Dr. Paul Nguyen is an internationally recognized expert in prostate cancer clinical care and research. He has published over 250 original research articles, has various national leadership roles and is the Dana-Farber Cancer Center Genitourinary Clinical Center Director for Radiation Oncology, Vice-Chair for Clinical Research in the Department of Radiation Oncology, and Associate Professor at Harvard Medical School.
Prostatepedia spoke with him about collaborations between healthcare and tech industries for prostate cancer.
Have you had any particular patients or cases that changed how you view your role as a doctor or how you practice medicine?
Dr. Paul Nguyen: Several years after treating him, I heard from a patient who recounted for me what it was like to meet with me when he had first been diagnosed with recurrent disease. He said he’d had a lot of uncertainty and anxiety about his future. He said that the way I spoke with him had changed it entirely for him. He said I had a plan for him, knew exactly what we were going to need to do, and that we were going to do it.
I didn’t do anything particularly different in that encounter than I normally do, but hearing that made me realize how patients really hang on our every word, our every facial expression, our every cadence, and the emotion that we project when we speak. This made me so aware and conscious of making sure that, at all times, in every encounter, I have that combination of being sure about what I need to do and maintaining hope and optimism in every part of our discussions.
That was a good learning cycle for me. I hadn’t thought of it that way when I was with a patient. You just don’t think that every intonation, every gesture has such a huge impact. But it does. That was a very valuable learning experience for me that has really shaped how I think about every patient encounter before I walk into the room.
What are your current research projects? Which are you most excited about?
Dr. Nguyen: I have spent my entire career using information from the medical record about patients’ health status and tumor characteristics to figure out which men should get hormone therapy and for how long. Now, I’m incredibly excited about the opportunity to unleash the power of genetic testing of tumors. This will help us understand, on a genetic and molecular level, which patients should be given hormone therapy and for exactly how long. This will be a lot more precise than the clinical information by itself. I’m working with Dr. Felix Feng and others, which has been a wonderful collaboration.
How do you see evolving technologies impacting prostate cancer research? Dr. Nguyen: Technology gives us opportunities to do the kinds of studies we never dreamed possible, which is amazing.
I’ll give you an example. Dr. Feng and I are about to take prostate cancer samples from biopsy tissues taken 25 years ago from men who had cancer, samples stored without a clear purpose in mind. I give a huge amount of credit to the people who designed these studies in the early 1990s. They had no way to analyze this tissue, but they knew that someday, this tissue would be important to humanity. There wasn’t a specific test that they were storing these samples for, but they knew some kind of technology could decode what was going on in those tumors, to study how the tumors work, and who should get which treatment.
I feel so fortunate to come along 25 years later, when we do have the technology to analyze this tissue, and research it. This is the research I’m about to do now, which would never have been possible without new technologies.
Do you see technology impacting how we design clinical trials from the get-go?
Dr. Nguyen: Absolutely, because now people are designing trials with technology. There’s a trial being led by Dr. Feng from UCSF and Dr. Dan Spratt at the University of Michigan that incorporates genetic technology.
All the patients are tested upfront with this new technology to help decide which arm the patient goes into, which is really cool. This new scientific technology is being worked into clinical trial design.
Which innovations or technologies have the biggest impact?
Dr. Nguyen: There are two kinds of impacts. One is the ability to do large-scale genomic studies for a relatively low price. That has been a game-changer because it used to be so expensive to sequence the DNA of patients, but now you can approximate that rather cheaply and then do studies on thousands of patients. This way, we can pick up very small signals, which are very valuable.
The other invaluable impact is the ability to detect very minute amounts of tumor in the blood, very tiny traces that can tell us a lot.
In the circulating tumor cell?
Dr. Nguyen: Exactly.
Do you think artificial intelligence will play a role?
Dr. Nguyen: For sure. I’ve spent most of my career working on simple, clinical data. You can see the patterns of simple data yourself by doing simple statistical analyses. But now, the patterns are much more complex. Instead of five datapoints, you might have two million datapoints per patient. So we need AI. We need sophisticated machine learning to help us discern some kind of pattern out of that huge amount of data, to help us make sense of it.
Are there any specific collaborations, other than the ones we’ve already discussed, that you think look promising?
Dr. Nguyen: We’re seeing a lot more collaborations across specialties and disciplines to get research done. So much of what we’re seeing now is team science whereas people used to do studies with their own group.
Now, if you look at a paper, it’s not just one group or one discipline. At each institution, it’s five disciplines, and then you might have ten institutions on a paper, each contributing something different because that’s just what it takes now.
Every group has its own, little special expertise that gets put together to get a big paper or a big trial done. That’s what has really exploded. We’ve all recognized that, in order to get good science done, we have to team up.
Is just it easier to collaborate with people now via email and sharing of data? Or is there something about the way cancer research has been funded that has fostered that collaboration?
Dr. Nguyen: Yes. Those factors definitely contribute. It is definitely easier to share data now with the internet. Efforts to fund team science have definitely led teams to be created that might not have been created organically before.
There’s something fundamental about the increasing use of technology in studies and trials where only certain groups have this kind of technology expertise. You might have one group that knows a lot about the technology and another group that has a large number of patients and ideas. And you have to reach outside of your little sphere in order to get these kinds of exciting studies done.
It seems like before everything was pretty much siloed: you had tech, you had healthcare, and then, within healthcare, you had prostate cancer versus pancreatic cancer versus breast cancer. But now, the walls are coming down between those silos, with things like increased genetic testing. Would you say that’s true?
Dr. Nguyen: Absolutely. For example, some of the cool studies done in prostate cancer genetics were modeled on similar research done in breast cancer genetics several years before. Breast cancer had the Oncotype study, and then prostate cancer developed the Oncotype test many years later. We’ve seen molecular subtypes of breast cancer (luminal A, luminal B, and basal), and now there’s a study led by Dr. Feng suggesting that you’ve got similar kinds of subtypes in prostate cancer. We have to be knowledgeable about other fields. You can’t just be in your own silo now.
Last week, I spoke with engineers at University of Pennsylvania who are working with microchip-based technologies and machine learning to increase liquid biopsy’s usefulness in pancreatic cancer. They said this allows them to process much more data than they could before. They hope this has potential in other cancers. I know that’s more along the lines of diagnostics than what you’re doing, but do you have any thoughts about that?
Dr. Nguyen: We are all trying to take those same kinds of approaches with the folks who do machine learning. We need them desperately now because we’ve got so much data, and we just can’t figure it out on our own.
That’s exactly where we’re all headed.