Review
Immunology
Roy A. Mariuzza, Daichao Wu, Brian G. Pierce
Summary: This review summarizes recent studies on the structural and biophysical aspects of T cell receptor (TCR) recognition of shared cancer neoantigens derived from oncogenes. The findings reveal the correlation between different mutations and the antigen presentation, and discuss the potential of TCR-mimic antibodies as an alternative to TCRs for targeting cancer neoantigens. Additionally, the review highlights recent computational advances and the significance of structural information in predicting neoepitope immunogenicity.
FRONTIERS IN IMMUNOLOGY
(2023)
Article
Multidisciplinary Sciences
Dan Lu, Yuan Chen, Min Jiang, Jie Wang, Yiting Li, Keke Ma, Wenqiao Sun, Xing Zheng, Jianxun Qi, Wenjing Jin, Yu Chen, Yan Chai, Catherine W. H. Zhang, Hao Liang, Shuguang Tan, George F. Gao
Summary: This study identifies TCRs specific for the 9-mer KRAS-G12V mutant neoantigen in the context of HLA-A*11:01, and demonstrates their potential for tumor therapy by mediating T cell responses to eliminate tumor cells expressing the KRAS-G12V mutation. The study also reveals the mechanisms of presentation and TCR recognition of KRAS-G12V mutant peptide, providing important insights for tumor immunotherapy.
NATURE COMMUNICATIONS
(2023)
Article
Immunology
Petra Baumgaertner, Julien Schmidt, Carla-Marisa Costa-Nunes, Natacha Bordry, Philippe Guillaume, Immanuel Luescher, Daniel E. Speiser, Nathalie Rufer, Michael Hebeisen
Summary: This study investigates the impact of peptide:HLA and TCR-pHLA affinities on CD8 T cell responses and cross-reactivity. It is found that vaccines containing native tumor epitopes generate T cells with better functionality and superior cross-reactivity against low affinity escape epitopes. Moreover, different clonotypes of tumor antigen-specific T cells display heterogeneous functional and cross-reactive profiles.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Immunology
Sarah Hall-Swan, Jared Slone, Mauricio M. Rigo, Dinler A. Antunes, Gregory Lizee, Lydia E. Kavraki
Summary: PepSim is a method for predicting T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs. It accurately separates cross-reactive from non-crossreactive pHLAs in diverse datasets, making it a valuable tool for designing safe and effective T-cell immunotherapies.
FRONTIERS IN IMMUNOLOGY
(2023)
Article
Multidisciplinary Sciences
Cristina Puig-Saus, Barbara Sennino, Songming Peng, Clifford L. Wang, Zheng Pan, Benjamin Yuen, Bhamini Purandare, Duo An, Boi B. Quach, Diana Nguyen, Huiming Xia, Sameeha Jilani, Kevin Shao, Claire McHugh, John Greer, Phillip Peabody, Saparya Nayak, Jonathan Hoover, Sara Said, Kyle Jacoby, Olivier Dalmas, Susan P. Foy, Andrew Conroy, Michael C. Yi, Christine Shieh, William Lu, Katharine Heeringa, Yan Ma, Shahab Chizari, Melissa J. Pilling, Marc Ting, Ramya Tunuguntla, Salemiz Sandoval, Robert Moot, Theresa Hunter, Sidi Zhao, Justin D. Saco, Ivan Perez-Garcilazo, Egmidio Medina, Agustin Vega-Crespo, Ignacio Baselga-Carretero, Gabriel Abril-Rodriguez, Grace Cherry, Deborah J. Wong, Jasreet Hundal, Bartosz Chmielowski, Daniel E. Speiser, Michael T. Bethune, Xiaoyan R. Bao, Alena Gros, Obi L. Griffith, Malachi Griffith, James R. Heath, Alex Franzusoff, Stefanie J. Mandl, Antoni Ribas
Summary: We isolated neoantigen-specific T cells from blood and tumors of melanoma patients using newly developed technologies. Multiple T cell clones with different neoTCR sequences recognized a limited number of mutations in samples from patients with clinical responses. These recurring neoTCR clonotypes were detected in blood and tumors over time. Patients with no response also showed neoantigen-specific T cell responses, but with lower TCR polyclonality and were not recurrently detected in sequential samples. Reconstructing neoTCRs in donor T cells demonstrated specific recognition and cytotoxicity to patient-matched melanoma cell lines.
