Magdalena Feldhahn

OptiType: precision HLA typing from next-generation sequencing data

Motivation: The human leukocyte antigen (HLA) gene cluster plays a crucial role in adaptive immunity and is thus relevant in many biomedical applications. While next-generation sequencing data are often available for a patient, deducing the HLA genotype is difficult because of substantial sequence similarity within the cluster and exceptionally high variability of the loci. Established approaches, therefore, rely on specific HLA enrichment and sequencing techniques, coming at an additional cost and extra turnaround time. Result: We present OptiType, a novel HLA genotyping algorithm based on integer linear programming, capable of producing accurate predictions from NGS data not specifically enriched for the HLA cluster. We also present a comprehensive benchmark dataset consisting of RNA, exome and whole-genome sequencing data. OptiType significantly outperformed previously published in silico approaches with an overall accuracy of 97% enabling its use in a broad range of applications. Contact: szolek@informatik.uni-tuebingen.de Supplementary information: Supplementary data are available at Bioinformatics online.

FRED-a framework for T-cell epitope detection

Summary: Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different methods become increasingly important. We have developed FRED, an extendable, open source software framework for key tasks in immunoinformatics. In this, its first version, FRED offers easily accessible prediction methods for MHC binding and antigen processing as well as general infrastructure for the handling of antigen sequence data and epitopes. FRED is implemented in Python in a modular way and allows the integration of external methods. Availability: FRED is freely available for download at http://www-bs.informatik.uni-tuebingen.de/Software/FRED. Contact: feldhahn@informatik.uni-tuebingen.de

EpiToolKit—a web server for computational immunomics

Predicting the T-cell-mediated immune response is an important task in vaccine design and thus one of the key problems in computational immunomics. Various methods have been developed during the last decade and are available online. We present EpiToolKit, a web server that has been specifically designed to offer a problem-solving environment for computational immunomics. EpiToolKit offers a variety of different prediction methods for major histocompatibility complex class I and II ligands as well as minor histocompatibility antigens. These predictions are embedded in a user-friendly interface allowing refining, editing and constraining the searches conveniently. We illustrate the value of the approach with a set of novel tumor-associated peptides. EpiToolKit is available online at www.epitoolkit.org.

miHA-Match: Computational detection of tissue-specific minor histocompatibility antigens

Allogenic stem cell transplantation has shown considerable success in a number of hematological malignancies, in particular in leukemia. The beneficial effect is mediated by donor T cells recognizing patient-specific HLA-binding peptides. These peptides are called minor histocompatibility antigens (miHAs) and are typically caused by single nucleotide polymorphisms. Tissue-specific miHAs have successfully been used in anti-tumor therapy without causing unspecific graft-versus-host reactions. However, only a small number of miHAs have been identified to date, limiting the clinical use. Here we present an immunoinformatics pipeline for the identification of miHAs. The pipeline can be applied to large-scale miHA screening, for example, in the development of diagnostic tests. Another interesting application is the design of personalized miHA-based cancer therapies based on patient-donor pair-specific miHAs detected by this pipeline. The suggested method covers various aspects of genetic variant detection, effects of alternative transcripts, and HLA-peptide binding. A comparison of our computational pipeline and experimentally derived datasets shows excellent agreement and coverage of the computationally predicted miHAs.

T-cell epitope prediction based on self-tolerance

T-cell epitopes, i.e., peptides capable of inducing a T-cell mediated immune response, represent suitable components for vaccines against infectious diseases and cancer. The development of accurate T-cell epitope prediction methods is thus of great interest to immunologists and the pharmaceutical industry. Whether a particular peptide is a T-cell epitope depends on the availability of (a) an MHC molecule capable of presenting the peptide on the cell surface and (b) a suitable T cell. In order to ensure self-tolerance of the immune system, T cells reactive to self-peptides are eliminated via negative selection processes. The composition of the T-cell repertoire thus depends on the host proteome. These complex dependencies along with a lack of data render T-cell epitope prediction a rather challenging problem. It is commonly reduced to the simpler MHC binding prediction problem. While state-of-the-art MHC binding prediction methods are highly accurate, the actual prediction of T-cell epitopes leaves room for improvement. Previously proposed approaches to T-cell epitope prediction do not take the dependencies on the host proteome into account but utilize peptide sequence information only. Their low prediction accuracies can be attributed to this limited view on T-cell reactivity and to a biased data basis. Moving from simple sequence-based predictors to predictors taking system-wide properties into account, we present a novel approach to T-cell epitope prediction combining sequence information and information on self-tolerance. In a thorough study on a small but unbiased data set, our method outperforms purely sequence-based predictors, indicating the validity of our approach.

