Associations of circulating cell-free DNA (cfDNA) and clinical outcomes in metastatic castrate-resistant prostate cancer (mCRPC): A Discussion on the Data - Daniel Shevrin

April 21, 2021

Genomic profiling of patients with metastatic castration-resistant prostate cancer (mCRPC) is becoming more widely used to assist both in prognosis and informing on treatment. Recently, the FDA approved two cell-free DNA tests for the purpose of looking for those genes, the FoundationOne, and the Guardant360. Joining Alicia Morgans is Daniel Shevrin to discuss questions that still remain regarding the validity of cell-free DNA assays, particularly for non-DDR genes, and how this analysis is helping to get a better understanding of the associations of these gene alterations with the cell-free DNA assay in relevant clinical outcomes.  

Biographies:

Daniel H. Shevrin, MD, GU Medical Oncologist, Division of Hematology-Oncology, Northshore University Health System, Evanston, Illinois

Alicia Morgans, MD, MPH Associate Professor of Medicine in the Division of Hematology/Oncology at the Northwestern University Feinberg School of Medicine in Chicago, Illinois.


Read the Full Video Transcript

Alicia Morgans: Hi, my name is Alicia Morgans and I'm an Associate Professor of Medicine and a GU Medical Oncologist at Northwestern University. Today, I'm so excited to have here with me today, a friend and colleague, Dr. Dan Shevrin, who is a GU Medical Oncologist at Northshore University Health System in Evanston, Illinois, also a good friend and collaborator on the SPORE that we have at Northwestern. Thank you so much for being here with me today, Dr. Shevrin.

Dan Shevrin: No, thank you so much, Alicia. Really, thank you so much for inviting me to discuss this abstract and the work we've been doing at Northshore. The title, I think, as you said, was Associations of Circulating Cell-Free DNA in Clinical Outcomes in Metastatic Castrate-Resistant Prostate Cancer. The group that we put together here is both from hematology-oncology, as well as the Mark Newman Center for personalized medicine, as well as our bioinformatics group.

The background of the work is, I think is fair to say, that genomic profiling of patients with mCRPC is becoming much more widely utilized to assist both in prognosis and informing us on treatment. Based on the data from TOPARP, TRITON2, and more recently PROfound, treatment with olaparib and rucaparib, PARP inhibitors, are now approved if there is a presence of a DNA damage repair gene alteration, specifically BRCA2 and ATM. And in fact, last summer, which was actually after this data was collected, the FDA approved two cell-free DNA tests for purposes of looking for those genes, the FoundationOne, and the Guardant360, with some specific restrictions on that. So, things have moved along. But, irrespective of that, we felt that questions still remain regarding the validity of cell-free DNA assays, particularly for non-DDR genes, and as well as getting a better understanding of the associations of these gene alterations with the cell-free DNA assay in relevant clinical outcomes.

This was a retrospective analysis of 82 patients with mCRPC who underwent CLIA-approved cell-free DNA testing. They received standard treatments with the androgen response signal inhibitors, abiraterone, and enzalutamide as well as chemotherapy. The relevant clinical outcomes data were recorded in a structured note that we developed in the EMR, and was stored and analyzed so that we could then look closely at this data. The relevant clinical endpoints included overall survival and time-on-treatment with the ARSi drugs.

Of the 82 men studied, 73 received ARSi drugs and 40 received both. Of the patients who received both ARSi drugs, 40 patients, 22 received the sequence of enza to abi, and 18 received the sequence of abi to enza. So, a little bit about the structured note. It's a note that allows entry of clinical data, for instance, which drugs are being used and time-on-treatment, and it is entered into the electronic medical record and stored in the EDW, the electronic data warehouse. Clinical treatment and outcomes data were then extracted from the EDW, retroactively, in this case, looking specifically at treatment type, start/end date, stop reason, so time-on-treatment with these drugs, as well as overall survival. And then this clinical data was integrated with the cell-free DNA genomic data.

The results of this analysis were as follows. First of all, we use univariate analysis initially to assess for any potential associations, however, after multiple hypothesis correction, these results lost statistical significance. As you know, multiple hypothesis corrections are used when you have multiple hypotheses and you need to have a more rigorous p-value, called the q-value, in order to avoid any false associations or false positives. So it is a much more rigorous announced statistical analysis.

What we observed were several things. We observed a shorter median overall survival for patients with BRAF alterations, as well as NF1 alterations. We also observed a shorter median overall survival for patients with detectable levels at all of cell-free DNA, compared to those where there was insufficient cell-free DNA to do the test at all. We also observed a shorter median overall survival for men who had co-occurrence of AR alterations in BRAF, and actually, this was the only association that did hold up and was significant under multiple hypotheses of correction. In terms of time-on-treatment, we did also observe a shorter median time-on-treatment with men who had a BRCA2 mutation.

The conclusions that we made from this are that it is, first of all, a retrospective analysis, which we did identify several potential significant associations between specific genes and overall survival, but, other than one association, lost significance with a more rigorous multiple hypothesis correction. We observed this trend with BRAF and NF1 alterations in a short and overall survival with co-occurrence of AR and BRAF. The presence of a sufficient amount of cell-free DNA was associated with shorter survival, and the sequence of abi to enza actually showed a longer time-on-treatment than the reverse sequence.

The future directions of this study is that this workflow is able to capture these data endpoints and is being used in a larger prospective study in men with CRPC, in which we are doing a more comprehensive genomic analysis of, in addition to cell-free DNA, also tissue with both archival and germline analysis, in order to get a better comprehensive look at their genomic profile, in order to help better understand how to inform us in prognosis and treatment.

