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Elli Papaemmanuil Explains How Genomics Will Transform Cancer Care

Episode Summary

This week Harry speaks with molecular geneticist Elli Papaemmanuil about how newly available genomic data could lead to major improvements in the standard of care for cancer patients, leading to an age of true precision medicine.

Episode Notes

This week Harry speaks with molecular geneticist Elli Papaemmanuil about how newly available genomic data could lead to major improvements in the standard of care for cancer patients, leading to an age of true precision medicine.

Papaemmanuil is an assistant professor of computational oncology at Memorial Sloan Kettering Cancer Center in New York. Her lab’s research is built around the idea that the genetic sequences of tumor cells reveal distinctive acquired mutations that can allow doctors to predict the course of the disease in specific patients and help them to design individualized treatments. That idea isn’t new—but it isn’t yet standard practice in oncology, a situation Emmanuil is working to change, in part by using AI and data-driven approaches to analyze the vast number of genetic variations in diseases like leukemia and reduce them to a manageable number of classes amenable to customized treatment approaches.

Papaemmanuil says she decided to become a cancer geneticist from the moment she learned about the Human Genome Project as a young person growing up in Greece. She obtained her PhD at the University of London, and now she’s working to understand “how we can use genomic technology and genomic data to inform patient care.” She was an early adopter of microarrays to conduct genome-wide linkage studies and identify common genetic variations that predispose people to colorectal cancer, leukemia, and other cancers. More recently she’s used rapid genome sequencing technology to help complete the first catalog of genes that are commonly mutated in cancer.  She says this kind of information could help identify which patients are at risk for cancer; carry out screening to find patients with early-stage cancer, when treatment outcomes are much better; and most fundamentally, to create data-driven treatment models that account for a patient’s age, gender, lifestyle, radiographic data, and genomic parameters.

“At the moment our standard of care represents brute force,” Papaemmanuil says. “Now we understand that there’s a lot of complexity [in cancer], and that if we study large enough patient cohorts, and we have genetic information with very good clinical annotation and outcomes, we can bring the AI component into the process and use classification and prediction tools” to, in effect, put a powerful computational advisor in every oncology exam room.

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Harry Glorikian: Hello, I’m Harry Glorikian. And this is Moneyball Medicine. The show where we meet executives, entrepreneurs, physicians, and scientists using the power of data to reinvent healthcare from machine learning to genomics, to personalized medicine. We look at the biggest trends in patient care and healthcare management.

And we talk to people behind the trends to find out where data is making the biggest differs.

Here’s a question I am sure you are all familiar with. Did anyone ever ask you, what do you want to be when you grow up? Now I’m sure a lot of you or a majority of you will apply with some fascinating occupations, such as firefighters, astronauts, police, officers, doctors, lawyers, engineers, or even some more ambitious ones like superheroes, but this was not the case for our next guest.

She became absolutely captivated with the idea of genomic research in cancer and vowed to become a scientist, which led her to phenomenal success in her field today. Her work revolving around precision medicine, using data collected from a population and application of it for treatment of a single patient led to alternative views of cancer care.

For example, in the case of leukemia, part of the reason why it is so hard to treat is due to its drastically varying genetics, but by using AI and data-driven approaches, she and her team were able to reduce the number of variations. Down to make the cancer simpler for treatment. I know you were thinking of this is science really complex in many different ways.

Well, in my conversation today, my guest is able to break this all down for me. And truly I have to say her works are nothing short of amazing in the making without further ado. Here’s our next guest, Dr. Elli Papaemmanuel,

Harry Glorikian: Elli. Welcome to the show.

Elli Papaemmanuil: Pleasure to be here.

Harry Glorikian: Hi Elli. I don’t even know where to start. I mean, there’s, there’s like, there’s a million different things that we could talk about and, you know, but, but, you know, I guess for everybody who’s interested in this space and, and, and whether they’re young or, or even thinking about, you know, what’s happening in this space, like, how did you start in this whole area?

And, really you’re like on the bleeding edge of all this different technology shifts within precision medicine and genomics. So tell me the story.

Elli Papaemmanuil: So yes, it’s been a very eventful decade and very much the rudder of at least my career trajectory has been these technological advances starting from the human genome project to where we are now.

So I can tell you a little bit more about it in that I was 18 years old. Just about when the human genome project was coming into maturity. So-

Harry Glorikian: Now you’re making me feel old.

