42
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Autophagy promotes immune evasion of pancreatic cancer by degrading MHC-I

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Immune evasion is a major obstacle for cancer treatment. Common mechanisms include impaired antigen presentation through mutations or loss of heterozygosity (LOH) of the major histocompatibility complex class I (MHC-I), which has been implicated in resistance to immune checkpoint blockade (ICB) therapy 13 . However, in pancreatic ductal adenocarcinoma (PDAC), a malignancy refractory to most therapies including ICB 4 , mutations causing MHC-I loss are rarely found 5 despite the frequent downregulation of MHC-I expression 68 . Here we find that, in PDAC, MHC-I molecules are selectively targeted for lysosomal degradation through an autophagy-dependent mechanism that involves the autophagy cargo receptor NBR1. PDAC cells display reduced MHC-I cell surface expression and instead demonstrate predominant localization within autophagosomes and lysosomes. Notably, autophagy inhibition restores surface MHC-I levels, leading to improved antigen presentation, enhanced anti-tumour T cell response and reduced tumour growth in syngeneic hosts. Accordingly, anti-tumour effects of autophagy inhibition are reversed by depleting CD8 + T cells or reducing surface MHC-I expression. Autophagy inhibition, either genetically or pharmacologically with Chloroquine (CQ), synergizes with dual ICB (anti-PD1 and anti-CTLA4), and leads to an enhanced anti-tumour immune response. Our findings uncover a role for enhanced autophagy/lysosome function in immune evasion through selective targeting of MHC-I molecules for degradation, and provide a rationale for the combination of autophagy inhibition and dual ICB as a therapeutic strategy against PDAC.

          Related collections

          Most cited references58

          • Record: found
          • Abstract: found
          • Article: not found

          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
                Bookmark

                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                23 April 2020
                22 April 2020
                May 2020
                22 October 2020
                : 581
                : 7806
                : 100-105
                Affiliations
                [1 ]Department of Radiation Oncology, Perlmutter Cancer Center, New York University School of Medicine, New York, NY 10016, USA
                [2 ]Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA
                [3 ]Columbia Center for Translational Immunology, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA
                [4 ]Columbia Stem Cell Initiative, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA
                [5 ]Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
                [6 ]Department of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA
                [7 ]Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
                [8 ]Division of Radiation and Genome Stability, Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02115, USA
                [9 ]Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
                [10 ]Weill Cornell Medicine, New York, NY 10065, USA
                [11 ]Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK
                Author notes
                [*]

                These authors contributed equally.

                Author contributions

                K.Y. and A.V. performed the majority of experiments and wrote the manuscript. J.Y. assisted with cloning and performed the proximity biotinylation and ubiquitylation experiments. D.E.B. and A.S.W.S. assisted with animal studies. S.G. performed immunofluorescence and analysis of patient PDAC specimens. M.K. assisted with the analysis of flow cytometry data and RNA-seq data. S.M. assisted with immunoblotting and preparing shRNAs. E.Y.L. and S.J.P. cloned fluorescent constructs. K.W.W. and G.E.K. provided PDAC patient specimens and analysis. J.D. provided GFP-NBR1 and GFP-NBR1 dUBA constructs. R.S.B. assisted with transcriptome data analysis. J.D.M. and J.A.P. performed proteomics analysis. D.T.F. provided intellectual feedback and support. R.M.P. and A.C.K. conceived the project, supervised the research, and wrote and edited the paper.

                [] Correspondence and requests for materials should be addressed to R.M.P. and A.C.K. ( rushika.perera@ 123456ucsf.edu ) and A.C.K. ( alec.kimmelman@ 123456nyulangone.org )
                Article
                NIHMS1580291
                10.1038/s41586-020-2229-5
                7296553
                32376951
                c7408c88-402a-485a-b62e-d7077cef8c3a

                Reprints and permissions information is available at www.nature.com/reprints.

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Categories
                Article

                Uncategorized
                Uncategorized

                Comments

                Comment on this article