APERIM – project finished: results and impact

Overall objectives of the project
Personalized cancer immunotherapy has tremendous potential to provide an incredible benefit to society. Without doubt, successful cancer immunotherapies will not only reduce health care costs, but will also increase the quality of life of the affected patients. Clinical trials have demonstrated profound tumor regression including complete cure in patients with metastatic cancer after treatment with immune checkpoint blockers. Importantly, technological advances such as next-generation sequencing (NGS) allow for the first time 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 of leading experts in bioinformatics and cancer immunology was working on methods development, methods validation, software implementation, and software testing. The major tools developed and assembled for this platform are:

  • A database for the integration of NGS data, images of whole tissue slides of tumour sections, and clinical data;
  • A tool for the quantification of tumor-infiltrating lymphocytes using RNA-seq and imaging data;
  • Analytical pipeline for NGS-guided personalised cancer vaccines;
  • Novel tools for the characterization of T-cell receptor (TCR) sequences including software that identifies epitopes that are likely to elicit a cytotoxic T-cell immune response;
  • Database of antigen-specific TCR sequences.

The bioinformatics methods we 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.

Results achieved
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 e integrative analyses of NGS data and imaging data.


  • Efremova M, et al. Targeting immune checkpoints potentiates immunoediting and changes the dynamics of tumor evolution Nature Communications 9, 2018
  • Charoentong P, et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep. 2017
  • Tappeiner E, et al. TIminer: NGS data mining pipeline for cancer immunology and immunotherapy. 2017
  • Finotello F, Trajanoski Z. New strategies for cancer immunotherapy: targeting regulatory T cells. Genome Med. 2017
  • Hackl H, Charoentong P, Finotello F, Trajanoski Z. Computational genomics tools for dissecting tumour-immune cell interactions. Nat Rev Genet. 2016
  • 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 (stromal TILs, epithelial TILs) and has performed region classifications (epithelium/stroma/necrosis) 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 tumor infiltrating lymphocytes (TIL) in tissue slides. This novel tool CRC classifier, based on the immunoscore software, 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 RNA-Seq samples starting from scRNASeq . The method is based on a Deep Learning strategy, using a Deep Neural Network (DNN) model that allows quantification not only of lymphocytes as a general population but also to identify the exact amount of specific CD8+, CD4Tmem, CD4Th and CD4Tregs, as well as B-cells and Stromal content. On top of the specificity of the subpopulations identified, the signatures were built from scRNASeq data from the tumour, preserving the specific characteristics of the tumour microenvironment as opposite to other approaches in which cells were isolated from blood.


  • Mlecnik B, et al. Integrative analyses of colorectal cancer show Immunoscore is a stronger predictor of patient survival than microsatellite instability, Immunity, 2016
  • Mlecnik B, et al. The tumor microenvironment and Immunoscore are critical determinants of dissemination to distant metastasis. Science Transl. 2016
  • Mlecnik B, et al. Comprehensive Intrametastatic Immune Quantification and Major Impact of Immunoscore on Survival. J Natl Cancer Inst. 2018
  • 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 standardized prototype pipeline for fusion gene detection and validation, which analyzes the data for a single patient. The pipeline manages the different tasks, which depend on each other, including short sequence read alignment, somatic mutation detection, determination of mutation effects and prediction of antigenicity. The group of TRON will reach the objective soon by a report on the performance, which includes key metrics as run-time per patient, number of detected mutations and neo-antigens and the ability to detect known neo-antigens in a set of well characterized patient data sets. Recently the group has extended the space of accessible neo antigens by in depth testing and characterization of NGS based fusion-transcript detection methods and the subsequent prediction of neo antigen candidates.

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 outputs a set of scores that are relevant for predicting the potential of query epitopes (peptide-MHC complexes) to elicit effective cytotoxic T-cell responses. Immunopredictor 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, depending on different levels of information. 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, while minimizing the “vaccine failure” risk. They further extended the framework with options to specifically include and exclude specific peptides and to use rank-based immunogenicity estimates. To improve accessibility and usability, UT implemented a web-based graphical user interface for the software product, which has been deployed as part of partner UT’s platform for biomedical research qPortal. In addition, UT developed an immunoinformatics toolbox (ImmunoNodes) that is fully integrated into the visual workflow environment KNIME and facilitates the usage of tools including (neo)epitope prediction and HLA typing (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. SOPs have been developed for use of the software at TRON.

Derivatives of the implemented software is used in clinical neoantigen vaccination trials of collaborating pharmaceutical companies. TRON uses the established SOPs and software for internal research and development and collaborations with international partners from academia and industry.