Elli Papaemmanuil: Not at all, not at all. And I remember just being entirely fascinated and captivated by the concept that there was this international efforts of scientists coming together to decode that the sequence of the human genome. And without really having a deep understanding of what that even meant at that time. I knew that that’s what I wanted to do for the rest of my life.

And I should say that I was a kid in Greece and I very vividly remember the moment where I was walking down an old street in Athens it’s summer. Everyone has the windows open and listening to the TV and I was just tired of listening to the question what will you do when you grow up? And as I was walking down the streets and hear about the announcement of the human genome through a TV channel, And I remember the one where I stopped and my heart just said, that’s what I want to do.

And essentially that set off a trajectory where I packed my bags and I went to the UK to my entire family’s despair 

Harry Glorikian: Wow

Elli Papaemmanuil: And studied genetics. My first day, at the auditorium, at my genetics course, my lecturer comes in and says the human genome has 80,000 genes. And every year when we started our academic Congress, that number went from 80,000 to 60,000 and it went all the way down to the 20,000 that it is now.

Harry Glorikian: Yea 

Elli Papaemmanuil: And I think starting a discipline at the time where that discipline is almost being reborn or reinvented through a big technological advance. One thing most, um, rocket fuelling experiences when it comes from a young individual who goes through university and it’s ready to go head on into identifying their career path.

Harry Glorikian: Well, and I have to tell you, um, I still feel like it’s going through the technological revolution and changing, you know, I always tell people I’m like, I don’t think I can read fast enough to keep up with the changes that are happening from multiple directions. And so whenever anybody makes like this scientific prediction, I’m like, eh, Okay. Let’s see how that goes.

Elli Papaemmanuil: 100%. That is one of the most fascinating things about being a scientist is that you never come out of college. You never graduate. You will always have way more to learn and understand that what you have mastered in terms of both technology and facts.

Harry Glorikian: I wish, I wish we had scientists that thought like that more, more than you-

I remember when someone said dark matter, Dark matter is not important in the genome. I was like, how can you say that? We don’t even know

Elli Papaemmanuil: And then came the In.code project.

Harry Glorikian: So, so tell me what you’re doing now.

Elli Papaemmanuil: So, yes, I didn’t answer your first question. So where I am now is I’m an assistant professor at computational oncology at MSKCC that’s Memorial Sloan Kettering Cancer Center in New York.

And I really feel that I have the privilege to work side, alongside some of the best scientists and physician scientists in oncology to very much understand how we can use both genomic technologies as well as genomic data to inform patient care.

Harry Glorikian: Now, I know that you’ve worked on like different technologies on the way.

I mean, you’ve seem like you’re constantly graduating to the next level where did you start? I mean, I know that I think it had something to do with colorectal cancer and, and microarrays. Was that correct?

Elli Papaemmanuil: Correct. So, as I was graduating from my university, I had to choose where I would go on and do my PhD.

And I remember {inaudible} through so many labs and that’s so important in science in that you really need to follow something that makes your heart tick. And for me, that has always been at the intersection of new technologies and data. So that brought me from my PhD at the Institute of cancer research, where to do a PhD.

That was one of the first, I was one of the first adopters of micro-ray technologies at that time. And to do genome-wide linkage studies. So that is to interrogate the genome of families, where there are multiple occurrences of colorectal cancer and map 10 thousands points across the human genome in order to identify chromosomal regions that may associate more often than you would expect,

Harry Glorikian: Right

Elli Papaemmanuil:- Like a child or segregate patients with cancer. Then these led me to be one of the first genome-wide association studies, where we use the population genomic approaches to identifying common genomic regions that predispose to leukemia. And at that time there was the big switch from high throughput, genotyping platforms such as micro rays to next generation sequencing technologies. We’re now for the first time we were in a position to sequence the entire exome, all the genes in the human genome or the entire genome. And that took me straight to the Welcome Trust Sanger center. One of the best genome centers in the world.

Where I had the privileged to be part of the International Cancer Genome Consortium and that effort was very much concentrated in performing whole exome or whole genome sequencing in the 15 most common cancer types with the aim to identify all the genes that are mutated in cancer. And just to give some perspective on that this, this initiative started only 12 years ago.

Harry Glorikian: Yeah. 

Elli Papaemmanuil: And it was only five years ago where we could say that with confidence with, have we have concluded the first catalog of the genes that are mutated in cancer. So the next 

Harry Glorikian: Yeah.

