Several novel and highly promising immunotherapeutic approaches are now in development, such as vaccination with synthesized long peptides or vaccines that encode relevant neoepitope sequences, as well as activated T cell therapies that recognize neoepitopes. Neoantigens (aka neoepitopes) are derived from tumor-specific somatic mutations accumulated during cancer progression. The discovery of patient specific high affinity neoepitopes is challenging, as several genomics attributes of the tumor and germline cells need to be measured and integrated to derive accurate predictions. Most of the existing bio-informatics pipelines for neoepitope prediction are complex sets of command line scripts that are complicated to operate, difficult to run efficiently on cloud or High-Performance Computing (HPC) environments and difficult to validate for the use of clinical trials and submissions.
We established a bio-informatics pipeline that allows calling of somatic variants, the estimation of the expression levels of the mutated proteins and the calling of patient specific MHC genotypes from paired tumor-normal tissue samples. The information can be used for predicting and selecting the most specific patient neoepitopes. We have ensured that the pipeline can run directly on most scalable cloud and HPC environments. The pipeline also includes the required regulatory features so that it can be used in clinical trials, and the results can be directly submitted to regulatory authorities.
We present the automated neoepitope prediction pipeline and show that it includes all compliance features required for operating in regulated clinical environments. We also demonstrate the scientific validation of the pipeline by reproducing and confirming the results from a published medical case of a metastatic breast cancer patient that was successfully treated with tumor-infiltrating lymphocytes (TILs) reactive against 4 patient specific mutated proteins.
We provide a standardized bio-informatics pipeline for neoepitope prediction that can be automated, runs highly efficient on the most scalable HPC environments and can be validated to support clinical trials and decisions.
Genedata AG.
Has not received any funding.
N. Masloboeva-Siwach: Employee of Genedata Inc. S. Ribi, H. Lempiäinen, T. Rujan: Employee of Genedata AG. M. Flesch: Employee of Genedata GmbH.