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Gene Expression Profile of Stromal Factors in Cancer-Associated Fibroblasts from Prostate Cancer

ABSTRACT: Recent investigations point at the stromal microenvironment to assess additional diagnostic information and provide new therapeutic targets in cancer. The aim of the study was to contribute to the characterization of the phenotype of cancer-associated fibroblasts (CAFs) in prostate cancer (PCa) compared with normal prostate-associated fibroblasts (NAFs) and fibroblasts from benign prostatic hyperplasia (BPH). Three patient populations were prospectively recruited: 23 patients with new localized PCa, 14 patients with advanced PCa treated with androgenic deprivation therapy (ADT), and 7 patients with BPH. Gene expression of 20 stroma-derived factors, including the androgen receptor (AR), chaperones (HSPA1A and HSF1), growth factors (FGF2, FGF7, FGF10, HGF, PDGFB, and TGFβ), proteins implicated in invasion (MMP2, MMP9, and MMP11), inflammation (IL6, IL17RB, NFκB, and STAT3), and in-stroma/epithelium interaction (CDH11, CXCL12, CXCL14, and FAP), was evaluated. Localized PCa CAFs showed a significant higher expression of FGF7, IL6, MMP2, and MMP11 compared with NAFs or IL17RB compared with BPH fibroblasts, but significantly lower expression of FGF10 and IL17RB compared with NAFs or CXCL14 compared with BPH fibroblasts. In addition, CAFs from ADT-resistant PCa showed significantly higher MMP11 and NFκB but significant lower TGFβ expression compared with CAFs from ADT-sensitive tumors. Our results contribute to defining the CAFs phenotypes associated to PCa progression, which may contribute to the diagnosis and design of alternative therapies in PCa.

SUBMITTER: Eiro N

PROVIDER: S-EPMC9325062 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundClinical grading systems using clinical features alongside nomograms lack precision in guiding treatment decisions in prostate cancer (PCa). There is a critical need for identification of biomarkers that can more accurately stratify patients with primary PCa.ObjectiveTo identify a robust prognostic signature to better distinguish indolent from aggressive prostate cancer (PCa).Design, setting, and participantsTo develop the signature, whole-genome and whole-transcriptome sequencing was conducted on five PCa patient-derived xenograft (PDX) models collected from independent foci of a single primary tumor and exhibiting variable metastatic phenotypes. Multiple independent clinical cohorts including an intermediate-risk cohort were used to validate the biomarkers.Outcome measurements and statistical analysisThe outcome measurement defining aggressive PCa was metastasis following radical prostatectomy. A generalized linear model with lasso regularization was used to build a 93-gene stroma-derived metastasis signature (SDMS). The SDMS association with metastasis was assessed using a Wilcoxon rank-sum test. Performance was evaluated using the area under the curve (AUC) for the receiver operating characteristic, and Kaplan-Meier curves. Univariable and multivariable regression models were used to compare the SDMS alongside clinicopathological variables and reported signatures. AUC was assessed to determine if SDMS is additive or synergistic to previously reported signatures.Results and limitationsA close association between stromal gene expression and metastatic phenotype was observed. Accordingly, the SDMS was modeled and validated in multiple independent clinical cohorts. Patients with higher SDMS scores were found to have worse prognosis. Furthermore, SDMS was an independent prognostic factor, can stratify risk in intermediate-risk PCa, and can improve the performance of other previously reported signatures.ConclusionsProfiling of stromal gene expression led to development of an SDMS that was validated as independently prognostic for the metastatic potential of prostate tumors.Patient summaryOur stroma-derived metastasis signature can predict the metastatic potential of early stage disease and will strengthen decisions regarding selection of active surveillance versus surgery and/or radiation therapy for prostate cancer patients. Furthermore, profiling of stroma cells should be more consistent than profiling of diverse cellular populations of heterogeneous tumors.

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Gene Expression Profile of Stromal Factors in Cancer-Associated Fibroblasts from Prostate Cancer

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