Advanced Immunoinformatics Approaches for Precision Medicine

Genomic sequencing and other ’-omic’ technologies are slowly changing biomedical practice. As a result, patients now can be treated based on their molecular profile. Especially the immune system’s variability, in particular that of the human leukocyte antigen (HLA) gene cluster, makes such a paradigm indispensable when treating illnesses such as cancer, autoimmune diseases, or infectious diseases. It can be, however, costly and time-consuming to determine the HLA genotype with traditional means, as these methods do not utilize often pre-existing sequencing data. We therefore proposed an algorithmic approach that can use these data sources to infer the HLA genotype. HLA genotyping inference can be cast into a set covering problem under special biological constraints and can be solved efficiently via integer linear programming. Our proposed approach outperformed previously published methods and remains one of the most accurate methods to date. We then introduced two applications in which a HLA-based stratification is vital for the efficacy of the treatment and the reduction of its adverse effects. In the first example, we dealt with the optimal design of string-of-beads vaccines (SOB). We developed a mathematical model that maximizes the efficacy of such vaccines while minimizing their side effects based on a given HLA distribution. Comparisons of our optimally designed SOB with experimentally tested designs yielded promising results. In the second example, we considered the problem of anti-drug antibody (ADA) formation of biotherapeutics caused by HLA presented peptides. We combined a new statistical model for mutation effect prediction together with a quantitative measure of immunogenicity to formulate an optimization problem that finds alterations to reduce the risk of ADA formation. To efficiently solve this bi-objective problem, we developed a distributed solver that is up to 25-times faster than state-of-the art solvers. We used our approach to design the C2 domain of factor VIII, which is linked to ADA formation in hemophilia A. Our experimental evaluations of the proposed designs are encouraging and demonstrate the prospects of our approach. Bioinformatics is an integral part of modern biomedical research. The translation of advanced methods into clinical use is often complicated. To ease the translation, we developed a programming library for computational immunology and used it to implement a Galaxy-based web server for vaccine design and a KNIME extension for desktop PCs. These platforms allow researchers to develop their own immunoinformatics workflows utilizing the platform’s graphical programming capabilities

Vaccines meet big data: State-ofthe- Art and future prospects. From the classical 3is ("isolate-inactivate- inject") vaccinology 1.0 to vaccinology 3.0, vaccinomics, and Beyond: A historical overview

Vaccines are public health interventions aimed at preventing infections-related mortality, morbidity, and disability. While vaccines have been successfully designed for those infectious diseases preventable by preexisting neutralizing specific antibodies, for other communicable diseases, additional immunological mechanisms should be elicited to achieve a full protection. "New vaccines" are particularly urgent in the nowadays society, in which economic growth, globalization, and immigration are leading to the emergence/ reemergence of old and new infectious agents at the animal-human interface. Conventional vaccinology (the so-called "vaccinology 1.0") was officially born in 1796 thanks to the contribution of Edward Jenner. Entering the twenty-first century, vaccinology has shifted from a classical discipline in which serendipity and the Pasteurian principle of the three Is (isolate, inactivate, and inject) played a major role to a science, characterized by a rational design and plan ("vaccinology 3.0"). This shift has been possible thanks to Big Data, characterized by different dimensions, such as high volume, velocity, and variety of data. Big Data sources include new cutting-edge, high-throughput technologies, electronic registries, social media, and social networks, among others. The current mini-review aims at exploring the potential roles as well as pitfalls and challenges of Big Data in shaping the future vaccinology, moving toward a tailored and personalized vaccine design and administration. © 2018 Bragazzi, Gianfredi, Villarini, Rosselli, Nasr, Hussein, Martini and Behzadifar

Vaccines meet big data: State-ofthe- Art and future prospects. From the classical 3is ("isolate-inactivate- inject") vaccinology 1.0 to vaccinology 3.0, vaccinomics, and Beyond: A historical overview

Vaccines are public health interventions aimed at preventing infections-related mortality, morbidity, and disability. While vaccines have been successfully designed for those infectious diseases preventable by preexisting neutralizing specific antibodies, for other communicable diseases, additional immunological mechanisms should be elicited to achieve a full protection. "New vaccines" are particularly urgent in the nowadays society, in which economic growth, globalization, and immigration are leading to the emergence/ reemergence of old and new infectious agents at the animal-human interface. Conventional vaccinology (the so-called "vaccinology 1.0") was officially born in 1796 thanks to the contribution of Edward Jenner. Entering the twenty-first century, vaccinology has shifted from a classical discipline in which serendipity and the Pasteurian principle of the three Is (isolate, inactivate, and inject) played a major role to a science, characterized by a rational design and plan ("vaccinology 3.0"). This shift has been possible thanks to Big Data, characterized by different dimensions, such as high volume, velocity, and variety of data. Big Data sources include new cutting-edge, high-throughput technologies, electronic registries, social media, and social networks, among others. The current mini-review aims at exploring the potential roles as well as pitfalls and challenges of Big Data in shaping the future vaccinology, moving toward a tailored and personalized vaccine design and administration. © 2018 Bragazzi, Gianfredi, Villarini, Rosselli, Nasr, Hussein, Martini and Behzadifar

