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论文题目: Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer
英文论文题目: Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer
第一作者: Banerji, CRS; Severini, S; Caldas, C; Teschendorff, AE
英文第一作者: Banerji, CRS; Severini, S; Caldas, C; Teschendorff, AE
联系作者: Banerji, CRS (reprint author), UCL, Stat Canc Genom, UCL Canc Inst, Paul OGorman Bldg, London WC1E 6BT, England.
英文联系作者: Banerji, CRS (reprint author), UCL, Stat Canc Genom, UCL Canc Inst, Paul OGorman Bldg, London WC1E 6BT, England.
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发表年度: 2015
卷: 11
期: 3
页码: -
摘要: 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.
英文摘要: 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.
刊物名称: PLOS COMPUTATIONAL BIOLOGY
英文刊物名称: PLOS COMPUTATIONAL BIOLOGY
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学科: Biochemistry & Molecular Biology; Mathematical & Computational Biology
英文学科: Biochemistry & Molecular Biology; Mathematical & Computational Biology
影响因子: 4.62
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论文类别: Article
英文论文类别: Article
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