Elli Papaemmanuil: –Step that came from that is what do we do with all this information?

Harry Glorikian: Right, it’s, it’s funny. I mean, if you, if you, I don’t know if you saw, there was somebody that wrote a piece the other day, again, one of these pieces of, Oh, you know, there was this promise of genomic medicine and on what doing the genome will do, and it’s done almost nothing.

And it drives me bananas when I see these things. I’m like, wait a minute. It’s the fundamental foundation for any modern biotech like that you could possibly think of,

Elli Papaemmanuil: But you know what, we’re all a little bit culprits for that because, and I’ll tell you my quickly, my thoughts about that every time we discovered a new gene, right?

It was, -I can’t describe the moment of that discovery. And then both the scientific and clinical community would come out. I was part of many of those discoveries and we said, would discovered this new gene and then both with the press announcements, both the publications, as well as the media wanted us to wrap up to say, and is going to help us cure that type of cancer. So that leads to this expectation of precision medicine and, and in a way resulted in not unreasonable hopes, but potentially the timeframes or the steps that we need to go from now to the realization of this vision of precision medicine potentially were not clearly understood.

So that’s what I’m working on right now.

Harry Glorikian: I try to make sure that we are talking with people, understand like data on the show. Like, what is the purpose of this data and where do you see the different uses in the lab that you’re in now? And what is your vision for where you’d like to see it go. Because I know you’re very passionate about the direction of this,

Elli Papaemmanuil: Right?

So we’re now sitting at probably at the middle of the Hill. We understand what the building blocks of cancer are. And we have this vision about precision medicine. So we should start by, um, understanding which Individuals are at risk of developing cancer in the first place, before they even develop pain.

Once early symptoms or early screening approaches, we can diagnose patients with earlier stage disease, um, using appropriate technologies in order, and in order to mitigate so survival of patients or treatment outcomes of patients with early stage disease are much better than ones with later stage disease, but fundamentally once a patient has been diagnosed with cancer and you have a rehab station where they’re sitting in their oncologists room next to a computer. And as part of the medical record for that patient, we have data driven models that consider the age of the patients, the gender of their patients. The genomic parameters, their lifestyle parameters, and they can inform their pathology or radiographic data.

And they built an accurate picture of that patient’s diagnosis. And also they deliver predictions or proposals with what the best line of treatment might be for that particular patient. Given both the genetic makeup, their age, as well as all their other clinical presentation parameters.

Harry Glorikian: So you’re talking about you, you’re going to have to use some form of AI and machine learning to take the base data and make it digestible. Right?

Because even some of the best, you know, that I know have to pour into this data at a unbelievable level of focus, um, and understanding to then, you know, come up with, okay. here’s what we think.

Elli Papaemmanuil: Exactly. So there is so much hope and also expectation on the use of AI based approaches in healthcare overall and cancer in particular.

So, but for that, and the basis of that is that we can learn from many patients in order to inform the treatment of a single patient. That’s the fundamental premise of that. So how can we leverage all the data historic and existing as well as prospective data from thousands and thousands of patients and convert them to confident and evidence-based decisions for the end of one.

Uh, that is sitting in that room with that oncologist that day. And most importantly, how can we empower that oncologist to make the right decision for that very patient?

Harry Glorikian: So, you know, speaking of leukemia, if I’m not mistaken, right, that was one of the fundamental papers that you presented and you’ve built a consortium around it to do more work in that area.

So hypothetically speaking,, you know, you’re a leukemia patient, right. But you’re If you were telling a patient what steps to take for taking advantage of this, what would you sort of tell them?

Elli Papaemmanuil: So I think we’re addressing more the oncologist and-

Harry Glorikian: Okay

Elli Papaemmanuil: The treats that the treating groups, uh, in order to support and help that patient.

And I think that examples that we’re saying leukemia are also translatable across cancer types. But to give you an example that of how unique every patient is. Leukemia is probably one of the simplest cancers from a genomic perspective. So just the basic building blocks of cancer is that every cancer occurs as a result of multiple mutations.

Harry Glorikian: Right

Elli Papaemmanuil: Usually those mutations happening in sequence. We have a first hit those cells expand first mutation, and then we need secondary, third events that eventually will transform a normal cell into a mutated cancer cell population. We performed a study and so currently what’s happening clinically is that a new patients will get diagnosed and a biopsy from their tumor or their leukemia is going to be taken.

