A molecular signature predictive of indolent prostate cancer

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

CRIPTO's multifaceted role in driving aggressive prostate cancer unveiled by in vivo, organoid, and patient data

CRIPTO (or CR-1 or TDGF1) is a protein that plays an active role in tumor initiation and progression. We have confirmed that increased expression of CRIPTO is associated with clinical and prostate-specific antigen (PSA) progression in human prostate tissues. Our approach involved gaining insight into the role of CRIPTO signaling in castration-resistant Nkx3.1-expressing cells (CARNs), targets for oncogenic transformation in prostate cancer (PCa), by integrating the existing Criptoflox/flox into CARNs model. The most aggressive stage was modeled using an inducible Cre under control of the Nkx3.1 promoter conferring Nkx3.1 inactivation and driving Pten inactivation, oncogenic Kras activation, and lineage tracing with yellow fluorescence protein (EYFP) upon induction. Our findings provide evidence that selective depletion of Cripto in epithelial cells in vivo reduced the invasive phenotype, particularly in more advanced tumor stages. Moreover, in vitro experiments with Cripto overexpression demonstrated alterations in the physical features of organoids, which correlated with increased tumorigenic activity. Transcriptomic analyses revealed a unique CRIPTO/MYC co-activation signature associated with PSA progression in a human PCa cohort. Taken together, our data highlights a role for CRIPTO in tumor invasiveness and progression, which carries implications for biomarkers and targeted therapies.

Interruption of KLF5 acetylation promotes PTEN-deficient prostate cancer progression by reprogramming cancer-associated fibroblasts

Inactivation of phosphatase and tensin homolog (PTEN) is prevalent in human prostate cancer and causes high-grade adenocarcinoma with a long latency. Cancer-associated fibroblasts (CAFs) play a pivotal role in tumor progression, but it remains elusive whether and how PTEN-deficient prostate cancers reprogram CAFs to overcome the barriers for tumor progression. Here, we report that PTEN deficiency induced Krüppel-like factor 5 (KLF5) acetylation and that interruption of KLF5 acetylation orchestrated intricate interactions between cancer cells and CAFs that enhance FGF receptor 1 (FGFR1) signaling and promote tumor growth. Deacetylated KLF5 promoted tumor cells to secrete TNF-α, which stimulated inflammatory CAFs to release FGF9. CX3CR1 inhibition blocked FGFR1 activation triggered by FGF9 and sensitized PTEN-deficient prostate cancer to the AKT inhibitor capivasertib. This study reveals the role of KLF5 acetylation in reprogramming CAFs and provides a rationale for combined therapies using inhibitors of AKT and CX3CR1.

Parathyroid hormone related-protein (PTHrP) in tissues with poor prognosis in prostate cancer patients

BACKGROUND

Parathyroid hormone-related peptide (PTHrP) is known to have a pivotal role in the progression of various solid tumors, among which prostate cancer stands out. However, the extent of PTHrP expression and its clinical implications in prostate cancer patients remain shrouded in obscurity. The primary objective of this research endeavor was to shed light on the relevance of PTHrP in the context of prostate cancer patients and to uncover the potential underlying mechanisms.

METHODS

The expression of PTHrP, E-cadherin, and vimentin in tumor tissues of 88 prostate cancer patients was evaluated by immunohistochemical technique. Subsequently, the associations between PTHrP and clinicopathological parameters and prognosis of patients with prostate cancer were analyzed.

RESULTS

Immunohistochemical analysis showed that the expression rates of PTHrP, E-cadherin, and vimentin in prostate cancer tissues were 95.5%, 88.6%, and 84.1%, respectively. Patients with a high level of PTHrP had a decreased expression of E-cadherin (P = .013) and an increased expression of vimentin (P = .010) compared with patients with a low level of PTHrP. Besides, the high expression of PTHrP was significantly correlated with a higher level of initial prostate-specific antigen (P = .026), positive lymph node metastasis (P = .010), osseous metastasis (P = .004), and Gleason score (P = .026). Moreover, patients with a high level of PTHrP had shorter progression-free survival (P = .002) than patients with a low level of PTHrP.

CONCLUSION

The present study indicates that PTHrP is associated with risk factors of poor outcomes in prostate cancer, while epithelial-mesenchymal transition may be involved in this process.

Identification of an anaplastic subtype of prostate cancer amenable to therapies targeting SP1 or translation elongation

Histopathological heterogeneity is a hallmark of prostate cancer (PCa). Using spatial and parallel single-nucleus transcriptomics, we report an androgen receptor (AR)-positive but neuroendocrine-null primary PCa subtype with morphologic and molecular characteristics of small cell carcinoma. Such small cell-like PCa (SCLPC) is clinically aggressive with low AR, but high stemness and proliferation, activity. Molecular characterization prioritizes protein translation, represented by up-regulation of many ribosomal protein genes, and SP1, a transcriptional factor that drives SCLPC phenotype and overexpresses in castration-resistant PCa (CRPC), as two potential therapeutic targets in AR-indifferent CRPC. An SP1-specific inhibitor, plicamycin, effectively suppresses CRPC growth in vivo. Homoharringtonine, a Food And Drug Administration-approved translation elongation inhibitor, impedes CRPC progression in preclinical models and patients with CRPC. We construct an SCLPC-specific signature capable of stratifying patients for drug selectivity. Our studies reveal the existence of SCLPC in admixed PCa pathology, which may mediate tumor relapse, and establish SP1 and translation elongation as actionable therapeutic targets for CRPC.

