(Guest Lecture) So Many Choices: Deciding Which Treatment is Best - Matthew Butler, MD | RT Austin, TX Mar 25, 2023

Thanks, Greg. I'm Matt Butler. I'm just down the road in San Antonio and I kind of focus on multiple myeloma. And so I spend a lot of time sitting in rooms with people talking about treatment options. You know, we could do this or we could do that. We have these medicines, we have, you know, kind of tried and true approaches, newer, more emerging approaches. And so the obvious question is, well, which one's best? That's a decision that Dr. Masoudi talked about, shared decision making. So it's ultimately your decision because it's your body and it's your health. But it's not a decision you can make on your own. It's someone you have to kind of, you know, talk about and reach a consensus. And so part of the job of the doctor is to at least make a recommendation. Here's what I think would be the best. Or here are two options that are more or less equal. Here are some pros and cons to help you choose. And so what I want to do with this talk is just kind of step back a little bit from that so you can just have a sense of where do those recommendations come from? How do I decide? How do I, you know, give you advice on on what treatment A being a better choice for you than treatment B? What kind of information am I using to do that? It's not all based on personal experience because none of us has enough, you know, accumulated knowledge in our lifetime to kind of understand all of the nuance about these diseases in these medicines. It has to be based on literature that we read and data that we learn from the research community. So when we say a treatment is good, we can mean a few different things. Obviously, we want it to be effective against the disease in multiple myeloma. We want to control the disease. We want it to respond. We want the measurements of myeloma to go down in the blood. We want obviously symptoms to get better. And usually those things go hand in hand. Once it responds, there's different levels of response that we talk about. Did it go down a little bit? Did it go down to the point that we can no longer detect it? And then even nowadays, even how hard did we try? So if we run more sensitive tests, you know, to find some trace of it remaining and those tests don't see it, then that's an even better response. Then of course, how long do those responses last? What we know about this disease is it can come back and for many people, it does come back somewhere in the future, but there's a big difference between that being the near future and the far future. And then of course, the ultimate question is, are our treatments helping people live longer? Hand in hand with that, we really try to not, to, you know, always be looking on the other side of the equation for our treatments, which is, are they safe? Are they well tolerated? Do they cause their own set of problems? And it's well known. In fact, it's a common thing when I talk to someone who's had a loved one go through treatment for other forms of cancer, or maybe someone who's gone through cancer treatment many years ago, when the options were fewer, you know, where they have a sense that the treatment can be worse than the disease or can cause problems that are, you know, terrible in their own right. And that is true. It is something we obviously do everything we can to avoid, and it's much less true than it used to be. But the toxicity or the harms of treatment is always something we need to think about hand in hand with the effectiveness. So we measure all these things, we try to quantify them with numbers. And then we, and then as a doctor trying to make a treatment choice, I have to decide what those numbers mean. And so if I say that a drug has a certain response rate, I have to have some context for what a response rate should be, what it can be, what the best treatment and the worst treatment, you know, would do so that the number means something. A response rate of 80% nowadays for an upfront treatment for standard risk myeloma would actually not be that good. We can do better than that usually. Whereas in other settings, in more difficult circumstances, maybe after many other treatments, a response rate like that would be excellent. So we always have to know, you know, where did those numbers come from and what group of patients did they, was this data generated from? What was their disease like? What did they have in common? And this is always a challenge because there are no perfect identical humans. Every human being is unique. Every case of myeloma is unique, although we can group them into categories and we're getting better at doing that with genetic testing. But I can never, if I'm talking to sitting down a room with an individual person, I can never find a research study that was done on people exactly like them or with myeloma that was exactly like theirs because there are too many variables and too many differences. So we have to generalize and we have to find a, you know, past experience that is as similar as we can and that's applicable. And to do that, we need to study groups of people and try to average out over a lot of those differences and control for those differences as best we can. And the most powerful way we have of controlling for differences is randomization, which is, you know, deliberately, you know, putting in a random factor that is the only reason that somebody is given one treatment versus the other. In other words, it's not biased, it's not influenced by who they are, where they're treated and which doctor they're seeing and how they're feeling that day and their own individual choices because all those things can kind of separate out people based on other differences and we want to eliminate those differences. So I think it's important to understand about randomized trials and how we, how that, how important that is for us getting good data, but also what it means for someone to be part of a randomized trial. And