A 12-gene expression signature is associated with aggressive histological in prostate cancer: SEC14L1 and TCEB1 genes are potential markers of progression

Identification of feature genes and molecular mechanisms involved in cell communication in uveal melanoma through analysis of single‑cell sequencing data

Uveal melanoma (UM) is a highly metastatic cancer with resistance to immunotherapy. The present study aimed to identify novel feature genes and molecular mechanisms in UM through analysis of single-cell sequencing data. For this purpose, data were downloaded from The Cancer Genome Atlas and National Center for Biotechnology Information Gene Expression Omnibus public databases. The statistical analysis function of the CellPhoneDB software package was used to analyze the ligand-receptor relationships of the feature genes. The Metascape database was used to perform the functional annotation of notable gene sets. The randomForestSRC package and random survival forest algorithm were applied to screen feature genes. The CIBERSORT algorithm was used to analyze the RNA-sequencing data and infer the relative proportions of the 22 immune-infiltrating cell types. In vitro, small interfering RNAs were used to knockdown the expression of target genes in C918 cells. The migration capability and viability of these cells were then assessed by gap closure and Cell Counting Kit-8 assays. In total, 13 single-cell sample subtypes were clustered by t-distributed Stochastic Neighbor Embedding and annotated by the R package, SingleR, into 7 cell categories: Tissue stem cells, epithelial cells, fibroblasts, macrophages, natural killer cells, neurons and endothelial cells. The interactions in NK cells|Endothelial cells, Neurons|Endothelial cells, CD74_APP, and SPP1_PTGER4 were more significant than those in the other subsets. T-Box transcription factor 2, tropomyosin 4, plexin D1 (PLXND1), G protein subunit α I2 (GNAI2) and SEC14-like lipid binding 1 were identified as the feature genes in UM. These marker genes were found to be significantly enriched in pathways such as vasculature development, focal adhesion and cell adhesion molecule binding. Significant correlations were observed between key genes and immune cells as well as immune factors. Relationships were also observed between the expression levels of the key genes and multiple disease-related genes. Knockdown of PLXND1 and GNAI2 expression led to significantly lower viability and gap closure rates of C918 cells. Therefore, the results of the present study uncovered cell communication between endothelial cells and other cell types, identified innovative key genes and provided potential targets of gene therapy in UM.

Towards key genes identification for breast cancer survival risk with neural network models

Breast cancer, one common malignant tumor all over the world, has a considerably high rate of recurrence, which endangers the health and life of patients. While more and more data have been available, how to leverage the gene expression data to predict the survival risk of cancer patients and identify key genes has become a hot topic for cancer research. Therefore, in this work, we investigate the gene expression and clinical data of breast cancer patients, specifically a novel framework is proposed focusing on the survival risk classification and key gene identification task. We firstly combine the differential expression and univariate Cox regression analysis to achieve dimensional reduction of gene expression data. The median survival time is subsequently proposed as the risk classification threshold and a learning model based on neural network is trained to classify the survival risk of patients. Innovatively, in this work, the activation region visualization technology is selected as the identification tool, which identify 20 key genes related to the survival risk of breast cancer patients. We further analyze the gene function of these 20 key genes based on STRING database. It is critical to learn that, the genetic biomarkers identified in this paper may possess value for the following clinical treatment of breast cancer according to the literature findings. Importantly, the genetic biomarkers identified in this paper may possess value for the following clinical treatment of breast cancer according to the literature findings. Our work accomplishes the objective of proposing a targeted approach to enhancing the survival analysis and therapeutic strategies in breast cancer through advanced computational techniques and gene analysis.

Tumoral periprostatic adipose tissue exovesicles-derived miR-20a-5p regulates prostate cancer cell proliferation and inflammation through the RORA gene

BACKGROUND

From the first steps of prostate cancer (PCa) initiation, tumours are in contact with the most-proximal adipose tissue called periprostatic adipose tissue (PPAT). Extracellular vesicles are important carriers of non-coding RNA such as miRNAs that are crucial for cellular communication. The secretion of extracellular vesicles by PPAT may play a key role in the interactions between adipocytes and tumour. Analysing the PPAT exovesicles (EVs) derived-miRNA content can be of great relevance for understanding tumour progression and aggressiveness.

METHODS

A total of 24 samples of human PPAT and 17 samples of perivesical adipose tissue (PVAT) were used. EVs were characterized by western blot and transmission electron microscopy (TEM), and uptake by PCa cells was verified by confocal microscopy. PPAT and PVAT explants were cultured overnight, EVs were isolated, and miRNA content expression profile was analysed. Pathway and functional enrichment analyses were performed seeking potential miRNA targets. In vitro functional studies were evaluated using PCa cells lines, miRNA inhibitors and target gene silencers.

RESULTS

Western blot and TEM revealed the characteristics of EVs derived from PPAT (PPAT-EVs) samples. The EVs were up taken and found in the cytoplasm of PCa cells. Nine miRNAs were differentially expressed between PPAT and PVAT samples. The RORA gene (RAR Related Orphan Receptor A) was identified as a common target of 9 miRNA-regulated pathways. In vitro functional analysis revealed that the RORA gene was regulated by PPAT-EVs-derived miRNAs and was found to be implicated in cell proliferation and inflammation.

CONCLUSION

Tumour periprostatic adipose tissue is linked to PCa tumour aggressiveness and could be envisaged for new therapeutic strategies.

FOXA1 regulates ribosomal RNA transcription in prostate cancer

BACKGROUND

Ribosome biogenesis is excessively activated in tumor cells, yet it is little known whether oncogenic transcription factors (TFs) are involved in the ribosomal RNA (rRNA) transactivation.

METHODS

Nucleolar proteomics data and large-scale immunofluorescence were re-analyzed to jointly identify the proteins localized at nucleolus. RNA-Seq data of five prostate cancer (PCa) cohorts were combined and integrated with multi-dimensional data to define the upregulated nucleolar TFs in PCa tissues. Then, ChIP-Seq data of PCa cell lines and two PCa clinical cohorts were re-analyzed to reveal the TF binding patterns at ribosomal DNA (rDNA) repeats. The TF binding at rDNA was validated by ChIP-qPCR. The effect of the TF on rRNA transcription was determined by rDNA luciferase reporter, nascent RNA synthesis, and global protein translation assays.

