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Aguadé-Gorgorió G, Anderson ARA, Solé R. Modeling tumors as species-rich ecological communities. bioRxiv 2024:2024.04.22.590504. [PMID: 38712062 PMCID: PMC11071393 DOI: 10.1101/2024.04.22.590504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Many advanced cancers resist therapeutic intervention. This process is fundamentally related to intra-tumor heterogeneity: multiple cell populations, each with different mutational and phenotypic signatures, coexist within a tumor and its metastatic nodes. Like species in an ecosystem, many cancer cell populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity nor are able to predict its consequences. Here we propose that the Generalized Lotka-Volterra model (GLV), a standard tool to describe complex, species-rich ecological communities, provides a suitable framework to describe the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties, such as outgrowth and multistability, provide a new understanding of the disease. Additionally, we discuss potential extensions of the model and their application to three active areas of cancer research, namely phenotypic plasticity, the cancer-immune interplay and the resistance of metastatic tumors to treatment. Our work outlines a set of questions and a tentative road map for further research in cancer ecology.
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Truex N, Mohapatra S, Melo M, Rodriguez J, Li N, Abraham W, Sementa D, Touti F, Keskin DB, Wu CJ, Irvine DJ, Gómez-Bombarelli R, Pentelute BL. Design of Cytotoxic T Cell Epitopes by Machine Learning of Human Degrons. ACS Cent Sci 2024; 10:793-802. [PMID: 38680558 PMCID: PMC11046456 DOI: 10.1021/acscentsci.3c01544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/13/2024] [Accepted: 02/16/2024] [Indexed: 05/01/2024]
Abstract
Antigen processing is critical for therapeutic vaccines to generate epitopes for priming cytotoxic T cell responses against cancer and pathogens, but insufficient processing often limits the quantity of epitopes released. We address this challenge using machine learning to ascribe a proteasomal degradation score to epitope sequences. Epitopes with varying scores were translocated into cells using nontoxic anthrax proteins. Epitopes with a low score show pronounced immunogenicity due to antigen processing, but epitopes with a high score show limited immunogenicity. This work sheds light on the sequence-activity relationships between proteasomal degradation and epitope immunogenicity. We anticipate that future efforts to incorporate proteasomal degradation signals into vaccine designs will lead to enhanced cytotoxic T cell priming by these vaccines in clinical settings.
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Affiliation(s)
- Nicholas
L. Truex
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States
- Department
of Chemistry and Biochemistry, University
of South Carolina, Columbia, South Carolina 29208, United States
| | - Somesh Mohapatra
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
- Machine
Intelligence and Manufacturing Operations Group, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Mariane Melo
- The
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
- Ragon Institute
of Massachusetts General Hospital, Massachusetts
Institute of Technology, and Harvard University, Cambridge, Massachusetts 02139, United States
| | - Jacob Rodriguez
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States
| | - Na Li
- The
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
| | - Wuhbet Abraham
- The
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
| | - Deborah Sementa
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States
| | - Faycal Touti
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States
| | - Derin B. Keskin
- Department
of Medical Oncology, Dana-Farber Cancer
Institute, Boston, Massachusetts 02215, United States
- Harvard
Medical School, Boston, Massachusetts 02115, United States
- Broad
Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Translational
Immunogenomics Laboratory (TIGL), Dana-Farber
Cancer Institute, Boston, Massachusetts 02215, United States
- Department
of Computer Science, Metropolitan College, Boston University, Boston, Massachusetts 02215, United States
- Section
for Bioinformatics, Department of Health Technology, Technical University of Denmark, Lyngby DK-2800, Denmark
| | - Catherine J. Wu
- Department
of Medical Oncology, Dana-Farber Cancer
Institute, Boston, Massachusetts 02215, United States
- Harvard
Medical School, Boston, Massachusetts 02115, United States
- Broad
Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02115, United States
| | - Darrell J. Irvine
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
- The
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
- Ragon Institute
of Massachusetts General Hospital, Massachusetts
