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BMC Med. 2017 Jun 26;15(1):118. doi: 10.1186/s12916-017-0874-9.

Overcoming intratumoural heterogeneity for reproducible molecular risk stratification: a case study in advanced kidney cancer.

Author information

1
MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
2
Present Address: Vanderbilt University School of Medicine, Vanderbilt University, Nashville, Tennessee, USA.
3
Scottish Collaboration On Translational Research into Renal Cell Cancer (SCOTRRCC), Scotland, UK.
4
Present Address: Academic Urology Group, University of Cambridge, Box 43, Addenbrooke's Hospital, Cambridge Biomedical Campus, Hill's Road, Cambridge, CB2 0QQ, UK.
5
School of Medicine, University of St Andrews, St Andrews, Fife, KY16 9TF, UK.
6
Department of Pathology, Western General Hospital, Edinburgh, EH4 2XU, UK.
7
Barts Cancer Institute, Experimental Cancer Medicine Centre, Queen Mary University of London, London, EC1M 6BQ, UK.
8
MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK. ian.overton@ed.ac.uk.
9
Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, EH16 4UX, UK. ian.overton@ed.ac.uk.

Abstract

BACKGROUND:

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.

KEYWORDS:

Cancer; Prognostic markers; Renal cell carcinoma; Tumour biomarkers; Tumour heterogeneity

PMID:
28648142
PMCID:
PMC5483837
DOI:
10.1186/s12916-017-0874-9
[Indexed for MEDLINE]
Free PMC Article

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