  • Schubert B, Kohlbacher O: Designing string-of-beads vaccines with optimal spacers, Genome Medicine 2016
  • Benjamin Schubert et al. FRED 2 – An Immunoinformatics Framework for Python. Bioinformatics 2016
  • Schubert B, et al: ImmunoNodes – graphical development of complex immunoinformatics workflows, BMC Bioinformatics, 2017
  • Strønen E, et al.: Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science 2016
  • To predict TCR specificity a TCR Analyser was developed by the German company AptaIT which comprises a software tool that enables the analysis of next generation sequencing (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 and can therewith be made accessible for bioinformatic analysis. NGS results into big data files, which provide the input for the TCR Analyser software. The output is a SQLite database of the TCR repertoire. The database comprises the CDR3-sequences of alpha and beta TCR-chains, their V- and J-genes annotated according to the immuno-genetics (IMGT) reference database, the counts of the genes as well as the entire TCR-sequences (i.e. the connectivity of the individual V- and J-genes and CDR3-sequences).

In order to support various kinds of experiments the TCR Analyser software can assemble the results from paired-end sequencing runs before parsing the TCR repertoire. Further aim was the processing of paired alpha and beta chain data, which was achieved in the second period of the project. Paired chains data sets from both, multiwell and emulsion PCR approaches can be processed now. In addition, a dynamic graphical user interface (GUI) was implemented to ease the usage of the TCR Analyser (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. Three years of work from partners 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 is a python module that was developed by partner UU and can be found on github: https://github.com/ewaldvandyk/TCR2Epitope, including an extensive explanation on usage. The present version of the software can rank TCR sequences based on their epitope specificity on a validation set from the VDJdb database. Although a set of patient specific neo-epitopes can already be ranked based on their probability of binding, the ranking is not yet satisfactory and it still needs further work and data to develop a model that can reliably predict TCR binding to neoantigens. The team is currently implementing an automated training module for TCR2Epitope. This allows the models to be retrained automatically every time new data is added to VDJdb database.

The Platform VDJdb https://vdjdb.cdr3.net/ was developed by Partner MU and is a comprehensive database of antigen-specific T-cell receptor (TCR) sequences acquired by manual processing of published studies that report the ligand specificities of defined T-cell clonotypes. The primary goal of VDJdb is to facilitate access to existing information on TCR antigen specificities, i.e. the ability to recognize known epitopes presented by known major histocompatibility complex (MHC) class I and II molecules. The mission was to aggregate TCR specificity information on a continuous basis and establish a curated repository to store these data in the public domain. In the period of 2017-2018 VDJdb has grown substantially owing to constant efforts in aggregation of previously published results and input from our collaborators and the community. A milestone of 20,000 records was reached in November 2017, and a chunk of 12 studies describing a variety of CMV-associated epitopes was added in May 2018, covering a majority of known CMV-specific clonotypes up to this date.

The Netherland Caner Institute, (NKI) developed a data set plus matched TCR vectors that can be used to validate TCR2epitope and other TCR specificity prediction algorithms.

      • Shugay M, et al. VDJdb: a curated database of T-cell receptor sequences with known antigen specificity. Nucleic Acids Res. 2017
      • Shagin DA, et al. Application of nonsense-mediated primer exclusion (NOPE) for preparation of unique molecular barcoded libraries. BMC Genomics. 2017
      • Bolotin DA, et al. Antigen receptor repertoire profiling from RNA-seq data. Nat Biotechnol. 2017
      • Shugay M, et al. MAGERI: Computational pipeline for molecular-barcoded targeted resequencing. PLoS Comput Biol. 2017

The socio-economic impact
As cancer immunotherapy is developing at rapid pace with several approved drugs as well as application to increasing number of common malignancies, 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. Already derivatives of the implemented NGSanalyzer software are used in clinical neoantigen vaccination trials of collaborating pharmaceutical companies (ClinicalTrials.gov Identifiers: NCT02316457, NCT02035956 and NCT03289962) of TRON.

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.

Researchers at the Med Uni Innsbruck describe mechanisms of resistance to cancer immunotherapy

Cancer immunotherapy is changing the way we treat cancer. Approved drugs target the immune system’s own ability to eliminate tumor cells and combat the cancer. However, only 10-20% of the patients are responding to these drugs. Moreover, recent data show that some of these patients develop resistance to cancer immunotherapeutics after one or two years. Austrian researchers now provide a first explanation for this unfavorable course.
Cancer progression is a complex process. Tumor cells develop very differently depending on their place of origin, genetic makeup or tumor microenvironment and they have numerous mutations. This heterogeneity of the tumor is currently a hot topic in cancer research. A joint research work by APERIM coordinator Zlatko Trajanoski and cell genetics experts of the Medical University of Innsbruck is now providing possible answers on the mechanisms of resistance to cancer immunotherapy. The findings were published recently in Nature Communications.

