Mantle Cell Lymphoma Podcast

Unpacking the Addition of New Variables to the Mantle Cell Lymphoma International Prognostic Index and MRD

Peter Martin, MD; Eva Hoster, PhD


January 31, 2023

This transcript has been edited for clarity. For more episodes, download the Medscape app or subscribe to the podcast on Apple Podcasts, Spotify, or your preferred podcast provider.

Peter Martin, MD: Hello. I'm Dr Peter Martin from the lymphoma program at Weill Cornell Medicine and the Meyer Cancer Center in New York City. Welcome to Medscape's InDiscussion series on mantle cell lymphoma. Today we'll be discussing prognosis in mantle cell lymphoma. First, let me introduce my guest, Dr Eva Hoster. Dr Hoster is professor of medical biometry in the department of medicine at Ludwig-Maximilians University of Munich. Dr Hoster, welcome to InDiscussion.

Eva Hoster, PhD: Hello.

Martin: Congratulations on your recent presentation on minimal residual disease (MRD) at the 2022 American Society of Hematology (ASH) meeting. We'll get back to that topic in a little bit if that's okay with you. First, I wanted to fill the audience in on your background. In addition to being the only person in this mantle cell lymphoma podcast series from outside of the United States, you're also the only mathematician. You've been studying mantle cell lymphoma for a long time — probably as long as anybody else we're talking to. Your first paper was in 2008 on the Mantle Cell Lymphoma International Prognostic Index (MIPI). And you can correct me if I'm wrong, but I think that was part of your doctoral thesis. So, you actually have a PhD in mantle cell lymphoma, which makes you pretty special.

Hoster: Correct. It was my PhD thesis — the development of the Mantle Cell Lymphoma International Prognostic Index.

Martin: How did you get interested in mantle cell lymphoma?

Hoster: It was quite a coincidence. I had studied mathematics and also a few years of human medicine. When I had my diploma in mathematics, I searched a way to combine mathematics and medicine. That's how I came into the hematology department of the university hospital here in Munich. This was a group who had been doing randomized trials in indolent lymphoma, including mantle cell lymphoma. They had already found out that mantle cell lymphoma is special because it has a poorer prognosis than other lymphoma entities. I was also part of a group at our Institute for Medical Biometry that was doing prognostic research. With these two contexts, I was able to use the data from the randomized trials to do prognostic factors research in mantle cell lymphoma. Actually, mantle cell lymphoma was only acknowledged as a separate lymphoma entity in 1994. The group had started trials in 1996, and there was not much research done before that time. So I could really use the data to reveal new information in this not-so-well-described entity.

Martin: The interest really is in trying to understand something that at that time was pretty poorly understood and had a limited amount of new data on which to develop these models. When you have this idea in mind that you're going to come up with a prognostic index, how do you go about doing that?

Hoster: I was in this group, and they had done prognostic indices in chronic myeloid leukemia, so I thought it would be interesting to do it in mantle cell lymphoma as well. There had been the IPI, the International Prognostic Index, but mantle cell lymphoma patients were not clearly included there. There were some, but they couldn't be recognized. And on the other hand, we have this large dataset of homogeneously treated mantle cell lymphoma patients who were treated within randomized trials. And there had been the FLIPI, the Follicular Lymphoma International Prognostic Index, as the first index for a specific lymphoma entity. So I checked the prognostic capacity of the IPI and the FLIPI, and our data on mantle cell lymphoma, and the separation of survival data were not so clear. That was the starting point when I decided to develop a new prognostic index specifically for mantle cell lymphoma patients.

Martin: There are a couple of the features of the MIPI I think are unique. One of them, which is really intuitive, is to use variables as a continuous variable as opposed to dichotomizing them — like age, for example. An 80 year old is not necessarily the same as a 60 year old, whereas in the IPI they would be. The other feature I think is unique is the transformation using logarithms, which is not something I'm familiar with in prognostic indices. Where you were coming from in terms of thinking about both of those variables — is this just standard in developing prognostic indices, or was this something you thought of uniquely?

