1
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Abbosh C, Frankell AM, Harrison T, Kisistok J, Garnett A, Johnson L, Veeriah S, Moreau M, Chesh A, Chaunzwa TL, Weiss J, Schroeder MR, Ward S, Grigoriadis K, Shahpurwalla A, Litchfield K, Puttick C, Biswas D, Karasaki T, Black JRM, Martínez-Ruiz C, Bakir MA, Pich O, Watkins TBK, Lim EL, Huebner A, Moore DA, Godin-Heymann N, L'Hernault A, Bye H, Odell A, Roberts P, Gomes F, Patel AJ, Manzano E, Hiley CT, Carey N, Riley J, Cook DE, Hodgson D, Stetson D, Barrett JC, Kortlever RM, Evan GI, Hackshaw A, Daber RD, Shaw JA, Aerts HJWL, Licon A, Stahl J, Jamal-Hanjani M, Birkbak NJ, McGranahan N, Swanton C. Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA. Nature 2023; 616:553-562. [PMID: 37055640 PMCID: PMC7614605 DOI: 10.1038/s41586-023-05776-4] [Citation(s) in RCA: 81] [Impact Index Per Article: 81.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 01/30/2023] [Indexed: 04/15/2023]
Abstract
Circulating tumour DNA (ctDNA) can be used to detect and profile residual tumour cells persisting after curative intent therapy1. The study of large patient cohorts incorporating longitudinal plasma sampling and extended follow-up is required to determine the role of ctDNA as a phylogenetic biomarker of relapse in early-stage non-small-cell lung cancer (NSCLC). Here we developed ctDNA methods tracking a median of 200 mutations identified in resected NSCLC tissue across 1,069 plasma samples collected from 197 patients enrolled in the TRACERx study2. A lack of preoperative ctDNA detection distinguished biologically indolent lung adenocarcinoma with good clinical outcome. Postoperative plasma analyses were interpreted within the context of standard-of-care radiological surveillance and administration of cytotoxic adjuvant therapy. Landmark analyses of plasma samples collected within 120 days after surgery revealed ctDNA detection in 25% of patients, including 49% of all patients who experienced clinical relapse; 3 to 6 monthly ctDNA surveillance identified impending disease relapse in an additional 20% of landmark-negative patients. We developed a bioinformatic tool (ECLIPSE) for non-invasive tracking of subclonal architecture at low ctDNA levels. ECLIPSE identified patients with polyclonal metastatic dissemination, which was associated with a poor clinical outcome. By measuring subclone cancer cell fractions in preoperative plasma, we found that subclones seeding future metastases were significantly more expanded compared with non-metastatic subclones. Our findings will support (neo)adjuvant trial advances and provide insights into the process of metastatic dissemination using low-ctDNA-level liquid biopsy.
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Affiliation(s)
- Christopher Abbosh
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
| | - Alexander M Frankell
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | | | - Judit Kisistok
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | | | | | - Selvaraju Veeriah
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | | | | | - Tafadzwa L Chaunzwa
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Jakob Weiss
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Freiburg University Hospital, Freiburg, Germany
| | | | - Sophia Ward
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Advanced Sequencing Facility, The Francis Crick Institute, London, UK
| | - Kristiana Grigoriadis
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | | | - Kevin Litchfield
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Tumour Immunogenomics and Immunosurveillance Laboratory, University College London Cancer Institute, London, UK
| | - Clare Puttick
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Dhruva Biswas
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Bill Lyons Informatics Centre, University College London Cancer Institute, London, UK
| | - Takahiro Karasaki
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - James R M Black
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Carlos Martínez-Ruiz
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Maise Al Bakir
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Oriol Pich
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Thomas B K Watkins
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Emilia L Lim
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Ariana Huebner
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - David A Moore
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | | | | | | | | | | | - Fabio Gomes
- The Christie NHS Foundation Trust, Manchester, UK
| | - Akshay J Patel
- University Hospital Birmingham NHS Foundation Trust, Birmingham, UK
| | - Elizabeth Manzano
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Crispin T Hiley
