In October, at BCRF’s annual Symposium and Awards Luncheon, more than 250 esteemed BCRF-supported investigators were recognized for their achievements in breast cancer research and their devotion to ending breast cancer. The event also announced the Foundation’s research investment for the coming year.
This year’s program included an extraordinary symposium, “Three Decades of Innovation and Progress,” featuring an expert panel of BCRF investigators who discussed current breaking topics in breast cancer research and what’s new on the horizon.
The panel was co-moderated by BCRF Founding Scientific Director Dr. Larry Norton of Memorial Sloan Kettering Cancer Center and BCRF Scientific Director Dr. Judy E. Garber of Dana-Farber Cancer Institute and Harvard Medical School. Joining Drs. Norton and Garber were symposium panelists, Dr. Constance D. Lehman, Dr. Seema Khan, and Dr. Olufunmilayo I. Olopade, who received the 2023 Jill Rose Award for Scientific Excellence.
Dr. Larry Norton: This is one of the most fun things that we do all year because this is our chance to speak about our work, to speak about breast cancer in general, to have contact with all of you who are obviously supporters, but also a chance for our array of investigators.
We have 150 in New York. We had a meeting yesterday to answer questions about their area of expertise and other questions that came up as well. So we have a sterling panel today. Of course, you see our scientific director, Judy Garber is sitting right there, and next to her is Funmi Olopade. I always have problems knowing where to put the accent. But it’s Connie Lehman and Seema Khan. I’m going to let themselves introduce themselves, their institution and their interests, and speak a little bit about their work.
We’ll start with Funmi—this is what we call her—who is the recipient of the Jill Rose Award this year, which is the highest honor that we give to an investigator every year, or to a scientist clinician who has made a very significant contribution to the field. And the list of contributions Funmi has made is so legion that again, it would eat up the entire time if we gave her a chance, if I started to roll through that. We’re very honored to have her here.
And then my other colleagues who are luminaries in their field will talk about their work, and then we’ll have a discussion, open up to your questions and discuss it among ourselves. I always like to think of this as very interactive and any questions you have, anything related to breast cancer, it doesn’t have to be the topics that we’re going to be talking about, is entirely appropriate to answer. And if I can’t answer the questions, Judy is here and she’s going to save me. So let’s take it away. Funmi, let’s start with you.
Dr. Olufunmilayo I. Olopade: Good morning, and thank you so much to the Scientific Advisory Board for naming me the Jill Rose Award winner for this year. And it’s always wonderful for me to come to New York because I live in Chicago. It was that second city, but now I’m even told it’s a third city because we’re always looking to be better than New York. And my work has actually gotten me to think about how we organize cities because I came to Chicago 40 years ago and our city is organized south, north, west, and east, and I live on the south side of Chicago, the best place to live on the planet. But also a place that’s segregated and a place where the University of Chicago has been working hard to make sure that every researcher, all research we do has global impact. And thinking about global impact of our work, I see patients who come to my clinic and they come in different shapes, sizes, colors. I work in genetics.
I was very fascinated about genetics and I happened to have married a man who is very concerned about the environment because our first daughter had asthma. And so every time we’re in the clinic and we’re seeing different types of patients, the question we ask is, “Are they having a severe form of asthma or breast cancer because they’re black or is it because we haven’t really had a good understanding of the root causes of every chronic illness?” And so we’re adult doctors, but our patients actually really teach us a lot. And so when I joined up with my colleagues who were looking to get a better understanding of the genetic basis of breast cancer, that’s when I ran into my amazing colleague Mary-Claire King. And we wanted to know, the families with breast cancer that come from different parts of the city of Chicago, what’s the origin of their breast cancer?
Why are they getting breast cancer at 30, at 25, at 21? And why do some old ladies on the south side of Chicago live with their breast cancer and they won’t show up to our emergency room until it’s coming out of their breast? And so I began to ask, “Is this America, and why is it so diverse in terms of the questions that we can ask?” So that’s why we began really thinking about who has BRCA1, BRCA2, and who doesn’t have a genetic reason for their breast cancer and how can we study the fundamental questions of why does a woman get breast cancer? And the answer is, the most important risk factor is you are a woman, and whether we know why you got the breast cancer or not, we ought to be able to treat you effectively. So that’s why I then started asking why are some people living and doing really well, surviving with great quality of life, and some don’t even make it to one year after a diagnosis of breast cancer?
So I’ve joined up with a lot of amazing people to ask those questions. What should we do to treat every patient successfully so that not only do they survive, but they thrive after a diagnosis of breast cancer? And my work is taking me to Nigeria and back and the lessons I’ve learned, it’s really been powerful to be able to think about all of us and the promise of precision medicine. So in a nutshell, that’s what I do and I’m passionate about it and I can’t thank all of you who support BCRF enough for giving us the idea to ask any question and to be creative with our research. Thank you.
Dr. Norton: Connie.
Dr. Constance D. Lehman: Good morning. I’m Connie Lehman. I’m just delighted to be here. I’m a breast imager in Boston at Mass General and Harvard, and I am so grateful for the Breast Cancer Research Foundation’s willingness to fund and support high-risk, cutting edge, future-thinking research. And nothing could be more true than in the area of artificial intelligence. There are many groups when they hear AI, it actually raises more of a sense of fear, particularly in the healthcare domain. Can we trust this? It’s a black box. We won’t understand it. The BCRF support of myself, of Regina Barzilay, of Adam Yala, really they’re standing by their commitment to push the field forward for a paradigm shift. I’m so excited about it because in my entire career as a breast imager, I’ve seen the limitations of how we screen. We had screening mammography in the late 1960s in the US. Not a lot has changed.
We’re still making recommendations for how women should be screened mainly based on their age and breast density when we have so much data about women where we could be more precise, more effective, and save more lives. We also have a lot of work to do in early detection, to have it more equitable and to have not only access more equitable, but also the outcomes. It’s not just about people being engaged, but about reaching the right people with the right tests at the right time. So probably 25 years ago, I was studying my grandmother’s CAD, old school cad. That was machine learning. And you may all have had mammograms where you know that it was interpreted with CAD, an older technology of-
Dr. Norton: Explain what CAD is.
Dr. Constance D. Lehman: Machine learning. Computer-aided detection or computer-aided diagnosis. So computers assisting radiologists isn’t new. We’ve been working on this area going back to the 70s and 80s.
