Clinical and genomic analysis of metastatic prostate cancer progression with a background of postoperative biochemical recurrence

Analysis of genetic biomarkers, polymorphisms in ADME-related genes and their impact on pharmacotherapy for prostate cancer

Prostate cancer (PCa) is a non-cutaneous malignancy in males with wide variation in incidence rates across the globe. It is the second most reported cause of cancer death. Its etiology may have been linked to genetic polymorphisms, which are not only dominating cause of malignancy casualties but also exerts significant effects on pharmacotherapy outcomes. Although many therapeutic options are available, but suitable candidates identified by useful biomarkers can exhibit maximum therapeutic efficacy. The single-nucleotide polymorphisms (SNPs) reported in androgen receptor signaling genes influence the effectiveness of androgen receptor pathway inhibitors and androgen deprivation therapy. Furthermore, SNPs located in genes involved in transport, drug metabolism, and efflux pumps also influence the efficacy of pharmacotherapy. Hence, SNPs biomarkers provide the basis for individualized pharmacotherapy. The pharmacotherapeutic options for PCa include hormonal therapy, chemotherapy (Docetaxel, Mitoxantrone, Cabazitaxel, and Estramustine, etc.), and radiotherapy. Here, we overview the impact of SNPs reported in various genes on the pharmacotherapy for PCa and evaluate current genetic biomarkers with an emphasis on early diagnosis and individualized treatment strategy in PCa.

Racial disparity in the genomics of precision oncology of prostate cancer

BACKGROUND

Significant racial disparities in prostate cancer incidence and mortality have been reported between African American Men (AAM), who are at increased risk for prostate cancer, and European American Men (EAM). In most of the studies carried out on prostate cancer, this population is underrepresented. With the advancement of genome-wide association studies, several genetic predictor models of prostate cancer risk have been elaborated, as well as numerous studies that identify both germline and somatic mutations with clinical utility.

RECENT FINDINGS

Despite significant advances, the AAM population continues to be underrepresented in genomic studies, which can limit generalizability and potentially widen disparities. Here we outline racial disparities in currently available genomic applications that are used to estimate the risk of individuals developing prostate cancer and to identify personalized oncology treatment strategies. While the incidence and mortality of prostate cancer are different between AAM and EAM, samples from AAM remain to be unrepresented in different studies.

CONCLUSION

This disparity impacts the available genomic data on prostate cancer. As a result, the disparity can limit the predictive utility of the genomic applications and may lead to the widening of the existing disparities. More studies with substantially higher recruitment and engagement of African American patients are necessary to overcome this disparity.

Tissue- and Liquid-Based Biomarkers in Prostate Cancer Precision Medicine

Worldwide, prostate cancer (PC) is the second-most-frequently diagnosed male cancer and the fifth-most-common cause of all cancer-related deaths. Suspicion of PC in a patient is largely based upon clinical signs and the use of prostate-specific antigen (PSA) levels. Although PSA levels have been criticised for a lack of specificity, leading to PC over-diagnosis, it is still the most commonly used biomarker in PC management. Unfortunately, PC is extremely heterogeneous, and it can be difficult to stratify patients whose tumours are unlikely to progress from those that are aggressive and require treatment intensification. Although PC-specific biomarker research has previously focused on disease diagnosis, there is an unmet clinical need for novel prognostic, predictive and treatment response biomarkers that can be used to provide a precision medicine approach to PC management. In particular, the identification of biomarkers at the time of screening/diagnosis that can provide an indication of disease aggressiveness is perhaps the greatest current unmet clinical need in PC management. Largely through advances in genomic and proteomic techniques, exciting pre-clinical and clinical research is continuing to identify potential tissue, blood and urine-based PC-specific biomarkers that may in the future supplement or replace current standard practices. In this review, we describe how PC-specific biomarker research is progressing, including the evolution of PSA-based tests and those novel assays that have gained clinical approval. We also describe alternative diagnostic biomarkers to PSA, in addition to biomarkers that can predict PC aggressiveness and biomarkers that can predict response to certain therapies. We believe that novel biomarker research has the potential to make significant improvements to the clinical management of this disease in the near future.

Genetic polymorphism and carbonic anhydrase 9 expression can predict nodal metastatic prostate cancer risk in patients with prostate-specific antigen levels ≤10 ng/ml at initial biopsy

OBJECTIVES

Active surveillance is a common management method for low-risk prostate cancer (CaP). However, devising a method to prevent disease progression is crucial. Carbon anhydrase 9 (CA9) plays a vital role in cell adhesion and intercellular communication correlated to tumor metastasis. Our study explored the impact of CA9 genetic polymorphism on the clinicopathological features and prognosis of CaP.