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Nicolas Carey
- Cancer Research Centre, University of Leicester, Leicester, UK
| | - Joan Riley
- Cancer Research Centre, University of Leicester, Leicester, UK
| | - Daniel E Cook
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | | | | | | | | | - Gerard I Evan
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Allan Hackshaw
- Cancer Research UK & UCL Cancer Trials Centre, London, UK
| | | | - Jacqui A Shaw
- Cancer Research Centre, University of Leicester, Leicester, UK
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
| | | | | | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Nicolai J Birkbak
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
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2
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Banchereau R, Leng N, Zill O, Sokol E, Liu G, Pavlick D, Maund S, Liu LF, Kadel E, Baldwin N, Jhunjhunwala S, Nickles D, Assaf ZJ, Bower D, Patil N, McCleland M, Shames D, Molinero L, Huseni M, Sanjabi S, Cummings C, Mellman I, Mariathasan S, Hegde P, Powles T. Molecular determinants of response to PD-L1 blockade across tumor types. Nat Commun 2021; 12:3969. [PMID: 34172722 PMCID: PMC8233428 DOI: 10.1038/s41467-021-24112-w] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 05/13/2021] [Indexed: 02/06/2023] Open
Abstract
Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis lead to durable clinical responses in subsets of cancer patients across multiple indications, including non-small cell lung cancer (NSCLC), urothelial carcinoma (UC) and renal cell carcinoma (RCC). Herein, we complement PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB) with RNA-seq in 366 patients to identify unifying and indication-specific molecular profiles that can predict response to checkpoint blockade across these tumor types. Multiple machine learning approaches failed to identify a baseline transcriptional signature highly predictive of response across these indications. Signatures described previously for immune checkpoint inhibitors also failed to validate. At the pathway level, significant heterogeneity is observed between indications, in particular within the PD-L1+ tumors. mUC and NSCLC are molecularly aligned, with cell cycle and DNA damage repair genes associated with response in PD-L1- tumors. At the gene level, the CDK4/6 inhibitor CDKN2A is identified as a significant transcriptional correlate of response, highlighting the association of non-immune pathways to the outcome of checkpoint blockade. This cross-indication analysis reveals molecular heterogeneity between mUC, NSCLC and RCC tumors, suggesting that indication-specific molecular approaches should be prioritized to formulate treatment strategies.
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Affiliation(s)
| | - Ning Leng
- Genentech, South San Francisco, CA, USA
| | | | | | | | | | | | | | | | - Nicole Baldwin
- Baylor Institute for Immunology Research, Dallas, TX, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Thomas Powles
- Barts Experimental Cancer Medicine Centre, Barts Cancer Institute, Queen Mary University of London, London, UK.
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3
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Khodayari Moez E, Hajihosseini M, Andrews JL, Dinu I. Longitudinal linear combination test for gene set analysis. BMC Bioinformatics 2019; 20:650. [PMID: 31822265 PMCID: PMC6902471 DOI: 10.1186/s12859-019-3221-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 11/13/2019] [Indexed: 11/12/2022] Open
Abstract
Background Although microarray studies have greatly contributed to recent genetic advances, lack of replication has been a continuing concern in this area. Complex study designs have the potential to address this concern, though they remain undervalued by investigators due to the lack of proper analysis methods. The primary challenge in the analysis of complex microarray study data is handling the correlation structure within data while also dealing with the combination of large number of genetic measurements and small number of subjects that are ubiquitous even in standard microarray studies. Motivated by the lack of available methods for analysis of repeatedly measured phenotypic or transcriptomic data, herein we develop a longitudinal linear combination test (LLCT). Results LLCT is a two-step method to analyze multiple longitudinal phenotypes when there is high dimensionality in response and/or explanatory variables. Alternating between calculating within-subjects and between-subjects variations in two steps, LLCT examines if the maximum possible correlation between a linear combination of the time trends and a linear combination of the predictors given by the gene expressions is statistically significant. A generalization of this method can handle family-based