The Potential of Extracellular RNAs as Biomarkers and a Liquid Biopsy Approach
Ann Nguyen:
Hi. I'm Ann Nguyen, Senior Associate Conference Producer with Cambridge Healthtech Institute. We’re here for a podcast interview for The Liquid Biopsy Summit, which takes place June 22-24 in San Francisco, California. We're chatting with one of our keynote speakers, Dr. Muneesh Tewari, Associate Professor of Internal Medicine and Biomedical Engineering and Anderson-Sprague Memorial Research Professor at the University of Michigan.
Hello, Muneesh! Thanks so much for your time today.
Muneesh Tewari:
Thank you, Ann. Happy to be here.
Ann Nguyen:
You're trained in internal medicine and medical oncology as well as systems biology and genetics from the University of Michigan to Dana-Farber Cancer Institute and Harvard Medical School. How have these research areas informed your work on circulating microRNAs as biomarkers for cancer and other diseases?
Muneesh Tewari:
I think they've made our work pretty multidisciplinary and enabled my team to bring a variety of different approaches together in ways that aren't often brought together, especially in more translational research like biomarker research. Just to give an example, when we first reported that we could find extracellular microRNAs in the plasma, in the circulation, and also that they were incredibly stable at least in plasma isolated from whole blood, we ended up actually bringing together a variety of just different approaches both from the very translational side but also from the very basic side. We were end labeling using P32 RNA that was isolated from plasma. So it's an old school RNA biochemistry approach and brought that together with really human clinical samples. I think that has been one of the most useful things to us in this research.
Later on in later studies when we were trying to understand why the microRNAs were so stable, we were able to bring in again, sort of classical biochemistry like size-exclusion chromatography but apply it to human plasma. Even later on we were trying to study exosomes and then we were able to bring in nanoparticle sort of technology. It's really enabled us to bring in diverse approaches, diverse ways of thinking, diverse models of how to approach a scientific problem. That's been I think the benefit of having such an eclectic background of training.
Ann Nguyen:
On June 24, you'll be talking about circulating extracellular RNAs as biomarkers during your keynote presentation. You'll cover the history, current knowledge, considerations, pitfalls and the future, so it will be quite comprehensive. Why do you find the prospects for extracellular RNA as a liquid biopsy approach so rich and what's the main theme you'd like your peers at the conference to absorb?
Muneesh Tewari:
Well, I think the prospects for extracellular RNA again in the circulation for liquid biopsy are rich for a couple of reasons. One of them really is the fact that RNA in general represents a lot of variety. I mean, there are so many different varieties of RNA sequences. That degree of variety is actually, we're finding out just in recent years, that it's actually much broader than maybe was envisioned 5 or 10 years ago even. What that variety means is that it at least has the potential to have much greater information content that that represents about different tissues, about different disease processes and so forth.
Related to that variety in terms of sequence is also variety in terms of dynamics. That is, as opposed to DNA sequence which can change under certain circumstances like cancer for example, RNA sequences and also especially RNA levels of course, but even sequences like through splicing are changing all the time and they're changing very dynamically in response to changes in tissues. I think the extracellular RNA space represents a particularly information-rich space that can inform both with respect to diversity of tissues and processes but also with respect to dynamic changes over time.
I think in addition to that, the thing that makes it very attractive right now is that you've got this combination of a space of molecules, really, that have the potential to be very information rich, actually really is very information rich. Now we have the technologies through advances in next-generation sequencing of course, to really explore that space and tap into it in a very deep way. If we compare that to protein for example, the protein space is also extremely rich with all the protein modifications and variety of proteins and also dynamic changes. One of the challenges historically has been with proteomics limitations as to the depth at which that space can be explored.
I see extracellular RNA as the really up-and-coming new space that has a lot of information that can potentially inform of dynamic changes and at the same time is one in which the technology has matured to the point that this can really be tapped pretty deeply with respect to discovery. That said, it's an emerging area right now so a lot of the work is really in the discovery phases and there's still more technology development to be done.
I mean, just to give you maybe one example about the dynamics to try to make it more concrete is, one of the findings that we had a few years ago in our lab with circulating microRNAs, is that a very specific microRNA that's specifically activated in cells or at least induced to much higher levels by low-oxygen conditions or hypoxia, we found that we could take patients with metastatic prostate cancer and group them into two categories on the basis of whether they had substantially elevated, and I mean 10-fold or more elevated levels of this particular microRNA called miR-210.