MATERIALS AND METHODS

In total, 579 patients with CaP who underwent robot-assisted radical prostatectomy were enrolled, 270 of whom had an initial prostate-specific antigen (PSA) level ≤10 ng/ml and 309 had initial one >10 ng/ml. Three single-nucleotide polymorphisms of CA9 gene were examined using real-time polymerase chain reaction assay.

RESULTS

After adjusting the confounding factors, participants carrying at least one G allele at CA9 rs3829078 had a 2.241-fold change in PSA compared with the wild-type carrier (AA), leading to an initial PSA level of ≤10 ng/ml. Furthermore, patients with CaP with an initial PSA level ≤10 ng/ml who carried at least one G allele at CA9 rs3829078 had a 4.532-fold and 3.484-fold risk of lymph node metastasis and lymphovascular invasion, respectively. Moreover, The Cancer Genome Atlas database showed that the CA9 mRNA expression significantly increased N1 disease risk and worsened overall survival trends.

CONCLUSION

The rs3829078 polymorphic genotype of CA9 can predict the risk of lymph node metastasis of CaP with an initial PSA level ≤10 ng/ml. This is the first study to report a correlation between CA9 gene polymorphisms/CA9 mRNA expression and early detection of CaP.

Gene expression signature of Gleason score is associated with prostate cancer outcomes in a radical prostatectomy cohort

Prostate cancer (PCa) is a leading cause of cancer-related mortality worldwide. Gleason score (GS) is one of the best predictors of PCa aggressiveness, but additional tumor biomarkers may improve its prognostic accuracy. We developed a gene expression signature of GS to enhance the prediction of PCa outcomes. Elastic net was used to construct a gene expression signature by contrasting GS 8-10 vs. ≤6 tumors in The Cancer Genome Atlas (TCGA) dataset. The constructed signature was then evaluated for its ability to predict recurrence and metastatic-lethal (ML) progression in a Fred Hutchinson (FH) patient cohort (N=408; NRecurrence=109; NMLprogression=27). The expression signature included transcripts representing 49 genes. In the FH cohort, a 25% increase in the signature was associated with a hazard ratio (HR) of 1.51 (P=2.7×10-5) for recurrence. The signature's area under the curve (AUC) for predicting recurrence and ML progression was 0.68 and 0.76, respectively. Compared to a model with age at diagnosis, pathological stage and GS, the gene expression signature improved the AUC for recurrence (3%) and ML progression (6%). Higher levels of the signature were associated with increased expression of genes in cell cycle-related pathways and decreased expression of genes in androgen response, estrogen response, oxidative phosphorylation, and apoptosis. This gene expression signature based on GS may improve the prediction of recurrence as well as ML progression in PCa patients after radical prostatectomy.

Current Management Strategy for Active Surveillance in Prostate Cancer

PURPOSE OF REVIEW

Active surveillance has been increasingly utilized as a strategy for the management of favorable-risk, localized prostate cancer. In this review, we describe contemporary management strategies of active surveillance, with a focus on traditional stratification schemes, new prognostic tools, and patient outcomes.

RECENT FINDINGS

Patient selection, follow-up strategy, and indication for delayed intervention for active surveillance remain centered around PSA, digital rectal exam, and biopsy findings. Novel tools which include imaging, biomarkers, and genetic assays have been investigated as potential prognostic adjuncts; however, their role in active surveillance remains institutionally dependent. Although 30-50% of patients on active surveillance ultimately undergo delayed treatment, the vast majority will remain free of metastasis with a low risk of dying from prostate cancer. The optimal method for patient selection into active surveillance is unknown; however, cancer-specific mortality rates remain excellent. New prognostication tools are promising, and long-term prospective, randomized data regarding their use in active surveillance will be beneficial.

Trends in Gene Expression Profiling for Prostate Cancer Risk Assessment: A Systematic Review

Objectives

The aim of the study is to review biotechnology advances in gene expression profiling on prostate cancer (PCa), focusing on experimental platform development and gene discovery, in relation to different study designs and outcomes in order to understand how they can be exploited to improve PCa diagnosis and clinical management.

Methods

We conducted a systematic literature review on gene expression profiling studies through PubMed/MEDLINE and Web of Science between 2000 and 2016. Tissue biopsy and clinical gene profiling studies with different outcomes (e.g., recurrence, survival) were included.

Results

Over 3,000 papers were screened and 137 full-text articles were selected. In terms of technology used, microarray is still the most popular technique, increasing from 50 to 70% between 2010 and 2015, but there has been a rise in the number of studies using RNA sequencing (13% in 2015). Sample sizes have increased, as well as the number of genes that can be screened all at once, but we have also observed more focused targeting in more recent studies. Qualitative analysis on the specific genes found associated with PCa risk or clinical outcomes revealed a large variety of gene candidates, with a few consistent cross-studies.