Article
Biochemistry & Molecular Biology
Daichao Wu, Ragul Gowathaman, Brian G. Pierce, Roy A. Mariuzza
Summary: Adoptive cell therapy with tumor-specific T cells can lead to long-term cancer regression. This study focuses on understanding how T cells recognize cancer-specific antigens at the atomic level, particularly the p53R175H mutation. The researchers found that TCR 6-11, unlike other TCRs, recognizes the mutant p53 indirectly, without directly contacting the mutation site. The difference in binding affinity between mutant and wild-type p53 is mainly attributed to the energetic cost of desolvation in the wild-type peptide. This study highlights the diversity of recognition strategies and provides insights into developing T cell therapies with selectivity against tumor cells.
JOURNAL OF BIOLOGICAL CHEMISTRY
(2022)
Article
Biochemistry & Molecular Biology
Kaixuan Diao, Jing Chen, Tao Wu, Xuan Wang, Guangshuai Wang, Xiaoqin Sun, Xiangyu Zhao, Chenxu Wu, Jinyu Wang, Huizi Yao, Casimiro Gerarduzzi, Xue-Song Liu
Summary: Seq2Neo is a pipeline that predicts the immunogenicity of neoantigens by providing a solution for neoepitope feature prediction using raw sequencing data. It supports different types of genome DNA alterations and includes a CNN-based model that shows improved performance in immunogenicity prediction compared to currently available tools.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Immunology
Guadalupe Nibeyro, Veronica Baronetto, Juan I. Folco, Pablo Pastore, Maria Romina Girotti, Laura Prato, Gabriel Moron, Hugo D. Lujan, Elmer A. Fernandez
Summary: Identification and evaluation of tumor specific neoantigen (TSN) immunogenicity is important for developing anti-tumoral vaccines and immunotherapies. In this study, a new curated TSN database was developed and 16 metrics were evaluated as predictors for immunogenicity. The results showed high performance variability among methods and the need for improvement. Recommendations for clinical application and the database are provided to promote development and comparison of computational TSN immunogenicity predictors.
FRONTIERS IN IMMUNOLOGY
(2023)
Article
Immunology
Markus Muller, Florian Huber, Marion Arnaud, Anne I. Kraemer, Emma Ricart Altimiras, Justine Michaux, Marie Taillandier-Coindard, Johanna Chiffelle, Baptiste Murgues, Talita Gehret, Aymeric Auger, Brian J. Stevenson, George Coukos, Alexandre Harari, Michal Bassani-Sternberg
Summary: This study reprocessed datasets to identify the crucial role of neoantigens in cancer immunotherapy and discovered new factors affecting immunogenicity. The classifiers' accuracy was validated, providing valuable data for developing and benchmarking companion algorithms for neoantigen-based immuno-therapies.
Article
Multidisciplinary Sciences
Andrew Poole, Vijaykumar Karuppiah, Annabelle Hartt, Jaafar N. Haidar, Sylvie Moureau, Tomasz Dobrzycki, Conor Hayes, Christopher Rowley, Jorge Dias, Stephen Harper, Keir Barnbrook, Miriam Hock, Charlotte Coles, Wei Yang, Milos Aleksic, Aimee Bence Lin, Ross Robinson, Joe D. Dukes, Nathaniel Liddy, Marc Van der Kamp, Gregory D. Plowman, Annelise Vuidepot, David K. Cole, Andrew D. Whale, Chandramouli Chillakuri
Summary: This study presents the identification and development of an affinity-enhanced T cell receptor (TCR) and a bispecific protein targeting cancer cells with a high-frequency mutation in the KRAS oncogene.
NATURE COMMUNICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Benjamin Alexander Albert, Yunxiao Yang, Xiaoshan M. M. Shao, Dipika Singh, Kellie N. N. Smith, Valsamo Anagnostou, Rachel Karchin
Summary: Researchers propose a method based on long short-term memory ensembles and transfer learning to predict effective neoepitopes that elicit an immune response. This method can help address the challenge of predicting immunogenicity of neoepitopes in developing personalized cancer vaccines. Compared to other state-of-the-art classifiers, this method significantly improves the prediction of epitope presentation.