And it’s going to be evaluated across a number of genetic markers. And the reason we do that is so that we can inform the diagnosis of each patient better. We can determine a more accurate molecular subtype and inform treatments for that patient. So we did this exercise recently. We’re just wrapping up this study in 3,400 patients with one particular type of leukemia.

So that is a single disease. That’s uniformly treated, by in the same way by hematologists around the world. So in these 3,400 patients we determined two and a half thousand. different genetic profiles. So that very much showcases why cancer still is today only five years after the completion of the genome project, a complex disease to tackle with regards to precision medicine, however, using a kind of machine learning based approaches, we were able to reduce this complexity.

To 15 common themes. So 15 classes, if you will, of genetic themes that not only helped us understand that these represent distinct biological entities, but clinically patients with each in each one of those 15 groups presented differently. Some of them may be young. Some of them may be older and most importantly, respond to standard of care or novel interventional therapies in different ways.

Harry Glorikian: Right? Right. It sounds like whack-a-mole like, you’re going to need more than one approach, potentially more than one therapy on the same patient, but it really says this standard of care.

Elli Papaemmanuil: At the moment. Uh, our standard of care represents more brute force and we’re treating more or less diseases uniformly.

Now through the ability to look at each patient’s genomes, we understand that there’s a lot of complexity, but then if we study large enough patient samples or cohorts, That are where we have matched genetic information with very good clinical annotation and outcomes. We can now bring the AI component into the process and start to developing both classification and prediction tools that will effectively become or inform that web portal tool of the future, where an oncologist sits with the patients.

Harry Glorikian: Right. And I can see it where if you can bin these groups together, it will send patients down a different path. But not only that, if you’re looking at this disease at different stages and you can build a movie, you could dial forward or dial backwards to be able to look at the disease, which can inform drug discovery, patient management, all sorts of information.

Elli Papaemmanuil: So, so you just touched upon two really important concepts in cancer care. So the first one is that we tend to look so far, we tend to look at single biomarkers, but we’re learning that no patient is a single biomarker is not as every patient is a complex and dynamic system.

It’s almost as if you imagine that if you have a chair with four broken legs and you try and fix the one leg, you can’t expect that chair to maintain the equilibrium and be fixed within one fixed length. Okay.

Harry Glorikian: Uh, Ellie you’re you’re, you’re expecting everybody now to, to understand complex concepts,

Elli Papaemmanuil: Right? So, so the question is how do we understand everything that’s broken and try and fix kind of bring back stability by looking at the whole picture,

Harry Glorikian: Which is impossible to do without AI and machine learning as a tool to assist.

Elli Papaemmanuil: Correct, or we can be, we can get there faster. Um, if we bring systematic approaches and data driven approaches to inform those decisions. So, so far we have a number of success stories where we have data driven models to inform patient care, but these ones so far, and as it should be are grassroots scientific movement.

So how can now learn from those grassroots scientific successes and understand the process and how can we embed data driven and AI approaches into essentially the development of infrastructures and at an institutional and national level that can enable and accelerate AI based solutions in healthcare and medicine.

Harry Glorikian: So now help me here, because what you’re talking about is a serious interdisciplinary team of people that need to come together that have expertise, because if you could mold all that into one person, these, these are, you know, yeah. I always think of Star Trek when we get to that type of person. Right. But your team has got to be composed of that.

Elli Papaemmanuil:  That is correct. This cannot be hap- this cannot be materialised by, um, one flavour of a scientist, scientists today can be treating physicians they can be biologists they can be computational genomicists or data scientists who have trained in machine learning and AI driven analytical approaches.

And my team is very much a reflection of this type of composites

Harry Glorikian: That doesn’t, I mean, let’s face it. You can’t have that everywhere all the time. Right. This is a, you know, you can’t have the winning NBA team everywhere all the time. And so how do you envision this being widely available?

Elli Papaemmanuil: You can think about this in creating the right resource and ecosystem to support these initiatives and these type of initiatives can be created and supported within academic institutions.

Let’s say for example, Memorial Sloan Kettering, or through national initiatives, and one can imagine, and we know that for example, scientists to date have been supported to perform their experiments by accessing biological specimen, biobanks, every time a scientist wanted to do an investigation or a next time it’s in the lab, they would go to the biobank and identify some samples and perform their experiments.

