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Dhall A, Jain S, Sharma N, Naorem LD, Kaur D, Patiyal S, Raghava GPS. In silico tools and databases for designing cancer immunotherapy. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 129:1-50. [PMID: 35305716 DOI: 10.1016/bs.apcsb.2021.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.
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Affiliation(s)
- Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Shipra Jain
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Neelam Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Leimarembi Devi Naorem
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India.
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Zhang W, Zeng B, Lin H, Guan W, Mo J, Wu S, Wei Y, Zhang Q, Yu D, Li W, Chan GCF. CanImmunother: a manually curated database for identification of cancer immunotherapies associating with biomarkers, targets, and clinical effects. Oncoimmunology 2021; 10:1944553. [PMID: 34345532 PMCID: PMC8288037 DOI: 10.1080/2162402x.2021.1944553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 06/12/2021] [Accepted: 06/15/2021] [Indexed: 12/01/2022] Open
Abstract
As immunotherapy is evolving into an essential armamentarium against cancers, numerous translational studies associated with relevant biomarkers, targets, and clinical effects have been reported in recent years. However, a large amount of associated experimental data remains unexplored due to the difficulty in accessibility and utilization. Here, we established a comprehensive high-quality database for cancer immunotherapy called CanImmunother (http://www.biomedical-web.com/cancerit/) through manual curation on 4515 publications. CanImmunother contains 3267 experimentally validated associations between 218 cancer sub-types across 34 body parts and 484 immunotherapies with 642 biomarkers, 108 targets, and 121 control therapies. Each association was manually curated by professional curators, incorporated with valuable annotation and cross references, and assigned with an association score for prioritization. To help clinicians and researchers in identifying and discovering better cancer immunotherapy and their respective biomarkers and targets, CanImmunother offers user-friendly web applications including search, browse, excel table, association prioritization, and network visualization. CanImmunother presents a landscape of experimental cancer immunotherapy association data, serving as a useful resource to improve our insight and to facilitate further discovery of advanced immunotherapy options for cancer patients.
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Affiliation(s)
- Wenliang Zhang
- Department of Pediatrics, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China
- Department of Bioinformatics, Outstanding Biotechnology Co., Ltd.-Shenzhen, Shenzhen, China
| | - Binghui Zeng
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Huancai Lin
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Wen Guan
- Department of Bioinformatics, Outstanding Biotechnology Co., Ltd.-Shenzhen, Shenzhen, China
- Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Institute of Zoology, Guangdong Academy of Sciences, Guangzhou, China
| | - Jing Mo
- Department of Bioinformatics, Outstanding Biotechnology Co., Ltd.-Shenzhen, Shenzhen, China
| | - Song Wu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yanjie Wei
- Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China
- Center for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- CAS Key Laboratory of Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Qianshen Zhang
- Department of Pediatrics, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Dongsheng Yu
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Weizhong Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Tropical Disease Control of Ministry of Education, Sun Yat-sen University,Guangzhou, China
| | - Godfrey Chi-Fung Chan
- Department of Pediatrics, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Pediatrics and Adolescent Medicine, Faculty of Medicine, The University of Hong Kong, Hong Kong
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Parvizpour S, Razmara J, Omidi Y. Breast cancer vaccination comes to age: impacts of bioinformatics. ACTA ACUST UNITED AC 2018; 8:223-235. [PMID: 30211082 PMCID: PMC6128970 DOI: 10.15171/bi.2018.25] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Revised: 04/02/2018] [Accepted: 04/03/2018] [Indexed: 01/01/2023]
Abstract
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Introduction: Breast cancer, as one of the major causes of cancer death among women, is the central focus of this study. The recent advances in the development and application of computational tools and bioinformatics in the field of immunotherapy of malignancies such as breast cancer have emerged the new dominion of immunoinformatics, and therefore, next generation of immunomedicines .
Methods: Having reviewed the most recent works on the applications of computational tools, we provide comprehensive insights into the breast cancer incidence and its leading causes as well as immunotherapy approaches and the future trends. Furthermore, we discuss the impacts of bioinformatics on different stages of vaccine design for the breast cancer, which can be used to produce much more efficient vaccines through a rationalized time- and cost-effective in silico approaches prior to conducting costly experiments.
Results: The tools can be significantly used for designing the immune system-modulating drugs and vaccines based on in silico approaches prior to in vitro and in vivo experimental evaluations. Application of immunoinformatics in the cancer immunotherapy has shown its success in the pre-clinical models. This success returns back to the impacts of several powerful computational approaches developed during the last decade.
Conclusion: Despite the invention of a number of vaccines for the cancer immunotherapy, more computational and clinical trials are required to design much more efficient vaccines against various malignancies, including breast cancer.