The FDA approved the first CAD product to interpret mammograms in 1998, but we found with the Breast Cancer Surveillance Consortium that it wasn’t translating to improved mammographic interpretations. And that was a hard lesson, but one we’ve carried forward to today as we’re now studying the next level of computer data detection about that. So we’re now going from CAD to deep learning. And what was probably one of the more exciting times in the field was around 2010 or so when computer vision really started to show promise in being able to use neural networks so that computers could separate out different types of images.
So then we realized all of the images in healthcare, whether it’s a lesion on the skin, an image of the body, an MRI, an ultrasound, a CT, a mammogram, the back of your retina, all of these images we can use with these big databases to train AI models, machine learning and deep learning models to be able to read these images independently, either to assist the physician or in some domains we are going to have examples where we can replace the physician. I think what I became most excited about when I first met with Regina Barzilay and we started talking about the potential of this was to leverage AI to do things that humans are not doing. We’re not terrible at finding present cancers on a mammogram, but we could certainly do better. But what we can’t do is look at a mammogram and judge a woman’s future risk of breast cancer, but we can with AI.
So that is the area that’s particularly exciting and where our work comes together. If we want to have prevention methods, wouldn’t it be great if we had a tool that could assess if that intervention is decreasing a woman’s risk? Many of my friends that have gone through breast cancer and are then put on risk-reduction strategies, and I find out after a few years that they just don’t stay engaged. There’s other side effects or problems, but I really believe that if they saw the impact it was having on their risk, it would give them an incentive. And if it wasn’t having an impact on their risk as assessed by the AI mammogram, to shift to a different type of intervention. Whether we’re working in obesity or hypertension or diabetes, we can give patients feedback about the interventions and how they’re influencing their risk. I think in the future, we’re going to do that with breast cancer as well and it really opens up so many different areas.
The last thing I’ll say before passing on to Seema is what was very exciting as Regina and Adam Yala and I were studying the outcomes of these tools in our clinical populations, we’re seeing that there is equity across diverse populations. We haven’t had that with our other risk tools, and we’re very excited about that possibility to provide accessible, affordable, and equitable care to more women in assessing their risk, intervening early, and preventing cancer. So thank you for your support, and I look forward to our questions and answer.
Dr. Seema Khan: Thank you, Connie. So I too would like to thank BCRF and all the support that they have provided to myself and other researchers like me who are focused on breast cancer prevention mainly. But the avenue to effective prevention is actually identifying women who are at risk for breast cancer and estimating that risk. So a lot of what I’m going to say is going to relate to what Connie Lehman has just told you, but I’m a breast surgeon at Northwestern University in Chicago, and I’ve been interested in breast cancer risk and prevention since early in my career. If you remember, this whole field was actually started by a surgeon. So a good role model, Dr. Bernie Fisher led the first prevention trial testing the value of tamoxifen for risk reduction, and this was published in the late 1990s.
There was a lot of excitement at the time because tamoxifen appeared to reduce the risk of breast cancer in women at increased risk by 50 percent, so halving the risk of breast cancer, and there was real anticipation that this intervention would make a difference in the occurrence of breast cancer and help women who were at increased risk. Unfortunately, over the next decade or so, we realized that what was anticipated to be a safe and tolerable intervention treatment for high-risk women turned out to be not so tolerable. And it was not perceived as safe enough for high-risk women to use when they were healthy if breast cancer hadn’t happened yet. So since then, over the past decade, I think there’s been a lot of attention paid to this question of how we can take an effective medication and reduce the risk of toxicity and increase the tolerance so that women who would benefit from such intervention would accept it.
There are many people who worked on this. Dr. Jack Cuzick is here, Dr. Andrea De Censi, and people from my institution. We’ve all been approaching it from different directions. The tech that we chose initially was to try to develop a gel formulation that would be applied to the skin of the breast, would concentrate in the breast, but have very low circulating levels. So this would deliver the benefit of the medication to the breast, but would avoid side effects. So with BCRF support, we were able to work on this for some time. This is a process in development at the moment. The initial data are encouraging, but more work needs to be done. And again, we really appreciate the support of the BCRF and of all the BCRF supporters as well.
The newer tech is low-dose tamoxifen, and that is something that we are pursuing in the next phase of work based on trials done by Dr. De Censi. So we think of this as a renaissance in tamoxifen use and in medical prevention, because obviously the alternative is surgical prevention, which is effective, but is also a big burden for women who are considering it. So we have to strike the right balance and offer the right intervention to women who need it.
Dr. Norton: Thank you. I would just say that it is such a shift in the kinds of discussions that we’ve had here in front of you, I don’t know how many years we’ve been doing this, but a whole lot of years in terms of contact, from some of the issues that we were discussing back in the beginning to where we are now. If you just think how remarkable it is, we have among our panelists, an expert in genetic in things you inherited from your mother or your father that may give you risk, ancestry in terms of geographical ancestry, social determinants of cancer, and an expert in breast imaging.
We always think of breast imaging as you find a cancer so you can go and take care of it. But now we’re getting into the area of actually looking at a breast that is normal and saying, “Gee, this is at higher risk,” and we have to take into account social determinants and geographical ancestry andusing the most modern tool that we have in analyzing complex data sets, which is artificial intelligence and machine learning. And an expert, a surgeon who’s trying to put herself out of business by preventing breast cancer in the first place. So she has nothing to operate on. And then she and I are going to open a bakery together. That’s our ultimate plan.
And this is a prevention strategy that is so benign that everybody could take advantage of it and without fear of side effects. So we’re talking about identifying people at high risk, we’re talking about not only detecting cancer, but finding by a mammographic image the probability of developing cancer and intervention strategies. It was inconceivable when we started this that we’d be talking about prevention to this degree and in this way, and especially with these advanced tools that we’re talking about. Aa\nd genetic analysis is very complicated, very detailed, very hard to do. Obviously mammograms have so much information in it that you need computers to help you interpret it, and pharmacological advances, advances in medicinal chemistry that allow us to deliver these drugs. I’m just blown away by this. We also have another expert here happens to be in genetics, and I’m going to pass the baton over to Judy to have further discussion on this topic.
Dr. Garber: Well thanks, Larry. And I have to say Funmi has been a colleague on the genetics journey all my career. And there are many others in this room who are expert. But I think genetics fits in. If we’re thinking about how do we find women at highest risk, some of them have genetic risk, some of them have no family history, no genetic risk, they get breast cancer anyway. Are there subtle genes that are still less powerful but able to help us identify who’s at risk? And we could say if we could figure out who is at risk, however we do it, we might be able to have a much more efficient system.