RESULTS

In this study, we reveal the role of oncogenic TF FOXA1 in regulating rRNA transcription within nucleolar organization regions. By analyzing human TFs in prostate cancer clinical datasets and nucleolar proteomics data, we identified that FOXA1 is partially localized in the nucleolus and correlated with global protein translation. Our extensive FOXA1 ChIP-Seq analysis provides robust evidence of FOXA1 binding across rDNA repeats in prostate cancer cell lines, primary tumors, and castration-resistant variants. Notably, FOXA1 occupancy at rDNA repeats correlates with histone modifications associated with active transcription, namely H3K27ac and H3K4me3. Reducing FOXA1 expression results in decreased transactivation at rDNA, subsequently diminishing global protein synthesis.

CONCLUSIONS

Our results suggest FOXA1 regulates aberrant ribosome biogenesis downstream of oncogenic signaling in prostate cancer.

Identification of tumor-agnostic biomarkers for predicting prostate cancer progression and biochemical recurrence

The diverse clinical outcomes of prostate cancer have led to the development of gene signature assays predicting disease progression. Improved prostate cancer progression biomarkers are needed as current RNA biomarker tests have varying success for intermediate prostate cancer. Interest grows in universal gene signatures for invasive carcinoma progression. Early breast and prostate cancers share characteristics, including hormone dependence and BRCA1/2 mutations. Given the similarities in the pathobiology of breast and prostate cancer, we utilized the NanoString BC360 panel, comprising the validated PAM50 classifier and pathway-specific signatures associated with general tumor progression as well as breast cancer-specific classifiers. This retrospective cohort of primary prostate cancers (n=53) was stratified according to biochemical recurrence (BCR) status and the CAPRA-S to identify genes related to high-risk disease. Two public cohort (TCGA-PRAD and GSE54460) were used to validate the results. Expression profiling of our cohort uncovered associations between PIP and INHBA with BCR and high CAPRA-S score, as well as associations between VCAN, SFRP2, and THBS4 and BCR. Despite low levels of the ESR1 gene compared to AR, we found strong expression of the ER signaling signature, suggesting that BCR may be driven by ER-mediated pathways. Kaplan-Meier and univariate Cox proportional hazards regression analysis indicated the expression of ESR1, PGR, VCAN, and SFRP2 could predict the occurrence of relapse events. This is in keeping with the pathways represented by these genes which contribute to angiogenesis and the epithelial-mesenchymal transition. It is likely that VCAN works by activating the stroma and remodeling the tumor microenvironment. Additionally, SFRP2 overexpression has been associated with increased tumor size and reduced survival rates in breast cancer and among prostate cancer patients who experienced BCR. ESR1 influences disease progression by activating stroma, stimulating stem/progenitor prostate cancer, and inducing TGF-β. Estrogen signaling may therefore serve as a surrogate to AR signaling during progression and in hormone-refractory disease, particularly in prostate cancer patients with stromal-rich tumors. Collectively, the use of agnostic biomarkers developed for breast cancer stratification has facilitated a precise clinical classification of patients undergoing radical prostatectomy and highlighted the therapeutic potential of targeting estrogen signaling in prostate cancer.

ATP8B1: A prognostic prostate cancer biomarker identified via genetic analysis

BACKGROUND

Controlling the asymmetric distribution of phospholipids across biological membranes plays a pivotal role in the life cycle of cells; one of the most important contributors that maintain this lipid asymmetry are phospholipid-transporting adenosine triphosphatases (ATPases). Although sufficient information regarding their association with cancer exists, there is limited evidence linking the genetic variants of phospholipid-transporting ATPase family genes to prostate cancer in humans.

METHODS

In this study, we investigated the association of 222 haplotype-tagging single-nucleotide polymorphisms (SNPs) in eight phospholipid-transporting ATPase genes with cancer-specific survival (CSS) and overall survival (OS) of 630 patients treated with androgen-deprivation therapy (ADT) for prostate cancer.

RESULTS

After multivariate Cox regression analysis and multiple testing correction, we found that ATP8B1 rs7239484 was remarkably associated with CSS and OS after ADT. A pooled analysis of multiple independent gene-expression datasets demonstrated that ATP8B1 was under-expressed in tumor tissues and that a higher ATP8B1 expression was associated with a better patient prognosis. Moreover, we established highly invasive sublines using two human prostate cancer cell lines to mimic cancer progression traits in vitro. The expression of ATP8B1 was consistently downregulated in both highly invasive sublines.

CONCLUSION

Our study indicates that rs7239484 is a prognostic factor for patients treated with ADT and that ATP8B1 can potentially attenuate prostate cancer progression.

Evaluation of Saccharomyces cerevisiae -like 1 (SEC14L1) in Gynecologic Malignancies Shows Overexpression in Endometrial Serous Carcinoma

Saccharomyces cerevisiae -like 1 ( SEC14L1 ) is a member of the SEC14 family and is involved in liposoluble vitamin transfer, and in a large cohort of breast cancer cases, was one of the genes most significantly associated with lymphovascular invasion (LVI), and had a significant relationship with human epidermal growth factor receptor 2 status, survival, and histologic grade. In this study, 111 separate gynecologic tumors were studied for SEC14L1 protein expression, including: uterine adenosarcoma, ovarian clear cell carcinoma, endometrial stromal sarcoma, endometrioid carcinoma of the uterus, high-grade serous carcinoma, ovarian endometrioid carcinoma, uterine leiomyosarcoma, low-grade serous carcinoma, uterine carcinosarcoma, and uterine serous carcinoma (USC). Overall, LVI was noted in 31/111 (28%) cases, highest in uterine carcinosarcoma (5/11; 45%), high-grade serous carcinoma (9/21; 43%), and ovarian clear cell carcinoma (4/10; 40%). SEC14L1 was positive in 25/111 (23%) cases; the highest percentage and only statistically significant finding by tumor type was USC at 9/12 (75%) cases positive. No relation between LVI or survival and SEC14L1 expression was noted. The relation between USC, a tumor known to show human epidermal growth factor receptor 2 overexpression and SEC14L1 is a novel finding, the significance of which warrants further study.

