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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] [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: 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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Liu MF, Ma RX, Cao XB, Zhang H, Zhou SH, Jiang WH, Jiang Y, Sun JW, Yang QT, Li XZ, Sun YN, Shi L, Wang M, Song XC, Chen FQ, Zhang XS, Wei HQ, Yu SQ, Zhu DD, Ba L, Cao ZW, Xiao XP, Wei X, Lin ZH, Chen FH, Shan CG, Wang GK, Ye J, Qu SH, Zhao CQ, Wang ZL, Li HB, Liu F, Cui XB, Ye SN, Liu Z, Xu Y, Cai X, Hang W, Zhang RX, Zhao YL, Yu GD, Shi GG, Lu MP, Shen Y, Zhao YT, Pei JH, Xie SB, Yu LG, Liu YH, Gu SS, Yang YC, Cheng L, Liu JF. [Incidence and prognosis of olfactory and gustatory dysfunctions related to infection of SARS-CoV-2 Omicron strain: a national multi-center survey of 35 566 population]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2023; 58:579-588. [PMID: 37339898 DOI: 10.3760/cma.j.cn115330-20230316-00117] [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] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
Objective: This cross-sectional investigation aimed to determine the incidence, clinical characteristics, prognosis, and related risk factors of olfactory and gustatory dysfunctions related to infection with the SARS-CoV-2 Omicron strain in mainland China. Methods: Data of patients with SARS-CoV-2 from December 28, 2022, to February 21, 2023, were collected through online and offline questionnaires from 45 tertiary hospitals and one center for disease control and prevention in mainland China. The questionnaire included demographic information, previous health history, smoking and alcohol drinking, SARS-CoV-2 vaccination, olfactory and gustatory function before and after infection, other symptoms after infection, as well as the duration and improvement of olfactory and gustatory dysfunction. The self-reported olfactory and gustatory functions of patients were evaluated using the Olfactory VAS scale and Gustatory VAS scale. Results: A total of 35 566 valid questionnaires were obtained, revealing a high incidence of olfactory and taste dysfunctions related to infection with the SARS-CoV-2 Omicron strain (67.75%). Females(χ2=367.013, P<0.001) and young people(χ2=120.210, P<0.001) were more likely to develop these dysfunctions. Gender(OR=1.564, 95%CI: 1.487-1.645), SARS-CoV-2 vaccination status (OR=1.334, 95%CI: 1.164-1.530), oral health status (OR=0.881, 95%CI: 0.839-0.926), smoking history (OR=1.152, 95%CI=1.080-1.229), and drinking history (OR=0.854, 95%CI: 0.785-0.928) were correlated with the occurrence of olfactory and taste dysfunctions related to SARS-CoV-2(above P<0.001). 44.62% (4 391/9 840) of the patients who had not recovered their sense of smell and taste also suffered from nasal congestion, runny nose, and 32.62% (3 210/9 840) suffered from dry mouth and sore throat. The improvement of olfactory and taste functions was correlated with the persistence of accompanying symptoms(χ2=10.873, P=0.001). The average score of olfactory and taste VAS scale was 8.41 and 8.51 respectively before SARS-CoV-2 infection, but decreased to3.69 and 4.29 respectively after SARS-CoV-2 infection, and recovered to 5.83and 6.55 respectively at the time of the survey. The median duration of olfactory and gustatory dysfunctions was 15 days and 12 days, respectively, with 0.5% (121/24 096) of patients experiencing these dysfunctions for more than 28 days. The overall self-reported improvement rate of smell and taste dysfunctions was 59.16% (14 256/24 096). Gender(OR=0.893, 95%CI: 0.839-0.951), SARS-CoV-2 vaccination status (OR=1.334, 95%CI: 1.164-1.530), history of head and facial trauma(OR=1.180, 95%CI: 1.036-1.344, P=0.013), nose (OR=1.104, 95%CI: 1.042-1.171, P=0.001) and oral (OR=1.162, 95%CI: 1.096-1.233) health status, smoking history(OR=0.765, 95%CI: 0.709-0.825), and the persistence of accompanying symptoms (OR=0.359, 95%CI: 0.332-0.388) were correlated with the recovery of olfactory and taste dysfunctions related to SARS-CoV-2 (above P<0.001 except for the indicated values). Conclusion: The incidence of olfactory and taste dysfunctions related to infection with the SARS-CoV-2 Omicron strain is high in mainland China, with females and young people more likely to develop these dysfunctions. Active and effective intervention measures may be required for cases that persist for a long time. The recovery of olfactory and taste functions is influenced by several factors, including gender, SARS-CoV-2 vaccination status, history of head and facial trauma, nasal and oral health status, smoking history, and persistence of accompanying symptoms.
