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SUMMARY:Steering the evolutionary dynamics of cancer through space and tim
 e -  Jeffrey West\, Ph.D. Assistant Member\, Integrated Mathematical Oncol
 ogy H. Lee Moffitt Cancer Center &amp\; Research Institute
DTSTART:20250203T123000Z
DTEND:20250203T133000Z
UID:TALK226030@talks.cam.ac.uk
CONTACT:Kate Davenport
DESCRIPTION:In the first part of the talk\, we focus on the conceptual dev
 elopment of alternative treatment strategies that leverage the principles 
 of evolution to mitigate treatment resistance. We introduce this broad cla
 ss of drug scheduling strategies known as evolutionary therapies and expla
 in how mathematical modeling can aid by providing patient-specific predict
 ions as a decision-support tool for providing clinical insight.  Next\, we
  explore the practical implementation of an evolutionary therapy steering 
 strategy within an in vivo model of non-small-cell lung cancer treated wit
 h ALK inhibitors. Treatment-naïve tumors are associated with more convex 
 exposure-response curves (low doses provide sufficient responses) while ev
 olved-resistance tumors are generally more concave (requiring high doses f
 or equivalent responses). Resistance to ALK inhibitors in vivo occurs grad
 ually\, as tumors acquire cooperating genetic and epigenetic adaptive chan
 ges. Thus\, we hypothesized the existence of a critical point in the time-
 evolution of ALK-positive tumors where it is optimal to switch from contin
 uous treatment to high-dose / low-dose to mitigate the onset of gradual re
 sistance. In vivo validation provides evidence that evolutionary steering 
 is a viable strategy for predicting the onset of resistance and developing
  resistance management treatment strategies. Thus far\, we neglect the fac
 t that cancer growth can be described as a caricature of the renewal proce
 ss of the tissue of origin\, where the tissue architecture and spatial cor
 relations have a strong influence on the evolutionary dynamics within a tu
 mor. To incorporate these characteristics\, we introduce agent-based model
 ing methods that integrate clinical spatial data to make inferences on the
  role of microenvironmental mechanism of immune escape\, and define implic
 ations on therapy.
LOCATION:CRUK CI Lecture Theatre
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