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

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.We acknowledge financial support from the Royal Society of Edinburgh Scottish Government Fellowship co-funded 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) and Melville Trust (AL)

Characterisation of male breast cancer: a descriptive biomarker study from a large patient series

Male breast cancer (MBC) is rare. We assembled 446 MBCs on tissue microarrays and assessed clinicopathological information, together with data from 15 published studies, totalling 1984 cases. By immunohistochemistry we investigated 14 biomarkers (ERα, ERβ1, ERβ2, ERβ5, PR, AR, Bcl-2, HER2, p53, E-cadherin, Ki67, survivin, prolactin, FOXA1) for survival impact. The main histological subtype in our cohort and combined analyses was ductal (81%, 83%), grade 2; (40%, 44%), respectively. Cases were predominantly ERα (84%, 82%) and PR positive (74%, 71%), respectively, with HER2 expression being infrequent (2%, 10%), respectively. In our cohort, advanced age (>67) was the strongest predictor of overall (OS) and disease free survival (DFS) (p = 0.00001; p = 0.01, respectively). Node positivity negatively impacted DFS (p = 0.04). FOXA1 p = 0.005) and AR p = 0.009) were both positively prognostic for DFS, remaining upon multivariate analysis. Network analysis showed ERα, AR and FOXA1 significantly correlated. In summary, the principle phenotype of MBC was luminal A, ductal, grade 2. In ERα+ MBC, only AR had prognostic significance, suggesting AR blockade could be employed therapeutically

Expression of glycolytic enzymes in ovarian cancers and evaluation of the glycolytic pathway as a strategy for ovarian cancer treatment

Table S2. Spearman correlation of the expression of four glycolytic enzymes in a cohort of 380 ovarian cancers. Spearman rho correlation values (top value) along with the respective adjusted P value (bottom value) of statistically significant correlations thresholded at FDR P < 0.01 are summarised. (DOCX 21 kb

Sunitinib Treatment Exacerbates intratumoral Heterogeneity in Metastatic Renal Cancer

This work was supported by the Chief Scientist Office, Scotland (ETM37; to G.D. Stewart, A.C.P. Riddick, M. Aitchison, and D.J. Harrison), Cancer Research UK (Experimental Cancer Medicine Centre; to T. Powles, London and D.J. Harrison, Edinburgh), Medical Research Council (to A. Laird and D.J. Harrison), Royal College of Surgeons of Edinburgh (to A. Laird), Melville Trust (to A. Laird), Medical Research Council (MC_UU_12018/25; to I.M. Overton), Royal Society of Edinburgh Scottish Government Fellowship cofunded by Marie Curie Actions (to I.M. Overton), Renal Cancer Research Fund (to G.D. Stewart), Kidney Cancer Scotland (to G.D. Stewart) and an educational grant from Pfizer (to T. Powles).Purpose: The aim of this study was to investigate the effect of VEGF targeted therapy (sunitinib) on molecular intratumoral heterogeneity (ITH) in metastatic clear cell renal cancer (mccRCC). Experimental design: Multiple tumor samples (n=187 samples) were taken from the primary renal tumors of mccRCC patients who were sunitinib treated (n=23, SuMR clinical trial) or untreated (n=23, SCOTRRCC study). ITH of pathological grade, DNA (aCGH), mRNA (Illumina Beadarray) and candidate proteins (reverse phase protein array) were evaluated using unsupervised and supervised analyses (driver mutations, hypoxia and stromal related genes). ITH was analysed using intratumoral protein variance distributions and distribution of individual patient aCGH and gene expression clustering. Results: Tumor grade heterogeneity was greater in treated compared to untreated tumors (P=0.002). In unsupervised analysis, sunitinib therapy was not associated with increased ITH in DNA or mRNA. However, there was an increase in ITH for the driver mutation gene signature (DNA and mRNA) as well as increasing variability of protein expression with treatment (p<0.05). Despite this variability, significant chromosomal and transcript changes to key targets of sunitinib, such as VHL, PBRM1 and CAIX, occurred in the treated samples. Conclusions: These findings suggest that sunitinib treatment has significant effects on the expression and ITH of key tumor and treatment specific genes/proteins in mccRCC. The results, based on primary tumor analysis, do not support the hypothesis that resistant clones are selected and predominate following targeted therapy.PostprintPeer reviewe