Multiple Myeloma Research
How a Rocket Scientist is Trying to Help Cancer Patients
We’ve been hearing about emerging treatments for multiple myeloma, in particular for relapsed/refractory patients. There are novel therapies, new combinations, and more about CAR T-cell therapy and bispecifics.
There’s a lesser known area of study, helmed in part by someone with fresh eyes – Praneeth Sudalagunta, Ph.D., a former aerospace engineer who specialized in computational models to help solve programs.
So the question is: can we use computers and patient data to help them figure out the next best treatment for them? Explore below.
- Moffitt: Studying myeloma outside the body
- Method of studying tumors outside the body (ex-vivo)
- New study: the relationship between selinexor & daratumumab
- Looking ahead with this kind of research
Thanks to Karyopharm Therapeutics for its support of our educational program.
Stephanie Chuang: Hi, everyone, this is Stephanie with The Patient Story, and I am hoping that you’re doing well wherever you’re joining us from. I’m really excited. This is actually a first of its kind interview for us because it features a rocket scientist, and I know that that’s a little joke that you’ve heard before. So I’ll welcome, without further ado, Praneeth Sudalagunta, Ph.D., applied research scientist at Moffitt Cancer Center. So welcome, Praneeth.
Praneeth Sudalagunta, PhD: Thank you for having me. I’m really happy to be here and talk to you today.
What drew you to work in cancer research?
Stephanie Chuang: I’m excited because this is a different angle we’re talking about. Usually, we’re talking to, as I’d mentioned, patients and people in that area and then also to a lot of specialists, myeloma specialists, people who are studying and researching it, as well as treating patients in the clinic. You come from a different background. The way you’re studying to help myeloma patients and their families is very different. It’s unique, and that’s why I’m excited to dive in.
You’re studying these myeloma cells and plasma cells and everything in the lab. What drew you to be a scientist at a cancer center? Because you could do this in a number of different settings.
Praneeth Sudalagunta, PhD: Yes. That’s a fantastic question, because there’s actually an anecdote that I usually tell people because I do get this question a lot, especially because of the transition that I’ve made from aerospace engineering to cancer research.
A specific setting is when I’m taking an Uber ride. Then the driver just really makes small talk and then they ask, What do you do? I say, I work at a cancer hospital and then here at Tampa, Moffitt is kind of like your neighborhood cancer hospital where everybody knows Moffitt.
The moment I say that I do cancer research, there’s usually like a three to four second pause. And then there comes like my mother or my brother or my uncle, my father, somebody everybody knows somebody who has cancer and that hits them at a very personal level. That is something that is incredibly compelling about working at a cancer center because it puts you literally close to patients and you have access to patient data in a way that maybe a research facility at a university may not have. So I think that’s what’s really exciting about being at a cancer center.
Stephanie Chuang: Wonderful, and yeah, it’s so true. Unfortunately, there are very few degrees of separation. Lots of people who are impacted in some way by cancer. I had non-Hodgkin lymphoma, a blood cancer, and so very grateful for any research that’s happening in this space.
You and I agreed that there’s a need to translate this medical scientific jargon into understandable, digestible information for people. We know that myeloma is not curable right now. There is no cure. But what we do know and why we have so many of these series now is because there’s so much going on in the landscape of myeloma drug research and a lot of that in combination, which we’ll talk about later. It makes it tricky to understand how people are really responding to a particular drug, and that’s where you come in.
Moffitt: Studying myeloma outside the body
The focus on relapsed/refractory myeloma patients
Praneeth Sudalagunta, PhD: When you have a newly diagnosed patient who has never seen any therapy, they typically respond to the frontline therapy they get. And what happens after that is that once they relapse to that, it happens at some point. For some patients, it’s very quickly. For some patients, it may take a while. But whenever they do relapse, it’s kind of all bets are off really because you really don’t know what they get.
Then as they keep relapsing, the complexity keeps increasing exponentially because now you have different sets of patients who have received different treatments in different sequences, right? And a lot of patients we’ve noticed that over the several lines of therapy, develop what we refer to as multi-drug resistant state, which basically is that they might have been exposed to four or maybe six, four lines of therapy. And then they’re all of a sudden refractory to like 20 drugs.
