Identification and validation of NFIA as a novel prognostic marker in renal cell carcinoma.

Prognostic tools are an essential component of the clinical management of patients with renal cell carcinoma (RCC). Although tumour stage and grade can provide important information, they fail to consider patient- and tumour-specific biology. In this study, we set out to find a novel molecular marker of RCC by using hepatocyte nuclear factor 4A (HNF4A), a transcription factor implicated in RCC progression and malignancy, as a blueprint. Through transcriptomic analyses, we show that the nuclear factor I A (NFIA)-driven transcription network is active in primary RCC and that higher levels of NFIA confer a survival benefit. We validate our findings using immunohistochemical staining and analysis of a 363-patient tissue microarray (TMA), showing for the first time that NFIA can independently predict poor cancer-specific survival in clear cell RCC (ccRCC) patients (hazard ratio = 0.46, 95% CI = 0.24-0.85, p value = 0.014). Furthermore, we confirm the association of HNF4A with higher grades and stages in ccRCC in our TMA cohort. We present novel data that show HNF4A protein expression does not confer favourable prognosis in papillary RCC, confirming our survival analysis with publicly available HNF4A RNA expression data. Further work is required to elucidate the functional role of NFIA in RCC as well as the testing of these markers on patient material from diverse multi-centre cohorts, to establish their value for the prognostication of RCC.

Size-based isolation and detection of renal carcinoma cells from whole blood.

Renal cell carcinoma (RCC) is a tumour type with an indolent growth pattern and rather vague symptoms. The present study developed a platform for liquid biopsy of RCC based upon the isolation of circulating tumour cells (CTCs). Founded on the observation that RCC tumour cells are considerably larger than leucocytes, the present study employed a microfluidics-based system for isolation of RCC CTCs from whole blood. Using this system, it was revealed that 66% of spiked-in RCC tumour cells could be retrieved using this approach. Furthermore, it was demonstrated that these cells could be molecularly detected with digital PCR using RCC-specific genes down to one tumour cell, whilst avoiding detection in samples lacking tumour cells. Finally, subtype specific transcripts were identified to distinguish the different subtypes of RCC, which were then validated in patient tumours. The present study established a novel workflow for the isolation of RCC CTCs from whole blood, with the potential to detect these cells irrespective of subtype.