Influence of Molecular Design on the Tumor Targeting and Biodistribution of PSMA-Binding Tracers Labeled with Technetium-99m

Previously, we designed the EuK-based PSMA ligand BQ0413 with an maE3 chelator for labeling with technetium-99m. It showed efficient tumor targeting, but our preclinical data and preliminary clinical results indicated that the renal excretion levels need to be decreased. We hypothesized that this could be achieved by a decrease in the ligand's total negative charge, achieved by substituting negatively charged glutamate residues in the chelator with glycine. The purpose of this study was to evaluate the tumor targeting and biodistribution of two new PSMA inhibitors, BQ0411 and BQ0412, compared to BQ0413. Conjugates were radiolabeled with Tc-99m and characterized in vitro, using PC3-pip cells, and in vivo, using NMRI and PC3-pip tumor-bearing mice. [99mTc]Tc-BQ0411 and [99mTc]Tc-BQ0412 demonstrated PSMA-specific binding to PC3-pip cells with picomolar affinity. The biodistribution pattern for the new conjugates was characterized by rapid excretion. The tumor uptake for [99mTc]Tc-BQ0411 was 1.6-fold higher compared to [99mTc]Tc-BQ0412 and [99mTc]Tc-BQ0413. [99mTc]Tc-BQ0413 has demonstrated predominantly renal excretion, while the new conjugates underwent both renal and hepatobiliary excretion. In this study, we have demonstrated that in such small targeting ligands as PSMA-binding EuK-based pseudopeptides, the structural blocks that do not participate in binding could have a crucial role in tumor targeting and biodistribution. The presence of a glycine-based coupling linker in BQ0411 and BQ0413 seems to optimize biodistribution. In conclusion, the substitution of amino acids in the chelating sequence is a promising method to alter the biodistribution of [99mTc]Tc-labeled small-molecule PSMA inhibitors. Further improvement of the biodistribution properties of BQ0413 is needed.

Parathyroid hormone-related protein as a potential prostate cancer biomarker: Promoting prostate cancer progression through upregulation of c-Met expression

Parathyroid hormone-related protein (PTHrP) plays a significant role in various tumor types, including prostate cancer. However, its specific role and underlying mechanisms in prostate cancer remain unclear. This study investigates the role of PTHrP and its interaction with the c-Met in prostate cancer. PTHrP was overexpressed and knocked down in prostate cancer cell lines to determine its effect on cell functions. Xenograft tumor models were employed to assess the impact of PTHrP overexpression on tumor growth. To delve into the interaction between PTHrP and c-Met, rescue experiments were conducted. Clinical data and tissue samples from prostate cancer patients were gathered and analyzed for PTHrP and c-Met expression. PTHrP overexpression in prostate cancer cells upregulates c-Met expression and augments cell functions. In contrast, PTHrP-knockdown diminishes c-Met expression and inhibits cell functions. In vivo experiments further demonstrated that PTHrP overexpression promoted tumor growth in xenograft models.Moreover, modulating c-Met expression in rescue experiments led to concurrent alterations in prostate cancer cell functions. Immunohistochemical analysis of clinical samples displayed a significant positive correlation between PTHrP and c-Met expression. Additionally, PTHrP expression correlated with clinical parameters like prostate-specific antigen (PSA) levels, tumor stage, lymph node involvement, distant metastasis, and Gleason score. PTHrP plays a crucial role in prostate cancer progression by upregulating c-Met expression. These insights point to PTHrP as a promising potential biomarker for prostate cancer.

Systematic review and integrated analysis of prognostic gene signatures for prostate cancer patients

Prostate cancer (PC) is one of the most common cancers in men and becoming the second leading cause of cancer fatalities. At present, the lack of effective strategies for prognosis of PC patients is still a problem to be solved. Therefore, it is significant to identify potential gene signatures for PC patients' prognosis. Here, we summarized 71 different prognostic gene signatures for PC and concluded 3 strategies for signature construction after extensive investigation. In addition, 14 genes frequently appeared in 71 different gene signatures, which enriched in mitotic and cell cycle. This review provides extensive understanding and integrated analysis of current prognostic signatures of PC, which may help researchers to construct gene signatures of PC and guide future clinical treatment.

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.

Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated "Omics" Approaches to Explore Measurable Metrics

Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.

Prognostic Values of Gene Copy Number Alterations in Prostate Cancer

Whilst risk prediction for individual prostate cancer (PCa) cases is of a high priority, the current risk stratification indices for PCa management have severe limitations. This study aimed to identify gene copy number alterations (CNAs) with prognostic values and to determine if any combination of gene CNAs could have risk stratification potentials. Clinical and genomic data of 500 PCa cases from the Cancer Genome Atlas stable were retrieved from the Genomic Data Commons and cBioPortal databases. The CNA statuses of a total of 52 genetic markers, including 21 novel markers and 31 previously identified potential prognostic markers, were tested for prognostic significance. The CNA statuses of a total of 51/52 genetic markers were significantly associated with advanced disease at an odds ratio threshold of ≥1.5 or ≤0.667. Moreover, a Kaplan-Meier test identified 27/52 marker CNAs which correlated with disease progression. A Cox Regression analysis showed that the amplification of MIR602 and deletions of MIR602, ZNF267, MROH1, PARP8, and HCN1 correlated with a progression-free survival independent of the disease stage and Gleason prognostic group grade. Furthermore, a binary logistic regression analysis identified twenty-two panels of markers with risk stratification potentials. The best model of 7/52 genetic CNAs, which included the SPOP alteration, SPP1 alteration, CCND1 amplification, PTEN deletion, CDKN1B deletion, PARP8 deletion, and NKX3.1 deletion, stratified the PCa cases into a localised and advanced disease with an accuracy of 70.0%, sensitivity of 85.4%, specificity of 44.9%, positive predictive value of 71.67%, and negative predictive value of 65.35%. This study validated prognostic gene level CNAs identified in previous studies, as well as identified new genetic markers with CNAs that could potentially impact risk stratification in PCa.

