28A. Advancing cellular immunotherapies for Diffuse Midline Glioma using BEHAV3D, a multi-omics 3D image-based platform

Cellular immunotherapy, including T cells engineered to recognize and kill tumor cells, are receiving increasing academic and clinical attention, as cellular immunotherapy has become a predominant focus of cancer therapy development. Also for Diffuse Midline Glioma (DMG), a highly aggressive pediatric brain tumor with currently no effective treatment options, T cell therapies are now entering clinical trials. Time-lapse imaging offers a unique readout to capture the highly dynamic mode-of-action of these cellular therapies, creating opportunities to better understand and improve their working mechanism. Multiomics data integration of single-cell dynamics with single-cell gene expression profiles is, thereby, essential for elucidating the underlying pathways of T cell dynamics and identify targets to induce more effective behavior.

Along these efforts, we recently developed BEHAV3D, a tumor organoid and T cell imaging platform that incorporates behavioral-guided transcriptomics to integrate dynamic imaging data with transcriptomic profiling and unravel the molecular determinants of tumor-targeting behavior (Dekkers, Alieva et al. Nature Biotechnology, 2022). In this project, we will further exploit this technology advance, to build a comprehensive atlas of engineered T cell behavior. We will include diverse T cell therapies, including the most advanced next generation products targeting DMG and other tumor subtypes. We will use behavior-guided transcriptomics to uncover underlying molecular pathways and match behavioral sub-populations with known tumor-infiltrating T cell subsets. In doing so, we will uncover leads for developing more effective therapies, for instance by counteracting tumor-derived signaling that induces dysfunctional behavior in T cells. To achieve these goals, we are looking for a PhD candidate with a strong bioinformatics background that can develop and apply multi-omics tools to integrate different types of single-cell imaging and transcriptomic datasets and identify candidate genes and pathways for improving the clinical successes of T cell therapies.

Necessary skills for this position:

• A computational background