During the course of the project period APERIM partners set up architectures and tools towards the development of novel analytical software pipelines.
• An advanced bioinformatics database (The Cancer Immunome Atlas http://tcia.at ) was developed by the Medical University of Innsbruck, Austria (I-Med) with contains data of the immunogenomic characterization for 20 solid cancers with >8000 tumor samples of The Cancer Genome Atlas. This database is publicly available and provides for the first time comprehensive view of the cellular composition of the intratumoral immune infiltrates. The database enables also integration of images from whole-tissue slides (digital pathology) and thereby the integrative analyses of NGS data and imaging data.
• To develop tools for the automated quantification of TILs partner Definiens from Germany set up a database for TIL quantification by cell segmentation and classification and has performed region classifications and tumor core annotations for slides with clinical data. INSERM from France worked on the development of a fully automatic image analysis solution to detect TILs in tissue slides. This novel tool CRC classifier, can be taken to stratify patients into short and long survivor and to further develop targets for novel immunotherapies.
The digital TILSorter has been developed by the Spanish partner CNIC in collaboration with I-MED to enumerate and quantify the immune infiltration in colorectal and breast cancer.
• To reach the aim of providing an analytical pipeline for NGS-guided personalised cancer vaccines, various models were developed and are compatible with each other, so that they can be combined to a real pipeline. Partner TRON from Germany has established the iCaM2.0 NGS data analyser, a pipeline for fusion gene detection and validation, which analyzes the data for a single patient. The software Immunopredictor was developed by University of Utrecht (UU), Netherlands and is a pipeline that provides predictors for antigen presentation on the cell surface. It was released on Github
https://github.com/APERIM-EU/WP3-ImmunePredictor(odnośnik otworzy się w nowym oknie).
For the selection of sets of potential neo-epitope targets, multiple strategies have been implemented by the University of Tuebingen (UT), Germany. The models have been implemented into a software product that is disseminated through the established infrastructure (
https://github.com/APERIM-EU/WP3-EpitopeSelector(odnośnik otworzy się w nowym oknie) https://hub.docker.com/r/aperim/epitopeselector/ ) and uses the output of NGSanalyser and Immunopredictor. The models integrate neo-antigen and HLA allele expression, self-similarity as well as binding strength/immunogenicity of the neo-epitopes, and optimize overall immunogenicity and antigen coverage. To improve accessibility and usability, UT implemented a web-based graphical user interface for the software product (
https://github.com/qbicsoftware/vaccine-designer-portlet(odnośnik otworzy się w nowym oknie)). The complete analytical pipeline for cancer vaccines has been evaluated with other software tools and clinical data at TRON and NKI. Derivatives of the implemented software are already used in clinical neoantigen vaccination trials of collaborating pharmaceutical companies.
• To predict TCR specificity a TCR Analyser was developed by the German company AptaIT which comprises a software tool that enables the analysis of NGS data derived from T-cells in order to provide T-cell-receptor (TCR) repertoire results. The TCR repertoire reflecting the tumour status of individuals can be digitalized by NGS (aperimTCRKit.pl ).
The most challenging part of the project was the aim to develop a TCR2Epitope package. Aim within APERIM was to evaluate the feasibility to predict epitope characteristics from the primary sequence information of TCRs. The Team of University of Utrecht (UU) and Masarykova University (MU), from Brno, Czech republic demonstrated that, predicting CDR3 beta chain characteristics associated with epitope binding, is feasible. The software TCR2epitope can be found on github:
https://github.com/ewaldvandyk/TCR2Epitope(odnośnik otworzy się w nowym oknie). It is based on the Platform VDJdb
https://vdjdb.cdr3.net/(odnośnik otworzy się w nowym oknie) which was developed by Partner MU and is a comprehensive database of antigen-specific T-cell receptor (TCR) sequences with the goal to facilitate access to existing information on TCR antigen specificities, i.e. the ability to recognize known epitopes presented by known MHC molecules. The Netherland Cancer Institute, (NKI) developed a data set plus matched TCR vectors that can be used to validate TCR2epitope and other TCR specificity prediction algorithms.