Immunoarray Measurements of Parathyroid Hormone-Related Peptides Combined with Other Biomarkers to Diagnose Aggressive Prostate Cancer

Parathyroid hormone-related peptide (PTHrP) is related to bone metastasis and hypercalcemia in prostate and breast cancers and should be an excellent biomarker for aggressive forms of these cancers. Current clinical detection protocols for PTHrP are immunoradiometric assay and radioimmunoassay but are not sensitive enough to detect PTHrPs at early stages. We recently evaluated a prostate cancer biomarker panel, including serum monocyte differentiation antigen (CD-14), ETS-related gene protein, pigment epithelial-derived factor, and insulin-like growth factor-1, with promise for identifying aggressive prostate cancers. This panel predicted the need for patient biopsy better than PSA alone. In the present paper, we report an ultrasensitive microfluidic assay for PTHrPs and evaluate their diagnostic value and the value of including them with our prior biomarker panel to diagnose aggressive forms of prostate cancer. The immunoarray features screen-printed carbon sensor electrodes coated with 5 nm glutathione gold nanoparticles with capture antibodies attached. PTHrPs are bound to a secondary antibody attached to a polyhorseradish peroxidase label and delivered to the sensors to provide high sensitivity when activated by H₂O₂ and a mediator. We obtained an unprecedented 0.3 fg mL^-1 limit of detection for PTHrP bioactive moieties PTHrP 1-173 and PTHrP 1-86. We also report the first study of PTHrPs in a large serum pool to identify aggressive malignancies. In assays of 130 human patient serum samples, PTHrP levels distinguished between aggressive and indolent prostate cancers with 83-91% clinical sensitivity and 78-96% specificity. Logistic regression identified the best predictive model as a combination of PTHrP 1-86 and vascular endothelial growth factor-D. PTHrP 1-173 alone also showed a high ability to differentiate aggressive and indolent cancers.

Characterization of a castrate-resistant prostate cancer xenograft derived from a patient of West African ancestry

BACKGROUND

Prostate cancer is a clinically and molecularly heterogeneous disease, with highest incidence and mortality among men of African ancestry. To date, prostate cancer patient-derived xenograft (PCPDX) models to study this disease have been difficult to establish because of limited specimen availability and poor uptake rates in immunodeficient mice. Ancestrally diverse PCPDXs are even more rare, and only six PCPDXs from self-identified African American patients from one institution were recently made available.

METHODS

In the present study, we established a PCPDX from prostate cancer tissue from a patient of estimated 90% West African ancestry with metastatic castration resistant disease, and characterized this model's pathology, karyotype, hotspot mutations, copy number, gene fusions, gene expression, growth rate in normal and castrated mice, therapeutic response, and experimental metastasis.

RESULTS

This PCPDX has a mutation in TP53 and loss of PTEN and RB1. We have documented a 100% take rate in mice after thawing the PCPDX tumor from frozen stock. The PCPDX is castrate- and docetaxel-resistant and cisplatin-sensitive, and has gene expression patterns associated with such drug responses. After tail vein injection, the PCPDX tumor cells can colonize the lungs of mice.

CONCLUSION

This PCPDX, along with others that are established and characterized, will be useful pre-clinically for studying the heterogeneity of prostate cancer biology and testing new therapeutics in models expected to be reflective of the clinical setting.

Correlation between stage of prostate cancer and tyrosine and tryptophan in urine samples measured electrochemically

Prostate cancer (PCa) is the second most common cancer in men and one of the leading causes of cancer-related deaths. Early detection is the key to successful treatment and provides the greatest chance to cure the patient. Currently, early detection involves screening for prostate-specific antigen levels in blood, which is not a tumor-specific biomarker. There is a critical need to develop clinically useful methods for screening for more reliable biomarkers. Here, we introduce an electrochemical biosensor that measures the concentrations of the amino acids tyrosine and tryptophan, and propose it as a possible diagnostic and prognostic tool for PCa. The limits of detection of tyrosine and tryptophan using the electrochemical sensors were 1.15 and 1.13 μmol/L in 1:10 urine: PBS, respectively. This study is the first to present electrochemical measurements of tyrosine and tryptophan directly in patient urine samples. We demonstrated an inverse correlation between the measured electrochemical signals and the severity of PCa. The most notable observation was a significant difference between controls and metastatic PCa patients (P ≤ 0.001). This observation was further validated using Liquid-Chromatography-Mass Spectrometry. Our data provides the basis for further research with electrochemical measurements of tyrosine and tryptophan as potential biomarkers for PCa.

A new perspective on breast cancer diagnostic guidelines to reduce overdiagnosis

Overdiagnosis of breast cancer, defined as diagnosing a cancer that would otherwise not cause symptoms or death in a patient's lifetime, costs U.S. health care system over $1.2 billion annually. Overdiagnosis rates, estimated to be around 10%-40%, may be reduced if indolent breast findings can be identified and followed with noninvasive imaging rather than biopsy. However, there are no validated guidelines for radiologists to decide when to choose imaging options recognizing cancer grades and types. The aim of this study is to optimize breast cancer diagnostic decisions based on cancer types using a large-scale finite-horizon Markov decision process (MDP) model with 4.6 million states to help reduce overdiagnosis. We prove the optimality of a divide-and-search algorithm that relies on tight upper bounds on the optimal decision thresholds to find an exact optimal solution. We project the high-dimensional MDP onto two lower dimensional MDPs and obtain feasible upper bounds on the optimal decision thresholds. We use real data from two private mammography databases and demonstrate our model performance through a previously validated simulation model that has been used by the policy makers to set the national screening guidelines in the United States. We find that a decision-analytical framework optimizing diagnostic decisions while accounting for breast cancer types has a strong potential to improve the quality of life and alleviate the immense costs of overdiagnosis. Our model leads to a20 % reduction in overdiagnosis on the screening population, which translates into an annual savings of approximately $300 million for the U.S. health care system.

Comprehensive evaluation of machine learning models and gene expression signatures for prostate cancer prognosis using large population cohorts

Overtreatment remains a pervasive problem in prostate cancer (PCa) management due to the highly variable and often indolent course of disease. Molecular signatures derived from gene expression profiling have played critical roles in guiding PCa treatment decisions. Many gene expression signatures have been developed to improve the risk stratification of PCa and some of them have already been applied to clinical practice. However, no comprehensive evaluation has been performed to compare the performance of these signatures. In this study, we conducted a systematic and unbiased evaluation of 15 machine learning (ML) algorithms and 30 published PCa gene expression-based prognostic signatures leveraging 10 transcriptomics datasets with 1,558 primary PCa patients from public data repositories. This analysis revealed that survival analysis models outperformed binary classification models for risk assessment, and the performance of the survival analysis methods - Cox model regularized with ridge penalty (Cox-Ridge) and partial least squares regression for Cox model (Cox-PLS) - were generally more robust than the other methods. Based on the Cox-Ridge algorithm, several top prognostic signatures displayed comparable or even better performance than commercial panels. These findings will facilitate the identification of existing prognostic signatures that are promising for further validation in prospective studies and promote the development of robust prognostic models to guide clinical decision-making. Moreover, this study provides a valuable data resource from large primary PCa cohorts, which can be used to develop, validate, and evaluate novel statistical methodologies and molecular signatures to improve PCa management.

