Clinical and Genetic Tumor Characteristics of Responding and Non-Responding Patients to PD-1 Inhibition in Hepatocellular Carcinoma.

Immune checkpoint inhibitors (ICIs) belong to the therapeutic armamentarium in advanced hepatocellular carcinoma (HCC). However, only a minority of patients benefit from immunotherapy. Therefore, we aimed to identify indicators of therapy response. This multicenter analysis included 99 HCC patients. Progression-free (PFS) and overall survival (OS) were studied by Kaplan-Meier analyses for clinical parameters using weighted log-rank testing. Next-generation sequencing (NGS) was performed in a subset of 15 patients. The objective response (OR) rate was 19% median OS (mOS)16.7 months. Forty-one percent reached a PFS > 6 months; these patients had a significantly longer mOS (32.0 vs. 8.5 months). Child-Pugh (CP) A and B patients showed a mOS of 22.1 and 12.1 months, respectively. Ten of thirty CP-B patients reached PFS > 6 months, including 3 patients with an OR. Tumor mutational burden (TMB) could not predict responders. Of note, antibiotic treatment within 30 days around ICI initiation was associated with significantly shorter mOS (8.5 vs. 17.4 months). Taken together, this study shows favorable outcomes for OS with low AFP, OR, and PFS > 6 months. No specific genetic pattern, including TMB, could identify responders. Antibiotics around treatment initiation were associated with worse outcome, suggesting an influence of the host microbiome on therapy success

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

Computergestützte Methoden für die Entwicklung von personalisierten Krebstherapien basierend auf genomischen Daten

Despite the considerable progress in understanding cancer biology and cancer development that has been made over the last decades, the treatment options for cancer are still insufficient. This can be attributed to the tremendous heterogeneity of cancers, with respect to appearance, clinical outcome, and underlying genetic alterations. In traditional concepts of drug design and drug administration, pathologically similar diseases are treated with the same drugs. These approaches are not adequate to face the complexity of cancer. Personalized or individualized approaches, targeting individual characteristics of tumors, are promising concepts to develop successful treatment options for cancer with little side-effects. The human organism is equipped with a powerful system that is capable of targeting abnormal cells specifically and efficiently: the immune system. T cells can distinguish healthy cells from infected or aberrant cells by scanning peptides that are presented on the surface of other cells. Genetic alterations in cancer cells can lead to the presentation of cancer-specific peptides that drive a very specific immune reaction against the cancer cells. These peptides are called cancer-specific T-cell epitopes. Each patient’s immune system is individual with respect to the peptides that can elicit an immune response. The design of tailor-made immunotherapies against individual tumors can thus be realized by using sets of patient- and tumor-specific T-cell epitopes in so-called epitope-based vaccines. A first major challenge in the development of such individualized therapies lies in the analysis of genetic information of individual cancers, which is necessary to detect cancer-specific mutations. A second challenge is the correct identification and selection of T-cell epitopes resulting from these mutations. In this thesis, we present computational methods that address these challenges. Starting from next-generation sequencing data of cancer and normal tissue from individual patients, we identify those mutations that are uniquely present in the tumor. We integrate information from gene expression, biological pathways, and functional annotation of genes and proteins to select suitable mutations. These mutations form the basis for potential targets for individualized immunotherapies. We present prediction algorithms based on machine learning approaches that identify T-cell epitopes that are specific for a patient’s tumor and immune system. In order to bring the computational methods to clinical applications, results have to be obtained in a reliable, reproducible, and timely manner, and have to be made available to clinical researchers in an easy-to-use and intuitive way. An additional focus of this thesis is thus the development of pipelines, tools, and user-interfaces that facilitate a close integration between the computational analysis with the experimental application in a clinical setting. We apply the presented methods to clinical data. The results show that a combination of high-throughput data, computational data analysis, and accurate prediction methods with clinical research can promote the development of new individualized treatment options for cancer.Die Fortschritte der letzten Jahrzehnte in der Krebsforschung haben zu einem deutlich verbesserten Verständnis der Ursachen und Entwicklung von Krebs geführt. Dieses Wissen konnte bisher allerdings nur in relativ geringem Maß in neue Therapieoptionen für Krebs umgesetzt werden. Eine Erklärung hierfür ist die große Heterogenität von Krebs in Bezug auf das Erscheinungsbild, den klinischen Verlauf und auf die dem Krebs zugrunde liegenden genetischen Veränderungen. Bei traditionellen Ansätzen der Wirkstoffentwicklung und der medikamentösen Therapie werden pathologisch ähnliche Krankheiten mit den gleichen Wirkstoffen behandelt. Diese Ansätze sind nicht ausreichend um der großen Komplexität von Krebs zu begegnen. Personalisierte oder individualisierte Ansätze, die gezielt individuelle Eigenschaften von Tumoren angreifen, sind dagegen ein vielversprechendes Konzept für die Entwicklung wirksamer und nebenwirkungsarmer Krebstherapien. Der menschliche Organismus ist mit dem Immunsystem bereits mit einem System ausgestattet, das ist der Lage ist abnorme Zellen gezielt und effizient anzugreifen. Mit Hilfe von auf der Oberfläche von Körperzellen präsentierten Peptiden sind T-Zellen in der Lage, gesunde Zellen von entarteten zu unterscheiden. Welche Peptide dabei erkannt werden können unterscheidet sich von Patient zu Patient. Genetische Veränderungen in Krebszellen können zur Präsentation von krebsspezifischen Peptiden führen, die eine gezielte Immunantwort gegen die Krebszellen auslösen. Solche krebsspezifischen T-Zell-Epitope können in Form von epitopbasierten Impfstoffen zur Bekämpfung von Tumoren verwendet werden. Dieses Verfahren bietet einen Ansatzpunkt für die Entwicklung von maßgeschneiderten Immuntherapien. Eine große Herausforderung bei der Entwicklung solcher individueller Therapieansätze ist die Auswertung genetischer Informationen von einzelnen Tumoren für die Detektion krebsspezifischer Mutationen. Eine weitere große Herausforderung ist die Identifikation von T-Zell-Epitopen, die durch diese Mutationen erzeugt werden. In dieser Arbeit stellen wir Algorithmen und Methoden zur Lösung dieser Herausforderungen vor. Ausgehend von Sequenzierungsdaten von Tumor- und Normalgewebe von einzelnen Patienten werden Mutationen identifiziert, die zwar im Tumor aber nicht im Normalgewebe vorkommen. Informationen über Genexpression, biologische Netzwerke und funktionelle Annotation von Genen und Proteinen werden in die Auswahl von Mutationen einbezogen, die als Angriffspunkt für eine Immuntherapie geeignet sind. Wir stellen Algorithmen zur Identifikation von T-Zell-Epitopen vor, die spezifisch für den Tumor und gleichzeitig auf Immunsystem des Patienten abgestimmt sind. Damit computergestützte Methoden in der klinischen Forschung zum Einsatz kommen können müssen deren Ergebnisse zuverlässig und reproduzierbar sein und Kooperationspartnern in der Klinik zeitnah und verständlich zur Verfügung gestellt werden. Die Entwicklung von Analysepipelines und intuitiven Benutzeroberflächen, die eine enge Verknüpfung zwischen spezialisierten bioinformatischen Analysen und klinischer Forschung erleichtern, ist daher ein weiterer Schwerpunkt dieser Arbeit. Die vorgestellten Methoden werden im zweiten Teil der Arbeit auf klinische Daten angewendet. Die Ergebnisse zeigen, dass die Kombination von Hochdurchsatzdaten, rechnergestützter Datenanalyse, zuverlässigen Vorhersagemethoden und klinischer Forschung einen wichtigen Beitrag zur Entwicklung individualisierter Krebstherapien leisten kann