No evidence of viral genomes in whole-transcriptome sequencing of three melanoma metastases

Abstract:  Several viruses are known to cause cancer, such as human herpes virus 8 in Kaposi sarcoma and human papilloma viruses in cervical cancer. Recently, Merkel cell polyoma virus (MCPyV) has been described in 80% of Merkel cell carcinomas (MCC). Similarly to MCC and Kaposi sarcoma, melanoma incidence is increased in immunosuppressed patients. We asked whether infection by known or yet unknown viruses may play a role in melanoma development as well. To detect viral sequences expressed in melanoma cells, we analysed three melanoma metastases by whole‐transcriptome sequencing and digital transcriptome subtraction. None of the samples investigated harboured viral sequences. In contrast, artificial viral sequences and MCPyV transcripts used as a positive control for the bioinformatics analysis were detected. This renders it less likely that viruses are frequently involved in melanoma induction. A larger number of melanoma transcriptome sequencings are required to rule out viruses as a relevant pathogen.

Abstract A113: iVacALL: A personalized peptide-vaccination design platform for pediatric acute lymphoblastic leukemia patients based on patient-individual tumor-specific variants

We established a platform for the design of patient-individual peptide vaccination cocktails by combination of whole exome sequencing of tumor and normal tissue with in silico epitope prediction algorithms for individual patient HLA types. Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy. Standard chemotherapy is a successful treatment in 80% of patients, only about 20% develop a relapse, however these patients have a dismal prognosis. Prevention of relapse after first-line chemotherapy or stem cell transplantation (SCT) is therefore mandatory. Accumulation of somatic mutations is one characteristic feature of malignant cells. These single nucleotide variants (SNVs) can lead to altered amino acid sequences of the translated proteins, which in turn can be presented as antigenic peptides on HLA molecules of the malignant cells. Mutated peptides represent ideal T cell targets as they are true neoantigens, specific for the tumor, and should not have been subject to central tolerance selection mechanisms. A peptide vaccination composed of mutated T cell epitopes specific for individual patient tumors is therefore a promising approach to prevent relapse in high-risk patients. For this purpose we detect nonsynonymous mutations by whole exome and transcriptome sequencing of patient leukemic blasts and normal reference tissue. HLA binding peptides harboring the altered amino acids are subsequently predicted in silico by algorithms SYFPEITHI, NetMHC and NetMHCpan for the patients9 individual HLA type. Whole exome sequencing was performed for 17 c-ALL, 2 cortical T-ALL and 1 pro-B-ALL sample pairs. ALL-specific SNVs, as well as insertions and deletions (InDels) were identified using a comparative bioinformatics pipeline. The determined variants were further validated by deep sequencing in 9/20 patients so far, with an average of 12 (+/- 8) validated mutations per patient. For all patients with validated variants, MHC class I and MHC class II epitopes could be predicted successfully. We applied the platform for 3 patients based on compassionate need and designed individual peptide vaccines. One patient underwent haploidentical SCT with relapsed c-ALL, a second patient received autologous SCT with ependymoma and the third patient got allogeneic SCT with pro-B ALL. In all cases validated mutations could be identified and epitope prediction was performed for MHC I & II binders. The predicted peptides were synthesized and vaccination cocktails were formulated. The vaccination schedule provides 16 vaccinations over 33 weeks using GM-CSF and Imiquimod as adjuvant. The vaccination was generally well tolerated. Response to the vaccination was monitored by detection of T cells recognizing the vaccinated peptides occurring over time in peripheral blood of the patients. Monitoring was performed for each vaccination time point by prestimulation with the peptides and subsequent intracellular cytokine staining (ICS) of T cells and FACS analysis. In all 3 patients we could detect a developing CD4+ T cell response against the vaccinated mutated MHC II binding peptides. Whole exome sequencing of pediatric ALL patients is feasible and yields a small amount of validated mutations per patients. However, these few mutations seem sufficient to predict HLA-binding peptides that are immunogenic when vaccinated and elicit specific T cell responses in patients. Moreover, the platform is not limited to ALL/leukemia but can also be applied for solid tumor patients. Citation Format: Armin Rabsteyn, Christina Kyzirakos, Christopher Schroder, Marc Sturm, Christopher Mohr, Mathias Walzer, Ulrike Pfluckhahn, Michael Walter, Magdalena Feldhahn, Karoline Laske, Michael Bonin, Martin Ebinger, Stefan Stevanovic, Peter Bauer, Oliver Kohlbacher, Cecile Gouttefangeas, Hans-Georg Rammensee, Rupert Handgretinger, Peter Lang. iVacALL: A personalized peptide-vaccination design platform for pediatric acute lymphoblastic leukemia patients based on patient-individual tumor-specific variants. [abstract]. In: Proceedings of the CRI-CIMT-EATI-AACR Inaugural International Cancer Immunotherapy Conference: Translating Science into Survival; September 16-19, 2015; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(1 Suppl):Abstract nr A113.

