JCO Precision Oncology Conversations

by American Society of Clinical Oncology (ASCO)

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JCO Precision Oncology Conversations is a monthly podcast featuring conversations with authors of clinically relevant and significant articles published in the JCO Precision Oncology journal. JCO Precision Oncology Conversations is hosted by the journal's social media editor, Dr. Abdul Rafeh Naqash.

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April 30, 2025

JCO PO Article Insights: Exceptional Responders with Abexinostat and Pazopanib

In this JCO PO Article Insights episode, host Harold Tan summarizes Low Kynurenine Levels Among Exceptional Responders on Phase Ib Trial of the HDAC Inhibitor Abexinostat with Pazopanib by Tsang et al, published November 07, 2024. <h2>Transcript</h2> Harold Nathan Tan: Welcome to <a href="https://ascopubs.org/journal/po" target="_blank" rel= "noopener">JCO Precision Oncology Article Insights</a>, where we explore cutting-edge discoveries in the world of cancer treatment and research. I'm Harold Nathan Tan, your host, and today we're taking a focused look at a <a href="https://ascopubs.org/doi/10.1200/PO.24.00328" target= "_blank" rel="noopener">compelling phase Ib trial</a> led by Dr. Tsang, which investigates a combination of abexinostat, a histone deacetylase inhibitor, with pazopanib, a VEGF-targeting tyrosine kinase inhibitor, in patients with advanced solid tumors. VEGF inhibition has long been an established therapeutic strategy across a wide range of tumor types, including colorectal, ovarian, sarcoma, and renal cell carcinoma. These agents function by disrupting tumor angiogenesis, effectively limiting oxygen and nutrient delivery to malignant cells and contributing to improved survival outcomes. However, over time, acquired resistance remains a significant challenge. A key mechanism implicated in this resistance involves the upregulation of hypoxia-inducible factor 1-alpha, or HIF-1-alpha for short, a master regulator of angiogenesis that restores VEGF signaling under hypoxic conditions. Interestingly, HIF-1-alpha overexpression is mediated by histone deacetylases, especially HDAC2. Preclinical studies suggest that HDAC2 inhibition can suppress tumor cell migration and downregulate HIF-1-alpha activity, effectively disabling a critical escape pathway used by tumors under VEGF pressure. Moreover, combining HDAC inhibition with VEGF blockade has demonstrated synergy in pazopanib-resistant tumor models, forming a compelling rationale for this dual approach. The phase Ib trial by Tsang et al. was designed to evaluate the safety, tolerability, and preliminary efficacy of this dual-targeted approach in patients with heavily pretreated advanced solid tumors. A dose-expansion cohort focused on individuals with renal cell carcinoma, allowing for more detailed evaluation in this population. A central component of this study was the incorporation of biomarker analysis, particularly regarding HDAC2 expression levels. The results were noteworthy. Patients with high HDAC2 expression achieved a progression-free survival of 7.7 months compared to only 3.5 months in those with low expression. Even more compelling, overall survival reached 32.3 months for those with a high HDAC2 expression versus just 9.2 months for those with low expression. This suggests the potential role for HDAC2 as a predictive biomarker for response to combination HDAC and VEGF-targeted therapy. The authors also explored the metabolic landscape of these patients, conducting metabolomic analysis focused on kynurenine, a key tryptophan catabolite known to contribute to the immune suppression in the tumor microenvironment. Its reduction is driven by HIF-1-alpha and inflammatory cytokines, including interleukin-6 and tumor necrosis factor-alpha. What they found was striking. Exceptional responders, defined as patients with treatment responses lasting more than 3 years, had consistently lower levels of kynurenine both before and after treatment. This finding introduces kynurenine as a potential metabolic biomarker. It suggests that patients with lower kynurenine levels may have a less immunosuppressive microenvironment, making them more responsive to the combined effects of HDAC inhibition and VEGF blockade. Of note, VEGF levels themselves did not significantly differ between responders and nonresponders, highlighting that the treatment benefit is not purely VEGF-mediated but likely driven by epigenetic and metabolic modulation. On the safety front, the combination of abexinostat and pazopanib was generally well tolerated. However, this study did report a correlation between higher plasma concentrations of abexinostat and an increased incidence of thrombocytopenia, a class effect associated with HDAC inhibitors. This trial introduces several key considerations for future research. First, it calls for validation of HDAC2 as a predictive biomarker. If confirmed in larger cohorts, HDAC2 expression could be used to select patients most likely to benefit