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Li M, Hua X, Li S, Wu MC, Zhao N. A multi-bin rarefying method for evaluating alpha diversities in TCR sequencing data. Bioinformatics 2024; 40:btae431. [PMID: 38950175 PMCID: PMC11246167 DOI: 10.1093/bioinformatics/btae431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/17/2024] [Accepted: 06/28/2024] [Indexed: 07/03/2024] Open
Abstract
MOTIVATION T cell receptors (TCRs) constitute a major component of our adaptive immune system, governing the recognition and response to internal and external antigens. Studying the TCR diversity via sequencing technology is critical for a deeper understanding of immune dynamics. However, library sizes differ substantially across samples, hindering the accurate estimation/comparisons of alpha diversities. To address this, researchers frequently use an overall rarefying approach in which all samples are sub-sampled to an even depth. Despite its pervasive application, its efficacy has never been rigorously assessed. RESULTS In this paper, we develop an innovative "multi-bin" rarefying approach that partitions samples into multiple bins according to their library sizes, conducts rarefying within each bin for alpha diversity calculations, and performs meta-analysis across bins. Extensive simulations using real-world data highlight the inadequacy of the overall rarefying approach in controlling the confounding effect of library size. Our method proves robust in addressing library size confounding, outperforming competing normalization strategies by achieving better-controlled type-I error rates and enhanced statistical power in association tests. AVAILABILITY AND IMPLEMENTATION The code is available at https://github.com/mli171/MultibinAlpha. The datasets are freely available at https://doi.org/10.21417/B7001Z and https://doi.org/10.21417/AR2019NC.
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Affiliation(s)
- Mo Li
- Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA, 70504, United States
| | - Xing Hua
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, United States
| | - Shuai Li
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205, United States
| | - Michael C Wu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, United States
| | - Ni Zhao
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205, United States
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2
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Beckabir W, Zhou M, Lee JS, Vensko SP, Woodcock MG, Wang HH, Wobker SE, Atassi G, Wilkinson AD, Fowler K, Flick LM, Damrauer JS, Harrison MR, McKinnon KP, Rose TL, Milowsky MI, Serody JS, Kim WY, Vincent BG. Immune features are associated with response to neoadjuvant chemo-immunotherapy for muscle-invasive bladder cancer. Nat Commun 2024; 15:4448. [PMID: 38789460 PMCID: PMC11126571 DOI: 10.1038/s41467-024-48480-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 05/02/2024] [Indexed: 05/26/2024] Open
Abstract
Neoadjuvant cisplatin-based chemotherapy is standard of care for muscle-invasive bladder cancer (MIBC). Immune checkpoint inhibition (ICI) alone, and ICI in combination with chemotherapy, have demonstrated promising pathologic response (
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Affiliation(s)
- Wolfgang Beckabir
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
| | - Mi Zhou
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jin Seok Lee
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, USA
| | - Steven P Vensko
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mark G Woodcock
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hsing-Hui Wang
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
| | - Sara E Wobker
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Gatphan Atassi
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alec D Wilkinson
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kenneth Fowler
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Leah M Flick
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jeffrey S Damrauer
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael R Harrison
- Division of Medical Oncology, Department of Medicine, Duke Cancer Institute, Duke University, Durham, NC, USA
| | - Karen P McKinnon
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
| | - Tracy L Rose
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Matthew I Milowsky
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jonathan S Serody
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA.
- Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - William Y Kim
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Division of Hematology, Department of Medicine, UNC School of Medicine, Chapel Hill, NC, USA.
| | - Benjamin G Vincent
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, USA.
- Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Computational Medicine Program, UNC School of Medicine, Chapel Hill, NC, USA.
