what's driving those timelines. So you have to kind of take a top-down approach and okay, I'm going to, I'm going to diverge a little bit and tell an anecdote. Both of my parents are university professors. And so my dad teaches mathematics, so pure math. And my mom is a professor of statistics.

And I remember when I was describing, you know, my day-to-day because they've only been in the academic world and they're like, you know, what do you, what do you do every day? And I'm like, well, I sit in like five or six hours of meetings every day. And they're like, what do you, why do you need to sit in so many meetings? Like, what are you doing? And I think a lot of it is just, you know, getting everyone and all the teams, like these very disparate teams doing very disparate activities on the same page, because you have to drive forward these programs where you need biology to run biology experiments. You need computational folks like myself to run computational experiments. You need chemistry to make the chemical matter.

And trying to get everyone on the same page to agree on what is the best path forward. What is, you know, what are the go, no-go decisions and what are the timelines in terms of, yeah, so what are the timelines and how long can your company survive for, you know, especially as a small company? That part is really important. So that takes a lot of effort. You don't necessarily have to think about that in an academic setting as much. And it turns out that alignment takes a lot of time.

Transitioning into bioinformatics

Yeah, I don't know much about finance. But I do know that people, at least early on, you know, when I was in grad school, people made this change between going the finance route and going the bioinformatics route. They made it pretty fluidly at the time. I think that that's probably different after you've been in the workforce for a little while. So I think after that, it becomes a little bit more challenging, but it's not impossible. Especially if you're willing to start in like an entry level role.

I think that, and I'm biased here, but, you know, one of the things that I love about working in startups is that, you know, you don't have to have a lot of experience. I think what you typically need to be able to do in a startup environment is to play many different roles and start up very quickly.

So I'll give a little bit of an example from my own experience, which is not quite what you're, you know, what you're talking about, but I think it has some relation. So in my first industry job, I was at a place called Ceres Therapeutics and we were very, I mean, we were a microbiome therapeutics company. And so I worked there for almost six years. And when I was there, everything was about microbial profiling. And, you know, we were like processing stool samples. We were thinking about what is the best algorithm? What is the best database for determining what kinds of bacteria are in a sample?

Nothing like human data related at all, basically. And towards the end of that, I was thinking, you know, I wanted to broaden my experience a bit to, you know, working in like to learn more about oncology, to broaden outside of the microbiome space. And, you know, it's not like I could jump directly into, you know, like a principal scientist or senior director of oncology at one of these small startup companies, right? Because I didn't have the experience.

And so I ended up going to a small company that didn't have a bioinformatics function at all. And so I got to, you know, A, learn how to build up that bioinformatics infrastructure, spin it up in the cloud. But B, at the same time, I had a mentor who basically took a chance on me and gave me a little bit of time to sort of ramp up on oncology. You know, how do people think about, you know, problems in oncology? Specifically, I was in this space called the Antibody Drug Conjugates. So that was completely out of my wheelhouse at the time.

But it's not something that you can't learn, right? So I think, you know, what's really important is to understand your own capacity for learning new things. And I think that if you're confident that you have that capacity, that you can do it. It's just how much effort is it going to take? And are you willing to commit that effort? But I'm a strong believer in that, especially in startup spaces, because, you know, one day this company is going to be here, and one day it's not, you're going to have to go somewhere else and probably pick up new skills while you're at it.

I know that's probably not like super helpful, but I think it's possible is my answer. And it's just about finding, you know, the time to do some training on your own, but also finding the right fit in terms of a new job, you know, in the space that you're interested in going into.

A lot of it is trying to draw parallels. So maybe you don't have a specific skill. You haven't done this particular analysis before. But in data science, you know, one of the great things is that if you're working with data, you just have to know what kind of data it is in order to know how to treat it and how to do the analysis, right? So maybe I haven't, you know, done an analysis with human gene expression before, but I've done a lot of bacterial, you know, composition analysis. So you can draw those kinds of parallels. And I think it's easier to do that in the data science space than in some other spaces.