Conclusions

The last 15 years of research in gene expression in PCa have brought a large volume of data and information that has been decoded only in part, but advancements in high-throughput sequencing technology are increasing the amount of data that can be generated. The variety of findings warrants the execution of both validation studies and meta-analyses. Genetic biomarkers have tremendous potential for early diagnosis of PCa and, if coupled with other diagnostics (e.g., imaging), can effectively be used to concretize less-invasive, personalized prediction of PCa risk and progression.

The context of prostate cancer genomics in personalized medicine

Prostate cancer is one of the most common types of cancer in males. Heterogeneous genomic aberrations may lead to prostate cancer onset, progression and metastasis. This heterogeneity also contributes to the variety in cancer risk and outcomes, different drug responses and progression, observed between individual patients. Classical prognostic factors, including prostate-specific antigen, Gleason Score and clinical tumor staging, are not sufficient to portray the complexity of a clinically relevant cancer diagnosis, risk prognosis, treatment choice and therapy monitoring. There is a requirement for novel genetic biomarkers in order to understand the oncogenic heterogeneity in a patient-personalized clinical setting and to improve the efficacy of risk prognosis and treatment choice. A number of biomarkers and gene panels have been established from patient sample cohort studies. These previous studies have provided distinct information to the investigation of heterogeneous malignancy in prostate cancer, which aids in clinical decision-making. Biomarker-guided therapies may facilitate the effective selection of drugs during early treatment; therefore, are beneficial to the individual patient. A non-invasive approach allows for convenient and repeated sampling to screen for cancer and monitor treatment response without the requirement for invasive tissue biopsies. With the current availability of numerous advanced technologies, reliable detection of the minimal tumor residues present following treatment may become clinical practice and, therefore, inform further in the field of personalized medicine.

Genomic tests to guide prostate cancer management following diagnosis

INTRODUCTION

Prostate cancer (PCa) is a common cancer in men, but variable clinical behaviors make its management challenging. Risk stratification is a key issue in disease management. Patient-tailored strategies are strongly advocated to reduce unnecessary treatment while maximizing the oncological outcomes of patient who need active treatment in the primary, adjuvant or salvage setting. Recently, tissue-based biomarkers or genomic tests have become available to improve the clinical decision-making. Areas covered: In this review, the authors present recent evidence about these tissue-based biomarkers, discussing the application of each of them in the clinical setting, focusing on the tests aimed to provide a better risk stratification and to guide decision-making after the diagnosis of PCa (i.e. OncotypeDX^Ⓡ, Prolaris^Ⓡ, ProMark^Ⓡ, Ki-67, Decipher^Ⓡ, PTEN, PORTOS, AR-V7 and DNA repair gene mutations). Expert commentary: Even if the clinicopathologic features are still the most frequently-used predictors of disease progression, these tools can be helpful in decision-making at every stage of the PCa management. Actually, OncotypeDX^Ⓡ, Prolaris^Ⓡ and Decipher^Ⓡ are recommended in the clinical setting by guidelines at different steps of PCa management. Consequently, further studies are indispensable to better tailor the right therapy for the right patient and at the right time.

The Use of Biomarkers in Prostate Cancer Screening and Treatment

Prostate cancer screening and diagnosis has been guided by prostate-specific antigen levels for the past 25 years, but with the most recent US Preventive Services Task Force screening recommendations, as well as concerns regarding overdiagnosis and overtreatment, a new wave of prostate cancer biomarkers has recently emerged. These assays allow the testing of urine, serum, or prostate tissue for molecular signs of prostate cancer, and provide information regarding both diagnosis and prognosis. In this review, we discuss 12 commercially available biomarker assays approved for the diagnosis and treatment of prostate cancer. The results of clinical validation studies and clinical decision-making studies are presented. This information is designed to assist urologists in making clinical decisions with respect to ordering and interpreting these tests for different patients. There are numerous fluid and biopsy-based genomic tests available for prostate cancer patients that provide the physician and patient with different information about risk of future disease and treatment outcomes. It is important that providers be able to recommend the appropriate test for each individual patient; this decision is based on tissue availability and prognostic information desired. Future studies will continue to emphasize the important role of genomic biomarkers in making individualized treatment decisions for prostate cancer patients.

New developments in prostate cancer biomarkers

PURPOSE OF REVIEW

The initial management of localized prostate cancer is increasingly complex with the identification of a growing number of prognostic subgroups. Molecular and genetic biomarkers have been proposed to help clinicians and patients navigate treatment decisions.

