Phillip Koo: Hello, I'm Phillip Koo, and welcome to UroToday. Today we have the pleasure of speaking with Dr. Jeremie Calais who's the Director of Clinical Research at the Ahmanson Translational Research Center at UCLA. Obviously, it's a very exciting time for UCLA and UCSF as they were just able to get FDA approval for the first PSMA PET product in the United States, gallium 68 PSMA-11. So today we wanted to talk about outcomes and I think a big question that urologists and medical oncologists often have when it comes to these new imaging technologies is: So what? How does it improve patient care? How does it improve clinical outcomes? So if someone asked you that Dr. Calais, how do you respond?

Jeremie Calais: So first, it is true. It's coming all the time and when you come with a new imaging technique, people say that it really impacts the outcome. It's good. You have a more sensitive test. You have probably a better diagnostic imaging technique, but what's the point? So these questions come back a lot from referring physicians, from guideline institutions, from regulatory institutions such as the FDA, from reviewers in the papers all the time. That comes back a lot. So our last meeting with the FDA, they really told us, "Okay, now the next step is to get some outcomes that are from the approval of such imaging tests, or at least if you go for a new imaging technique in the future, please try to put some outcome data endpoints." Just by outcome data, it's really to show approval of any type of survival with the scan comparison to without the scan. So that's what everybody wants to see.

I have some, let's say, some broad thoughts about that. I'm wondering like all the conventional imaging techniques, I'm not sure it has been shown before. When did we have a trial randomized showing CT patients do better than patients without imaging? It's not done. There was no proof of doing CT versus no imaging or MRI versus no imaging. It's just people are doing it, it's approved and everybody does it. And for PET tracers, I feel there is some, it's not lobbying, but it's a way of thinking, way of seeing things. And that when you look at the amount of published literature of PET imaging compared to the amount of literature with conventional imaging, it's 10 to 100 fold difference. This means that even with data, people don't want to believe in it. They say, "What's the point? It's complicated. There's radiation." So I think there is a lot of belief and misconception a little bit about that.

And the diagnostic imaging tests, the trials to show outcome are very difficult to design. When you try to design such trials, we have two undergoing now with UCLA, you always have some kind of unsolvable issues. The main thing that the test imaging can do is to select patients to have a good therapy and to individualize treatments. And so patients with more accurate tests will have different treatments than patients without the test, for example, if your test works well. But if you do different treatments, how do you measure the outcome in the same cohort of patients? They have different treatments. So just the endpoint measure becomes difficult.

For example, if you do a more sensitive test, such as PET imaging, PSMA PET imaging in prostate cancer, usually you have a stage migration towards metastatic disease because the test is more sensitive. And skin lesions before now become visible and patients migrate from N0 to N1 or from M0 to M1. And so this patient that was before would get, I don't know, surgery or radiation therapy only. Now they would get, for example, systemic therapy. But the way you assess outcomes after systemic therapy or surgery is not the same. Your PSA if a patient gets two years of adjuvant therapy versus something else, you can not have the same outcome inputs.

Now if we design a trial, for example, the two trials we design at UCLA, are based on patient selection, like a biomarker principle. It means it doesn't say that everyone who gets the test will do better. It says that the test will better select patients for each treatment and patients who are receiving the treatments after the test are better selected and we have a better outcome.

So for example, let's take radiation therapy, and you have a patient you intend to do radiation therapy. You randomize the patient with or without the imaging scan. The imaging scanning is more sensitive and will show metastatic disease in some patients. But in these, you can not ignore that they have metastatic disease and do the same treatment with the other ones. So you have ethical considerations to put that here because the scans show something and you know that your treatment will fail because you see the metastasis elsewhere. And so you can not do radiation therapy on these patients anymore, under the same way you would do it in the control group. So if you exclude these patients, you enrich your treatment group with patients who are less likely to fail. And that's what we want to show in comparison to the control group who keep these unseen metastatic patients.