Genomic profiling and network-level understanding uncover the potential genes and the pathways in hepatocellular carcinoma

Data integration with phenotypes such as gene expression, pathways or function, and protein-protein interactions data has proven to be a highly promising technique for improving human complex diseases, particularly cancer patient outcome prediction. Hepatocellular carcinoma is one of the most prevalent cancers, and the most common cause is chronic HBV and HCV infection, which is linked to the majority of cases, and HBV and HCV play a role in multistep carcinogenesis progression. We examined the list of known hepatocellular carcinoma biomarkers with the publicly available expression profile dataset of hepatocellular carcinoma infected with HCV from day 1 to day 10 in this study. The study covers an overexpression pattern for the selected biomarkers in clinical hepatocellular carcinoma patients, a combined investigation of these biomarkers with the gathered temporal dataset, temporal expression profiling changes, and temporal pathway enrichment following HCV infection. Following a temporal analysis, it was discovered that the early stages of HCV infection tend to be more harmful in terms of expression shifting patterns, and that there is no significant change after that, followed by a set of genes that are consistently altered. PI3K, cAMP, TGF, TNF, Rap1, NF-kB, Apoptosis, Longevity regulating pathway, signaling pathways regulating pluripotency of stem cells, Cytokine-cytokine receptor interaction, p53 signaling, Wnt signaling, Toll-like receptor signaling, and Hippo signaling pathways are just a few of the most commonly enriched pathways. The majority of these pathways are well-known for their roles in the immune system, infection and inflammation, and human illnesses like cancer. We also find that ADCY8, MYC, PTK2, CTNNB1, TP53, RB1, PRKCA, TCF7L2, PAK1, ITPR2, CYP3A4, UGT1A6, GCK, and FGFR2/3 appear to be among the prominent genes based on the networks of genes and pathways based on the copy number alterations, mutations, and structural variants study.

Antizyme Inhibitor 1 regulates matrikine expression and enhances the metastatic potential of aggressive primary prostate cancer

Molecular drivers of metastasis in patients with high-risk localized prostate cancer (PCa) are poorly understood. Therefore, we aim to study molecular drivers of metastatic progression in high-risk PCa patients. A retrospective matched case-control study of two clinico-pathologically identical groups of high-risk PCa patients was undertaken. One group developed metastatic recurrence (n=19) while the other did not (n=25). The primary index tumor was identified by a uro-pathologist, followed by DNA and RNA extraction for somatic copy number aberration (CNA) analysis and whole-transcriptome gene expression analysis. In vitro and in vivo studies included cell line manipulation and xenograft models. The integrative CNA and gene expression analyses identified an increase in AZIN1 gene expression within a focal amplification of 8q22.3, which was associated with metastatic recurrence of high-risk PCa patients in four independent cohorts. The effects of AZIN1 knockdown were evaluated, due to its therapeutic potential. AZIN1 knockdown effected proliferation and metastatic potential of PCa cells and xenograft models. RNA sequencing after AZIN1 knockdown in PCa cells revealed upregulation of genes coding for collagen subunits. The observed effect on cell migration after AZIN1 knockdown was mimicked when exposing PCa cells to bio-active molecules deriving from COL4A1 and COL4A2. Our integrated CNA and gene expression analysis of primary high-risk PCa identified the AZIN1 gene as a novel driver of metastatic progression, by altering collagen subunit expression. Future research should further investigate its therapeutic potential in preventing metastatic recurrence. Implications: AZIN1 was identified as driver of metastatic progression in high-risk PCa through matrikine regulation.

Vitamins D2 and D3 Have Overlapping But Different Effects on the Human Immune System Revealed Through Analysis of the Blood Transcriptome

Vitamin D is best known for its role in maintaining bone health and calcium homeostasis. However, it also exerts a broad range of extra-skeletal effects on cellular physiology and on the immune system. Vitamins D₂ and D₃ share a high degree of structural similarity. Functional equivalence in their vitamin D-dependent effects on human physiology is usually assumed but has in fact not been well defined experimentally. In this study we seek to redress the gap in knowledge by undertaking an in-depth examination of changes in the human blood transcriptome following supplementation with physiological doses of vitamin D₂ and D₃. Our work extends a previously published randomized placebo-controlled trial that recruited healthy white European and South Asian women who were given 15 µg of vitamin D₂ or D₃ daily over 12 weeks in wintertime in the UK (Nov-Mar) by additionally determining changes in the blood transcriptome over the intervention period using microarrays. An integrated comparison of the results defines both the effect of vitamin D₃ or D₂ on gene expression, and any influence of ethnic background. An important aspect of this analysis was the focus on the changes in expression from baseline to the 12-week endpoint of treatment within each individual, harnessing the longitudinal design of the study. Whilst overlap in the repertoire of differentially expressed genes was present in the D₂ or D₃-dependent effects identified, most changes were specific to either one vitamin or the other. The data also pointed to the possibility of ethnic differences in the responses. Notably, following vitamin D₃ supplementation, the majority of changes in gene expression reflected a down-regulation in the activity of genes, many encoding pathways of the innate and adaptive immune systems, potentially shifting the immune system to a more tolerogenic status. Surprisingly, gene expression associated with type I and type II interferon activity, critical to the innate response to bacterial and viral infections, differed following supplementation with either vitamin D₂ or vitamin D₃, with only vitamin D₃ having a stimulatory effect. This study suggests that further investigation of the respective physiological roles of vitamin D₂ and vitamin D₃ is warranted.

Identification of the similarly expressed genes in patients with polycystic ovary syndrome and transsexuals

ABSTRACT

Polycystic ovary syndrome (PCOS) is a common female infertility, which may be caused by excessive androgen, but its mechanism remains unknown. Transsexuals are women who take androgen drugs for a long time, and gradually have male signs. Their ovaries may have received high concentrations of androgen, which leads to the failure of ovarian reproductive function. Therefore, we searched the relevant data of PCOS and transsexuals in gene expression omnibus database, used limma package to identify the most similarly genes, and then analyzed the possible mechanism of PCOS through gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway analysis. Then, the protein-protein interaction network was constructed by searching the String database, and the top 5 hub genes were identified by the cytohubba plug-in of Cytoscape. Finally, ubiquitin conjugating enzyme E2 E1 (UBE2E1), ubiquitin C (UBC), transcription elongation factor B subunit 1 (TCEB1), ubiquitin conjugating enzyme E2 N (UBE2N), and ring finger protein 7 (RNF7) genes were identified as the most similarly expressed genes between PCOS and Transsexuals. They may cause the ubiquitination of androgen receptor and eventually lead to sinus follicular growth arrest. In conclusion, 5 Central genes were identified in PCOS and transsexuals. These genes can be used as targets for early diagnosis or treatment of PCOS.