Computational biology vs. data science titles

Yeah, so my question was about computational biology and bioinformatics versus data science. So my background is actually a bit similar to yours. I had a computational biology slash bioinformatics degree and then a little bit of postdoc experience. And then I transitioned to data science. But I feel like that it's, for me, it's always kind of, so I'm not doing bioinformatics anymore because I feel like I probably don't want to, or maybe I was a bit too traumatized with like the academia experience and like working in the lab and like all of this. But I sometimes think of going back.

So right now you're director of data science, right? But so do you feel that this role is different or is it because like you have data science in your title or it is still more like computational biology but just named as a data science? Definitely the latter. This is a trend in the industry that I really only noticed in the last couple of years. You know, five, six, seven years ago when I was getting started, everything was comp bio, bioinformatics. And then in the last couple of years, I started seeing everyone's title started shifting towards being data science.

I don't know what was driving that trend. I still consider what I do computational biology. You know, maybe people want to say data science because it seems to have more like broader implications. So we do more than just, you know, genomic pipelines, for example, like we analyze all kinds of data. So in that sense, OK, you know, data science, that's fine. But it's definitely still the same thing.

Keeping up with a fast-moving field

So if so, first of all, you know, in a lot of companies, we have resources that go out and find patents and IP and interesting what appears to be, you know, interesting related publications every week or every month. And we get that kind of report. But also what has been really helpful, especially when you're in like a new field, is to just set up a ton of Google Scholar alerts. So I have Google Scholar alerts for all sorts of things. And then every, you know, it should be every day. So it's not every day. So it does pile up over time. I think I have like 90 unread ones right now. But I try, right? I try to get through the pile every couple of weeks or so.

And so you can that way you can really keep on top of like cutting edge, what is being published? What are people saying? It's like the biology side, the clinical side and the technology side. I think that that is actually really helpful and low lift, except for when you're, you know, trying to go through your inbox.

Yeah, I think I think that actually ties into something you and I had chatted about a little bit before, but you said in order to be the best data scientist, you can be you have to know the right questions to ask in that space. And other ways that you kind of went about that when you first started in your role.

Yeah, I think that that is an area of growth that most people go through, you know, as they become more senior in their roles. So, you know, when you are fresh out of grad school, I think most of the people who, you know, when you first graduate, you have a lot of really sharp technical skills. And so you really just need someone to tell you, you know, solve this problem. Tell me, you know, very basic question. What are the differentially expressed genes? What are the pathways that are differentially expressed? And everyone can do that, right?

If you're coming from a bioinformatics background, I think as you progress and you start, you know, or in my case, moving away from just the bioinformatics and going into computational biology, into the translational questions. At some point, someone like there isn't going to be someone to tell you what are the questions to ask, right? And at that point, you either, you know, need to find someone who can do that for you, or you need to start thinking of those questions yourself.

I think that part can be pretty challenging because you have to understand not just what your tool set is able to answer, but what are the data that are available? You know, what are the relevant biological questions in a particular indication or in a particular space that's relevant for your company to help drive the science forward? And that, I think that takes, you know, it can take a while to learn to pick up.

But I think that's where people who have the biology background, you know, people who come into bioinformatics have all different kinds of backgrounds, right? So, some people come in from a math background like myself. Some people come in from a physics or a chemistry background. And some people, I've known actually several people who come from like an ecology or a biology background. And so they actually, I think, have the benefit of having already been trying to ask those kinds of specific questions, which, you know, for someone like myself, who really avoided a lot of biology classes growing up, you know, I had to sort of learn that on the fly.

Career advice and making the leap

Yeah, I mean, so I think personally speaking, the most important thing to me is to not be afraid to make the leap into something new. Every time I started, I have started a new job, I have been terrified that it's not going to work out or I'm not going to have the skills. This happened when I was going to grad school. It happened when I moved my postdoc. It happened when I was, you know, looking for my new job and my second job and my third job every single time.