there's some misconceptions about that. Whenever we do a study like this, we want to compare two treatments that are very similar. If they weren't similar, there would be no point in comparing them because we would already know the answer. So we don't compare or we do our best not to compare a treatment against a weaker or an inferior treatment. We shouldn't do studies like that. We should do studies against a weaker treatment. We should do studies against two treatments that are both very good. And the question is, is one a little better than the other? And that question has to be unanswered for us to be, for it to be worth our while and worth the patient's while to participate and do the trial. We do not do placebo-controlled trials. There's other diseases where that might be a fine thing to do and there's still a role for that in medical research, but it's virtually unheard of in diseases like myeloma where the stakes are very high and where there are already good treatment options. We don't need to know that a treatment is better than a sugar pill. That's irrelevant. We don't care about that. We want to know that a treatment is better than another good treatment. And to find these subtle differences where we can say we have a treatment we know, we understand, we believe in it, but maybe we can do a little better. And that little better and little better, that incremental improvement in outcomes is how we make progress. So this is kind of what that progress looks like visually. If you get deep into this and start looking at clinical research for myeloma, you'll see a lot of graphs like this. So I think it's useful to just to know that, you know, what they mean and how to interpret them and know that if you don't look at something like this, almost certainly your doctor does. And, you know, when when you get a recommendation, well, I think you should get the red stuff. Well, this is why this red line is better. Why is it better? Well, this graph shows progression-free survival. That's the endpoint that we talk about the most in myeloma. There are curves that talk about just survival, just being alive. But in myeloma, fortunately, we spend less focus on that nowadays because survival has become so good and people live so long with the disease that we really, you know, have to look at progression because the overall survival curves tend to be pretty flat and don't separate very well. So progression happens when the disease grows. Maybe it responded initially, but once it starts going up again and there's a certain threshold that it has to meet where we would say, OK, this treatment is no longer working. The disease is active or it's creating, you know, new increasing protein levels or new bone lesions or something like that. And when that happens, this line goes down a little bit. Somebody is, you know, at the beginning, we have 100 percent of people who haven't progressed, but each point in time a few people do and the line drops. And the less the line drops, the more people are still doing well with the treatment. And so we kind of want these curves to get higher and higher and eventually be a flat line across the top, meaning nobody's progressed. And so you can see that the differences between these two treatments aren't huge in the sense that, you know, people progress on both, but there's a clear separation. One is definitely better than the other. And, you know, this is from a major trial a number of years ago that basically it became the standard that, you know, once this data came out, people just stopped using this blue treatment because we knew it was inferior. Sometimes the studies we do don't give such clear answers and sometimes it's a little harder to interpret what they mean. But this is a basic concept that we talk about when we say, you know, what's one drug gives a better outcome than another. But here's the trouble. Running a study like that, it takes many, many, the efforts of many, many people, it takes many, many patients willing to be a part of it, it takes many dollars and takes time. And so we can't run studies comparing every possible drug with every other possible drug. It's just not feasible. Not only is it not feasible, it wouldn't be, it wouldn't make any sense scientifically. And it wouldn't make sense for patients to be part of those studies because I told you that we never want to deliberately compare something against something we already know or we already suspect is worse. What we want to do is take something that we believe is good and then see if we can do something that's a little bit better. But because of that, because we have to make these choices, okay, we're going to start a study, fund it, organize it, run it. You know, most of these studies have to be run in many different centers by many different people. It can leave blind spots. We can have a question where I say, I have these two treatments, I think they're both good, but I really can't tell you which one is better because they've never been directly compared. And so that we have to extrapolate from the data we do have and make some judgment calls. And that's why there isn't a simple algorithm for, well, you should, you know, we can't automate this. We can't say, well, we'll just look up what the best treatment is for you right now and the book will tell us or the guideline will tell us. The guidelines give us some ideas, but we still have to make a lot of hard choices. So this is just to give you a sense, and this is very old. If you did this graph today, it would look much more complicated. But this each line is a comparison study that was done between two different treatments for multiple myeloma. And there's a lot of lines. People are, you know, doing studies all the time and there's a tremendous effort in generating this research and answering these questions. And this is research that's accumulated over decades, you know, this comparison to dexamethasone alone, that wouldn't have been a reasonable treatment for myeloma for