Best practices for bioinformatic characterization of neoantigens for clinical utility

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types

Sequence analysis methods for the design of cancer vaccines that target tumor-specific mutant antigens (neoantigens)

The human adaptive immune system is programmed to distinguish between self and non-self proteins and if trained to recognize markers unique to a cancer, it may be possible to stimulate the selective destruction of cancer cells. Therapeutic cancer vaccines aim to boost the immune system by selectively increasing the population of T cells specifically targeted to the tumor-unique antigens, thereby initiating cancer cell death.. In the past, this approach has primarily focused on targeted selection of ‘shared’ tumor antigens, found across many patients. The advent of massively parallel sequencing and specialized analytical approaches has enabled more efficient characterization of tumor-specific mutant antigens, or neoantigens. Specifically, methods to predict which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell recognition improve predictions of immune checkpoint therapy response and identify one or more neoantigens as targets for personalized vaccines. Selecting the best/most immunogenic neoantigens from a large number of mutations is an important challenge, in particular in cancers with a high mutational load, such as melanomas and smoker-associated lung cancers. To address such a challenging task, Chapter 1 of this thesis describes a genome-guided in silico approach to identifying tumor neoantigens that integrates tumor mutation and expression data (DNA- and RNA-Seq). The cancer vaccine design process, from read alignment to variant calling and neoantigen prediction, typically assumes that the genotype of the Human Reference Genome sequence surrounding each somatic variant is representative of the patient’s genome sequence, and does not account for the effect of nearby variants (somatic or germline) in the neoantigenic peptide sequence. Because the accuracy of neoantigen identification has important implications for many clinical trials and studies of basic cancer immunology, Chapter 2 describes and supports the need for patient-specific inclusion of proximal variants to address this previously oversimplified assumption in the identification of neoantigens. The method of neoantigen identification described in Chapter 1 was subsequently extended (Chapter 3) and improved by the addition of a modular workflow that aids in each component of the neoantigen prediction process from neoantigen identification, prioritization, data visualization, and DNA vaccine design. These chapters describe massively parallel sequence analysis methods that will help in the identification and subsequent refinement of patient-specific antigens for use in personalized immunotherapy

Desarrollo de una vacuna oral contra Helicobacter pylori basada en la expresión de un antígeno multi-epítopo en Lactococcus lactis recombinante.