Hoster: I come from an environment where biostatistics is very strong, and so the people doing methodological research in prognostic models really recommend not to dichotomize continuous variables because you lose information. I also included the age and white blood cell count on a continuous scale. It's also important to see what the distribution looks like, and lactate dehydrogenase and white blood cell count especially had a skewed distribution. That's where these logarithms come in. But don't worry too much about this. There is an online calculator available. We also have a calculator where you can type into the variables, and then you get the MIPI score and the MIPI risk groups on the German Lymphoma Alliance website. So that's easy. I also developed a simplified MIPI, which uses not dichotomized variables but a few small groups for the variables — up to three points for each variable. Then, you sum them up, and there are some cutoffs, and you can also get the risk group with the simplified version.

Martin: I've used the online calculator and one on my phone. I put the MIPI score in all of my clinic notes. The one caveat is that you have to get the units correct on the white blood cell count. That's been a mistake I have made. And I know I'm not alone there.

Hoster: That's correct. That is an important point. You really have to watch that the unit that you input is the unit mentioned on the calculator.

Martin: Not long after you developed the MIPI, you were already involved in describing updates with the value of Ki-67, which then was also incorporated into the MIPI score. That's interesting because it starts to incorporate more of the disease biology. I'm wondering how you get that idea. Why you think Ki-67 is the most important variable? And are there other variables you come back to now and that you continue to look at as they relate to prognosis?

Hoster: That's a very good question. I've been working together with hematopathologists for a long time. They are also part of our European Mantle Cell Lymphoma Network. From the beginning, we aimed to incorporate more biological variables into the MIPI. There were two aims — I wanted to have one clinical prognostic index that is easily applicable in clinical practice but also a more biological version. This is why we investigated the prognostic value of proliferation, which means the percentage of cells dividing in the cell cycle. This is most easily captured with the Ki­-67 index, and the European Mantle Cell Lymphoma Network has derived some guidelines on how to reproducibly calculate the amount of proliferating cells in the tumor samples. We looked at this cytology, especially the blastoid variant, and also some growth patterns. It turned out that we published in the later publication that the Ki­-67 index covers most of the prognostic information contained in the other histopathological parameters. However, there is one additional biological variable, which is the p53 expression or TP53 mutational alteration. This also has an independent prognostic value that has not yet been included in the MIPI. But we have done some research integrating that, too. We could show that the Ki­-67 index has an independent prognostic impact on survival that is independent of the clinical variables. It adds to the prognostic value. In the first publication in 2008, I already had some MIPI-B, a version of a combined biological MIPI, but then afterward we realized there's a better way to combine it. This is now called MIPI-C, MIPI combined, and it also integrates MIPI and Ki­-67 and can separate not only three groups with different survival but even four groups.

Martin: When I am reading papers or as a researcher writing papers, I am routinely trying to include their MIPI-C score as a priority because I think the biology is key, but I often see just MIPI score written and then Ki-67 and p53 written in addition in tables of characteristics. Do you have a bias as to which way you would prefer us to report it and which way you like to see it written?

Hoster: That's a good question. I think it's good to show the characteristics first separately for the different variables. And even for MIPI, I prefer to have all the four MIPI variables described and then the MIPI score, the MIPI groups, Ki-67, TP53 — what you have as separate variables — and then of course show the risk groups, the percentage of patients in each risk group, MIPI risk group, and also MIPI-C risk groups. This gives the full picture. This is for describing data and if you do adjusted analysis. So, if you are in statistical analysis with data on MIPI and Ki-67, I would recommend using the two variables separately. The MIPI score is best used continuously and then for Ki-67, we have established a cutoff of 30%. I would include that binary variable. Adjusting for both MIPI score and Ki-67 separately is kind of adjusting for MIPI-C. It's preferable to adjust for MIPI-C groups because you will have some residual confounding with only groups and not this continuous picture. Of course, you could also, if you have the data, include p53, but you have to keep in mind that if you add more variables, if you have missing values — that's a reality with Ki-67 and p53 data — you would lose statistical power. Then I would recommend to do one adjustment with MIPI alone and then combined with the biological variables.