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Affiliation(s)
- M F Liu
- Graduate School of Beijing University of Chinese Medicine, Beijing 100029, China Department of Otorhinolaryngology Head and Neck Surgery, China-Japan Friendship Hospital, Beijing 100029, China
| | - R X Ma
- Department of Otorhinolaryngology Head and Neck Surgery, the First People's Hospital of Yinchuan, Yinchuan 750001, China
| | - X B Cao
- Department of Otorhinolaryngology, the First People's Hospital of Yunnan Province, Kunming 650100, China
| | - H Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - S H Zhou
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310003, China
| | - W H Jiang
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital Central South University, Changsha 410008, China
| | - Y Jiang
- Department of Otorhinolaryngology Head and Neck Surgery, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - J W Sun
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of USTC, Hefei 230001, China
| | - Q T Yang
- Department of Otorhinolaryngology Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - X Z Li
- Department of Otorhinolaryngology Head and Neck Surgery, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Y N Sun
- Department of Otorhinolaryngology Head and Neck Surgery, the Second Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - L Shi
- Department of Rhinology and Allergy, Shandong Provincial ENT Hospital, Shandong University, Jinan 250299, China
| | - M Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Peking University People's Hospital, Beijing 100032, China
| | - X C Song
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China
| | - F Q Chen
- Department of Otorhinolaryngology Head and Neck Surgery, Xijing Hospital, the Fourth Military Medical University, Xi'an 710032, China
| | - X S Zhang
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - H Q Wei
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - S Q Yu
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical University, Shanghai 200065, China
| | - D D Zhu
- Department of Otorhinolaryngology Head and Neck Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - L Ba
- Department of Otorhinolaryngology Head and Neck Surgery, Xizang Autonomous Region People's Hospital, Lasa 850000, China
| | - Z W Cao
- Department of Otorhinolaryngology Head and Neck Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - X P Xiao
- Department of Otorhinolaryngology Head and Neck Surgery, Hunan Provincial People's Hospital, Changsha 410005, China
| | - X Wei
- Department of Otorhinolaryngology Head and Neck Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China
| | - Z H Lin
- Department of Otorhinolaryngology Head and Neck Surgery, the Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310009, China
| | - F H Chen
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - C G Shan
- Department of Otorhinolaryngology Head and Neck Surgery, the Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
| | - G K Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Henan Provincial People's Hospital, Zhengzhou 450003, China
| | - J Ye
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - S H Qu
- Department of Otorhinolaryngology Head and Neck Surgery, Guangxi Zhuang Autonomous Region People's Hospital, Nanning 530021, China
| | - C Q Zhao
- Department of Otorhinolaryngology Head and Neck Surgery, Shanxi Medical University Affiliated Second Hospital, Taiyuan 030001, China
| | - Z L Wang
- Department of Otorhinolaryngology Head and Neck Surgery, XuanWu Hospital, Capital Medical University, Beijing 100053, China
| | - H B Li
- Department of Otorhinolaryngology Head and Neck Surgery, Eye, Ear, Nose and Throat Hospital, Shanghai Medical College, Fudan University, Shanghai 200031, China
| | - F Liu
- Department of Otorhinolaryngology Head and Neck Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - X B Cui
- Department of Otorhinolaryngology Head and Neck Surgery, Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010059, China
| | - S N Ye
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China
| | - Z Liu
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Y Xu
- Department of Otorhinolaryngology Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - X Cai
- Department of Otorhinolaryngology Head and Neck Surgery, Qinghai Provincial People's Hospital, Xining 810000, China
| | - W Hang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - R X Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China
| | - Y L Zhao
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - G D Yu
- Department of Otorhinolaryngology Head and Neck Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China
| | - G G Shi
- Department of Otorhinolaryngology Head and Neck Surgery, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, Jinan 250021, China
| | - M P Lu
- Department of Otorhinolaryngology, the First Affiliated Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Y Shen
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Y T Zhao
- Department of Otorhinolaryngology Head and Neck Surgery, the First People's Hospital of Yinchuan, Yinchuan 750001, China
| | - J H Pei
- Department of Otorhinolaryngology, the First People's Hospital of Yunnan Province, Kunming 650100, China
| | - S B Xie
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital Central South University, Changsha 410008, China
| | - L G Yu
- Department of Otorhinolaryngology Head and Neck Surgery, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Y H Liu
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - S S Gu
- Department of Otorhinolaryngology Head and Neck Surgery, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Y C Yang
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - L Cheng
- Department of Otorhinolaryngology, the First Affiliated Hospital, Nanjing Medical University, Nanjing 210029, China
| | - J F Liu
- Department of Otorhinolaryngology Head and Neck Surgery, China-Japan Friendship Hospital, Beijing 100029, China
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Luo Y, Gu SS, Wang MM, He SJ, Zhang XX, Li XZ. [Multimodality therapy of locally recurrent nasopharyngeal carcinoma in the sinuses and nasal cavity: two case reports]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2023; 58:496-498. [PMID: 37150998 DOI: 10.3760/cma.j.cn115330-20221231-00779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Affiliation(s)
- Y Luo
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
| | - S S Gu
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
| | - M M Wang
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
| | - S J He
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
| | - X X Zhang
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
| | - X Z Li
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
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Zhang Y, Xiang G, Jiang AY, Lynch A, Zeng Z, Wang C, Zhang W, Fan J, Kang J, Gu SS, Wan C, Zhang B, Liu XS, Brown M, Meyer CA. MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment. Nat Commun 2023; 14:2634. [PMID: 37149682 PMCID: PMC10164163 DOI: 10.1038/s41467-023-38333-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 04/26/2023] [Indexed: 05/08/2023] Open
Abstract
Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.