A m6Avalue predictive of prostate cancer stemness, tumor immune landscape and immunotherapy response

The molecular mechanisms underpinning prostate cancer (PCa) progression are incompletely understood, and precise stratification of aggressive primary PCa (pri-PCa) from indolent ones poses a major clinical challenge. Here, we comprehensively dissect, genomically and transcriptomically, the m⁶A (N ⁶-methyladenosine) pathway as a whole in PCa. Expression, but not the genomic alteration, repertoire of the full set of 24 m⁶A regulators at the population level successfully stratifies pri-PCa into three m⁶A clusters with distinct molecular and clinical features. These three m⁶A modification patterns closely correlate with androgen receptor signaling, stemness, proliferation and tumor immunogenicity of cancer cells, and stroma activity and immune landscape of tumor microenvironment (TME). We observe a discrepancy between a potentially higher neoantigen production and a deficiency in antigen presentation processes in aggressive PCa, offering insights into the failure of immunotherapy. Identification of PCa-specific m⁶A phenotype-associated genes provides a basis for construction of m⁶Avalue to measure m⁶A methylation patterns in individual patients. Tumors with lower m⁶Avalue are relatively indolent with abundant immune cell infiltration and stroma activity. Interestingly, m⁶Avalue separates PCa TME into fibrotic and nonfibrotic phenotypes (instead of previously reported immune-proficient or -desert phenotypes in other cancer types). Significantly, m⁶Avalue can be used to predict drug response and clinical immunotherapy efficacy in both castration-resistant PCa and other cancer types. Therefore, our study establishes m⁶A methylation modification pattern as a determinant in PCa progression via impacting cancer cell aggressiveness and TME remodeling.

Patterns of indolence in prostate cancer (Review)

Although prostate cancer is a major cause of cancer-related mortality worldwide, most patients will have a relatively indolent clinical course. Contrary to most other types of cancer, even the diagnosis of locally advanced or metastatic disease is not always lethal. The present review aimed to summarize what is known regarding the underlying mechanisms related to the indolent course of subsets of prostate cancer, at various stages. The data suggested that no specific gene alteration by itself was responsible for carcinogenesis or disease aggressiveness. However, pathway analysis identified genetic aberrations in multiple critical pathways that tend to accumulate over the course of the disease. The progression from indolence into aggressive disease is associated with a complex interplay in which genetic and epigenetic factors are involved. The effect of the immune tumor microenvironment is also very important. Emerging evidence has suggested that the upregulation of pathways related to cellular aging and senescence can identify patients with indolent disease. In addition, a number of tumors enter a long-lasting quiescent state. Further research will determine whether halting tumor evolution is a feasible option, and whether the life of patients can be markedly prolonged by inducing tumor senescence or long-term dormancy.

MCAM is associated with metastasis and poor prognosis in osteosarcoma by modulating tumor cell migration

Background

Although there are standard treatment options for osteosarcoma (OS), the prognoses of patients with OS remain varied. Therefore, it is important to profile OS patients at a high risk of mortality to develop focused interventions. Although tumor biomarkers are closely associated with clinical outcomes, data on prognostic biomarkers for OS remain scarce.

Methods

We collected RNA expression profiles and clinical data of 90 OS patients from the GEO database (dataset GSE21257 and GSE39055) and 96 patients in the TARGET program. The data were analyzed using univariate Kaplan‐Meier survival analysis to screen candidate gene sets that might be associated with OS survival.

Results

Our analysis demonstrated that melanoma cell adhesion molecule (MCAM) was associated with overall survival of patients with OS in the three cohorts. The data showed that MCAM was upregulated in OS patients who had metastases within 5 years compared to those without metastases. GO analysis revealed that genes correlated with MCAM were mainly involved in cell migration and wound healing processes. In addition, wound healing assays and gene set enrichment analysis results from RNA sequencing data of small interfering (si)‐MCAM‐transfected OS cells demonstrated that MCAM modulated tumor cell migration.

Conclusions

Our data demonstrate that MCAM may be a novel prognostic biomarker for OS. MCAM is associated with increased cell migration ability and risk of metastasis, thus leading to poor prognoses in OS patients.

NKX3.1 Localization to Mitochondria Suppresses Prostate Cancer Initiation

Mitochondria provide the first line of defense against the tumor-promoting effects of oxidative stress. Here we show that the prostate-specific homeoprotein NKX3.1 suppresses prostate cancer initiation by protecting mitochondria from oxidative stress. Integrating analyses of genetically engineered mouse models, human prostate cancer cells, and human prostate cancer organotypic cultures, we find that, in response to oxidative stress, NKX3.1 is imported to mitochondria via the chaperone protein HSPA9, where it regulates transcription of mitochondrial-encoded electron transport chain (ETC) genes, thereby restoring oxidative phosphorylation and preventing cancer initiation. Germline polymorphisms of NKX3.1 associated with increased cancer risk fail to protect from oxidative stress or suppress tumorigenicity. Low expression levels of NKX3.1 combined with low expression of mitochondrial ETC genes are associated with adverse clinical outcome, whereas high levels of mitochondrial NKX3.1 protein are associated with favorable outcome. This work reveals an extranuclear role for NKX3.1 in suppression of prostate cancer by protecting mitochondrial function. SIGNIFICANCE: Our findings uncover a nonnuclear function for NKX3.1 that is a key mechanism for suppression of prostate cancer. Analyses of the expression levels and subcellular localization of NKX3.1 in patients at risk of cancer progression may improve risk assessment in a precision prevention paradigm, particularly for men undergoing active surveillance.See related commentary by Finch and Baena, p. 2132.This article is highlighted in the In This Issue feature, p. 2113.

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.

Wait-and-See Treatment Strategy Could be Considered for Lung Adenocarcinoma with Special Pleural Dissemination Lesions, and Low Genomic Instability Correlates with Better Survival

BACKGROUND

This study aimed to evaluate the feasibility of a wait-and-see strategy for non-small cell lung cancer (NSCLC) patients with special pleural dissemination lesions (r-pM1a and s-pM1a). Furthermore, the study characterized genomic alternations about disease progression.