Institute of Technology, and Harvard University, Cambridge, Massachusetts 02139, United States
- Department
of Biological Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
- Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, United States
| | - Rafael Gómez-Bombarelli
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Bradley L. Pentelute
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States
- The
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
- Broad
Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Center
for Environmental Health Sciences, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
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Sheikhlary S, Lopez DH, Moghimi S, Sun B. Recent Findings on Therapeutic Cancer Vaccines: An Updated Review. Biomolecules 2024; 14:503. [PMID: 38672519 PMCID: PMC11048403 DOI: 10.3390/biom14040503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/06/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Cancer remains one of the global leading causes of death and various vaccines have been developed over the years against it, including cell-based, nucleic acid-based, and viral-based cancer vaccines. Although many vaccines have been effective in in vivo and clinical studies and some have been FDA-approved, there are major limitations to overcome: (1) developing one universal vaccine for a specific cancer is difficult, as tumors with different antigens are different for different individuals, (2) the tumor antigens may be similar to the body's own antigens, and (3) there is the possibility of cancer recurrence. Therefore, developing personalized cancer vaccines with the ability to distinguish between the tumor and the body's antigens is indispensable. This paper provides a comprehensive review of different types of cancer vaccines and highlights important factors necessary for developing efficient cancer vaccines. Moreover, the application of other technologies in cancer therapy is discussed. Finally, several insights and conclusions are presented, such as the possibility of using cold plasma and cancer stem cells in developing future cancer vaccines, to tackle the major limitations in the cancer vaccine developmental process.
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Affiliation(s)
- Sara Sheikhlary
- Department of Biomedical Engineering, College of Engineering, The University of Arizona, Tucson, AZ 85721, USA
| | - David Humberto Lopez
- Department of Pharmacology and Toxicology, College of Pharmacy, The University of Arizona, Tucson, AZ 85721, USA; (D.H.L.); (S.M.)
| | - Sophia Moghimi
- Department of Pharmacology and Toxicology, College of Pharmacy, The University of Arizona, Tucson, AZ 85721, USA; (D.H.L.); (S.M.)
| | - Bo Sun
- Department of Pharmacology and Toxicology, College of Pharmacy, The University of Arizona, Tucson, AZ 85721, USA; (D.H.L.); (S.M.)
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Ragone C, Cavalluzzo B, Mauriello A, Tagliamonte M, Buonaguro L. Lack of shared neoantigens in prevalent mutations in cancer. J Transl Med 2024; 22:344. [PMID: 38600547 PMCID: PMC11005154 DOI: 10.1186/s12967-024-05110-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 03/19/2024] [Indexed: 04/12/2024] Open
Abstract
Tumors are mostly characterized by genetic instability, as result of mutations in surveillance mechanisms, such as DNA damage checkpoint, DNA repair machinery and mitotic checkpoint. Defect in one or more of these mechanisms causes additive accumulation of mutations. Some of these mutations are drivers of transformation and are positively selected during the evolution of the cancer, giving a growth advantage on the cancer cells. If such mutations would result in mutated neoantigens, these could be actionable targets for cancer vaccines and/or adoptive cell therapies. However, the results of the present analysis show, for the first time, that the most prevalent mutations identified in human cancers do not express mutated neoantigens. The hypothesis is that this is the result of the selection operated by the immune system in the very early stages of tumor development. At that stage, the tumor cells characterized by mutations giving rise to highly antigenic non-self-mutated neoantigens would be efficiently targeted and eliminated. Consequently, the outgrowing tumor cells cannot be controlled by the immune system, with an ultimate growth advantage to form large tumors embedded in an immunosuppressive tumor microenvironment (TME). The outcome of such a negative selection operated by the immune system is that the development of off-the-shelf vaccines, based on shared mutated neoantigens, does not seem to be at hand. This finding represents the first demonstration of the key role of the immune system on shaping the tumor antigen presentation and the implication in the development of antitumor immunological strategies.