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Affiliation(s)
- Yi Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Guanjue Xiang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Alva Yijia Jiang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Allen Lynch
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Zexian Zeng
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Chenfei Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Wubing Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Jingyu Fan
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Jiajinlong Kang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Shengqing Stan Gu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Changxin Wan
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Boning Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - X Shirley Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA.
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Myles Brown
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Clifford A Meyer
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA.
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
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Sahu A, Wang X, Munson P, Klomp JP, Wang X, Gu SS, Han Y, Qian G, Nicol P, Zeng Z, Wang C, Tokheim C, Zhang W, Fu J, Wang J, Nair NU, Rens JA, Bourajjaj M, Jansen B, Leenders I, Lemmers J, Musters M, van Zanten S, van Zelst L, Worthington J, Liu JS, Juric D, Meyer CA, Oubrie A, Liu XS, Fisher DE, Flaherty KT. Discovery of Targets for Immune-Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha. Cancer Discov 2023; 13:672-701. [PMID: 36745048 PMCID: PMC9975674 DOI: 10.1158/2159-8290.cd-22-0244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 09/13/2022] [Accepted: 11/23/2022] [Indexed: 02/07/2023]
Abstract
Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer cell-specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway and then used it to identify 38 candidate immune-metabolic regulators. We show the tumor activities of these regulators stratify patients with melanoma by their response to anti-PD-1 using machine learning and deep neural approaches, which improve the predictive power of current biomarkers. The topmost identified regulator, ESRRA, is activated in immunotherapy-resistant tumors. Its inhibition killed tumors by suppressing energy metabolism and activating two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization, and (ii) antigen-presentation stimulation, recruiting CD8+ T cells into tumors. We also demonstrate a wide utility of BipotentR by applying it to angiogenesis and growth suppressor evasion pathways. BipotentR (http://bipotentr.dfci.harvard.edu/) provides a resource for evaluating patient response and discovering drug targets that act simultaneously through multiple mechanisms. SIGNIFICANCE BipotentR presents resources for evaluating patient response and identifying targets for drugs that can kill tumors through multiple mechanisms concurrently. Inhibition of the topmost candidate target killed tumors by suppressing energy metabolism and effects on two immune mechanisms. This article is highlighted in the In This Issue feature, p. 517.
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Affiliation(s)
- Avinash Sahu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Obstetrics and Gynecology, University of Colorado School of Medicine, Aurora, Colorado
- Corresponding Authors: Keith T. Flaherty, Developmental Therapeutics, Massachusetts General Hospital Cancer Center, 55 Fruit Street, Boston, MA 02114. Phone: 617-724-4000; E-mail: ; David E. Fisher, Charlestown Navy Yard Building 149, 149 13th Street, Charlestown, MA 02129. Phone: 617-643-5428; E-mail: ; and Avinash Sahu, Department of Data Sciences, Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 02115. Phone: 240-391-8125; E-mail:
| | - Xiaoman Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Phillip Munson
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts
| | | | - Xiaoqing Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Shengqing Stan Gu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Ya Han
- School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Gege Qian
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Phillip Nicol
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Zexian Zeng
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Chenfei Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Collin Tokheim
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Wubing Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jingxin Fu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jin Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | | | | | - Bas Jansen