from HDAC inhibitor-based regimens, transforming how we approach trial enrollment and treatment planning. Second, the link between low kynurenine and exceptional response supports further investigation into how metabolic pathways can influence treatment response to combined HDAC and VEGF inhibition. Overall, HDAC inhibitors hold significant promise in precision oncology. Realizing their full therapeutic potential requires a deeper understanding of HDAC biology, refined combination strategies, and thorough preclinical and clinical evaluations tailored to individual patient profiles. This study exemplifies the potential of epigenetic-metabolic crosstalk as a therapeutic vulnerability and underscores the importance of precision stratification in clinical trial design. As research in this space progresses, the integration of molecular, epigenetic, and metabolic profiling will be essential in optimizing the use of HDAC inhibitors and expanding their role within precision oncology. Thank you for tuning into <a href="https://ascopubs.org/journal/po" target= "_blank" rel="noopener">JCO Precision Oncology Article Insights</a>. Don't forget to <a href= "https://www.asco.org/news-initiatives/podcasts" target="_blank" rel="noopener">subscribe</a> and join us next time as we explore more groundbreaking research shaping the future of oncology. Until then, stay informed and stay inspired. The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions. Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.

April 16, 2025

Prognostic Artificial Intelligence Scores and Outcomes in Nonmetastatic Prostate Cancer

JCO PO author Dr. Timothy Showalter at Artera and University of Virginia shares insights into his JCO PO article, “<a href= "https://ascopubs.org/doi/abs/10.1200/PO-24-00653" target="_blank" rel="noopener">Digital Pathology–Based Multimodal Artificial Intelligence Scores and Outcomes in a Randomized Phase III Trial in Men With Nonmetastatic Castration-Resistant Prostate Cancer</a>” . Host Dr. Rafeh Naqash and Dr. Showalter discuss how multimodal AI as a prognostic marker in nonmetastatic castration-resistant prostate cancer may serve as a predictive biomarker with high-risk patients deriving the greatest benefit from treatment with apalutamide. TRANSCRIPT Dr. Rafeh Naqash: Hello and welcome to <a href= "https://ascopubs.org/journal/po/podcast">JCO Precision Oncology Conversations</a> where we'll bring you engaging conversations with authors of clinically relevant and highly significant <a href= "https://ascopubs.org/journal/po">JCO PO</a> articles. I'm your host, Dr. Rafeh Naqash, podcast Editor for <a href= "https://ascopubs.org/journal/po">JCO Precision Oncology</a> and assistant professor at the OU Health Stephenson Cancer Center at the University of Oklahoma. Today, we are excited to be joined by Dr. Timothy Showalter, Chief Medical Officer at Artera and professor of Radiation Oncology at the University of Virginia and author of the JCO Precision Oncology article entitled, “<a href= "https://ascopubs.org/doi/abs/10.1200/PO-24-00653">Digital Pathology Based Multimodal Artificial Intelligence Scores and Outcomes in a Randomized Phase 3 Trial in Men with Non-Metastatic Castration Resistant Prostate Cancer</a>.” At the time of this recording, our guest’s disclosures will be linked in the transcript. Dr. Showalter, it's a pleasure to have you here today. Dr. Timothy Showalter: It's a pleasure to be here. Thanks for having me. Dr. Rafeh Naqash: I think this is going to be a very interesting discussion, not just from a biomarker perspective, but also in terms of how technologies have evolved and how we are trying to stratify patients, trying to escalate or deescalate treatments based on biomarkers. And this article is a good example of that. One of the things I do want to highlight as part of this article is that Dr. Felix Feng is the first author for this article. Unfortunately, <a href= "https://ascopost.com/issues/february-25-2025/felix-feng-md-leader-in-genitourinary-cancer-dies-at-age-48/"> Dr. Felix Feng passed away in December of 2024</a>. He was a luminary in this field of prostate cancer research. He was also the Chair of the NRG GU Committee as well as Board of Directors for RTOG Foundation and has mentored a lot of individuals from what I have heard. I didn't know Dr. Feng but heard a lot about him from my GU colleagues. It's a huge loss for the community, but it was an interesting surprise for me when I saw his name on this article as I was reviewing it. Could you briefly talk about Dr. Feng for a minute and how you knew him and how he's been an asset to the field? Dr. Timothy Showalter: Yeah. I'm always happy to talk about Felix whenever there's an opportunity. You know, I was fortunate to know Felix Feng for about 20 years as we met during our residency programs through a career development workshop that we both attended and stayed close ever since. And you know, he's someone who made an impact on hundreds of lives of cancer researchers and other radiation oncologists and physicians in