many years. Probably some people in this room that weren't alive when this was standard treatment. But then you can see there's a lot of places where there is no line. And interestingly, at the time when this paper came out, this treatment here, VRD, was the standard. It was considered the best of everything on here. This is what was recommended for initial treatment for myeloma. It's still a very good treatment for myeloma. And you can see that it's only ever been compared to one thing directly in a randomized head to head fashion. So how do we know it's better than this and better than this and better than this? Well, we have to extrapolate and say, well, if this outperformed this in that study, and then this outperformed this and then this outperformed this, we can kind of, you know, jump to the conclusion. And it's usually, you know, we try to do that carefully. And it's sometimes tempting to overinterpret the data or to get, you know, overly excited about a small study in a select group of patients and try to read too much into that. But I just put this up just to give you a sense of this is how complicated that the choices are. This is how many options are out there. And navigating between them is always a nuanced decision. So all of this data that we have, all of these results, both about how well treatments work and about whether they're safe or not, they come from clinical trials. When drugs are developed, they study them in labs and in cell cultures and in experimental animals for a while. But we can't learn all that much. We need to know how do people do who are living with the disease in the real world when they're treated this way. And that requires people to choose to be part of this research. And that's what pushes, that's what has got us to the point we are at, which is a point of incredible progress compared to where we were 20 years ago with myeloma. And it's a progress that still has momentum behind it and we're still, you know, making advances. And all of that is built on the courage and the generosity of people that sign up to be part of clinical research, to be part of the scientific process. So, you know, that's an incredible commitment and gift that patients make by becoming subjects. And when we do research and we offer people studies, we owe to them, you know, a lot of, we take on a big responsibility ourselves to make sure that the research that we're involving people in is good research, well thought out, well designed, and that the treatments that we're giving are good. We can't be, if they were proven, if everything was known about them, the research would already be done. So there are, it's always some uncertainty. But there's a very careful process that goes into building up evidence and confidence in the treatments, in their effectiveness, you know, step by step before we start putting large numbers of people on them. And so by the time someone has offered a trial, particularly by the time someone has offered a phase three or a randomized trial, there's already an enormous amount known about the treatment and there's already a pretty high degree of confidence that it's good. And so there's different reasons why people choose to do this. There is a potential for personal benefit, meaning the treatment that you can get on a trial may be the best treatment available to you. It's certainly going to be among the latest or newest treatments. Newest doesn't always mean best, but, you know, generally progress moves forward. And so we at least hope and think that a new treatment may be the best or may be better for you. And so we're hoping that you're going to be able to get better than what you would get otherwise. And of course, the further along you are in the disease, in the, you know, in a situation where you don't have a lot of other options or the other options aren't great, that gives you more reason to think about, you know, being on an experimental treatment. But then at the same time that you're getting treatment that can help you, we also believe that you're helping others. You're helping that, you know, you benefit from research that's been done in the past that, you know, that you can then pay some of that forward to the future. And some people just enjoy being part of the scientific process itself. It's kind of exciting to be on a, you know, the latest thing. But of course, there's risks that come with that, and we never downplay those risks. There are some unknowns about new treatments, you know, just how well they work, just how safe they are. And then there's also just some logistical commitments you have to make to be a clinical trial subject. You have to get a few more blood tests, usually, maybe some additional procedures or imaging scans, because you're going to be watched extremely closely, because we want to learn as much as possible from you as a subject. And so, you know, you have to be willing to make that commitment and do a little more to do this. If we're running low on time, I don't need to talk too much about the phases of trials, other than just not all clinical trials are the same. So when we talk about, you know, the very cutting edge of a drug that is just brand new, and we're just learning the basics about it, these tend to be very small trials. And they tend to be done when there aren't a lot of other good choices. The larger studies that most people participate in are drugs that are already quite mature, and we already know quite a lot about them. And those are the ones where there may be a randomization step where you may get one of two different things. But by this point, we're already pretty sure that both of those things are effective. It's just a question of is one a little better than the other. I think, yeah, okay. So I think that's what that's the time that I have. But I'm happy to chat about this some more. And then you'll hear a lot about trials that are going on now and trials that have recently changed the treatment landscape. And there's exciting stuff. So