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      Multi-omics landscape and clinical significance of a SMAD4-driven immune signature: Implications for risk stratification and frontline therapies in pancreatic cancer

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          Highlights

          • SMAD4 mutation affect the oncogenesis, progression and immunity of pancreatic cancer.

          • Combined with immune subtypes, a SMAD4-driven immune signature (SDIS) was established.

          • SDIS could robustly predict prognosis and efficacy in six independent cohorts.

          • SDIS might serve as an attractive platform to further tailor decision-making.

          Abstract

          SMAD4 mutation was recently implicated in promoting invasion and poor prognosis of pancreatic cancer (PACA) by regulating the tumor immune microenvironment. However, SMAD4-driven immune landscape and clinical significance remain elusive. In this study, we applied the consensus clustering and weighted correlation network analysis (WGCNA) to identify two heterogeneous immune subtypes and immune genes. Combined with SMAD4-driven genes determined by SMAD4 mutation status, a SMAD4-driven immune signature (SDIS) was developed in ICGC-AU2 (microarray data) via machine learning algorithm, and then was validated by RNA-seq data (TCGA, ICGC-AU and ICGC-CA) and microarray data (GSE62452 and GSE85916). The high-risk group displayed a worse prognosis, and multivariate Cox regression indicated that SDIS was an independent prognostic factor. In six cohorts, SDIS also displayed excellent accuracy in predicting prognosis. Moreover, the high-risk group was characterized by higher frequencies of TP53/ CDKN2A mutations and SMAD4 deletion, superior immune checkpoint molecules expression and more sensitive to chemotherapy and immunotherapy. Meanwhile, the low-risk group was significantly enriched in metabolism-related pathways and suggested the potential to target tumor metabolism to develop specific drugs. Overall, SDIS could robustly predict prognosis in PACA, which might serve as an attractive platform to further tailor decision-making in chemotherapy and immunotherapy in clinical settings.

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          Most cited references35

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          The blockade of immune checkpoints in cancer immunotherapy.

          Among the most promising approaches to activating therapeutic antitumour immunity is the blockade of immune checkpoints. Immune checkpoints refer to a plethora of inhibitory pathways hardwired into the immune system that are crucial for maintaining self-tolerance and modulating the duration and amplitude of physiological immune responses in peripheral tissues in order to minimize collateral tissue damage. It is now clear that tumours co-opt certain immune-checkpoint pathways as a major mechanism of immune resistance, particularly against T cells that are specific for tumour antigens. Because many of the immune checkpoints are initiated by ligand-receptor interactions, they can be readily blocked by antibodies or modulated by recombinant forms of ligands or receptors. Cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) antibodies were the first of this class of immunotherapeutics to achieve US Food and Drug Administration (FDA) approval. Preliminary clinical findings with blockers of additional immune-checkpoint proteins, such as programmed cell death protein 1 (PD1), indicate broad and diverse opportunities to enhance antitumour immunity with the potential to produce durable clinical responses.
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            ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

            Summary: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery. Availability: ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/). Contact: mwilkers@med.unc.edu Supplementary Information: Supplementary data are available at Bioinformatics online.
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              Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.

              The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia.at/). Cellular characterization of the immune infiltrates showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning, we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) antibodies in two independent validation cohorts. Our findings and this resource may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.
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                Author and article information

                Contributors
                Journal
                Comput Struct Biotechnol J
                Comput Struct Biotechnol J
                Computational and Structural Biotechnology Journal
                Research Network of Computational and Structural Biotechnology
                2001-0370
                02 March 2022
                2022
                02 March 2022
                : 20
                : 1154-1167
                Affiliations
                [a ]Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
                [b ]Institute of Hepatobiliary and Pancreatic Diseases, Zhengzhou University, Zhengzhou 450052, Henan Province, China
                [c ]Zhengzhou Basic and Clinical Key Laboratory of Hepatopancreatobiliary Diseases, Zhengzhou, China
                [d ]Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
                [e ]Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
                Author notes
                [* ]Corresponding authors at: Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China (Y. Sun). fcchanxw@ 123456zzu.edu.cn ylsun@ 123456zzu.edu.cn
                [1]

                These authors have contributed equally to this work.

                Article
                S2001-0370(22)00073-3
                10.1016/j.csbj.2022.02.031
                8908051
                35317237
                c6575cb1-eaaa-4edf-a560-2b40f948960f
                © 2022 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 12 November 2021
                : 27 February 2022
                : 28 February 2022
                Categories
                Research Article

                pancreatic cancer,smad4 mutation,signature,prognosis,therapeutic response

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