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3
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Pothuri VS, Hogg GD, Conant L, Borcherding N, James CA, Mudd J, Williams G, Seo YD, Hawkins WG, Pillarisetty VG, DeNardo DG, Fields RC. Intratumoral T-cell receptor repertoire composition predicts overall survival in patients with pancreatic ductal adenocarcinoma. Oncoimmunology 2024; 13:2320411. [PMID: 38504847 PMCID: PMC10950267 DOI: 10.1080/2162402x.2024.2320411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/14/2024] [Indexed: 03/21/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is refractory to immune checkpoint inhibitor therapy. However, intratumoral T-cell infiltration correlates with improved overall survival (OS). Herein, we characterized the diversity and antigen specificity of the PDAC T-cell receptor (TCR) repertoire to identify novel immune-relevant biomarkers. Demographic, clinical, and TCR-beta sequencing data were collated from 353 patients across three cohorts that underwent surgical resection for PDAC. TCR diversity was calculated using Shannon Wiener index, Inverse Simpson index, and "True entropy." Patients were clustered by shared repertoire specificity. TCRs predictive of OS were identified and their associated transcriptional states were characterized by single-cell RNAseq. In multivariate Cox regression models controlling for relevant covariates, high intratumoral TCR diversity predicted OS across multiple cohorts. Conversely, in peripheral blood, high abundance of T-cells, but not high diversity, predicted OS. Clustering patients based on TCR specificity revealed a subset of TCRs that predicts OS. Interestingly, these TCR sequences were more likely to encode CD8+ effector memory and CD4+ T-regulatory (Tregs) T-cells, all with the capacity to recognize beta islet-derived autoantigens. As opposed to T-cell abundance, intratumoral TCR diversity was predictive of OS in multiple PDAC cohorts, and a subset of TCRs enriched in high-diversity patients independently correlated with OS. These findings emphasize the importance of evaluating peripheral and intratumoral TCR repertoires as distinct and relevant biomarkers in PDAC.
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Affiliation(s)
- Vikram S. Pothuri
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Graham D. Hogg
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Leah Conant
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Nicholas Borcherding
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - C. Alston James
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Jacqueline Mudd
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Greg Williams
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Yongwoo David Seo
- Department of Surgery, University of Washington School of Medicine, Seattle, WA, USA
- Department of Surgical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - William G. Hawkins
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MOUSA
| | - Venu G. Pillarisetty
- Department of Surgery, University of Washington School of Medicine, Seattle, WA, USA
- Fred Hutchinson Cancer Center, Seattle, WAUSA
| | - David G. DeNardo
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MOUSA
| | - Ryan C. Fields
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MOUSA
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4
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Mason M, Lapuente-Santana Ó, Halkola AS, Wang W, Mall R, Xiao X, Kaufman J, Fu J, Pfeil J, Banerjee J, Chung V, Chang H, Chasalow SD, Lin HY, Chai R, Yu T, Finotello F, Mirtti T, Mäyränpää MI, Bao J, Verschuren EW, Ahmed EI, Ceccarelli M, Miller LD, Monaco G, Hendrickx WRL, Sherif S, Yang L, Tang M, Gu SS, Zhang W, Zhang Y, Zeng Z, Das Sahu A, Liu Y, Yang W, Bedognetti D, Tang J, Eduati F, Laajala TD, Geese WJ, Guinney J, Szustakowski JD, Vincent BG, Carbone DP. A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer. J Transl Med 2024; 22:190. [PMID: 38383458 PMCID: PMC10880244 DOI: 10.1186/s12967-023-04705-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/05/2023] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.