Proteomic-Based Analysis of Hypoxia- and Physioxia-Responsive Proteins and Pathways in Diffuse Large B-Cell Lymphoma

Hypoxia is a common feature in most tumors, including hematological malignancies. There is a lack of studies on hypoxia- and physioxia-induced global proteome changes in lymphoma. Here, we sought to explore how the proteome of diffuse large B-cell lymphoma (DLBCL) changes when cells are exposed to acute hypoxic stress (1% of O₂) and physioxia (5% of O₂) for a long-time. A total of 8239 proteins were identified by LC–MS/MS, of which 718, 513, and 486 had significant changes, in abundance, in the Ri-1, U2904, and U2932 cell lines, respectively. We observed that changes in B-NHL proteome profiles induced by hypoxia and physioxia were quantitatively similar in each cell line; however, differentially abundant proteins (DAPs) were specific to a certain cell line. A significant downregulation of several ribosome proteins indicated a translational inhibition of new ribosome protein synthesis in hypoxia, what was confirmed in a pathway enrichment analysis. In addition, downregulated proteins highlighted the altered cell cycle, metabolism, and interferon signaling. As expected, the enrichment of upregulated proteins revealed terms related to metabolism, HIF1 signaling, and response to oxidative stress. In accordance to our results, physioxia induced weaker changes in the protein abundance when compared to those induced by hypoxia. Our data provide new evidence for understanding mechanisms by which DLBCL cells respond to a variable oxygen level. Furthermore, this study reveals multiple hypoxia-responsive proteins showing an altered abundance in hypoxic and physioxic DLBCL. It remains to be investigated whether changes in the proteomes of DLBCL under normoxia and physioxia have functional consequences on lymphoma development and progression.

Limitations of Explainability for Established Prognostic Biomarkers of Prostate Cancer

High-throughput technologies do not only provide novel means for basic biological research but also for clinical applications in hospitals. For instance, the usage of gene expression profiles as prognostic biomarkers for predicting cancer progression has found widespread interest. Aside from predicting the progression of patients, it is generally believed that such prognostic biomarkers also provide valuable information about disease mechanisms and the underlying molecular processes that are causal for a disorder. However, the latter assumption has been challenged. In this paper, we study this problem for prostate cancer. Specifically, we investigate a large number of previously published prognostic signatures of prostate cancer based on gene expression profiles and show that none of these can provide unique information about the underlying disease etiology of prostate cancer. Hence, our analysis reveals that none of the studied signatures has a sensible biological meaning. Overall, this shows that all studied prognostic signatures are merely black-box models allowing sensible predictions of prostate cancer outcome but are not capable of providing causal explanations to enhance the understanding of prostate cancer.

Androgen signaling uses a writer and a reader of ADP-ribosylation to regulate protein complex assembly

Androgen signaling through the androgen receptor (AR) directs gene expression in both normal and prostate cancer cells. Androgen regulates multiple aspects of the AR life cycle, including its localization and post-translational modification, but understanding how modifications are read and integrated with AR activity has been difficult. Here, we show that ADP-ribosylation regulates AR through a nuclear pathway mediated by Parp7. We show that Parp7 mono-ADP-ribosylates agonist-bound AR, and that ADP-ribosyl-cysteines within the N-terminal domain mediate recruitment of the E3 ligase Dtx3L/Parp9. Molecular recognition of ADP-ribosyl-cysteine is provided by tandem macrodomains in Parp9, and Dtx3L/Parp9 modulates expression of a subset of AR-regulated genes. Parp7, ADP-ribosylation of AR, and AR-Dtx3L/Parp9 complex assembly are inhibited by Olaparib, a compound used clinically to inhibit poly-ADP-ribosyltransferases Parp1/2. Our study reveals the components of an androgen signaling axis that uses a writer and reader of ADP-ribosylation to regulate protein-protein interactions and AR activity.

Type 2 Diabetes, Obesity, and Cancer Share Some Common and Critical Pathways

Diabetes and cancer are among the most frequent and complex diseases. Epidemiological evidence showed that the patients suffering from diabetes are significantly at higher risk for a number of cancer types. There are a number of evidence that support the hypothesis that these diseases are interlinked, and obesity may aggravate the risk(s) of type 2 diabetes and cancer. Multi-level unwanted alterations such as (epi-)genetic alterations, changes at the transcriptional level, and altered signaling pathways (receptor, cytoplasmic, and nuclear level) are the major source which promotes a number of complex diseases and such heterogeneous level of complexities are considered as the major barrier in the development of therapeutic agents. With so many known challenges, it is critical to understand the relationships and the commonly shared causes between type 2 diabetes and cancer, which is difficult to unravel and understand. Furthermore, the real complexity arises from contended corroborations that specific drug(s) (individually or in combination) during the treatment of type 2 diabetes may increase or decrease the cancer risk or affect cancer prognosis. In this review article, we have presented the recent and most updated evidence from the studies where the origin, biological background, the correlation between them have been presented or proved. Furthermore, we have summarized the methodological challenges and tasks that are frequently encountered. We have also outlined the physiological links between type 2 diabetes and cancers. Finally, we have presented and summarized the outline of the hallmarks for both these diseases, diabetes and cancer.

