The Transcriptional Landscape of Radiation-Treated Human Prostate Cancer: Analysis of a Prospective Tissue Cohort

Abstract

The resistance of prostate cancer to radiation therapy (RT) is a significant clinical issue and still largely unable to be guided by patient-specific molecular characteristics. The present study describes the gene expression changes induced in response to RT in human prostate tissue obtained from a prospective tissue acquisition study designed for radiobiology research. A prospective cohort of 5 men with intermediate-risk and clinically localized tumors were treated with high-dose-rate brachytherapy with 2×10-Gy fractions. Image-guided transperineal biopsy specimens were taken immediately before and 14days after the first high-dose-rate brachytherapy fraction. Using genome-wide 3' RNA sequencing on total RNA extracted from 10 biopsy specimens, we obtained quantitative expression data for a median of 13,244 genes. We computed the fold-change information for each gene and extracted high-confidence lists of transcripts with either increased or decreased expression (≥1.5-fold) after radiation in ≥4 of the 5 patients. Several gene ontology analyses were then used to identify functionally enriched pathways. The predominant change in response to RT was elevation of the transcript levels, including that of DNA damage binding protein 2 and p21, and collagens, laminins, and integrins. We observed strong upregulation of the p53 pathway, without observable dysregulation of p53 itself. Interstitial remodeling, extracellular matrix proteins, and focal adhesion pathways were also strongly upregulated, as was inflammation. Functional network analysis showed clustering of the changes inherent in apoptosis and programmed cell death, extracellular matrix organization, and immune regulation. In the present prospective study of matched clinical tissues, we successfully recognized known radiation-sensitive transcriptional pathways and identified numerous other novel and significantly altered genes with no current association with RT. These data could be informative in the development of future personalized therapeutic agents.