Briefings in Bioinformatics Advance Access published July 14, 2015 Briefings in Bioinformatics, 2015, 1–15 doi: 10.1093/bib/bbv046 Paper

Personalized cancer immunotherapy using Systems Medicine approaches

Corresponding authors: Olaf Wolkenhauer, Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany and Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa. Tel.: (49) 381 498 7570. E-mail: [email protected]; Julio Vera, Laboratory of Systems Tumor Immunology, Department of Dermatology, Faculty of Medicine, University of Erlangen-Nuremberg, Erlangen, Germany. Tel.: (49) 9131 85 45876. E-mail: [email protected]

Abstract The immune system is by definition multi-scale because it involves biochemical networks that regulate cell fates across cell boundaries, but also because immune cells communicate with each other by direct contact or through the secretion of local or systemic signals. Furthermore, tumor and immune cells communicate, and this interaction is affected by the tumor microenvironment. Altogether, the tumor-immunity interaction is a complex multi-scale biological system whose analysis requires a systemic view to succeed in developing efficient immunotherapies for cancer and immune-related diseases. In this review we discuss the necessity and the structure of a systems medicine approach for the design of anticancer immunotherapies. We support the idea that the approach must be a combination of algorithms and methods from bioinformatics and patient-data-driven mathematical models conceived to investigate the role of clinical interventions in the tumor–immunity interaction. For each step of the integrative approach proposed, we review the advancement with respect to the computational tools and methods available, but also successful case studies. We particularized our idea for the case of identifying novel tumor-associated antigens and therapeutic targets by integration of patient’s immune and tumor profiling in case of aggressive melanoma. Key words: personalized vaccine; cancer immunotherapy; systems biology; multi-epitope vaccine; melanoma

Introduction Unlike infectious diseases, where the nonself (pathogens) can easily be distinguished from the self (human cells), developing cancer immunotherapy is a major challenge owing to the great

resemblance of cancer cells to the somatic cells. Many recent studies suggest that immune therapy can be used as an important therapeutic option for some types of metastatic tumors, including aggressive melanoma [1–5].

Shailendra K Gupta is a postdoctoral fellow at the Department of Systems Biology & Bioinformatics, University of Rostock and senior scientist at CSIRIndian Institute of Toxicology Research, Lucknow, India. His research focuses on the structural modeling of molecule complexes and the simulation of molecular dynamics in biomedical phenomena. Tanushree Jaithly is a PhD student at Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen, FriedrichAlexander-University Erlangen-Nuremberg, Germany. Her main research focus is to develop computational methods to optimize personalize tumor vaccine. Ulf Schmitz is a postdoctoral researcher at the Department of Systems Biology & Bioinformatics, University of Rostock. His research focuses on the design of integrative workflows combining various computational disciplines with experimentation to approach molecular biological and medical problems. Gerold Schuler leads the Department of Dermatology, University Hospital Erlangen. He is expert in Tumorimmunology. His research focuses on the development of anticancer therapeutic vaccines based on patient monocyte-derived dendritic cells. Olaf Wolkenhauer is head of the Department of Systems Biology & Bioinformatics, University of Rostock. His research interest is the analysis of data from complex biological and biomedical systems. He uses statistical techniques, mathematical modeling, computer simulations and develops tools for use in the areas of bioinformatics, systems biology and systems medicine. Julio Vera is professor in Systems Tumor Immunology at the University of Erlangen-Nuremberg. He develops computational methods for data-driven mathematical modeling of biochemical pathways and networks relevant to diseases. Submitted: 2 May 2015; Received (in revised form): 11 June 2015 C The Author 2015. Published by Oxford University Press. For Permissions, please email: [email protected] V

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Shailendra K Gupta, Tanushree Jaitly, Ulf Schmitz, Gerold Schuler, Olaf Wolkenhauer and Julio Vera

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Tools and resources for epitope-based personalized cancer vaccines Identification of functional T-cell epitopes from the targeted tumor-associated antigens (TAA) is a key process for the development of effective cancer immunotherapy. Thanks to the advancement in the high-throughput sequencing of cancer genomes and established downstream pipelines for the analysis of genome-wide variants and prediction of novel epitopes that contain both tumor and patient-specific mutations, it is theoretically possible to achieve the ultimate goal of synthesizing personalized tumor vaccines. T-cell epitopes, recognized by the immune system, are the part of TAA that are located on the

surface of an antigen-presenting cell after their binding to the major histocompatibility (MHC) molecules [20]. Similarly, B-cell epitopes are important in generating humoral responses to produce antibodies from B-cells for tumor rejection [21].