Helicobacter pylori es un agente infeccioso que coloniza la mucosa gástrica de la mitad de la población mundial. Esta bacteria ha sido reconocida como carcinógeno perteneciente al grupo 1 por la OMS por su papel en el desarrollo de gastritis, úlceras pépticas y cáncer. Debido al aumento de la resistencia a los antibióticos utilizados en la terapia convencional anti-H. pylori, el desarrollo de una vacuna eficaz es una alternativa de gran interés, que sigue siendo un desafío. Por lo tanto, es necesario un diseño de vacuna racional, estratégico y eficiente contra H. pylori donde el uso de las herramientas bioinformáticas más actuales pueda ayudar a lograrlo. En este estudio, se empleó un enfoque de inmunoinformático para diseñar una nueva vacuna oral multi-epítopo contra H. pylori. Nuestra vacuna multi-epítopo contiene un adyuvante de mucosa con la subunidad B de la toxina del cólera (CTB), que se usa para mejorar la inmunogenicidad oral. CTB se fusionó con once epítopos predichos de proteínas de H. pylori relacionados a los mecanismos de patogenicidad (UreB170-189, VacA459-478, CagA1103-1122, GGT106-126, NapA30-44 y OipA211-230) y colonización (HpaA33-52, FlaA487-506, FecA437-456, BabA129-149 y SabA540-559) y el péptido CKS9 (CKSTHPLSC), que dirige la vacuna hacia las células epiteliales de micropliegue para mejorar su absorción intestinal. La vacuna está compuesta por 373 aminoácidos y la predicción de la estructura secundaria mostró que contiene un 35% de hélices alfa, un 14% de láminas beta y un 49% de otras estructuras (bobina aleatoria y giro beta). Nuestros resultados indicaron que la calidad y la estabilidad del modelo 3D refinado final se mejoraron notablemente con base a las predicciones Ramachandran. Se obtuvo una puntuación de antigenicidad de 0.5547. La predicción de alergenicidad, demostró que la vacuna no es alergénica. El peso molecular y el pI teórico de la proteína fueron 40.7 kDa y 9.36, respectivamente. La solubilidad en la sobreexpresión en E. coli fue de 0.821589. La vida media se estimó en 30 h en los reticulocitos de mamíferos, >20 h en levadura y >10 h en E. coli. La vacuna fue catalogada como estable con un índice de inestabilidad (II) de 32.58. Los valores de GRAVY e índice alifático fueron -0.485 y 67.77, respectivamente. Por otro lado, se introdujeron los sitios de restricción NcoI y HindIII para su clonación en pNZ8084 para formar una construcción plasmática de 4,566 bp, lo que se confirmó por PCR y secuenciación. Se observó una banda de un peso aproximado de ~40 kDa en SDS-Page y Western blot tras la inducción con 10 ng/mL de nisina durante 5 h a 37°C en condiciones anaerobias. Observamos una inmunoreactividad del 100% en pacientes infectados por H. pylori mediante ensayos in vitro por ELISA. Nuestro nuevo diseño de vacuna oral podría ser un buen candidato contra H. pylori. Sin embargo, para validar los efectos profilácticos y terapéuticos de nuestro diseño de vacuna oral, se requieren estudios inmunológicos in vivo. ABSTRACT Helicobacter pylori is an infectious agent that colonizes the gastric mucosa of half the world population. This bacterium has been recognized as a carcinogen belonging to group 1 by the WHO for its role in the development of gastritis, peptic ulcers, and cancer. Due to the increased resistance to antibiotics used in conventional anti-H. pylori therapy, the development of an effective vaccine is an alternative of great interest, which remains a challenge. Therefore, a rational, strategic and efficient vaccine design against H. pylori is necessary where the use of the most current bioinformatics tools can help achieve this. In this study, an immunoinformatic approach was used to design a new multi-epitope oral vaccine against H. pylori. Our multi-epitope vaccine is composed of the cholera toxin B subunit (CTB) from Vibrio cholerae that is used as a mucosal adjuvant to improve oral immunogenicity. CTB was fused with eleven predicted epitopes of H. pylori proteins related to pathogenicity (UreB170-189, VacA459-478, CagA1103-1122, GGT106-126, NapA30-44 and OipA211-230) and colonization (HpaA33-52, FlaA487-506, FecA437-456, BabA129-149 and SabA540-559) mechanisms, and a CKS9 peptide (CKSTHPLSC), which directs the vaccine towards the micropliegue epithelial cells to improve its intestinal absorption. The vaccine is composed of 373 amino acids and the prediction of the secondary structure showed that it contains 35% alpha helices, 14% beta sheets and 49% other structures (random coil and beta rotation). Our results indicated that the quality and stability of the final refined 3D model were remarkably improved based on the Ramachandran predictions. An antigenicity score of 0.5547 was obtained. The prediction of allergenicity, showed that the vaccine is not allergenic. The molecular weight and the theoretical pI of the protein were 40.7 kDa and 9.36, respectively. The solubility in overexpression in E. coli was 0.821589. The half-life was estimated at 30 h in mammalian reticulocytes,> 20 h in yeast and> 10 h in E. coli. The vaccine was classified as stable with an instability index (II) of 32.58. The values of GRAVY and aliphatic index were -0.485 and 67.77, respectively. On the other hand, NcoI and HindIII restriction sites were introduced for cloning into pNZ8084 to form a plasmatic construct of 4,566 bp, which was confirmed by PCR and sequencing. A band with an approximate weight of ~ 40 kDa was observed in SDS-Page and Western blot after induction with 10 ng/mL nisin for 5 h at 37°C under anaerobic conditions. We observed a 100% immunoreactivity in patients infected with H. pylori by in vitro tests by ELISA. Our new oral vaccine design could be a good candidate against H. pylori. However, to validate the prophylactic and therapeutic effects of our oral vaccine design, immunological studies in vivo are required

Computational Methods towards Personalized Cancer Vaccines and their Application through a Web-based Platform

