Diagnostic associations of gene expression signatures in prostate cancer tissue

Unlocking the potential of oncology biomarkers: advancements in clinical theranostics

INTRODUCTION

Cancer biomarkers have revolutionized the field of oncology by providing valuable insights into tumor changes and aiding in screening, diagnosis, prognosis, treatment prediction, and risk assessment. The emergence of "omic" technologies has enabled biomarkers to become reliable and accurate predictors of outcomes during cancer treatment.

CONTENT

In this review, we highlight the clinical utility of biomarkers in cancer identification and motivate researchers to establish a personalized/precision approach in oncology. By extending a multidisciplinary technology-based approach, biomarkers offer an alternative to traditional techniques, fulfilling the goal of cancer therapeutics to find a needle in a haystack.

SUMMARY AND OUTLOOK

We target different forms of cancer to establish a dynamic role of biomarkers in understanding the spectrum of malignancies and their biochemical and molecular characterization, emphasizing their prospective contribution to cancer screening. Biomarkers offer a promising avenue for the early detection of human cancers and the exploration of novel technologies to predict disease severity, facilitating maximum survival and minimum mortality rates. This review provides a comprehensive overview of the potential of biomarkers in oncology and highlights their prospects in advancing cancer diagnosis and treatment.

Systematic review of comparative transcriptomic studies of cellular resistance to genotoxic stress

The development of resistance by tumor cells to various types of therapy is a significant problem that decreases the effectiveness of oncology treatments. For more than two decades, comparative transcriptomic studies of tumor cells with different sensitivities to ionizing radiation and chemotherapeutic agents have been conducted in order to identify the causes and mechanisms underlying this phenomenon. However, the results of such studies have little in common and often contradict each other. We have assumed that a systematic analysis of a large number of such studies will provide new knowledge about the mechanisms of development of therapeutic resistance in tumor cells. Our comparison of 123 differentially expressed gene (DEG) lists published in 98 papers suggests a very low degree of consistency between the study results. Grouping the data by type of genotoxic agent and tumor type did not increase the similarity. The most frequently overexpressed genes were found to be those encoding the transport protein ABCB1 and the antiviral defense protein IFITM1. We put forward a hypothesis that the role played by the overexpression of the latter in the development of resistance may be associated not only with the stimulation of proliferation, but also with the limitation of exosomal communication and, as a result, with a decrease in the bystander effect. Among down regulated DEGs, BNIP3 was observed most frequently. The expression of BNIP3, together with BNIP3L, is often suppressed in cells resistant to non-platinum genotoxic chemotherapeutic agents, whereas it is increased in cells resistant to ionizing radiation. These observations are likely to be mediated by the binary effects of these gene products on survival, and regulation of apoptosis and autophagy. The combined data also show that even such obvious mechanisms as inhibition of apoptosis and increase of proliferation are not universal but show multidirectional changes.

Recent advances in nanomaterials for prostate cancer detection and diagnosis

Despite the significant progress in the discovery of biomarkers and the exploitation of technologies for prostate cancer (PCa) detection and diagnosis, the initial screening of these PCa-related biomarkers using current...

Precision Medicine Approach in Prostate Cancer

Prostate cancer is the most prevalent type of cancer and the second cause of death in men worldwide. Various diagnostic and treatment procedures are available for this type of malignancy, but High-grade or locally advanced prostate cancers showed the potential to develop to lethal phase that can be causing dead. Therefore, new approaches are needed to prolong patients' survival and to improve their quality of life. Precision medicine is a novel emerging field that plays an essential role in identifying new sub-classifications of diseases and in providing guidance in treatment that is based on individual multi-omics data. Multi-omics approaches include the use of genomics, transcriptomics, proteomics, metabolomics, epigenomics and phenomics data to unravel the complexity of a disease-associated biological network, to predict prognostic biomarkers, and to identify new targeted drugs for individual cancer patients. We review the impact of multi-omics data in the framework of systems biology in the era of precision medicine, emphasising the combination of molecular imaging modalities with highthroughput techniques and the new treatments that target metabolic pathways involved in prostate cancer.

Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum

Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to the development of a less-invasive assay to perform active surveillance monitoring. Metabolomic profiling of prostate tissue and serum samples was performed. Metabolite levels and metabolite sets were compared across Gleason scores. Machine learning algorithms were trained and tuned to predict transformation or differentiation status from metabolite data. A total of 135 metabolites were significantly different (P _adjusted < 0.05) in tumor versus normal tissue, and pathway analysis identified one sugar metabolism pathway (P _adjusted = 0.03). Machine learning identified profiles that predicted tumor versus normal tissue (AUC of 0.82 ± 0.08). In tumor tissue, 25 metabolites were associated with Gleason score (unadjusted P < 0.05), 4 increased in high grade while the remainder were enriched in low grade. While pyroglutamine and 1,5-anhydroglucitol were correlated (0.73 and 0.72, respectively) between tissue and serum from the same patient, no metabolites were consistently associated with Gleason score in serum. Previously reported as well as novel metabolites with differing abundance were identified across tumor tissue. However, a "metabolite signature" for Gleason score was not obtained. This may be due to study design and analytic challenges that future studies should consider. IMPLICATIONS: Metabolic profiling can distinguish benign and neoplastic tissues. A novel unsupervised machine learning method can be utilized to achieve this distinction.

The ternary complex factor protein ELK1 is an independent prognosticator of disease recurrence in prostate cancer

BACKGROUND

Both hormone-sensitive and castration- and enzalutamide-resistant prostate cancers (PCa) depend on the ternary complex factor (TCF) protein ELK1 to serve as a tethering protein for the androgen receptor (AR) to activate a critical set of growth genes. The two sites in ELK1 required for AR binding are conserved in other members of the TCF subfamily, ELK3 and ELK4. Here we examine the potential utility of the three proteins as prognosticators of disease recurrence in PCa.

METHODS

Transcriptional activity assays; Retrospective analysis of PCa recurrence using data on 501 patients in The Cancer Genome Atlas (TCGA) database; Unpaired Wilcoxon rank-sum test and multiple comparison correction using the Holm's method; Spearman's correlations; Kaplan-Meier methods; Univariable and multivariable Cox regression analyses; LASSO-based penalized Cox regression models; Time-dependent area under the receiver operating characteristic (ROC) curve.

RESULTS

ELK4 but not ELK3 was coactivated by AR similar to ELK1. Tumor expression of neither ELK3 nor ELK4 was associated with disease-free survival (DFS). ELK1 was associated with higher clinical T-stage, pathology T-stage, Gleason score, prognostic grade, and positive lymph node status. ELK1 was a negative prognosticator of DFS, independent of ELK3, ELK4, clinical T-stage, pathology T-stage, prognostic grade, lymph node status, age, and race. Inclusion of ELK1 increased the abilities of the Oncotype DX and Prolaris gene panels to predict disease recurrence, correctly predicting disease recurrence in a unique subset of patients.

CONCLUSIONS

ELK1 is a strong, independent prognosticator of disease recurrence in PCa, underscoring its unique role in PCa growth. Inclusion of ELK1 may enhance the utility of currently used prognosticators for clinical decision making in prostate cancer.

A novel combined miRNA and methylation marker panel (miMe) for prediction of prostate cancer outcome after radical prostatectomy