Heterogeneous versus homogeneous tumor development The researchers were using genetic and immunological methods and were able to demonstrate that tumors become genetically more homogeneous during the course of immunotherapy. Subsequently, the tumor cells are not recognized by the immune system and the tumor grows larger. “During the course of cancer Immunotherapy a so-called immune editing occurs, meaning that tumor cells with certain mutations are eliminated and thereby reducing the genetic heterogeneity of the tumor ,” Trajanoski explains the new finding. Extensive methodology Using an immunodeficient mouse model, researchers were able to distinguish the effects of the immune system from genetic influences. Suprisingly, the tumor development in the mouse model was neutral, ie without positive or negative selection. The findings of the Innsbruck research work are based on a remarkably comprehensive methodology with Next Generation Sequencing technology, immunological characterization and bioinformatics analyzes.

The researchers propose that in order to predict the development of resistance, a comprehensive analysis of the tumor sample for its genetic heterogeneity should be carried out. This could eventually result in an adaptation of the therapy in terms of dosage and time Management.

Original paper full text


APERIM data demonstrate that immune repertoire profiling of tumor-infiltrating lymphocytes can be efficient from RNA-Seq

The knowledge about the vast heterogeneity of immune receptors and their role in the anti -tumor response has gained high importance for precision cancer medicine. Scientists therefore have an immense interest to collect information and profiling data of immune receptor repertoires – T-cell receptors (TCR) and immunoglobulins.

However, research groups often face the problem that available tumor material is insufficient to perform immune repertoire profiling along with other analyses such as Exome-Seq and RNA-Seq. Additional analyses also need additional resources, which burdens the large scale clinical trials.

The idea then was to bypass these limiting factors and to extract TCR and immunoglobulin repertoires from the bulk transcriptome sequencing data of tumor RNA. RNA-Seq is routinely performed and could be a useful alternative way to obtain the intratumoral repertoires of immune receptors.

MiLaboratory LLC recently upgraded its flagship software product, MiXCR, enabling it to efficiently extract TCR and immunoglobulin repertoires from RNA-Seq data.

Using MiXCR RNA-Seq mode to analyse bulk transcriptomic data of human melanoma, the APERIM researchers demonstrated that the extracted TCR repertoires of medium and large tumor-infiltrating T cell clones are very similar to those obtained using targeted TCR profiling performed from the same RNA samples.

In this work that involved a bundle of research groups it was also shown that high intratumoral expression of clonal IgG1 antibodies is associated with the best prognosis in human melanoma.

Finally, the work demonstrates that RNA-Seq performed for the pure sorted T cells allows to extract nearly complete TCR repertoires for these cells, which converts bulk transcriptomic profiling into a powerful universal approach for the functional analysis of T and B cell subpopulations.

All these findings, which were recently published in Nature biotechnology, should help to use RNA-Seq data as a source for antigen receptor repertoire profiling, amplifying the possibilities for adaptive immunity studies and rising the chances to find further clinically relevant biomarkers for cancer immunotherapy.

Original paper:

Online Database of T Cell Receptor Sequences was recently published

The database called VDJdb, developed by research group around Dmitriy Chudakov from Masarykova University, helps to reach the next step towards a new level of understanding the adaptive immune system.

Modern sequencing technologies are generating huge numbers of TCR sequences. However, up to now the sequencing data could hardly be linked to functionality of the phenotype TCRs i.e. the ability to recognize certain epitopes presented on a cell surface. Within APERIM finally the comprehensive repository VDJdb was developed, which collects information on TCR sequences with known antigen specificities. The primary goal of this work was to create an open source database and to facilitate access to existing information on T-cell receptor antigen specificities.

Within the cooperation in the APERIM network the establishment of this database is also essential for the development of a further software, called TCR2Epitope. Can Keşmir and her colleagues from the University of Utrecht are working on that visionary tool, which would in the future allow to predict the interaction between TCRs and certain epitopes. The therapeutic application of that tool presents an innovative method to support T cell-mediated cancer immunotherapy.

The infrastructure behind VDJdb allows community-driven data acquisition, proofreading and aggregation in order to establish a comprehensive repository of T-cell receptor sequences with known antigen specifities. The VDJdb database accumulates data from both – previously published papers and obtained via personal communications. Several research groups around the world could be attracted to fulfil the database. Currently the VDJdb includes more than 12000 TCR variants with known specificities and it is rapidly growing.

