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Inositol-requiring enzyme 1[α] (IRE1[α])–X-box binding protein spliced (XBP1) signaling maintains endoplasmic reticulum (ER) homeostasis while controlling immunometabolic processes. Yet, the physiological consequences of IRE1a–XBP1 activation in leukocytes remain unexplored. We found that induction of prostaglandin-endoperoxide synthase 2 (Ptgs2/Cox-2) and prostaglandin E synthase (Ptges/mPGES-1) was compromised in IRE1a-deficient myeloid cells undergoing ER stress or stimulated through pattern recognition receptors. Inducible biosynthesis of prostaglandins, including the pro-algesic mediator prostaglandin E2 (PGE2), was decreased in myeloid cells that lack IRE1a or XBP1 but not other ER stress sensors. Functional XBP1 transactivated the human PTGS2 and PTGES genes to enable optimal PGE2 production. Mice that lack IRE1α–XBP1 in leukocytes, or that were treated with IRE1a inhibitors, demonstrated reduced pain behaviors in PGE2-dependent models of pain. Thus, IRE1a–XBP1 is a mediator of prostaglandin biosynthesis and a potential target to control pain.

SummaryDendritic cells (DCs) are required to initiate and sustain T cell-dependent anti-cancer immunity. However, tumors often evade immune control by crippling normal DC function. The endoplasmic reticulum (ER) stress response factor XBP1 promotes intrinsic tumor growth directly, but whether it also regulates the host anti-tumor immune response is not known. Here we show that constitutive activation of XBP1 in tumor-associated DCs (tDCs) drives ovarian cancer (OvCa) progression by blunting anti-tumor immunity. XBP1 activation, fueled by lipid peroxidation byproducts, induced a triglyceride biosynthetic program in tDCs leading to abnormal lipid accumulation and subsequent inhibition of tDC capacity to support anti-tumor T cells. Accordingly, DC-specific XBP1 deletion or selective nanoparticle-mediated XBP1 silencing in tDCs restored their immunostimulatory activity in situ and extended survival by evoking protective type 1 anti-tumor responses. Targeting the ER stress response should concomitantly inhibit tumor growth and enhance anti-cancer immunity, thus offering a unique approach to cancer immunotherapy.

Tumours evade immune control by creating hostile microenvironments that perturb T cell metabolism and effector function 1?4 . However, it remains unclear how intra-tumoral T cells integrate and interpret metabolic stress signals. Here we report that ovarian cancer?an aggressive malignancy that is refractory to standard treatments and current immunotherapies 5?8 ?induces endoplasmic reticulum stress and activates the IRE1α?XBP1 arm of the unfolded protein response 9,10 in T cells to control their mitochondrial respiration and anti-tumour function. In T cells isolated from specimens collected from patients with ovarian cancer, upregulation of XBP1 was associated with decreased infiltration of T cells into tumours and with reduced IFNG mRNA expression. Malignant ascites fluid obtained from patients with ovarian cancer inhibited glucose uptake and caused N-linked protein glycosylation defects in T cells, which triggered IRE1α?XBP1 activation that suppressed mitochondrial activity and IFNγ production. Mechanistically, induction of XBP1 regulated the abundance of glutamine carriers and thus limited the influx of glutamine that is necessary to sustain mitochondrial respiration in T cells under glucose-deprived conditions. Restoring N-linked protein glycosylation, abrogating IRE1α?XBP1 activation or enforcing expression of glutamine transporters enhanced mitochondrial respiration in human T cells exposed to ovarian cancer ascites. XBP1-deficient T cells in the metastatic ovarian cancer milieu exhibited global transcriptional reprogramming and improved effector capacity. Accordingly, mice that bear ovarian cancer and lack XBP1 selectively in T cells demonstrate superior anti-tumour immunity, delayed malignant progression and increased overall survival. Controlling endoplasmic reticulum stress or targeting IRE1α?XBP1 signalling may help to restore the metabolic fitness and anti-tumour capacity of T cells in cancer hosts. Fil: Song, Minkyung. Weill Cornell Medicine; Estados Unidos Fil: Sandoval, Tito A.. Weill Cornell Medicine; Estados Unidos Fil: Chae, Chang-Suk. Weill Cornell Medicine; Estados Unidos Fil: Chopra, Sahil. Weill Cornell Medicine; Estados Unidos Fil: Tan, Chen. Weill Cornell Medicine; Estados Unidos Fil: Rutkowski, Melanie R.. University of Virginia; Estados Unidos Fil: Raundhal, Mahesh. Dana Farber Cancer Institute; Estados Unidos. Harvard Medical School; Estados Unidos Fil: Chaurio, Ricardo A.. H. Lee Moffitt Cancer Center & Research Institute; Estados Unidos Fil: Payne, Kyle K.. H. Lee Moffitt Cancer Center & Research Institute; Estados Unidos Fil: Konrad, Csaba. Weill Cornell Medicine; Estados Unidos Fil: Bettigole, Sarah E.. Quentis Therapeutics Inc.; Estados Unidos Fil: Shin, Hee Rae. Quentis Therapeutics Inc.; Estados Unidos Fil: Crowley, Michael J. P.. Weill Cornell Graduate School of Medical Sciences; Estados Unidos Fil: Cerliani, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina Fil: Kossenkov, Andrew V.. The Wistar Institute; Estados Unidos Fil: Motorykin, Ievgen. Weill Cornell Medicine,; Estados Unidos Fil: Zhang, Sheng. Weill Cornell Medicine,; Estados Unidos Fil: Manfredi, Giovanni. Weill Cornell Medicine,; Estados Unidos Fil: Zamarin, Dmitriy. Memorial Sloan Kettering Cancer Center; Estados Unidos Fil: Holcomb, Kevin. Weill Cornell Medicine,; Estados Unidos Fil: Rodriguez, Paulo C.. H. Lee Moffitt Cancer Center & Research Institute; Estados Unidos Fil: Rabinovich, Gabriel Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; Argentina Fil: Conejo Garcia, Jose R.. H. Lee Moffitt Cancer Center & Research Institute; Estados Unidos Fil: Glimcher, Laurie H.. Dana Farber Cancer Institute; Estados Unidos. Harvard Medical School; Estados Unidos Fil: Cubillos-Ruiz, Juan R.. Weill Graduate School Of Medical Sciences; Estados Unidos. Weill Graduate School Of Medical Sciences; Estados Unidos