Improved prognostic biomarkers are needed to guide personalized prostate cancer (PC) treatment decisions. Due to the prominent molecular heterogeneity of PC, multimarker panels may be more robust. Here, 25 selected top-candidate miRNA and methylation markers for PC were profiled by qPCR in malignant radical prostatectomy (RP) tissue specimens from 198 PC patients (Cohort 1, training). Using GLMnet, we trained a novel multimarker model (miMe) comprising nine miRNAs and three methylation markers that predicted postoperative biochemical recurrence (BCR) independently of the established clinicopathological CAPRA-S nomogram in Cox multivariate regression analysis in Cohort 1 (HR [95% CI]: 1.53 [1.26-1.84], p < 0.001). This result was successfully validated in two independent RP cohorts (Cohort 2, n = 159: HR [95% CI]: 1.35 [1.06-1.73], p = 0.015. TCGA, n = 350: HR [95% CI]: 1.34 [1.01-1.77], p = 0.04). Notably, in CAPRA-S low-risk patients, a high miMe score was associated with >6 times higher risk of BCR, suggesting that miMe may help identify PC patients at high risk of progression despite favorable clinicopathological factors postsurgery. Finally, miMe was a significant predictor of cancer-specific survival (p = 0.019, log-rank test) in a merged analysis of 357 RP patients. In conclusion, we trained, tested and validated a novel 12-marker panel (miMe) that showed significant independent prognostic value in three RP cohorts. In the future, combining miMe score with existing clinical nomograms may improve PC risk stratification and thus help guide treatment decisions.

Development of a stress response therapy targeting aggressive prostate cancer

Oncogenic lesions up-regulate bioenergetically demanding cellular processes, such as protein synthesis, to drive cancer cell growth and continued proliferation. However, the hijacking of these key processes by oncogenic pathways imposes onerous cell stress that must be mitigated by adaptive responses for cell survival. The mechanism by which these adaptive responses are established, their functional consequences for tumor development, and their implications for therapeutic interventions remain largely unknown. Using murine and humanized models of prostate cancer (PCa), we show that one of the three branches of the unfolded protein response is selectively activated in advanced PCa. This adaptive response activates the phosphorylation of the eukaryotic initiation factor 2-α (P-eIF2α) to reset global protein synthesis to a level that fosters aggressive tumor development and is a marker of poor patient survival upon the acquisition of multiple oncogenic lesions. Using patient-derived xenograft models and an inhibitor of P-eIF2α activity, ISRIB, our data show that targeting this adaptive brake for protein synthesis selectively triggers cytotoxicity against aggressive metastatic PCa, a disease for which presently there is no cure.

New biomarkers for diagnosis and prognosis of localized prostate cancer

The diagnostics and management of localized prostate cancer is complicated because of cancer heterogeneity and differentiated progression in various subgroups of patients. As a prostate cancer biomarker, FDA-approved detection assay for serum prostate specific antigen (PSA) and its derivatives are not potent enough to diagnose prostate cancer, especially high-grade disease (Gleason ≥7). To date, a collection of new biomarkers was developed. Some of these markers are superior for primary screening while others are particularly helpful for cancer risk stratification, detection of high-grade cancer, and prediction of adverse events. Two of those markers such as proPSA (a part of the Prostate Health Index (PHI)) and prostate specific antigen 3 (PCA3) (a part of the PCA3 Progensa test) were recently approved by FDA for clinical use. Other markers are not PDA-approved yet but are available from Clinical Laboratory Improvement Amendment (CLIA)-certified clinical laboratories. In this review, we characterize diagnostic performance of these markers and their diagnostic and prognostic utility for prostate cancer.

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.

Abiraterone acetate and prednisone in chemotherapy-naïve prostate cancer patients: rationale, evidence and clinical utility