So we can start thinking about a similar concept, but with digital biobanks. So what about if at an institutional and why not national level? We start introducing the concept of digital biobanks, where we have, um, infrastructures that allow us to a, um, have high dimensional data, genomic imaging radiographic in a structured format that is readily computable, but most importantly, linked to clinical annotation and outcome metadata.

So once one builds, and these are things that a hospital or a research Institute, or even a diagnostic company could start thinking about building with partnerships, or this could be mandated through national funding initiatives and promote it where we now start to build architectures of these datasets.

And then these can represent that ecosystem that scientists can access with specific questions to start building models. Um, for those patients, we can, we have the option as a community to lead these into a {inaudible} of grassroots movement. Or we really say that that point in time where we understand what needs to be done and we start thinking.

A little bit more globally, more ambitiously in some way.

Harry Glorikian: Yeah, no, I wish we were thinking about this at a national level. You know, that would be my- right? You know, if I was, I would be like, if you’re getting public dollars, we’d like to see it in this format, but you’re just trying to set that up.

And then when I think about the IT infrastructure of, you know, a normal hospital. I break out into hives most of the time, right? It’s not exactly what you would consider the epitome of that technological solution.

Elli Papaemmanuil: The EMRs are challenging. And this is not to say that I don’t appreciate the challenge it’s like solving the EMR data problem is, is for a reason, one of the most challenging problems to crack around the world throughout time.

Harry Glorikian: But that requires the EMR companies to want to fix the system. Right. And I am I just in my heart, I don’t believe that is a goal of those organisations, which is why I believe most of the time the change is going to come from externally into healthcare or places like, um, I think it’s Northwell in, um, New York has announced that they’re going to build their own EMR because they hate the commercial EMRs

Elli Papaemmanuil: I think we could devote an entire podcast,{inaudible}

Harry Glorikian: Yes, yes,yes 

Elli Papaemmanuil:  But I should say that coming from, for example, MSKCC has developed um, uh, molecular profiling as a {inaudible} and the majority of patients with advanced disease that are being cared for at MSKCC have access to molecular profiling of the tumor, because this is happening, at such high rapids for a large proportion of the patients.

Um, there’s been an enormous effort and initiative to have all that data, essentially data-based and updated on a nightly basis and available through the C bio portal at MSKCC. So this high throughput technologies are now providing the embedding of this high throughput technologies and the way that, that data is structured then linked and visualized through web portals is starting to bring the concepts into within institutional walls. And once you have those data sets present, once that’s thinking, and certainly the chief of computational oncology, Dr. Shah MSK building such initiatives, where one says, now we have that in place. What about we start layering base with additional data, 

Harry Glorikian: Yes

Elli Papaemmanuil: And then you can start buying tumor types or cross-sectional projects.And then as we layer data , you see that an ecosystem is born that will allow, um, and will accelerate the development of AI based research in healthcare.

Harry Glorikian: But take one step back. I mean, just having some sort of a system like that, I can see how the payers, when they have some level of standardization of analytics might be more apt to want to pay for sequencing as a service, right? Because right now I can’t say they’re jumping up and down because depending on where you get it, you’re, you might get different results or different information. And so a system like this might actually help provide a layer of standardization on top of that.

Elli Papaemmanuil: Also, if I return for investment in that we can enter our data into this type of infrastructures, but then the return of investments to the many and of ones 

Harry Glorikian: Yes

Elli Papaemmanuil: Of patients being treated and cared by systems that are informed and supported by this type of infrastructures is very significant.

Harry Glorikian: Do you have any success stories that you could, you feel comfortable sharing where, where you’ve utilized this system that, that has been available or that you’re building that has informed let’s say a unique or insightful therapeutic direction that might not have been chosen from quote, standard of care.

Elli Papaemmanuil: Right. So, um, we’ve been leading an initiative at MSKCC to evaluate, both the visibility, as well as the advantage of looking at whole genome sequencing in upfront clinical care. So, as I mentioned earlier, the majority of molecular diagnostics in cancer and looking at very specific genetic regions, they will look at the number of genes.

This is a very practical and robust, scalable solution for the many, many thousands of patients. However in the time that we’ve implemented this targeting gene sequencing tests, technological advances have happened that allow us to equally rapidly and cost cost-effectively look at a hundred percent of the genome.

So the reason that whole genome sequencing has not been tested or implemented in clinical care yet is because a) has been very expensive, but most importantly, The scale of the data that, that generates and the ability to analyze and interpret that data has been put into question as well as whether they sense any value in the clinical setting above and beyond the few hundred genes we understand very well.

