Cargando…
Profiling networks of distinct immune-cells in tumors
BACKGROUND: It is now clearly evident that cancer outcome and response to therapy is guided by diverse immune-cell activity in tumors. Presently, a key challenge is to comprehensively identify networks of distinct immune-cell signatures present in complex tissue, at higher-resolution and at various...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4932723/ https://www.ncbi.nlm.nih.gov/pubmed/27377892 http://dx.doi.org/10.1186/s12859-016-1141-3 |
_version_ | 1782441115933212672 |
---|---|
author | Clancy, Trevor Hovig, Eivind |
author_facet | Clancy, Trevor Hovig, Eivind |
author_sort | Clancy, Trevor |
collection | PubMed |
description | BACKGROUND: It is now clearly evident that cancer outcome and response to therapy is guided by diverse immune-cell activity in tumors. Presently, a key challenge is to comprehensively identify networks of distinct immune-cell signatures present in complex tissue, at higher-resolution and at various stages of differentiation, activation or function. This is particularly so for closely related immune-cells with diminutive, yet critical, differences. RESULTS: To predict networks of infiltrated distinct immune-cell phenotypes at higher resolution, we explored an integrated knowledge-based approach to select immune-cell signature genes integrating not only expression enrichment across immune-cells, but also an automatic capture of relevant immune-cell signature genes from the literature. This knowledge-based approach was integrated with resources of immune-cell specific protein networks, to define signature genes of distinct immune-cell phenotypes. We demonstrate the utility of this approach by profiling signatures of distinct immune-cells, and networks of immune-cells, from metastatic melanoma patients who had undergone chemotherapy. The resultant bioinformatics strategy complements immunohistochemistry from these tumors, and predicts both tumor-killing and immunosuppressive networks of distinct immune-cells in responders and non-responders, respectively. The approach is also shown to capture differences in the immune-cell networks of BRAF versus NRAS mutated metastatic melanomas, and the dynamic changes in resistance to targeted kinase inhibitors in MAPK signalling. CONCLUSIONS: This integrative bioinformatics approach demonstrates that capturing the protein network signatures and ratios of distinct immune-cell in the tumor microenvironment maybe an important factor in predicting response to therapy. This may serve as a computational strategy to define network signatures of distinct immune-cells to guide immuno-pathological discovery. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1141-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4932723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-49327232016-07-08 Profiling networks of distinct immune-cells in tumors Clancy, Trevor Hovig, Eivind BMC Bioinformatics Methodology Article BACKGROUND: It is now clearly evident that cancer outcome and response to therapy is guided by diverse immune-cell activity in tumors. Presently, a key challenge is to comprehensively identify networks of distinct immune-cell signatures present in complex tissue, at higher-resolution and at various stages of differentiation, activation or function. This is particularly so for closely related immune-cells with diminutive, yet critical, differences. RESULTS: To predict networks of infiltrated distinct immune-cell phenotypes at higher resolution, we explored an integrated knowledge-based approach to select immune-cell signature genes integrating not only expression enrichment across immune-cells, but also an automatic capture of relevant immune-cell signature genes from the literature. This knowledge-based approach was integrated with resources of immune-cell specific protein networks, to define signature genes of distinct immune-cell phenotypes. We demonstrate the utility of this approach by profiling signatures of distinct immune-cells, and networks of immune-cells, from metastatic melanoma patients who had undergone chemotherapy. The resultant bioinformatics strategy complements immunohistochemistry from these tumors, and predicts both tumor-killing and immunosuppressive networks of distinct immune-cells in responders and non-responders, respectively. The approach is also shown to capture differences in the immune-cell networks of BRAF versus NRAS mutated metastatic melanomas, and the dynamic changes in resistance to targeted kinase inhibitors in MAPK signalling. CONCLUSIONS: This integrative bioinformatics approach demonstrates that capturing the protein network signatures and ratios of distinct immune-cell in the tumor microenvironment maybe an important factor in predicting response to therapy. This may serve as a computational strategy to define network signatures of distinct immune-cells to guide immuno-pathological discovery. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1141-3) contains supplementary material, which is available to authorized users. BioMed Central 2016-07-04 /pmc/articles/PMC4932723/ /pubmed/27377892 http://dx.doi.org/10.1186/s12859-016-1141-3 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Clancy, Trevor Hovig, Eivind Profiling networks of distinct immune-cells in tumors |
title | Profiling networks of distinct immune-cells in tumors |
title_full | Profiling networks of distinct immune-cells in tumors |
title_fullStr | Profiling networks of distinct immune-cells in tumors |
title_full_unstemmed | Profiling networks of distinct immune-cells in tumors |
title_short | Profiling networks of distinct immune-cells in tumors |
title_sort | profiling networks of distinct immune-cells in tumors |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4932723/ https://www.ncbi.nlm.nih.gov/pubmed/27377892 http://dx.doi.org/10.1186/s12859-016-1141-3 |
work_keys_str_mv | AT clancytrevor profilingnetworksofdistinctimmunecellsintumors AT hovigeivind profilingnetworksofdistinctimmunecellsintumors |