Cancer immunotherapy is a treatment option that involves or uses components of a patient’s immune system. Today, it is heading towards becoming an integral part of treatment plans together with chemotherapy, surgery, and radiotherapy. Personalized epitope-based vaccines (EVs) serve as one strategy that is truly personalized. Each patient possesses a distinct immune system, and each tumor is unique, rendering the design of a potent vaccine challenging and dependent on the patient and the tumor. The potency of a vaccine is reliant on the ability of its constituent epitopes – short, immunogenic antigen fragments – to trigger an immune response. To assess this ability, one has to take into account the individuality of the immune system, among others conditioned by the variability of the human leukocyte antigen (HLA) gene cluster. Determining the HLA genotype with traditional experimental techniques can be time- and cost-intensive. We proposed a novel HLA genotyping algorithm based on integer linear programming that is independent of dedicated data generation for the sole purpose of HLA typing. On publicly available next-generation sequencing (NGS) data, our method outperformed previously published approaches. HLA binding is a prerequisite for T-cell recognition, and precise prediction algorithms exist. However, this information is not sufficient to assess the immunogenic potential of a peptide. To induce an immune response, reactive T-cell clones with receptors specific for a peptide-HLA complex have to be present. We suggested a method for the prediction of immunogenicity that includes peripheral tolerance models, based on gut microbiome data, in addition to central tolerance, previously shown to increase performance. The comparison to a previously published method suggests that the incorporation of gut microbiome data and HLA-binding stability estimates do not enhance prediction performance. High-throughput sequencing provides the basis for the design of personalized EVs. Through genome and transcriptome sequencing of tumor and matched non-malignant tissue samples, cancer-specific mutations can be identified, which can be further validated using other technologies such as mass spectrometry (MS). Multi-omics approaches can result in the acquisition of several hundreds of gigabytes of data. Handling and analysis of such data usually require data management solutions and high-performance computing (HPC) infrastructures. We developed the web-based platform qPortal for data-driven biomedical research that allows users to manage and analyze quantitative biological data intuitively. To emphasize the advantages of our data-driven approach with an integrated workflow system, we conducted a comparison to Galaxy. Building on qPortal, we implemented the web-based platform iVacPortal for the design of personalized EVs to facilitate data management and data analysis in such projects. Further, we applied the implemented methods through iVacPortal in two studies of two distinct cancer entities, indicating the added value of our platform for the assessment of personalized EV candidates and alternative targets for cancer immunotherapy

Identifizierung und Charakterisierung antigenspezifischer T-Zell-Antworten gegen Glioblastome