We decided to put this into a test. So we’ve developed, um, a rapid turn around clinical grades research, but clinical prototype whole-genome sequencing platform. And we’ve developed the study for pediatric patients with rare cancers. And we asked where there um, applying such comprehensive genomic profiling approaches could help us optimize care for those patients.

And we just about concluding that one year pilot study. We showed that with working, by working with advanced software engineers, we can turn around whole-genome sequencing data from rural sequencing to a report within two days that is within the research setting, but it shows a path of how this might be implemented within a clinical diagnostic laboratory.

And most importantly, what this, what this allowed us to evaluate is the sheer impact of now looking at hundred percent of the data in a cancer genome, as opposed to a few spots {inaudible}.

Harry Glorikian: Yes. yes

Elli Papaemmanuil: And. I I’ve been at part of this molecular tumor boards and I’ve been leading the genomic part of this analysis together with my team and week on week witnessing then value of having the complete picture as opposed to a few regions, helps us understand a lot more about what, whether that patient, for example, has an underlying germline defect or a risk predisposition mutation in cancer through kind of a defect and DNA damage response mechanism. It helps us to map out all the genes from mutations that have gone RA, um, in that patient’s tumor, it helps us assess the proportion of the genome.

That has been altered or affected. There are things we call now such as mutation signatures or mutation processes, um, that may allude towards potential therapies as well as evaluate. Um, if more systemic, not only targeted, but also systemic therapeutic approaches, such as immunotherapeutic approaches would be suitable for those patients.

Harry Glorikian: Amazing. Having the whole picture actually gives you better information. What an amazing concept. I mean, you know, this is like the blind man who like, is feeling the trunk of the elephant and trying to figure out the rest of the animal. Right. Fascinates me that, you know, this is such a novel concept, right?

I mean, you’re laughing, but I, you know, I’ve, I’ve had, I’ve had to deal with people in, in the industry for a long time on these different issues,

Elli Papaemmanuil: You know? Today you speak to most oncologists and they will say like, we have to also realize that oncologists are looking at those reports and they say, how do I translate this into an actionable clinical intervention?

So the thought of looking at the entire genome. Can be first innodating, but second we haven’t done that yet in clinical practice,

Harry Glorikian: I would argue that there are a lot of oncologists that don’t even look at the report or don’t even order the report to begin with. Then there are other ones that look at the report, the data is there and they decide that standard of care or some other pathway is a better way to go and not follow the data. Right. So-

Elli Papaemmanuil: It’s down to us, it’s down- I think it’s in our hands for both. We’re bringing in new technologies and new concepts, and I think the responsibility is in our hands to work alongside the oncologists and then clinical community and identify and surface those examples that are really life-changing.

Uh, in our pilot, we had patients at their end of treatment where a whole genome sequencing allowed us to identify targets and drugs that are available today, that we would have never thought about giving prior to now. And they were {inaudible}, but we need to move from anecdotal examples to um, demonstrating and providing the evidence of where and when that adds value and a clear path forward for the implementation, the safe implementation, and most importantly, without adding burden to the oncologists, but embedding it within their comfort zone.

And once we start materializing change, And showcasing the value that we can bring. We will never go back {inaudible}

Harry Glorikian: I’m with you. I’m with you. I mean, I want to see that I always have trouble sometimes when I’m talking to other people because they sort of fight it, um, or they argue with it. I mean, I remember there was the first time I gave my talk about genomic medicine. And somebody had asked me to come and give grand rounds someplace.

And I had gone there and after their talk, this doctor came and started yelling at me. We will never practice medicine this way. I was like, why are you yelling at me? I’m I’m reporting to you the data. I, it’s not my opinion. Now all of that said, you’re now at the end of this pilot, what’s next.

Elli Papaemmanuil: Well, we, we need to make it, we need to bring it into validated clinical tests.

We need to make it accessible to more than the few and demonstrate at scale, the value that more systematic approaches can bring, uh, to both patient care and oncology.

Harry Glorikian: But now with, with a platform like this, I mean, my, this is a hypothesis, you know what it does, and I haven’t seen it, but you could identify diagnostic biomarkers, prognostic biomarkers.

I mean, people could be looking at this and utilizing this in a number of different ways that I think we haven’t even touched upon.