Connie talks about risk-based mammographic screening, so we wouldn’t all have to go sign up at 40 or 50, depending where you live, every year, every other year. If we had more sophisticated risk estimation, we could stratify who needs to be screened frequently or less frequently, like we do for colonoscopy. And if Seema is able to give us really effective, acceptable prevention strategies and we work on those things too, then great. We wouldn’t all be as afraid of this as we are. And then we could come back to Funmi and ask Funmi, you covered so many things, you’ve had so many interests in your time. Where do you think are our greatest opportunities for moving forward now? Thinking the way you do so broadly, so globally, how can we make the biggest difference?
Dr. Olopade: Yes, thanks Judy. When we started really mapping genes one gene at a time, we had a promise that we can map the entire human genome. And I remember when we got the good news that Congress was going to give us $5 billion dollars to map the human genome. And it was a bipartisan congress that gave us $5 billion. It seemed like a lot of money. And we talk about the fact that it took $1 billion and $5 billion to find the first map of the human genome, and now we can actually do it for less than a thousand dollars. How powerful is that? And Larry’s always talked about mathematics because he’s a genius. He thinks mathematically.
Dr. Norton: It’s true. It’s totally true.
Dr. Olopade: And we had at our symposium yesterday a talk about mathematics in medicine. Who would’ve thought? If I knew mathematics was going to come back to medicine, maybe I wouldn’t have been a doctor. Because what you need is data, and you need technology. And some of us are concerned about data, about whether the internet and people are fixated on social media, and that’s bad for us. But there’s technology for public good. And one of the amazing things that we are doing now is getting data from all over the world. Women get breast cancer in every country and in every neighborhood, and we can actually map where the worst survivorship for breast cancer is in this country because from 1975 till now we’ve been recording cancer as a reportable disease. It’s the only disease that you actually must report from every hospital. So imagine if we can pinpoint where the most vulnerable patients are.
And by the way, my husband is in the audience, and he tells me we don’t need to do breast cancer research. We only need to fix the environment. And so between the environment and genetics, we have this tension in my family because I think we have a foundation. We’re born with the genes we’re born with and then life happens. So what are those lifestyle trajectories that we can actually intercept? So I’m very excited at this moment that we are finally having the tools to pull all of it together so that we can do cancer interception. We cannot wait until people walk into our door with advanced cancer to treat them. We can monitor them, we can check them, we can tell them to exercise.
I ran the marathon last year. Why? Because everybody told me I have to increase my metabolism. I shouldn’t gain weight after menopause. Those are all important things to help us change our lifestyle. So I’m very optimistic that it’s not whether we do this or that. It’s like we’ve all to come together in solidarity to use the evidence we have now and keep building on it. And luckily we have young men like Dr. Yala, who as a graduate student working with Connie at MIT, was thinking about women with breast cancer and about developing tools that those of us who never knew mathematics can actually press the button and then it gives us the answer. How amazing is that?
Dr. Larry Norton: Connie? Do you think AI is going to help sort out all of the complexity of the data that Funmi is talking about?
Dr. Lehman: I think it’s certainly going to be huge. It’s so fortuitous that at the same time, we had diagnostic methods that were collecting more and more volumes of data, whether it’s genomics or radiomics, the imaging data, at the same time we were having computers have such increased speed to be able to process and manage that data. Many of us in healthcare were worried that we were getting so much more data out of our patients that we could possibly process. And that’s where I think AI is really going to influence every domain of breast cancer.
Dr. Norton: You said something that sent a chill down my spine, is maybe we can eliminate doctors, Connie. That’s a terrifying thought for many of us in the room. No, I’m just joking. Actually, I am impressed even to this day by how much information I can get while I’m talking to a patient just by taking my smartphone out and asking a question and getting some further information in that regard. So this interface I think is one of the really important areas. You have a comment on that?
Dr. Lehman: Absolutely. And I am so grateful. Many of you in the audience are also helping spread the word of these paradigms that are shifting. So depending on our age, we’re either extremely comfortable with computers being a part of our lives, how we feel about a self-driving car, whatever our experiences are, AI is permeating so many different facets of our life. It can be a little bit fear inducing to think about it in your healthcare. Many of my patients will say, “I trust you as my doctor. How can I trust a computer?” But that’s where we in the medical environment are going to be educating ourselves and educating others on how this is really enhancing our jobs.
My friends that are breast imagers, we cannot wait until we can take some of the tasks that we do that we know computers can do better and offload those onto the computer so we have more time to spend with our patients, to provide greater access, to be much more refined in our conversations and discussions about the best personalized screening strategy, the best personalized diagnostic pathway for the patient in front of us. So hematologists aren’t looking under a microscope counting red blood cells anymore. We pass that off to computers. There are a lot of domains where it was a change, but we were all very excited about that transition.
And the same will happen with interpreting images. We have the FDA involved because the regulatory process is extremely important in AI. The FDA has already approved a new domain for AI, which is triage to take exams, whether it’s a scan of the brain looking for a stroke or early signs of a stroke or a mammogram, to scan them and say, “This really doesn’t have anything remarkable. Let’s put this at the bottom of the list. These others need extra attention.” So we’re already seeing the FDA engage in this process to move the field forward, and we’re going to continue to see that trend.
Dr. Norton: How is this influencing cancer surgery? What are the changes that we’re seeing in approaches to cancer surgery that are happening with this data revolution and other things that we’ve been talking about?
Dr. Khan: Well, surgery, as we all know, is the oldest treatment for cancer. So it has stood the test of time and it is an integral part of cancer treatment today and will remain so for some time, I believe. The way that AI advances are influencing cancer surgery will probably not be totally apparent for some time. I think where the information that Connie is talking about does influence us is in risk estimation. So again, as I’ve said, surgeons are very interested in preventing cancer as well.
And so many of us counsel high-risk women about interventions, and it’s a partnership between the breast imagers and the breast surgeons counseling women about the appropriate imaging strategy for them. And that’s where AI is likely to make a difference in the near future. The longer-term effect on cancer surgery might have to do with predicting a response to neoadjuvant therapy, for instance, which is increasingly used, as you probably know. Somewhere around half of women are now being considered for medical treatment prior-
Dr. Norton: That’s using drugs before we operate to shrink the tumor down and make it disappear.