study designs when the subjects are not independent. This method is also applicable to time-course microarray, with the ability to identify gene sets that exhibit significantly different expression patterns over time. Based on the results from a simulation study, LLCT outperformed its alternative: pathway analysis via regression. LLCT was shown to be very powerful in the analysis of large gene sets even when the sample size is small. Conclusions This self-contained pathway analysis method is applicable to a wide range of longitudinal genomics, proteomics, metabolomics (OMICS) data, allows adjusting for potentially time-dependent covariates and works well with unbalanced and incomplete data. An important potential application of this method could be time-course linkage of OMICS, an attractive possibility for future genetic researchers. Availability: R package of LLCT is available at: https://github.com/its-likeli-jeff/LLCT
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4
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Hong S, Banchereau R, Maslow BSL, Guerra MM, Cardenas J, Baisch J, Branch DW, Porter TF, Sawitzke A, Laskin CA, Buyon JP, Merrill J, Sammaritano LR, Petri M, Gatewood E, Cepika AM, Ohouo M, Obermoser G, Anguiano E, Kim TW, Nulsen J, Nehar-Belaid D, Blankenship D, Turner J, Banchereau J, Salmon JE, Pascual V. Longitudinal profiling of human blood transcriptome in healthy and lupus pregnancy. J Exp Med 2019; 216:1154-1169. [PMID: 30962246 PMCID: PMC6504211 DOI: 10.1084/jem.20190185] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 02/28/2019] [Accepted: 03/04/2019] [Indexed: 12/22/2022] Open
Abstract
Healthy and uncomplicated lupus pregnancies exhibit early and sustained transcriptional modulation of lupus-related pathways. This might contribute to fetal tolerance while predisposing pregnant women to certain infections. Failure to modulate these pathways is associated with lupus pregnancy complications. Systemic lupus erythematosus carries an increased risk of pregnancy complications, including preeclampsia and fetal adverse outcomes. To identify the underlying molecular mechanisms, we longitudinally profiled the blood transcriptome of 92 lupus patients and 43 healthy women during pregnancy and postpartum and performed multicolor flow cytometry in a subset of them. We also profiled 25 healthy women undergoing assisted reproductive technology to monitor transcriptional changes around embryo implantation. Sustained down-regulation of multiple immune signatures, including interferon and plasma cells, was observed during healthy pregnancy. These changes appeared early after embryo implantation and were mirrored in uncomplicated lupus pregnancies. Patients with preeclampsia displayed early up-regulation of neutrophil signatures that correlated with expansion of immature neutrophils. Lupus pregnancies with fetal complications carried the highest interferon and plasma cell signatures as well as activated CD4+ T cell counts. Thus, blood immunomonitoring reveals that both healthy and uncomplicated lupus pregnancies exhibit early and sustained transcriptional modulation of lupus-related signatures, and a lack thereof associates with adverse outcomes.
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Affiliation(s)
- Seunghee Hong
- Drukier Institute for Children's Health, Weill Cornell Medicine, New York, NY.,Department of Pediatrics, Weill Cornell Medicine, New York, NY.,Baylor Institute for Immunology Research, Dallas, TX
| | - Romain Banchereau
- Baylor Institute for Immunology Research, Dallas, TX.,Oncology Biomarker Development, Genentech, South San Francisco, CA
| | | | - Marta M Guerra
- Department of Medicine and Program in Inflammation and Autoimmunity, Hospital for Special Surgery, New York, NY
| | | | - Jeanine Baisch
- Drukier Institute for Children's Health, Weill Cornell Medicine, New York, NY.,Department of Pediatrics, Weill Cornell Medicine, New York, NY.,Baylor Institute for Immunology Research, Dallas, TX
| | - D Ware Branch
- University of Utah Health Sciences Center, Salt Lake City, UT.,Intermountain Healthcare, Salt Lake City, UT
| | - T Flint Porter
- University of Utah Health Sciences Center, Salt Lake City, UT.,Intermountain Healthcare, Salt Lake City, UT
| | - Allen Sawitzke
- University of Utah Health Sciences Center, Salt Lake City, UT
| | - Carl A Laskin
- Mount Sinai Hospital and the University of Toronto, Toronto, Ontario, Canada
| | - Jill P Buyon
- New York University School of Medicine, New York, NY
| | - Joan Merrill
- Oklahoma Medical Research Foundation, Oklahoma City, OK
| | - Lisa R Sammaritano
- Department of Medicine and Program in Inflammation and Autoimmunity, Hospital for Special Surgery, New York, NY.,Department of Medicine, Weill Cornell Medicine, New York, NY
| | - Michelle Petri
