Stromal Gene Expression is Predictive for Metastatic Primary Prostate Cancer

Abstract

Background: Clinical 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.

Objective: To identify a robust prognostic signature to better distinguish indolent from aggressive prostate cancer (PCa).

Design, setting, and participants: To 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 analysis: The 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 limitations: A 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.

Conclusions: Profiling of stromal gene expression led to development of an SDMS that was validated as independently prognostic for the metastatic potential of prostate tumors.

Patient summary: Our 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.

Keywords: Genomic profiling; Prognostic biomarkers; Prostate cancer metastasis; RNA sequencing; Stromal gene.

Fig. 1 –

Workflow overview. Comprehensive genomic and transcriptomic profiling led to the discovery of a stromal gene signature. This signature was further validated in multiple large independent clinical cohorts, including a cohort of patients with intermediate-risk tumors.

Fig. 2 –

Genomic profiling revealed limited heterogeneity among multiple LTL-313 patient-derived xenograft (PDX) models. (A) Matrix showing the genomic alterations identified in each PDX model. The asterisk before each gene name indicates an alteration event that was not supported by the transcriptome sequencing data. For example, the RB1 gene had a single copy number loss but no change in expression; the FAT2 allele contained a nonsynonymous single nucleotide variant (SNV) that was not expressed. (B) The number of unique, shared (by any two models), and common (to all five models) chromosome breakpoints among the five PDX models. The phylogenetic tree was constructed based on the chromosome breakpoints, and demonstrates the inferred evolutionary relationships among PDX models. It shows the copy number profile similarity within each PDX model. (C) The numbers of unique, shared, and common genomic (black bars) and nonsilent (reddish bars) mutations among PDX models. The phylogenetic tree was built based on whole-genome mutations. Again, it did not separate the nonmetastatic model (LTL-313B) from the metastatic models. (D) Reconstruction of the subclonal composition over time was inferred from the mutational profiles of each PDX model. Eight subclones (a-h) were identified; the percentage range indicates the prevalence of each subclone within each PDX model. The pie chart illustrates the relative ratio of each subclone proportion across the five PDX models. In summary, there was no association between the differences in subclonal composition and the differences in metastatic phenotypes of LTL-313 PDX models.

Fig. 3 –

Unsupervised hierarchical clustering based on protein-coding gene expression showed the distinct separation between the stromal transcriptomes of nonmetastatic and metastatic xenografts. (A) Unsupervised hierarchical clustering of total human tumor cell gene expression and 4361 top differentially expressed human tumor cell genes. (B) Subcluster heatmap showing the correlation among patient-derived xenograft (PDX) models. (C) Unsupervised hierarchical clustering of total murine stromal gene expression and 3471 top differentially expressed murine stromal genes. (D) Subcluster heatmap showing the correlation among PDX models. The nonmetastatic xenografts (LTL-313B and LTL-418) were separated from the metastatic xenografts by the top differentially expressed murine stromal genes. Bootstrap probability values are indicated in blue. Nonmetastatic PDX models are highlighted in red.

Fig. 4 –

Area under the curve (AUC) and Kaplan-Meier survival analysis for the stroma-derived metastasis signature (SDMS) in multiple independent clinical cohorts demonstrated that the SDMS can distinguish indolent primary prostate tumors from those with metastatic potential. (A) Receiver operating characteristic curves show that the SDMS can separate patients with metastatic potential from patients with indolent tumors: Mayo Clinic (MC) II, AUC = 0.77; Cleveland Clinic Foundation (CCF), AUC = 0.83; Johns Hopkins Medical Institutions (JHMI) AUC = 0.62. (B) Kaplan-Meier curves show that patients from the high-score group, based on a median split of SDMS scores within each cohort (low/high), have worse outcome in terms of metastasis (Mets)-free survival: MC II, p<0.001; CCF, p<0.001; JHMI, p<0.005.

Fig. 5 –

Kaplan-Meier survival analysis for the stroma-derived metastasis signature (SDMS) for patients with intermediate-risk Gleason 7 tumors and improvement in prognostic performance of previously validated prognostic signatures. (A) Kaplan-Meier curves show that intermediate-risk patients with high SDMS scores, based on a cohort median split (low/high), have worse outcome. (B) Addition of the SDMS to previously validated signatures improves their predictive power. Combined logistic regression models were trained in the Mayo Clinic I cohort and evaluated in the pooled validation cohort. An improvement in the area under the receiver operating characteristic curve is observed on addition of the SDMS to the Wu, Bibikova, and Xie signatures.