addition to the cancer patients he helped, either through direct clinical care or through his innovation. For this project in particular, I first became involved soon after Felix had co-founded Artera, which is, you know the company that developed this. And because Felix was such a prolific researcher, he was actually involved in this and this research project from all different angles, both from the multimodal digital pathology tool to the trial itself and being part of moving the field forward in that way. It's really great to be able to sort of celebrate a great example of Felix's legacy, which is team science, and really moving the field forward in terms of translational projects based on clinical trials. So, it's a great opportunity to highlight some of his work and I'm really happy to talk about it with you. Dr. Rafeh Naqash: Thanks, Tim. Definitely a huge loss for the scientific community. And I did see a while back that there was an international symposium organized, showcasing his work for him to talk about his journey last year where more than 200, 250 people from around the globe actually attended that. That speaks volumes to the kind of impact he's had as an individual and impact he's had on the scientific side of things as well. Dr. Timothy Showalter: Yes. And we just had the second annual Feng Symposium the day before ASCO GU this year with, again, a great turnout and some great science highlighted, as well as a real focus on mentorship and team science and collaboration. Dr. Rafeh Naqash: Thank you so much for telling us all about that. Now going to what you guys published in <a href= "https://ascopubs.org/journal/po">JCO Precision Oncology</a>, which is this article on using a biomarker approach to stratify non-metastatic prostate cancer using this artificial intelligence based H&E score. Could you tell us the background for what started off this project? And I see there is a clinical trial data set that you guys have used, but there's probably some background to how this score or how this technology came into being. So, could you superficially give us an idea of how that started? Dr. Timothy Showalter: Sure. So, the multimodal AI score was first published in a peer reviewed journal back in 2022 and the test was originally developed through a collaboration with the Radiation Therapy Oncology Group or Energy Oncology Prostate Cancer Research Team. The original publication describes development and validation of a risk stratification tool designed to predict distant metastasis and prostate cancer specific mortality for men with localized prostate cancer. And the first validation was in men who were treated with definitive radiation therapy. There have been subsequent publications in that context and there's a set of algorithms that have been validated in localized prostate cancer and there's a test that's listed on NCCN guidelines based on that technology. The genesis for this paper was really looking at extending that risk stratification tool that was developed in localized prostate cancer to see if it could one, validate in a non-metastatic castrate refractory prostate cancer population for patients enrolled on the SPARTAN trial. And two, whether there was a potential role for the test output in terms of predicting benefit from apalutamide for patients with non-metastatic prostate cancer. For patients who are enrolled on the SPARTAN study, almost 40% of them had H&E stain biopsy slide material available and were eligible to be included in this study. Dr. Rafeh Naqash: Going a step back to how prostate cancer, perhaps on the diagnostic side using the pathology images is different as you guys have Gleason scoring, which to the best of my knowledge is not necessarily something that most other tumor types use. Maybe Ki-67 is somewhat of a comparison in some of the neuroendocrine cancers where high Ki-67 correlates with aggressive biology for prognosis. And similarly high Gleason scores, as we know for some of the trainees, correlates with poor prognosis. So, was the idea behind this based on trying to stratify or sub-stratify Gleason scoring further, where you may not necessarily know what to do with the intermediate high Gleason score individual tumor tissues? Dr. Timothy Showalter: Well, yeah. I mean, Gleason score is a really powerful risk stratification tool. As you know, our clinical risk groupings are really anchored to Gleason scores as an important driver for that. And while that's a powerful tool, I think, you know, some of the original recognition for applying computer vision AI into this context is that there are likely many other features located in the morphology that can be used to build a prognostic model. Going back to the genesis of the discovery project for the multimodal AI model, I think Felix Feng would have described it as doing with digital pathology and computer vision AI what can otherwise be done with gene expression testing. You know, he would have approached it from a genomic perspective. That's what the idea was. So, it's along the line of what