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Affiliation(s)
- Concetta Ragone
- Lab of Innovative Immunological Models Unit, Istituto Nazionale Tumori, IRCCS - "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Beatrice Cavalluzzo
- Lab of Innovative Immunological Models Unit, Istituto Nazionale Tumori, IRCCS - "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Angela Mauriello
- Lab of Innovative Immunological Models Unit, Istituto Nazionale Tumori, IRCCS - "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Maria Tagliamonte
- Lab of Innovative Immunological Models Unit, Istituto Nazionale Tumori, IRCCS - "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy.
| | - Luigi Buonaguro
- Lab of Innovative Immunological Models Unit, Istituto Nazionale Tumori, IRCCS - "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy.
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Manoutcharian K, Gevorkian G. Are we getting closer to a successful neoantigen cancer vaccine? Mol Aspects Med 2024; 96:101254. [PMID: 38354548 DOI: 10.1016/j.mam.2024.101254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/16/2024]
Abstract
Although significant advances in immunotherapy have revolutionized the treatment of many cancer types over the past decade, the field of vaccine therapy, an important component of cancer immunotherapy, despite decades-long intense efforts, is still transmitting signals of promises and awaiting strong data on efficacy to proceed with regulatory approval. The field of cancer vaccines faces standard challenges, such as tumor-induced immunosuppression, immune response in inhibitory tumor microenvironment (TME), intratumor heterogeneity (ITH), permanently evolving cancer mutational landscape leading to neoantigens, and less known obstacles: neoantigen gain/loss upon immunotherapy, the timing and speed of appearance of neoantigens and responding T cell clonotypes and possible involvement of immune interference/heterologous immunity, in the complex interplay between evolving tumor epitopes and the immune system. In this review, we discuss some key issues related to challenges hampering the development of cancer vaccines, along with the current approaches focusing on neoantigens. We summarize currently well-known ideas/rationales, thus revealing the need for alternative vaccine approaches. Such a discussion should stimulate vaccine researchers to apply out-of-box, unconventional thinking in search of new avenues to deal with critical, often yet unaddressed challenges on the road to a new generation of therapeutics and vaccines.
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Affiliation(s)
- Karen Manoutcharian
- Instituto de Investigaciones Biomedicas, Universidad Nacional Autonoma de Mexico (UNAM), CDMX, Apartado Postal 70228, Cuidad Universitaria, Mexico DF, CP, 04510, Mexico.
| | - Goar Gevorkian
- Instituto de Investigaciones Biomedicas, Universidad Nacional Autonoma de Mexico (UNAM), CDMX, Apartado Postal 70228, Cuidad Universitaria, Mexico DF, CP, 04510, Mexico.
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Terai M, Sato T. Individualised neoantigen cancer vaccine therapy. Lancet 2024; 403:590-591. [PMID: 38246193 DOI: 10.1016/s0140-6736(23)02463-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 11/01/2023] [Indexed: 01/23/2024]
Affiliation(s)
- Mizue Terai
- Department of Medical Oncology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Takami Sato
- Department of Medical Oncology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA.
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Adams AC, Macy AM, Borden ES, Herrmann LM, Brambley CA, Ma T, Li X, Hughes A, Roe DJ, Mangold AR, Buetow KH, Wilson MA, Baker BM, Hastings KT. Distinct sets of molecular characteristics define tumor-rejecting neoantigens. bioRxiv 2024:2024.02.13.579546. [PMID: 38405868 PMCID: PMC10888839 DOI: 10.1101/2024.02.13.579546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Challenges in identifying tumor-rejecting neoantigens limit the efficacy of neoantigen vaccines to treat cancers, including cutaneous squamous cell carcinoma (cSCC). A minority of human cSCC tumors shared neoantigens, supporting the need for personalized vaccines. Using a UV-induced mouse cSCC model which recapitulated the mutational signature and driver mutations found in human disease, we found that CD8 T cells constrain cSCC. Two MHC class I neoantigens were identified that constrained cSCC growth. Compared to the wild-type peptides, one tumor-rejecting neoantigen exhibited improved MHC binding and the other had increased solvent accessibility of the mutated residue. Across known neoantigens that do not impact MHC binding, structural modeling of the peptide/MHC complexes indicated that increased solvent accessibility, which will facilitate TCR recognition of the neoantigen, distinguished tumor-rejecting from non-immunogenic neoantigens. This work reveals characteristics of tumor-rejecting neoantigens that may be of considerable importance in identifying optimal vaccine candidates in cSCC and other cancers.