This is something that we are able to figure out because what what was developed here at Moffitt Cancer Center by Dr. Ariosto Silva and Dr. Ken Shain, whose lab I work in, is that they’ve pioneered this technology where they take cancer cells from a patient, which is donated by these patients at Moffitt Cancer Center.
Method of studying tumors outside the body (ex-vivo)
Praneeth Sudalagunta, PhD: What we do is that we culture them outside the human body, basically in a petri dish. We trick the cancer cells into thinking that they’re still inside the patient’s body by giving them enough resources to survive, and then we treat these cells with various drugs. This is an incredibly powerful piece of information because this is something that you wouldn’t otherwise get in a clinical setting because these patients, they always receive combination therapies.
So if a patient has responded, you don’t know if they’ve responded to one particular drug or of the three drugs that they’ve received. Or is it like a combination. You basically have six possibilities. That’s why this platform that was developed here at Moffitt Cancer Center is so powerful is because we were able to characterize drug sensitivity for a given patient to various drugs individually. That’s what really powers my research project.
Stephanie Chuang: The way that people are figuring out resistance or sensitivity to drugs, meaning if they respond to a drug is by having to go into this clinical trial or just taking the combination if it’s already approved and then figuring out that way. Does it work? Does it not work?
That’s hard, right? It’s hard for people in so many different ways, especially when you’re dealing with something that’s chronic, this relapsed refractory multiple myeloma population. They are physically going through a lot and mentally and emotionally, they’re not really getting a break. So being able to test all these things in a lab is incredible.
How do you work with the physicians?
Praneeth Sudalagunta, PhD: Right now, what we do is that we generate a PDF report for a physician here at Moffitt, for every patient who donates their bone marrow specimen. And then we actually have these bar plots, which are like, okay, for this particular combination, this is what the response is going to be like, so there’s a predictive response. That kind of gives the physician a relative view of these are the different drugs. And relatively speaking, this is how these drugs respond compared to each other. So that is something that we want to develop.
Stephanie Chuang: Ok, great, we’ll dive more into this in a bit, but let’s rewind a little bit into talking about personalized medicine and predictive, right? We keep hearing predictive and this is promising for people for all the reasons we mentioned earlier.
Can you talk about what it means to identify predictive biomarkers like what are you looking at? How is it being studied in the lab effectively?
What exactly are predictive biomarkers in multiple myeloma?
Praneeth Sudalagunta, PhD: Absolutely. So in order for predictive biomarkers, on one hand, we have the the readout, which is the drug sensitivity, right? So we have this drug sensitivity for each of the patients. And in order to be able to predict the drug sensitivity, we need to identify a feature in a given patient.
So the question is, what are these features now? What we rely on is that there are certain genetic features and there are certain other cancers where there are certain mutations, for example, that make a particular patient more sensitive or more resistant to a given drug. And this information is widely known in those cancers.
But in myeloma, there’s no real baseline for this. There’s really not a lot of data, so there’s a lot of potential for improvement. And what we do is that we use gene expression data. Now, gene expression might sound like a very absurd scientific jargon term, so I will try my best, again an aerospace engineer’s perspective of what a gene expression is.
What is gene expression?
Let’s think about a cell as a factory. Now, every factory produces something. Let’s say, this factory produces T-shirts, right? And there are other factories that might be producing belts, another factory that may be producing trousers. And collectively, what they do is they clothe the population, the society. It’s what happens inside a complex organism’s tissue and an organ is that you have these different types of cells and they all are doing their part in making sure that the organ functions in a certain way.
But sometimes when there is a particular type of cell that’s not functioning as expected, which is typically what happens when that particular cell turns malignant is that you have a supply chain issue like kind of we’re all experiencing right now. What happens in that organ is that you have too much of something and then relatively you have too little of something else. And that’s what leads to potentially organ failure, and it could be very catastrophic.
