RNA biomarkers associated with metastatic progression in prostate cancer: a multi-institutional high-throughput analysis of SChLAP1

Abstract

Background Improved clinical predictors for disease progression are needed for localized prostate cancer, where only a minority of patients experience poor outcomes. We undertake an unbiased large-scale analysis of genes associated with aggressive clinical course. Methods Prostate cancer samples, obtained from patients treated with radical prostatectomy at three academic institutions, were analyzed for gene expression using a clinical-grade, high-density Affymetrix GeneChip platform, encompassing >1 million genomic loci. Nomination of prognostic candidate genes was performed on a discovery cohort (n=545) and validated on three independent cohorts (n=463), totaling 1,008 patients. Molecular assays were performed in a CLIA-certified (Clinical Laboratory Improvement Amendments) laboratory facility. Multivariate analyses were performed for the primary endpoint of metastasis. The top prostate-specific gene was evaluated in urine samples from 230 patients using PCR. Findings Among all known genes, the long noncoding RNA SChLAP1 ranked first for elevated expression in patients with metastatic progression by receiver-operator-curve (ROC) area-under-the-curve (AUC) analyses. Of the top five prognostic genes, SChLAP1 was the only prostate-specific gene. Validation in three independent cohorts confirmed the prognostic value of SChLAP1 for metastasis. On multivariate modeling, SChLAP1 expression independently predicted metastasis within ten years (odds ratio (OR) = 2·45, 95% confidence interval (CI) 1·70 – 3·53), death within ten years (OR = 1·93, 95% CI 1·31 – 2·85), and biochemical recurrence within five years (OR = 1·76, 95% CI 1·28 – 2·41) with odds ratios comparable to Gleason score. Evaluation of SChLAP1 expression in 230 urine sediment samples with either biopsy-confirmed cancer or biopsy-negative tissue demonstrated increased incidence and expression of SChLAP1 RNA in patients at a higher risk for disease progression. Interpretation We perform the largest high-throughput, unbiased study of prostate cancer prognostic biomarkers to date and discover SChLAP1 as one of the best genes for the prediction of metastasis. We validate SChLAP1 extensively using a clinical-grade assay in a CLIA-certified laboratory. We show feasibility of a non-invasive urine test for SChLAP1, and suggest that SChLAP1 represents a very promising biomarker for aggressive clinical course. Funding Prostate Cancer Foundation, National Institutes of Health, Department of Defense, Early Detection Research Network, Doris Duke Charitable Foundation, and Howard Hughes Medical Institute.

High-Throughput Transcriptomic Analysis Nominates Proteasomal Genes As Age Specific Biomarkers and Therapeutic Targets in Prostate Cancer

“…17 Patients from the CC cohort were obtained from a case-control study in which 2317 conservatively treated high-risk RP patients who did not receive adjuvant therapy were sampled to achieve a 3:1 ratio for nonmetastatic versus metastatic progression, for a total of 183 samples. 18 RNA extraction and microarray hybridization were performed using clinicalgrade techniques in a Clinical Laboratory Improvement Amendmentscertified laboratory facility (GenomeDx Biosciences, San Diego, CA, USA). The normalization and summarization of the microarray samples were done with the Single Channel Array Normalization algorithm with quality control performed as described previously.…”

“…The normalization and summarization of the microarray samples were done with the Single Channel Array Normalization algorithm with quality control performed as described previously. [16][17][18][19] Gene expression for each gene was calculated using the Affymetrix Core level summaries for annotated genes. Microarray data are available on the NCBI Gene Expression Omnibus as accession numbers GSE46691 (MCI), GSE62116 (MCII) and GSE62667 (CC).…”

“…21,22 The performance of a gene was evaluated using a REM comparing the 10-year metastasis rate in high versus low expression also using the R 'meta' package. Pooled multivariate logistic regression analysis was performed, with age analyzed per year, Gleason and PSA stratified into high/low (Gleason 8-10/ ⩽ 7, PSA48/ ⩽ 8), and stratification by cohort as described previously 18 to account for baseline differences, both measured and unmeasured, between cohorts. Kaplan-Meier curves were generated and a P-value was calculated using the Log-rank test.…”

Application of a Clinical Whole-Transcriptome Assay for Staging and Prognosis of Prostate Cancer Diagnosed in Needle Core Biopsy Specimens

“…15 Use of this expression array protocol also allows for evaluation of various combinations of PSRs, and thus permits one to simultaneously assess other expression marker panels and data sets, 10,16,17 as well as evaluate new ones. To explore the transcriptomic differences between prostate biopsy and matched RP samples, and evaluate the effects of heterogeneity, we compared transcriptomic data generated using Human Exon arrays obtained from 158 different prostate tissue samples from 33 patients seen at three different institutions.…”

Prostate cancer markers: An update

“…Recent study has utilized genomic testing as a biomarker for aggressive prostate cancer. A recent discovery of the long noncoding RNA SChLAP1 in the prostate has provided a novel biomarker that not only adds to the ability to identify prostate cancer, but also to conventional risk stratification (73). SChLAP1 has been clinically validated for the prognosis of aggressive prostate cancer and integration of genomic tests may advance the diagnosis of prostate cancer through early identification of high-risk patients.…”