Oops! It appears that you have disabled your Javascript. In order for you to see this page as it is meant to appear, we ask that you please re-enable your Javascript!

Functional genomic landscape of cancer-intrinsic evasion of killing by T cells

  • 1.

    Chen, D. S. & Mellman, I. Elements of cancer immunity and the cancer-immune set point. Nature 541, 321–330 (2017).

    ADS  CAS  PubMed  Google Scholar 

  • 2.

    Hegde, P. S. & Chen, D. S. Top 10 challenges in cancer immunotherapy. Immunity 52, 17–35 (2020).

    CAS  PubMed  Google Scholar 

  • 3.

    Binnewies, M. et al. Understanding the tumor immune environment (TIME) for effective therapy. Nat. Med. 24, 541–550 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 4.

    Hugo, W. et al. Genomic and transcriptomic features of response to anti-pd-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 5.

    Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 6.

    Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 7.

    Kearney, C. J. et al. Tumor immune evasion arises through loss of TNF sensitivity. Sci. Immunol. 3, eaar3451 (2018).

    PubMed  Google Scholar 

  • 8.

    Manguso, R. T. et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547, 413–418 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 9.

    Pan, D. et al. A major chromatin regulator determines resistance of tumor cells to T cell-mediated killing. Science 359, 770–775 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • 10.

    Patel, S. J. et al. Identification of essential genes for cancer immunotherapy. Nature 548, 537–542 (2017).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • 11.

    Vredevoogd, D. W. et al. Augmenting immunotherapy impact by lowering tumor TNF cytotoxicity threshold. Cell 178, 585–599 (2019).

    CAS  PubMed  Google Scholar 

  • 12.

    Hart, T. et al. Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. G3 7, 2719–2727 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 13.

    Hart, T., Brown, K. R., Sircoulomb, F., Rottapel, R. & Moffat, J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics. Mol. Syst. Biol. 10, 733 (2014).

    PubMed  PubMed Central  Google Scholar 

  • 14.

    Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).

    CAS  PubMed  Google Scholar 

  • 15.

    Hart, T. & Moffat, J. BAGEL: a computational framework for identifying essential genes from pooled library screens. BMC Bioinformatics 17, 164 (2016).

    PubMed  PubMed Central  Google Scholar 

  • 16.

    Li, B. et al. A comprehensive mouse transcriptomic BodyMap across 17 tissues by RNA-seq. Sci. Rep. 7, 4200 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  • 17.

    Söllner, J. F. et al. An RNA-seq atlas of gene expression in mouse and rat normal tissues. Sci. Data 4, 170185 (2017).

    PubMed  PubMed Central  Google Scholar 

  • 18.

    Zimmermann, M. et al. CRISPR screens identify genomic ribonucleotides as a source of PARP-trapping lesions. Nature 559, 285–289 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • 19.

    Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).

    PubMed  PubMed Central  Google Scholar 

  • 20.

    Weichhart, T. & Säemann, M. D. The multiple facets of mTOR in immunity. Trends Immunol. 30, 218–226 (2009).

    CAS  PubMed  Google Scholar 

  • 21.

    Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 22.

    Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e14 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 23.

    Smith, C. C. et al. Endogenous retroviral signatures predict immunotherapy response in clear cell renal cell carcinoma. J. Clin. Invest. 128, 4804–4820 (2018).

    PubMed  PubMed Central  Google Scholar 

  • 24.

    Liddicoat, B. J. et al. RNA editing by ADAR1 prevents MDA5 sensing of endogenous dsRNA as nonself. Science 349, 1115–1120 (2015).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • 25.

    Starr, R. et al. A family of cytokine-inducible inhibitors of signalling. Nature 387, 917–921 (1997).

    ADS  CAS  PubMed  Google Scholar 

  • 26.

    Ishizuka, J. J. et al. Loss of ADAR1 in tumours overcomes resistance to immune checkpoint blockade. Nature 565, 43–48 (2019).

    ADS  CAS  PubMed  Google Scholar 

  • 27.

    Miranda, D. A. et al. Fat storage-inducing transmembrane protein 2 is required for normal fat storage in adipose tissue. J. Biol. Chem. 289, 9560–9572 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 28.

    Becuwe, M. et al. FIT2 is a lipid phosphate phosphatase crucial for endoplasmic reticulum homeostasis. Preprint at bioRxiv https://doi.org/10.1101/291765 (2018).

  • 29.

    Robke, L. et al. Phenotypic identification of a novel autophagy inhibitor chemotype targeting lipid kinase VPS34. Angew. Chem. Int. Edn Engl. 56, 8153–8157 (2017).

    CAS  Google Scholar 

  • 30.

    Tan, J. M. J., Mellouk, N. & Brumell, J. H. An autophagy-independent role for ATG16L1: promoting lysosome-mediated plasma membrane repair. Autophagy 15, 932–933 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 31.

    Diebold, S. S., Cotten, M., Koch, N. & Zenke, M. MHC class II presentation of endogenously expressed antigens by transfected dendritic cells. Gene Ther. 8, 487–493 (2001).

    CAS  PubMed  Google Scholar 

  • 32.

    Mair, B. et al. Essential gene profiles for human pluripotent stem cells identify uncharacterized genes and substrate dependencies. Cell Rep. 27, 599–615 (2019).

    CAS  PubMed  Google Scholar 

  • 33.

    Colic, M. et al. Identifying chemogenetic interactions from CRISPR screens with drugZ. Genome Med. 11, 52 (2019).

    PubMed  PubMed Central  Google Scholar 

  • 34.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    ADS  CAS  PubMed  Google Scholar 

  • 35.

    Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  • 36.

    Lee, I., Date, S. V., Adai, A. T. & Marcotte, E. M. A probabilistic functional network of yeast genes. Science 306, 1555–1558 (2004).

    ADS  CAS  PubMed  Google Scholar 

  • 37.

    Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).

    CAS  PubMed  Google Scholar 

  • 38.

    Giurgiu, M. et al. CORUM: the comprehensive resource of mammalian protein complexes-2019. Nucleic Acids Res. 47, D559–D563 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 39.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Google Scholar 

  • 40.

    Seki, A. & Rutz, S. Optimized RNP transfection for highly efficient CRISPR/Cas9-mediated gene knockout in primary T cells. J. Exp. Med. 215, 985–997 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 41.

    Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).

    ADS  PubMed  PubMed Central  Google Scholar 

  • 42.

    Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719–720 (2008).

    CAS  PubMed  Google Scholar 

  • Leave a Reply

    Your email address will not be published. Required fields are marked *

    Top