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      Genome-wide association analysis identifies genetic correlates of immune infiltrates in solid tumors

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          Abstract

          Therapeutic options for the treatment of an increasing variety of cancers have been expanded by the introduction of a new class of drugs, commonly referred to as checkpoint blocking agents, that target the host immune system to positively modulate anti-tumor immune response. Although efficacy of these agents has been linked to a pre-existing level of tumor immune infiltrate, it remains unclear why some patients exhibit deep and durable responses to these agents while others do not benefit. To examine the influence of tumor genetics on tumor immune state, we interrogated the relationship between somatic mutation and copy number alteration with infiltration levels of 7 immune cell types across 40 tumor cohorts in The Cancer Genome Atlas. Levels of cytotoxic T, regulatory T, total T, natural killer, and B cells, as well as monocytes and M2 macrophages, were estimated using a novel set of transcriptional signatures that were designed to resist interference from the cellular heterogeneity of tumors. Tumor mutational load and estimates of tumor purity were included in our association models to adjust for biases in multi-modal genomic data. Copy number alterations, mutations summarized at the gene level, and position-specific mutations were evaluated for association with tumor immune infiltration. We observed a strong relationship between copy number loss of a large region of chromosome 9p and decreased lymphocyte estimates in melanoma, pancreatic, and head/neck cancers. Mutations in the oncogenes PIK3CA, FGFR3, and RAS/RAF family members, as well as the tumor suppressor TP53, were linked to changes in immune infiltration, usually in restricted tumor types. Associations of specific WNT/beta-catenin pathway genetic changes with immune state were limited, but we noted a link between 9p loss and the expression of the WNT receptor FZD3, suggesting that there are interactions between 9p alteration and WNT pathways. Finally, two different cell death regulators, CASP8 and DIDO1, were often mutated in head/neck tumors that had higher lymphocyte infiltrates. In summary, our study supports the relevance of tumor genetics to questions of efficacy and resistance in checkpoint blockade therapies. It also highlights the need to assess genome-wide influences during exploration of any specific tumor pathway hypothesized to be relevant to therapeutic response. Some of the observed genetic links to immune state, like 9p loss, may influence response to cancer immune therapies. Others, like mutations in cell death pathways, may help guide combination therapeutic approaches.

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          Regulatory T Cells Exhibit Distinct Features in Human Breast Cancer.

          Regulatory T (Treg) cells reside in lymphoid organs and barrier tissues where they control different types of inflammatory responses. Treg cells are also found in human cancers, and studies in animal models suggest that they contribute to cancer progression. However, properties of human intratumoral Treg cells and those present in corresponding normal tissue remain largely unknown. Here, we analyzed features of Treg cells in untreated human breast carcinomas, normal mammary gland, and peripheral blood. Tumor-resident Treg cells were potently suppressive and their gene-expression pattern resembled that of normal breast tissue, but not of activated peripheral blood Treg cells. Nevertheless, a number of cytokine and chemokine receptor genes, most notably CCR8, were upregulated in tumor-resident Treg cells in comparison to normal tissue-resident ones. Our studies suggest that targeting CCR8 for the depletion of tumor-resident Treg cells might represent a promising immunotherapeutic approach for the treatment of breast cancer.
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            Nuclear FAK Controls Chemokine Transcription, Tregs, and Evasion of Anti-tumor Immunity

            Summary Focal adhesion kinase (FAK) promotes anti-tumor immune evasion. Specifically, the kinase activity of nuclear-targeted FAK in squamous cell carcinoma (SCC) cells drives exhaustion of CD8+ T cells and recruitment of regulatory T cells (Tregs) in the tumor microenvironment by regulating chemokine/cytokine and ligand-receptor networks, including via transcription of Ccl5, which is crucial. These changes inhibit antigen-primed cytotoxic CD8+ T cell activity, permitting growth of FAK-expressing tumors. Mechanistically, nuclear FAK is associated with chromatin and exists in complex with transcription factors and their upstream regulators that control Ccl5 expression. Furthermore, FAK’s immuno-modulatory nuclear activities may be specific to cancerous squamous epithelial cells, as normal keratinocytes do not have nuclear FAK. Finally, we show that a small-molecule FAK kinase inhibitor, VS-4718, which is currently in clinical development, also drives depletion of Tregs and promotes a CD8+ T cell-mediated anti-tumor response. Therefore, FAK inhibitors may trigger immune-mediated tumor regression, providing previously unrecognized therapeutic opportunities.
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              Discovery of meaningful associations in genomic data using partial correlation coefficients.