Alicia Morgans: That's fantastic and I appreciate you going through that process with us. And also, I want to acknowledge how much effort must have gone into having that specific structured clinical note that you've been able to integrate into your EMR, and then use to capture ongoing data. And then of course, once you had that process within your operating procedures within your clinic, you can, as you said, leverage that to do prospective studies. That in itself seems like a huge accomplishment when we are trying to think about how to use real-world data to understand how patients may do in these settings. Can you tell us how you were able to make that work? Did you require extra FTEs? Where was there extra time spent in documentation? Was it a labor of love? How did you and your clinical partners make that work?

Dan Shevrin: Definitely a labor of love. I appreciate that. Yeah, we have a very robust bioinformatics group up at Northshore, and a number of specialties have looked at developing structured notes with the help of our bioinformatics HIT group to store data and ultimately be able to analyze it, so I thought it was a good opportunity to do that since I always felt it was hard to really understand the database that our patients had and what treatments they were receiving. It did take them, I would say, almost 2 years of fairly frequent meetings with our bioinformatics group to develop this note with the elements that I felt were important. And this actually, is needed to be modified as new drugs are available and new treatments, so it is a bit of a dynamic situation.

It does take a little longer to enter the data. A lot of clicks, but not that much more. I think some of the efforts also for the informatics group is to extract this data from the EDW is also a laborious task. It is not as easy as one would think. I say, "Well, it's in the EDW, so just get it," and it does take them some effort. And they don't always, as you know, understand some of the clinical relevance of things that we do and some of the data is simply not relevant. It has been a labor of love, but I feel like it has allowed us to gather important clinical data.

Alicia Morgans: That's fantastic, and I think really to acknowledge your comment about pulling data from the EDW, your electronic data warehouse, just because the data may exist there doesn't mean that it is cleanly pulled from that setting.

Dan Shevrin: Exactly.

Alicia Morgans: As you also mentioned in your clinical note, your documenting reason for treatment cessation, which is something that's almost never easily pulled because it's not usually structured, so I think that is a major accomplishment that you've been able to get that to work. I encourage other practices that are interested in trying to collect their own real-world data to consider that model and of course to reach out to you and your colleagues at Northshore if that is something that they are interested in doing. Perhaps you can get a collaboration going.

Dan Shevrin: Sure.

Alicia Morgans: But at least to be a model for others, as they are trying to do that. It is no easy task.

Dan Shevrin: Well, thank you.

Alicia Morgans: As you think about this data, how would you give a take-home message to folks on this initial work that you've done?

Dan Shevrin: Yeah, no, that's the right relevant question, and I was thinking about that. This is a work in progress, obviously, and it's very preliminary. It is a retrospective study with a relatively small number of men, 82, so you have to be cautious to really make too many bold proclamations, particularly when you are dealing with so many different genes and so many associations.

I have to say when I was first doing this and I saw the univariate analysis and I got excited. Once we did the more rigorous statistical analysis, you realize that you have to be more careful. But, I think my main message is caution, I guess, and not that we shouldn't be ordering cell-free DNA, and certainly the two tests are now FDA approved, which is good. And we know that if they have a specific DDR gene like BRCA2, I think most of us would feel pretty comfortable ordering a PARP inhibitor.

But beyond that, because, really, depending on what series you look at in terms of DDR genes, even the Memorial group found about 20% in a tissue-based study, others have found more or less. Initially, with this retrospective group, it was about 10%, 12% of mine had a DDR gene, maybe a little bit more, but it may not be as much as we would like. And so therefore you have other genes that show up, other gene alterations, and it's not always clear to me what they mean and how important they are clinical.

Obviously we're always looking to target them and that's an important part of it in the prospective study, is to determine if the gene alteration actually led to a difference in a recommendation for treatment and whether it actually was the drug could be obtained. Did it work? And so this is also data to find out, is it all worth it to do all of this genomic analysis? I think we all think it is, but we have a lot of work to do.

We're not as far along as lung cancer and some melanomas, or even GI cancers. So for instance, BRAF mutation, I don't know exactly what to do with that. With specific mutations, you can use a MEK inhibitor, but that's proven in melanoma, but we have no idea if it is going to be effective in prostate cancer. So, I think caution, but I'm excited that we have the ability to do cell-free DNA, since it is often challenging to get tissue in men with bony mets, as you know.

Alicia Morgans: Absolutely. And I think that any study that really helps us to understand how we can pull apart these findings using our cell-free DNA is certainly a worthwhile study. Just to emphasize that, you did find findings that were similarly demonstrated in phase 2s, for example, that sequence of abi to enza maybe being allowing patients to be on treatment a little bit longer than enza to abi, which has been demonstrated. Though, of course, we do try to change the mechanism of action. There are many reasons why we don't always do that. And so that really substantiates that in the real world, we see what we are seeing and that this is certainly a step in the right direction.

To your point, there's so much work to be done. Studies like TAPUR the NCI-MATCH, combo match, and eventually iMATCH, are going to study where we can potentially put patients like this on those clinical trials that are more of a basket-type approach, because we don't know how those agents are going to work in men with prostate cancer, and we don't always have the opportunity to open a trial for 1 or 2 patients that we may see over years that have these alterations.

Dan Shevrin: Right.

Alicia Morgans: But as a community, we can come together. So, thank you so much for sharing this work and I commend you on the effort that it clearly took to get to this point, and I look forward to hearing how things continue to go in the future. We really appreciate your time.

Dan Shevrin: Thank you very much for inviting me. This was a pleasure.
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