ASH 2025 | Developing and refining predictive models in Hodgkin lymphoma using the HoLISTIC consortium

Really excited to be here at the annual ASH meeting in Orlando, Florida. Our entire group at RWJBarnabas Health actually had 93 presentations, believe it or not. I was happy to have 10 for my group. And we had several different ones that we included oral presentations as well as poster presentations. The ones that are really a labor of love are what’s part of the HoLISTIC Consortium. HoLISTIC stands for Hodgkin Lymphoma International Study for Individual Care. This is a global consortium I co-founded several years ago with Dr. Susan Parsons, who’s at Tufts Medical Center, and it consists of very large amounts of data that’s been synthesized across the world. And when I say data, that means large phase three clinical trials conducted in Hodgkin lymphoma over the last 15 or so years, phase three randomized trials, but importantly, as well as registries, cancer registries, that are enriched with long-term follow-up. And we’ve been doing this really to garner modern, dynamic prediction models that are robust using what’s called tripod guidelines. And so this has been a continuation of that. 

One of the presentations was on what’s called a multi-state model, where we want to understand, great, what’s the contribution of clinical factors, but what about PET imaging? What about other factors, such as different treatments that you receive? So that was one oral presentation given by Jenny Cui, who was a Tufts fellow and is now faculty at Brown. 

The second oral presentation was looking at one of our HoLISTIC predictive models called the A-HIPI in the landmark clinical trial S1826 that was published in the New England Journal last year. And we just wanted to see with contemporary treatment, in particular checkpoint immunotherapy, Nivo AVD, is it still predictive? And the long story short is, yes, it was. And so we’re really excited about these presentations, but continuing really the science where we really look forward to this coming year to integrate into these predictive models to estimate the risk of post-acute and late effects based on patient characteristics as well as exact milligrams per meter squared of chemotherapy, radiation, etc.

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