- Johns Hopkins University School of Medicine, Baltimore, MD
| | | | | | - Marina Ohouo
- Drukier Institute for Children's Health, Weill Cornell Medicine, New York, NY.,Department of Pediatrics, Weill Cornell Medicine, New York, NY.,Baylor Institute for Immunology Research, Dallas, TX
| | | | | | - Tae Whan Kim
- Drukier Institute for Children's Health, Weill Cornell Medicine, New York, NY.,Department of Pediatrics, Weill Cornell Medicine, New York, NY.,Baylor Institute for Immunology Research, Dallas, TX
| | - John Nulsen
- University of Connecticut School of Medicine, Farmington, CT
| | | | | | - Jacob Turner
- Baylor Institute for Immunology Research, Dallas, TX
| | | | - Jane E Salmon
- Department of Medicine and Program in Inflammation and Autoimmunity, Hospital for Special Surgery, New York, NY.,Department of Medicine, Weill Cornell Medicine, New York, NY
| | - Virginia Pascual
- Drukier Institute for Children's Health, Weill Cornell Medicine, New York, NY.,Department of Pediatrics, Weill Cornell Medicine, New York, NY.,Baylor Institute for Immunology Research, Dallas, TX
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5
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Meng H, Yaari G, Bolen CR, Avey S, Kleinstein SH. Gene set meta-analysis with Quantitative Set Analysis for Gene Expression (QuSAGE). PLoS Comput Biol 2019; 15:e1006899. [PMID: 30939133 PMCID: PMC6461294 DOI: 10.1371/journal.pcbi.1006899] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 04/12/2019] [Accepted: 02/24/2019] [Indexed: 01/20/2023] Open
Abstract
Small sample sizes combined with high person-to-person variability can make it difficult to detect significant gene expression changes from transcriptional profiling studies. Subtle, but coordinated, gene expression changes may be detected using gene set analysis approaches. Meta-analysis is another approach to increase the power to detect biologically relevant changes by integrating information from multiple studies. Here, we present a framework that combines both approaches and allows for meta-analysis of gene sets. QuSAGE meta-analysis extends our previously published QuSAGE framework, which offers several advantages for gene set analysis, including fully accounting for gene-gene correlations and quantifying gene set activity as a full probability density function. Application of QuSAGE meta-analysis to influenza vaccination response shows it can detect significant activity that is not apparent in individual studies.
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Affiliation(s)
- Hailong Meng
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Gur Yaari
- Bioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel
| | - Christopher R. Bolen
- Department of Microbiology and Immunology, Stanford University, Stanford, California, United States of America
| | - Stefan Avey
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Steven H. Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut, United States of America
- * E-mail:
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6
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Gu J, Wang X, Chan J, Baldwin NE, Turner JA. Phantom: investigating heterogeneous gene sets in time-course data. Bioinformatics 2018; 33:2957-2959. [PMID: 28595310 DOI: 10.1093/bioinformatics/btx348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2016] [Accepted: 06/02/2017] [Indexed: 12/19/2022] Open
Abstract
Motivation Gene set analysis is a powerful tool to study the coordinative change of time-course data. However, most existing methods only model the overall change of a gene set, yet completely overlooked heterogeneous time-dependent changes within sub-sets of genes. Results We have developed a novel statistical method, Phantom, to investigate gene set heterogeneity. Phantom employs the principle of multi-objective optimization to assess the heterogeneity inside a gene set, which also accounts for the temporal dependency in time-course data. Phantom improves the performance of gene set based methods to detect biological changes across time. Availability and implementation Phantom webpage can be accessed at: http://www.baylorhealth.edu/Phantom . R package of Phantom is available at https://cran.r-project.org/web/packages/phantom/index.html . Contact jinghua.gu@bswhealth.org. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jinghua Gu
- Baylor Research Institute, 3310 Live Oak St, Dallas, TX 75204, USA
| | - Xuan Wang
- Baylor Research Institute, 3310 Live Oak St, Dallas, TX 75204, USA
| | - Jinyan Chan
- Baylor Research Institute, 3310 Live Oak St, Dallas, TX 75204, USA
| | - Nicole E Baldwin
- Baylor Research Institute, 3310 Live Oak St, Dallas, TX 75204, USA
| | - Jacob A Turner
- Baylor Research Institute, 3310 Live Oak St, Dallas, TX 75204, USA
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7
