There is a clear need to improve risk stratification and to identify novel therapeutic targets in aggressive prostate cancer. The goal of this study was to investigate genes with outlier expression ...with prognostic association in high-risk prostate cancer patients as potential biomarkers and drug targets.
We interrogated microarray gene expression data from prostatectomy samples from 545 high-risk prostate cancer patients with long-term follow-up (mean 13.4 years). Three independent clinical datasets totaling an additional 545 patients were used for validation. Novel prognostic outlier genes were interrogated for impact on oncogenic phenotypes in vitro using siRNA-based knockdown. Association with clinical outcomes and comparison with existing prognostic instruments was assessed with multivariable models using a prognostic outlier score.
Analysis of the discovery cohort identified 20 prognostic outlier genes. Three top prognostic outlier genes were novel prostate cancer genes; NVL, SMC4, or SQLE knockdown reduced migration and/or invasion and outlier expression was significantly associated with poor prognosis. Increased prognostic outlier score was significantly associated with poor prognosis independent of standard clinicopathologic variables. Finally, the prognostic outlier score prognostic association is independent of, and adds to existing genomic and clinical tools for prognostication in prostate cancer (Decipher, the cell-cycle progression signature, and CAPRA-S).
To our knowledge, this study represents the first unbiased high-throughput investigation of prognostic outlier genes in prostate cancer and demonstrates the potential biomarker and therapeutic importance of this previously unstudied class of cancer genes.
We aimed to validate Decipher to predict adverse pathology (AP) at radical prostatectomy (RP) in men with National Comprehensive Cancer Network (NCCN) favorable-intermediate risk (F-IR) prostate ...cancer (PCa), and to better select F-IR candidates for active surveillance (AS).
In all, 647 patients diagnosed with NCCN very low/low risk (VL/LR) or F-IR prostate cancer were identified from a multi-institutional PCa biopsy database; all underwent RP with complete postoperative clinicopathological information and Decipher genomic risk scores. The performance of all risk assessment tools was evaluated using logistic regression model for the endpoint of AP, defined as grade group 3-5, pT3b or higher, or lymph node invasion.
The median age was 61 years (interquartile range 56-66) for 220 patients with NCCN F-IR disease, 53% classified as low-risk by Cancer of the Prostate Risk Assessment (CAPRA 0-2) and 47% as intermediate-risk (CAPRA 3-5). Decipher classified 79%, 13% and 8% of men as low-, intermediate- and high-risk with 13%, 10%, and 41% rate of AP, respectively. Decipher was an independent predictor of AP with an odds ratio of 1.34 per 0.1 unit increased (p value = 0.002) and remained significant when adjusting by CAPRA. Notably, F-IR with Decipher low or intermediate score did not associate with significantly higher odds of AP compared to VL/LR.
NCCN risk groups, including F-IR, are highly heterogeneous and should be replaced with multivariable risk-stratification. In particular, incorporating Decipher may be useful for safely expanding the use of AS in this patient population.
The Decipher 22-gene genomic classifier (GC) may help in post-radical prostatectomy (RP) decision making given its superior prognostic performance over clinicopathologic variables alone. However, ...most studies evaluating the GC have had a modest representation of African-American men (AAM). We evaluated the GC within a large Veteran Affairs cohort and compared its performance to CAPRA-S for predicting outcomes in AAM and non-AAM after RP.
GC scores were generated for 548 prostate cancer (PC) patients, who underwent RP at the Durham Veteran Affairs Medical Center between 1989 and 2016. This was a clinically high-risk cohort and was selected to have either pT3a, positive margins, seminal vesicle invasion, or received post-RP radiotherapy. Multivariable Cox models and survival C-indices were used to compare the performance of GC and CAPRA-S for predicting the risk of metastasis and PC-specific mortality (PCSM).
Median follow-up was 9 years, during which 37 developed metastasis and 20 died from PC. Overall, 55% (n = 301) of patients were AAM. In multivariable analyses, GC (high vs. intermediate and intermediate vs. low) was a significant predictor of metastasis in all men (all p < 0.001). Consistent with prior studies, relative to CAPRA-S, GC had a higher C-index for 5-year metastasis (0.78 vs. 0.72) and 10-year PCSM (0.85 vs. 0.81). There was a suggestion GC was a stronger predictor in AAM than non-AAM. Specifically, the 5-year metastasis risk C-index was 0.86 in AAM vs. 0.69 in non-AAM and the 10-year PCSM risk C-index was 0.91 in AAM vs. 0.78 in non-AAM. However, the test for interaction of race and the performance of the GC in the Cox model was not significant for either metastasis or PCSM (both p ≥ 0.3).