Summary We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.

Cancer is caused by accumulated changes in a cell's DNA sequence that disrupt the regulation of genetic networks. Mutation type, cell of origin, and tissue microenvironment all influence the initiation and progression of disease. The Cancer Genome Atlas (TCGA) catalogues the molecular changes of thousands of tumor samples of various tumor types using different data modalities including genomic, transcriptomic, proteomic, and epigenomic views. The goal is to find similarities amongst cancers of different tissues and to reveal clinically-relevant subtypes sharing common molecular abnormalities. While various analyses for interpreting these rich datasets exist, few methods are available to enable intuitive global overviews of these rich compendia. There is a need for methods that can tap the statistical power of large cohorts, to aid in analysis of smaller cohorts and of individual patient samples. Patterns present in many tumors may reveal driving genomic aberrations and pathway signatures that inform therapy. Here, we present a TumorMap, a tool that generates a map of cancer samples for interactive exploration, data overlay visualization, and statistical analysis. TumorMap arranges samples on a hexagonal 2-dimensional grid based on sample similarity. Different maps can be made for each distinct platform of data. Herein we demonstrate the utility of TumorMap for revealing commonalities between cancers of different tissue types, and its ability to aid in pan-cancer hypothesis generation. TumorMap maps for various cancer cohorts are available for free online at http://tumormap.ucsc.edu. Citation Format: Yulia Newton, Adam Novak, Teresa Swatlowski, Sahil Chopra, Sofie Salama, Olena Morozova, David Haussler, Joshua Stuart. UCSC TumorMap: Exploring cancer signatures on an interactive dynamic landscape. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-290.