Abiraterone acetate 1000 mg/day, combined with prednisone 5 mg PO twice daily, is indicated for the treatment of metastatic castration-resistant prostate cancer (mCRPC). Abiraterone acetate is the oral prodrug of abiraterone, a specific CYP17 inhibitor that blocks androgen biosynthesis within the adrenal glands, testes and tumor microenvironment. In a phase III trial of men with asymptomatic or minimally symptomatic, chemotherapy-naïve mCRPC, treatment with oral abiraterone acetate plus prednisone led to a statistically significant improvement in the co-primary endpoints of overall survival and radiographic progression-free survival when compared with placebo plus prednisone. In long-term follow-up of phase III trials, the incidence of corticosteroid-associated adverse events was 25.5% in the abiraterone acetate plus prednisone arm compared with 23.3% in the placebo plus prednisone arm. The need for regular patient monitoring and appropriate management of symptoms during long-term use of prednisone must be placed in context with the improvement in survival seen with abiraterone plus prednisone. Within the multidisciplinary environment that is emerging to meet quality and cost imperatives, abiraterone acetate plus prednisone is suitable for use in the chemotherapy-naïve population with minimal symptoms as well as in patients who have been treated with docetaxel and may have symptomatic disease. Ongoing trials are evaluating the role of abiraterone acetate plus prednisone in patients with nonmetastatic CRPC and metastatic hormone-sensitive prostate cancer, while further trials in the mCRPC setting are evaluating its use in combination regimens.

Utilization and predictors of expectant management among elderly men with low-and intermediate-risk localized prostate cancer in U.S. urological practice

INTRODUCTION

Expectant management (EM) reduces overtreatment in low-risk but not intermediate-risk localized prostate cancer (PCa). We assessed the use and predictors of EM to understand its uptake in U.S. practice.

METHODS

Using the U.S. SEER-Medicare database, we conducted a retrospective cohort study of men 66 years and older diagnosed with low-risk (N=25,506) or intermediate-risk (N=25,597) localized PCa between 2004 - 2011 and followed through December 31, 2012. We defined EM as no definitive therapy (DT) and at least one prostate-specific antigen (PSA) test or re-biopsy 4 - 12 months post diagnosis; or receiving DT after PSA testing or re-biopsy 7 - 12 months after diagnosis. We performed separate analyses for low-risk and intermediate-risk groups using multiple logistic regressions.

RESULTS

For men diagnosed with PCa in 2004-2011, EM increased from 22% to 43% in the low-risk group and from 15% to 18% in the intermediate-risk group. In the low-risk group, EM increased with patients' age (adjusted odds ratio [aOR] = 1.26 for 71-75 years; 2.21 for 76-80 years; 6.33 for older then 80, p<0.0001, compared to 66-70 years). EM uptake was higher among men with comorbidities (aOR=1.29), and residing in the Pacific region (aOR=0.56, compared to the East Coast).

CONCLUSIONS

In U.S. practice, the utilization of EM steadily increased in low-risk PCa and remained low in the intermediate-risk group over time. While patients with advanced age or comorbidities were more likely to receive EM, its use varied substantially by geographic region. Our findings bring attention to the presence of multiple barriers for EM implementation.

Precision Oncology Medicine: The Clinical Relevance of Patient-Specific Biomarkers Used to Optimize Cancer Treatment

Precision medicine in oncology is the result of an increasing awareness of patient-specific clinical features coupled with the development of genomic-based diagnostics and targeted therapeutics. Companion diagnostics designed for specific drug-target pairs were the first to widely utilize clinically applicable tumor biomarkers (eg, HER2, EGFR), directing treatment for patients whose tumors exhibit a mutation susceptible to an FDA-approved targeted therapy (eg, trastuzumab, erlotinib). Clinically relevant germline mutations in drug-metabolizing enzymes and transporters (eg, TPMT, DPYD) have been shown to impact drug response, providing a rationale for individualized dosing to optimize treatment. The use of multigene expression-based assays to analyze an array of prognostic biomarkers has been shown to help direct treatment decisions, especially in breast cancer (eg, Oncotype DX). More recently, the use of next-generation sequencing to detect many potential "actionable" cancer molecular alterations is further shifting the 1 gene-1 drug paradigm toward a more comprehensive, multigene approach. Currently, many clinical trials (eg, NCI-MATCH, NCI-MPACT) are assessing novel diagnostic tools with a combination of different targeted therapeutics while also examining tumor biomarkers that were previously unexplored in a variety of cancer histologies. Results from ongoing trials such as the NCI-MATCH will help determine the clinical utility and future development of the precision-medicine approach.

