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SUMMARY:Digital Cancer Twins: From Mechanistic Insights to Therapeutic App
 lications - Professor Jasmin Fisher\, UCL Cancer Institute
DTSTART:20240701T113000Z
DTEND:20240701T123000Z
UID:TALK216766@talks.cam.ac.uk
CONTACT:Kate Davenport
DESCRIPTION:Cancer is a complex systemic disease driven by genetic and epi
 genetic aberrations that impact a multitude of signalling pathways operati
 ng in different cell types. The dynamic evolving nature of the disease lea
 ds to tumour heterogeneity and an inevitable resistance to treatment\, whi
 ch poses considerable challenges for the design of therapeutic strategies 
 to combat cancer. Digital twins for cancer tumours are emerging as a trans
 formative tool in oncology to enable a more personalised and dynamic appro
 ach to cancer treatment. In this talk\, I will showcase a growing library 
 of mechanistic\, data-driven computational models\, focused on the signall
 ing pathways within tumour cells and their microenvironment in various can
 cer types (e.g.\, triple-negative breast cancer\, non-small cell lung canc
 er\, melanoma and glioblastoma). These computational models are mechanisti
 cally interpretable\, enabling us to better understand and anticipate inev
 itable resistance mechanisms and predict patient-specific treatment plans 
 that counteract the forces of clonal selection and phenotypic plasticity t
 hat ultimately lead to treatment relapse. I will demonstrate how these mod
 els are initialised with genetic data to create digital twins for individu
 al patient tumours and predict optimised treatment strategies and outcomes
  for their evolving disease. We anticipate our cancer digital twins to be 
 a starting point for a powerful and clinically deployable software applica
 tion to optimise treatment plans and outcomes for hard-to-treat cancers.
LOCATION:CRUK CI Lecture Theatre
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