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SUMMARY:From PD-L1 to PSA: Virtual Clones and Cohorts in Action Across Can
 cer Types and Therapeutic Modalities - Trachette Jackson (University of Mi
 chigan)
DTSTART:20250919T083000Z
DTEND:20250919T093000Z
UID:TALK235204@talks.cam.ac.uk
DESCRIPTION:The promise of oncological digital twins hinges on our ability
  to build virtual replicas of tumors and their microenvironments that fait
 hfully forecast the diversity of patient-specific responses to various the
 rapeutic options and inform clinical decision-making. In this talk\, I pre
 sent an integrative modeling framework that uses parameter identifiability
  and data-driven virtual tumor clones and virtual patient cohorts to enhan
 ce the predictive reliability of mathematical models in cancer treatment. 
 Drawing on two recent studies&mdash\;one focused on immune checkpoint inhi
 bitor (ICI) therapy in bladder cancer\, the other on androgen deprivation 
 therapy (ADT) in prostate cancer&mdash\;we demonstrate how virtual clones 
 and cohorts can be used to test hypotheses\, optimize therapies\, and reve
 al information insights about the reliability of clinical biomarkers like 
 immune cell infiltration and PSA levels.\n&nbsp\;\nOur bladder cancer stud
 y uses multiple in vivo datasets to construct virtual tumors under differe
 nt immune conditions and treatment regimens. We show that models calibrate
 d with insufficient data or based on unidentifiable parameters systematica
 lly overestimate tumor reduction and survival. Similarly\, in prostate can
 cer\, we construct virtual cohorts parameterized by clinical PSA trajector
 ies and experimentally measured PSA expression rates to enhance the predic
 tion of ADT failure. These virtual clones reveal that post-nadir PSA dynam
 ics\, rather than the initial decline\, hold significant prognostic value.
  Together\, these studies offer practical guidelines for developing data-d
 riven digital twins. We highlight the importance of identifying minimal bu
 t informative datasets for calibration\, assessing parameter identifiabili
 ty\, and tailoring virtual cohorts to reflect both parameter uncertainty a
 nd patient-specific biology. These insights chart a path toward translatio
 nally robust digital twins capable of forecasting individualized treatment
  outcomes\, providing critical insights for optimizing treatment and monit
 oring schedules and adapting to emerging clinical data.\n&nbsp\;
LOCATION:Seminar Room 1\, Newton Institute
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