METHODS

For this study, 131 NSCLC patients with a diagnosis of pM1a were retrospectively selected. Survival differences were evaluated among patients treated with three different initial postoperative treatments: chemotherapy, targeted therapy, and wait-and-see strategy. Whole-exome sequencing (WES) was performed on primary and metastatic tumors of 10 patients with dramatic progression and 13 patients with gradual progression.

RESULTS

The wait-and-see group showed better progression-free survival (PFS) than the chemotherapy group (p < 0.001) but PFS similar to that of targeted group (p = 0.984). This pattern persisted in epidermal growth factor receptor (EGFR)-positive patients. For patients with EGFR-negative/unknown status, PFS was longer in the wait-and-see group than in the two treatment groups. Furthermore, better overall survival (OS) was observed for the patients who received chemotherapy or targeted therapy after the wait-and-see strategy than for those who received chemotherapy or targeted therapy immediately. Lymph node status was an independent prognostic factor for PFS and OS. Finally, WES analysis showed that a high genomic instability index (GIS) and chromosome 18q loss were more common in metastatic tumors, and low GIS was significantly associated with better PFS (p = 0.016).

CONCLUSIONS

The wait-and-see strategy could be considered for special pM1a patients without lymph nodes metastasis, and patients with a low GIS may be suitable for this strategy.

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.

Can epigenetic and inflammatory biomarkers identify clinically aggressive prostate cancer?

Prostate cancer (PCa) is a highly prevalent malignancy and constitutes a major cause of cancer-related morbidity and mortality. It emerges through the acquisition of genetic and epigenetic alterations. Epigenetic modifications include DNA methylation, histone modifications and microRNA deregulation. These generate heritable transformations in the expression of genes but do not change the DNA sequence. Alterations in DNA methylation (hypo and hypermethylation) are the most characterized in PCa. They lead to genomic instability and inadequate gene expression. Major and minor-specific modifications in chromatin recasting are involved in PCa, with signs suggesting a dysfunction of enzymes modified by histones. MicroRNA deregulation also contributes to the initiation of PCa, including involvement in androgen receptor signalization and apoptosis. The influence of inflammation on prostate tumor carcinogenesis is currently much better known. Recent discoveries about microbial species resident in the urinary tract suggest that these are the initiators of chronic inflammation, promoting prostate inflammatory atrophy and eventually leading to PCa. Complete characterization of the relationship between the urinary microbiome and prostatic chronic inflammation will be crucial to develop plans for the prevention of PCa. The prevalent nature of epigenetic and inflammatory alterations may provide potential biomarkers for PCa diagnosis, treatment decisions, evaluation of prognosis and posttreatment surveillance.

TIP5 primes prostate luminal cells for the oncogenic transformation mediated by PTEN-loss

The cell of origin and the temporal order of oncogenic events in tumors play important roles for disease state. This is of particular interest for PCa due to its highly variable clinical outcome. However, these features are difficult to analyze in tumors. We established an in vitro murine PCa organoid model taking into account the cell of origin and the temporal order of events. We found that TIP5 primes luminal prostate cells for Pten-loss mediated oncogenic transformation whereas it is dispensable once the transformation is established. Cross-species transcriptomic analyses revealed a PTEN-loss gene signature that identified a set of aggressive tumors with PTEN-del, or low PTEN expression, and high-TIP5 expression. This paper provides a powerful tool to elucidate PCa mechanisms.

Prostate cancer (PCa) is the second leading cause of cancer death in men. Its clinical and molecular heterogeneities and the lack of in vitro models outline the complexity of PCa in the clinical and research settings. We established an in vitro mouse PCa model based on organoid technology that takes into account the cell of origin and the order of events. Primary PCa with deletion of the tumor suppressor gene PTEN (PTEN-del) can be modeled through Pten-down-regulation in mouse organoids. We used this system to elucidate the contribution of TIP5 in PCa initiation, a chromatin regulator that is implicated in aggressive PCa. High TIP5 expression correlates with primary PTEN-del PCa and this combination strongly associates with reduced prostate-specific antigen (PSA) recurrence-free survival. TIP5 is critical for the initiation of PCa of luminal origin mediated by Pten-loss whereas it is dispensable once Pten-loss mediated transformation is established. Cross-species analyses revealed a PTEN gene signature that identified a group of aggressive primary PCas characterized by PTEN-del, high-TIP5 expression, and a TIP5-regulated gene expression profile. The results highlight the modeling of PCa with organoids as a powerful tool to elucidate the role of genetic alterations found in recent studies in their time orders and cells of origin, thereby providing further optimization for tumor stratification to improve the clinical management of PCa.

Merging new-age biomarkers and nanodiagnostics for precision prostate cancer management

The accurate identification and stratified treatment of clinically significant early-stage prostate cancer have been ongoing concerns since the outcomes of large international prostate cancer screening trials were reported. The controversy surrounding clinical and cost benefits of prostate cancer screening has highlighted the lack of strategies for discriminating high-risk disease (that requires early treatment) from low-risk disease (that could be managed using watchful waiting or active surveillance). Advances in molecular subtyping and multiomics nanotechnology-based prostate cancer risk delineation can enable refinement of prostate cancer molecular taxonomy into clinically meaningful and treatable subtypes. Furthermore, the presence of intertumoural and intratumoural heterogeneity in prostate cancer warrants the development of novel nanodiagnostic technologies to identify clinically significant prostate cancer in a rapid, cost-effective and accurate manner. Circulating and urinary next-generation prostate cancer biomarkers for disease molecular subtyping and the newest complementary nanodiagnostic platforms for enhanced biomarker detection are promising tools for precision prostate cancer management. However, challenges in merging both aspects and clinical translation still need to be overcome.

Role of the DNA damage response in prostate cancer formation, progression and treatment

BACKGROUND

Clinical and preclinical studies have revealed that alterations in DNA damage response (DDR) pathways may play an important role in prostate cancer (PCa) etiology and progression. These alterations can influence PCa responses to radiotherapy and anti-androgen treatment. The identification of DNA repair gene aberrations in PCa has driven the interest for further evaluation whether these genetic changes may serve as biomarkers for patient stratification.