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Affiliation(s)
- Sepideh Parvizpour
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Jafar Razmara
- Department of Computer Science, Faculty of mathematical Sciences, University of Tabriz, Tabriz, Iran
| | - Yadollah Omidi
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.,Department of Pharmaceutics, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
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dbCPG: A web resource for cancer predisposition genes. Oncotarget 2018; 7:37803-37811. [PMID: 27192119 PMCID: PMC5122350 DOI: 10.18632/oncotarget.9334] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2015] [Accepted: 04/28/2016] [Indexed: 01/13/2023] Open
Abstract
Cancer predisposition genes (CPGs) are genes in which inherited mutations confer highly or moderately increased risks of developing cancer. Identification of these genes and understanding the biological mechanisms that underlie them is crucial for the prevention, early diagnosis, and optimized management of cancer. Over the past decades, great efforts have been made to identify CPGs through multiple strategies. However, information on these CPGs and their molecular functions is scattered. To address this issue and provide a comprehensive resource for researchers, we developed the Cancer Predisposition Gene Database (dbCPG, Database URL: http://bioinfo.ahu.edu.cn:8080/dbCPG/index.jsp), the first literature-based gene resource for exploring human CPGs. It contains 827 human (724 protein-coding, 23 non-coding, and 80 unknown type genes), 637 rats, and 658 mouse CPGs. Furthermore, data mining was performed to gain insights into the understanding of the CPGs data, including functional annotation, gene prioritization, network analysis of prioritized genes and overlap analysis across multiple cancer types. A user-friendly web interface with multiple browse, search, and upload functions was also developed to facilitate access to the latest information on CPGs. Taken together, the dbCPG database provides a comprehensive data resource for further studies of cancer predisposition genes.
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Hou XL, Wang L, Ding YL, Xie Q, Diao HY. Current status and recent advances of next generation sequencing techniques in immunological repertoire. Genes Immun 2016; 17:153-64. [PMID: 26963138 DOI: 10.1038/gene.2016.9] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Revised: 01/20/2016] [Accepted: 01/20/2016] [Indexed: 01/26/2023]
Abstract
To ward off a wide variety of pathogens, the human adaptive immune system harbors a vast array of T-cell receptors (TCRs) and B-cell receptors (BCRs), collectively referred to as the immune repertoire. High-throughput sequencing (HTS) of TCR/BCR genes allows in-depth molecular analysis of T/B-cell clones, providing an unprecedented level of detail when examining the T/B-cell repertoire of individuals. It can evaluate TCR/BCR complementarity-determining region 3 (CDR3) diversity and assess the clonal composition, including the size of the repertoire; similarities between repertoires; V(D)J segment use; nucleotide insertions and deletions; CDR3 lengths; and amino acid distributions along the CDR3s at sequence-level resolution. Deep sequencing of B-cell and T-cell repertoires offers the potential for a quantitative understanding of the adaptive immune system in healthy and disease states. Recently, paired sequencing strategies have also been developed, which can provide information about the identity of immune receptor pairs encoded by individual T or B lymphocytes. HTS technology provides a previously unimaginable amount of sequence data, accompanied, however, by numerous challenges associated with error correction and interpretation that remain to be solved. The review details some of the technologies and some of the recent achievements in this field.
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Affiliation(s)
- X-L Hou
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - L Wang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Y-L Ding
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Q Xie
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - H-Y Diao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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Angelova M, Charoentong P, Hackl H, Fischer ML, Snajder R, Krogsdam AM, Waldner MJ, Bindea G, Mlecnik B, Galon J, Trajanoski Z. Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy. Genome Biol 2015; 16:64. [PMID: 25853550 PMCID: PMC4377852 DOI: 10.1186/s13059-015-0620-6] [Citation(s) in RCA: 393] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2014] [Accepted: 02/24/2015] [Indexed: 12/26/2022] Open
Abstract
Background While large-scale cancer genomic projects are comprehensively characterizing the mutational spectrum of various cancers, so far little attention has been devoted to either define the antigenicity of these mutations or to characterize the immune responses they elicit. Here we present a strategy to characterize the immunophenotypes and the antigen-ome of human colorectal cancer. Results We apply our strategy to a large colorectal cancer cohort (n = 598) and show that subpopulations of tumor-infiltrating lymphocytes are associated with distinct molecular phenotypes. The characterization of the antigenome shows that a large number of cancer-germline antigens are expressed in all patients. In contrast, neo-antigens are rarely shared between patients, indicating that cancer vaccination requires individualized strategy. Analysis of the genetic basis of the tumors reveals distinct tumor escape mechanisms for the patient subgroups. Hypermutated tumors are depleted of immunosuppressive cells and show upregulation of immunoinhibitory molecules. Non-hypermutated tumors are enriched with immunosuppressive cells, and the expression of immunoinhibitors and MHC molecules is downregulated. Reconstruction of the interaction network of tumor-infiltrating lymphocytes and immunomodulatory molecules followed by a validation with 11 independent cohorts (n = 1,945) identifies BCMA as a novel druggable target. Finally, linear regression modeling identifies major determinants of tumor immunogenicity, which include well-characterized modulators as well as a novel candidate, CCR8, which is then tested in an orthologous immunodeficient mouse model. Conclusions The immunophenotypes of the tumors and the cancer antigenome remain widely unexplored, and our findings represent a step toward the development of personalized cancer immunotherapies. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0620-6) contains supplementary material, which is available to authorized users.
