Integrative Modelling of Plasticity, Evolution and Prognosis of Cancer

• Applicant: Dr. Othman Al-Sawaf
• Subject Area: Hematology, Oncology
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 524342988

Cancer is a leading cause of death worldwide and despite the curative potential of some modern therapies, many patients experience cancer recurrences. Cancer cells can adapt to pressures exerted by therapy and undergo a constant evolution that leads to treatment resistance. Understanding cancer evolution is therefore critical to delineate and exploit the mechanisms by which cancers withstand therapy. Leukaemia is a highly informative model for cancer plasticity and evolution: It is characterized by the clonal expansion of malignant cells in peripheral blood and bone marrow. It is therefore possible to longitudinally profile the disease from simple blood or bone marrow draws. To this end, this proposal addresses four aims: The first aim is to reconstruct the evolution of chronic lymphocytic leukaemia (CLL), a common, indolent kind of leukaemia. Most of our knowledge on CLL has been derived in the era of chemotherapy, however, chemo-free therapies have become the standard-of-care for CLL. How CLL evolves in the context of continuous versus fixed-duration targeted therapy is largely unknown. Hence, we will profile the clonal composition of CLL in three different continuous and fixed-duration targeted therapy contexts, before, during and after therapy until disease progression. Using ultra-sensitive sequencing methods, we will track the trajectories of individual CLL subclones in relation to specific treatment modalities, and thereby identify optimal timepoints for potential salvage interventions. This approach will be mirrored in a cohort of patients with acute lymphoblastic leukaemia (ALL), an aggressive leukaemia that is treated with intensive chemotherapy and where the evolutionary dynamics in adult patients are understudied. The second aim will be to interrogate the plasticity of the tumour microenvironment (TME) by using single cell techniques to deconvolute the TME of patients with CLL and ALL. By integrating single cell sequencing as well as spatial imaging mass cytometry techniques, we will dissect TME modulations that occur under selective therapeutic pressures and that contribute to persistence of minimal residual disease (MRD). For the third aim of the study, we hypothesize that certain genomic aberrations like TP53 aberrations, which can so far only be detected by molecular-/cytogenetic assays, have morphological features that can be captured by digital cytology. Using a deep-learning framework, will build morphology-based classifier of clinically relevant genomic aberrations that will widen the access to genetic diagnostics for patients with leukaemia. The final aim of this study is to use the multi-layered data generated in this study to build a machine-learning-based prognostic score for CLL to predict survival, MRD response and quality of life metrics according to different treatment modalities. This will eventually improve treatment decisions based on individual patient and disease features to allow for optimal treatment outcomes.

• DFG Programme: Independent Junior Research Groups