Data resources required for predicting novel TAA Numerous web resources are being made publically available by the cancer research community worldwide [22]. These resources include both raw and processed data from tumor patients or cell lines covering whole-genome/whole-exome sequence data, copy number variations, epigenetic profiles, single nucleotide variants and gene/miRNA expression profiles from a variety of tumor entities. Some of the widely used resources for the prediction of novel tumor epitopes are highlighted in Table 1. In an attempt to compile all published tumor-associated antigenic peptides from human, Vigneron et al. developed a peptide database hosted at www.cancerimmunity.org/peptide, which currently comprises 403 tumor antigenic peptides [26]. They categorized all the published tumor antigens into five groups: i. Unique tumor antigens resulting from mutations in a particular tumor. Currently there are 52 epitopes containing one or more residues modified by mutation. This group also contains two human leukocyte antigen (HLA) genes (HLAA2 from renal cell carcinoma [34] and HLA-A11 from melanoma [35]) where mutations alter whole genes and the mutated products from these genes are recognized by T cells. ii. Shared tumor-specific antigens expressed in various tumors. This group contains epitopes from the proteins expressed exclusively in different tumors but not in the healthy tissue. These antigens are the product of genes that are silent in normal tissues (except placental trophoblasts and testicular germ cells, which are devoid of MHC molecules so that no antigen is presented on their surface) but reactivated in different tumors [36, 37]. There are >120 epitopes derived from tumor-specific proteins such as members of G antigen (GAGE), melanoma antigen (MAGE) and B melanoma antigen (BAGE) families described in >100 publications. iii. Differentiation antigens, which are expressed in both tumors and the normal healthy tissue from which tumors originate. This group contains 85 epitopes mostly derived from tyrosinase, tyrosinase related protein (TRP)-1, TRP-2, NY-melanocyte differentiation antigen-1 (NY-MEL-1), Melan-A and gp100 protein in case of melanoma, carcinoembryonic antigen (CEA) protein in gut carcinoma and prostatic acid phosphatase (PAP), prostate-specific antigen (PSA) protein from prostate carcinoma [38, 39]. iv. Antigens overexpressed in tumors but also expressed in a wide variety of normal tissues. The predictions regarding the safety of these antigens are difficult as low level of expression is sufficient for cytotoxic T lymphocytes (CTL) recognition in normal tissues, which may result in epitope presentation on the surface of normal cells that can provide resistance to CTL recognition in case of tumor immune therapy [40]. v. The last group has references in >240 scientific publications highlighting potential epitopes from a variety of tumors and also from a number of viruses associated with these malignancies.

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Broadly, vaccines are of two types: (1) prophylactic (preventative); and (2) therapeutic [6]. While prophylactic vaccines are administered to individuals who are at high risk of a disease to provide protection before its occurrence, therapeutic vaccines are delivered after the onset of the disease to boost immunity against it and to prepare the immune systems for posttreatment disease relapse. In case of pathogen-driven cancers, such as cervical cancer caused by human papillomavirus, hepatocellular carcinoma caused by hepatitis B and hepatitis C virus, Hodgkin’s lymphoma by Epstein–Barr virus, T-cell leukemia by Human T-cell leukemia virus, Kaposi’s sarcoma by Kaposi’s sarcoma herpes virus, there has been considerable success in designing prophylactic cancer vaccines using traditional vaccine development approaches, and many of them are currently in use or in the advanced stages of clinical trials [7–11]. During the past several decades, various approaches attempted to harness the innate power of immune systems in the development of effective immunotherapy to treat nonpathogenic cancer. The complex interactions between highly dynamic tumor and immune systems provide a perfect pitch to deploy tools, methodologies and the philosophy of systems biology for designing effective and personalized tumor vaccines. Overall, cancer immunotherapeutic strategies can be divided into two categories: (1) Passive immunotherapeutic approaches based on monoclonal antibodies (mAb); immune checkpoint inhibitors/ modulators; cytokines; adoptive cell transfer mainly tumor-infiltrating lymphocytes, T cells, dendritic cells for inducing nonspecific immune stimulation; and (2) Active immunotherapeutic approaches based on tumor-specific antigens [12]. In the past few years, the generation of quantitative high-throughput data, by, for example, genome sequencing, mass cytometry, expression profiling of proteins, mRNAs, microRNAs (miRNAs), has become technically and economically affordable [13, 14]. These data, when analyzed in an integrative manner for the identification of tumor neoantigens, provide opportunities to design personalized immunotherapy in case of multifactorial diseases, such as cancer [15–18]. Recently Osada and coworkers showed that mature dendritic cells when electroporated with tumor-derived mRNA, induces high antigenspecific T-cell responses in colon cancer-bearing mice mode, and thus, proposed that these dendritic cells can be used as potential cancer immunotherapy when personalized tumorspecific antigens are largely unknown [19]. In the following text, we first review immunoinformatics tools and resources widely used to design and optimize cancer vaccine and then provide detailed overview of how these tools can be combined together with mathematical models in the form of an integrated systems biology workflow for the design of personalized tumor immunotherapeutics.