- Lead Pharma, Kloosterstraat, Oss, the Netherlands
| | | | - Jaap Lemmers
- Lead Pharma, Kloosterstraat, Oss, the Netherlands
| | - Mark Musters
- Lead Pharma, Kloosterstraat, Oss, the Netherlands
| | | | | | | | - Jun S. Liu
- Department of Statistics, Harvard University, Cambridge, Massachusetts
| | - Dejan Juric
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts
| | - Clifford A. Meyer
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | | | - X. Shirley Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - David E. Fisher
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts
- Department of Dermatology, Massachusetts General Hospital, Boston, Massachusetts
- Corresponding Authors: Keith T. Flaherty, Developmental Therapeutics, Massachusetts General Hospital Cancer Center, 55 Fruit Street, Boston, MA 02114. Phone: 617-724-4000; E-mail: ; David E. Fisher, Charlestown Navy Yard Building 149, 149 13th Street, Charlestown, MA 02129. Phone: 617-643-5428; E-mail: ; and Avinash Sahu, Department of Data Sciences, Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 02115. Phone: 240-391-8125; E-mail:
| | - Keith T. Flaherty
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts
- Corresponding Authors: Keith T. Flaherty, Developmental Therapeutics, Massachusetts General Hospital Cancer Center, 55 Fruit Street, Boston, MA 02114. Phone: 617-724-4000; E-mail: ; David E. Fisher, Charlestown Navy Yard Building 149, 149 13th Street, Charlestown, MA 02129. Phone: 617-643-5428; E-mail: ; and Avinash Sahu, Department of Data Sciences, Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 02115. Phone: 240-391-8125; E-mail:
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Feng X, Fu Q, Gu SS, Ye P, Wang J, Duan C, Cai XL, Zhang LQ, Ni SL, Li XZ. [Endoscopic resection of type D trigeminal schwannoma through nasal sinus approach]. Zhonghua Wai Ke Za Zhi 2023; 61:232-238. [PMID: 36650970 DOI: 10.3760/cma.j.cn112139-20220725-00323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Objective: To examine the feasibility and surgical approach of removing type D trigeminal schwannoma through nasal cavity and nasal sinus under endoscope. Methods: Eleven patients with trigeminal schwannoma who were treated in the Department of Otorhinolaryngology, Qilu Hospital of Shandong University from December 2014 to August 2021 were analyzed retrospectively in this study. There were 7 males and 4 females, aged (47.5±13.5) years (range: 12 to 64 years). The neoplasm involved the pterygopalatine fossa, infratemporal fossa, ethmoidal sinus, sphenoid sinus, cavernous sinus, and middle cranial fossa. The size of tumors were between 1.6 cm×2.0 cm×2.0 cm and 5.7 cm×6.0 cm×6.0 cm. Under general anesthesia, the tumors were resected through the transpterygoid approach in 4 cases, through the prelacrimal recess approach in 4 cases, through the extended prelacrimal recess approach in 2 cases, and through the endoscopic medial maxillectomy approach in 1 case. The nasal endoscopy and imaging examination were conducted to detect whether neoplasm recurred or not, and the main clinical symptoms during follow-up. Results: All the surgical procedures were performed under endonasal endoscope, including Gross total resection in 10 patients. The tumor of a 12-year-old patient was not resected completely due to huge tumor size and limited operation space. One patient was accompanied by two other schwannomas located in the occipital region and the ipsilateral parotid gland region originating from the zygomatic branch of the facial nerve, both of which were removed concurrently. After tumor resection, the dura mater of middle cranial fossa was directly exposed in the nasal sinus in 2 cases, including 1 case accompanied by cerebrospinal fluid leakage which was reconstructed by a free mucosal flap obtained from the middle turbinate, the other case was packed by the autologous fat to protect the dura mater. The operation time was (M(IQR)) 180 (160) minutes (range: 120 to 485 minutes). No complications and deaths were observed. No recurrence was observed in the 10 patients with total tumor resection during a 58 (68) months' (range: 10 to 90 months) follow-up. No obvious change was observed in the facial appearance of all patients during the follow-up. Conclusion: Type D trigeminal schwannoma involving pterygopalatine fossa and infratemporal fossa can be removed safely through purely endoscopic endonasal approach by selecting the appropriate approach according to the size and involvement of the tumor.