              A major challenge of systems biology is to infer biochemical interactions from large-scale observations, such as transcriptomics, proteomics and metabolomics. We propose to use a partial correlation analysis to construct approximate Undirected Dependency Graphs from such large-scale biochemical data. This approach enables a distinction between direct and indirect interactions of biochemical compounds, thereby inferring the underlying network topology. The method is first thoroughly evaluated with a large set of simulated data. Results indicate that the approach has good statistical power and a low False Discovery Rate even in the presence of noise in the data. We then applied the method to an existing data set of yeast gene expression. Several small gene networks were inferred and found to contain genes known to be collectively involved in particular biochemical processes. In some of these networks there are also uncharacterized ORFs present, which lead to hypotheses about their functions. Programs running in MS-Windows and Linux for applying zeroth, first, second and third order partial correlation analysis can be downloaded at: http://mendes.vbi.vt.edu/tiki-index.php?page=Software. Supplementary information can be found at: URL to be decided.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                27 July 2017
                2017
                : 12
                : 7
                : e0179726
                Affiliations
                [1 ] Translational Bioinformatics, Bristol-Myers Squibb, Redwood City, California, United States of America
                [2 ] Translational Bioinformatics, Bristol-Myers Squibb, Hopewell, New Jersey, United States of America
                [3 ] Oncology Discovery, Bristol-Myers Squibb, Lawrenceville, New Jersey, United States of America
                Universidade de Sao Paulo, BRAZIL
                Author notes

                Competing Interests: The authors are employed at Bristol-Myers Squibb, which discovered and commercialized ipilimumab and nivolumab therapies for cancer. There are no patents, clinical research programs, nor marketed products to declare that are directly related to this manuscript. Bristol-Myers Squibb has major efforts in the area of immuno-oncology in general. Our affiliation with Bristol-Myers Squibb does not alter our adherence to PLOS ONE policies on sharing data and materials.

                • Conceptualization: NOS.

                • Formal analysis: NOS HC JLH SDC.

                • Investigation: NOS HC JLH PRM.

                • Methodology: NOS HC JLH SDC.

                • Software: NOS HC JLH.

                • Supervision: SDC.

                • Validation: NOS JLH.

                • Visualization: NOS.

                • Writing – original draft: NOS.

                • Writing – review & editing: NOS PRM SDC JDS CFV.

                Article
                PONE-D-16-37475
                10.1371/journal.pone.0179726
                5531551
                28749946
                a36871ce-5274-42cb-82b0-acb2f4a7c36c
                © 2017 Siemers et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 18 September 2016
                : 2 June 2017
                Page count
                Figures: 6, Tables: 8, Pages: 24
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100002491, Bristol-Myers Squibb;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100002491, Bristol-Myers Squibb;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100002491, Bristol-Myers Squibb;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100002491, Bristol-Myers Squibb;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100002491, Bristol-Myers Squibb;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100002491, Bristol-Myers Squibb;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100002491, Bristol-Myers Squibb;
                Award Recipient :
                All of the authors of this manuscript are salaried employees of Bristol-Myers Squibb Company, a Biopharma company in the business of developing and marketing therapeutic drugs. The Company did not influence study design, data collection, nor analysis, but supported our research in general, and approved the release of the publication. The specific roles of these authors are articulated in the 'author contributions' section.
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                Data from TCGA (TCGA Research Network: Http://cancergenome.nih.gov/) was obtained from the University of California Santa Cruz Xena TCGA Pan-Cancer (PANCAN) repositories ( http://xena.ucsc.edu/public-hubs/). The R, R markdown code, and related files used to perform the analysis and generate this paper are available at https://github.com/siemersn/GENIO. However, the TCGA public data contains too many indirect patient identifiers to be provided via public deposition. A snapshot of the UCSC Xena team's November 2015 release of TCGA data used in this work may be made available upon request. Requests may be sent to the corresponding author.

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