Now let’s zoom into what happens inside the cell. Now the cell, like I said, it’s a factory that makes T-shirts. It has something like a family recipe. It’s like your grandmother’s recipe for biscuits or something like that. It’s hardcoded and written into a giant world which is called the nucleus, and this family recipe is basically the DNA. Then on the floor of the factory, you have a lot of machines, and these machines are known as proteins.
Proteins are aptly termed as molecular machines, and they do all the work inside the cell. So you have this giant family recipe that’s inside the vault that is locked, not everybody can walk in there because it’s protected and then you have all these machines on the floor. So how does this information, this recipe from the vault go into these machines because these machines need to know what to do, right?
Because they need to be a machine that draws the cloth, another machine that cuts it, another one that folds it and then stitches and then prints. So all this needs to happen in the sequence, and they all have to be done in a particular way because you need to have t-shirts which are big or small and so on and so forth.
What happens is that these messages are being sent from inside the nucleus using basically you can call them supervisors, these are managers. These are upper manager level people who have access to the vault. They have exclusive access to the vault. And then they send this information down to the factory workers on the floor. And what gene expression really is wire tapping that information that comes from inside the nucleus to these molecular machines.
What we do is that before they go into that, we capture that information just like wire tapping. Then we’re trying to estimate what the cell is telling. It’s trying to estimate basically sequence mRNA, which is messenger RNA. That’s what gene expression is, and it’s like it says – messenger, right? So it’s the messages that are going to these proteins and that’s what we captured.
So this gene expression we capture for over 20,000 genes. Now you have a problem – you have 20,000 genes and you just don’t know which genes are really important for you. That is something that we figured out.
Using our approach is that we try to figure out which genes are actually driving resistance to a given drug and which genes are driving sensitivity to given drug. And we are able to do that because we have gene expression for 500 patients on one hand.
On the other hand, we have the drug sensitivity for all those patients, so we’re able to match them, use computational methods and say these patterns lead to resistance to this drug and these patterns lead to the sensitivity to the other drug and so on and so forth. That’s basically not in a nutshell what a predictive biomarker is.
Stephanie Chuang: Well, it’s hard to do in a nutshell for that topic! If I’m hearing correctly again boiling it down to the very essence of what you’re saying, you’re studying the messaging that’s happening from the nucleus or the vault to the proteins, which are the machines, the molecular machines, right? You’re capturing this somehow.
And some of the messaging allows for a pattern of sensitivity to a particular drug and other messaging with a certain pattern shows you, oh, they’re more resistant to this drug or something like that. Is that ball park?
Praneeth Sudalagunta, PhD: Perfect.
Testing tumor behaviors outside of the body
Stephanie Chuang: It’s very interesting because it’s fresh eyes, a different perspective with that background, using the mathematical, computational models that you have from one field applying it here, I think that’s fabulous.
So again, what I’m hearing, you had mentioned the two things you’re studying the drug sensitivity and the gene expression as part of this research, those are the two important parts. Now specifically, first of all, you had mentioned the actual process.
So these patients at Moffitt, they were getting bone marrow biopsies or basically they were getting extractions to get those cells from the bone marrow right? And that was what’s being studied in the petri dish. Is the experimental the pioneered approach with the tumor microenvironment. So making sure that they would stay alive outside of the body?
Praneeth Sudalagunta, PhD: What we do is that we take these cells and we culture them in a petri dish. But the actual term is that it’s a multi-well plate. It has (about) 384 wells. Now we’re trying to use a 1536-well plate where you’re able to test 1536 experimental conditions on one patient simultaneously. So this is allows you to test several drugs at different concentrations. You need some technical replicates in case there are any issues. And we need to make sure that if there’s any variation to account for things like that.
So what we do there is that in that well, we culture these cells with bone marrow stromal cells. These are from the patients. It’s not from the same patient. It’s usually from another patient a few months ago or like a few weeks ago. We capture these cells in a flask and then allow them to replicate.
Then we use those cells to kind of give them that microenvironment. It provides them those growth factors to survive. You also use that patient’s plasma and then we add that into that. We think that they’re still in that microenvironment for them to survive.