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Affiliation(s)
- X Feng
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
| | - Q Fu
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
| | - S S Gu
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
| | - P Ye
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
| | - J Wang
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
| | - C Duan
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
| | - X L Cai
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
| | - L Q Zhang
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
| | - S L Ni
- Department of Neurosurgery, Qilu Hospital of Shandong University, Jinan 250012, China
| | - X Z Li
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, National Health Commission Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China
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Zeng Z, Gu SS, Ouardaoui N, Tymm C, Yang L, Wong CJ, Li D, Zhang W, Wang X, Weirather JL, Rodig SJ, Hodi FS, Brown M, Liu XS. Hippo Signaling Pathway Regulates Cancer Cell-Intrinsic MHC-II Expression. Cancer Immunol Res 2022; 10:1559-1569. [PMID: 36219700 DOI: 10.1158/2326-6066.cir-22-0227] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 07/02/2022] [Accepted: 10/06/2022] [Indexed: 01/10/2023]
Abstract
MHC-II is known to be mainly expressed on the surface of antigen-presenting cells. Evidence suggests MHC-II is also expressed by cancer cells and may be associated with better immunotherapy responses. However, the role and regulation of MHC-II in cancer cells remain unclear. In this study, we leveraged data mining and experimental validation to elucidate the regulation of MHC-II in cancer cells and its role in modulating the response to immunotherapy. We collated an extensive collection of omics data to examine cancer cell-intrinsic MHC-II expression and its association with immunotherapy outcomes. We then tested the functional relevance of cancer cell-intrinsic MHC-II expression using a syngeneic transplantation model. Finally, we performed data mining to identify pathways potentially involved in the regulation of MHC-II expression, and experimentally validated candidate regulators. Analyses of preimmunotherapy clinical samples in the CheckMate 064 trial revealed that cancer cell-intrinsic MHC-II protein was positively correlated with more favorable immunotherapy outcomes. Comprehensive meta-analyses of multiomics data from an exhaustive collection of data revealed that MHC-II is heterogeneously expressed in various solid tumors, and its expression is particularly high in melanoma. Using a syngeneic transplantation model, we further established that melanoma cells with high MHC-II responded better to anti-PD-1 treatment. Data mining followed by experimental validation revealed the Hippo signaling pathway as a potential regulator of melanoma MHC-II expression. In summary, we identified the Hippo signaling pathway as a novel regulator of cancer cell-intrinsic MHC-II expression. These findings suggest modulation of MHC-II in melanoma could potentially improve immunotherapy response.
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Affiliation(s)
- Zexian Zeng
- Department of Data Science, Dana Farber Cancer Institute, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Shengqing Stan Gu
- Department of Data Science, Dana Farber Cancer Institute, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Nofal Ouardaoui
- Department of Data Science, Dana Farber Cancer Institute, Boston, Massachusetts
| | - Carly Tymm
- Department of Data Science, Dana Farber Cancer Institute, Boston, Massachusetts
| | - Lin Yang
- Department of Data Science, Dana Farber Cancer Institute, Boston, Massachusetts
| | - Cheryl J Wong
- Department of Data Science, Dana Farber Cancer Institute, Boston, Massachusetts
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Dian Li
- Department of Data Science, Dana Farber Cancer Institute, Boston, Massachusetts
| | - Wubing Zhang
- Department of Data Science, Dana Farber Cancer Institute, Boston, Massachusetts
- School of Life Science and Technology, Tongji University, Shanghai, China
| | - Xiaoqing Wang
- Department of Data Science, Dana Farber Cancer Institute, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jason L Weirather
- Department of Data Science, Dana Farber Cancer Institute, Boston, Massachusetts
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Scott J Rodig
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts
| | - F Stephen Hodi
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Myles Brown
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - X Shirley Liu
- Department of Data Science, Dana Farber Cancer Institute, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
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Zeng Z, Gu SS, Wong CJ, Yang L, Ouardaoui N, Li D, Zhang W, Brown M, Liu XS. Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response. Sci Adv 2022; 8:eabm8564. [PMID: 36240281 PMCID: PMC9565795 DOI: 10.1126/sciadv.abm8564] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 08/30/2022] [Indexed: 06/16/2023]
Abstract
Most patients with cancer are refractory to immune checkpoint blockade (ICB) therapy, and proper patient stratification remains an open question. Primary patient data suffer from high heterogeneity, low accessibility, and lack of proper controls. In contrast, syngeneic mouse tumor models enable controlled experiments with ICB treatments. Using transcriptomic and experimental variables from >700 ICB-treated/control syngeneic mouse tumors, we developed a machine learning framework to model tumor immunity and identify factors influencing ICB response. Projected on human immunotherapy trial data, we found that the model can predict clinical ICB response. We further applied the model to predicting ICB-responsive/resistant cancer types in The Cancer Genome Atlas, which agreed well with existing clinical reports. Last, feature analysis implicated factors associated with ICB response. In summary, our computational framework based on mouse tumor data reliably stratified patients regarding ICB response, informed resistance mechanisms, and has the potential for wide applications in disease treatment studies.