CGAP: Cancer Genome Anatomy Project

Oncomine

Array express

cBioPortal for cancer genomics

CellMiner

GEO: Gene expression omnibus

COSMIC: Catalogue of somatic mutations in cancer

ICGC: The International Genome Consortium

TCGA: The Cancer Genome Atlas

CIG: Cancer-related Immunological Gene database

CID: Cancer Immunome Database

SYFPEITHI

IEDB

TANTIGEN: Tumor T-cell Antigen Database

PeptideDatabase CTDatabase: Cancer-testis database

R IMGT/mAb-DB: the IMGTV database for therapeutic mAb

www.ebi.ac.uk/ipd

Database and tools to study polymorphisms in genes of the immune system [23] Searchable repository of highly curated HLA sequences. With 13 023 allele sequences in the current release 3.20 [24] Resource on immunoglobulins or mAb with clinical indications, and on fusion proteins for immune applications (FPIA). As of April 2015, the database contains 516 entries for 344 mAb [25] Human tumor antigens recognized by CD4þ or CD8þ T cells [26] Collection of cancer-testis antigens genes, expression profile and immune responses [27] Data of 4006 curated human tumor antigens representing 251 unique proteins T-cell and B-cell epitopes, MHC binding peptides with experimental data from published literature [28] Database of >7000 peptide sequences known to bind MHC class I & II molecules A database on gene products against which immune response is known in cancer patients Database of human and mouse immunoglobulin and T-cell receptor genes investigated in cancer studies Database on gene/miRNA expression, copy number, epigenetic data and clinical information from >11 000 cases in 34 cancer types Catalogues of somatic mutations, abnormal expression of genes and epigenetic modifications in tumors from 50 different cancer types and/or subtypes [29] Catalogue of >2 million coding point and 6 million noncoding point mutations from >1 million tumor samples [30] High-throughput array and sequence-based functional genomics data repository A database and online query tool to facilitate integration of molecular data sets on NCI-60 cell lines [31] cBioPortal provides visualization, analysis and download of largescale cancer genomics data sets covering 89 cancer studies from 20 958 tumor samples [32] Repository of both micro array and RNA-seq expression data of many human cancer cell lines Cancer microarray database with integrated data-mining platform with 715 data sets from >86 000 samples [33] Gene expression profiles of normal, precancer and cancer cells for multiple tumor types

http://cgap.nci.nih.gov

www.oncomine.org

www.ebi.ac.uk/arrayexpress

http://discover.nci.nih.gov/ cellminer www.cbioportal.org

http://ncbi.hlm.nih.gov/geo

http://cancer.sanger.ac.uk

https://icgc.org

http://cancergenome.nih.gov/ dataportal

http://ludwig-sun5.unil.ch/ CancerImmunomeDB http://www.scchr-cigdb.jp

www.syfpeithi.de

www.iedb.org

http://cvc.dfci.harvard.edu/tadb

http://cancerimmunity.org/peptide www.cta.lncc.br

www.imgt.org/

www.ebi.ac.uk/ipd/imgt/hla

Web link

Description

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Cancer genomics database

IPD: Immuno Polymorphism Database

Immunology Databases

IMGT/ HLA

Name

Category

Table 1. List of important immunology and cancer genomics databases for predicting novel tumor epitopes

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are personalized to patient and can be used in case of personalized vaccination. (B) Antigens can be detected from proteins expressed only in tumor tissues. (C) Protein expressed in the tumor tissues and the normal tissues from which tumor originates and proteins that are overexpressed in tumor tissues compared with healthy tissues (D) form a large data set to detect tumor antigens; however, in some cases autoimmunity can create a problem. (E) Immunogenic proteins from pathogens associated with malignancies are always an ideal choice for immunotherapy in case of pathogen-born tumors.

Protein products affected from all sort of genomic variations (e.g. point mutation, frame-shift mutations, reactivation of silent genes, alternative splicing and overexpression) that distinguish tumor from healthy cells have been used to detect TAA [15, 41] as shown in Figure 1. The key for designing a personalized tumor vaccine is the mapping of mutations in the exonic regions of a gene. These mutations are the potential source for novel peptides identified by immune cells as nonself and presented by MHC molecules or recognized by antibodies. Technological advancements resulted in the availability of an enormous amount of variation data, for example, the Catalogue of Somatic Mutations in Cancer contains >2 million coding and 6 million noncoding point mutations from >1 million tumor samples [30]. Mapping of such vast amount of data requires sophisticated tools and methods for predicting unique immunogens for use in tumor vaccination strategies [42].

Computational methods and tools for predicting epitopes from tumor antigens Prediction of novel epitopes either from foreign proteins or selfproteins is the main focus of immunoinformatics research since decades. Epitopes can be classified into two groups: (1) continuous epitopes: linear peptide fragment (9–12 mers) mostly recognized by Th-cells or T-cell receptors (TCRs) and (2) discontinuous epitopes: nonlinear peptide fragments (15–22 mers) sharing common spatial confirmation owing to protein folding and are recognized by B cells, antibodies and Th-cells. Based on the input data types and output processing, computational approaches for epitope prediction can be divided into four broad groups: (1) sequence-based methods, (2) structurebased methods, (3) hybrid methods and (4) consensus methods [43]. While sequence-based methods are less reliable than the structure-based methods, the former are most widely used

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Figure 1. Protein data sources important for the prediction of tumor antigens. (A) Antigens derived from tumor-specific mutated proteins. Sometimes mutated proteins

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ing of novel peptide mimic of epitopes, molecular docking and molecular dynamics simulations for the selection of potential epitopes. Hybrid methods are based on a set of features from both sequence and structure. Consensus methods take the output generated by two or more methods and based on a consensus score, prioritize epitopes.