But with such a design, we have many concerns from reviewers, regulatory institutions. And because people say it's unfair, you're excluding patients, so I just want to say that it's difficult to design because an imaging test is very different than the treatment to look at the outcome. So I found it sometimes not very constructive and relevant to ask for outcomes from a diagnostic test because the outcome is driven by treatment. And in terms of designing trials, when you go deep into that, you see how difficult it is because treatment outcome is easy to show. You do give the treatment, you don't give the treatment. One does better than the other. Diagnostic accuracy is easy to show. You look at a test and you have a gold standard, you compare. But showing the outcome of treatment for diagnostic tests, that's much more difficult.

So I know the field is asking for that, and I think we'll try to do it and the more we have approved procedures, we can build in more tests into treatment trials, and then we can get some outcomes out of it. But I think the whole community should also look at PET imaging, not as an outcome improvement tool per se, it's an imaging test. So I think that's important to say.

Phillip Koo: Yeah, I agree. I think it wasn't quite fair. It's not quite fair to expect a diagnostic test to be able to show those outcomes because as you eloquently described it, it's hard to design that type of trial. And it's a little unfair because as you mentioned, MRI, CT, all these different technologies in the past never were given that bar yet. So we've been doing this in the clinic and all of a sudden with PET, which is transformational when it comes to prostate cancer, you're running into these expectations that aren't so easy to meet.

That being said, I have tremendous respect for you. Rather than being frustrated, you've gone out there and designed some clinical trials to really try to answer pieces of this as best as you can. And I think you're really raising the elevation and you're inspiring hopefully more people to think of themselves as clinician-researchers from the nuclear medicine subspecialty, which I think to me is what we need more of. We need more people like yourself I think who are going to design some of these trials and work closely with the radiation oncologists and surgeons to help answer these questions. So you're right, I think it's going to be challenging, but it seems like you're adapting and finding ways to answer this.

So where do you think we're going to head next with regards to clinical trials? Is it going to be some more of these types of trials about patient selection? Or are we going to see more trials looking at using imaging to assess treatment response?

Jeremie Calais: Yes, you have several ways of how to use an imaging test for directing treatment or improving outcome. I think once you have a test that is not research anymore, and that is considered as a clinical procedure standard of care, you can put these standards of care procedures into trials of new treatments. And so for these treatments, at least you will have the data available with these newly approved techniques so it will be much easier to implement now PSMA PET into trials because it's approved. So first that will be the first thing for the new treatment trials. You can now build into this trial PSMA PET because it's not a secondary research procedure. It would be considered standard of care. So on that point, we can make some progress.

Now in terms of trial design building, I think patient selection is like the biomarker principle. It's like HER2 in breast cancer, patients can receive Herceptin® if they are positive for HER2 expression. And so you will do the HER intercepting test before you do Herceptin® treatments. And you know the ones that are negative, you will not give them the treatments. Well, imaging should be seen in the same way. You classify a patient as M1 or N0 or whatever and then you exclude the patients who are not likely to respond like the biomarker test when you don't have the expression of the target you're looking for. So I think staging should be seen like that and that would be the patient selection way of doing things.

Now you also have some prognostic values, which are a bit linked. You know all the Kaplan-Meier curve, the TNM score basically separate very well T0, N1, N0, M1 and the more you have disease you'll have the Kaplan-Meier curve that decreases and patients are doing worse. And while we have to redo the same trials with PSMA PET and showing that PSMA N1 disease has a better outcome than PSMA PET M1 disease and has a worst outcome than PSMA negative scans. And that maybe sometime later in the future we'll say, "Okay, to beneficiate from this therapy, you need to have a negative PSMA PET scan. That's what you need to have." So things like that, biomarker ways of thinking.

Phillip Koo: I think a lot of times when people think of imaging, they think of it too simplistically. People outside of the field think of it as just a tool that could be used for staging. But as you mentioned, it has so many more layers and possibilities and applications. Prognosis as you mentioned, biomarker patient selection, guiding treatment. So I think this to me, as you mentioned, is just the beginning and I look forward to seeing what comes of this now that it will hopefully become more available throughout the US. So thank you very much for spending some time with us and sharing your knowledge.

Jeremie Calais: Thank you, it was a pleasure as always. Hopefully next time again.