Stephanie Chuang: Wonderful. Thank you for that. I know that there was a recent – it’s ASH, so it’s a huge blood disease blood cancer conference where brilliant minds gather, and there was a presentation specifically about this kind of research applied to daratumumab and selinexor. I’d love to hear about that research.
New study: the relationship between selinexor & daratumumab
Praneeth Sudalagunta, PhD: There are these predictive biomarkers. So what we’ve done is we’ve tested 500 patients with one 180 drugs, some of them being used in multiple myeloma but most of them are drugs that are used in other cancers. And what we did is for these 500 patients, we also have their gene expression data and we combine the two of them to identify basically like a fingerprint, like a genetic fingerprint for each drug. And we did this for all the 180 drugs.
We were particularly interested in those pairs of drugs which had an exact opposite fingerprint, like if there are certain genes which have which are implicated in resistance to one drug. Are those exact same genes implicated in sensitivity to another drug?
We did this massive analysis with all these 180 drugs, and then we found that daratumumab, which is a monoclonal antibody, anti-CD38 monoclonal antibody that’s currently approved for use in multiple myeloma and selinexor, which is a selective nuclear export inhibitor, also approved for use in relapsed multiple myeloma, is that these two drugs actually have this anti correlative profile, where the fingerprint of one drug is exactly the opposite of the fingerprint of the other. That is something that caught our eye and then what we did is that we basically reached out to and have an active collaboration with Karyopharm Therapeutics.
We reached out to them and said, in your trials where you’re treating selinexor patients, could you look at the treatment history of the patients and split them into patients who have been exposed to an anti-CD38 monoclonal antibody and those who did not and then compare their survival?
Clinical validation of computational, data-driven approach
What they did is they essentially compared the two. In two clinical trials, STOMP and XPO028 , they noticed that patients who received an anti-CD38 monoclonal antibody survived longer versus those who did not. That’s basically a clinical validation of an inference that we’ve made based on a completely data-driven approach where we did not use any known biological knowledge or any mechanism.
None of that. We just used this data and then we fed it into this computational system. It basically spits out this pair of drugs and we go into a trial and see if there is any difference. Lo and behold, we find that difference. And it’s a validation that this whole computational approach does work. That’s what the ASH talk that you were referring to is focused on.
Stephanie Chuang: Wow. So what I’m hearing you say is there’s all the computational models, the data that’s coming out. And then this was verified by the clinical research data, real world data from patients and how they had performed on the the daratumumab type drugs, so the anti CD38 monoclonal antibodies. What you’re saying is what you found with your data was that those with the daratumumab resistance were sensitive to the selinexor. So was it vice versa as well?
Determining if patients with selinexor resistance are sensitive to daratumumab
Praneeth Sudalagunta, PhD: That’s a very good question. So based on our inference, we expect it to work both ways because we don’t know… I mean, it’s a correlation, so we don’t know off directionality. Since we reached out to Karyopharm Therapeutics and since they looked back in time, they were able to because their trials involve selinexor and they can always look back in time and say whether a patient has received daratumumab or not.
You could always flip that question and look at a patient who’s enrolled in a daratumumab trial and see if the patient has prior exposure to selinexor and so on and so forth, potentially if it works both ways. We don’t know this yet, but potentially as a hypothesis, it works both ways.
In research: could you alternate 2 drugs like daratumumab & selinexor and prolong patient survival?
You could have daratumumab-selinexor, and once the patient is exposed to selinexor now, something that commonly happens when you expose the patient cells to a particular drug, they acquire resistance to that drug. So the first we’re exposed to daratumumab, now they acquire less resistance to daratumumab.
And now you treat them with selienxor since we know that they’re sensitive to selinexor, then they acquire resistance to selinexor, could you then treat with daratumumab again? Right? So basically, what we would be doing is that from a patient’s perspective, we would be taking them back in time in terms of the tumor. The tumor would go back in time to the point where they were first treated with daratumumab, and that’s essentially prolonging survival by that much amount.