Molecular tracing of prostate cancer lethality

Prostate cancer is diagnosed mostly in men over the age of 50 years, and has favorable 5-year survival rates due to early cancer detection and availability of curative surgical management. However, progression to metastasis and emergence of therapeutic resistance are responsible for the majority of prostate cancer mortalities. Recent advancement in sequencing technologies and computational capabilities have improved the ability to organize and analyze large data, thus enabling the identification of novel biomarkers for survival, metastatic progression and patient prognosis. Large-scale sequencing studies have also uncovered genetic and epigenetic signatures associated with prostate cancer molecular subtypes, supporting the development of personalized targeted-therapies. However, the current state of mainstream prostate cancer management does not take full advantage of the personalized diagnostic and treatment modalities available. This review focuses on interrogating biomarkers of prostate cancer progression, including gene signatures that correspond to the acquisition of tumor lethality and those of predictive and prognostic value in progression to advanced disease, and suggest how we can use our knowledge of biomarkers and molecular subtypes to improve patient treatment and survival outcomes.

Development and Validation of a Novel TP53 Mutation Signature That Predicts Risk of Metastasis in Primary Prostate Cancer

INTRODUCTION

Prostate tumors with TP53 gene mutations are molecularly heterogenous, and the presence of TP53 gene mutations has been linked to inferior outcomes. We developed an RNA-based gene signature that detects underlying TP53 gene mutations and identifies wild-type prostate tumors that are analogous to TP53-mutant tumors.

MATERIALS AND METHODS

Using genomic expression profiles from The Cancer Genome Atlas, we developed a mutation signature score to predict prostatic tumors with a molecular fingerprint similar to tumors with TP53 mutations. Area under the receiver operating characteristic curve assessed model accuracy in predicting TP53 mutations, and Cox regression models measured association between the signature and progression-free survival and metastasis-free survival (MFS).

RESULTS

The TP53 signature score achieved an area under the receiver operating characteristic curve of 0.84 in the training and 0.82 in the validation cohorts for predicting an underlying mutation. In three retrospective cohorts, a high score was prognostic for poor 5-year MFS: 46% versus 81% (hazard ratio [HR], 3.05; P < .0001; Johns Hopkins University cohort), 64% versus 83% (HR, 2.77; P < .0001; Mayo Clinic cohort), and 71% versus 97% (HR, 6.8; P = .0001; Brigham and Women's Hospital cohort). The signature also identified TP53 wild-type tumors molecularly analogous to TP53 mutant tumors, wherein high signature score correlated with worse 5-year MFS (50% vs. 82%; HR, 3.05; P < .0001).

CONCLUSIONS

This novel mutational signature predicted tumors with TP53 mutations, identified TP53 wild-type tumors analogous to mutant tumors, and was independently associated with poor MFS. This signature can therefore be used to strengthen existing clinical risk-stratification tools.

Convergence of Prognostic Gene Signatures Suggests Underlying Mechanisms of Human Prostate Cancer Progression

The highly heterogeneous clinical course of human prostate cancer has prompted the development of multiple RNA biomarkers and diagnostic tools to predict outcome for individual patients. Biomarker discovery is often unstable with, for example, small changes in discovery dataset configuration resulting in large alterations in biomarker composition. Our hypothesis, which forms the basis of this current study, is that highly significant overlaps occurring between gene signatures obtained using entirely different approaches indicate genes fundamental for controlling cancer progression. For prostate cancer, we found two sets of signatures that had significant overlaps suggesting important genes (p < 10⁻³⁴ for paired overlaps, hypergeometrical test). These overlapping signatures defined a core set of genes linking hormone signalling (HES6-AR), cell cycle progression (Prolaris) and a molecular subgroup of patients (PCS1) derived by Non Negative Matrix Factorization (NNMF) of control pathways, together designated as SIG-HES6. The second set (designated SIG-DESNT) consisted of the DESNT diagnostic signature and a second NNMF signature PCS3. Stratifications using SIG-HES6 (HES6, PCS1, Prolaris) and SIG-DESNT (DESNT) classifiers frequently detected the same individual high-risk cancers, indicating that the underlying mechanisms associated with SIG-HES6 and SIG-DESNT may act together to promote aggressive cancer development. We show that the use of combinations of a SIG-HES6 signature together with DESNT substantially increases the ability to predict poor outcome, and we propose a model for prostate cancer development involving co-operation between the SIG-HES6 and SIG-DESNT pathways that has implication for therapeutic design.

A Radiogenomic Approach for Decoding Molecular Mechanisms Underlying Tumor Progression in Prostate Cancer

Prostate cancer (PCa) is a genetically heterogeneous cancer entity that causes challenges in pre-treatment clinical evaluation, such as the correct identification of the tumor stage. Conventional clinical tests based on digital rectal examination, Prostate-Specific Antigen (PSA) levels, and Gleason score still lack accuracy for stage prediction. We hypothesize that unraveling the molecular mechanisms underlying PCa staging via integrative analysis of multi-OMICs data could significantly improve the prediction accuracy for PCa pathological stages. We present a radiogenomic approach comprising clinical, imaging, and two genomic (gene and miRNA expression) datasets for 298 PCa patients. Comprehensive analysis of gene and miRNA expression profiles for two frequent PCa stages (T2c and T3b) unraveled the molecular characteristics for each stage and the corresponding gene regulatory interaction network that may drive tumor upstaging from T2c to T3b. Furthermore, four biomarkers (ANPEP, mir-217, mir-592, mir-6715b) were found to distinguish between the two PCa stages and were highly correlated (average r = ± 0.75) with corresponding aggressiveness-related imaging features in both tumor stages. When combined with related clinical features, these biomarkers markedly improved the prediction accuracy for the pathological stage. Our prediction model exhibits high potential to yield clinically relevant results for characterizing PCa aggressiveness.

Unbiased data mining identifies cell cycle transcripts that predict non-indolent Gleason score 7 prostate cancer

BACKGROUND

Patients with newly diagnosed non-metastatic prostate adenocarcinoma are typically classified as at low, intermediate, or high risk of disease progression using blood prostate-specific antigen concentration, tumour T category, and tumour pathological Gleason score. Classification is used to both predict clinical outcome and to inform initial management. However, significant heterogeneity is observed in outcome, particularly within the intermediate risk group, and there is an urgent need for additional markers to more accurately hone risk prediction. Recently developed web-based visualization and analysis tools have facilitated rapid interrogation of large transcriptome datasets, and querying broadly across multiple large datasets should identify predictors that are widely applicable.