NATURE MACHINE INTELLIGENCE
(2023)
Article
Immunology
Huiming Xia, Joshua McMichael, Michelle Becker-Hapak, Onyinyechi C. Onyeador, Rico Buchli, Ethan McClain, Patrick Pence, Suangson Supabphol, Megan M. Richters, Anamika Basu, Cody A. Ramirez, Cristina Puig-Saus, Kelsy C. Cotto, Sharon L. Freshour, Jasreet Hundal, Susanna Kiwala, S. Peter Goedegebuure, Tanner M. Johanns, Gavin P. Dunn, Antoni Ribas, Christopher A. Miller, William E. Gillanders, Todd A. Fehniger, Obi L. Griffith, Malachi Griffith
Summary: Neoantigens are tumor-specific peptide sequences resulting from somatic DNA mutations that can trigger T cell recognition when loaded onto MHC molecules. Accurate neoantigen identification is crucial for cancer vaccine design and predicting response to immunotherapies. Consideration of the mutation position within the peptide relative to anchor positions on MHC molecules is important for predicting T cell responses.
SCIENCE IMMUNOLOGY
(2023)
Article
Oncology
Catherine M. Ade, Matthew J. Sporn, Sudipto Das, Zhiya Yu, Ken-ichi Hanada, Yue A. Qi, Tapan Maity, Xu Zhang, Udayan Guha, Thorkell Andresson, James C. Yang
Summary: This article introduces a method of using mass spectrometry to identify common tumor-specific neoepitopes derived from mutated oncogenes, and develop TCRs based on these data. The results of the study show that this method successfully identified precise neoepitopes derived from KRAS, EGFR, BRAF, and PIK3CA presented by HLA-A*03:01 and/or HLA-A*11:01 across multiple biological replicates.
JOURNAL FOR IMMUNOTHERAPY OF CANCER
(2023)
Review
Oncology
Lindy Davis, Ashley Tarduno, Yong-Chen Lu
Summary: Cancer immunotherapy revolutionizes cancer treatment by utilizing the patient's own immune system to fight and potentially cure cancer. T cells play a critical role in recognizing and killing tumor cells in melanoma patients by targeting neoantigens. Clinical trials have shown significant clinical responses in patients with metastatic cutaneous melanoma through checkpoint blockade immunotherapy or adoptive cell therapy.
Review
Oncology
Remco Nagel, Abhijeet Pataskar, Julien Champagne, Reuven Agami
Summary: Immune-checkpoint blockade therapy has shown success in treating cancers with high mutational burden and abundant neoantigens. However, tumors lacking classic genetically derived neoantigens require novel approaches to enhance immunotherapy efficacy. Recent discoveries of non-genetically encoded and inducible neoepitopes offer new avenues for therapeutic development to improve sensitivity to immunotherapies.
Article
Genetics & Heredity
Kerstin Haase, Anja Moesch, Dmitrij Frishman
BMC MEDICAL GENOMICS
(2015)
Article
Biochemical Research Methods
Filippo Grazioli, Pierre Machart, Anja Mosch, Kai Li, Leonardo Castorina, Nico Pfeifer, Martin Renqiang Min
Summary: We propose a multi-sequence generalization of Variational Information Bottleneck called Attentive Variational Information Bottleneck (AVIB). AVIB utilizes multi-head self-attention to implicitly approximate a posterior distribution over latent encodings conditioned on multiple input sequences. AVIB is applied to predict interactions between T-cell receptors (TCRs) and peptides in immuno-oncology, outperforming state-of-the-art methods. Additionally, AVIB demonstrates effective unsupervised detection of out-of-distribution amino acid sequences.
Article
Immunology
Filippo Grazioli, Anja Moesch, Pierre Machart, Kai Li, Israa Alqassem, Timothy J. O'Donnell, Martin Renqiang Min
Summary: This study investigates the generalization ability of state-of-the-art deep learning models for TCR-peptide/-pMHC binding prediction to unseen peptides. The results show that these models fail to perform well on peptides that are not included in the training set, and provide an explanation for this phenomenon.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Maria Littmann, Katharina Selig, Liel Cohen-Lavi, Yotam Frank, Peter Hoenigschmid, Evans Kataka, Anja Moesch, Kun Qian, Avihai Ron, Sebastian Schmid, Adam Sorbie, Liran Szlak, Ayana Dagan-Wiener, Nir Ben-Tal, Masha Y. Niv, Daniel Razansky, Bjoern W. Schuller, Donna Ankerst, Tomer Hertz, Burkhard Rost
NATURE MACHINE INTELLIGENCE
(2020)
Article
Oncology
Victor Jaravine, Anja Moesch, Silke Raffegerst, Dolores J. Schendel, Dmitrij Frishman