And could we do this over and over again? Could we do this once? These are all open questions and this is something that we’re actively investigating, but based on our data, itself, we don’t know off directionality, we just know this much that there is a negative relationship. We just don’t know what’s the extent of it. And that’s something that’s worth investigating. It’s a very good question.
Stephanie Chuang: Yeah, it’s fascinating, all of the different questions that this poses. So if I’m a patient and a caregiver now listening to this, it’s like, OK, so now you know that daratumumab resistance, you may respond well to selinexor. So that’s one data point that might be helpful, although I think right now a lot of specialists and physicians are trying to do these different combinations. If you haven’t, we’ll add this as the next regimen. What else can patients take away from just that part? It’s so fascinating if you were able to kind of do both back and forth.
I just want to be very clear. So one, is according to both your data and also the selinexor clinical trial data, people who were on daratumumab and then selinexor did better than those right who were just on selinexor themselves.
Praneeth Sudalagunta, PhD: Yes.
Stephanie Chuang: So you have that data, OK, which is really important. Then the second question that came up that needs to be studied is well, we need to figure out the directionality of it. And could it be if you could figure out with daratumumab that the inverse is true, that then you could do daratumumab, then selinexor and then daratumumab again? And then it’s like, well, how many times could you do that to prolong survival? Right? And of course, this is all without talking about side effects. And of course, people develop those. But just purely on the scientific sort of level, that would be the line of questioning to follow.
Praneeth Sudalagunta, PhD: Yes. That’s something that we’re actively investigating. So based on our data, like I said, we don’t infer directionality and we’re thinking about that. Could we develop some kind of a computational approach? Can we use a particular type of data set to infer this directionality? That’s basically something that we are looking at.
Could we look at a patient who has the gene expression of a patient before they were exposed to selinexor versus after exposure? They can look at this difference between their gene expression and what difference does that make? Does it flip this fingerprint from one side to the other? That is something that we’re actively investigating.
Looking ahead with this kind of research
Stephanie Chuang: So you’re actively investigating a lot of different things. And I know you said there were a lot of drugs, but in myeloma, we know the four major classes of drugs. Then you also have the novel mechanisms of action like selinexor, so is there going to be testing like, it’s really a function of how these different drugs act and you’ll also test the proteasome inhibitor versus the monoclonal antibody versus the IMiDs, that kind of thing.
How do you approach this research with the different classes of myeloma drugs?
Praneeth Sudalagunta, PhD: Yes. When we look at these drugs first, we look at them in an agnostic sense. We don’t really classify them based on classes because what we know is that drugs which belong to the same class, they sometimes have a different pharmacodynamic effect, which is basically if a drug has a particular target, how does that inhibit that particular target and how does that vary across time?
That effect that the targeted effect of a particular drug that’s basically the pharmacodynamics and the pharmacokinetics is when a patient takes a pill like, basically equal to the ADME process, it gets absorbed. You have that drug available at the site of the tumor. And how does that vary as a function of time? So a few years ago, I was reading this textbook just to understand pharmacokinetics and pharmacodynamics. Then the author wrote a really nice statement which made a lot of sense to me and stayed with me.
It’s that pharmacokinetics is what the body does to the drug, and pharmacodynamics is what the drug does to the body. There’s a yin yang effect there, which is really cool for me to remember.
Stephanie Chuang: That is really interesting. And so I just want to make sure I’m understanding this correctly. So what that means is it’s agnostic to the classes because while you can say how the drug is supposed to react to the body, we actually don’t know how the body will – we don’t know the other part of it. So it is very patient specific, and that’s why it’s personalized medicine.
Praneeth Sudalagunta, PhD: It’s patient specific, but also it’s drug specific because drugs within a class, they behave very differently. And this is kind of known but not very well known in the field. So there’s a lot of complexity that’s associated with it.
Stephanie Chuang: That answers my question because I was asking, if you test one class, does it apply to all? And the answer is no. This has to be research that’s done with each drug in different classes.
Praneeth Sudalagunta, PhD: Exactly.