I think this is where it's really important to know for yourself internally what you think you're capable of doing. Right, so yes, you have to convince someone else to hire you that you're capable of doing what you say, but you also have to know that. And so I think once you are comfortable with that, it makes it, you know, then you don't fear as much the unknown of what the new job is going to look like. It's more that you know that it's going to be, you know, challenging for a couple of weeks or a couple of months as you start up, but that you're going to get there and it's going to be fine. Yeah, I think fear is natural. Being nervous is natural, but I think that, you know, people are able to overcome that.

The most important thing to me is to not be afraid to make the leap into something new. I think this is where it's really important to know for yourself internally what you think you're capable of doing.

Something that you said to me also, which was like sticking with me is a boss said to you before, in an ideal world, what would you have accomplished five years from now in an interview? And I thought that was, it's just, I've just been thinking a lot about it too. So I was wondering if you could share that story with everybody.

Yeah, so I was, so this was for my very first job. I'd never had a, like a job job before. So I was a postdoc. I was interviewing. I was really excited for this opportunity because in my postdoc, I had been studying the microbiome and then I had this opportunity through a recruiter to work at a microbiome therapeutics company. I knew zero about drug discovery. I knew nothing.

So, you know, the, the, my hiring manager who later became my boss, she asked me like the last question before the end of the interviews, she was like, well, in an ideal world, you know, what would you like to have accomplished five years from now? And I like, I really hate that question because like, you know, what do you, what do you know? What, what's life going to look like in five years? Nobody knows.

So I was thinking to myself, I was like, well, you know, what, what, what would be really cool is if I could be part of the team that brought the first like microbiome therapeutic to the market. I had no idea how unlikely of a, of a, of a goal that could be. I didn't know what timelines look like in drug discovery. And, you know, five years is not a, not a very long time. And we were working on a novel therapeutic and a novel space, nothing like no microbiome therapeutic had been approved before.

But I, you know, what I like about this story is it kind of showcases, you know, when you are new to an industry and you don't necessarily know what to expect, that you can kind of aim for anything and, you know, not be held back by your preconceived notions of what's possible. And it, as it turned out, like five years later, after being at that company, five and a half years later, we were actually on track to, to FDA approval for that therapeutic. It's really amazing.

It's like kind of an incredible Cinderella story, which I love to tell because actually I'm going to add a little bit to that, to that story, which is when I started that job, I started on Monday and on the Friday of my first week and my first job ever, we got this really, really terrible news that our phase two trial had completely flopped. It was for the same program. And you can feel like the, the disappointment and the heartbreak was really palpable in the room, right? Everybody had worked so hard on this program and the results were so bad. There was like nothing. There's no signal at all.

And I thought I was like going to not have a job on Monday, basically. But somehow really, I think, attesting to the scientific leadership, you know, everyone really, really dug down deep and I was fortunate to be a part of this effort. We went back, looked at the data, reanalyzed the data, built new algorithms, you know, built new software, got new data to, to really figure out what happened to identify all of the issues that led to that failed phase two trial. And we didn't, when we went back to the FDA, we didn't have to rerun the phase two trial. We justified like what we, you know, what we had found and what, why we thought we saw what we did in the phase two and we were able to proceed to a phase three trial. It was really an amazing story.

It's like kind of an incredible Cinderella story, which I love to tell because actually I'm going to add a little bit to that, to that story, which is when I started that job, I started on Monday and on the Friday of my first week and my first job ever, we got this really, really terrible news that our phase two trial had completely flopped.

The microbiome pill and FDA submissions

So FMT, this fecal microbiota transplantation, this is something that has existed for, for several years, for a long time for the treatment of recurrent C. diff. This was a procedure that was not regulated by the FDA, but it is like very efficacious for the treatment of C. diff infections. So what Ceres was trying to do was to make this kind of a regulated, a clean, clean process, because an FMT is really just like a fecal enema, which nobody really wants to have done.

