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Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer
The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer ce...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4368751/ https://www.ncbi.nlm.nih.gov/pubmed/25793737 http://dx.doi.org/10.1371/journal.pcbi.1004115 |
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author | Banerji, Christopher R. S. Severini, Simone Caldas, Carlos Teschendorff, Andrew E. |
author_facet | Banerji, Christopher R. S. Severini, Simone Caldas, Carlos Teschendorff, Andrew E. |
author_sort | Banerji, Christopher R. S. |
collection | PubMed |
description | The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample’s genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers. |
format | Online Article Text |
id | pubmed-4368751 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-43687512015-03-27 Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer Banerji, Christopher R. S. Severini, Simone Caldas, Carlos Teschendorff, Andrew E. PLoS Comput Biol Research Article The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample’s genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers. Public Library of Science 2015-03-20 /pmc/articles/PMC4368751/ /pubmed/25793737 http://dx.doi.org/10.1371/journal.pcbi.1004115 Text en © 2015 Banerji et al http://creativecommons.org/licenses/by/4.0/ 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 properly credited. |
spellingShingle | Research Article Banerji, Christopher R. S. Severini, Simone Caldas, Carlos Teschendorff, Andrew E. Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer |
title | Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer |
title_full | Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer |
title_fullStr | Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer |
title_full_unstemmed | Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer |
title_short | Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer |
title_sort | intra-tumour signalling entropy determines clinical outcome in breast and lung cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4368751/ https://www.ncbi.nlm.nih.gov/pubmed/25793737 http://dx.doi.org/10.1371/journal.pcbi.1004115 |
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