Just by really taking a sample of their serum we could, in fact, group them into patients who presumably had tumors with significant tumor hypoxia versus patients who didn't. In fact, that really correlated very well with whether or not those patients were responding to treatment or not. That all actually makes sense because hypoxia is a major resistance mechanism for a variety of different treatments. I just give that as an example of how RNA can give types of information. I think that's unique especially compared to and complementary in many cases to, for example, circulating tumor DNA which is a much more mature area at the moment although still under development.
Again, the big theme I'd like I think for people to take away is that this is really an emerging area that has potential for high information content and for dynamic measurements over time of normal and disease physiology.
Ann Nguyen:
One of your goals is to develop novel biomarker approaches for human health and disease that involve serial monitoring at high-time resolution and much lower cost. What considerations or barriers -- scientific, technological and otherwise -- need to be addressed to make real progress?
Muneesh Tewari:
I think there are both scientific and technological barriers. I might start with the technological first and just give us a little bit of a backdrop of why this is one of our goals. It really comes from working in the biomarker field, especially the sort of minimally invasive or noninvasive biomarker field in particular for cancer, but also for some other diseases for a number of years now. One thing that struck me is that for the amount of resources and effort that's put into discovery research of biomarkers and for the number of early stage biomarkers that are discovered, very few of them ultimately have made it to the clinic.
It's my conviction, or you could also call it a working hypothesis right now, that a big reason for this is that in large part, the paradigm for biomarker discovery and even implementation is sort of static paradigm, where you would take a blood sample, for example, measure a particular analyte and then compare it to some reference range, some normal reference range defined by other individuals and then try to decide if this is ... You know, make a diagnosis for example or make a prediction. I think the variability of human beings, between person-variability as well as even within-person variability, is so high that this often confounds the biomarkers from really being robust enough for clinical use.
The alternative is this serial, very high-time resolution longitudinal measurement. The technological barriers to this are really just the expense of collecting blood, for example, and then doing the purification of the analyte and then doing the measurements and then relaying all that information to the physician or central hospital. To do all that is just a very, very expensive process right now. I think one of the technological barriers is how can we make that really much more inexpensive while at the same time still maintaining the robustness of the data.
There are a few solutions to that. One of the solutions I think is to try to bring a lot of the technology that's being developed for rapid point-of-care diagnosis, for example, including for diseases in developing countries, for example, to bring some of that technology to bear upon a more diverse class of biomarkers that are -- let's say clinically are relevant. That's one solution and it's a solution that we're pursuing with collaborators but there's a lot of activity in that space, just nationally and internationally.
The other part of that solution is, of course, tying that into the existing information technology infrastructure, so once these results are obtained, they can be transmitted, for example, through cell phones. I know there's a lot of interest in that. Ultimately I guess I see this moving in the direction of a lot of this data being collected at home rather than in a clinical lab as it is right now. With respect to the scientific barriers, one of them is just that this is an untested hypothesis of whether really high-time resolution measurement biomarkers over time within an individual is going to make a big difference.
It is my conviction that it will but some of the science that needs to be done around this has to also be directed around the specific biology and the specific context of particular disease process that one's trying to make a prediction about or trying to make a diagnosis for example, at a specific clinical indication, the specific clinical utility. I think there's a lot of science that is going to be very specific to very specific applications that has to be done. They sort of go hand-in-hand. That can't be done without removing the technological barriers. I think it's sort of two-pronged and parallel approach.
Ann Nguyen:
Thank you, Muneesh. That's just the tip of the iceberg of what you'll be able to share with us later at the conference. We'll wrap up for now and we'll look forward to hearing more of your insights later on this summer.
Muneesh Tewari:
My pleasure. Thank you for having me here.
Ann Nguyen:
That was Dr. Muneesh Tewari of the University of Michigan. He'll be giving his keynote presentation as part of the conference agenda at The Liquid Biopsy Summit, happening in San Francisco this June 22-24.
To learn more from Dr. Tewari, visit www.LiquidBiopsySummit.com for registration info and enter the keycode “Podcast”. I'm Ann Nguyen. Thanks for listening.