Strategies for countering treatment resistance in cancer by tracing and targeting clonal dynamics

Rationale: Most children dying from cancer under modern healthcare do so because of relapsing, treatment-resistant disease.

The mechanisms underlying resistance are largely unexplored but there is strong evidence that Darwinian evolution of tumor cells under therapy is a key contributor.Aims and purpose: We will explore the principles by which childhood cancer genomes evolve under therapy to predict mutational patterns at relapse and stall the time to resistance by timing new treatments according to evolutionary models.Work plan: Using multiregional sequencing and bioinformatics inspired by species evolution we will analyze >100 childhood cancers to: (1) Define evolutionary trajectories towards treatment resistance under chemotherapy in a large retrospective cohort.(2) Prospectively test the clinical value of evolutionary parameters in an ongoing nationwide precision medicine study for children with cancer(3) Use the detected trajectories of cancer evolution as a substrate for mathematical models of clonal dynamics under treatment(4) Assess the capability of these evolutionary models to improve survival in childhood cancer animal systems by an improved timing of treatmentSignificance: Our research results have already been translated into new diagnostic routines.

The now proposed project gathers critical data for the future development of precision oncology protocols that circumvent treatment resistance by taking cancer´s capacity for evolution and adaptation into account.