RECENT FINDINGS

Three commercially available tests, the Genomic Prostate score, Cell Cycle Progression score, and Genomic Classifier appear to currently have the most supporting data for their use in localized prostate cancer. All three have been shown to identify men at higher risk for poor outcome following radical prostatectomy in retrospective studies whereas the first two have also shown promise in addressing which men might be appropriate for active surveillance. Only the Genomic Classifier has data supporting its use as a predictive marker in addition to a prognostic marker.

SUMMARY

Over the past several years, the management of localized prostate cancer has seen the development of several novel biomarkers aimed at improving decision making. Although a lack of prospective validation makes it challenging to know how best to change management based on the results from any of the tests, the growing body of retrospective data suggests significant promise in this arena.

Incorporation of tissue-based genomic biomarkers into localized prostate cancer clinics

Localized prostate cancer (PCa) is a clinically heterogeneous disease, which presents with variability in patient outcomes within the same risk stratification (low, intermediate or high) and even within the same Gleason scores. Genomic tools have been developed with the purpose of stratifying patients affected by this disease to help physicians personalize therapies and follow-up schemes. This review focuses on these tissue-based tools. At present, four genomic tools are commercially available: Decipher™, Oncotype DX®, Prolaris® and ProMark®. Decipher™ is a tool based on 22 genes and evaluates the risk of adverse outcomes (metastasis) after radical prostatectomy (RP). Oncotype DX® is based on 17 genes and focuses on the ability to predict outcomes (adverse pathology) in very low-low and low-intermediate PCa patients, while Prolaris® is built on a panel of 46 genes and is validated to evaluate outcomes for patients at low risk as well as patients who are affected by high risk PCa and post-RP. Finally, ProMark® is based on a multiplexed proteomics assay and predicts PCa aggressiveness in patients found with similar features to Oncotype DX®. These biomarkers can be helpful for post-biopsy decision-making in low risk patients and post-radical prostatectomy in selected risk groups. Further studies are needed to investigate the clinical benefit of these new technologies, the financial ramifications and how they should be utilized in clinics.

What is the Need for Prostatic Biomarkers in Prostate Cancer Management?

Discriminating patients with a low risk of progression from those with lethal prostate cancer is one of the main challenges in prostate cancer management. Indeed, such discrimination is essential if we aim to avoid overtreatment in men with indolent disease and to improve survival in those men with lethal disease. We are reporting on the current literature on such prognostic tools that are now available, their clinical role and their limitations in individualizing care. There is an urgent need to incorporate such genomic tools into new platform-based clinical trial structures to further develop and validate prognostic and predictive biomarkers and provide prostate cancer patients with an effective and cost-efficient access to new drugs in the setting of personalized treatment.

Evolving transcriptomic fingerprint based on genome-wide data as prognostic tools in prostate cancer

Background Information

Prostate cancer (PCa) is a common disease but only a small subset of patients are at risk of developing metastasis and lethal disease, and identifying which patients will progress is challenging because of the heterogeneity underlying tumour progression. Understanding this heterogeneity at the molecular level and the resulting clinical impact is a critical step necessary for risk stratification. Defining genomic fingerprint elucidates molecular variation and may improve PCa risk stratification, providing more accurate prognostic information of tumour aggressiveness (or lethality) for prognostic biomarker development. Therefore, we explored transcriptomic differences between patients with indolent disease outcome and patients who developed metastasis post‐radical prostatectomy using genome‐wide expression data in the post radical prostatectomy clinical space before metastatic spread.

Results

Based on differential expression analysis, patients with adverse pathological findings who are at higher risk of developing metastasis have a distinct transcriptomic fingerprint that can be detected on surgically removed prostate specimens several years before metastasis detection. Nearly half of the transcriptomic fingerprint features were non‐coding RNA highlighting their pivotal role in PCa progression. Protein‐coding RNA features in the fingerprint are involved in multiple pathways including cell cycle, chromosome structure maintenance and cytoskeleton organisation. The metastatic transcriptomic fingerprint was determined in independent cohorts verifying the association between the fingerprint and metastatic patients. Further, the fingerprint was confirmed in metastasis lesions demonstrating that the fingerprint represents early metastatic transcriptomic changes, suggesting its utility as a prognostic tool to predict metastasis and provide clinical value in the early radical prostatectomy setting.

Conclusions

Here, we show that transcriptomic patterns of metastatic PCa exist that can be detected early after radical prostatectomy. This metastatic fingerprint has potential prognostic ability that can impact PCa treatment management potentially circumventing the requirements for unnecessary therapies.