you're saying, which is to think about assigning a stronger Gleason score. But I think really more broadly, the motivation was to come up with an advanced complementary risk stratification tool that can be used in conjunction with clinical risk factors to help make better therapy recommendations potentially. So that was the motivation behind it. Dr. Rafeh Naqash: Sure. And one of the, I think, other important teaching points we try to think about, trainees of course, who are listening to this podcast, is trying to differentiate between prognostic and predictive scores. So, highlighting the results that you guys show in relation to the MMAI score, the digital pathology score, and outcomes as far as survival as well as outcomes in general, could you try to help the listeners understand the difference between the prognostic aspect of this test and the predictive aspect of this test? Dr. Timothy Showalter: So let me recap for the listeners what we found in the study and how it kind of fits into the prognostic and the predictive insights. So, one, you know, as I mentioned before, this is ultimately a model that was developed and validated for localized prostate cancer for risk stratification. So, first, the team looked at whether that same tool developed in localized prostate cancer serves as a prognostic tool in non-metastatic castrate-refractory prostate cancer. So, we applied the tool as it was previously developed and identified that about 2/3 of patients on the SPARTAN trial that had specimens available for analysis qualified as high risk and 1/3 of patients as either intermediate or low risk, which we called in the paper ‘non-high risk’. And we're able to show that the multimodal AI score, which ranges from 0 to 1, and risk group, was associated with metastasis free survival time to second progression or PFS 2 and overall survival. And so that shows that it performs as a prognostic tool in this setting. And this paper was the first validation of this tool in non-metastatic castrate-refractory prostate cancer. So, what that means to trainees is basically it helps you understand how aggressive that cancer is or better stratify the risk of progression over time. So that's the prognostic performance. Dr. Rafeh Naqash: Thank you for trying to explain that. It's always useful to get an example and understand the difference between prognostic and predictive. Now again, going back to the technology, which obviously is way more complicated than the four letter word MMAI, I per se haven't necessarily done research in this space, but I've collaborated with some individuals who've done digital pathology assessments, and one of the projects we worked on was TIL estimation and immune checkpoint related adverse events using some correlation and something that one of my collaborators had sent to me when we were working on this project as part of this H&E slide digitalization, you need color deconvolution, you need segmentation cell profiling. Superficially, is that something that was done as part of development of this MMAI score as well? Dr. Timothy Showalter You need a ground truth, right? So, you need to train your model to predict whatever the outcome is. You know, if you're designing an AI algorithm for Ki-67 or something I think you mentioned before, you would need to have a set of Ki-67 scores and train your models to create those scores. In this case, the clinical annotation for how we develop the multimodal AI algorithm is the clinical endpoints. So going back to how this tool was developed, the computer vision AI model is interpreting a set of features on the scan and what it's trying to do is identify high risk features and make a map that would ultimately predict clinical outcomes. So, it's a little bit different than the many digital pathology algorithms where the AI is being trained to predict a particular morphological finding. In this case, the ground truth that the model is trained to predict is the clinical outcome. Dr. Rafeh Naqash: Sure. And from what you explained earlier, obviously, tumors that had a high MMAI score were the ones that were benefiting the most from the ADT plus the applausive. Is this specific for this androgen receptor inhibitor or is it interchangeable with other inhibitors that are currently approved? Dr. Timothy Showalter: That's a great question and we don't know yet. So, as you're alluding to, we did find that the MMAI risk score was predictive for benefit from apalutamide and so it met the statistical definition of having a significant interaction p value so we can call it a predictive performance. And so far, we've only looked in this population for apalutamide. I think you're raising a really interesting point, which is the next question is, is this generalizable to other androgen receptor inhibitors? There will be future research looking at that, but I think it's too early to say. Just for summary, I think I mentioned before, there are about 40% of patients enrolled on the SPARTAN study had specimens available for inclusion in this analysis. So, the SPARTAN