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Autophagy promotes immune evasion of pancreatic cancer by degrading MHC-I 期刊论文
NATURE, 2020, 581 (7806) : 100-+
作者:  Waszak, Sebastian M.;  Robinson, Giles W.;  Gudenas, Brian L.;  Smith, Kyle S.;  Forget, Antoine;  Kojic, Marija;  Garcia-Lopez, Jesus;  Hadley, Jennifer;  Hamilton, Kayla V.;  Indersie, Emilie;  Buchhalter, Ivo;  Kerssemakers, Jules;  Jager, Natalie;  Sharma, Tanvi;  Rausch, Tobias;  Kool, Marcel;  Sturm, Dominik;  Jones, David T. W.;  Vasilyeva, Aksana;  Tatevossian, Ruth G.;  Neale, Geoffrey;  Lombard, Berangere;  Loew, Damarys;  Nakitandwe, Joy;  Rusch, Michael;  Bowers, Daniel C.;  Bendel, Anne;  Partap, Sonia;  Chintagumpala, Murali;  Crawford, John;  Gottardo, Nicholas G.;  Smith, Amy;  Dufour, Christelle;  Rutkowski, Stefan;  Eggen, Tone;  Wesenberg, Finn;  Kjaerheim, Kristina;  Feychting, Maria;  Lannering, Birgitta;  Schuz, Joachim;  Johansen, Christoffer;  Andersen, Tina V.;  Roosli, Martin;  Kuehni, Claudia E.;  Grotzer, Michael;  Remke, Marc;  Puget, Stephanie;  Pajtler, Kristian W.;  Milde, Till;  Witt, Olaf;  Ryzhova, Marina;  Korshunov, Andrey;  Orr, Brent A.;  Ellison, David W.;  Brugieres, Laurence;  Lichter, Peter;  Nichols, Kim E.;  Gajjar, Amar;  Wainwright, Brandon J.;  Ayrault, Olivier;  Korbel, Jan O.;  Northcott, Paul A.;  Pfister, Stefan M.
收藏  |  浏览/下载:38/0  |  提交时间:2020/07/03

Immune evasion is a major obstacle for cancer treatment. Common mechanisms of evasion include impaired antigen presentation caused by mutations or loss of heterozygosity of the major histocompatibility complex class I (MHC-I), which has been implicated in resistance to immune checkpoint blockade (ICB) therapy(1-3). However, in pancreatic ductal adenocarcinoma (PDAC), which is resistant to most therapies including ICB4, mutations that cause loss of MHC-I are rarely found(5) despite the frequent downregulation of MHC-I expression(6-8). Here we show that, in PDAC, MHC-I molecules are selectively targeted for lysosomal degradation by an autophagy-dependent mechanism that involves the autophagy cargo receptor NBR1. PDAC cells display reduced expression of MHC-I at the cell surface and instead demonstrate predominant localization within autophagosomes and lysosomes. Notably, inhibition of autophagy restores surface levels of MHC-I and leads to improved antigen presentation, enhanced anti-tumour T cell responses and reduced tumour growth in syngeneic host mice. Accordingly, the anti-tumour effects of autophagy inhibition are reversed by depleting CD8(+) T cells or reducing surface expression of MHC-I. Inhibition of autophagy, either genetically or pharmacologically with chloroquine, synergizes with dual ICB therapy (anti-PD1 and anti-CTLA4 antibodies), and leads to an enhanced anti-tumour immune response. Our findings demonstrate a role for enhanced autophagy or lysosome function in immune evasion by selective targeting of MHC-I molecules for degradation, and provide a rationale for the combination of autophagy inhibition and dual ICB therapy as a therapeutic strategy against PDAC.


Inhibition of the autophagy-lysosome system upregulates surface expression of MHC class I proteins and enhances antigen presentation, and evokes a potent anti-tumour immune response that is mediated by CD8(+) T cells.


  
Classification with a disordered dopantatom network in silicon 期刊论文
NATURE, 2020, 577 (7790) : 341-+
作者:  Vagnozzi, Ronald J.;  Maillet, Marjorie;  Sargent, Michelle A.;  Khalil, Hadi;  Johansen, Anne Katrine Z.;  Schwanekamp, Jennifer A.;  York, Allen J.;  Huang, Vincent;  Nahrendorf, Matthias;  Sadayappan, Sakthivel;  Molkentin, Jeffery D.
收藏  |  浏览/下载:24/0  |  提交时间:2020/07/03

Classification is an important task at which both biological and artificial neural networks excel(1,2). In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable(3,4), simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers. A promising approach is to exploit physical materials systems that perform this nonlinear projection intrinsically, because of their high computational density(5), inherent parallelism and energy efficiency(6,7). However, existing approaches either rely on the systems'  time dynamics, which requires sequential data processing and therefore hinders parallel computation(5,6,8), or employ large materials systems that are difficult to scale up(7). Here we use a parallel, nanoscale approach inspired by filters in the brain(1) and artificial neural networks(2) to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction(9-11) through an electrically tunable network of boron dopant atoms in silicon, reconfiguring the network through artificial evolution to realize different computational functions. We first solve the canonical two-input binary classification problem, realizing all Boolean logic gates(12) up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters(2) that can perform four-input binary classification on the Modified National Institute of Standards and Technology handwritten digit database. Implementation of our material-based filters substantially improves the classification accuracy over that of a linear classifier directly applied to the original data(13). Our results establish a paradigm of silicon-based electronics for smallfootprint and energy-efficient computation(14).