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Arostegui JI, Anton J, Calvo I, Robles A, Iglesias E, López-Montesinos B, Banchereau R, Hong S, Joubert Y, Junge G, Pascual V, Yagüe J. Open-Label, Phase II Study to Assess the Efficacy and Safety of Canakinumab Treatment in Active Hyperimmunoglobulinemia D With Periodic Fever Syndrome. Arthritis Rheumatol 2017; 69:1679-1688. [DOI: 10.1002/art.40146] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 05/02/2017] [Indexed: 12/11/2022]
Affiliation(s)
- Juan I. Arostegui
- Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer; Barcelona Spain
| | | | | | | | | | | | | | - Seunghee Hong
- Baylor Institute for Immunological Research; Dallas Texas
| | | | | | - Virginia Pascual
- Baylor Institute for Immunological Research and Texas Scottish Rite Hospital for Children; Dallas Texas
| | - Jordi Yagüe
- Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer; Barcelona Spain
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8
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Cabrera CP, Manson J, Shepherd JM, Torrance HD, Watson D, Longhi MP, Hoti M, Patel MB, O’Dwyer M, Nourshargh S, Pennington DJ, Barnes MR, Brohi K. Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study. PLoS Med 2017; 14:e1002352. [PMID: 28715416 PMCID: PMC5513400 DOI: 10.1371/journal.pmed.1002352] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 06/12/2017] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Severe trauma induces a widespread response of the immune system. This "genomic storm" can lead to poor outcomes, including Multiple Organ Dysfunction Syndrome (MODS). MODS carries a high mortality and morbidity rate and adversely affects long-term health outcomes. Contemporary management of MODS is entirely supportive, and no specific therapeutics have been shown to be effective in reducing incidence or severity. The pathogenesis of MODS remains unclear, and several models are proposed, such as excessive inflammation, a second-hit insult, or an imbalance between pro- and anti-inflammatory pathways. We postulated that the hyperacute window after trauma may hold the key to understanding how the genomic storm is initiated and may lead to a new understanding of the pathogenesis of MODS. METHODS AND FINDINGS We performed whole blood transcriptome and flow cytometry analyses on a total of 70 critically injured patients (Injury Severity Score [ISS] ≥ 25) at The Royal London Hospital in the hyperacute time period within 2 hours of injury. We compared transcriptome findings in 36 critically injured patients with those of 6 patients with minor injuries (ISS ≤ 4). We then performed flow cytometry analyses in 34 critically injured patients and compared findings with those of 9 healthy volunteers. Immediately after injury, only 1,239 gene transcripts (4%) were differentially expressed in critically injured patients. By 24 hours after injury, 6,294 transcripts (21%) were differentially expressed compared to the hyperacute window. Only 202 (16%) genes differentially expressed in the hyperacute window were still expressed in the same direction at 24 hours postinjury. Pathway analysis showed principally up-regulation of pattern recognition and innate inflammatory pathways, with down-regulation of adaptive responses. Immune deconvolution, flow cytometry, and modular analysis suggested a central role for neutrophils and Natural Killer (NK) cells, with underexpression of T- and B cell responses. In the transcriptome cohort, 20 critically injured patients later developed MODS. Compared with the 16 patients who did not develop MODS (NoMODS), maximal differential expression was seen within the hyperacute window. In MODS versus NoMODS, 363 genes were differentially expressed on admission, compared to only 33 at 24 hours postinjury. MODS transcripts differentially expressed in the hyperacute window showed enrichment among diseases and biological functions associated with cell survival and organismal death rather than inflammatory pathways. There was differential up-regulation of NK cell signalling pathways and markers in patients who would later develop MODS, with down-regulation of neutrophil deconvolution markers. This study is limited by its sample size, precluding more detailed analyses of drivers of the hyperacute response and different MODS phenotypes, and requires validation in other critically injured cohorts. CONCLUSIONS In this study, we showed how the hyperacute postinjury time window contained a focused, specific signature of the response to critical injury that led to widespread genomic activation. A transcriptomic signature for later development of MODS was present in this hyperacute window; it showed a strong signal for cell death and survival pathways and implicated NK cells and neutrophil populations in this differential response.
