The development of targeted agents and the rapid advancement of next generation gene sequencing technologies have resulted in a realistic prospect of personalizing treatment for malignancies within the next decade. Glioblastoma is driven by the accumulation of genetic mutations that results in malfunctioning of cell signaling cascades and knowledge about the genetic make-up of the glioblastoma cell could provide guidance for a more patient tailored treatment. A major challenge for researchers is to expedite the development of novel, targeted, therapies.

Although targeted therapy has been a valuable addition to treatment strategies leading to the development of a portfolio of potentially successful new drugs, it has not yet delivered the much needed relief in glioma patient populations. We believe that the development of these agents is mainly hampered by (1) our lack of successful patient selection and (2) the redundancy of signaling pathways that can mediate a resistance to single agents and/or contribute to the broad resistance against death of tumor cells.

Large-scale gene expression profile studies in glioblastoma have demonstrated that transcriptional profiles reflect underlying tumor biology and can be used to predict tumor classification (e.g. being a surrogate for pathological grading), patient outcome, and response to treatment. A major benefit in defining subtypes within a tumor entity is the prospect of a patient tailored treatment, leading to more homogenous treatment responses and identifying successful therapeutic agents.

Cancer immunogenomics represents a complementary approach to the application of genomics in developing novel treatment strategies for malignancies. Using this approach, putative tumor-specific neoantigens derived from expressed, nonsynonymous missense or frameshift mutations in the exome are prioritized based on predicted processing and binding affinity to a patient’s individual HLA (human leukocyte antigen) molecules. Thus, rather than stratifying mutational targets based on the “drivers” and “passengers” classification, the predicted immunodominance of a mutational alteration is given precedence, creating a “mutation-to-antigenic target” paradigm. This approach is increasingly being applied to neoantigen identification both preclinically and clinically.

The focus group aims are to improve our understanding of brain cancer biology and to identify changes to the genome that are causing brain cancer and their relationship to treatment response; to identify possible drugable targets and neoantigens in treatment development to benefit patients in the future; to build a cancer immunogenetics pipeline, to expand knowledge on cancer immunogenomics and its translational implications; to build on research collaborations to enhance the clinical interpretation and validation of whole genome sequencing and immunogenomics; to collaborate widely with other research collaborations and form partnerships with industry to convert findings into treatments; to train the next generation scientists, analysts and clinicians in genomic medicine.

Filip De Vos, UMC Utrecht

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