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Affiliation(s)
- Mike Mason
- Bristol Myers Squibb, Princeton, NJ, USA
| | - Óscar Lapuente-Santana
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Anni S Halkola
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Wenyu Wang
- Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar
- Department of Immunology, St. Jude Children's Research Hospital, P.O. Box 38105, Memphis, TN, USA
- Biotechnology Research Center, Technology Innovation Institute, P.O. Box 9639, Abu Dhabi, United Arab Emirates
| | - Xu Xiao
- School of Informatics, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Jacob Kaufman
- Department of Medicine, Duke University, Durham, NC, USA
- The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Jingxin Fu
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | | | - Han Chang
- Bristol Myers Squibb, Princeton, NJ, USA
| | | | | | | | | | - Francesca Finotello
- Institute of Molecular Biology, University of Innsbruck, Innsbruck, Austria
- Digital Science Center (DiSC), University of Innsbruck, Innsbruck, Austria
| | - Tuomas Mirtti
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- Department of Biomedical Engineering, School of Medicine, Emory University, Atlanta, GA, USA
| | - Mikko I Mäyränpää
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Jie Bao
- Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Emmy W Verschuren
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Eiman I Ahmed
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
| | - Michele Ceccarelli
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples "Federico II", 80125, Naples, Italy
- BIOGEM Institute of Molecular Biology and Genetics, Via Camporeale, Ariano Irpino, Italy
| | - Lance D Miller
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Atrium Health Wake Forest Baptist Comprehensive Cancer Center, Winston-Salem, NC, USA
| | - Gianni Monaco
- BIOGEM Institute of Molecular Biology and Genetics, Via Camporeale, Ariano Irpino, Italy
| | - Wouter R L Hendrickx
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 26999, Doha, Qatar
| | - Shimaa Sherif
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 26999, Doha, Qatar
| | - Lin Yang
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ming Tang
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | - Yi Zhang
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Zexian Zeng
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Yang Liu
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Davide Bedognetti
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 26999, Doha, Qatar
- Department of Internal Medicine and Medical Specialties, University of Genoa, Genoa, Italy
| | - Jing Tang
- Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Teemu D Laajala
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- FICAN West Cancer Centre, University of Turku and Turku University Hospital, Turku, Finland
- Department of Pharmacology, Anschutz Medical Campus, University of Colorado, Denver, CO, USA
| | | | | | | | - Benjamin G Vincent
- Department of Medicine, Division of Hematology, Department of Microbiology and Immunology, Curriculum in Bioinformatics and Computational Biology, Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - David P Carbone
- The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA.
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Luo R, Chyr J, Wen J, Wang Y, Zhao W, Zhou X. A novel integrated approach to predicting cancer immunotherapy efficacy. Oncogene 2023; 42:1913-1925. [PMID: 37100920 PMCID: PMC10244162 DOI: 10.1038/s41388-023-02670-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 03/10/2023] [Accepted: 03/15/2023] [Indexed: 04/28/2023]
Abstract
Immunotherapies have revolutionized cancer treatment modalities; however, predicting clinical response accurately and reliably remains challenging. Neoantigen load is considered as a fundamental genetic determinant of therapeutic response. However, only a few predicted neoantigens are highly immunogenic, with little focus on intratumor heterogeneity (ITH) in the neoantigen landscape and its link with different features in the tumor microenvironment. To address this issue, we comprehensively characterized neoantigens arising from nonsynonymous mutations and gene fusions in lung cancer and melanoma. We developed a composite NEO2IS to characterize interplays between cancer and CD8+ T-cell populations. NEO2IS improved prediction accuracy of patient responses to immune-checkpoint blockades (ICBs). We found that TCR repertoire diversity was consistent with the neoantigen heterogeneity under evolutionary selections. Our defined neoantigen ITH score (NEOITHS) reflected infiltration degree of CD8+ T lymphocytes with different differentiation states and manifested the impact of negative selection pressure on CD8+ T-cell lineage heterogeneity or tumor ecosystem plasticity. We classified tumors into distinct immune subtypes and examined how neoantigen-T cells interactions affected disease progression and treatment response. Overall, our integrated framework helps profile neoantigen patterns that elicit T-cell immunoreactivity, enhance the understanding of evolving tumor-immune interplays and improve prediction of ICBs efficacy.
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Affiliation(s)
- Ruihan Luo
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jacqueline Chyr
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jianguo Wen
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yanfei Wang
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, USA.