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Affiliation(s)
- Zexian Zeng
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
| | - Shengqing Stan Gu
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Cheryl J. Wong
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115 USA
| | - Lin Yang
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Nofal Ouardaoui
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Dian Li
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Wubing Zhang
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
- School of Life Science and Technology, Tongji University, Shanghai 200060, China
| | - Myles Brown
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - X. Shirley Liu
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
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9
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Gu SS, Shen ZS, Deng HX, Qiu SJ, Ye D. [Cell heterogeneity of laryngeal carcinoma and evolution trajectory of epithelial cells]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2022; 57:168-177. [PMID: 35196760 DOI: 10.3760/cma.j.cn115330-20211217-00805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To analyze the classification and functions of cell subsets in laryngeal carcinoma and metastatic lymph nodes, and to explore the evolution trajectory of epithelial cells to tumor cells. Methods: Single-cell RNA sequencing was performed on 5 cases of laryngeal cancer, matched metastatic lymph nodes and 3 normal tissues. Patients were admitted to Ningbo Medical Center Lihuili Hospital from October 22, 2019 to December 16, all patients were male, aged 53-70 years old. Cell subsets of the above-mentioned tissues were analyzed by the Seurat, and the biological functions of cell subpopulation were investigated by functional enrichment analysis. Malignant epithelial cells were identified using copy number variation (CNV). The evolutionary trajectory of epithelial cells to cancer cells was analyzed by cell trajectory analysis, and cancerous transitional cells were identified. The highly expressed genes in transitional cells were analyzed by the FindAllMarker of the Seurat and verified by immunohistochemistry. Results: A total of 66 969 high-quality cells were obtained in 9 major clusters: epithelial cells, T cells, B cells, fibroblasts, endothelial cells, myeloid cells, mast cells, plasmacytoid dendritic cells and nerve cells. The first 5 cell clusters were divided into 8, 6, 4, 3 and 2 subgroups, respectively. Four epithelial cell subsets (C0, C1, C2 and C5) were derived from tumor tissues and metastatic lymph nodes, and had high levels of CNV and tumor cell content. Cell trajectory analysis showed that the evolution trajectory of epithelial cells was from normal epithelial subpopulation C4 to early cancerous cell population C0, which differentiated into three major malignant cell subsets C1, C3, and C5. Epithelial cell C0 may represent the transitional cell population of carcinogenesis, and were enriched in biological processes such as epithelial-mesenchymal transformation and angiogenesis. C0 highly expressed sulforaphane (SFN) which may be related to the occurrence and development of cancer. Immunohistochemistry confirmed that SFN was highly expressed in tumor tissues and metastatic lymph nodes compared with paracancerous tissues. Conclusion: Single-cell sequencing may be used to elucidate the diversity of cells and functions in laryngeal carcinoma tissues and metastatic lymph nodes, and cell population C0 plays a key role in the evolution of cells.
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Affiliation(s)
- S S Gu
- Department of Otorhinolaryngology Head and Neck Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo 315040, China
| | - Z S Shen
- Department of Otorhinolaryngology Head and Neck Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo 315040, China
| | - H X Deng
- Department of Otorhinolaryngology Head and Neck Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo 315040, China
| | - S J Qiu
- Department of Otorhinolaryngology Head and Neck Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo 315040, China
| | - D Ye
- Department of Otorhinolaryngology Head and Neck Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo 315040, China
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Wang X, Tokheim C, Gu SS, Wang B, Tang Q, Li Y, Traugh N, Zeng Z, Zhang Y, Li Z, Zhang B, Fu J, Xiao T, Li W, Meyer CA, Chu J, Jiang P, Cejas P, Lim K, Long H, Brown M, Liu XS. In vivo CRISPR screens identify the E3 ligase Cop1 as a modulator of macrophage infiltration and cancer immunotherapy target. Cell 2021; 184:5357-5374.e22. [PMID: 34582788 PMCID: PMC9136996 DOI: 10.1016/j.cell.2021.09.006] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [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: 11/25/2020] [Revised: 06/14/2021] [Accepted: 09/01/2021] [Indexed: 12/26/2022]
Abstract
Despite remarkable clinical efficacy of immune checkpoint blockade (ICB) in cancer treatment, ICB benefits for triple-negative breast cancer (TNBC) remain limited. Through pooled in vivo CRISPR knockout (KO) screens in syngeneic TNBC mouse models, we found that deletion of the E3 ubiquitin ligase Cop1 in cancer cells decreases secretion of macrophage-associated chemokines, reduces tumor macrophage infiltration, enhances anti-tumor immunity, and strengthens ICB response. Transcriptomics, epigenomics, and proteomics analyses revealed that Cop1 functions through proteasomal degradation of the C/ebpδ protein. The Cop1 substrate Trib2 functions as a scaffold linking Cop1 and C/ebpδ, which leads to polyubiquitination of C/ebpδ. In addition, deletion of the E3 ubiquitin ligase Cop1 in cancer cells stabilizes C/ebpδ to suppress expression of macrophage chemoattractant genes. Our integrated approach implicates Cop1 as a target for improving cancer immunotherapy efficacy in TNBC by regulating chemokine secretion and macrophage infiltration in the tumor microenvironment.