Dr. Khan: Right. Medical treatment prior to surgery to try to shrink the tumor and get some information about the effectiveness of the medical treatment, the chemotherapy for that particular tumor in that particular woman. So that information is very useful both in planning surgery and also in planning downstream medical therapy. And that’s where these kinds of algorithms may fit in to help the surgeon decide on the appropriate surgical recommendation. But at the moment, it’s in detection and risk estimation that AI is having the biggest impact, I think.
Dr. Norton: Very good.
Dr. Garber: So Larry, I’m going to take what could be the last AI question. We may have to cover some other things, but this is really for you. How do you see the future of clinical trials? How can AI better inform drug discovery?
Dr. Norton: Well, there’s two questions there. The future clinical trials and the question of AI going forward in that regard. Clinical trials are absolutely essential. I differ from a lot of my colleagues in one important respect. I still think strongly that randomized clinical trials going forward are the way that we get clear answers, and that’s when somebody who’s a candidate for the trial can get one treatment or the other or can get standard therapy versus other therapy rather than just trying something and seeing how it works and comparing it to historical experience. But that is happening as well. And we can debate it and we can argue it. I think that the big transformation that has to occur is that we have to democratize the clinical trial process. We have to get clinical trials out for everybody, certainly in the United States, maybe even the world, that has a certain disease that’s a candidate for the clinical trial and bring the clinical trial to them.
One of the really painful things that I have to deal with all the time is getting phone calls from somebody who has advanced disease, metastatic disease, that has the potential of being helped by an experimental drug that is coming down the pipe that looks very promising., And we may even have early evidence that the drug works but we’re not be able to get that drug to them because they don’t have access to the clinical trials where the drug could be provided by geography or by economics or by inclination because of past experiences for themselves or their family or even their cultural group. So I think that one of the major things that we have to do is we have to make clinical trials available for everybody. And I think that’s really one of the most important things that we have to address. All right, and that’s the last question I hope I’m going to have to answer.
Dr. Garber: You’re not going to do the AI question?
Dr. Olopade: Actually I can maybe help my colleague because there’s something that’s actually fascinating with AI in every area of our life, and I’ll give you the example of what happened to me this summer. We had students in the college coming to do experiments in the lab. And because we have mapped all the genes, all the proteins, and we know how the proteins are configured, and you can just go to the computer and say, “If this mutation occurs in a breast cancer cell, how can we drug it?” And these literally third-year college kids came back and said, “This is where we can drug this because the protein is being mapped.”
So AI will influence how we even begin to test which drug we should go after. And there are lots of ways in which we do chemical screening—there’s what is called now chemical biology, computer biology, so that we are all looking at a way to accelerate and make things happen faster. Some of the work that BCRF has funded is I-SPY, where we want to get a biomarker to the right patient at the right time. And lastly, you take wedding photographs using drones. Imagine if we can deliver drugs to somebody’s doorstep with a drone. It’s going to happen.
Dr. Norton: Connie?
Dr. Lehman: I love your out-of-the box thinking. And also I think in the specific domain of drug development, we’ve seen some examples in the past where drugs—especially those designed to prevent a cancer occurrence—where it’s been very difficult to do those trials and wait to see who gets cancer and who doesn’t get cancer. So some groups have said, “Well, could we impact the breast density? Could we look at surrogate markers of risk?” And those have shown some interesting results. But AI on the mammogram, if it’s showing us risk and it’s showing modifiable risk factors that will change that risk on the mammogram, you could have an earlier indicator of promise in certain drugs versus others.
The second domain is we know, and it’s so frustrating when you see drug trials where it helped some women so much and didn’t help others. In the domain of precision medicine, can AI help us identify those that will benefit from that drug and move out the subgroups? The same thing with mammography. Mammography is a great tool for a lot of women, and it’s a really poor tool for others. Can we start to be more precise because of the AI power to help us categorize women more effectively and look for changes in risk with different drug trials and interventions?
Dr. Norton: Seema?
Dr. Khan: So yes, I just want to add that what Connie has been mentioning is actually going to be tested in our BCRF-supported clinical trial that will begin soon, hopefully in a few months, where we’ll be starting premenopausal women on low-dose tamoxifen five milligrams a day based on Dr. De Censi’s work, and then testing their response in terms of breast density. So breast density is known to be reduced in many women by the use of tamoxifen, and we will be following their tamoxifen response using breast density and also assessing their risk reduction with the AI tools that we’ve been talking about. So this new generation of trials will be putting these concepts into practice, at least at the level of clinical trial testing.
Dr. Norton: Yes, I can’t help but jump in with a couple thoughts in this regard. The thing that really is impressive about AI is that the way science has been done in large data sets—the way science has been done for centuries—is you have to have a hypothesis. You have to have an idea, and then you have to design an experiment to test that idea. By looking at a tremendous amount of information now using these modern electronic tools, the idea can come from the data rather than having the idea first and then looking for data to support the idea.
Look how many centuries it took us to figure out that cigarette smoking caused lung cancer. It would take an AI tool a few milliseconds to make that observation, People do a lot of things, of which smoking is only one of the thousand things that people do, and if you actually had the data, it would find it instantaneously and then you could also test it going forward. The other point I want to make is yes, I’m a mathematician, and we have other mathematicians researchers that are all arrayed around you in this regard. I think the interface between machine learning and AI and mathematics still is an area of great potential of development. AI is a tool for looking at large data and finding patterns and discerning them.
I’m reminded of the analogy I always use in this regard, which is that you can look at the night sky forever and collect all the data about the stars, but actually making sense of the motions of the planetary bodies can only happen when you have a mathematical hypothesis, which is the theory of universal gravitation and the mathematics of calculus, then it all makes sense. But you can’t develop calculus and the theory of universal gravitation unless you have the data and unless you’re able to analyze the data. And that’s why it took so many centuries to actually figure this thing out.
Had we had computers at the time of Kepler, for example, Copernicus, it all would’ve sorted out extremely quickly because you could test a lot of ideas about the way the planets interact very, very quickly by looking at the data. So we really are in a different world. This is one of those quantum leaps, I think, of human intelligence where the data itself can generate the hypotheses that we can look at, and where it’s all going to lead is really exciting—alittle frightening but actually profoundly exciting moving forward. We have a question from the audience there.
Dr. Garber: We do, and I would just add one thing, which is that AI is a fabulous tool, but as everyone said, it’s a tool. It has to help us be smarter and we’ll still need clinical trials to prove that what we think is the right tool is actually predicting correctly and that the ways that we modify actually work. So I don’t think we’re finished yet.