owing to the scarcity of 3D structure of immunogenic peptide– receptor complexes. In case of prediction of epitopes from tumor antigens, data on misfolded 3D structures owing to nonsynonymous point mutations are rarely available, and thus, most frequently used methods are based on sequence features. Hybrid methods are more accurate, as they use both sequence and structural information for the prediction. Consensus methods combine the output generated by other methods for better sensitivity and specificity in epitope prediction. Overall computational methods available for predicting epitopes are summarized in Figure 2 and reviewed elsewhere in detail [15, 44–47]. Table 2 highlights important tools and web servers available for predicting epitopes. Sequence-based methods for predicting T-cell/B-cell epitopes These methods use primary sequence information of antigen proteins and are based on the philosophy that identical sequence leads to identical structure and function [48]. The first sequence-based epitope prediction method was developed back in 1981 by Hopp and Woods for the prediction of epitopes from a given protein sequence based on the highest local average

hydrophilicity score [49]. Thereafter a large number of methods were developed for the prediction of epitopes by making use of various physicochemical properties of peptides, such as exposed surface area [50–52], solvent accessibility, location of antigenic sites in protein [53], protein secondary structure [54] and chain flexibility [55]. Various approaches such as binding motif-based search, machine learning-based techniques [mainly Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Hidden Markov Models (HMM)], Quantitative Metrics (QMs) and linear programming have been used to predict epitopes from diseaseassociated antigens in the past. Motif-based programs use a library of patterns of amino-acid residues (motifs) from the known binders and non-binders [56]. The accuracy of motif-based algorithms is only 60–70% because of the fact that recognizable patterns cannot be detected from all binding peptides [57, 58]. QMs are matrices of frequencies or likelihood for the occurrence of a particular amino acid in a given position of the motif [45, 59]. These matrices were constructed for various MHC class I, class II alleles and also for linear B-cell epitopes using

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Figure 2. Computational methods available for the prediction of epitopes. Sequence-based methods use features and information extracted from the primary aminoacid sequence data of potential antigens. Structure-based methods use surface properties of antigen-binding receptors, various 3D descriptors in case of QSAR, design-

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Table 2. Important tools and web servers available for predicting T-/B-cell epitopes Method

Tool/webserver

Description

Web link

Sequence-based method

EPIPREDICT MOTIF SYFPEITHI

Motif-based tool for MHC class-II binding epitope Epitopes binding to HLA-A*0201 allele Identification of 8-11 mer MHC Type I and 15 mer for Type II epitopes Based on ANN and weight matrices Linear B-cell epitope prediction

www.epipredict.de Available on request www.syfpeithi.de

NetMHC Bcipep PickPocket 1.1 BIMAS EpiJen

Structure-based method

EpiDOCK EpiTOP ElliPro DiscoTope 2.0

Hybrid/ Consensus method

MIMOX EpiSearch NetMHCcons 1.1 NetMHC PERUN IEDB T-cell epitope prediction tools

experimental data from known binders [60–63] based on which many popular epitope prediction tools, such as EpiMatrix [64], SYFPEITHI [65], RANKPEP [66], EpiJen [67], TEPITOPE [68] and ProPred [69], were developed. Sturniolo et al. developed virtual matrices to model the pocket profile of each human leukocyte antigen-D related (HLA-DR) allele, which is a quantitative representation of the interaction of all amino acid residues with a given pocket. These virtual matrices were then integrated in the software TEPITOPE, which is used to predict epitopes for all HLA-DR alleles that share similar structural features of the binding groove [68]. Cochlovius et al. used TEPITOPE to design seven HLA-DR promiscuous peptides from melanomaassociated Ag glycoprotein, which when loaded to dendritic cells generated Th-cell responses [70]. Although QMs are more efficient in predicting epitopes in comparison with the motifbased search, these methods cannot handle nonlinear data. As conformational epitopes are nonlinear in nature, various statistical methods such as HMM and machine learning methods (ANN and SVMs) are used to predict reliable epitopes. HMMs have been extensively used in the analysis of flexible biological sequences and are probabilistic statistical models designed to describe the observable elements of a system that depend on internal hidden factors (states) [71]. The first HMMbased method to predict peptides that bind to HLA-A*0201 and DR1 MHC molecules was developed by Mamitsuka in 1998 [72]. Motivated by Mamitsuka, various researchers developed HMM models for predicting binding peptides to other MHC alleles [73, 74]. Like HMM, ANN- and SVM-based methods are also

http://www-bimas.cit.nih.gov/molbio/ hla_bind www.ddg-pharmfac.net/epijen/EpiJen/ EpiJen.htm http://imed.med.ucm.es/Tools/ rankpep.html http://epidock.ddg-pharmfac.net www.pharmfac.net/EpiTOP http://tools.immuneepitope.org/ellipro www.cbs.dtu.dk/services/DiscoTope http://immunet.cn/mimox http://curie.utmb.edu/episearch.html www.cbs.dtu.dk/services/NetMHCcons/ www.cbs.dtu.dk/services/NetMHC Software available on request to [email protected]. au http://tools.immuneepitope.org/main/ tcell/

frequently used in biological sequence analysis for pattern recognition and for describing both linear and nonlinear data. Yu and colleagues compared binding motifs-, QMs-, HMM- and ANN-based methods for the prediction of peptide binding with MHC molecules and found that in case of a small training data set, binding motif search is the most useful method to select candidate binders; however, ANN and HMM can outperform binding motif search with training data sets of >100 known binding peptides [75]. Brusic et al. developed ANN-based methods first for predicting MHC class I binders [76] and later for class II binding peptides with high-, moderate-, low- and zerobinding affinity [77]. Later, Nielsen and colleagues presented an improved ANN model by combining different sequence-encoding schemes including HMM for predicting MHC class I binders and developed the NetMHC prediction server (www.cbs.dtu.dk/ services/NetMHC) with the ability to predict binders to 12 MHC supertypes [78, 79]. SVM-based methods always outperform other sequence-based epitope prediction methods when the dimensionality of the data is high and the number of observations is limited [80]. Several groups developed SVM-based methods for predicting MHC class I, II and B-cell epitopes [81–86]. Structure-based methods for predicting T-cell/B-cell epitopes Structure-based epitopes prediction methods use information such as geometry, and electrostatic complementarities between receptors and ligands. Molecular docking- and molecular dynamics simulations-based methods with successful applications in computer-aided drug design are also used to predict