Stephanie Chuang: You have different sequences. What’s the goal? I know that this study has been ongoing, so it’s been 500 plus myeloma patients. That’s a great number. What’s the next hallmark moment for for the study? What are the goals here to expand to? The major goals – you talked about some, but what are the next major steps here?
What are the next major steps in this research?
Praneeth Sudalagunta, PhD: Right now, the way traditional treatment is given in the clinic is that a newly diagnosed patient comes in or the patient is early relapsed or refractory or late relapsed refractory. You know where they stand in terms of that timeline.
Then those patients are given a certain treatment that’s for an established clinical pathway system. What we want to do with this particular research is that transformed that into a predictive, biomarker driven decision making system.
When a patient comes in, you’re able to identify this fingerprint that the patient has, and that’s from the gene expression data. And then we are able to tell that this particular patient would be best suited with this drug. It’s not something that’s defined by this sort of clinical pathway system. The hope and what we’ve shown in our study is that when you do things this way, the patients do survive longer.
That’s what we’ve showed with the daratumumab-selienxor study in a microcosm. But potentially if you could apply this to all kinds of fingerprints that might exist out there for different patients, you could potentially match them to a drug that they would best respond to.
So you have subsets of patients responding to subclasses of drugs and then you’d kind of have treatment prescribed in that particular fashion. It’s kind of a predictive, biomarker driven therapeutic intervention. That’s how I would put it with respect to the next step for this study, yes.
Stephanie Chuang: A couple of follow ups there. You would just need a bone marrow biopsy from patients to be able to do this research right for the gene expression?
Praneeth Sudalagunta, PhD: Yes, so that’s the cool thing. So what we need here is since we’ve already done ex vivo drug sensitivity characterization on a cohort of patients, that’s kind of the patient. It’s like a training set and we’ve learned a lot of things from that training set.
We no longer would need to do that going forward with a new patient. So the new patient would basically give us, would donate their bone marrow specimen and we would get the gene expression data from it. We would not need to do the ex vivo drug sensitivity characterization. And the idea is purely based on gene expression.
Could we make a certain conclusion in terms of which drug or which particular subclass of drugs the patient would be best suited to? And alternatively, what we could do is if we can have cells to be able to characterize their ex vivo drug sensitivity, they can also match drugs.
Ex vivo drug sensitivity screening also does the same thing, but in a very different way, right? So even there, you can identify a particular drug that the patient is more sensitive to, just like this fingerprint from the gene expression data does. And what I personally think is that the future is really in a multimodal imaging or decision making system where you have these multiple modalities kind of coming together and informing a particular common decision making system. So that’s what I would say that that’s really where I kind of foresee the future of cancer treatment really is.
Stephanie Chuang: Which is exciting. I want to clarify just in terms of what patients would need. So as a new patient wanting to be part of this process to see if it would work for them. You’re saying you would still need bone marrow cells from the bone marrow, right? But you wouldn’t need for them to have to go through a treatment like that data, is that what you’re saying to show the sensitivity or resistance?
Praneeth Sudalagunta, PhD: If you go back to the the prime objective of the study is that we have two sets of data, ex vivo drug sensitivity and gene expression. So we have this for 500 patients and we use this to inform the model. Going forward,we just use the gene expression data from a patient and then that just informs the process.
Stephanie Chuang: Perfect. So that saves the time, of course, a huge amount of time.
Praneeth Sudalagunta, PhD: And definitely cost because the gene expression data is something that is a lot more affordable than doing this ex vivo drug sensitivity screening because it involves a lot of personnel, a lot of equipment. That kind of brings up the cost. So you’ll be able to do more with less money.
Future possibilities with other combinations of myeloma drugs
Stephanie Chuang: So you were mentioning before, too, about how this particular part of research came about, which actually started with another study looking at four combinations with selinexor and different drug combinations, right? What was the result of that that prompted this study and what are the possibilities with other pairs of drugs?