Okay, so we’ll shift gears a little bit. Here’s a question, maybe Seema, you might want to start this one, which is about breast cancer in men. So breast cancer in men is a little better understood than before. We’re recognizing that breast cancer in men certainly happens. We’ve known that. What are we learning from women that might inform our ability to take care of men?
Dr. Khan: Breast cancer in men, of course, happens much more rarely. It’s about 1 percent of the rate that breast cancer happens in women. But we have learned over the past decade or more that there are some genetic susceptibilities that increase the risk of breast cancer in men, BRCA2 being the most prominent, but also some others including BRCA1. And so the one thing we’ve learned when we see a man with breast cancer is that this is an indication for testing of that man as well as his family members if his cancer proves to be related to a genetic inherited mutation. And then those men are also at increased risk for prostate cancer. So that sets in place the whole sequence of other considerations that apply to people with genetic susceptibility.
In terms of the actual treatment of breast cancer in men, it seems to be fairly frequently hormone sensitive that is responding to anti-estrogen medications, and those are used regularly in men. The surgical treatment is mainly similar to that of women. Typically in a man’s breast, a breast cancer occupies enough space and there isn’t enough breast tissue to really think about breast conservation. So most men with breast cancer are treated with mastectomy, and then the follow-up treatment is really based on the characteristics of the tumor. But again, many times endocrine therapy, anti-estrogen therapy is very effective. One other issue about breast cancer in a man, the risk is increased after hematologic malignancies. So the data from the surveillance and results reporting from the big database that’s maintained by the NCI suggests that breast cancer as second malignancies is also more frequent in men.
Dr. Norton: Do you have other thoughts to add to that?
Dr. Olopade: Well, she’s covered everything.
Dr. Norton: All right. A question for you, Connie. We’ve been talking about mammograms. What about MRIs? People out there are being recommended to get MRIs. Some of them are getting an MRI every year, some of them are getting mammograms every year, but other countries recommend mammograms less frequently than that. What are your thoughts about that and how it relates to your work?
Dr. Lehman: We have so much good data that tells us one thing. If we want to find more cancers above and beyond what we can see on mammography, we need to do vascular imaging. I’m not saying contrast-enhanced MRI, I’m saying vascular imaging. Contrast-enhanced mammography—which BCRF is supporting via a fabulous trial with Chris Comstock and GE and the American College of Radiology—appears to have the same power of contrast-enhanced MRI, but at a fraction of the cost, and it’s globally accessible. If you have a modern mammography unit, you can upgrade that mammography unit and then inject a contrast agent and get the same power that you get from MRI. So I’m a huge proponent, huge fan of vascular imaging, and I hope in my lifetime we shift from MRI to contrast-enhanced mammography, as the globally accessible, very powerful way to find cancers that are hidden on a regular mammogram.
Dr. Khan: Hear, hear.
Dr. Garber: Amen.
Dr. Olopade: Maybe I can offer another example of why we’re talking about precision. When you have a problem, you want to make sure that we are working on the best tools to actually find that cancer. I talked to a lot of physicists and they’re telling us now that we can actually find the smallest molecules that are becoming deranged to cause cancer. So it’s not just about imaging to find cancer early. Yesterday we heard about early detection, multi-cancer analytes, things that are shared by the earliest signs of cancer that we can get from a blood test. Now when I say it’s coming, the same thing about the human genome, we thought it would never cost a thousand dollars. Phones will never cost a fraction, but the science keeps getting better. And what we want is what’s going to work. In the meantime, we did an experiment with Dr. Comstock in Chicago when he was younger.
What Dr. Comstock wanted to do at that time was to have me get my patients with BRCA1 mutation who will get breast cancer at the age of 30 . There’s no recommendation for how you find cancer in a 30-year-old or a 25-year-old. And that technology has matured to the point that those women will come to us now and they can get an MRI, they can get it faster, cheaper than when we first started, that in four minutes we can actually image their breasts. So these women can have babies, can do whatever they want, and at the time that they’re done having their children, they can make a decision whether they want to have surgery, go to my friend to go and have their breast off, or just keep screening. That experiment started in 2004, and guess what? We’re no longer in 2004, we’ve gotten it faster, and cheaper because the technology allows us to reassure these young women that “Yes, you were born with a BRCA1 mutation, but come to us, we’ll take care of you.”
And what we learned by doing that study was their anxiety level went way down. They had their babies, they breastfed their babies, and they’re going on with their lives. That’s the power of integrating science and technology so that every woman has a chance to get the right treatment at the right time for the right disease. And so for us, we’re now testing this study nationally, and that’s what WISDOM study is about. Let’s find the highes- risk women. Have you all done your risk assessment? I’ve done mine because one size doesn’t fit all. I want to know, it’s a bell curve, am I on the extreme of risk or am I on the extreme of I don’t have any risk, or am I a little to the right? And I happen to be a little to the right, so I’m going to do something to reduce my risk.
That’s what precision prevention is about, and it gives us an opportunity to lower risk and catch cancer early. So we continue to do the research because we don’t know what’s going to be better in the future. And that’s why BCRF brings us together and we can test a lot of different ideas so we can finally begin to think about how to get it right for every woman. Because right now we don’t know enough on who’s going to get cancer and when they’re going to get cancer. And if we can make sure that they never die prematurely from breast cancer. That’s the goal.
Dr. Norton: That’s where we’re at. Dr. Comstock, are you here? Tell them about BCRF’s supported study.
Dr. Christopher Comstock: Larry refers patients, and he sees the power, as Connie mentioned, in vascular-based screening. And our biggest, I think our quickest, fix to lowering mortality because too many women still die of breast cancer each year is that mammographic. Our current gold standard of 3D mammography is maybe 30 percent sensitive. And so we have twice as many cancers walk out the door that we find after looking through all those images, and vascular-based screening can significantly, by two and a half fold, increase our sensitivity. As Connie mentioned, it’s very accessible and it can be done on most systems throughout the country by just a modification and then giving contrast. So it’s vascular-based screening and it’s not affected by breast density. So that’s the problem with mammographic screening. The more dense breast tissue, the harder it is to find cancer.