study did show in the entire clinical trial set that patients with non-metastatic castrate-refractory prostate cancer benefited from apalutamide. The current study did show that there seems to be a larger magnitude of benefit for those patients who are multimodal AI high risk scores. And I think that's very interesting research and suggests that there's some interaction there. But I certainly would want to emphasize that we have not shown that patients with intermediate or low risk don't benefit from apalutamide. I think we can say that the original study showed that that trial showed a benefit and that we've got this interesting story with multimodal AI as well. Dr. Rafeh Naqash: Sure. And I think from a similar comparison, ctDNA where ctDNA shows prognostic aspects, I treat people with lung cancer especially, and if you're ctDNA positive at a 3 to 4-month period, likely chances of you having a shorter disease-free interval is higher. Same thing I think for colorectal cancers. And now there are studies that are using ctDNA as an integral biomarker to stratify patients positive/negative and then decide on escalation/de-escalation of treatment. So, using a similar approach, is there something that is being done in the context of the H&E based stratification to de-intensify or intensify treatments based on this approach? Dr. Timothy Showalter: You're hitting right on the point in the most promising direction. You know, as we pointed out in the manuscript, one of the most exciting areas as a next step for this is to use a tool like this for stratification for prospective trials. The multimodal AI test is not being used currently in clinical trials of non-metastatic castrate-refractory prostate cancer, which is a disease setting for this paper. There are other trials that are in development or currently accruing where multimodal AI stratification approach is being taken, where you see among the high-risk scores, at least in the postoperative setting for a clinical trial that's open right now, high risk score patients are being randomized to basically a treatment intensification question. And then the multimodal AI low risk patients are being randomized to a de-intensification experimental arm where less androgen deprivation therapy is being given. So, I think it's a really promising area to see, and I think what has been shown is that this tool has been validated really across the disease continuum. And so, I think there are opportunities to do that in multiple clinical scenarios. Dr. Rafeh Naqash: Then moving on to the technological advancements, very fascinating how we've kind of evolved over the last 10 years perhaps, from DNA based biomarkers to RNA expression and now H&E. And when you look at cost savings, if you were to think of H&E as a simpler, easier methodology, perhaps, with the limitations that centers need to digitalize their slides, probably will have more cost savings. But in your experience, as you've tried to navigate this H&E aspect of trying to either develop the model or validate the model, what are some of the logistics that you've experienced can be a challenge? As we evolve in this biomarker space, how can centers try to tackle those challenges early on in terms of digitalizing data, whether it's simple data or slides for that matter? Dr. Timothy Showalter: I think there's two main areas to cover. One, I think that the push towards digitalization is going to be, I think, really driven by increasing availability and access to augmentative technologies like this multimodal AI technology where it's really adding some sort of a clinical insight beyond what is going to be generated through routine human diagnostic pathology. I think that when you can get these sorts of algorithms for patient care and have them so readily accessible with a fast turnaround time, I think that's really going to drive the field forward. Right now, in the United States, the latest data I've seen is that less than 10% of pathology labs have gone digital. So, we're still at an early stage in that. I hope that this test and similar ones are part of that push to go more digital. The other, I think, more interesting challenge that's a technical challenge but isn't about necessarily how you collect the data, but it certainly creates data volume challenges, is how do you deal with image robustness and sort of translating these tools into routine real-world settings. And as you can imagine, there's a lot of variation for staining protocols, intensity scanner variations, all these things that can affect the reliability of your test. And at least for this research group that I'm a part of that has developed this multimodal AI tool can tell you that the development is sophisticated, but very data and energy intensive in terms of how to deal with making a tool that can be consistent across a whole range of image parameters. And so that presents its own challenges for dealing with a large amount of compute time and AI cycles to make robust algorithms like that. And practically speaking, I think moving into