The database was recently published in Nucleic Acids Research (full paper).

The online database VDJdb is available under https://vdjdb.cdr3.net/

BioNTech and collaboration partner TRON published promising results in “Nature”

First-ever clinical study demonstrates personalized RNA-based vaccine using mutant neo-epitopes as antigens activates immune system against individual mutations and exerts anti-cancer activity.

BioNTech AG, a fully-integrated biotechnology company pioneering individualized cancer immunotherapy, announced Phase I trial results demonstrating its IVAC® MUTANOME, an individualized RNA vaccine based on patient-specific mutations, induces strong immunogenicity as well as promising anti-tumor activity in high-risk patients with late-stage melanoma. Additionally, in this early trial, a majority of patients showed prolonged progression-free survival in comparison to historical controls. The first-in-human study applied a process covering the comprehensive identification of individual mutations from routine tumor biopsies to next generation sequencing, the computational prediction of potential neo-epitopes as vaccine targets, and the design and manufacturing of an RNA vaccine encoding multiple neo-epitopes unique for each patient. The data, published in Nature, were obtained from research conducted in collaboration with clinical partners and the translational research institute, TRON. These data are now available to APERIM consortium partners to further test developed software modules.

Press release
Nature Article – full text


“Austrian Platform for Precision Oncology” receives national funding

The Medical University of Innsbruck further expands its facilities for precision cancer.

The establishment of an accurate infrastructure for precision oncology is a major challenge for research organizations and clinics, as expertise and high-end equipment are required in various fields such as laboratory diagnostics, oncology, cellular and molecular biology as well as bioinformatics. Three Austrian medical universities in Innsbruck, Graz and Vienna have therefore bundled complementary expertise aiming to develop an Austrian platform for precision oncology.

The platform contains three components: (1) molecular characterization with next generation sequencing and T cell receptor sequencing, (2) cellular phenotyping for the determination of immune infiltrates, and (3) functional diagnostics with 3D cell culture and CRISP / Cas9 technology. “This nationwide initiative will accelerate the implementation of precision-oncology and enable the standardization of clinical procedures,” explains Zlatko Trajanoski, who is also responsible for this project, which is funded with 1.5 million € over a five-year period.

APERIM Partners present preliminary results at the annual meeting in Utrecht

The venerable University of Utrecht, in short distance to Amsterdam, was venue of the 2nd partner meeting of APERIM. From 3rd to 4th of April APERIM partners met to present and discuss first project results. In particular, developed software and databases to address specific challenges in the field of cancer immunology were presented. Regarding the planned objectives, good progress could be achieved. Various software prototypes have been developed and a number of web databases developed during the course of the project are now publicly accessible:

Data Integration and development of an advanced bioinformatic platform
The Cancer Immunome Atlas Database – TCIA was launched by the Medical University of Innsbruck. TCIA provides a comprehensive view of the cellular composition of the intratumoral immune infiltrates as well as cancer antigens of >8000 samples from The Cancer Genome Atlas (TCGA), which were analysed using state-of-the-art immunogenomic analytical pipelines.
Link: http://tcia.at

Automated quantification tool for tumor infiltrating lymphocytes (TILs) to stratify colorectal cancer patients
Partner Definiens developed the TIL analyser software as an image analysis solution to detect, quantify and evaluate tumour infiltrating lymphocytes in slide H&E images. This software is now tested and evaluated by APERIM partner data. INSERM in parallel is working the annotation of whole slide images in order to stratify CRC patients using immunoscore. Partner CNIC worked on the digital TIL sorter to quantify TILs from RNA-Seq data. A prototype software is established and will now be tested and evaluated, before it is integrated in TCIA bioinformatic platform.

Development of an analytical pipeline for NGS-guided personalised cancer vaccines
Three software parts will form this pipeline package. Partner TRON already successfully developed the iCaM2.0 NGSanalyser software, which is actually evaluated with clinical data. To predict which targets of the tumor surface will be immunogenic, University of Utrecht is working intensively on the second software, the immunopredictor with the planned delivery date in fall. As further important parts of the pipeline, University of Tübingen developed the EpitopeSelector as an open source software, further a novel approach to solve the subsequent problem of assembling the selected neo-epitopes into the final vaccine and a necessary framework to rapidly develop such advanced computational immunology approaches.


The integration of all applications in one pipeline to identify optimal cancer vaccine targets will be reached by the end of the Project.