And these tools, MRI and contrast mammography, overcome that limitation, reducing sensitivity. So with BCRF, along with GE and the ACR, we’ve just launched our trial at four sites. We hope to have 10 sites up and running soon. The trial is looking at contrast mammography as a new paradigm for women to have screening if they have dense breast tissue. We couldn’t have done this without Larry and BCRF. As Connie mentioned, I have a slide of pictures of different aspects of society from mobile phones to computers. And in the 50s, when mammographic screening started, a 5MB hard drive was two and a half tons, a massive piece of equipment, and now it’s a little card in your phone that holds a terabyte.
But mammographic screening is just improved slightly with, we went from analog to digital, added tomosynthesis. I call it the art car phenomenon. On the West Coast, San Francisco, you see these pictures of cars where they’ve glued on all these different ornaments and statues and they’ve made it fancy. But the problem is that’s mammography. It’s this art car phenomenon where we’ve added all these little increments but we haven’t compared them to the rest of society. And now with AI, we’ve got a whole other model of doing screening, and hopefully with this trial BCRF will usher that in.
Dr. Norton: Thank you.
Dr. Garber: I was just going to point out that we hear all of this. It’s so exciting, it’s so promising, but you have to prove it. Before you have the country decide that we’re going to invest in upgrading all of our mammography machines, we have to prove they work. That’s why there’s research. That’s why there are trials, because of course we’re all excited about things that sound new, but we have to do the rigorous work to prove that they’re better before we change practice in the world.
Dr. Lehman: And we’ve learned again and again and again that where we had the excitement, the enthusiasm, the ideas, the creativity, rigorous scientific trials showed us we needed to go in a different direction.
Dr. Norton: You asked me the question before is a well-designed clinical trial, whatever the outcome is, is going to be important information going forward. And so that’s the art of clinical trial design, which we have many people here that do it moving forward. Judy?
Dr. Garber: So I have a great question, but it’s also for somebody not on the stage. Dr. Finn, would you speak to research being done on breast cancer vaccines? Dr. Olivera Finn is at the University of Pittsburgh, and she has spent her career in immunology working on vaccines in colon cancer, but we’ve enticed her to work on breast cancer.
Dr. Olivera Finn: Thank you very much, Judy, and thank you for the question. This is my very first BCRF event, because I’m your new grantee, but I’m really excited. It’s also the 30th anniversary event, because exactly 30 years ago, in 1993 December, I started the very first clinical trial in a cancer vaccine in patients with advanced colon, pancreatic, and breast cancer. And my very first patient was a breast cancer patient who was basically dying of breast cancer and she signed up for the vaccine and she told me, “I know this is not going to do anything for me, but I really don’t want anybody to live through this through what I’ve just lived through.”
So the same vaccine with slight tweaks is now what BCRF is funding for breast cancer prevention. And we have spent 30 years showing that it is safe, that it can be effective and more effective if the woman is not suffering from advanced cancer because we have learned that in the setting of advanced tumor, not only our vaccine but all cancer vaccines—and there have been many tested in clinical trials—are very strongly impaired and compromised in their effectiveness by the immune suppressive environment of the cancer. So it is much better if we can use them early. And you know, vaccines have saved the world many times before against infectious disease. The latest example is our COVID-19 vaccine.
So the same way we are preventing infectious disease, we are going to prevent cancer. And we are doing this in baby steps because as Judy pointed out, we have to prove that this works. And the baby steps are not taking a seven-year-old and giving her a vaccine or a 15-year-old girl and giving her a vaccine, but taking women at very high risk, however we define the risk, and we had the whole discussion on that. So, we have to work on this together. Right now, what we are doing for my trial, our trial in Pittsburgh as well as several trials going on all around the country, one at the University of Pennsylvania for the BRCA 1 and 2 mutations, is to diagnose pre-malignant disease. Judy mentioned our colon cancer work. We actually vaccinated people with a history of advanced polyps that are immediate precursors to colon cancer. And those polyps are removed via colonoscopy, but over 60 percent of people who have those polyps can recur with other polyps one to three years later. So, we vaccinated right after the polyp removal, and then we observed for five years, and in people who responded well to the vaccine (because even at that level there’s some level of suppression at that stage), there’s some level of suppression, but we had a lot of responders. Those who were vaccinated had a 38 percent reduction in polyp recurrence and (are still polyp-free). Which means that there is a 38 percent reduction in your risk of developing new polyps that can progress to colon cancer.
The breast cancer equivalent here now in what we are trying to do is women with DCIS, ductal carcinoma in situ. You all know that is not a cancer. However, we don’t know what the risk for the woman with DCIS is for progressing to cancer, so it’s surgically removed, otherwise other times differently treated. We are going to vaccinate women newly diagnosed with DCIS with our vaccine. The vaccine is expressed on all breast tumors and on DCIS, and we are going to measure their ability to respond to generate a very strong immune response. And then the women will have standard of care—those who want surgery will have surgery and we will have tissue to compare the initial biopsy with the tissue post-vaccination.
If we see that we have one, induced a very strong immune response to our vaccine and two, that we have brought all the immune cells to the site of DCIS before it was taken out, we might in the next phase of our trial just wait because the immune system should have the capacity to get rid of that lesion without having surgery. And it should also protect the woman in the future from developing new lesions. As you know, that can happen in the other breast, et cetera. So, we are very excited about that. Similarly, in the study at Penn, they are using a very similar vaccine but in carriers in BRCA 1 and 2 carriers. Would you like to comment on that?
Dr. Laura Esserman: Yes, I’m presenting at San Diego at AACR on Saturday. We have been doing a phase one study using a Moderna mRNA with a cocktail. I’ve been working on this for several years. BCRF actually funded a lot of the work that led to this. So if you directly inject into the DCIS and let the DCIS be its own vaccine so you can organize it, you can bring the immune system cells in great numbers. Now most DCIS are slow-growing and hormone-driven. They’re not immune-driven, but there’s about 10 to 20 percent that are, and they can be these very big lesions, but we used to think those were the worst DCIS. But you have to ask how is the body already containing them? And it’s probably the immune system that’s keeping them intact. So we now have, when we combine pembrolizumab ( Keytruda), one of the drugs that takes the brakes off the immune system.
But again, you don’t want to give it to the whole body when someone’s got pre-cancer. You want to just give it locally. And then we added this mRNA cocktail to our 10 patients. Eight of the 10 patients had the big immune cells in their DCIS already. And all of them responded. Four of them went away completely, and three of those patients have not had surgery and they’re now over a year out without anything left. I think we’ve already shown that this is possible now and we’re expanding it. And it’s another way of saying we don’t have to know. It’s as if you were injecting the polyp right there, we can inject the DCIS right there and have it go away.
