Show simple item record

Files in this item

Item metadata

dc.contributor.authorLubbock, Alexander L. R.
dc.contributor.authorStewart, Grant D.
dc.contributor.authorO'Mahoney, Fiach C.
dc.contributor.authorLaird, Alexander
dc.contributor.authorMullen, Peter
dc.contributor.authorO'Donnell, Marie
dc.contributor.authorPowles, Thomas
dc.contributor.authorHarrison, David J.
dc.contributor.authorOverton, Ian M.
dc.date.accessioned2017-06-27T10:30:13Z
dc.date.available2017-06-27T10:30:13Z
dc.date.issued2017-06-26
dc.identifier.citationLubbock , A L R , Stewart , G D , O'Mahoney , F C , Laird , A , Mullen , P , O'Donnell , M , Powles , T , Harrison , D J & Overton , I M 2017 , ' Overcoming intratumoural heterogeneity for reproducible molecular risk stratification : a case study in advanced kidney cancer ' BMC Medicine , vol. 15 , 118 . DOI: 10.1186/s12916-017-0874-9en
dc.identifier.issn1741-7015
dc.identifier.otherPURE: 250007022
dc.identifier.otherPURE UUID: b914d85c-787c-4aaf-9723-a91c97f803b1
dc.identifier.otherScopus: 85021207534
dc.identifier.urihttp://hdl.handle.net/10023/11092
dc.descriptionWe acknowledge financial support from the Royal Society of Edinburgh Scottish Government Fellowship cofunded by Marie Curie Actions (IMO), Carnegie Trust (50115; IMO, DJH, GDS), IGMM DTF (IMO, GDS), Medical Research Council (MC_UU_12018/25; IMO), Chief Scientist Office Scotland (ETM37; GDS, DJH), Cancer Research UK (Experimental Medicine Centre; TP, DJH), Renal Cancer Research Fund (GDS), Kidney Cancer Scotland (GDS), MRC Clinical Training Fellowship (AL), RCSEd Robertson Trust (AL), Melville Trust (AL).en
dc.description.abstractBackground: Metastatic clear cell renal cell cancer (mccRCC) portends a poor prognosis and urgently requires better clinical tools for prognostication as well as for prediction of response to treatment. Considerable investment in molecular risk stratification has sought to overcome the performance ceiling encountered by methods restricted to traditional clinical parameters. However, replication of results has proven challenging, and intratumoural heterogeneity (ITH) may confound attempts at tissue-based stratification. Methods: We investigated the influence of confounding ITH on the performance of a novel molecular prognostic model, enabled by pathologist-guided multiregion sampling (n = 183) of geographically separated mccRCC cohorts from the SuMR trial (development, n = 22) and the SCOTRRCC study (validation, n = 22). Tumour protein levels quantified by reverse phase protein array (RPPA) were investigated alongside clinical variables. Regularised wrapper selection identified features for Cox multivariate analysis with overall survival as the primary endpoint. Results: The optimal subset of variables in the final stratification model consisted of N-cadherin, EPCAM, Age, mTOR (NEAT). Risk groups from NEAT had a markedly different prognosis in the validation cohort (log-rank p = 7.62 × 10−7; hazard ratio (HR) 37.9, 95% confidence interval 4.1–353.8) and 2-year survival rates (accuracy = 82%, Matthews correlation coefficient = 0.62). Comparisons with established clinico-pathological scores suggest favourable performance for NEAT (Net reclassification improvement 7.1% vs International Metastatic Database Consortium score, 25.4% vs Memorial Sloan Kettering Cancer Center score). Limitations include the relatively small cohorts and associated wide confidence intervals on predictive performance. Our multiregion sampling approach enabled investigation of NEAT validation when limiting the number of samples analysed per tumour, which significantly degraded performance. Indeed, sample selection could change risk group assignment for 64% of patients, and prognostication with one sample per patient performed only slightly better than random expectation (median logHR = 0.109). Low grade tissue was associated with 3.5-fold greater variation in predicted risk than high grade (p = 0.044). Conclusions:  This case study in mccRCC quantitatively demonstrates the critical importance of tumour sampling for the success of molecular biomarker studies research where ITH is a factor. The NEAT model shows promise for mccRCC prognostication and warrants follow-up in larger cohorts. Our work evidences actionable parameters to guide sample collection (tumour coverage, size, grade) to inform the development of reproducible molecular risk stratification methods.en
dc.format.extent12en
dc.language.isoeng
dc.relation.ispartofBMC Medicineen
dc.rights© The Author(s). 2017 Open Access. This 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.en
dc.subjectCanceren
dc.subjectTumour heterogeneityen
dc.subjectPrognostic markersen
dc.subjectRenal cell carcinomaen
dc.subjectTumour biomarkersen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectDASen
dc.subject.lccRC0254en
dc.titleOvercoming intratumoural heterogeneity for reproducible molecular risk stratification : a case study in advanced kidney canceren
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Cellular Medicine Divisionen
dc.identifier.doihttps://doi.org/10.1186/s12916-017-0874-9
dc.description.statusPeer revieweden


The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record