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RANKPEP

Predict peptide binding to know MHC molecules using position-specific weight matrices Rank epitopes based on half-time prediction of dissociation to HLA-I molecules T-cell epitope prediction considering proteasome cleavage and transporter associated with antigen processing (TAP) binding Prediction of MHC class I and II epitopes using position-specific scoring matrix (PSSM) Molecular docking-based tool for MHC class II epitopes Proteochemometric tool for MHC class II binding prediction Predicts linear and discontinuous antibody epitopes Predicts discontinuous B-cell epitopes from protein 3D structure Mimotope explorer based on phage display analysis Mapping of conformational epitopes Consensus method for predicting MHC class I epitopes Based on ANN and weight matrices MHC class II epitopes using evolutionary algorithm and ANN Prediction of MHC class I & II epitopes using consensus prediction method

www.cbs.dtu.dk/services/NetMHC www.imtech.res.in/raghava/bcipep/ index.html www.cbs.dtu.dk/services/PickPocket

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binding affinity and thereby suggested the improvement in structure of drug for better efficacy. [112]. Design of novel cancer immunotherapeutics can also be benefited from these established methods by considering patient/ tumor specific mutations around epitopes/ mimtopes binding sites in the receptor cavities. As more and more 3D structures of tumor antigens and receptors are being deposited in the databases, the designs of effective antitumor vaccination are on the horizon. Hybrid and consensus methods for epitope prediction Structure-based epitope prediction methods are more reliable; however, owing to the unavailability of confirmed X-ray structures for the large number of MHC alleles, TCR, B-cell receptors (BCR) and structures containing patient-specific mutations, these methods have limited scope. To overcome this, several attempts were made in the past to develop hybrid methods combining various sequence- and structure-based features for the prediction of epitopes. Bhasin and Raghava developed an accurate hybrid method for predicting promiscuous MHC class I restricted T-cell epitopes by combining QM- and ANN-based methods [113]. Consensus methods combine results from several prediction methods to rank potential epitopes with higher sensitivity and specificity in comparison with individual tools [114]. Wang et al. developed a consensus prediction approach by calculating the median rank of top three predictive methods for each MHC class II molecule and found that the prediction performance significantly improved for the majority of MHC class II molecules tested [115]. The method was later updated by integrating NN-align, an ANN-based alignment algorithm for MHC class II peptide binding prediction, along with SMM-align, a stabilization matrix alignment methods, and combinatorial peptide scanning libraries and used in IEDB MHC class I & II binding prediction tools (www.iedb.org) [116]. Combining various tools to generate a consensus score is not straight forward owing to different output format and prediction strategies by individual tools. Several integration strategies were developed to generate consensus scores, which are: majority voting (averaging the predicted scores of individual predictors), weighted linear combinations (weighted sum of the prediction score of each predictor based on their estimated performance on training data sets), and meta-learning (meta-classifier trained using the output of individual predictors) [117]. Besides epitope/mimotopes prediction methods, other strategies such as epitopes population coverage analysis by considering MHC alleles frequency in a targeted ethnicity are critical for the design of potential vaccines [118]. These methods are used in the design of various ethnicity-tailored immunodiagnostic and immunotherapeutic approaches in the past [119–122].

Mathematical modeling in anticancer vaccination: on the way to the post-genomic era The design and personalization of immune-based therapies against cancer, especially anticancer vaccination, would benefit strongly from the use of systems biology-inspired data-driven mathematical modeling [123]. When interacting, the immune system and the tumor constitute a remarkable case of multilevel biological system: the recruitment, activation and interaction of immune cells at the tumor site depend on fine-tuned, nested paracrine and autocrine feedback loops, while aggressive tumors can deploy a number of feedback looped mechanisms to sense and counteract the immune attack. Within the immune