For the hormone-driven DCIS, we’ve also shown that if you give endocrine risk-reducing agents, these are the people who are the most at risk for ER-positive disease that maybe 60, 70 percent of people will never progress. And we of course use MRI as a way to measure this and to really look at who’s got endocrine-sensitive and endocrine-insensitive disease. We’re starting a study across the country called RECAST where we are testing new endocrine types of agents, testosterone, looking at endoxifen, which is the active agent for tamoxifen.
Dr. Norton: I have to ask this question and the right person to ask this is Dorraya El-Ashry, the chief scientific officer from BCRF. One of the things we’re hearing about here is the interactive-ness of the various scientists and the various collaborators that we have. Once we start on a topic, essentially anybody could stand up and talk about how they’re related to this topic. Do you have data on how interactive we are as an organization?
Dr. Olivera Finn: Larry, I’ll just add that everybody who I talked to at this meeting about our trial asked how many patients are involvedand can they also contribute? So that’s the other thing, that all BCRF investigators here who, including Seema, I was sitting next to her last night, said, “If you have slow accrual, get in touch with us.” Beth Mittendorf offered. So this is a community that immediately can form these sorts of-
Dr. Garber: Interactions.
Dr. Norton: Absolutely.
Dr. Garber: Thank you.
Dr. Laura Esserman: And Larry, I just wanted to say, the WISDOM study. Anyone here who hasn’t had cancer can participate.
Dr. Garber: One more take.
Dr. Laura Esserman: Wisdomstudy.org. Thank you. Dorraya?
Dr. Dorraya El-Ashry: Good morning, everyone. You’ve been hearing about the most impactful and tremendous progress that is being made in the fields of disparities and prevention this morning. But what I also hope you’ve heard, and what Larry mentioned, is how much of this is being done in a collaborative way, and that this is the future of taking great ideas forward. And BCRF has had as its hallmark fostering collaboration. In fact, as we do our five-year impact reviews and reports, we pull together this data on collaborations of BCRF investigators, both with other BCRF investigators that are fostered at events we hold, like the research retreat we had yesterday as well as at many think tanks as well as with outside investigators, which only extends the impact and reach of BCRF funding.
From a cohort that we have looked at and from each of these years, BCRF investigators collaborate on average with three other BCRF investigators and with clinical trial groups that we support at a tremendous rate, and at a rate of five to six times non-BCRF investigators. And so the amount of collaboration that is both fostered by BCRF, but also because all of these investigators know that the way we move things forward is through team science, has just been tremendous.
Dr. Garber: Funmi, there are some communities that have a very high incidence of young, advanced disease women with metastatic breast cancer. Are there environmental factors or social factors that you think might contribute to this particular problem?
Dr. Olopade: Yes, thank you for asking that question, and I hope some of the epidemiologists in the audience—and there are many of them here—would chime in. When I started my introduction, I talked about the south side of Chicago. One of the areas that we’ve really been focused on is environmental justice. Dumps and every toxic chemical is put in poor neighborhoods. And we have been very concerned about not just environmental pollution that you see outdoors, but household air pollution. About three million women and children die globally from household air pollution because they burn dirty fuels.
Just this morning we were talking about the PM 2.4, particulate matters that we may think don’t matter. We’ve spent a lot of our time looking for genetics. What are the genetic causes of breast cancer? And we’re now actually also funded by BCRF to look at the epigenetics. As we are all sitting here, mutations occur. Most of us have the immune system to wipe away the mutations, but somehow some mutations cause derangement of other genes, and you have this cascade going on that then leads to advanced metastatic breast cancer. Globally, the fastest-growing breast cancer community are premenopausal women. It’s become an epidemic, and the World Health Organization has actually put in a call to say, “Why are young women everywhere getting breast cancer before the age of 50?” What’s in the environment? What’s in our lifestyle? My mother had six children and breastfed for 21 years. Who does that anymore?
Dr. Garber: More recovery.
Dr. Olopade: Right. So, it’s a women’s health issue because we don’t know what’s happening to the modern woman and what’s happening to our environment, and I think this is where we need political action to have more support for women in the workforce, more support to diagnose cancer if you’re going to get it before the age of 50. And of course, to always really think about environmental justice, because it is true that when we map the areas with the most disparity, where people are most likely to die from breast cancer, it’s also the communities that are most affected by environmental injustice. And we all have to really do something about that.
Dr. Norton: Yes. Connie, you’ve done some fantastic work on differences—I call it geographical ancestry— in terms of AI and risk prediction using your models. Do you want to talk about that a little bit and then maybe answer the question that I handed to you?
Dr. Lehman: Sure. So many of us have heard of the domains with AI where errors were made because the trials used predominantly light-skinned patients. So for example, some of the dermatology AI tools cannot accurately diagnose disease in people with dark skin, and that was extremely concerning because we have a long, long history of racially biased research and clinical care around the world. We were delighted to find with the AI applied to interpreting the mammogram for a future breast cancer event, that it was equitable across races. We studied this carefully at Mass General, populations across Hispanic, Asian, African American, and Caucasian, and in other domains in other centers as well. So I think one of the reasons is because the model can learn from the image itself how all these differences are being expressed and displayed, whether it is the endocrine challenges that a woman has gone through during her life or different factors and features of race, et cetera.
So we were very enthusiastic about that, but think it is imperative, as Judy points out, that we continue to rigorously test. We now have, and this gets to one of the questions that I was asked here, “Well, is this available?” Right now there are about four or five different risk assessment AI-type tools that are being used. The FDA has made it very clear that these will need to be regulated by the FDA, so that’s a murky area right now. Vignesh Arasu at Kaiser studied and presented with a single database with known five-year outcome of cancer outcomes in a cohort of patient— both the clinical Breast Cancer Surveillance Consortium, a clinical risk assessment method that is more traditional of how many family members have breast cancer, how many children did you have, did you breastfeed, et cetera—and compared that to these different AI tools. He found that the AI tools that have been developed at multiple different centers, including NYU, including some different companies, performed significantly better than the clinical approach.