GC was a very strong predictor of poor outcome and performed well in both AAM and non-AAM. Our data support the use of GC for risk stratification in AAM post-RP. While our data suggest that GC may actually work better in AAM, given the limited number of events, further validation is needed.
Glucagon-like peptide-1 (GLP-1), an incretin hormone renowned for its role in post-meal blood sugar regulation and glucose-dependent insulin secretion, has gained attention as a novel treatment for ...diabetes through GLP-1 receptor agonists (GLP-1-RA). Despite their efficacy, concerns have been raised regarding the potential associations between GLP-1-RA and certain malignancies, including medullary thyroid cancer. However, evidence of its association with prostate cancer (PCa) remains inconclusive. This review delves into the intricate relationship between GLP-1-RA and PCa, exploring the mechanisms through which GLP-1-Rs may impact PCa cells. We discuss the potential pathways involving cAMP, ERK, AMPK, mTOR, and P27. Furthermore, we underscore the imperative for additional research to elucidate the impact of GLP-1-RA treatment on PCa progression, patient outcomes, and potential interactions with existing therapies. Translational studies and clinical trials are crucial for a comprehensive understanding of the role of GLP-1-RA in PCa management.
Genomic classifiers (GC) have been shown to improve risk stratification post prostatectomy. However, their clinical benefit has not been prospectively demonstrated. We sought to determine the impact ...of GC testing on postoperative management in men with prostate cancer post prostatectomy.
Two prospective registries of prostate cancer patients treated between 2014 and 2019 were included. All men underwent Decipher tumor testing for adverse features post prostatectomy (Decipher Biosciences, San Diego, CA). The clinical utility cohort, which measured the change in treatment decision-making, captured pre- and postgenomic treatment recommendations from urologists across diverse practice settings (n = 3455). The clinical benefit cohort, which examined the difference in outcome, was from a single academic institution whose tumor board predefined "best practices" based on GC results (n = 135).
In the clinical utility cohort, providers' recommendations pregenomic testing were primarily observation (69%). GC testing changed recommendations for 39% of patients, translating to a number needed to test of 3 to change one treatment decision. In the clinical benefit cohort, 61% of patients had genomic high-risk tumors; those who received the recommended adjuvant radiation therapy (ART) had 2-year PSA recurrence of 3 vs. 25% for those who did not (HR 0.1 95% CI 0.0-0.6, p = 0.013). For the genomic low/intermediate-risk patients, 93% followed recommendations for observation, with similar 2-year PSA recurrence rates compared with those who received ART (p = 0.93).
The use of GC substantially altered treatment decision-making, with a number needed to test of only 3. Implementing best practices to routinely recommend ART for genomic-high patients led to larger than expected improvements in early biochemical endpoints, without jeopardizing outcomes for genomic-low/intermediate-risk patients.
We investigated whether tumors from diagnostic biopsies of primary rhabdomyosarcoma (RMS) contain relevant prognostic information in the form of gene expression signatures that can be used to model ...and predict outcome of patients.
A 22,000-probe set microarray was used to evaluate 120 RMS specimens and correlate gene expression patterns to survival. Multivariate gene expression models or metagenes were developed using cross-validated Cox regression proportional hazards modeling and were evaluated using Kaplan-Meier analysis.
A 34-metagene, based on expression patterns of 34 genes, was highly predictive of outcome. It was not highly correlated with individual clinical risk factors such as patient age, stage, tumor size, or histology. However, it was correlated with a risk classification used by the Children's Oncology Group and the biologic subsets of alveolar histology tumors.
These data support further evaluation of RMS metagenes to discriminate patients with good prognosis from those with poor prognosis, with the potential to direct risk-adapted therapy.