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novel peptides with high binding affinity toward MHC molecules (for T-cell epitopes) and B-cell receptors (for B-cell epitopes). Docking-based methods were previously applied to screen peptide libraries for specific sets of MHC class I and II alleles [87–90]. The major limitations of such methods are the availability of accurate 3D structures of MHC receptor molecules. Sinigaglia and colleagues developed a curated repository of 3D structures of immunogenic peptide and MHC class I complexes with 182 nonredundant pMHC-I complexes from two human and two murine alleles [91]. Atanasova et al. developed a structure-based molecular docking tool named EpiDOCK for predicting peptide binders for the 23 most frequent human MHC class II proteins with overall accuracy of 83% [92]. Haste Anderson et al. developed the method DiscoTope for predicting discontinuous B-cell epitopes from the 3D structure of antigen proteins by calculating surface accessibility and amino-acid propensity score [93]. In silico Quantitative Structure-Activity Relationship (QSAR)based approaches are also being used for accurate prediction of B-cell or T-cell epitopes [94–96]. While 2D-QSAR methods are largely based on the calculation of physicochemical properties affecting the epitope–MHC interactions, 3D-QSAR methods are more reliable in predicting the binding affinity of epitopes by mapping 3D interaction potentials on the structure of molecules being investigated [44]. Motivated by proteo-chemometrics QSAR approach developed by Lapinsh and colleagues [97] for the analysis of drug receptor interactions, EpiTOP server was developed for predicting MHC class II binding peptides. Like in the proteo-chemometric QSAR method, where the descriptor matrix contains information from both receptor and ligand, the binding affinity of a peptide to a particular MHC molecule is predicted as a function of the structure of both peptide and targeted protein in the EpiTOP server [98]. Bui et al. developed an algorithm, PePSSI (peptide-MHC prediction of structure through solvated interfaces) for flexible structural prediction of peptides bound to MHC molecules HLA-A2 by sampling the peptide backbone conformation and flexible movement of MHC side chains from 38 X-ray structures of the HLA-A2/ peptide complexes available in the protein data bank [99]. This algorithm was later applied to predict novel epitopes form the sequence of cancer-testis antigen KU-CT-1 and its binding affinity for HLA-A2 [100]. Similarly, the Ellipro server available at the Immune Epitope DataBase and analysis resource (IEDB) predict both linear and discontinuous antibody epitopes based on antigen 3D structures [101]. Ellipro also includes the MODELLER software to draw the 3D structure of antigens from sequence data via homology modeling in its pipeline. Based on structural information of epitopes binding to receptor proteins, mimotopes are designed in several studies aiming toward the development of anticancer vaccinations [102–108]. Mimotopes are small peptides that mimic the binding surface to an antigen to provide the same response as that of a real epitope. Huang et al. developed a web-based tool, MIMOX, to map native epitope of an antibody based on one or more user-supplied mimotopes and the antigen structure using phase display methods [109]. They also developed a database named MimoDB, which currently stores 20 840 unique peptide sequences grouped into 2463 mimotope sets from >1100 publications (http://immunet.cn/mimodb) [110]. Sun et al. recently presented a method combining antigen preprocessing and mimotopes analysis for the identification of B-cell epitopes [111]. Previous studies on protein-drug interactions successfully used molecular docking and molecular dynamics simulations based methods to analyze the impact of mutations on the drug

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Insights into immunotherapy mechanisms Mathematical modeling can be used to elucidate the mechanisms that immunotherapies have in the tumor-immune system interaction. Back in the 90s, Kuznetsov and coworkers already presented a model based on ordinary differential equations (ODE) that mimics the activation of the cytotoxic T cells in response to the growth of an immunogenic B-lymphoma in the spleen of mice [126]. Owen and Sherratt modeled the interaction between macrophages, tumor and tissue cells. The authors focused on how tumor cell mutations impact the ability of macrophages to initiate an effective immune response [127]. D’Onofrio derived an entire set of ODE models, inspired by ecological models, to account for the tumor–immune system interaction [128]. He used them to simulate and analyze the efficacy of different time profiles in the administration of immune therapy. Kim and Lee constructed a two-compartment hybrid mathematical model (ODE þ agent-based modeling) aiming at analyzing the suitability of preventive vaccination with cytotoxic T cells [129]. Interestingly, their computer simulations suggest the existence of a minimum size for the population of memory cytotoxic cells promoted by the vaccine, necessary to achieve full depletion of micrometastases. De Pillis and coworkers investigated via model simulation the ability of combinations of conventional chemotherapy, immunostimulation, T-cell adoptive transfer and therapeutic vaccination to control or deplete tumors [130]. Wilson and Levy investigated via model simulation the combination of TGF-b inhibitors and therapeutic vaccination as an alternative anticancer therapy [131]. Stochasticity can play an important role in the dynamics of some intracellular and cell-to-cell processes essential in the tumor immune system interaction, and therefore, mathematical models account for this could be a useful tool [132]. For example, Celli and coauthors (2012) used flow cytometry, 2-photon imaging to derived and characterize a spatial stochastic model accounting for the T-cell–DC interaction at the lymph nodes. Model analysis was used to quantify the probability of T-cell–DC encounters and thereby the

minimum number of antigen bearing DCs necessary to trigger a T-cell response [133]. Although most of these results are based in mathematical models that are too simple and not deeply characterized in terms of quantitative data, they still have delivered interesting insights into the design principles underlying immunotherapy. In addition, detailed versions of these models combined with tools for model analysis like sensitivity and bifurcation analysis can be used to detect critical steps in the tumor–immunity interaction whose modulation via immunotherapy or adjuvant therapy could enhance the immune response [134]. We have not found examples on this in the literature for immunotherapies, but this approach is regularly used by some pharma companies for the design of new targeted anticancer therapies [135, 136].