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Woodsworth DJ, Castellarin M, Holt RA. Sequence analysis of T-cell repertoires in health and disease. Genome Med 2013; 5:98. [PMID: 24172704 PMCID: PMC3979016 DOI: 10.1186/gm502] [Citation(s) in RCA: 130] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
T-cell antigen receptor (TCR) variability enables the cellular immune system to discriminate between self and non-self. High-throughput TCR sequencing (TCR-seq) involves the use of next generation sequencing platforms to generate large numbers of short DNA sequences covering key regions of the TCR coding sequence, which enables quantification of T-cell diversity at unprecedented resolution. TCR-seq studies have provided new insights into the healthy human T-cell repertoire, such as revised estimates of repertoire size and the understanding that TCR specificities are shared among individuals more frequently than previously anticipated. In the context of disease, TCR-seq has been instrumental in characterizing the recovery of the immune repertoire after hematopoietic stem cell transplantation, and the method has been used to develop biomarkers and diagnostics for various infectious and neoplastic diseases. However, T-cell repertoire sequencing is still in its infancy. It is expected that maturation of the field will involve the introduction of improved, standardized tools for data handling, deposition and statistical analysis, as well as the emergence of new and equivalently large-scale technologies for T-cell functional analysis and antigen discovery. In this review, we introduce this nascent field and TCR-seq methodology, we discuss recent insights into healthy and diseased TCR repertoires, and we examine the applications and challenges for TCR-seq in the clinic.
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Affiliation(s)
- Daniel J Woodsworth
- BC Cancer Agency, Michael Smith Genome Sciences Centre, Vancouver, British Columbia V5Z 1L3, Canada
- Genome Sciences & Technology Program, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Mauro Castellarin
- BC Cancer Agency, Michael Smith Genome Sciences Centre, Vancouver, British Columbia V5Z 1L3, Canada
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | - Robert A Holt
- BC Cancer Agency, Michael Smith Genome Sciences Centre, Vancouver, British Columbia V5Z 1L3, Canada
- Genome Sciences & Technology Program, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
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Charoentong P, Angelova M, Efremova M, Gallasch R, Hackl H, Galon J, Trajanoski Z. Bioinformatics for cancer immunology and immunotherapy. Cancer Immunol Immunother 2012; 61:1885-903. [PMID: 22986455 PMCID: PMC3493665 DOI: 10.1007/s00262-012-1354-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2012] [Accepted: 09/04/2012] [Indexed: 01/24/2023]
Abstract
Recent mechanistic insights obtained from preclinical studies and the approval of the first immunotherapies has motivated increasing number of academic investigators and pharmaceutical/biotech companies to further elucidate the role of immunity in tumor pathogenesis and to reconsider the role of immunotherapy. Additionally, technological advances (e.g., next-generation sequencing) are providing unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and ultimately enable individualized treatment. However, the increasing complexity of the generated data and the plethora of bioinformatics methods and tools pose considerable challenges to both tumor immunologists and clinical oncologists. In this review, we describe current concepts and future challenges for the management and analysis of data for cancer immunology and immunotherapy. We first highlight publicly available databases with specific focus on cancer immunology including databases for somatic mutations and epitope databases. We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling. Mathematical models are powerful tools that can predict and explain important patterns in the genetic and clinical progression of cancer. Therefore, a survey of mathematical models for tumor evolution and tumor-immune cell interaction is included. Finally, we discuss future challenges for individualized immunotherapy and suggest how a combined computational/experimental approaches can lead to new insights into the molecular mechanisms of cancer, improved diagnosis, and prognosis of the disease and pinpoint novel therapeutic targets.
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Affiliation(s)
- Pornpimol Charoentong
- Biocenter, Division of Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria
| | - Mihaela Angelova
- Biocenter, Division of Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria
| | - Mirjana Efremova
- Biocenter, Division of Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria
| | - Ralf Gallasch
- Biocenter, Division of Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria
| | - Hubert Hackl
- Biocenter, Division of Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria
| | - Jerome Galon
- INSERM U872, Integrative Cancer Immunology Laboratory, Paris, France
| | - Zlatko Trajanoski
- Biocenter, Division of Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria
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