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SUMMARY:Using clinically calibrated digital twins to generate personalized
  decision support for evolutionary therapy - Mark Robertson-Tessi (Moffitt
  Cancer Center)
DTSTART:20250919T094500Z
DTEND:20250919T095000Z
UID:TALK236098@talks.cam.ac.uk
DESCRIPTION:\nWe developed a digital twin framework for guiding therapy de
 cisions for individual patients. Given that cancer is an eco-evolutionary 
 system that adapts to evade therapy\, static treatment regimens eventually
  fail in many advanced cancers. A personalized\, proactive\, and dynamic a
 pproach to therapy has the potential to improve outcomes. To explore clini
 cal feasibility of implementing such a framework\, we developed a clinical
  trial (NCT04343365)\, the Evolutionary Tumor Board (ETB)\, which uses eco
 -evolutionary theory and predictive modeling to assist clinical decision m
 aking for individual cancer patients (n=24\, ongoing).\nThe framework reli
 es on detailed data curation and imaging measurements for each patient\, a
 nd mathematical modeling incorporates multi-lesion tumor growth\, treatmen
 t response\, and evolution of resistance. Digital twin models are calibrat
 ed by historical datasets of similar patients\, clinical trial data\, and 
 the patient&rsquo\;s longitudinal data. A &ldquo\;Phase i trial&rdquo\; ap
 proach accounts for prediction uncertainty\, and information is delivered 
 via a software interface.\nThe ETB has provided novel and useful decision 
 support for the patients and oncologists. At the same time\, our efforts s
 how that there are both challenges and opportunities in using digital twin
 s to predict and personalize therapy\, particularly in the context of real
 -time clinical care.\n&nbsp\;\n
LOCATION:Seminar Room 1\, Newton Institute
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