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Sebastiaan van Heesch

27B. Childhood cancer-specific proteins as targets for immunotherapy

Cancer immunotherapy, for which different components of the patient’s own immune system are directed against the cancer, has revolutionized the management and treatment of cancer in adults. However, immunotherapy options deduced from adults are likely not the best options for treatment of cancer in children. For instance, pediatric tumors show a largely different mutational landscape with a low mutational burden, yielding very few actionable events/targets.

The candidate will therefore exploit alternative, DNA mutation-independent sources of tumor-specific targets to enable immunotherapy development in children. They will use various sequencing (e.g., Ribo-seq, (long-read) RNA-seq) and computational workflows to identify and validate putative neoantigens. These will first be visualized as open reading frames (ORFs) as detected by ribosome profiling, computationally evaluated to establish their uniqueness for the tumor, and validated by proteomics technologies and immunogenicity assays. Cancer-specific ORFs likely provide a wealth of potential new biomarkers or targets for the development of off-the-shelf cellular immunotherapies (e.g., TCR-T or CAR-T) or therapeutic vaccines. In comparison to classical DNA mutation derived neoantigens, ORF-based neoantigens are anticipated to display a much greater dissimilarity to self (i.e., a higher specificity to the tumor cell), and are more likely to be public (shared amongst multiple patients or tumor types). This might be the only useable source of targetable antigens for tumors with limited DNA mutations—which holds true for almost all childhood cancers.

The candidate will benefit from our role in the Therapeutic Vaccines workstream of the nation-wide Oncode-PACT project, which aims to accelerate the preclinical development of cancer treatments (https://www.oncode.nl/oncode-pact), as well as local infrastructure and expertise in the development of immunotherapies.

The van Heesch group combines wet- and dry-lab approaches and affinity with (or a background in) computational biology is highly recommended. Knowledge of immunology is a pre.

Necessary skills for this position:

  • Affinity with (or a background in) computational biology
  • Knowledge of immunology

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