METHODS

We used camcAPP, cBioPortal, CRN, and NIH NCI GDC Data Portal to data mine publicly available large prostate cancer datasets. A test set of biomarkers was developed by identifying transcripts that had: 1) altered abundance in prostate cancer, 2) altered expression in patients with Gleason score 7 tumours and biochemical recurrence, 3) correlation of expression with time until biochemical recurrence across three datasets (Cambridge, Stockholm, MSKCC). Transcripts that met these criteria were then examined in a validation dataset (TCGA-PRAD) using univariate and multivariable models to predict biochemical recurrence in patients with Gleason score 7 tumours.

RESULTS

Twenty transcripts met the test criteria, and 12 were validated in TCGA-PRAD Gleason score 7 patients. Ten of these transcripts remained prognostic in Gleason score 3 + 4 = 7, a sub-group of Gleason score 7 patients typically considered at a lower risk for poor outcome and often not targeted for aggressive management. All transcripts positively associated with recurrence encode or regulate mitosis and cell cycle-related proteins. The top performer was BUB1, one of four key MIR145-3P microRNA targets upregulated in hormone-sensitive as well as castration-resistant PCa. SRD5A2 converts testosterone to its more active form and was negatively associated with biochemical recurrence.

CONCLUSIONS

Unbiased mining of large patient datasets identified 12 transcripts that independently predicted disease recurrence risk in Gleason score 7 prostate cancer. The mitosis and cell cycle proteins identified are also implicated in progression to castration-resistant prostate cancer, revealing a pivotal role for loss of cell cycle control in the latter.

Saccharomyces cerevisiae-like 1 (SEC14L1) is a prognostic factor in breast cancer associated with lymphovascular invasion

Lymphovascular invasion is strongly related to breast cancer metastasis. However, the underlying mechanisms of lymphovascular invasion and its driver molecules in breast cancer remain to be defined. In this study, we explore differential expression of genes in large molecularly characterized and clinically annotated datasets of invasive breast cancer patients (n = 8056) coupled with histological review and strict definition for lymphovascular invasion status. The METABRIC series was used to identify genes associated with lymphovascular invasion, as defined using hematoxylin and eosin staining supplemented by immunohistochemistry, at the genomic/transcriptomic levels. Saccharomyces cerevisiae-like 1 (SEC14L1) was identified as one of the most significant genes associated with lymphovascular invasion. The prognostic significance of SEC14L1 gene copy number and mRNA expression was further investigated in the METABRIC series and externally validated using the Breast Cancer Gene-Expression Miner v4.0. Protein expression of SEC14L1 was also assessed using immunohistochemistry in series of early stage breast cancer using tissue microarrays. SEC14L1 gene copy number gain was significantly associated with high histological grade and poor outcome. SEC14L1 mRNA expression showed positive association with higher grade, lymph node metastasis, and poor outcome. SEC14L1 protein overexpression was significantly associated with lymphovascular invasion (p < 0.0001), higher grade (p = 0.011), HER2 positivity (p = 0.036), and shorter survival (p = 0.00075). Our findings specify SEC14L1 as an independent prognostic factor in breast cancer. Its association, at both transcriptome and protein expression levels, with lymphovascular invasion and outcome could imply an important role in tumor progression. A further mechanistic insight into its molecular roles including potential therapeutic utility is warranted.

Integrating biomarkers across omic platforms: an approach to improve stratification of patients with indolent and aggressive prostate cancer

Classifying indolent prostate cancer represents a significant clinical challenge. We investigated whether integrating data from different omic platforms could identify a biomarker panel with improved performance compared to individual platforms alone. DNA methylation, transcripts, protein and glycosylation biomarkers were assessed in a single cohort of patients treated by radical prostatectomy. Novel multiblock statistical data integration approaches were used to deal with missing data and modelled via stepwise multinomial logistic regression, or LASSO. After applying leave‐one‐out cross‐validation to each model, the probabilistic predictions of disease type for each individual panel were aggregated to improve prediction accuracy using all available information for a given patient. Through assessment of three performance parameters of area under the curve (AUC) values, calibration and decision curve analysis, the study identified an integrated biomarker panel which predicts disease type with a high level of accuracy, with Multi AUC value of 0.91 (0.89, 0.94) and Ordinal C‐Index (ORC) value of 0.94 (0.91, 0.96), which was significantly improved compared to the values for the clinical panel alone of 0.67 (0.62, 0.72) Multi AUC and 0.72 (0.67, 0.78) ORC. Biomarker integration across different omic platforms significantly improves prediction accuracy. We provide a novel multiplatform approach for the analysis, determination and performance assessment of novel panels which can be applied to other diseases. With further refinement and validation, this panel could form a tool to help inform appropriate treatment strategies impacting on patient outcome in early stage prostate cancer.

Differentially expressed microRNAs in lung adenocarcinoma invert effects of copy number aberrations of prognostic genes

In many cancers, significantly down- or upregulated genes are found within chromosomal regions with DNA copy number alteration opposite to the expression changes. Generally, this paradox has been overlooked as noise, but can potentially be a consequence of interference of epigenetic regulatory mechanisms, including microRNA-mediated control of mRNA levels.

To explore potential associations between microRNAs and paradoxes in non-small-cell lung cancer (NSCLC) we curated and analyzed lung adenocarcinoma (LUAD) data, comprising gene expressions, copy number aberrations (CNAs) and microRNA expressions. We integrated data from 1,062 tumor samples and 241 normal lung samples, including newly-generated array comparative genomic hybridization (aCGH) data from 63 LUAD samples.

We identified 85 “paradoxical” genes whose differential expression consistently contrasted with aberrations of their copy numbers. Paradoxical status of 70 out of 85 genes was validated on sample-wise basis using The Cancer Genome Atlas (TCGA) LUAD data. Of these, 41 genes are prognostic and form a clinically relevant signature, which we validated on three independent datasets. By meta-analysis of results from 9 LUAD microRNA expression studies we identified 24 consistently-deregulated microRNAs. Using TCGA-LUAD data we showed that deregulation of 19 of these microRNAs explains differential expression of the paradoxical genes.

Our results show that deregulation of paradoxical genes is crucial in LUAD and their expression pattern is maintained epigenetically, defying gene copy number status.