Optimal therapy design Pharma companies have made extensive use of mathematical modeling since decades for optimizing the dosage regimen of conventional drugs. The idea here is that well-characterized mathematical models, accounting for the time profile of systemic drug concentration and its physiological effect, can be used to decide on the correct dosage and timing for drug administration (for targeted cancer inhibition see [137]; for antibody therapy of cancer, see [138]). In pharma companies the development of immune therapies based on the use of checkpoint blockade inhibitors, considered in this sense as ‘conventional drugs’, are certainly following similar modeling-based procedures to optimize their dosage regime. We think that the same principles can be applied to the optimization of therapeutic anticancer vaccines. In line with this idea, Castiglione and Piccoli proposed the use of mathematical modeling and optimal control techniques to design optimized immune therapy schedules [139]. Pennisi and coworkers derived and characterized a computational model for lung metastases in a mice model of breast cancer and their treatment using a combination of allogeneic tumor cell vaccination and interleukin 12 administrations [140]. They further used systematic model simulations together with genetic algorithms [141] to look for an optimal vaccination protocol allowing for (a) maximization of prevented metastases and (b) minimization of the vaccine injections. We note that this can be envisioned as a multi-objective optimization problem, which can produce multiple efficient alternative solutions deserving special analysis [142]. Palladini and coworkers (2010) used mathematical modeling to design in silico designed protocols aiming at reducing the number of vaccinations while increasing their efficacy. The model predictions were experimentally validated in vivo in a mouse model of preventive vaccination using HER-2/neu transgenic mice. A surprising result from their analysis is that the progressive aging of the immune system may play a role in the efficacy of anticancer vaccination: to compensate this effect they propose to boost immune responses in elderly hosts [143].

Therapy personalization A promising use for mathematical modeling is the personalization of anticancer treatments. When talking about conventional anticancer drugs, the options for personalization normally reduce to (a) an assessment of the potential efficacy of the drug in a patient-to-patient manner before its use, (b) personalization of the drug dose and timing schedule and (c) the personalized assessment of a combined drug therapy. Interestingly, immunotherapies and especially therapeutic vaccination offer much more options for personalization. For example, the profiling of

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and tumor cells, proliferation, differentiation and (resistance to) cell killing are controlled by feedback and feedforward controlled signaling and transcriptional networks whose response is guided by a myriad of chemical signals. These signals, primarily cytokines and chemokines, are secreted at the microenvironment by both the tumor and the immune system [124, 125]. To include in this picture therapeutic interventions like checkpoint blockade inhibitors, adopted therapy or vaccination makes the system more complex, and the interaction among the tumor and immune system more intricate. No need to say that this is an ideal framework for deploying a systems biology-inspired modeling strategy. Conventional drug discovery integrates different types of mathematical modeling aiming at establishing secure drug dosage protocols (a.k.a. pharmaco-kinetics) and certainty into the biochemical and physiological mechanism of drug action (a.k.a. pharmacodynamics). Having in mind that immunotherapies share with conventional drug the necessity to establish a secure, yet effective, dosage schedule, it is surprising that many initiatives launched in the past 15 years to design antitumor immunotherapy have not integrated mathematical modeling in their research and validation workflow. However, the field is not idle. In the past decade, a few papers have shown that modeling can be used to assess, design and personalize anticancer immunotherapy. Here we discuss some highly illustrative examples.

Personalized cancer immunotherapy

coworkers developed a mathematical model for dendritic cell vaccination of melanoma in a mouse model [149]. The model simulations were used to optimize administration protocols, while computational sensitivity analysis was used to determine the model parameters with higher impact on the effective vaccine-mediated disseminated tumor depletion.

Modeling of anticancer vaccination: next step? Although most of these works are brilliant examples of mathematical modeling and some hold extremely detailed descriptions of the cell-to-cell events underlying the tumor–immune system interaction and the therapy mechanism of action, they all belong to the pre-genomics era of immunotherapy. They all lack a connection to the intracellular networks governing the fate and phenotypic responses of both immune and tumor cells, and therefore, they cannot make use of the amazing amount of already existing (and soon available) ‘-omics’ data accounting for the immunogenic features of primary and metastatic tumors. This information will be the ultimate resource for the effective tailoring and optimization of all anticancer therapies, including therapeutic vaccination. Thus, future attempts to model immunotherapy must consider the biochemical networks controlling tumor and immune cells as a basis to make reliable model predictions based on molecular mechanisms, but also to provide molecular-level insights to re-engineer immunotherapies. In recent years, a number of papers have shown that mathematical

Figure 3. Mathematical models for the post-genomics era of Immunotherapy. Option 1: multi-level models containing interconnected equations, accounting for the dynamics of interacting cell populations and the one of the biochemical networks governing the phenotypic response of these cell populations. This model offers the option to simulate at-once systemic tumor and immune markers, the dynamics of cancer and immune cells at tumor site and the pathways regulating the behavior of these cells. Option 2: annotated cell population models, in which model parameters are annotated with precise and standard gene ontology terms. This way, modification in the value of this model parameters in simulations can be linked to differentially expressed genes in in vitro, in vivo or patient data and ‘-omics’ data.

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tumor mutations can be an excellent tool to personalize anticancer vaccines by customizing the selection of patient-specific, highly immunogenic epitopes [144]. Furthermore, this tumor profiling can be used to decide beforehand on the suitability of immunotherapy. Ulloa-Montoya and coworkers obtained biopsies from patients participating in two phase II studies for the evaluation of recombinant MAGE-A3 immunotherapy and analyzed them using microarray analysis [145]. The data were used to define and validate a pretreatment tumor gene expression signature with the ability to predict the responsiveness of the patients to the recombinant MAGE-A3 immunotherapy. Mathematical modeling can be an important element for the personalization of anticancer immunotherapy. The work of Agur and collaborators in the past years is illustrative in that regard. They personalized the design of an allogeneic whole-cell therapeutic vaccine against disseminated prostate cancer using mathematical modeling [146–148]. Using data generated in a clinical trial, they generated patient-specific mathematical models able to predict blood values of prostate-specific antigen levels after vaccination. They also used simulations of the patient-derived model for the personalized redesign of the vaccination protocols. Later, they adapted tools and modeling strategies used in pharmacodynamics to the personalization of adoptive T-cell therapy against metastatic melanoma [148]. The obtained modeling framework was able to use patient-specific parameters like growth rate or residual tumor size to customize critical features of the therapy, including the T-cell dosage and timing. Radunskaya and

