A prognostic microRNA-based signature for localized clear cell renal cell carcinoma: the Bio-miR study

Abstract

Background: Two thirds of renal cell carcinoma (RCC) patients have localized disease at diagnosis. A significant proportion of these patients will relapse. There is a need for prognostic biomarkers to improve risk-stratification and specific treatments for patients that relapse. The objective of this study is to determine the clinical utility of microRNA signatures as prognostic biomarkers in localized clear cell RCC (ccRCC) and propose new therapeutic targets in patients with a high-risk of relapse.

Patients and methods: The microRNA profiles from a discovery cohort of 71 T1-T2 ccRCC patients (n = 88) were analyzed using microarrays. MicroRNAs prognostic value was established, and a microRNAs signature predicting relapse for T1b-T3 disease was defined. Independent validation was carried out by qPCR in cohorts from UK (n = 75) and Spain (n = 180), and the TCGA cohort (n = 175). In the Spanish validation cohort, proteomics experiments were done. Proteins were extracted from FFPE tissue and analyzed using by data-independent acquisition mass spectrometry. Additionally, ccRCC TCGA RNA-seq data was also analyzed. Both protein and RNA-seq data was analyzed using Significance Analysis of Micorarrays (SAM) and probabilistic graphical models, which allow the identification of relevant biological processes between low and high-risk tumors.

Results: A 9-microRNAs signature, Bio-miR, classified patients into low- and high-risk with disease-free survival (DFS) at 5 years of 87.12 vs. 54.17% respectively (p = 0.0086, HR = 3.58, 95%CI: 1.37-8.3). Results were confirmed in the validation cohorts with 5-year DFS rates of 94% vs. 62% in the UK cohort (HR = 7.14, p = 0.001), 82.9% vs. 58.7% in the Spanish cohort (HR = 2.46, p = 0.0013), and 5-year overall survival rates of 72.7% vs. 44.5% in the TCGA cohort (HR = 2.43, p = 0.0012). Among low-risk patients according to adjuvant immunotherapy clinical trial criteria, Bio-miR identified a high-risk group. Maybe those patients ought to be considered to receive adjuvant therapy. Proteins overexpressed in the high-risk group were mainly related to focal adhesion, serine and inositol metabolism, and angiogenesis. Probabilistic graphical models defined eight functional nodes related to specific biological processes. Differences between low- and high-risk tumors were detected in complement activation and translation functional nodes. In ccRCC TCGA cohort, 676 genes were differentially expressed between low and high-risk patients, mainly related to complement activation, adhesion, and chemokine and cytokine cascades. In this case, probabilistic graphical models defined ten functional nodes. Calcium binding, membrane, adhesion, extracellular matrix, blood microparticle, inflammatory response and immune response had higher functional node activity, and metabolism node, containing genes related to retinol and xenobiotic and CYP450 metabolism, had lower activity in the high-risk group.

Conclusions: Bio-miR dichotomizes ccRCC patients with non-metastatic disease into those with low- and high-risk of relapse. This has implications for treatment and follow-up, identifying patients most likely to benefit from adjuvant treatment in clinical trials, preventing unnecessary exposure to side-effects, and providing health economics benefits. Additionally, promising therapeutic targets, as angiogenesis, immune response, metabolism, or complement activation, were found deregulated in high-risk ccRCC patients defined by Bio-miR. These findings may be useful to select patients for tailored, molecularly-driven clinical trials. Identifying which patients with kidney cancer are most at risk of their cancer coming back after surgery is critical, so that they can be prioritized for early treatment. We have identified a combination of biomarkers present in the cancer tissue (called BiomiR) which can help to do this.