Martin: That makes sense. I do both clinical medicine and research, and I mentioned that I will often include the MIPI score in my note, just out of habit, but there are limitations in terms of what the MIPI can help me understand with respect to patient prognosis. And I'm wondering if you can help us clinicians understand what the MIPI means to our individual patients as opposed to the populations of patients we see in trials?

Hoster: Yes, and that's a very good question. Most of the application of the MIPI is in research. So you can use it for adjustment. As we have said, you can use it to describe and compare patient cohorts with regard to their risk profile. And you can do stratified randomization, which we have done for the MIPI a lot of times, to get really balanced groups in randomized trials. Now, coming to the clinical practice, of course it would be very good to have a score accurately predicting the survival of patients and the outcomes. Unfortunately this is a very, very difficult task. We made an attempt with the MIPI, but what we can say for the moment is that if you calculate the risk group, then you can get a rough estimate on what the prognosis will be. It has to be at the time of diagnosis of the disease, and then you can have a rough estimate about median survival time for low risk, intermediate risk, and high risk patients. But there's still a lot of variability in this estimation. You have to explain to the patient that this is the median, and there's a variability. It gives you some kind of impression of what the rough estimate is, but then there is also some variation that partly could be explained with other variables. We should improve upon that. It's also partly the treatment and the characteristics of the patients. There is some random variation we will certainly not be able to explain, as well.

Martin: I think we have the same issue with the FLIPI in follicular lymphoma and the IPI in diffuse large B-cell lymphoma. I honestly struggle with this in discussions with patients where they find the prognostic index on the internet and they'll say it is their average. Many of them are familiar with high school statistics and a mean with standard deviations and a bell-shaped curve. But we don't really have something like that. I wonder if there's a way to communicate with patients that would be more helpful or valuable, like confidence intervals around the prediction, so that people can see the range is quite broad. If you just mention range, then you go from zero to infinity. That's also not so helpful. I don't know if you have suggestions there.

Hoster: That's a very good point. I would maybe show the Kaplan-Meier plots to show the variability because if patients are interested in that, they can really see the distribution of the events. You can see that there are very early events and late events, and you could explain to them where the median is. Then, for the median, it's a good point to say not only is this the median but you can also take the confidence interval. It gives an impression on the uncertainty of the estimate. I haven't calculated this, but I could also calculate the quantiles, or the time where 25% of the patients have survived or 75% have survived. My recommendation would be to give an impression on the variability. But of course, you would need to take that out of the curves, and maybe I could share the numbers later in a follow-up publication also.

Martin: That's an interesting component of medicine, particularly with indolent lymphomas, where we're looking at sequencing of therapies and people are living longer and longer time periods, especially with mantle cell lymphoma. I think that's where there's a lot of fear, which historically was well founded. It's important to be able to have these discussions with patients, but they're really complicated subjects. We're learning how to have these discussions and spending a bit more time thinking about how to communicate these complicated topics to patients — I think this will be interesting in the future. I also mentioned briefly that we would talk about MRD analysis, and your recent presentation at ASH looked at the role of rituximab maintenance in MRD analysis. Do you want to quickly summarize your findings?

