Large-scale public data reuse to model immunotherapy response and resistance

被引:588
|
作者
Fu, Jingxin [1 ,2 ,3 ]
Li, Karen [4 ]
Zhang, Wubing [1 ,2 ]
Wan, Changxin [1 ,2 ]
Zhang, Jing [3 ]
Jiang, Peng [2 ,5 ]
Liu, X. Shirley [2 ]
机构
[1] Tongji Univ, Sch Life Sci & Technol, Shanghai Pulm Hosp, Clin Translat Res Ctr, Shanghai 200433, Peoples R China
[2] Harvard TH Chan Sch Publ Hlth, Dana Farber Canc Inst, Dept Data Sci, Boston, MA 02215 USA
[3] Tongji Univ, Tongji Hosp, Sch Life Sci & Technol, Shanghai 200065, Peoples R China
[4] Winsor Sch, Boston, MA 02215 USA
[5] NCI, Canc Data Sci Lab, NIH, Bethesda, MD 20892 USA
关键词
Immunotherapy; Immune evasion; Data integration; Web platform; CANCER; CELLS; GENES;
D O I
10.1186/s13073-020-0721-z
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Despite growing numbers of immune checkpoint blockade (ICB) trials with available omics data, it remains challenging to evaluate the robustness of ICB response and immune evasion mechanisms comprehensively. To address these challenges, we integrated large-scale omics data and biomarkers on published ICB trials, non-immunotherapy tumor profiles, and CRISPR screens on a web platform TIDE (). We processed the omics data for over 33K samples in 188 tumor cohorts from public databases, 998 tumors from 12 ICB clinical studies, and eight CRISPR screens that identified gene modulators of the anticancer immune response. Integrating these data on the TIDE web platform with three interactive analysis modules, we demonstrate the utility of public data reuse in hypothesis generation, biomarker optimization, and patient stratification.
引用
收藏
页数:8
相关论文
共 50 条
  • [1] Large-scale public data reuse to model immunotherapy response and resistance
    Jingxin Fu
    Karen Li
    Wubing Zhang
    Changxin Wan
    Jing Zhang
    Peng Jiang
    X. Shirley Liu
    [J]. Genome Medicine, 12
  • [2] The public speaks: Using large-scale public comments data in public response research
    Dokshin, Fedor A.
    [J]. ENERGY RESEARCH & SOCIAL SCIENCE, 2022, 91
  • [3] The public speaks: Using large-scale public comments data in public response research
    Dokshin, Fedor A.
    [J]. ENERGY RESEARCH & SOCIAL SCIENCE, 2022, 91
  • [4] ARCHITECTURES FOR LARGE-SCALE REUSE
    BECK, RP
    DESAI, SR
    RYAN, DR
    TOWER, RW
    VROOM, DQ
    WOOD, LM
    [J]. AT&T TECHNICAL JOURNAL, 1992, 71 (06): : 34 - 45
  • [5] Dynamic consent, communication and return of results in large-scale health data reuse: Survey of public preferences
    Muller, Sam H. A.
    van Thiel, Ghislaine J. M. W.
    Mostert, Menno
    van Delden, Johannes J. M.
    [J]. DIGITAL HEALTH, 2023, 9
  • [6] DEVELOPING SOFTWARE FOR LARGE-SCALE REUSE
    SEIDEWITZ, E
    BALFOUR, B
    ADAMS, SS
    WADE, DM
    COX, B
    [J]. SIGPLAN NOTICES, 1993, 28 (10): : 137 - 143
  • [7] Outlier Ranking for Large-Scale Public Health Data
    Joshi, Ananya
    Townes, Tina
    Gormley, Nolan
    Neureiter, Luke
    Rosenfeld, Roni
    Wilder, Bryan
    [J]. THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20, 2024, : 22176 - 22184
  • [8] Data model for large-scale structural experiments
    Lee, Chang-Ho
    Chin, Chung H.
    Marullo, Thomas
    Bryan, Peter
    Sause, Richard
    Ricles, James M.
    [J]. JOURNAL OF EARTHQUAKE ENGINEERING, 2008, 12 (01) : 115 - 135
  • [9] A Hybrid Data Model for Large-Scale Analytics
    Feo, John
    [J]. 2018 ACM INTERNATIONAL CONFERENCE ON COMPUTING FRONTIERS, 2018, : 269 - 269
  • [10] OPSEC VS Leaked Credentials: Password reuse in Large-Scale Data Leaks
    Uzonyi, David Gabor
    Pitropakis, Nikolaos
    McKeown, Sean
    Politis, Ilias
    [J]. 2023 IEEE 28TH INTERNATIONAL WORKSHOP ON COMPUTER AIDED MODELING AND DESIGN OF COMMUNICATION LINKS AND NETWORKS, CAMAD 2023, 2023, : 74 - 79