Praneeth Sudalagunta, PhD: So basically, we looked at four two-drug combinations involving selinexor. They are:
- Selinexor-dexamethasone, which is already FDA approved combination
- Selinexor-pomalidomide, which is currently being studied in the STOMP trial
- Selinexor-daratumumab, which are (both) also being studied in the STOMP trial because it’s a multi-arm clinical trial
What we’ve observed is that in our ex vivo drug sensitivity cohort, in the patient cohort that we looked at, we noticed statistically significant synergy.
Synergy is basically it’s better than it’s not better than one drug, it’s basically better than the additive effect. So what is an additive effect? Additive effect is if one drug kills 50 percent of the cells, the other drug kills 50 percent of the cells. The additive effect is 75 percent. When drug kills 50 percent, the other one killed 50 percent of the remaining 50 percent, and now you have 75 percent. If the combination kills 87 percent, then you could say the 12 percent is a synergistic effect. So that’s synergistic effect. It’s kind of like a high bar when we think about combination therapies and that high bar was what we used to characterize these two drug combinations.
What we noticed is that out of the four combinations that we looked at, selinexor dexamethasone, selinexor pomalidomide, selinexor elotuzumab, was statistically significantly synergistic. And selinexor daratumumab was synergistic in a sub cohort of patients, which was very interesting to us because whenever we see that we’re thinking there is some kind of a predictive biomarker. There is something that is common among these patients that’s driving them to be synergistic and not in the other patients.
That is really exciting from a research standpoint, and that’s when we really got interested in looking at selinexor and daratumumab as a possible sequential therapy and basically looking at these predictive biomarkers. And this can be potentially applied to other drugs.
Basically, there are several drugs that are available currently in multiple myeloma. And if you think about the possible combinations, it just basically explodes combinatorially. All of that can be done using a simple computational model, which kind of prunes all these different pairs and gives you this curated list of pairs that you could look at for combination therapy and also for sequential therapy. And this could potentially be tested in a preclinical setting first and then in a clinical setting, and that’s when it would translate into clinical practice eventually.
Stephanie Chuang: Yes, which is an important point to make – a lot of this needs to still go into clinical trials, and after that data is when you’d see the practice happening at your health care provider.
But all this to say that this research is really exciting because you’re able to do the studies without having people actually have to go through everything first to get the data which again, is just so big if we can save people that stress and that weight of having to endure all of the treatments that they already have to anyway, then I think this is great for not just their survival, but their quality of life. So thank you.
Praneeth Sudalagunta, PhD: Thank you so much.
Stephanie Chuang: So my last question for you is what is the high level message and takeaway, especially relapsed refractory multiple myeloma patients and their families, with this kind of data? Big question, because we talked to a lot of people who do clinical trial research, and it’s always, how far away is this from me benefiting in real life? And what’s the five year, 10 year sort of picture for for patients?
How might myeloma patients benefit from this research in real life?
Praneeth Sudalagunta, PhD: That’s a great question, and the short answer is whatever study that we are doing right now, we’re putting all this information out there. The first step is always to go to a clinical trial, because a clinical trial would ensure that it’s actually done in a controlled condition. It’s basically proven, you know, like there’s a lot of thoroughness associated with it.
Once it goes to a clinical trial, that’s when it’s available to patients for improving their treatment therapy. So the idea here is to put out several novel therapeutic strategies based on these genetic fingerprints that we are trying to capture from gene expression data. And the idea would be matching patients to these fingerprints and then identifying them and matching them to the drug that they would best respond to.
And this drug would potentially prolong their survival, through that particular treatment and also this would be done the next time they would have to get treated so it would prolong their survival then, and the next time and the next time.
It has this compounding effect, and our hope is that when we do this at every juncture through a patient’s journey through multiple myeloma, potentially we could extend their survival to maybe like 10 to 15, maybe even 20 years. And that’s at that point. It’s kind of a new life, and that’s that’s that’s kind of the idea behind that.
Stephanie Chuang: Thank you so much for sharing this is exciting research. It’s so nice to have the aerospace engineer, rocket scientist, you know, perspective as fresh eyes and fresh knowledge in the cancer space. So thank you for the work you do.
Praneeth Sudalagunta, PhD: Thank you so much for giving an opportunity to talk about my research. Thank you.