Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq

被引:312
|
作者
Karaayvaz, Mihriban [1 ,2 ]
Cristea, Simona [3 ,4 ,5 ]
Gillespie, Shawn M. [1 ,2 ,6 ]
Patel, Anoop P. [2 ,7 ]
Mylvaganam, Ravindra [1 ,2 ,6 ]
Luo, Christina C. [1 ,2 ,6 ]
Specht, Michelle C. [2 ,8 ]
Bernstein, Bradley E. [1 ,2 ,6 ,9 ,10 ]
Michor, Franziska [3 ,4 ,5 ,9 ,10 ,11 ]
Ellisen, Leif W. [1 ,2 ]
机构
[1] Massachusetts Gen Hosp, Ctr Canc Res, Boston, MA 02114 USA
[2] Harvard Med Sch, Boston, MA 02114 USA
[3] Dana Farber Canc Inst, Dept Biostat & Computat Biol, Boston, MA 02115 USA
[4] Harvard TH Chan Sch Publ Hlth, Dept Biostat, Boston, MA 02215 USA
[5] Harvard Univ, Dept Stem Cell & Regenerat Biol, Cambridge, MA 02138 USA
[6] Massachusetts Gen Hosp, Dept Pathol, Boston, MA 02114 USA
[7] Massachusetts Gen Hosp, Dept Neurosurg, Boston, MA 02114 USA
[8] Massachusetts Gen Hosp, Dept Surg Oncol, Boston, MA 02114 USA
[9] Broad Inst Harvard & MIT, Cambridge, MA 02139 USA
[10] Ludwig Ctr Harvard, Boston, MA 02215 USA
[11] Dana Farber Canc Inst, Ctr Canc Evolut, Boston, MA 02115 USA
基金
瑞士国家科学基金会;
关键词
NEGATIVE BREAST-CANCER; TUMOR-INFILTRATING LYMPHOCYTES; STEM-CELLS; EXPRESSION; EVOLUTION; GLYCOSPHINGOLIPIDS; PATTERNS; SURVIVAL;
D O I
10.1038/s41467-018-06052-0
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by extensive intratumoral heterogeneity. To investigate the underlying biology, we conducted single-cell RNA-sequencing (scRNA-seq) of >1500 cells from six primary TNBC. Here, we show that intercellular heterogeneity of gene expression programs within each tumor is variable and largely correlates with clonality of inferred genomic copy number changes, suggesting that genotype drives the gene expression phenotype of individual subpopulations. Clustering of gene expression profiles identified distinct subgroups of malignant cells shared by multiple tumors, including a single subpopulation associated with multiple signatures of treatment resistance and metastasis, and characterized functionally by activation of glycosphingolipid metabolism and associated innate immunity pathways. A novel signature defining this subpopulation predicts long-term outcomes for TNBC patients in a large cohort. Collectively, this analysis reveals the functional heterogeneity and its association with genomic evolution in TNBC, and uncovers unanticipated biological principles dictating poor outcomes in this disease.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] Unravelling glioblastoma heterogeneity by means of single-cell RNA sequencing
    Hernandez Martinez, Ana
    Madurga, Rodrigo
    Garcia-Romero, Noemi
    Ayuso-Sacido, Angel
    CANCER LETTERS, 2022, 527 : 66 - 79
  • [22] Single-cell RNA-Seq reveals transcriptional heterogeneity and immune subtypes associated with disease activity in human myasthenia gravis
    Jin, Wanlin
    Yang, Qi
    Peng, Yuyao
    Yan, Chengkai
    Li, Yi
    Luo, Zhaohui
    Xiao, Bo
    Xu, Liqun
    Yang, Huan
    CELL DISCOVERY, 2021, 7 (01)
  • [23] Integrating single-cell RNA-seq and imaging with SCOPE-seq2
    Liu, Zhouzerui
    Yuan, Jinzhou
    Lasorella, Anna
    Iavarone, Antonio
    Bruce, Jeffrey N.
    Canoll, Peter
    Sims, Peter A.
    SCIENTIFIC REPORTS, 2020, 10 (01)
  • [24] Analysis of Single-Cell RNA-seq Data by Clustering Approaches
    Zhu, Xiaoshu
    Li, Hong-Dong
    Guo, Lilu
    Wu, Fang-Xiang
    Wang, Jianxin
    CURRENT BIOINFORMATICS, 2019, 14 (04) : 314 - 322
  • [25] Quantifying the clusterness and trajectoriness of single-cell RNA-seq data
    Lim, Hong Seo
    Qiu, Peng
    PLOS COMPUTATIONAL BIOLOGY, 2024, 20 (02)
  • [26] Exponential scaling of single-cell RNA-seq in the past decade
    Svensson, Valentine
    Vento-Tormo, Roser
    Teichmann, Sarah A.
    NATURE PROTOCOLS, 2018, 13 (04) : 599 - 604
  • [27] Locality Sensitive Imputation for Single-Cell RNA-Seq Data
    Moussa, Marmar
    Mandoiu, Ion I.
    BIOINFORMATICS RESEARCH AND APPLICATIONS, ISBRA 2018, 2018, 10847 : 347 - 360
  • [28] Processing single-cell RNA-seq datasets using SingCellaR
    Wang, Guanlin
    Wen, Wei Xiong
    Mead, Adam J.
    Roy, Anindita
    Psaila, Bethan
    Thongjuea, Supat
    STAR PROTOCOLS, 2022, 3 (02):
  • [29] Phylogenetic inference from single-cell RNA-seq data
    Liu, Xuan
    Griffiths, Jason I.
    Bishara, Isaac
    Liu, Jiayi
    Bild, Andrea H.
    Chang, Jeffrey T.
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [30] Quantitative single-cell RNA-seq with unique molecular identifiers
    Islam, Saiful
    Zeisel, Amit
    Joost, Simon
    La Manno, Gioele
    Zajac, Pawel
    Kasper, Maria
    Lonnerberg, Peter
    Linnarsson, Sten
    NATURE METHODS, 2014, 11 (02) : 163 - +