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Affiliation(s)
- Xiaoqing Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Collin Tokheim
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Shengqing Stan Gu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Binbin Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
| | - Qin Tang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Yihao Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Nicole Traugh
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Zexian Zeng
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Yi Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Ziyi Li
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Boning Zhang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Jingxin Fu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Tengfei Xiao
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Wei Li
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Clifford A Meyer
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Jun Chu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Key Laboratory of Xin'an Medicine, Ministry of Education, Anhui University of Chinese Medicine, Hefei, Anhui 230038, China
| | - Peng Jiang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Paloma Cejas
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Klothilda Lim
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Henry Long
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Myles Brown
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
| | - X Shirley Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
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11
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Gu SS, Zhang W, Wang X, Jiang P, Traugh N, Li Z, Meyer C, Stewig B, Xie Y, Bu X, Manos MP, Font-Tello A, Gjini E, Lako A, Lim K, Conway J, Tewari AK, Zeng Z, Sahu AD, Tokheim C, Weirather JL, Fu J, Zhang Y, Kroger B, Liang JH, Cejas P, Freeman GJ, Rodig S, Long HW, Gewurz BE, Hodi FS, Brown M, Liu XS. Therapeutically Increasing MHC-I Expression Potentiates Immune Checkpoint Blockade. Cancer Discov 2021; 11:1524-1541. [PMID: 33589424 DOI: 10.1158/2159-8290.cd-20-0812] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 11/13/2020] [Accepted: 01/13/2021] [Indexed: 11/16/2022]
Abstract
Immune checkpoint blockade (ICB) therapy revolutionized cancer treatment, but many patients with impaired MHC-I expression remain refractory. Here, we combined FACS-based genome-wide CRISPR screens with a data-mining approach to identify drugs that can upregulate MHC-I without inducing PD-L1. CRISPR screening identified TRAF3, a suppressor of the NFκB pathway, as a negative regulator of MHC-I but not PD-L1. The Traf3-knockout gene expression signature is associated with better survival in ICB-naïve patients with cancer and better ICB response. We then screened for drugs with similar transcriptional effects as this signature and identified Second Mitochondria-derived Activator of Caspase (SMAC) mimetics. We experimentally validated that the SMAC mimetic birinapant upregulates MHC-I, sensitizes cancer cells to T cell-dependent killing, and adds to ICB efficacy. Our findings provide preclinical rationale for treating tumors expressing low MHC-I expression with SMAC mimetics to enhance sensitivity to immunotherapy. The approach used in this study can be generalized to identify other drugs that enhance immunotherapy efficacy. SIGNIFICANCE: MHC-I loss or downregulation in cancer cells is a major mechanism of resistance to T cell-based immunotherapies. Our study reveals that birinapant may be used for patients with low baseline MHC-I to enhance ICB response. This represents promising immunotherapy opportunities given the biosafety profile of birinapant from multiple clinical trials.This article is highlighted in the In This Issue feature, p. 1307.
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Affiliation(s)
- Shengqing Stan Gu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Wubing Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,School of Life Science and Technology, Tongji University, Shanghai, China
| | - Xiaoqing Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Peng Jiang
- Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Nicole Traugh
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Ziyi Li
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,School of Life Science and Technology, Tongji University, Shanghai, China
| | - Clifford Meyer
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Blair Stewig
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Yingtian Xie
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Xia Bu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Michael P Manos
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Alba Font-Tello
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Evisa Gjini
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Ana Lako
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Klothilda Lim
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jake Conway
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Alok K Tewari
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Zexian Zeng
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Avinash Das Sahu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Collin Tokheim
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Jason L Weirather
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jingxin Fu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,School of Life Science and Technology, Tongji University, Shanghai, China
| | - Yi Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Benjamin Kroger
- The University of Texas Southwestern Medical School, Dallas, Texas
| | - Jin Hua Liang
- Department of Microbiology, Harvard Medical School, Boston, Massachusetts.,Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Paloma Cejas
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Gordon J Freeman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Scott Rodig
- Department of Pathologic Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Henry W Long
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Benjamin E Gewurz
- Department of Microbiology, Harvard Medical School, Boston, Massachusetts.,Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - F Stephen Hodi
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.,Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Myles Brown
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts. .,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - X Shirley Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts. .,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
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12
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Gu SS, Wang X, Hu X, Jiang P, Li Z, Traugh N, Bu X, Tang Q, Wang C, Zeng Z, Fu J, Meyer C, Zhang Y, Cejas P, Lim K, Wang J, Zhang W, Tokheim C, Sahu AD, Xing X, Kroger B, Ouyang Z, Long H, Freeman GJ, Brown M, Liu XS. Clonal tracing reveals diverse patterns of response to immune checkpoint blockade. Genome Biol 2020; 21:263. [PMID: 33059736 PMCID: PMC7559192 DOI: 10.1186/s13059-020-02166-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 09/15/2020] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Immune checkpoint blockade (ICB) therapy has improved patient survival in a variety of cancers, but only a minority of cancer patients respond. Multiple studies have sought to identify general biomarkers of ICB response, but elucidating the molecular and cellular drivers of resistance for individual tumors remains challenging. We sought to determine whether a tumor with defined genetic background exhibits a stereotypic or heterogeneous response to ICB treatment. RESULTS We establish a unique mouse system that utilizes clonal tracing and mathematical modeling to monitor the growth of each cancer clone, as well as the bulk tumor, in response to ICB. We find that tumors derived from the same clonal populations showed heterogeneous ICB response and diverse response patterns. Primary response is associated with higher immune infiltration and leads to enrichment of pre-existing ICB-resistant cancer clones. We further identify several cancer cell-intrinsic gene expression signatures associated with ICB resistance, including increased interferon response genes and glucocorticoid response genes. These findings are supported by clinical data from ICB treatment cohorts. CONCLUSIONS Our study demonstrates diverse response patterns from the same ancestor cancer cells in response to ICB. This suggests the value of monitoring clonal constitution and tumor microenvironment over time to optimize ICB response and to design new combination therapies. Furthermore, as ICB response may enrich for cancer cell-intrinsic resistance signatures, this can affect interpretations of tumor RNA-seq data for response-signature association studies.