Clonal evaluation of prostate cancer foci in biopsies with discontinuous tumor involvement by dual ERG/SPINK1 immunohistochemistry

The presence of two or more prostate cancer foci separated by intervening benign tissue in a single core is a well-recognized finding on prostate biopsy. Cancer involvement can be measured by including intervening benign tissue or only including the actual cancer involved area. Importantly, this parameter is a common enrollment criterion for active surveillance protocols. We hypothesized that spatially distinct prostate cancer foci in biopsies may arise from separate clones, impacting cancer involvement assessment. Hence, we used dual ERG/SPINK1 immunohistochemistry to determine the frequency of separate clones-when separate tumor foci showed discordant ERG and/or SPINK1 status-in discontinuously involved prostate biopsy cores from two academic institutions. In our cohort of 97 prostate biopsy cores with spatially discrete tumor foci (from 80 patients), discontinuous cancer involvement including intervening tissue ranged from 20 to 100% and Gleason scores ranged from 6 to 9. Twenty-four (25%) of 97 discontinuously involved cores harbored clonally distinct cancer foci by discordant ERG and/or SPINK1 expression status: 58% (14/24) had one ERG(+) focus, and one ERG(-)/SPINK1(-) focus; 29% (7/24) had one SPINK1(+) focus and one ERG(-)/SPINK1(-) focus; and 13% (3/24) had one ERG(+) focus and one SPINK1(+) focus. ERG and SPINK1 overexpression were mutually exclusive in all tumor foci. In summary, our results show that ~25% of discontinuously involved prostate biopsy cores showed tumor foci with discordant ERG/SPINK1 status, consistent with multiclonal disease. The relatively frequent presence of multiclonality in discontinuously involved prostate biopsy cores warrants studies on the potential clinical impact of clonality assessment, particularly in cases where tumor volume in a discontinuous core may impact active surveillance eligibility.

Evolution of a CDC Public Health Research Agenda for Low-Risk Prostate Cancer

Men with prostate cancer face difficult choices when selecting a therapy for localized prostate cancer. Comparative data from controlled studies are lacking and clinical opinions diverge about the benefits and harms of treatment options. Consequently, there is limited guidance for patients regarding the impact of treatment decisions on quality of life. There are opportunities for public health to intervene at several decision-making points. Information on typical quality of life outcomes associated with specific prostate cancer treatments could help patients select treatment options. From 2003 to present, the Division of Cancer Prevention and Control at CDC has supported projects to explore patient information-seeking behavior post-diagnosis, caregiver and provider involvement in treatment decision making, and patient quality of life following prostate cancer treatment. CDC's work also includes research that explores barriers and facilitators to the presentation of active surveillance as a viable treatment option and promotes equal access to information for men and their caregivers. This article provides an overview of the literature and considerations that initiated establishing a prospective public health research agenda around treatment decision making. Insights gathered from CDC-supported studies are poised to enhance understanding of the process of shared decision making and the influence of patient, caregiver, and provider preferences on the selection of treatment choices. These findings provide guidance about attributes that maximize patient experiences in survivorship, including optimal quality of life and patient and caregiver satisfaction with information, treatment decisions, and subsequent care.

Endosomal gene expression: a new indicator for prostate cancer patient prognosis?

Prostate cancer continues to be a major cause of morbidity and mortality in men, but a method for accurate prognosis in these patients is yet to be developed. The recent discovery of altered endosomal biogenesis in prostate cancer has identified a fundamental change in the cell biology of this cancer, which holds great promise for the identification of novel biomarkers that can predict disease outcomes. Here we have identified significantly altered expression of endosomal genes in prostate cancer compared to non-malignant tissue in mRNA microarrays and confirmed these findings by qRT-PCR on fresh-frozen tissue. Importantly, we identified endosomal gene expression patterns that were predictive of patient outcomes. Two endosomal tri-gene signatures were identified from a previously published microarray cohort and had a significant capacity to stratify patient outcomes. The expression of APPL1, RAB5A, EEA1, PDCD6IP, NOX4 and SORT1 were altered in malignant patient tissue, when compared to indolent and normal prostate tissue. These findings support the initiation of a case-control study using larger cohorts of prostate tissue, with documented patient outcomes, to determine if different combinations of these new biomarkers can accurately predict disease status and clinical progression in prostate cancer patients.

