title: Intra-tumour signalling entropy determines clinical outcome in breast and lung cancer.
abstract: 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.
authors: Statistical Cancer Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, London WC1E 6BT, UK; Department of Computer Science, University College London, London WC1E 6BT, UK; Centre of Mathematics and Physics in the Life Sciences and Experimental Biology, University College London, London WC1E 6BT, UK.
authors: Christopher R S
authors: CR
authors: Banerji
authors: Department of Computer Science, University College London, London WC1E 6BT, UK.
authors: Simone
authors: S
authors: Severini
authors: Breast Cancer Functional Genomics Laboratory, Cancer Research UK, Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, UK.
authors: Carlos
authors: C
authors: Caldas
authors: Statistical Cancer Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, London WC1E 6BT, UK; CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai Institute for Biological Sciences, 320 Yue Yang Road, Shanghai 200031, China.
authors: Andrew E
authors: AE
authors: Teschendorff
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D019295:Computational Biology; D019277:Entropy; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D011379:Prognosis; D015398:Signal Transduction / Q000502:physiology*; D016896:Treatment Outcome