That study was fantastic because it also uncovered that some of these tools are actually finding cancers that are probably present at the time on the mammogram and the computer vision is identifying it, but it was missed by the radiologist that was reading the exam. So that’s why the clinical research is so important. We’re still early in understanding how this is working. The question was also are there prospective trials of MRI? This is a tricky area. The direct answer is there is not a prospective trial seeing the impact of MRI in clinic, that would take a minimum of five years if we wanted to see the prediction of a future breast cancer. And these models are evolving rapidly, so that’s a little challenging. What we’re doing in our very large Mass General cohort is looking to see how did MRI distinguish from those women that were undergoing screening with a high cancer burden and a lower cancer burden.
We can use registry data for that. We have done that and shown that we are better at identifying patients with cancer who undergo screening MRI, and we can identify those patients that probably didn’t benefit at all from the MRI. The same with more intensive mammographic screening and less intensive mammographic screening. We look to our mathematicians, our epidemiologists, our biostatisticians, for creative ways to develop and evaluate these risk prediction tools to try to set prospective trials with five or six years needed to see what the impact is as they’re developing so rapidly, and it’s going to be challenging.
Dr. Norton: Excellent, thank you. There was a lot of interest out there and I got a lot of questions about this, is the idea of getting tamoxifen out as a prevention strategy. What are the toxicities of tamoxifen that you’re trying to reduce by your methods?
Dr. Khan: What we know about the toxicity of tamoxifen is based almost entirely on the standard 20 milligram dose. And that dose was developed in cancer treatment trials. It was initially used in women with more advanced cancer and then as additional treatment after surgery in women with earlier cancer. In these cases, tamoxifen is very well-established for improving cancer outcomes. What we’re discussing here now is tamoxifen for prevention, and the calculus, the effect of toxicity in women who have had cancer and are offered a treatment for that compared to women who’ve never had cancer and are offered a drug for prevention of cancer in the future. That’s a different calculation. So, the toxicities that are very acceptable in women who are being treated are not so acceptable in women who have never had cancer. But I think it’s useful to divide those side effects of tamoxifen even among healthy women into two categories.
One is the quality-of-life side effects, and they are more common. So menopausal effects like hot flashes and sometimes sexual problems and vaginal dryness, those things happen more frequently. They’re ’tolerated variably by different women depending on their motivation. The serious side effects of tamoxifen are mainly twofold. One is that it results in a small increase in uterine cancer risk, and the other is that it may cause an increase in risk of blood clots. Those are seen mostly in older women. So for premenopausal women, those risks are really quite low and not that different from women who are not taking tamoxifen.
But for postmenopausal women, and particularly women in their sixties and older, those are serious considerations. Fortunately for that group of high-risk women, we do have alternative medications that don’t have these side effects. They have other side effects, of course. So as a general principle, the idea that we have to find the minimal effective dose rather than just doses that have been borrowed from treatment trials is a very valid principle, and that’s what many of us are working on. But yes, the tamoxifen risks are mainly quality of life, as I mentioned, and the uterine and clot risk.
Dr. Larry Norton: Judy?
Dr. Judy E. Garber: So I can’t believe it, but it’s time for the last question, which is about circulating tumor cells, circulating cell-free DNA. Last year, Funmi mentioned that among all this technology progress is the possibility that we could use a blood test to find cancer and, no offense Connie, skip the mammograms altogether. Where do we stand with that now? Does anybody want to comment on progress in blood detection?
Dr. Lehman: I hope it happens. I’m going to have you talk about how you think it’s going to happen, but as an imager, I hope it happens and it would be fantastic. We imagers have a lot of work to do in a lot of different domains, and if we have a simple blood test that can replace the mammogram, it would be fantastic.
Dr. Norton: Yes, I think it’s going to be an interactive, and I think there’s no “yes or no” answer on all of this. There are some already tools out there that look very, very promising because cancer is caused by abnormalities in DNA almost exclusively. Some of that DNA is spread into the blood, and by doing a blood test—and again, the technology has improved enormously about finding these abnormalities—it is possible you can find a piece of abnormal DNA and that might indicate that there is a tiny invisible cancer brewing somewhere. We’re still going to need imaging tests to find out where it is, I think. So I think there’s going to be an interaction in that nature. The other thing, which is not apparent—and we’re getting into a big topic when we have no time—is that as you get older, you’re going to get abnormalities in your DNA anyway, despite the fact that you don’t have cancer.
There’s something called clonal hematopoiesis, where you have actually mutations in your white blood cells that occurred as a function of age, and it actually does predispose you to developing things like leukemia. But most people have these abnormalities, and their blood cells don’t develop leukemia. And so when we actually measure DNA abnormalities in the blood, we’re identifying a lot of normal individuals who are never going to get cancer. And I think that even though they have those abnormalities, it’s just part of natural aging, so we’re going to have to be very careful that we don’t over-diagnose. And I think that all of our panelists have touched on some aspect of this, which again, the interactive-ness of the science, BCRF. If you have a known predisposition, if you have risk factors and you have a profile that suggests a higher risk and you have an abnormal piece of DNA, that’s going to have different meaning than somebody who doesn’t have that, for example.
If you have an abnormality that suggests, “Well, it’s most likely coming from the breast,” but very sophisticated imaging tools don’t show anything, then it may require further imaging tests or other tests in the future to follow that individual. But it also may indicate an opportunity for chemo prevention, cancer prevention with drugs. You have an abnormality, we can’t find it, but we have an approach to reduce your risk, and then we can follow that blood test and see if it disappears. And often with our cancer therapies, when patients have abnormal DNA in their blood, our cancer therapies make those abnormalities go down or disappear entirely. So it’s not any one thing. I’ll end with my favorite cliche. People ask, “What’s the most important part of what you’re doing?” And I say, “I’ll answer that question if you tell me what is the most important part of the airplane.”
Is it the left wing or is it the right wing? Is it the landing apparatus? Is it the navigation so that you land in Dubai and not in some other place where there’s a war going on? Which happened to me recently. The fact of the matter is it’s all important. What makes the airplane work is that all the parts work and they all work together. And that’s what BCRF is all about. We are developing all parts, everything that we can think of, everything that comes from the extraordinary intelligence of our investigators, everything that we learned from the field. We study the entire airplane, and we also want to make all the parts work together. And that’s why I am incredibly appreciative of this panel for bringing forward their ideas, for talking about their own work, for talking about the interactive-ness of it, and my colleagues in the room who are doing it as well. Please join me in thanking our session.
That was BCRF’s 2023 NY Symposium and a special Investigating Breast Cancer podcast. Thanks for listening. To learn more about breast cancer research or to subscribe to our podcast, go to BCRF.org/podcasts.
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