Serum levels of miR-320 family members are associated with clinical parameters and diagnosis in prostate cancer patients

We studied the association of the serum levels of the microRNA family members miR-320a/-b/-c with clinico-pathological data to assess their applicability as diagnostic biomarker in prostate cancer (PCa) patients. The levels of miR-320a/-b/-c in 3 groups were evaluated by qRT-PCR (145 patients with PCa, 31 patients with benign prostatic hyperplasia (BPH) and 19 healthy controls). The levels of the three family members of miR-320 were directly correlated within each group (P < 0.001), but they differed significantly among the three groups (P < 0.001). The serum levels of the miR-320 family members were significantly increased in older patients compared to younger patients (≤ 66 years vs. > 66 years, P ≤ 0.001). In addition, the levels of all three miR-320 family members were significantly different in patients with low tumor stage compared with those with high tumor stage (miR-320a: P = 0.034; miR-320b: P = 0.006; miR-320c: P = 0.007) and in patients with low serum PSA compared with those with high serum PSA (≤ 4 ng vs. > 4 ng; miR-320a: P = 0.003; miR-320b: P = 0.003; miR-320c: P = 0.006). The levels of these miRNAs were inversely correlated with serum PSA levels. Detection in the serum samples of PCa patients with or without PSA relapse revealed higher levels of miR-320a/-b/-c in the group without PSA relapse before/after radical prostatectomy than in that with PCa relapse. In summary, the differences among the PCa/BPH/healthy control groups with respect to miR-320a/-b/-c levels in conjunction with higher levels in patients without a PSA relapse than in those with a relapse suggest the diagnostic potential of these miRNA-320 family members in PCa patients.

Genome-wide CRISPR screen identifies HNRNPL as a prostate cancer dependency regulating RNA splicing

Alternative RNA splicing plays an important role in cancer. To determine which factors involved in RNA processing are essential in prostate cancer, we performed a genome-wide CRISPR/Cas9 knockout screen to identify the genes that are required for prostate cancer growth. Functional annotation defined a set of essential spliceosome and RNA binding protein (RBP) genes, including most notably heterogeneous nuclear ribonucleoprotein L (HNRNPL). We defined the HNRNPL-bound RNA landscape by RNA immunoprecipitation coupled with next-generation sequencing and linked these RBP-RNA interactions to changes in RNA processing. HNRNPL directly regulates the alternative splicing of a set of RNAs, including those encoding the androgen receptor, the key lineage-specific prostate cancer oncogene. HNRNPL also regulates circular RNA formation via back splicing. Importantly, both HNRNPL and its RNA targets are aberrantly expressed in human prostate tumors, supporting their clinical relevance. Collectively, our data reveal HNRNPL and its RNA clients as players in prostate cancer growth and potential therapeutic targets.

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.

Cancer/Testis Antigens: "Smart" Biomarkers for Diagnosis and Prognosis of Prostate and Other Cancers

A clinical dilemma in the management of prostate cancer (PCa) is to distinguish men with aggressive disease who need definitive treatment from men who may not require immediate intervention. Accurate prediction of disease behavior is critical because radical treatment is associated with high morbidity. Here, we highlight the cancer/testis antigens (CTAs) as potential PCa biomarkers. The CTAs are a group of proteins that are typically restricted to the testis in the normal adult but are aberrantly expressed in several types of cancers. Interestingly, >90% of CTAs are predicted to belong to the realm of intrinsically disordered proteins (IDPs), which do not have unique structures and exist as highly dynamic conformational ensembles, but are known to play important roles in several biological processes. Using prostate-associated gene 4 (PAGE4) as an example of a disordered CTA, we highlight how IDP conformational dynamics may regulate phenotypic heterogeneity in PCa cells, and how it may be exploited both as a potential biomarker as well as a promising therapeutic target in PCa. We also discuss how in addition to intrinsic disorder and post-translational modifications, structural and functional variability induced in the CTAs by alternate splicing represents an important feature that might have different roles in different cancers. Although it is clear that significant additional work needs to be done in the outlined direction, this novel concept emphasizing (multi)functionality as an important trait in selecting a biomarker underscoring the theranostic potential of CTAs that is latent in their structure (or, more appropriately, the lack thereof), and casts them as next generation or “smart” biomarker candidates.

Correlation of B7-H3 with androgen receptor, immune pathways and poor outcome in prostate cancer: an expression-based analysis

BACKGROUND

B7-H3 (CD276), part of the B7 superfamily of immune checkpoint molecules, has been shown to have an immunomodulatory role. Its regulation, receptor and mechanism of action remain unclear. B7-H3 protein expression correlates with prostate cancer outcomes, and humanized monoclonal antibodies (that is, enoblituzumab) are currently being investigated for therapeutic use. Here we used genomic expression data to examine the relationship between B7-H3 mRNA expression and prostate cancer.

METHODS

Prostatectomy tissue from 2781 patients were profiled using the Affymetrix HuEx 1.0 ST microarray. Pairwise comparisons were used to identify significant associations between B7-H3 expression and clinicopathologic variables, and survival analyses were used to evaluate the prognostic significance of B7-H3. Pearson's correlation analyses were performed to assess the relationship of B7-H3 expression with molecular subtypes and individual transcripts. Androgen receptor (AR) occupancy at the B7-H3 locus was determined using chromatin immunoprecipitation (ChIP), and androgen-dependent expression changes in B7-H3 was evaluated by quantitative reverse transcription PCR in LNCaP cell lines. Oncomine was queried to evaluate B7-H3 expression in metastatic disease.

RESULTS

B7-H3 mRNA expression was positively associated with higher Gleason score (P<0.001), tumor stage (P<0.001), and castrate resistant metastatic disease (P<0.0001). High B7-H3 expression correlated with the development of metastasis and prostate cancer specific mortality, but this was not significant on multi-variable analysis. B7-H3 expression correlated with ERG-positive disease (r=0.99) and AR expression (r=0.36). ChIP revealed an AR-binding site upstream of B7-H3, and the presence of androgens decreased B7-H3 expression in LNCaP suggesting potential direct AR regulation. Gene set enrichment analysis demonstrated an association of B7-H3 with androgen signaling as well as immune regulatory pathways.