Predicting T-cell receptor (TCR) specifity for adoptive T-cell cancer therapy
Based on available NGS data Partner AptaIT generated a TCR Kit to analyse deriving TCR sequences. This software will now be tested and evaluated with further data. In order to develop a novel method to predict TCR specificity, Masaryk University built a database containing more than 5000 TCRs with known specificities, which can be now extended with additional datasets.
Link: https://vdjdb.cdr3.net/
Additionally, together with research results from University of Utrecht concerning epitope structures (pMHC), a TCR ontology database was generated.
Link: https://github.com/antigenomics/vdjdb-db
All tools together will finally help to describe TCR reactivity in human cancers.

In the remaining project year, Partners with access to clinical data will provide these data to test and evaluate the developed software modules and to fill open access databases. Preliminary results already show the high value and successful progress of the innovative APERIM bioinformatics platform.

Group picture Aperim

Partners of the H2020 project APERIM, coordinated by Univ.-Prof. Zlatko Trajanoski (front left) met in Utrecht to present and discuss preliminary project results.



Partner CNIC presents APERIM research in The New York Academy of Sciences

March 7th,
Last week The New York Academy of Sciences hosted the symposium “Quantitative Approaches in Immuno-Oncology”. Dr Carlos Torroja from Centro National de Investigationes Cardiovasculares Carlos III (CNIC) thereby presented first APERIM results in a much-noticed poster.
The symposium aimed to explore the promising field of immunotherapy in cancer treatment, covering the breadth of approaches needed to quantify interactions between tumors and the immune system. Quantitative Immuno-Oncology—sitting at the interface between immuno-oncology and quantitative approaches from mathematics, physics, and computer science—has emerged as a field that can significantly advance the ability to interpret existing large datasets, and perform predictive analyses.
In his poster Carlos Torroja presented first results of the APERIM project. The reserachers around Fátima Sánchez-Cabo (CNIC) and Zlatko Trajanoski from the Medical University of Innsbruck applied deep learning on a set of markers selected as very predictive of the amount of lymphocytes and tested it on the 1207 breast cancer samples from TCGA. The results agreed relatively well with the annotated amount of lymphocytes from TCGA and furthermore also the predicted survival time of the groups.

Download Poster

Image: © Fátima Sánchez-Cabo

Further Information:
Prof Dr Fátima Sánchez Cabo
Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid 28029, Spain
Fátima Sánchez Cabo: fscabo@cnic.es

Science-paper with Input of APERIM´s work group

Researchers from the Netherlands Cancer Institute and University of Oslo/Oslo University Hospital show that even if one’s own immune cells cannot recognize and fight their tumors, someone else’s immune cells might. Their proof of principle study was published in the journal Science on May 19th.

The published data show that adding mutated DNA from cancer cells into immune stimulating cells from healthy donors create an immune response by the healthy immune cells. By inserting the tumor cell recognition elements from the donor immune cells back into the immune cells of the cancer patients, the researchers were able to make cancer patients’ own immune cells recognize cancer cells.

The APERIM work group of the Netherlands Cancer Institute contributed with first project results to this remarkable study. Within APERIM, Ton Schumacher aims to further investigate the role of tumor-specific mutations and the resulting neo-antigens as targets for immunotherapy, both to be able to predict T-cell reactivity and to find ways to enhance neo-antigen specific T cell immunity in cancer patients.


Original paper:

Press release Euerkalert:


Ton Schumacher
Senior Member NKI-AVL & Professor of Immunotechnology Leiden University
The Netherlands Cancer Institute
t.schumacher (at) nki.nl

Tumor instability and impact on patient survival: it all depends on the immune response.

Genetic and molecular characteristics are often used to classify tumors because stratification is the first step towards individualized cancer medicine with the aim to find the optimal treatment for each patient. In colorectal cancer for an example the diagnosis to have a genetic instable tumor indicates a favorable prognosis for the patient. Researchers from the Laboratory of Integrative Cancer Immunology led by Jérôme Galon (Inserm, Universités Pierre-

et-Marie-Curie et Paris Descartes, Cordeliers Research Center in Paris, France), in collaboration with MedImmune, the global biologics research and development arm of AstraZeneca, now could prove that the immunologic environment in and around colorectal cancer even plays a greater role to

stratify tumors than classification based on tumor (in)stability. These results could have important clinical implications for immunotherapy. The article detailing these results is published in the journal Immunity on March 15th 2016.

Press Release


Contact: Jérôme GALON

Laboratory of Integrative Cancer Immunology

INSERM UMRS1138, Cordeliers Research Center

15 rue de l’Ecole de Medecine, 75006, Paris, France

Email: jerome.galon@crc.jussieu.fr


Source: Integrative analyses of colorectal cancer show Immunoscore is a stronger predictor of patient survival than microsatellite instability, Immunity, 44, 1–14, March 15, 2016  Full text