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Network-based identification of key master regulators associated with an immune-silent cancer phenotype

A cancer immune phenotype characterized by an active T-helper 1 (Th1)/cytotoxic response is associated with responsiveness to immunotherapy and favorable prognosis across different tumors. However, in some cancers, such an intratumoral immune activation does not confer protection from progression or...

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Autores principales: Mall, Raghvendra, Saad, Mohamad, Roelands, Jessica, Rinchai, Darawan, Kunji, Khalid, Almeer, Hossam, Hendrickx, Wouter, M Marincola, Francesco, Ceccarelli, Michele, Bedognetti, Davide
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8574720/
https://www.ncbi.nlm.nih.gov/pubmed/33979427
http://dx.doi.org/10.1093/bib/bbab168
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author Mall, Raghvendra
Saad, Mohamad
Roelands, Jessica
Rinchai, Darawan
Kunji, Khalid
Almeer, Hossam
Hendrickx, Wouter
M Marincola, Francesco
Ceccarelli, Michele
Bedognetti, Davide
author_facet Mall, Raghvendra
Saad, Mohamad
Roelands, Jessica
Rinchai, Darawan
Kunji, Khalid
Almeer, Hossam
Hendrickx, Wouter
M Marincola, Francesco
Ceccarelli, Michele
Bedognetti, Davide
author_sort Mall, Raghvendra
collection PubMed
description A cancer immune phenotype characterized by an active T-helper 1 (Th1)/cytotoxic response is associated with responsiveness to immunotherapy and favorable prognosis across different tumors. However, in some cancers, such an intratumoral immune activation does not confer protection from progression or relapse. Defining mechanisms associated with immune evasion is imperative to refine stratification algorithms, to guide treatment decisions and to identify candidates for immune-targeted therapy. Molecular alterations governing mechanisms for immune exclusion are still largely unknown. The availability of large genomic datasets offers an opportunity to ascertain key determinants of differential intratumoral immune response. We follow a network-based protocol to identify transcription regulators (TRs) associated with poor immunologic antitumor activity. We use a consensus of four different pipelines consisting of two state-of-the-art gene regulatory network inference techniques, regularized gradient boosting machines and ARACNE to determine TR regulons, and three separate enrichment techniques, including fast gene set enrichment analysis, gene set variation analysis and virtual inference of protein activity by enriched regulon analysis to identify the most important TRs affecting immunologic antitumor activity. These TRs, referred to as master regulators (MRs), are unique to immune-silent and immune-active tumors, respectively. We validated the MRs coherently associated with the immune-silent phenotype across cancers in The Cancer Genome Atlas and a series of additional datasets in the Prediction of Clinical Outcomes from Genomic Profiles repository. A downstream analysis of MRs specific to the immune-silent phenotype resulted in the identification of several enriched candidate pathways, including NOTCH1, TGF- [Formula: see text] , Interleukin-1 and TNF- [Formula: see text] signaling pathways. TGFB1I1 emerged as one of the main negative immune modulators preventing the favorable effects of a Th1/cytotoxic response.
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spelling pubmed-85747202021-11-09 Network-based identification of key master regulators associated with an immune-silent cancer phenotype Mall, Raghvendra Saad, Mohamad Roelands, Jessica Rinchai, Darawan Kunji, Khalid Almeer, Hossam Hendrickx, Wouter M Marincola, Francesco Ceccarelli, Michele Bedognetti, Davide Brief Bioinform Problem Solving Protocol A cancer immune phenotype characterized by an active T-helper 1 (Th1)/cytotoxic response is associated with responsiveness to immunotherapy and favorable prognosis across different tumors. However, in some cancers, such an intratumoral immune activation does not confer protection from progression or relapse. Defining mechanisms associated with immune evasion is imperative to refine stratification algorithms, to guide treatment decisions and to identify candidates for immune-targeted therapy. Molecular alterations governing mechanisms for immune exclusion are still largely unknown. The availability of large genomic datasets offers an opportunity to ascertain key determinants of differential intratumoral immune response. We follow a network-based protocol to identify transcription regulators (TRs) associated with poor immunologic antitumor activity. We use a consensus of four different pipelines consisting of two state-of-the-art gene regulatory network inference techniques, regularized gradient boosting machines and ARACNE to determine TR regulons, and three separate enrichment techniques, including fast gene set enrichment analysis, gene set variation analysis and virtual inference of protein activity by enriched regulon analysis to identify the most important TRs affecting immunologic antitumor activity. These TRs, referred to as master regulators (MRs), are unique to immune-silent and immune-active tumors, respectively. We validated the MRs coherently associated with the immune-silent phenotype across cancers in The Cancer Genome Atlas and a series of additional datasets in the Prediction of Clinical Outcomes from Genomic Profiles repository. A downstream analysis of MRs specific to the immune-silent phenotype resulted in the identification of several enriched candidate pathways, including NOTCH1, TGF- [Formula: see text] , Interleukin-1 and TNF- [Formula: see text] signaling pathways. TGFB1I1 emerged as one of the main negative immune modulators preventing the favorable effects of a Th1/cytotoxic response. Oxford University Press 2021-05-13 /pmc/articles/PMC8574720/ /pubmed/33979427 http://dx.doi.org/10.1093/bib/bbab168 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Problem Solving Protocol
Mall, Raghvendra
Saad, Mohamad
Roelands, Jessica
Rinchai, Darawan
Kunji, Khalid
Almeer, Hossam
Hendrickx, Wouter
M Marincola, Francesco
Ceccarelli, Michele
Bedognetti, Davide
Network-based identification of key master regulators associated with an immune-silent cancer phenotype
title Network-based identification of key master regulators associated with an immune-silent cancer phenotype
title_full Network-based identification of key master regulators associated with an immune-silent cancer phenotype
title_fullStr Network-based identification of key master regulators associated with an immune-silent cancer phenotype
title_full_unstemmed Network-based identification of key master regulators associated with an immune-silent cancer phenotype
title_short Network-based identification of key master regulators associated with an immune-silent cancer phenotype
title_sort network-based identification of key master regulators associated with an immune-silent cancer phenotype
topic Problem Solving Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8574720/
https://www.ncbi.nlm.nih.gov/pubmed/33979427
http://dx.doi.org/10.1093/bib/bbab168
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