other diseases and making this widely available, the size of data required and the amount of cloud compute time will be a real challenge. Dr. Rafeh Naqash: Thank you for summarizing. I can say that definitely, you know, this is maybe a small step in prostate cancer biomarker research, but perhaps a big step in the overall landscape of biomarker research in general. So definitely very interesting. Now, moving on to the next part of the discussion is more about you as a researcher, as an individual, your career path, if you can summarize that for us. And more interestingly, this intersection between being part of industry as well as academia for perhaps some of the listeners, trainees who might be thinking about what path they want to choose. Dr. Timothy Showalter: Sure. So, as you may know, I'm a professor at the University of Virginia and I climbed the academic ladder and had a full research grant program and thought I'd be in academia forever. And my story is that along the way, I kind of by accident ended up founding a medical device company that was called Advaray and that was related to NCI SBIR funding. And I found myself as a company founder and ultimately in that process, I started to learn about the opportunity to make an impact by being an innovator within the industry space. And that was really the starting point for me. About four years ago, soon after Felix Feng co-founded Artera, he called me and told me that he needed me to join the company. For those who were lucky to know Felix well, at that very moment, it was inevitable that I was going to join Artera and be a part of this. He was just so persuasive. So, I will say, you know, from my experience of being sort of in between the academic and industry area, it's been a really great opportunity for me to enter a space where there's another way of making an impact within cancer care. I've gotten to work with top notch collaborators, work on great science, and be part of a team that's growing a company that can make technology like this available. Dr. Rafeh Naqash: Thank you so much, Tim, for sharing some of those thoughts and insights. We really appreciate you discussing this very interesting work with us and also appreciate you submitting this to <a href= "https://ascopubs.org/journal/po">JCO Precision Oncology</a> and hopefully we'll see more of this as this space evolves and maybe perhaps bigger more better validation studies in the context of this test. Thank you for listening to <a href= "https://ascopubs.org/journal/po/podcast">JCO Precision Oncology Conversations</a>. Don't forget to give us a rating or review and be sure to subscribe so you never miss an episode. You can find all ASCO shows at <a href= "https://www.asco.org/news-initiatives/podcasts">asco.org/podcast.</a> The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions. Guests on this podcast express their own opinions, experience and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.

March 26, 2025

JCO PO Article Insights: Prognostic Artificial Intelligence Nonmetastatic Prostate Cancer

In this JCO Precision Oncology Article Insights episode, Natalie DelRocco summarizes "<a href= "https://ascopubs.org/doi/10.1200/PO-24-00653">Digital Pathology–Based Multimodal Artificial Intelligence Scores and Outcomes in a Randomized Phase III Trial in Men With Nonmetastatic Castration-Resistant Prostate Cancer</a>" by Felix Y. Feng, et al published January 31, 2025. Come back for the next episode where JCO Precision Oncology Conversations host, Dr. Rafeh Naqash interviews the author of the JCO PO article discussed, Dr. Tim Showalter. TRANSCRIPT Natalie DelRocco: Hello and welcome to <a href= "https://ascopubs.org/journal/po">JCO Precision Oncology Article Insights</a>. I'm your host Natalie Del Rocco. Today, we'll be discussing the article, “<a href= "https://ascopubs.org/doi/10.1200/PO-24-00653">Digital Pathology-Based Multimodal Artificial Intelligence Scores and Outcomes in a Randomized Phase III Trial in Men With Nonmetastatic Castration-Resistant Prostate Cancer</a>.” We will also be discussing the accompanying editorial, “<a href= "https://ascopubs.org/doi/pdf/10.1200/PO-24-00877">Leveraging Artificial Intelligence to Improve Risk Stratification in Nonmetastatic Castration-Resistant Prostate Cancer</a>.” So, we're going to start by summarizing the original report and then we'll jump into a few of the high-level interpretations that were supplied by the editorial. The original report by Feng et. al. describes the application of multimodal artificial intelligence to data collected on a nonmetastatic castration-resistant prostate cancer. We will call this disease moving forward NMCRPC, a Clinical Trial. So, we're looking at data from an NMCRPC clinical trial. The SPARTAN trial was a randomized phase three trial and this study compared metastasis-free survival as the primary endpoint for those treated with traditional androgen deprivation therapy or ADT to those treated with androgen deprivation therapy plus apalutamide. Other secondary endpoints included