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Transcriptome-wide gene expression analysis of Formalin-Fixed Paraffin-Embedded (FFPE) biopsies of prostate cancer (PCa)

ABSTRACT: Clinical manifestation of PCa is highly variable. Aggressive tumors require radical treatment, while clinically non-significant ones may be suitable for active surveillance. We have previously developed the prognostic ProstaTrend signature mainly on prostatectomy specimens by application of transcriptome‐wide microarray and RNA-sequencing (RNA-Seq) analyses. We used a cohort of 185 tumor specimens obtained from FFPE biopsies for RNA-Seq to facilitate the application of ProstaTrend at the beginning of routine PCa diagnostic. All patients of the FFPE biopsy cohort were treated by radical prostatectomy (RPx) and median follow-up for biochemical recurrence (BCR) was 9 years. Based on the transcriptome data of the FFPE biopsies, we filtered ProstaTrend for genes susceptible to FFPE-associated degradation via regression analysis. ProstaTrend was additionally restricted to genes with concordant prognostic effects in the RNA-Seq TCGA prostate adenocarcinoma (PRAD) cohort to ensure robust and broad applicability. The prognostic relevance of the refined Transcriptomic Risk Score (TRS) was analyzed by Kaplan-Meier curves, Cox-regression models in our FFPE-biopsy cohort and 9 other public datasets from PCa patients with BCR as primary endpoint. The TRS based on the revised ProstaTrend signature, which included 204 genes, was significantly associated with BCR in the FFPE biopsy cohort (Cox-regression p-value <0.001). The TRS retained prognostic relevance when adjusted for Gleason Score (GS). We confirmed a significant association with BCR in 9 independent cohorts with a total of 1109 PCa patients. Comparison of the prognostic performance of the TRS with 17 other prognostically relevant PCa panels revealed that the revised ProstaTrend was among the best-ranked panels.

ORGANISM(S): Homo sapiens

PROVIDER: GSE220095 | GEO | 2023/09/08

REPOSITORIES: GEO

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Project description:Accurate estimation of recurrence risk is needed for optimal treatment of clinically localized prostate cancer (PCa) patients. We combined classical clinicopathological predictors of biochemical recurrence (BCR) and molecular markers into a new risk score for predicting BCR after radical prostatectomy (RP). A retrospective study which included 122 PCa patients who attended our department between 2000 and 2007 was conducted. Total RNA was isolated from formalin-fixed paraffin embedded PCa tissue. Gene expression patterns were analyzed in 21 selected samples from 7 localized, 6 locally advanced, and 8 metastatic PCa patients using Illumina microarrays. Expression levels of 41 genes were validated by quantitative PCR in an independent retrospective series of 101 patients with ≥ 10 years follow-up who underwent RP for clinically localized PCa. Univariate and multivariate logistic regression analysis were used to identify individual predictors of BCR. A risk score (RS) for predicting BCR including clinicopathological and gene expression data was developed and Kaplan-Meier curves were generated. Interaction networks between the genes of the model were built by GeneMANIA Cytoscape plugin. In a median follow-up of 120 months (range 3-190), 37 patients developed BCR (36.6%). Expression levels of 7,930 transcripts differed between clinically localized and locally advanced-metastatic PCa groups (FDR<0.1). A set of 41 genes were validated by quantitative PCR. Regression analysis showed that expression of ASF1B and MCL1 as well as Gleason score, extracapsular extension, seminal vesicle invasion and positive margins were independent prognostic factors of BCR. A RS generated using these gene expression and clinicopathological variables was able to discriminate between two groups of patients with a significantly different probability of BCR (HR 6.24; CI 3.23-12.4, p<0.01), improving the individual discriminative performance of each of these variables on their own. Direct interactions between the two genes of the model were not found. In conclusion, identification of new gene expression patterns associated with a high probability of BCR in clinically localized PCa patients treated with RP, and their combination with clinicopathological variables in a robust, easy-to-use and reliable classifier may contribute to improve PCa risk stratification and, consequently, to tailor treatment and surveillance strategies in these patients.

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Transcriptome-wide gene expression analysis of Formalin-Fixed Paraffin-Embedded (FFPE) biopsies of prostate cancer (PCa)

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