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Porciello N, Franzese O, D’Ambrosio L, Palermo B, Nisticò P. T-cell repertoire diversity: friend or foe for protective antitumor response? JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2022; 41:356. [PMID: 36550555 PMCID: PMC9773533 DOI: 10.1186/s13046-022-02566-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022]
Abstract
Profiling the T-Cell Receptor (TCR) repertoire is establishing as a potent approach to investigate autologous and treatment-induced antitumor immune response. Technical and computational breakthroughs, including high throughput next-generation sequencing (NGS) approaches and spatial transcriptomics, are providing unprecedented insight into the mechanisms underlying antitumor immunity. A precise spatiotemporal variation of T-cell repertoire, which dynamically mirrors the functional state of the evolving host-cancer interaction, allows the tracking of the T-cell populations at play, and may identify the key cells responsible for tumor eradication, the evaluation of minimal residual disease and the identification of biomarkers of response to immunotherapy. In this review we will discuss the relationship between global metrics characterizing the TCR repertoire such as T-cell clonality and diversity and the resultant functional responses. In particular, we will explore how specific TCR repertoires in cancer patients can be predictive of prognosis or response to therapy and in particular how a given TCR re-arrangement, following immunotherapy, can predict a specific clinical outcome. Finally, we will examine current improvements in terms of T-cell sequencing, discussing advantages and challenges of current methodologies.
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Affiliation(s)
- Nicla Porciello
- grid.417520.50000 0004 1760 5276Tumor Immunology and Immunotherapy Unit, IRCCS-Regina Elena National Cancer Institute, Rome, Italy
| | - Ornella Franzese
- grid.6530.00000 0001 2300 0941Department of Systems Medicine, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy
| | - Lorenzo D’Ambrosio
- grid.417520.50000 0004 1760 5276Tumor Immunology and Immunotherapy Unit, IRCCS-Regina Elena National Cancer Institute, Rome, Italy
| | - Belinda Palermo
- grid.417520.50000 0004 1760 5276Tumor Immunology and Immunotherapy Unit, IRCCS-Regina Elena National Cancer Institute, Rome, Italy
| | - Paola Nisticò
- grid.417520.50000 0004 1760 5276Tumor Immunology and Immunotherapy Unit, IRCCS-Regina Elena National Cancer Institute, Rome, Italy
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7
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Routh ED, Van Swearingen AED, Sambade MJ, Vensko S, McClure MB, Woodcock MG, Chai S, Cuaboy LA, Wheless A, Garrett A, Carey LA, Hoyle AP, Parker JS, Vincent BG, Anders CK. Comprehensive Analysis of the Immunogenomics of Triple-Negative Breast Cancer Brain Metastases From LCCC1419. Front Oncol 2022; 12:818693. [PMID: 35992833 PMCID: PMC9387304 DOI: 10.3389/fonc.2022.818693] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 05/30/2022] [Indexed: 11/23/2022] Open
Abstract
Background Triple negative breast cancer (TNBC) is an aggressive variant of breast cancer that lacks the expression of estrogen and progesterone receptors (ER and PR) and HER2. Nearly 50% of patients with advanced TNBC will develop brain metastases (BrM), commonly with progressive extracranial disease. Immunotherapy has shown promise in the treatment of advanced TNBC; however, the immune contexture of BrM remains largely unknown. We conducted a comprehensive analysis of TNBC BrM and matched primary tumors to characterize the genomic and immune landscape of TNBC BrM to inform the development of immunotherapy strategies in this aggressive disease. Methods Whole-exome sequencing (WES) and RNA sequencing were conducted on formalin-fixed, paraffin-embedded samples of BrM and primary tumors of patients with clinical TNBC (n = 25, n = 9 matched pairs) from the LCCC1419 biobank at UNC—Chapel Hill. Matched blood was analyzed by DNA sequencing as a comparison for tumor WES for the identification of somatic variants. A comprehensive genomics assessment, including mutational and copy number alteration analyses, neoantigen prediction, and transcriptomic analysis of the tumor immune microenvironment were performed. Results Primary and BrM tissues were confirmed as TNBC (23/25 primaries, 16/17 BrM) by immunohistochemistry and of the basal intrinsic subtype (13/15 primaries and 16/19 BrM) by PAM50. Compared to primary tumors, BrM demonstrated a higher tumor mutational burden. TP53 was the most frequently mutated gene and was altered in 50% of the samples. Neoantigen prediction showed elevated cancer testis antigen- and endogenous retrovirus-derived MHC class I-binding peptides in both primary tumors and BrM and predicted that single-nucleotide variant (SNV)-derived peptides were significantly higher in BrM. BrM demonstrated a reduced immune gene signature expression, although a signature associated with fibroblast-associated wound healing was elevated in BrM. Metrics of T and B cell receptor diversity were also reduced in BrM. Conclusions BrM harbored higher mutational burden and SNV-derived neoantigen expression along with reduced immune gene signature expression relative to primary TNBC. Immune signatures correlated with improved survival, including T cell signatures. Further research will expand these findings to other breast cancer subtypes in the same biobank. Exploration of immunomodulatory approaches including vaccine applications and immune checkpoint inhibition to enhance anti-tumor immunity in TNBC BrM is warranted.