CONCLUSIONS

Higher B7-H3 expression correlates with Gleason grade, prostate cancer stage and poor oncologic outcomes in prostatectomy cohorts. B7-H3 expression appears to be related to androgen signaling as well as the immune reactome.

Prognostic Utility of a New mRNA Expression Signature of Gleason Score

PURPOSE

Gleason score strongly predicts prostate cancer mortality; however, scoring varies among pathologists, and many men are diagnosed with intermediate-risk Gleason score 7. We previously developed a 157-gene signature for Gleason score using a limited gene panel. Using a new whole-transcriptome expression dataset, we verified the previous signature's performance and developed a new Gleason signature to improve lethal outcome prediction among men with Gleason score 7.

EXPERIMENTAL DESIGN

We generated mRNA expression data from prostate tumor tissue from men in the Physicians' Health Study and Health Professionals Follow-Up Study (N = 404) using the Affymetrix Human Gene 1.0 ST microarray. The Prediction Analysis for Microarrays method was used to develop a signature to distinguish high (≥8) versus low (≤6) Gleason score. We evaluated the signature's ability to improve prediction of lethality among men with Gleason score 7, adjusting for 3 + 4/4 + 3 status, by quantifying the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

We identified a 30-gene signature that best distinguished Gleason score ≤6 from ≥8. The AUC to predict lethal disease among Gleason score 7 men was 0.76 [95% confidence interval (CI), 0.67-0.84] compared with 0.68 (95% CI, 0.59-0.76) using 3 + 4/4 + 3 status alone (P = 0.0001). This signature was a nonsignificant (P = 0.09) improvement over our previous signature (AUC = 0.72).

CONCLUSIONS

Our new 30-gene signature improved prediction of lethality among men with Gleason score 7. This signature can potentially become a useful prognostic tool for physicians to improve treatment decision making. Clin Cancer Res; 23(1); 81-87. ©2016 AACRSee related commentary by Yin et al., p. 6.

Molecular etiology of an indolent lymphoproliferative disorder determined by whole-genome sequencing

In an attempt to assess potential treatment options, whole-genome and transcriptome sequencing were performed on a patient with an unclassifiable small lymphoproliferative disorder. Variants from genome sequencing were prioritized using a combination of comparative variant distributions in a spectrum of lymphomas, and meta-analyses of gene expression profiling. In this patient, the molecular variants that we believe to be most relevant to the disease presentation most strongly resemble a diffuse large B-cell lymphoma (DLBCL), whereas the gene expression data are most consistent with a low-grade chronic lymphocytic leukemia (CLL). The variant of greatest interest was a predicted NOTCH2-truncating mutation, which has been recently reported in various lymphomas.

Differential expression profiling of microRNAs in para-carcinoma, carcinoma and relapse human pancreatic cancer

PURPOSE

To explore the altered different expression of miRNAs and the mechanisms underlying the relapse and metastasis of pancreatic cancer.

MATERIALS AND METHODS

The most differentially expressed miRNAs were analyzed by gene ontology (GO) term analysis, Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis and protein interaction analysis. The potentially regulated target genes of the most differentially expressed miRNAs were also analyzed further by GO term analysis and KEGG pathway analysis, and quantitated by qRT-PCR.

RESULTS

In total, we found 12 miRNAs displayed at least a 30-fold increase or decrease in expression of carcinoma and relapse vs. para-carcinoma human pancreatic cancer (C/R vs. P). In addition, our study found that pancreatic cancer was related to pathways in cancer, including Jak-STAT signaling pathway, MAPK signaling pathway and PPAR signaling pathway.

CONCLUSIONS

The differential expressed miRNAs and their predicted target genes that involved in Jak-STAT signaling pathway, MAPK signaling pathway and PPAR signaling pathway indicating their potential roles in pancreatic carcinogenesis and progress.

Antisense transcription at the TRPM2 locus as a novel prognostic marker and therapeutic target in prostate cancer

Overwhelming evidence indicates that cancer is a genetic disease caused by the accumulation of mutations in oncogenes and tumor suppressor genes. It is also increasingly apparent, however, that cancer depends not only on mutations in these coding genes but also on alterations in the large class of non-coding RNAs. Here, we report that one such long non-coding RNA, TRPM2-AS, an antisense transcript of TRPM2, which encodes an oxidative stress-activated ion channel, is overexpressed in prostate cancer (PCa). The high expression of TRPM2-AS and its related gene signature were found to be linked to poor clinical outcome, with the related gene signature working also independently of the patient's Gleason score. Mechanistically, TRPM2-AS knockdown led to PCa cell apoptosis, with a transcriptional profile that indicated an unbearable increase in cellular stress in the dying cells, which was coupled to cell cycle arrest, an increase in intracellular hydrogen peroxide and activation of the sense TRPM2 gene. Moreover, targets of existing drugs and treatments were found to be consistently associated with high TRPM2-AS levels in both targeted cells and patients, ultimately suggesting that the measurement of the expression levels of TRPM2-AS allows not only for the early identification of aggressive PCa tumors, but also identifies a subset of at-risk patients who would benefit from currently available, but mostly differently purposed, therapeutic agents.

Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy

PURPOSE

Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.

METHODS

A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases--men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.

RESULTS

Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67-0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.

CONCLUSION

A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.

The genomic landscape of prostate cancer

By the age of 80, approximately 80% of men will manifest some cancerous cells within their prostate, indicating that prostate cancer constitutes a major health burden. While this disease is clinically insignificant in most men, it can become lethal in others. The most challenging task for clinicians is developing a patient-tailored treatment in the knowledge that this disease is highly heterogeneous and that relatively little adequate prognostic tools are available to distinguish aggressive from indolent disease. Next-generation sequencing allows a description of the cancer at an unprecedented level of detail and at different levels, going from whole genome or exome sequencing to transcriptome analysis and methylation-specific immunoprecipitation, followed by sequencing. Integration of all these data is leading to a better understanding of the initiation, progression and metastatic processes of prostate cancer. Ultimately, these insights will result in a better and more personalized treatment of patients suffering from prostate cancer. The present review summarizes current knowledge on copy number changes, gene fusions, single nucleotide mutations and polymorphisms, methylation, microRNAs and long non-coding RNAs obtained from high-throughput studies.