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: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Biomarkers Associated with Immune-Related Adverse Events under Checkpoint Inhibitors in Metastatic Melanoma

Immune checkpoint inhibitors (ICI) have revolutionized the therapeutic landscape of metastatic melanoma. However, ICI are often associated with immune-related adverse events (IRAE) such as colitis, hepatitis, pancreatitis, hypophysitis, pneumonitis, thyroiditis, exanthema, nephritis, myositis, encephalitis, or myocarditis. Biomarkers associated with the occurrence of IRAE would be desirable. In the literature, there is only little data available and furthermore mostly speculative, especially in view of genetic alterations. Our major aim was to check for possible associations between NGS-based genetic alterations and IRAE. We therefore analyzed 95 melanoma patients with ICI and evaluated their NGS results. We checked the data in view of potential associations between copy number variations (CNVs), small variations (VARs), human leucocyte antigen (HLA), sex, blood count parameters, pre-existing autoimmune diseases and the occurrence of IRAE. We conducted a literature research on genetic alterations hypothesized to be associated with the occurrence of IRAE. In total, we identified 39 genes that have been discussed as hypothetical biomarkers. We compared the list of these 39 genes with the tumor panel that our patients had received and focused our study on those 16 genes that were also included in the tumor panel used for NGS. Therefore, we focused our analyses on the following genes: AIRE, TERT, SH2B3, LRRK2, IKZF1, SMAD3, JAK2, PRDM1, CTLA4, TSHR, FAN1, SLCO1B1, PDCD1, IL1RN, CD274, UNG. We obtained relevant results: female sex was significantly associated with the development of hepatitis, combined immunotherapy with colitis, increased total and relative monocytes at therapy initiation were significantly associated with the development of pancreatitis, the same, pre-existing autoimmune diseases. Further significant associations were as follows: HLA homozygosity (hepatitis), and VARs on SMAD3 (pancreatitis). Regarding CNVs, significant markers included PRDM1 deletions and IL1RN (IRAE), CD274 duplications and SLCO1B1 (hepatitis), PRDM1 and CD274 (encephalitis), and PRDM1, CD274, TSHR, and FAN1 (myositis). Myositis and encephalitis, both, were associated with alterations of PRDM1 and CD274, which might explain their joined appearance in clinical practice. The association between HLA homozygosity and IRAE was clarified by finding HLA-A homozygosity as determining factor. We identified several genetic alterations hypothesized in the literature to be associated with the development of IRAE and found significant results concerning pre-existing autoimmune diseases and specific blood count parameters. Our findings can help to better understand the development of IRAE in melanoma patients. NGS might be a useful screening tool, however, our findings have yet to be confirmed in larger studies

Immunoinformatics: an integrated scenario

Genome sequencing of humans and other organisms has led to the accumulation of huge amounts of data, which include immunologically relevant data. A large volume of clinical data has been deposited in several immunological databases and as a result immunoinformatics has emerged as an important field which acts as an intersection between experimental immunology and computational approaches. It not only helps in dealing with the huge amount of data but also plays a role in defining new hypotheses related to immune responses. This article reviews classical immunology, different databases and prediction tools. It also describes applications of immunoinformatics in designing in silico vaccination and immune system modelling. All these efforts save time and reduce cost