Systematic evaluation of the prognostic impact and intratumour heterogeneity of clear cell renal cell carcinoma biomarkers

Abstract

Background: Candidate biomarkers have been identified for clear cell renal cell carcinoma (ccRCC) patients, but most have not been validated.

Objective: To validate published ccRCC prognostic biomarkers in an independent patient cohort and to assess intratumour heterogeneity (ITH) of the most promising markers to guide biomarker optimisation.

Design, setting, and participants: Cancer-specific survival (CSS) for each of 28 identified genetic or transcriptomic biomarkers was assessed in 350 ccRCC patients. ITH was interrogated in a multiregion biopsy data set of 10 ccRCCs.

Outcome measurements and statistical analysis: Biomarker association with CSS was analysed by univariate and multivariate analyses.

Results and limitations: A total of 17 of 28 biomarkers (TP53 mutations; amplifications of chromosomes 8q, 12, 20q11.21q13.32, and 20 and deletions of 4p, 9p, 9p21.3p24.1, and 22q; low EDNRB and TSPAN7 expression and six gene expression signatures) were validated as predictors of poor CSS in univariate analysis. Tumour stage and the ccB expression signature were the only independent predictors in multivariate analysis. ITH of the ccB signature was identified in 8 of 10 tumours. Several genetic alterations that were significant in univariate analysis were enriched, and chromosomal instability indices were increased in samples expressing the ccB signature. The study may be underpowered to validate low-prevalence biomarkers.

Conclusions: The ccB signature was the only independent prognostic biomarker. Enrichment of multiple poor prognosis genetic alterations in ccB samples indicated that several events may be required to establish this aggressive phenotype, catalysed in some tumours by chromosomal instability. Multiregion assessment may improve the precision of this biomarker.

Patient summary: We evaluated the ability of published biomarkers to predict the survival of patients with clear cell kidney cancer in an independent patient cohort. Only one molecular test adds prognostic information to routine clinical assessments. This marker showed good and poor prognosis results within most individual cancers. Future biomarkers need to consider variation within tumours to improve accuracy.

Keywords: Biomarker; Intratumour heterogeneity; Kidney cancer; Personalised medicine; Prognostic marker.

Fig. 1

Kaplan-Meier survival estimates for cancer-specific survival for clinical and genetic markers: (A) tumour stage; (B) Fuhrman grade; (C) BAP1 nonsynonymous (nonsyn) mutation status; (D) TP53 nonsyn mutation status; (E) chromosome (Chrom) 8q amplification (amp) status; (F) Chrom12 amp status; (G) Chrom20q focal amp status; (H) Chrom20 amp status; (I) Chrom4p deletion (del) status; (J) Chrom9p focal del status; (K) Chrom9p del status; (L) Chrom19 del status; (M) Chrom22q del status. WT = wild type.

Fig. 2

Kaplan-Meier survival estimates for cancer-specific survival for gene expression markers: (A) EDNRB expression levels; (B) TSPAN7 expression levels; (C) gene expression subgroup of patients, Kosari signature; (D) gene expression subgroup of patients, Zhao signature; (E) gene expression subgroup of patients, Lane signature; (F) gene expression subgroup of patients, ccA/ccB; (G) gene expression subgroup of patients, Beleut signature; (H) gene expression subgroup of patients according to tumour growth factor (TGF) β activity score.

Fig. 3

Heat map showing consensus non-negative matrix factorisation clustering analysis based on gene expression data of 103 ccA/ccB signature genes. Patient assignment to ccA and ccB prognostic subgroups is indicated by coloured bars at the top of the heat map. Coloured bars below the heat map depict the presence of poor prognosis genetic aberrations. The bar chart at the bottom of the figure represents the number of these genetic aberrations per patient. OR = odds ratio.

Fig. 4

(A) Comparison of the number of poor prognosis genetic aberrations per sample between ccA and ccB subgroups. Only aberrations that are enriched in the ccB subgroup were considered. (B) Box and whisker plot comparing median number of poor prognosis genetic aberrations between samples assigned to the ccA and the ccB group. (Wilcoxon test; p < 0.001). (C) Comparison of the number of number of genetic aberrations that did not pass univariate validation per sample between ccA and ccB subgroups. (D) Box plot and whisker plot showing the median number of genetic aberrations that did not pass univariate validation between ccA and ccB subgroups (Wilcoxon test; p = 0.138). (E) Box plot and whisker plot comparing weighted Genomic Instability Index (wGII) between ccA and ccB subgroups. wGII ≥0.2 is considered genomically unstable.

Fig. 5

Heterogeneity analysis of ccA/ccB expression profiles. The ccA or ccB profiles detected by consensus non-negative matrix factorisation clustering in a multiregion analysis data set from 10 clear cell renal cell carcinomas were mapped onto the phylogenetic trees of these tumours (adapted with permission from Nature Publishing Group [8]). Regional gene expression signatures were assigned to the dominant clones detected within the region. The minority clones detected in some regions in the original publication were omitted.