Abstract
Stromal-derived intratumoural heterogeneity (ITH) has been shown to undermine molecular stratification of patients into appropriate prognostic/predictive subgroups. Here, using several clinically relevant colorectal cancer (CRC) gene expression signatures, we assessed the susceptibility of these signatures to the confounding effects of ITH using gene expression microarray data obtained from multiple tumour regions of a cohort of 24 patients, including central tumour, the tumour invasive front and lymph node metastasis. Sample clustering alongside correlative assessment revealed variation in the ability of each signature to cluster samples according to patient-of-origin rather than region-of-origin within the multi-region dataset. Signatures focused on cancer-cell intrinsic gene expression were found to produce more clinically useful, patient-centred classifiers, as exemplified by the CRC intrinsic signature (CRIS), which robustly clustered samples by patient-of-origin rather than region-of-origin. These findings highlight the potential of cancer-cell intrinsic signatures to reliably stratify CRC patients by minimising the confounding effects of stromal-derived ITH.
Original language | English |
---|---|
Article number | 15657 |
Pages (from-to) | 15657 |
Journal | Nature Communications |
Volume | 8 |
DOIs | |
Publication status | Published - 31 May 2017 |
Fingerprint
Dive into the research topics of 'Cancer-cell intrinsic gene expression signatures overcome intratumoural heterogeneity bias in colorectal cancer patient classification'. Together they form a unique fingerprint.Student theses
-
Defining the clinical importance of epithelial-stroma-immune signalling in colorectal cancer using a molecular pathology approach
McCorry, A. (Author), Lawler, M. (Supervisor), Dunne, P. (Supervisor) & Salto-Tellez, M. (Supervisor), Jul 2021Student thesis: Doctoral Thesis › Doctor of Philosophy
File -
Developing computational methods to enhance understanding in glioma progression
Roddy, A. (Author), McArt, D. (Supervisor), Prise, K. (Supervisor) & Salto-Tellez, M. (Supervisor), Jul 2022Student thesis: Doctoral Thesis › Doctor of Philosophy
File
Profiles
-
Mark Lawler
- School of Medicine, Dentistry and Biomedical Sciences - Associate Pro-Vice-Chancellor and Professor of Digital Health
Person: Academic