Molecular Profiles of Prostate Cancer: To Treat or Not to Treat

A major dilemma in the selection of treatment for men with prostate cancer is the difficulty in accurately characterizing the risk posed by the cancer. This uncertainty has led physicians to recommend aggressive therapy for most men diagnosed with prostate cancer and has led to concerns about the benefits of screening and the adverse consequences of excessive treatment. Genomic analyses of prostate cancer reveal distinct patterns of alterations in the genomic landscape of the disease that show promise for improved prediction of prognosis and better medical decision making. Several molecular profiles are now commercially available and are being used to inform medical decisions. This article describes the clinical tests available for distinguishing aggressive from nonaggressive prostate cancer, reviews the new genomic tests, and discusses their advantages and limitations and the evidence for their utility in various clinical settings.

Cancer biomarker discovery and validation

With the emergence of genomic profiling technologies and selective molecular targeted therapies, biomarkers play an increasingly important role in the clinical management of cancer patients. Single gene/protein or multi-gene "signature"-based assays have been introduced to measure specific molecular pathway deregulations that guide therapeutic decision-making as predictive biomarkers. Genome-based prognostic biomarkers are also available for several cancer types for potential incorporation into clinical prognostic staging systems or practice guidelines. However, there is still a large gap between initial biomarker discovery studies and their clinical translation due to the challenges in the process of cancer biomarker development. In this review we summarize the steps of biomarker development, highlight key issues in successful validation and implementation, and overview representative examples in the oncology field. We also discuss regulatory issues and future perspectives in the era of big data analysis and precision medicine.

Integrative molecular profiling of routine clinical prostate cancer specimens

BACKGROUND

Comprehensive molecular profiling led to the recognition of multiple prostate cancer (PCa) molecular subtypes and driving alterations, but translating these findings to clinical practice is challenging.

PATIENTS AND METHODS

We developed a formalin-fixed paraffin-embedded (FFPE) tissue compatible integrative assay for PCa molecular subtyping and interrogation of relevant genetic/transcriptomic alterations (MiPC). We applied MiPC, which combines capture-based next generation sequencing and quantitative reverse transcription PCR (qRT-PCR), to 53 FFPE PCa specimens representing cases not well represented in frozen tissue cohorts, including 8 paired primary tumor and lymph node metastases. Results were validated using multiplexed PCR based NGS and Sanger sequencing.

RESULTS

We identified known and novel potential driving, somatic mutations and copy number alterations, including a novel BRAF T599_V600insHT mutation and CYP11B2 amplification in a patient treated with ketoconazole (a potent CYP11B2 inhibitor). qRT-PCR integration enabled comprehensive molecular subtyping and provided complementary information, such as androgen receptor (AR) target gene module assessment in advanced cases and SPINK1 over-expression. MiPC identified highly concordant profiles for all 8 tumor/lymph node metastasis pairs, consistent with limited heterogeneity amongst driving events. MiPC and exome sequencing were performed on separately isolated conventional acinar PCa and prostatic small cell carcinoma (SCC) components from the same FFPE resection specimen to enable direct comparison of histologically distinct components. While both components showed TMPRSS2:ERG fusions, the SCC component exclusively harbored complete TP53 inactivation (frameshift variant and copy loss) and two CREBBP mutations.

CONCLUSIONS

Our results demonstrate the feasibility of integrative profiling of routine PCa specimens, which may have utility for understanding disease biology and enabling personalized medicine applications.