METHODS

In this review, we summarize the current knowledge on DDR alterations in PCa, their potential impact on clinical interventions and prospects for improved management of PCa. We particularly focus on the influence of DDR gene mutations on PCa initiation and progression and describe the underlying mechanisms.

RESULTS AND CONCLUSIONS

A better understanding of these mechanisms, will contribute to better disease management as treatment strategies can be chosen based on the specific disease properties, since a growing number of treatments are targeting DDR pathway alterations (such as Poly(ADP-ribose) polymerase inhibitors). Furthermore, the recently discovered crosstalk between the DDR and androgen receptor signaling opens a new array of possible strategies to optimize treatment combinations. We discuss how these recent and ongoing studies will help to improve diagnostic, prognostic and therapeutic approaches for PCa management.

KLF4, A Gene Regulating Prostate Stem Cell Homeostasis, Is a Barrier to Malignant Progression and Predictor of Good Prognosis in Prostate Cancer

There is a considerable need to identify those individuals with prostate cancer who have indolent disease. We propose that genes that control adult stem cell homeostasis in organs with slow turnover, such as the prostate, control cancer fate. One such gene, KLF4, overexpressed in murine prostate stem cells, regulates their homeostasis, blocks malignant transformation, and controls the self-renewal of tumor-initiating cells. KLF4 loss induces the molecular features of aggressive cancer and converts PIN lesions to invasive sarcomatoid carcinomas; its re-expression in vivo reverses this process. Bioinformatic analysis links these changes to human cancer. KLF4 and its downstream targets make up a gene signature that identifies indolent tumors and predicts recurrence-free survival. This approach may improve prognosis and identify therapeutic targets for advanced cancer.

Available criteria for segregating prostate cancer patients into those requiring therapeutic intervention and those who can be followed are inadequate. Xiong et al. show that KLF4 and its downstream targets make up a gene signature that identifies indolent tumors. This approach may improve prognosis and identify therapeutic targets for advanced cancer.

Dual and multi-targeted nanoparticles for site-specific brain drug delivery

In recent years, nanomedicines have emerged as a promising method for central nervous system drug delivery, enabling the drugs to overcome the blood-brain barrier and accumulate preferentially in the brain. Despite the current success of brain-targeted nanomedicines, limitations still exist in terms of the targeting specificity. Based on the molecular mechanism, the exact cell populations and subcellular organelles where the injury occurs and the drugs take effect have been increasingly accepted as a more specific target for the next generation of nanomedicines. Dual and multi-targeted nanoparticles integrate different targeting functionalities and have provided a paradigm for precisely delivering the drug to the pathological site inside the brain. The targeting process often involves the sequential or synchronized navigation of the targeting moieties, which allows highly controlled drug delivery compared to conventional targeting strategies. Herein, we focus on the up-to-date design of pathological site-specific nanoparticles for brain drug delivery, highlighting the dual and multi-targeting strategies that were employed and their impact on improving targeting specificity and therapeutic effects. Furthermore, the background discussion of the basic properties of a brain-targeted nanoparticle and the common lesion features classified by neurological pathology are systematically summarized.

Molecular profiling stratifies diverse phenotypes of treatment-refractory metastatic castration-resistant prostate cancer

Metastatic castration-resistant prostate cancer (mCRPC) is a heterogeneous disease with diverse drivers of disease progression and mechanisms of therapeutic resistance. We conducted deep phenotypic characterization of CRPC metastases and patient-derived xenograft (PDX) lines using whole genome RNA sequencing, gene set enrichment analysis and immunohistochemistry. Our analyses revealed five mCRPC phenotypes based on the expression of well-characterized androgen receptor (AR) or neuroendocrine (NE) genes: (i) AR-high tumors (ARPC), (ii) AR-low tumors (ARLPC), (iii) amphicrine tumors composed of cells co-expressing AR and NE genes (AMPC), (iv) double-negative tumors (i.e. AR-/NE-; DNPC) and (v) tumors with small cell or NE gene expression without AR activity (SCNPC). RE1-silencing transcription factor (REST) activity, which suppresses NE gene expression, was lost in AMPC and SCNPC PDX models. However, knockdown of REST in cell lines revealed that attenuated REST activity drives the AMPC phenotype but is not sufficient for SCNPC conversion. We also identified a subtype of DNPC tumors with squamous differentiation and generated an encompassing 26-gene transcriptional signature that distinguished the five mCRPC phenotypes. Together, our data highlight the central role of AR and REST in classifying treatment-resistant mCRPC phenotypes. These molecular classifications could potentially guide future therapeutic studies and clinical trial design.

TRIM24 as an independent prognostic biomarker for prostate cancer

INTRODUCTION

Simply applicable biomarkers for prostate cancer patients predicting the clinical course are urgently needed. Recently, TRIM24 has been identified to promote androgen receptor signaling and to correlate with an aggressive prostate cancer phenotype. Based on these data, we proofed TRIM24 as a prognostic biomarker for risk stratification.

MATERIALS AND METHODS

We performed TRIM24 immunohistochemistry on 2 independent cohorts including a total of 806 primary tumors, 26 locally advanced/recurrent tumors, 30 lymph node metastases, 30 distant metastases, and 129 benign prostatic samples from 497 patients as well as on 246 prostate needle biopsies. Expression data were correlated with clinic-pathological data including biochemical recurrence-free survival (bRFS) as endpoint.

RESULTS

Benign samples show no or low TRIM24 expression in 94%, while tumor tissues demonstrate significant higher levels. Strongest expression is observed in advanced and metastatic tumors. In multivariate analyses, TRIM24 up-regulation on radical prostatectomy specimens correlates with shorter bRFS independent of other prognostic parameters. 5-(10-) year bRFS rates for TRIM24 negative, low, medium and high expressing tumors are 93.1(93.1)%, 75.4(68.5)%, 54.9(47.5)% and 43.1(32.3)%, respectively. Of interest, tumors diagnosed as indolent disease, TRIM24 expression stratifies patients into specific risk groups. Increased TRIM24 expression associates with higher grade group, positive nodal status and extraprostatic tumor growth. TRIM24 assessment on prostate needle biopsies taken prior to treatment decision at time of initial diagnosis significantly correlates with recurrence after surgery.