Glioblastome (GBM) sind die häufigsten primären Hirntumore. Trotz maximaler Therapie mit Operation und Radiochemotherapie kommt es meist zu Rezidiven, so dass GBM-Patienten nur ein geringes Gesamtüberleben von unter 16 Monaten im Median zeigen. Die Immuntherapie, insbesondere Immuncheckpoint-Inhibitoren und zelluläre Therapien, haben in der Krebstherapie große Erfolgte gezeigt. Auch in Glioblastomen werden aktuell zahlreiche klinische Studien dazu durchgeführt. Vielversprechend sind neue Ansätze zur personalisierten, zellulären Therapie von Glioblastom-Patienten. Essenziell ist dafür eine Plattform zur effizienten Antigen-Identifikation und Zell-Modifikation. Im ersten Teil der Dissertation wurden Methoden zur in vitro Testung von humanen Leukozytenantigenen (HLA)-abhängigen Immunantworten untersucht, um grundlegende Bausteine für eine solche Plattform zu etablieren. Als antigenpräsentierende Zellen (APZ) wurden Dendritische Zellen (DZ) evaluiert, indem die Prozessierung und Antigenpräsentation mittels Transfektion von RNA-kodierten Antigenen untersucht wurde. Nach Prozessierung zeigte sich ein hoher Anteil an lebenden Zellen mit Expression von MHCII und CD80 als Zeichen einer effizienten Antigenpräsentation, die Transfektion der DZ mit Antigen-RNA zeigte jedoch nur eine geringe Effizienz. Alternativ wurde ein in vitro Ko-Kultur-Modell etabliert, bestehend aus antigenpräsentierenden Zellen (APZ), die patientenspezifisch mit HLA-Allelen ausgestattet werden können, sowie modifizierbaren Effektorzellen zur Testung der Antigenreaktivität von T-Zell-Rezeptoren (TZR). Dafür wurden HLA-Sequenzen aus Genmaterial von HLA-typisierten Spendern kloniert und eine Vektorenbank mit verschiedenen HLA-Allelen etabliert. Es konnte gezeigt werden, dass K562-Zellen als APZ mit personalisierten HLA-Rezeptoren transduziert werden und erfolgreich HLA-konforme Antigene präsentieren können. Diese konnten wiederum von TZR-transduzierten Jurkat76-Zellen spezifisch erkannt werden. Daneben wurde auch die Transfektion von primären T-Zellen mit transgenen TZR etabliert. Die Spezifität der TZR-transfizierten T-Zellen konnte mittels Tetramer-Färbung in der Durchflusszytometrie gezeigt werden. Im Vergleich zu personalisierten Therapieansätzen mit genetisch modifizierten Zellen kann die Therapie mit expandierten, autologen Tumor-infiltrierenden Lymphozyten (TIL) zeitaufwändige und technologisch anspruchsvolle Methoden, insbesondere die Antigen-Identifikation und Zell-Modifikation umgehen. In klinischen Studien konnte bereits gezeigt werden, dass die zelluläre Therapie mit autologen TIL durchführbar und sicher ist. Im zweiten Teil der Dissertation wurde die Verwendung von autologen TIL für die zelluläre Therapie von Glioblastom-Patienten exploriert, indem die Spezifität und die Dynamik des TZR-Repertoires in TIL-Kulturen untersucht wurde. Dafür wurden TIL-Kulturen von GBM-Patienten für zwei Wochen in vitro expandiert und das TZR-Repertoire mittels TZR-Sequenzierungen (-Seq) untersucht. Es konnte gezeigt werden, dass die in vitro TIL-Expansion zu einem starken Anstieg in der Klonalität des TZR-Repertoires führt. Gleichzeitig gibt es nur wenig Überschneidung zwischen den dominierenden TZR-Klonotypen vor und nach Expansion, was auf starke Unterschiede in den Expansionskapazitäten der einzelnen TZR-Klonotypen hindeutet. GBM besitzen wenig Neoepitope, die zudem häufig patientenspezifisch und subklonal sind. Im Rahmen dieser Dissertation wurden daher die im GBM häufig überexprimierten, unmutierten Tumor-assoziierten Antigene (TAA) als Zielantigene untersucht. Mittels Microarray und RNA-Seq wurden vergleichende Expressionsanalysen der GBM-Proben sowie gesunder Gehirn-RNA durchgeführt, um überexprimierte TAA individuell für die Patienten zu identifizieren. Alle Studienpatienten wurden zudem HLA-typisiert. Epitope wurden auf Basis von experimentell, validierter Immunogenität und in silico prädizierter Wahrscheinlichkeit für intrazelluläre Prozessierung sowie HLA-Präsentation ausgewählt. 68 Epitope wurden als Peptide in Ko-Kultur mit autologen, expandierten TIL-Kulturen untersucht. Dabei zeigte sich lediglich eine spontane Immunantwort in einer TIL-Kultur gegen RPS4Y1. Im letzten Schritt wurde die Relevanz von transkriptionellen Signaturen zum Zeitpunkt der Tumorresektion auf die TIL-Expansion untersucht. Kombinierte Einzelzell (sc)-TZR/RNA-Seq und TZRb-Seq vor und nach Expansion wurden durchgeführt. Die Expansionskapazität der einzelnen T-Zell-Klonotypen wurde mittels Vergleich der relativen Häufigkeit ex vivo und nach Expansion bestimmt. Für expandierte TIL-Subpopulationen konnte eine Gensignatur identifiziert werden. Diese Gensignatur beinhaltet Granzyme A, Granzyme H, Chemokine ligand 5, Natural killer cell granule protein 7 und Granulysin und war in allen drei Studien-Patienten stark hochreguliert. Die Gen-Ontologie-Analyse der Gensignatur zeigte eine aktivierte, proinflammatorische Gensignatur und eine Assoziation mit Antigenverarbeitung und -präsentation sowie Zellaktivierung und Zytolyse. Zusammenfassend wurden in dieser Dissertation wichtige Grundsteine für die personalisierte Immuntherapie gelegt, indem Methoden zur Antigen-Identifikation und Zell-Modifikation etabliert wurden. Weiterführend wurde die Dynamik und Spezifität von in vitro TIL-Kulturen als möglicher Therapieansatz für autologe Zelltherapien untersucht. Die Ergebnisse dieser Dissertation bieten somit wichtige Ansatzpunkte für die weitere Entwicklung der zellulären Immuntherapie in Glioblastom-Patienten

EpiToolKit-a web-based workbench for vaccine design

Summary: EpiToolKit is a virtual workbench for immunological questions with a focus on vaccine design. It offers an array of immunoinformatics tools covering MHC genotyping, epitope and neo-epitope prediction, epitope selection for vaccine design, and epitope assembly. In its recently re-implemented version 2.0, EpiToolKit provides a range of new functionality and for the first time allows combining tools into complex workflows. For inexperienced users it offers simplified interfaces to guide the users through the analysis of complex immunological data sets. Availability and implementation: http://www.epitoolkit.de Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online