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Affiliation(s)
- Shengqing Stan Gu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Xiaoqing Wang
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Xihao Hu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Peng Jiang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Ziyi Li
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Science and Technology, Tongji University, Shanghai, 200433, China
| | - Nicole Traugh
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Xia Bu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Qin Tang
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Chenfei Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Zexian Zeng
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Jingxin Fu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Science and Technology, Tongji University, Shanghai, 200433, China
| | - Cliff Meyer
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Yi Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Paloma Cejas
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Klothilda Lim
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Jin Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Science and Technology, Tongji University, Shanghai, 200433, China
| | - Wubing Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Science and Technology, Tongji University, Shanghai, 200433, China
| | - Collin Tokheim
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Avinash Das Sahu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Xiaofang Xing
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Division of Gastrointestinal Cancer Translational Research Laboratory, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Benjamin Kroger
- University of Texas Southwestern Medical School, Dallas, TX, 75390, USA
| | - Zhangyi Ouyang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Henry Long
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Gordon J Freeman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
| | - Myles Brown
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
| | - X Shirley Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA.
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
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13
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Liang H, Gao Y, Liu Y, Gu SS, Cai N, Jiang M, Wang J, He F. [Predictive value of neutrophil-to-lymphocyte ratio in 30-day mortality of patients with acute paraquat poisoning]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 2019; 36:911-914. [PMID: 30812077 DOI: 10.3760/cma.j.issn.1001-9391.2018.12.007] [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] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the predictive value of neutrophil-to-lymphocyte Ratio (NLR) in 30-day mortality of patients with acute paraquat poisoning. Methods: We respectively reviewed the clinical parameters of 115 patients with acute paraquat poisoning. They were divided into survival (n=64) and non-survival (n=51) groups based on their 30-day outcome. Multivariate logistic regression was performed to identify risk factors of 30-day mortality. Receiver operating curve (ROC) test was applied to analysis to the predictive value of NLR in 30-day mortality ofacute paraquat poisoning patients. The correlations between NLR and severity index of paraquat poisoning (SIPP) were analyzed using Spearman's rank correlation coefficient. Results: Of the 115patients included in the study, 54 (46.96%) patients were males and 61 (53.04%) were females with a mean age of 38.96±13.58 years. The total mortality in 30-day was 44.35% (51/115) . The NLR at admission was an independently risk factor of 30-day mortality of patients with acute paraquat poisoning (OR 1.477, 95%CI 1.035-2.107, P<0.05) . The NLR to predict the death of the area under the ROC curve was 0.894 (95%CI: 0.8212-0.9663, P<0.01) ; the optimal cutoff threshold was 11.71; the sensitivity was 71.79% and the specificity was 94.29%; the positive predictive value was 93.33%and negative predictive value of 75.00%. Meanwhile, the positive likelihood ratio was 12.57 and the negative likelihood ratio was 0.30. The NLR was significantly associated with SIPP (Spearman rho 0.525; P<0.01) and it was significantly higher in patients with SIPP of ten or higher than in those with an SIPP less than 10 (15.02±12.40 vs. 6.19±2.54, P<0.05) . Conclusion: The increased NLR at admission was an independently risk factor of 30-day mortality of patients with acute paraquat poisoning and it was significantly correlated with SIPP score. Therefore, NLR was useful for predicting prognosis of patients with acute paraquat poisoning.
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Affiliation(s)
- H Liang
- Emergency Medical Department, the First Affiliated Hospital of Xi'An Jiao Tong University, XiAn 710061, China
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