1169PPANEL-BASED NEXT-GENERATION SEQUENCING OF CANCER-RELEVANT GENES AS TREATMENT DECISION SUPPORT

ABSTRACT Aim: Knowledge about somatic driver mutations in cancer is crucial for decision-making in personalized therapy. Samples with low tumor content cannot be sequenced reliably with traditional methods. We have developed a complete pipeline to sequence more than 550 cancer-relevant genes in parallel using next-generation sequencing of FFPE or frozen tumor tissue. Interpretation of somatic mutations yields a medical report for the oncologist to support treatment decision. Methods: FFPE or frozen tumor sections are macrodissected and DNA is extracted from tumor material and patients blood. Enrichment for more than 550 cancer-relevant genes is performed using a specially designed custom enrichment kit and the samples are sequenced to a very high depth. Somatic mutations are detected with very high sensitivity by comparison of sequencing data from the tumor and the blood sample. Clinical interpretation of mutations is performed by a team of scientists and medical doctors. A medical report listing actionable mutations, potentially beneficial targeted drugs and contraindications, as well as clinical trials the patient may wish to participate in. Results: Sensitive and specific detection of somatic mutations is achieved with this diagnostic sequencing panel, even in samples with a tumor content as low as 20%. A majority of patient samples contains actionable mutations, allowing personalized treatment. In addition, the panel allows the classification of CUP as metastasis of previous cancer. The analysis is also able to assign the source of metastasis to one of multiple primary tumors, where histology was not informative. In addition, the panel allowed the classification of CUP as metastasis of a previous cancer and assignment of metastases to one of multiple primary tumors, when histology was not informative. Conclusions: Conclusions: Comprehensive molecular profiling using panel-based next-generation sequencing can identify therapeutic targets in tumor patients. This helps to decide on an optimized therapy and to clarify the source of metastases. Early sequencing can also save costs and provide an advantage to the patient by excluding therapies that will likely have no benefit. Disclosure: M. Menzel: Employee of CeGaT; S. Armeanu-Ebinger: Employee of CeGaT; M. Feldhahn: Employee of CeGaT; S. Biskup: Founder and director of CeGaT.

miHA-match: computational detection of tissue-specific minor histocompatibility antigens

For certain hematologic malignancies, allogenenic hematopoietic stem cell transplantation (alloHCT) is a well-established therapy [1]. Initially alloHCT was used to strengthen the hematopoiesis after chemo-/radiotherapy, but nowadays the beneficial effects of graft-versus-leukemia (GvL) or graft-versus-tumor (GvT) is the main aim [2]. The beneficial effects are mediated by donor T cells recognizing patient-specific HLA-binding peptides. These peptides are called minor histocompatibility antigens (miHAs) and are typically caused by single nucleotide polymorphisms. Tissue-specific miHAs have successfully been used in anti-tumor therapy without causing unspecific graft-versus-host reactions. However, only a small number of miHAs have been identified to date, limiting the clinical use. There is thus a substantial need for fast and accurate identification of novel miHAs to enable immunotherapy for a large number of patients. Here we present an immunoinformatics pipeline for the identification of miHAs. The pipeline can be applied to large-scale miHA screening, for example, in the development of diagnostic tests. Another interesting application is the design of personalized miHA-based cancer therapies based on patient-donor pair-specific miHAs detected by this pipeline. The suggested method covers various aspects of genetic variant detection, effects of alternative transcripts, and HLA-peptide binding. A comparison of our computational pipeline and experimentally derived datasets shows excellent agreement and coverage of the computationally predicted miHAs.