Elli Papaemmanuil: Correct. And I think these are parallel journeys. So in order to, so the development of, or showcasing that we can now reliably look at 100% of a patient genomes and, and, um, and enable optimal diagnosis care and disease surveillance is a major milestone that we can accomplish. In parallel there is, there are waves of research that are focusing on how can we first characterise individual biomarkers. Right. How can we understand different mutations in genes? What is dosage of mutations? What is Linage to where these mutations happen in terms of which cell type, how do the co-occurring mutations from mutation signatures add information, and then third, how do we understand the clinical relationships of the individual biomarker and then all the biomarkers together as a whole. And, and how do we, how do we develop models or tools that can help us translate each biomarker and composites of biomarkers into actions, and then comes the platform that can detect all of those biomarkers.

Okay, so there are two different paths. Does this make sense? 

Harry Glorikian: Yes, no, I understand.

Elli Papaemmanuil: So there is what w what we were talking about, the first part of the talk is how do we make sense? How can we incorporate genomic information? And how can you use AI based tools to inform clinical practice? And there, we talked about the development of data-based and evidence-based models that are learning from many patients to inform the interpretation into the annual fund.

And to the second part is how do we make sure that we capture all the biomarkers in that tumor and that’s where whole genome sequencing comes from. Um, so we, as a community, we have this at least, these two, um, tasks to work on. We need to inform both and develop the models that will allow for the interpretation.

And we also need to develop the platforms that can rapidly and reliably extract all the relevant biomarkers for a particular patient and inform all the decisions.

Harry Glorikian: And, and you know, what I’m struggling with, this is how to make this standard of care, right? How to roll this out in a way that it’s universal. Right?

How do you have a -I don’t want to call it a call center, but a place where a pathologist can sort of utilize the software and then right. I’m not trying to belittle it like, you know, but you to get this to the level that we’re talking about, there has to be an industrialization, not art. And each place paints its own picture,

Elli Papaemmanuil: Right? Or there need to be specific standards.

Harry Glorikian: You’re more hopeful than I am on the standards thing.

Elli Papaemmanuil: The regulatory and compliance system does dictate for certain standards and we’ve seen the rolling out of this type of platforms. So in being realistic, there will be implementations of these type approaches within the big institutes and the big cancer care hospitals, 

Harry Glorikian: Yep

Elli Papaemmanuil: And then will be sent. There will be in the US centralized service providers from a commercial standpoint.

And we’ve seen many such providers, um, in the molecular diagnostic space. In the U.S or if we look across the pond in Europe, you will have government led centralized laboratory

Harry Glorikian: Yep

Elli Papaemmanuil:  Such as um, what we’re seeing in the UK. So they’re the necessities for some centralization and some standardization in order to be able to apply this at scale.

And in order to be able to gather that data that will allow continuous interrogation and optimization of both the technology, the models and the tools that will inform what will end up in that patient report?

Harry Glorikian: Yeah. I mean, I’ve, I’ve been advocating to try to build a genome center in Armenia, right. And to do whole genome and then use, you know, analyze at that level.

Right. So I can see how, if you had a central, uh, place to do this with access to software like this, it might make the whole process a lot smoother and easier.

Elli Papaemmanuil: It’s going to be a game changer. And countries such as England, Holland, Australia, Ukraine, many countries around the world are starting to build this initiative.

Harry Glorikian: So maybe that becomes the first place where you license this technology or make it available to them.

Elli Papaemmanuil: We can certainly help with that

Harry Glorikian: What else can you tell us about where you think, you know, this genomic. Approach would have an impact. I know it’s cancer. You’ve been spending all your time on, but if you let your imagination run wild, where do you see the sort of the next opportunity beyond cancer?

Elli Papaemmanuil: Beyond cancer? So since the sequencing of the human genome, every day, we’re witnessing the changes that having access to genome profiling technology can bring in healthcare. I would say that some of the biggest and most material, um, changes have happened in the setting of neonates where, um, we have newborns who will present with a range of symptoms that could dilute to a number of potential genetic disorders.

And there are programs now in place that will perform whole genome sequencing, um, of, um, a newborn. At the, at the first indication that something is going wrong and then use machine learning algorithms to both identify and prioritize most importantly, the most likely causal variance that, um, Inform at a particular clinical diagnosis.

And this leverages from whole genome sequencing data, um, that incorporation of data mining, the entire literature of symptoms, linking them to their genetic variants and then coming up with proposed solutions or treatment solutions and these kinds of revolution. Both the diagnosis and treatment of, um, developmental disorders at a, very early and very critical state.