And so what Ceres was developing was this pill, which was derived, which is derived from healthy patient stool samples that has been heavily, heavily processed, so that all that remains are these bacterial spores from commensal bacteria. And so I've actually seen those pills that are like, you know, just like a normal pill, little blue pill. And it's just packed full of healthy bacterial spores. And as, as, you know, one of the scientists who got to analyze the clinical data, what you see is when patients take this pill, they start out with a very dysbiotic microbiome, because this is, these are patients who are really sick. They've taken lots of antibiotics. Those antibiotics have really destroyed their gut microbiome. There's nothing there. And within like two to three days of taking this pill, the microbiome is just like, it's blossoming back. It's really, it's really incredible to see the kinetics.

AI and LLMs in drug development

Not my team. So I've actually been to a couple of conferences slash workshops where people are, you know, throwing around this generative AI idea in the drug development space. And what I have found is that in general, there's a lot of hype and not a lot of results, specifically when you're talking about gen AI, right?

And I also think that over time, what people call what I would call just statistics and analysis has changed. So, you know, seven years ago, it was, what are we doing with machine learning? This, you know, someone talking to me, right? And then, you know, five years ago, it's like, what are we doing with, you know, deep learning? And now it's like, what are we doing with AI? And I think that for the kinds of analyses we do, the technology is not there yet for that.

I think it's different in the chemistry space where you have a lot of data points going in and you have a lot of screens being run. So you have something that you can run on, that you can generate these models on. When you're talking about like microbiome patient data in like any kind of clinical trial, honestly, the sample sizes aren't large enough to build those kinds of models. And I don't think that people have a clear idea of really of even what are the right questions to ask with those kinds of models. That's my, sorry, it's kind of a downer, but that's my take on it right now.

You know, I was speaking to our co-founder a couple of weeks ago and he was laughing. He told me that, you know, he had spoken to this investor recently and she was like, yeah, you know, half of these companies that are touting their AI capabilities, like you can do what they do in Excel. It's not what you think it is.

I was curious, you know, there's a lot of scuttle and industry around how do we do FDA submissions in R only and, you know, for standard clinical trials, there's a lot of movement there. But what you at Roam do is a step beyond really. And hearing that you're using Python maybe makes it an extra step of challenging, like validating Python environments comes to mind. But yeah, what does that look like for you? Have you had to manage that? Like, how do you validate and ensure that the FDA or other regulators believe in the novel bioinformatics pipeline that you're implementing?

It's a really good question. We actually struggled with that quite a lot when I was at Ceres and we were preparing our BLA filing. And so, I mean, I don't think I have a really good answer for that. We do the best we can to perform the kind of validation experiments that we think need to be done to validate those environments. I mean, Kevin may know better, you know, being from the FDA. But, you know, we captured all of our environment variables. We were using R at the time. So, you know, there's a RM package, right? So, we used that. We used all of the packages that we could to make sure that our environment was unpackable and reproducible by anyone.

And that was really, I think at the time, we were also considering like Docker images. We were trying to, you know, we were wondering like what kind of data do we need to submit to the FDA? You probably don't want all of our FASTQ files, like our raw sequencing data. You probably don't care about that. But like at what level, what is the right level of data to submit? And we had a lot of internal struggles trying to define that as well.

I would like to connect with you, actually, after this to talk about this more, because we will have more of these kinds of issues coming up as we're also moving into the clinic here at Rome.

One nerdy question is this, how did you deal with human contamination in the reference genomes? I mean, we do, we do the double mapping. So first for the microbiome stuff, we map to the human reference genome first and remove all of that. In some indications, it's like much more critical than others. So we also had studies in ulcerative colitis and in the patient's stool samples that were, you know, that had a lot of blood contamination, for example, you can see that 95% of your sequence is coming from human data.

I think we're out of time. I know we're a little bit over. It sounds like this is a really fun conversation for everybody to have. Thank you so much, Leanne. I really appreciate you taking the time to join us today and sharing your experience. This has been great. Yeah. Thank you, everyone.