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Affiliation(s)
- Eric D. Routh
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Amanda E. D. Van Swearingen
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Maria J. Sambade
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Steven Vensko
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Marni B. McClure
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- National Cancer Center Research Institute, Tokyo, Japan
| | - Mark G. Woodcock
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Medicine, Division of Medical Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Shengjie Chai
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, United States
| | - Luz A. Cuaboy
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Amy Wheless
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Amy Garrett
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Lisa A. Carey
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Medicine, Division of Medical Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Alan P. Hoyle
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Joel S. Parker
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Benjamin G. Vincent
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Medicine, Division of Medical Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, United States
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Division of Hematology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Carey K. Anders
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Medicine, Division of Medical Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- *Correspondence: Carey K. Anders,
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8
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Innocenti F, Yazdani A, Rashid N, Qu X, Ou FS, Van Buren S, Bertagnolli M, Kabbarah O, Blanke CD, Venook AP, Lenz HJ, Vincent BG. Tumor Immunogenomic Features Determine Outcomes in Patients with Metastatic Colorectal Cancer Treated with Standard-of-Care Combinations of Bevacizumab and Cetuximab. Clin Cancer Res 2022; 28:1690-1700. [PMID: 35176136 PMCID: PMC9093780 DOI: 10.1158/1078-0432.ccr-21-3202] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/22/2021] [Accepted: 02/11/2022] [Indexed: 12/16/2022]
Abstract
PURPOSE CALGB/SWOG 80405 was a randomized phase III trial in first-line patients with metastatic colorectal cancer treated with bevacizumab, cetuximab, or both, plus chemotherapy. We tested the effect of tumor immune features on overall survival (OS). EXPERIMENTAL DESIGN Primary tumors (N = 554) were profiled by RNA sequencing. Immune signatures of macrophages, lymphocytes, TGFβ, IFNγ, wound healing, and cytotoxicity were measured. CIBERSORTx scores of naive and memory B cells, plasma cells, CD8+ T cells, resting and activated memory CD4+ T cells, M0 and M2 macrophages, and activated mast cells were measured. RESULTS Increased M2 macrophage score [HR, 6.30; 95% confidence interval (CI), 3.0-12.15] and TGFβ signature expression (HR, 1.35; 95% CI, 1.05-1.77) were associated with shorter OS. Increased scores of plasma cells (HR, 0.55; 95% CI, 0.38-0.87) and activated memory CD4+ T cells (HR, 0.34; 95% CI, 0.16-0.65) were associated with longer OS. Using optimal cutoffs from these four features, patients were categorized as having either 4, 3, 2, or 0-1 beneficial features associated with longer OS, and the median (95% CI) OS decreased from 42.5 (35.8-47.8) to 31.0 (28.8-34.4), 25.2 (20.6-27.9), and 17.7 (13.5-20.4) months respectively (P = 3.48e-11). CONCLUSIONS New immune features can be further evaluated to improve patient response. They provide the rationale for more effective immunotherapy strategies.