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modeling can be used to dissect the biochemical networks underlying the regulation of T cells [150–152], B cells [153, 154] and NK cells [155]. Thus, proof of principle for the modeling of the networks modulating the immune response is available. We foresee two alternative ways to connect cell-to-cell interaction models of immunotherapies, like the existing ones, to the biochemical networks that fine-tune the involved cellular processes (Figure 3). One possibility is develop multi-level mathematical models containing descriptions for both levels of regulation, that of the interacting cell population and the one of the biochemical networks embedded in the studied cells types. This procedure has been followed in other biomedical contexts, especially in cancer. We recently develop an ODE model following this strategy to detect genetic signatures in melanoma, providing resistance against conventional chemotherapy [156].

Another option is to assign a biological meaning to the processes included in the cell population model; the model parameters accounting for each process can be thereby associated to standard gene ontology terms. Later, proper annotation of differentially expressed genes in in vitro, in vivo or patient data and definition of aggregated metagenes can make it possible to connect ‘-omics’ data with model predictions.

Better together: toward a personalization of anticancer vaccine via the integration of immunoinformatics and data-driven mathematical modeling For the future of anticancer vaccines, we predict that advanced immunoinformatics tools and mathematical modeling will be

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Figure 4. A hypothetical (and ideal) workflow for the personalization of anticancer vaccination based on the integration of -omics data, immunoinformatics tools and data-driven mathematical modeling.

Personalized cancer immunotherapy

Conclusion and future perspective In this review, we presented the state of the art of computational tools and methods that can be used to personalize cancer immunotherapy by the identification of novel and patientspecific TAA. Further, we advocate the use of mathematical and computational models to get deeper insights into the mechanisms of action of immune therapy, their personalization and optimal design. We envisaged that an integrated workflow combining (a) patient derived high-throughput data analysis methods for assessing tumor heterogeneity and patient proteome/ mutanome profile; (b) prediction of effective tumor-associated epitopes in the light of patient immune profile; and (c) mathematical models for customizing and optimizing features of the clinical interventions will be in the origin of future successful personalized immunotherapies, specially anticancer vaccines.

Key Points • Communication between tumor and immune cells is a

complex multi-scale biological system that requires a systems medicine approach. • Several computational methods developed for the identification of novel tumor-associated antigens.

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• Personalized tumor vaccine is realized through the

integration of computational methods for tumor immunogenic peptide prediction combined with highthroughput analysis of patient’s mutation and immune profile. • Mathematical models can help gaining mechanistic insights, providing a basis for therapy optimization and therapy personalization.

Funding German Federal Ministry of Education and Research (BMBF) as part of e:Bio-SysMet [0316171 to S.K.G., J.V. and O.W.]; Council of Scientific and Industrial Research (CSIR) India [GENESIS (BSC0121) and INDEPTH (BSC0111) network projects to S.K.G.]; Erlangen University Hospital [ELAN funds, 1407-22-1-Vera-Gonza´lez and direct Faculty support to J.V.]; German Research Foundation (DFG) [GRK-1660 to J.V. and SFB643 (project C1) to G.S.].

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merged to achieve optimal vaccination schedules and personalized vaccine design (Figure 4). The aim should be to create integrative workflows, in which the knowledge generated by some computational tools (e.g. patient data analysis to look for gene expression signatures for tumor immunogenicity or a ranking of promising tumor epitopes) are used to enhance the predictive ability of other tools (e.g. model simulation used to decide whether a therapeutic vaccine will benefit a given patient). Whatever attempt in this direction, it must fulfill a basic requisite to succeed in the post-genomic era: it should make extensive but also integrative use of tumor and patient profiling to assess the benefit of an anticancer vaccine, but also to personalize its design. Figure 4 contains a conceptual sketch of one of these integrative workflows. In our ideal workflow, the patient and the primary tumor are extensively profiled: tumor RNAseq and exome sequencing are performed together with advanced patient blood profiling. In the first step, this information is processed and analyzed using computational models to assess the potential sensitivity of the tumor to immunotherapy (immunogenicity, see [145, 157]). The immunogenicity is a prerequisite to further progress in the personalization of the vaccine. Using a computer-based stratification of patients, a tumor with low immunogenicity would be discarded from this type of therapy. In case of an immunogenic tumor, tumor RNAseq and exome sequencing together with patient HLA profiling are used to select optimal tumor epitopes, whose suitability is assessed using in vitro experiments [144]. Mathematical model simulations can be used to optimize the vaccine administration schedule [140] but also some of the features of the vaccine, for example, the minimum number of different tumor epitopes that the vaccine should contain to target the majority of the metastatic clonal populations contained in the tumor. All this information is used to make a tailored design of the vaccine, adjusted to the tumor and the immune profile. This hypothesized sketch may sound ‘science fiction’, but every element of the workflow has already been performed independently and is therefore feasible.

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