CONCLUSION

Using 2 large independent radical prostatectomy specimen cohorts, we found that TRIM24 expression predicts patients' risk to develop disease recurrence with high accuracy and independent from other established biomarkers. Further, this is the first study exploring TRIM24 expression on prostate needle biopsies which represents the clinically relevant tissue type on which biomarkers guide treatment decisions. Thus, we strongly suggest introducing TRIM24 evaluation in prostate needle biopsies in clinical routine as an inexpensive and simple immunohistochemical test.

Cancer overdiagnosis: a biological challenge and clinical dilemma

For cancer screening to be successful, it should primarily detect cancers with lethal potential or their precursors early, leading to therapy that reduces mortality and morbidity. Screening programmes have been successful for colon and cervical cancers, where subsequent surgical removal of precursor lesions has resulted in a reduction in cancer incidence and mortality. However, many types of cancer exhibit a range of heterogeneous behaviours and variable likelihoods of progression and death. Consequently, screening for some cancers may have minimal impact on mortality and may do more harm than good. Since the implementation of screening tests for certain cancers (for example, breast and prostate cancers), a spike in incidence of in situ and early-stage cancers has been observed, but a link to reduction in cancer-specific mortality has not been as clear. It is difficult to determine how many of these mortality reductions are due to screening and how many are due to improved treatments of tumours. In cancers with lower incidence but high mortality (for example, pancreatic cancer), screening has focused on high-risk populations, but challenges similar to those for general population screening remain, particularly with regard to finding lesions with difficult-to-characterize malignant potential (for example, intraductal papillary mucinous neoplasms). More sensitive screening methods are detecting smaller and smaller lesions, but this has not been accompanied by a comparable reduction in the incidence of invasive cancers. In this Opinion article, we focus on the contribution of screening in general and high-risk populations to overdiagnosis, the effects of overdiagnosis on patients and emerging strategies to reduce overdiagnosis of indolent cancers through an understanding of tumour heterogeneity, the biology of how cancers evolve and progress, the molecular and cellular features of early neoplasia and the dynamics of the interactions of early lesions with their surrounding tissue microenvironment.

Cumulative Cancer Locations is a Novel Metric for Predicting Active Surveillance Outcomes: A Multicenter Study

BACKGROUND

Active surveillance (AS) of prostate cancer (PC) has increased in popularity to address overtreatment.

OBJECTIVE

To determine whether a novel metric, cumulative cancer locations (CCLO), can predict AS outcomes in a group of AS patients with low and very low risk.

DESIGN, SETTING, AND PARTICIPANTS

CCLO is obtained by summing the total number of histological cancer-positive locations in both diagnostic and confirmatory biopsies (Bx). The retrospective study cohort comprised three prospective AS cohorts (Helsinki University Hospital: n=316; European Institute of Oncology: n=204; and University of Münster: n=89).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We analyzed whether risk stratification based on CCLO predicts different AS outcomes: protocol-based discontinuation (PBD), Gleason upgrading (GU) during AS, and adverse findings in radical prostatectomy (RP) specimens.

RESULTS

In Kaplan Meier analyses, patients in the CCLO high-risk group experienced significantly shorter event-free survival for all outcomes (PBD, GU, and adverse RP findings; all p<0.002). In multivariable Cox regression analysis, patients in the CCLO high-risk group had a significantly higher risk of experiencing PBD (hazard ratio [HR] 12.15, 95% confidence interval [CI] 6.18-23.9; p<0.001), GU (HR 6.01, 95% CI 2.16-16.8; p=0.002), and adverse RP findings (HR 9.144, 95% CI 2.27-36.9; p=0.006). In receiver operating characteristic analyses, the area under the curve for CCLO outperformed the number of cancer-positive Bxs in confirmatory Bx in predicting PBD (0.734 vs 0.682), GU (0.655 vs 0.576) and adverse RP findings (0.662 vs 0.561) and the added value was supported by decision curve analysis.

CONCLUSIONS

CCLO is distinct from the number of positive Bx cores. Higher CCLO predicts AS outcomes and may aid in selection of patients for AS.

PATIENT SUMMARY

For patients on active surveillance for prostate cancer, the cumulative number of cancer-positive locations in diagnostic and confirmatory biopsies is a predictor of active surveillance outcomes.

A TMEFF2-regulated cell cycle derived gene signature is prognostic of recurrence risk in prostate cancer

Background

The clinical behavior of prostate cancer (PCa) is variable, and while the majority of cases remain indolent, 10% of patients progress to deadly forms of the disease. Current clinical predictors used at the time of diagnosis have limitations to accurately establish progression risk. Here we describe the development of a tumor suppressor regulated, cell-cycle gene expression based prognostic signature for PCa, and validate its independent contribution to risk stratification in several radical prostatectomy (RP) patient cohorts.

Methods

We used RNA interference experiments in PCa cell lines to identify a gene expression based gene signature associated with Tmeff2, an androgen regulated, tumor suppressor gene whose expression shows remarkable heterogeneity in PCa. Gene expression was confirmed by qRT-PCR. Correlation of the signature with disease outcome (time to recurrence) was retrospectively evaluated in four geographically different cohorts of patients that underwent RP (834 samples), using multivariate logistical regression analysis. Multivariate analyses were adjusted for standard clinicopathological variables. Performance of the signature was compared to previously described gene expression based signatures using the SigCheck software.

Results

Low levels of TMEFF2 mRNA significantly (p < 0.0001) correlated with reduced disease-free survival (DFS) in patients from the Memorial Sloan Kettering Cancer Center (MSKCC) dataset. We identified a panel of 11 TMEFF2 regulated cell cycle related genes (TMCC11), with strong prognostic value. TMCC11 expression was significantly associated with time to recurrence after prostatectomy in four geographically different patient cohorts (2.9 ≤ HR ≥ 4.1; p ≤ 0.002), served as an independent indicator of poor prognosis in the four RP cohorts (1.96 ≤ HR ≥ 4.28; p ≤ 0.032) and improved the prognostic value of standard clinicopathological markers. The prognostic ability of TMCC11 panel exceeded previously published oncogenic gene signatures (p = 0.00017).