The vast majority of deaths associated with cancer are a consequence of a complex phenotypic behavior, metastasis, by which tumor cells spread from their primary site of origin to regional and ...distant sites. This process requires the tumor cell to make numerous adjustments, both subtle and dramatic, to successfully reach, survive, and flourish at favorable secondary sites. It has been suggested that molecular mechanisms accounting for metastatic behavior can recapitulate those employed during embryogenesis. We have shown that the homeodomain transcription factor Six1, known to be required for normal development of migratory myogenic progenitor cells, is sufficient to promote metastatic spread in a mouse model of the pediatric skeletal muscle cancer rhabdomyosarcoma. Here, we report that Six1 is able to activate the expression of a set of protumorigenic genes (encoding cyclin D1, c-Myc, and Ezrin) that can control cell proliferation, survival, and motility. Although the role of Ezrin in cytoskeletal organization and adhesion has been well studied, the means by which its expression is regulated are poorly understood. We now show that the gene encoding Ezrin is a direct transcriptional target of Six1. Moreover, Ezrin is indispensable for Six1-induced metastasis and highly expressed in a panel of representative pediatric cancers. Our data indicate that Ezrin represents a promising therapeutic target for patients with advanced-stage rhabdomyosarcoma and perhaps other malignancies.
Regional lymph node disease (RLND) is a component of the risk-based treatment stratification in rhabdomyosarcoma (RMS). The purpose of this study was to determine the contribution of RLND to ...prognosis for patients with RMS.
Patient characteristics and survival outcomes for patients enrolled onto Intergroup Rhabdomyosarcoma Study IV (N = 898, 1991 to 1997) were evaluated among the following three patient groups: nonmetastatic patients with clinical or pathologic negative nodes (N0, 696 patients); patients with clinical or pathologic positive nodes (N1, 125 patients); and patients with a single site of metastatic disease (77 patients).
Outcomes for patients with nonmetastatic alveolar N0 RMS were significantly better than for patients with N1 RMS (5-year failure-free survival FFS, 73% v 43%, respectively; 5-year overall survival OS, 80% v 46%, respectively; P < .001). Patients with a single site of alveolar metastasis had even worse FFS and OS (23% FFS and OS, P = .01) when compared with patients with N1 RMS; however, the differences was not as large as the differences between patients with N0 RMS and N1 RMS. For embryonal RMS, there was no statistically significant difference in FFS or OS (P = .41 and P = .77, respectively) for patients with N1 versus N0 RMS. Gene array analysis of primary tumor specimens identified that genes associated with the immune system and antigen presentation were significantly increased in N1 versus N0 alveolar RMS.
RLND alters prognosis for alveolar but not embryonal RMS. For patients with N1 disease and alveolar histology, outcomes were more similar to distant metastatic disease rather than local disease. Current data suggest that more aggressive therapy for patients with alveolar N1 RMS may be warranted.
Muscle-invasive bladder cancer (MIBC) is a heterogeneous disease, and gene expression profiling has identified several molecular subtypes with distinct biological and clinicopathological ...characteristics. While MIBC subtyping has primarily been based on messenger RNA (mRNA), long non-coding RNAs (lncRNAs) may provide additional resolution.
LncRNA expression was quantified from microarray data of a MIBC cohort treated with neoadjuvant chemotherapy (NAC) and radical cystectomy (RC) (n = 223). Unsupervised consensus clustering of highly variant lncRNAs identified a four-cluster solution, which was characterized using a panel of MIBC biomarkers, regulon activity profiles, gene signatures, and survival analysis. The four-cluster solution was confirmed in The Cancer Genome Atlas (TCGA) cohort (n = 405). A single-sample genomic classifier (GC) was trained using ridge-penalized logistic regression and validated in two independent cohorts (n = 255 and n = 94).
NAC and TCGA cohorts both contained an lncRNA cluster (LC3) with favorable prognosis that was enriched with tumors of the luminal-papillary (LP) subtype. In both cohorts, patients with LP tumors in LC3 (LPL-C3) were younger and had organ-confined, node-negative disease. The LPL-C3 tumors had enhanced FGFR3, SHH, and wild-type p53 pathway activity. In the TCGA cohort, LPL-C3 tumors were enriched for FGFR3 mutations and depleted for TP53 and RB1 mutations. A GC trained to identify these LPL-C3 patients showed robust performance in two validation cohorts.
Using lncRNA expression profiles, we identified a biologically distinct subgroup of luminal-papillary MIBC with a favorable prognosis. These data suggest that lncRNAs provide additional information for higher-resolution subtyping, potentially improving precision patient management.
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