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Affiliation(s)
| | - Akram Yazdani
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Naim Rashid
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - Fang-Shu Ou
- Alliance Statistics and Data Management Center, Mayo Clinic, Rochester, MN
| | - Scott Van Buren
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | | | | | - Alan P. Venook
- University of California at San Francisco, San Francisco, CA
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9
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Fiorentino DF, Mecoli CA, Rosen MC, Chung LS, Christopher-Stine L, Rosen A, Casciola-Rosen L. Immune responses to CCAR1 and other dermatomyositis autoantigens are associated with attenuated cancer emergence. J Clin Invest 2022; 132:150201. [PMID: 35040440 PMCID: PMC8759791 DOI: 10.1172/jci150201] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 11/12/2021] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND The temporal clustering of a cancer diagnosis with dermatomyositis (DM) onset is strikingly associated with autoantibodies against transcriptional intermediary factor 1-γ (TIF1-γ). Nevertheless, many patients with anti–TIF1-γ antibodies never develop cancer. We investigated whether additional autoantibodies are found in anti–TIF1-γ–positive patients without cancer. METHODS Using a proteomic approach, we defined 10 previously undescribed autoantibody specificities in 5 index anti–TIF1-γ–positive DM patients without cancer. These were subsequently examined in discovery (n = 110) and validation (n = 142) cohorts of DM patients with anti–TIF1-γ autoantibodies. RESULTS We identified 10 potentially novel autoantibodies in anti–TIF1-γ–positive DM patients, 6 with frequencies ranging from 3% to 32% in 2 independent DM cohorts. Autoantibodies recognizing cell division cycle and apoptosis regulator protein 1 (CCAR1) were the most frequent, and were significantly negatively associated with contemporaneous cancer (discovery cohort OR 0.27 [95% CI 0.7–1.00], P = 0.050; validation cohort OR 0.13 [95% CI 0.03–0.59], P = 0.008). When cancer did emerge, it occurred significantly later in anti-CCAR1–positive compared with anti-CCAR1–negative patients (median time from DM onset 4.3 vs. 0.85 years, respectively; P = 0.006). Cancers that emerged were more likely to be localized (89% of anti-CCAR1–positive cancers presenting at stage 0 or 1 compared with 42% of patients without anti-CCAR1 antibodies, P = 0.02). As the number of additional autoantibody specificities increased in anti–TIF1-γ–positive DM patients, the frequency of cancer decreased (P < 0.001). CONCLUSION As the diversity of immune responses in anti–TIF1-γ DM patients increases, the likelihood of cancer emerging decreases. Our findings have important relevance for cancer risk stratification in DM patients and for understanding natural immune regulation of cancer in humans. TRIAL REGISTRATION Not applicable. FUNDING SOURCES The NIH, the Donald B. and Dorothy L. Stabler Foundation, and the Huayi and Siuling Zhang Discovery Fund.
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Affiliation(s)
- David F Fiorentino
- Department of Dermatology, Stanford University School of Medicine, Redwood City, California, USA
| | - Christopher A Mecoli
- Division of Rheumatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Matthew C Rosen
- Department of Neurobiology, The University of Chicago, Chicago, Illinois, USA
| | - Lorinda S Chung
- Division of Immunology and Rheumatology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Lisa Christopher-Stine
- Division of Rheumatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Antony Rosen
- Division of Rheumatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Livia Casciola-Rosen
- Division of Rheumatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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10
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Choueiri TK, Albiges L, Atkins MB, Bakouny Z, Bratslavsky G, Braun DA, Haas NB, Haanen JB, Hakimi AA, Jewett MA, Jonasch E, Kaelin WG, Kapur P, Labaki C, Lewis B, McDermott DF, Pal SK, Pels K, Poteat S, Powles T, Rathmell WK, Rini BI, Signoretti S, Tannir NM, Uzzo RG, Hammers HJ. From Basic Science to Clinical Translation in Kidney Cancer: A Report from the Second Kidney Cancer Research Summit. Clin Cancer Res 2021; 28:831-839. [DOI: 10.1158/1078-0432.ccr-21-3238] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/07/2021] [Accepted: 12/21/2021] [Indexed: 11/16/2022]
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