Hoster: I'm happy to. I'll start from the MIPI and Ki-67. This is based on variables recorded at the start of first-line treatment. So, it already gives a quite good picture on what happens, but it does not capture any information recorded later. For example, a special method to understand response to treatment on a highly sensitive level is by assessing minimum residual disease. This is done by a PCR test, and at the time of diagnosis, you get a patient-specific primer that can then, at later time points, be recognized, even when the lymphoma itself is not visible by CT scans anymore. We did this as part of our trials, and it was especially the Mantle Cell Lymphoma Elderly trial that investigated the effect of rituximab maintenance after induction R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) therapy vs R-FC (rituximab, fludarabine, and cyclophosphamide) in older patients with mantle cell lymphoma. We had the question whether the MRD status at end of induction predicts the efficacy of maintenance therapy. There are a lot of trials ongoing at the moment that try to reduce treatment in MRD-negative patients. So, we looked at the time point at end of induction where rituximab maintenance vs interferon maintenance was applied, and we were quite astonished that the result was different. The rituximab maintenance therapy worked especially well in patients who were MRD negative at end of induction, and in MRD-positive patients there was also an efficacy, but it seemed a bit reduced. And the conclusion from this was that treatment de-escalation in MRD-negative patients by omitting rituximab maintenance therapy would not be a good idea. On the other hand, we could show that in MRD-positive patients, the prognosis is quite poor. So, the time to disease progression is quite short. These are the patients for whom we should look in the future for new treatments, treatment intensification, and modification novel treatments. And this should be studied further in new clinical trials.

Martin: I thought this was great because it's in some ways counterintuitive that adding more therapy to somebody who has already done well is more beneficial to them than adding more therapy to somebody who hasn't done well. But that was also interestingly reported by you guys last year at ASH looking at the "R-squared" maintenance therapy in older patients. I suppose I don't have to ask, but I'm sure you're doing the same analysis in the TRIANGLE trial and the ibrutinib maintenance. It will be interesting in the future to see if the same story holds true, or if we find that different agents affect different patients differently.

Hoster: Yes. So, of course we have seen the data of the Mantle Cell Lymphoma R2 (Rituximab-Lenalidomide) Elderly Clinical Trial, where it was shown that the lenalidomide maintenance therapy was especially effective and also in MRD-negative patients. It seems to be something related to maintenance. It seems that maintenance is really what it says — maintenance. It works especially well when the disease is well-controlled in MRD-negative patients. And, of course, we are assessing MRD in the TRIANGLE trial, and the next task for the next months is to evaluate the MRD, see whether the MRD response is different, and see what effect the maintenance therapy has on patients' MRD status. This is now, of course, ibrutinib maintenance, almost an more even more specific treatment than rituximab.

Martin: Today we've talked with Dr Eva Hoster about MIPI, MRD, and mantle cell lymphoma. Specifically, we heard a little bit about the development of the MIPI, as well as the addition of newer variables like proliferation index and p53, and specifically the role of these variables in understanding research studies we might be reading. We also talked about the limitations of these data as they apply to individual patients. I think MRD is an area that will be very helpful to us in the future, but we're still trying to understand it because it seems to vary depending on when we measure it, how we measure it, and what we do with the information after we measure it. I'm looking forward to the updates in the TRIANGLE trial in MRD. Thank you all for tuning in. If you haven't done so already, take a moment to download the Medscape app and listen and subscribe to this podcast series on mantle cell lymphoma. This is Dr Peter Martin for InDiscussion.


Predictive Value of Minimal Residual Disease on Efficacy of Rituximab Maintenance in Mantle Cell Lymphoma: Results From the European MCL Elderly Trial

Mantle Cell Lymphoma

A New Prognostic Index (MIPI) for Patients With Advanced-Stage Mantle Cell Lymphoma

German Lymphoma Alliance Scores

Treatment of Older Patients With Mantle Cell Lymphoma (MCL): Long-term Follow-up of the Randomized European MCL Elderly Trial

Rituximab-Lenalidomide (R2) Maintenance Is Superior to Rituximab Maintenance After First-line Immunochemotherapy in Mantle Cell Lymphoma: Results of the MCL R2 Elderly Clinical Trial

ASCT After a Rituximab/Ibrutinib/Ara-c Containing iNduction in Generalized Mantle Cell Lymphoma

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