progression-free survival and overall survival, but the primary endpoint there was metastasis-free survival or MFS. This study found that the addition of apalutamide resulted in a significantly longer median metastasis-free survival compared to androgen deprivation therapy alone. And we should note that this is a double-blind placebo-controlled trial. In the overall study, 1,207 patients participated and over the course of this study histopathology slides were collected and they were digitized for future use. And that future use is what we are going to be discussing today. The authors do note that there are currently no good biomarkers for use in NMCRPC. The authors seem to be inspired by the ArteraAI prostate test, which was a recent application of multimodal artificial intelligence models. But in localized prostate cancer as opposed to NMCRPC, the authors constructed a multimodal artificial intelligence model or an MMAI model. They applied this to the SPARTAN trial with the intention of developing a risk score that could be used for risk stratification in NMCRPC. And we should note here that multimodal artificial intelligence or MMAI is a broad class of artificial intelligence models, and they can analyze different types of data at one time, hence the term multimodal. So in this example, the author's primary data source of interest were those digitized histopathology images because histopathology tells you a lot about NMCRPC. The authors though also wanted their model to consider traditional clinical factors that are known to be prognostic such as Gleason score, tumor stage, PSA level, and age. So those two different types of data, those histopathology images and that traditional clinical data are the two different types of data that make this model multimodal. So we should note here importantly, after dropping missing data, 420 patients contribute to this model, the MMAI model. The authors generate a risk score from this MMAI model and they categorize that risk score into low, intermediate, and high risk groups using clinical knowledge. The authors found in their results that an increase in this MMAI risk score was associated with an increased hazard of metastasis-free survival event with a hazard ratio from a Cox proportional hazards model of 1.72. To summarize how the authors arrived here, they derived a risk score from this MMAI model which incorporates both imaging and regular data. They plugged this risk score into a Cox proportional hazards mode,l modeling metastasis-free survival and they found that an increase in that MMAI based risk score is associated with increased hazard of metastasis-free event with a hazard ratio of 1.72, which is quite large. Additionally, the risk score seemed to be associated with PFS2 and OS, which were two of the secondary endpoints from the SPARTAN clinical trial, though the effect sizes were more modest. Those are the highlights from the original report, the methods and the results. The accompanying editorial notes that both histopathology and Gleason score specifically are very critical to understanding prostate cancer, and Gleason score alone is not sufficient to summarize the complexity of the disease, although it is a well validated prognostic factor for prostate cancer. So this makes MMAI an excellent tool in the setting described by the authors. We have an existing prognostic factor that doesn't describe the entire picture of the disease by itself and so we can use those digitized histopathology slides to help bolster our understanding and provide the model more information. MMAI allows you to do this because it can take in different types of data. So that was the main conclusion of the editorial. They also summarize a number of recent validations of MMAI models in prostate cancer research, noting that it will be an important tool for risk stratification and has already been shown to outperform classical techniques. The editorial though does highlight a number of weaknesses of this paper, limitations and I think the most important one to highlight, and we touched on this earlier, is that 420 patients from the SPARTAN clinical trial contributed to the development of this MMAI score. That is a small proportion of the roughly 1200 patients that did participate in the SPARTAN clinical trial. So we have a small subgroup analysis that can be limiting and this model will need to be validated in a broader population in the future. Thank you for listening to <a href= "https://ascopubs.org/journal/po/podcast">JCO Precision Oncology Article Insights</a>. Don't forget to give us a rating or a review and be sure to subscribe so that you never miss an episode. You can find all ASCO shows at <a href= "https://www.asco.org/news-initiatives/podcasts">asco.org/podcasts.</a> The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions. Guests on this podcast express their own opinions, experience and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity or therapy should not be construed as an ASCO endorsement.

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