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Han Y, Yang Y, Tian Y, Fattah FJ, von Itzstein MS, Hu Y, Zhang M, Kang X, Yang DM, Liu J, Xue Y, Liang C, Raman I, Zhu C, Xiao O, Dowell JE, Homsi J, Rashdan S, Yang S, Gwin ME, Hsiehchen D, Gloria-McCutchen Y, Pan K, Wu F, Gibbons D, Wang X, Yee C, Huang J, Reuben A, Cheng C, Zhang J, Gerber DE, Wang T. pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding. bioRxiv 2023:2023.12.01.569599. [PMID: 38105939 PMCID: PMC10723300 DOI: 10.1101/2023.12.01.569599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of αβ T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.
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Huang R, Zhou P, Chen B, Zhu Y, Chen X, Min Y. Stimuli-Responsive Nanoadjuvant Rejuvenates Robust Immune Responses to Sensitize Cancer Immunotherapy. ACS Nano 2023; 17:21455-21469. [PMID: 37897704 DOI: 10.1021/acsnano.3c06233] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/30/2023]
Abstract
Despite their immense therapeutic potential, cancer immunotherapies such as immune checkpoint blockers (ICBs) benefit only a small subset of patients. Toll-like receptor agonists reverse the immunosuppressive tumor microenvironment (TME) to enhance antitumor immunity, but their systemic administration induces side effects. This work describes a TME-responsive nanotherapeutic platform for the site-specific release of drug candidates in tumors with a significant antitumor efficacy. Imidazoquinoline (IMQ)-derived liposomal nanovesicles (LN-IMQ) triggered the antitumor ability of macrophages, mobilized T-cell immunity, and promoted the secretion of antitumor cytokines, explaining the synergistic effect of LN-IMQ with ICBs. LN-IMQ monotherapy observed complete tumor regression in 6/8 of 4T1-bearing mouse, and cured mice resisted secondary tumor challenge. Besides, LN-IMQ decreased the occurrence of lung metastases, being effective against advanced metastases. On the other hand, neoantigen-based cancer vaccine has very low immune responses. Here, we also verified that LN-IMQ can serve as an ideal tumor antigen delivery vector. Cancer cells in vitro treated with chemotherapeutic drugs included multiple neoantigens and high levels of damage-associated molecular patterns, which were then successfully encapsulated in LN-IMQ to obtain a "personalized nanovaccine" with artificially amplified antigenicity and adjuvant properties. This study developed an attractive potential personalized nanovaccine for chemotherapeutic-drug-induced tumor neoantigens and immunotherapy.
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Affiliation(s)
- Ruijie Huang
- Department of Chemistry, University of Science and Technology of China, Hefei 230026, China
| | - Peijie Zhou
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Bo Chen
- Department of Chemistry, University of Science and Technology of China, Hefei 230026, China
| | - Yang Zhu
- Department of Neurosurgery, Neurosurgery Research Institute, the First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
| | - Xiaoyuan Chen
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
- Nanomedicine Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore
| | - Yuanzeng Min
- Department of Chemistry, University of Science and Technology of China, Hefei 230026, China
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- CAS Key Lab of Soft Matter Chemistry, University of Science and Technology of China, Hefei 230026, China
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