Harry Glorikian: Uh, yeah. Yeah. I’ve, uh, I’ve had previous podcasts where I’ve interviewed, you know, the CEO of face to gene. Um, and then, uh, Robert Green, Dr. Robert Green from up here at Harvard, who’s sort of trying to pioneer a lot of the work that’s going in this area. So it’s very, It’s fascinating the impact.

I just wish that we would write about these things in the popular press, more so that the average person understood the impact of these capabilities and technologies.

Elli Papaemmanuil: So we have, we’ve seen some of the best examples of success, where we have what we call monogenic disorders.

Harry Glorikian: Yea

Elli Papaemmanuil: Okay. Where there is one more or less root cause of a particular clinical phenotype.

And we now trying to translate these to more complex diseases, cardiovascular disease, autism, cancer, to name some or mental health disorders. These are more complex systems. And the fact that it’s taking us some time to get there should not be confused with the fact that we won’t get there.

Harry Glorikian: No, absolutely.

Elli Papaemmanuil: So it’s just that, because these are complex systems, it means that we need to understand many pieces of the puzzle and in order for us to be successful in doing so, we need to pay equal focus on the single feature and as well as how all the features that we’re evaluating as data scientists come together to both result in that particular disease phenotype and how best to manage it. So it’s a complex task,

Harry Glorikian: No, of course. I mean, I think about it and I always say to people, you know, because people want to find the simple answer. And sometimes the simple answer is buried in a lot of different data that’s coming together.

And unfortunately the human brain was never designed to look at that many different data points coming together. And be able to distinguish one. This is why we have the scientific method. I’ve looked at a few things. I have a hypothesis. I’m going to go chase it down. But at some point, these complex diseases have so many different things coming together you can’t chase each one down one by one, it would take you a lifetime.

Elli Papaemmanuil: Exactly. So now we have, when it comes from diseases that we suspect have, or hypothesise that they’d have a genetical theology, we now have a toolkit in our hands. That can scan a large space of that genomic footprint. Okay. And this is where we can bring, we, we now start to understand what the roadmap is, so we know what the vision is.

We start to understand what we need to get there and to get there we need a. to work in multidisciplinary teams that understand each one of the facets, of disease, biology, genetic presentation, clinical care, as well as data management and data analysis. And then we need to develop this roadmap and this is why, and the roadmap will take either 50 years or it can take 10 years.

And the only way to accelerate this is by putting a design phasing process. A design for the architectures and the teams that we need to bring together in order to push this forward.

Harry Glorikian: I’m giving you a challenge three to five, three to five, not tem, three to five. I’m getting older. I need this to happen much faster.

I think you need to sit down with a Francis Collins and work on a, you know, Uh, getting some, some funding here to move this faster.

Elli Papaemmanuil: We need to have ambitious vision. We need to be ambitious and we need to be realistic at the same time, but without letting the realism tamper our ambition.

Harry Glorikian: We need, we need an Elon Musk in this, in this space.

Maybe, maybe you’re that person, right. Somebody who’s going to have audacious ideas and say, I’m going to not just launch a rocket, , but land it on a target landing, you know, floating in the ocean. Right. And do what NASA couldn’t do.

Elli Papaemmanuil: We need to start thinking like that 100%.

Harry Glorikian: I don’t, I don’t think that’s a problem.

I think there’s a few people out there that can help with this. Ellie. I want to thank you. It was wonderful speaking to you. Um, I’m sure our listeners will have a thousand more questions, so don’t be surprised if you get inundated with emails and calls. Um, but, uh, it was great to talk to you. I’m sure. I mean, like, I, I think when we started this, I said we could do a multi-part series on this discussion.

Elli Papaemmanuil: No, it was a real pleasure starting this conversation. I just feel that we can’t do it justice, but at least. Starting the conversation is often the biggest and most important part.

Harry Glorikian: Yeah. I mean, I try to convince people, you know, that having a genomic view of your disease is critical and it’s difficult because the lay person doesn’t understand that it’s at least a map.

It’s at least a, a piece of information that you can’t get in other ways. And it’s critical to how you might be managed in your disease state. So on that note, thank you.

Elli Papaemmanuil: Thank you so much.

Harry Glorikian: And that’s it for this episode. If you enjoyed Moneyball medicine, please head over to iTunes, to subscribe, rate, and leave a review. It is greatly appreciated. Hope you join us next time until then farewell.



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