We applied biologically-motivated feature transformations coupled with established machine learning methods to predict gene essentiality in CCLE cell line models. By leveraging additional large datasets, such as The Cancer Genome Atlas PanCancer12 data and MSigDB pathway definitions, we improved the robustness and biological interpretability of our models. We developed a multi-pathway learning (MPL) approach that associates a genetic pathway from MSigDB with a distinct kernel for use in a multiple kernel learning setting. We evaluated the performance of MPL compared to several other regression methods including random forests, kernel ridge regression, and elastic net linear models, We combined multiple approaches using an ensemble technique on the diverse set of predictors. We found that the best performing method was an ensemble combining MPL and random forest predictions. Both models utilized features derived from both gene expression and copy number data, the latter of which were filtered to those predicted as driver events in prior pan-cancer studies. The ensemble method was a joint winner in the recent DREAM 9 gene essentiality prediction challenge. MPL also demonstrated merit as a feature selector when used with other downstream methods. The ensemble performed best at predicting the essentiality of genes involved in cell cycle control (cyclins and cyclin-dependent kinases), protein degradation (proteasome complex), cell proliferation signaling (sonic hedgehog, Aurora-B, RAC1), apoptosis (RB1,TP53) and hypoxia response (VEGF, VHL). Many of the key genes in those pathways are known to be drivers of cancer progression, suggesting our method's utility as a biomarker for detecting key tumorigenic events. The advantage of MPL is that mechanistically coherent gene sets are automatically selected as high scoring pathway kernels (HSPKs). We investigated whether the HSPKs identify cellular processes relevant to the loss of key genes. To do this, we inspected the HSPKs for a few of the most abundantly mutated genes in cancer. The MPL predictor for TP53 included the targets of this transcription factor as well as HSPKs involved in apoptosis, a cellular process regulated by TP53. The retinoblastoma gene (RB1) MPL predictor included RB1 targets as well as HSPKs involved in the regulation of histone deacetylase (HDAC) that interacts with RB1 to suppress DNA synthesis. These findings suggest trends in the MPL results could reveal a pathway-level view of the synthetic lethal architecture of cells. Such a map, that links patterns of pathway expression to potential genetic vulnerabilities, could provide an invaluable tool for exploring new avenues to target cancer cells. Citation Format: Vladislav Uzunangelov, Evan Paull, Sahil Chopra, Daniel Carlin, Adrian Bivol, Kyle Ellrott, Kiley Graim, Yulia Newton, Sam Ng, Artem Sokolov, Joshua Stuart. Multiple Pathway Learning accurately predicts gene essentiality in the Cancer Cell Line Encyclopedia. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr PR10.

We applied biologically-motivated feature transformations coupled with established machine learning methods to predict gene essentiality in CCLE cell line models. By leveraging additional large datasets, such as The Cancer Genome Atlas PanCancer12 data and MSigDB pathway definitions, we improved the robustness and biological interpretability of our models. We developed a multi-pathway learning (MPL) approach that associates a genetic pathway from MSigDB with a distinct kernel for use in a multiple kernel learning setting. We evaluated the performance of MPL compared to several other regression methods including random forests, kernel ridge regression, and elastic net linear models, We combined multiple approaches using an ensemble technique on the diverse set of predictors. We found that the best performing method was an ensemble combining MPL and random forest predictions. Both models utilized features derived from both gene expression and copy number data, the latter of which were filtered to those predicted as driver events in prior pan-cancer studies. The ensemble method was a joint winner in the recent DREAM 9 gene essentiality prediction challenge. MPL also demonstrated merit as a feature selector when used with other downstream methods. The ensemble performed best at predicting the essentiality of genes involved in cell cycle control (cyclins and cyclin-dependent kinases), protein degradation (proteasome complex), cell proliferation signaling (sonic hedgehog, Aurora-B, RAC1), apoptosis (RB1,TP53) and hypoxia response (VEGF, VHL). Many of the key genes in those pathways are known to be drivers of cancer progression, suggesting our method's utility as a biomarker for detecting key tumorigenic events. The advantage of MPL is that mechanistically coherent gene sets are automatically selected as high scoring pathway kernels (HSPKs). We investigated whether the HSPKs identify cellular processes relevant to the loss of key genes. To do this, we inspected the HSPKs for a few of the most abundantly mutated genes in cancer. The MPL predictor for TP53 included the targets of this transcription factor as well as HSPKs involved in apoptosis, a cellular process regulated by TP53. The retinoblastoma gene (RB1) MPL predictor included RB1 targets as well as HSPKs involved in the regulation of histone deacetylase (HDAC) that interacts with RB1 to suppress DNA synthesis. These findings suggest trends in the MPL results could reveal a pathway-level view of the synthetic lethal architecture of cells. Such a map, that links patterns of pathway expression to potential genetic vulnerabilities, could provide an invaluable tool for exploring new avenues to target cancer cells. Citation Format: Vladislav Uzunangelov, Evan Paull, Sahil Chopra, Daniel Carlin, Adrian Bivol, Kyle Ellrott, Kiley Graim, Yulia Newton, Sam Ng, Artem Sokolov, Joshua Stuart. Multiple Pathway Learning accurately predicts gene essentiality in the Cancer Cell Line Encyclopedia. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR02.