APERIM: Advanced bioinformatics platform for PERsonalised cancer IMmunotherapy

Projektinformationen

APERIM

ID Finanzhilfevereinbarung: 633592

DOI

10.3030/633592

Projekt abgeschlossen

EK-Unterschriftsdatum 7 April 2015

Startdatum 1 Mai 2015

Enddatum 30 April 2018

Finanziert unter

• SOCIETAL CHALLENGES - Health, demographic change and well-being

Gesamtkosten

€ 2 999 287,50

EU-Beitrag

€ 2 999 287,50

Koordiniert durch

MEDIZINISCHE UNIVERSITAT INNSBRUCK
Austria

Periodic Reporting for period 2 - APERIM (APERIM: Advanced bioinformatics platform for PERsonalised cancer IMmunotherapy)

Berichtszeitraum: 2016-11-01 bis 2018-04-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Personalized cancer immunotherapy has tremendous potential to provide an incredible benefit to society. Successful cancer immunotherapies will not only reduce health care costs, but will also increase the quality of life of the affected patients.. Importantly, technological advances such as next-generation sequencing (NGS) allow the development of personalised cancer immunotherapies that target patient specific mutations. However, clinical application is currently hampered by specific bottlenecks in bioinformatics. The project APERIM aimed to accelerate the clinical translation and maximize the accessibility and utility of biomedical data in research and medicine. The overall objective of APERIM was to develop an advanced bioinformatics platform for personalised cancer immunotherapy. Towards this goal, a transdisciplinary network was working on methods development, methods validation, software implementation, and software testing. The bioinformatics methods the consortium developed are an important prerequisite and will enhance personalized health care in the context of cancer immunotherapy. The unique methods and the easy to use software tools enable comprehensive characterization of patients’ samples and will provide the basis for developing efficient therapeutic strategies and ultimately lead to a benefit for the society.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

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.
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 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). 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. It is based on the Platform VDJdb https://vdjdb.cdr3.net/ 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.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

As cancer immunotherapy is developing at rapid pace, we strongly believe that the scientific findings from the project and the developed methods and software tools will have huge impact on cancer diagnostics and therapy. For example, our pan-cancer analysis of the immunophenotypes and antigenomes implicates that both, mutational profiles and immunological profiles are highly diverse and tissue-dependent. Thus, successful cancer therapy leading to long-term benefit will likely require precision immune-oncology approach by rationally selecting drugs and/or drug combinations.
The impact of the project work on the translation into clinical application is unique: The development of the integrated NGSanalyser, immunopredictor, and epitopeselector solution will enable the rapid rational design of cancer vaccines for the treatment of solid cancers. We expect, that in the future the software tools will be generally useful for all cancer vaccine developers and will be specifically and immediately used in our on-going, regulatory-approved, and planned individualized cancer vaccine clinical trials.
In parallel with the translation of the results into clinical research there are ongoing efforts to apply regulatory principles to the manufacture and quality control of vaccines (Britten et al., 2013). Despite the fact that these therapeutic approaches pose unique regulatory challenges, the development of cancer vaccines may be pursued within the existing regulatory framework of the EU.

To develop a fully automated image analyser, the software has to be able to reliably classify lympho