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Affiliation(s)
- Claudia P. Cabrera
- Centre for Translational Bioinformatics, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Joanna Manson
- Centre for Trauma Sciences, The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Joanna M. Shepherd
- Centre for Trauma Sciences, The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- * E-mail:
| | - Hew D. Torrance
- Centre for Translational Medicine and Therapeutics, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - David Watson
- Centre for Translational Bioinformatics, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - M. Paula Longhi
- Heart Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Mimoza Hoti
- Department of Genetics, Evolution & Environment, University College London, London, United Kingdom
| | - Minal B. Patel
- Centre for Translational Bioinformatics, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Michael O’Dwyer
- Centre for Translational Medicine and Therapeutics, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Sussan Nourshargh
- Centre for Microvascular Research, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Daniel J. Pennington
- Centre for Immunobiology, Blizard Institute, Barts and The London School of Medicine and Dentistry, London, United Kingdom
| | - Michael R. Barnes
- Centre for Translational Bioinformatics, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Karim Brohi
- Centre for Trauma Sciences, The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
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Banchereau R, Hong S, Cantarel B, Baldwin N, Baisch J, Edens M, Cepika AM, Acs P, Turner J, Anguiano E, Vinod P, Kahn S, Obermoser G, Blankenship D, Wakeland E, Nassi L, Gotte A, Punaro M, Liu YJ, Banchereau J, Rossello-Urgell J, Wright T, Pascual V. Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients. Cell 2016; 165:551-65. [PMID: 27040498 PMCID: PMC5426482 DOI: 10.1016/j.cell.2016.03.008] [Citation(s) in RCA: 391] [Impact Index Per Article: 48.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Revised: 10/30/2015] [Accepted: 02/29/2016] [Indexed: 12/22/2022]
Abstract
Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by loss of tolerance to nucleic acids and highly diverse clinical manifestations. To assess its molecular heterogeneity, we longitudinally profiled the blood transcriptome of 158 pediatric patients. Using mixed models accounting for repeated measurements, demographics, treatment, disease activity (DA), and nephritis class, we confirmed a prevalent IFN signature and identified a plasmablast signature as the most robust biomarker of DA. We detected gradual enrichment of neutrophil transcripts during progression to active nephritis and distinct signatures in response to treatment in different nephritis subclasses. Importantly, personalized immunomonitoring uncovered individual correlates of disease activity that enabled patient stratification into seven groups, supported by patient genotypes. Our study uncovers the molecular heterogeneity of SLE and provides an explanation for the failure of clinical trials. This approach may improve trial design and implementation of tailored therapies in genetically and clinically complex autoimmune diseases. PAPERCLIP.
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Affiliation(s)
| | - Seunghee Hong
- Baylor Institute for Immunology Research, Dallas, TX 75204, USA
| | - Brandi Cantarel
- Baylor Institute for Immunology Research, Dallas, TX 75204, USA
| | - Nicole Baldwin
- Baylor Institute for Immunology Research, Dallas, TX 75204, USA
| | - Jeanine Baisch
- Baylor Institute for Immunology Research, Dallas, TX 75204, USA
| | - Michelle Edens
- Baylor Institute for Immunology Research, Dallas, TX 75204, USA
| | | | - Peter Acs
- Baylor Institute for Immunology Research, Dallas, TX 75204, USA
| | - Jacob Turner
- Baylor Institute for Immunology Research, Dallas, TX 75204, USA
| | | | - Parvathi Vinod
- Baylor Institute for Immunology Research, Dallas, TX 75204, USA
| | - Shaheen Kahn
- UT Southwestern Medical Center, Dallas, TX 75235, USA
| | | | | | | | - Lorien Nassi
- UT Southwestern Medical Center, Dallas, TX 75235, USA; Texas Scottish Rite Hospital for Children, Dallas, TX 75219, USA
| | - Alisa Gotte
- UT Southwestern Medical Center, Dallas, TX 75235, USA; Texas Scottish Rite Hospital for Children, Dallas, TX 75219, USA; Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Marilynn Punaro
- UT Southwestern Medical Center, Dallas, TX 75235, USA; Texas Scottish Rite Hospital for Children, Dallas, TX 75219, USA
| | - Yong-Jun Liu
- Baylor Institute for Immunology Research, Dallas, TX 75204, USA; MedImmune, Gaithersburg, MD 20878, USA
| | | | | | - Tracey Wright
- UT Southwestern Medical Center, Dallas, TX 75235, USA; Texas Scottish Rite Hospital for Children, Dallas, TX 75219, USA
| | - Virginia Pascual
- Baylor Institute for Immunology Research, Dallas, TX 75204, USA; Texas Scottish Rite Hospital for Children, Dallas, TX 75219, USA.
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