Conclusions

This study provides evidence that the TMCC11 gene signature is a robust independent prognostic marker for PCa, reveals the value of using highly heterogeneously expressed genes, like Tmeff2, as guides to discover prognostic indicators, and suggests the possibility that low Tmeff2 expression marks a distinct subclass of PCa.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5592-6) contains supplementary material, which is available to authorized users.

MiR-644a Disrupts Oncogenic Transformation and Warburg Effect by Direct Modulation of Multiple Genes of Tumor-Promoting Pathways

Castration-resistant prostate cancer (CRPC) is defined by tumor microenvironment heterogeneity affecting intrinsic cellular mechanisms including dysregulated androgen signaling, aerobic glycolysis (Warburg effect), and aberrant activation of transcription factors including androgen receptor (AR) and c-Myc. Using in vitro, in vivo, and animal models, we find a direct correlation between miR-644a downregulation and dysregulation of essential cellular processes. MiR-644a downregulated expression of diverse tumor microenvironment drivers including c-Myc, AR coregulators, and antiapoptosis factors Bcl-xl and Bcl2. Moreover, miR-644a modulates epithelial-mesenchymal transition (EMT) by directly targeting EMT-promoting factors ZEB1, cdk6, and Snail. Finally, miR-644a expression suppresses the Warburg effect by direct targeting of c-Myc, Akt, IGF1R, and GAPDH expression. RNA sequencing analysis revealed an analogous downregulation of these factors in animal tumor xenografts. These data demonstrate miR-644a mediated fine-tuning of oncogenesis, stimulating pathways and resultant potentiation of enzalutamide therapy in CRPC patients. SIGNIFICANCE: This study demonstrates that miR-644a therapeutically influences the CRPC tumor microenvironment by suppressing androgen signaling and additional genes involved in metabolism, proliferation, Warburg effect, and EMT, to potentiate the enzalutamide therapy.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/8/1844/F1.large.jpg.

Mechanisms of PTEN loss in cancer: It's all about diversity

PTEN is a phosphatase which metabolises PIP₃, the lipid product of PI 3-Kinase, directly opposing the activation of the oncogenic PI3K/AKT/mTOR signalling network. Accordingly, loss of function of the PTEN tumour suppressor is one of the most common events observed in many types of cancer. Although the mechanisms by which PTEN function is disrupted are diverse, the most frequently observed events are deletion of a single gene copy of PTEN and gene silencing, usually observed in tumours with little or no PTEN protein detectable by immunohistochemistry. Accordingly, with the exceptions of glioblastoma and endometrial cancer, mutations of the PTEN coding sequence are uncommon (<10%) in most types of cancer. Here we review the data relating to PTEN loss in seven common tumour types and discuss mechanisms of PTEN regulation, some of which appear to contribute to reduced PTEN protein levels in cancers.

Genetically Engineered Mouse Models of Prostate Cancer in the Postgenomic Era

Recent genomic sequencing analyses have unveiled the spectrum of genomic alterations that occur in primary and advanced prostate cancer, raising the question of whether the corresponding genes are functionally relevant for prostate tumorigenesis, and whether such functions are associated with particular disease stages. In this review, we describe genetically engineered mouse models (GEMMs) of prostate cancer, focusing on those that model genomic alterations known to occur in human prostate cancer. We consider whether the phenotypes of GEMMs based on gain or loss of function of the relevant genes provide reliable counterparts to study the predicted consequences of the corresponding genomic alterations as occur in human prostate cancer, and we discuss exceptions in which the GEMMs do not fully emulate the expected phenotypes. Last, we highlight future directions for the generation of new GEMMs of prostate cancer and consider how we can use GEMMs most effectively to decipher the biological and molecular mechanisms of disease progression, as well as to tackle clinically relevant questions.

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.

Exploring the transcriptome of hormone-naive multifocal prostate cancer and matched lymph node metastases

Background

The current inability to predict whether a primary prostate cancer (PC) will progress to metastatic disease leads to overtreatment of indolent PCs as well as undertreatment of aggressive PCs. Here, we explored the transcriptional changes associated with metastatic progression of multifocal hormone-naive PC.

Methods

Using total RNA-sequencing, we analysed laser micro-dissected primary PC foci (n = 23), adjacent normal prostate tissue samples (n = 23) and lymph node metastases (n = 9) from ten hormone-naive PC patients. Genes important for PC progression were identified using differential gene expression and clustering analysis. From these, two multi-gene-based expression signatures (models) were developed, and their prognostic potential was evaluated using Cox-regression and Kaplan–Meier analyses in three independent radical prostatectomy (RP) cohorts (>650 patients).

Results

We identified several novel PC-associated transcripts deregulated during PC progression, and these transcripts were used to develop two novel gene-expression-based prognostic models. The models showed independent prognostic potential in three RP cohorts (n = 405, n = 107 and n = 91), using biochemical recurrence after RP as the primary clinical endpoint.

Conclusions

We identified several transcripts deregulated during PC progression and developed two new prognostic models for PC risk stratification, each of which showed independent prognostic value beyond routine clinicopathological factors in three independent RP cohorts.

Cooperation of loss of NKX3.1 and inflammation in prostate cancer initiation

Although it is known that inflammation plays a critical role in prostate tumorigenesis, the underlying processes are not well understood. Based on analysis of genetically engineered mouse models combined with correlative analysis of expression profiling data from human prostate tumors, we demonstrate a reciprocal relationship between inflammation and the status of the NKX3.1 homeobox gene associated with prostate cancer initiation. We find that cancer initiation in aged Nkx3.1 mutant mice correlates with enrichment of specific immune populations and increased expression of immunoregulatory genes. Furthermore, expression of these immunoregulatory genes is similarly increased in human prostate tumors having low levels of NKX3.1 expression. We further show that induction of prostatitis in Nkx3.1 mutant mice accelerates prostate cancer initiation, which is coincident with aberrant cellular plasticity and differentiation. Correspondingly, human prostate tumors having low levels of NKX3.1 have de-regulated expression of genes associated with these cellular processes. We propose that loss of function of NKX3.1 accelerates inflammation-driven prostate cancer initiation potentially via aberrant cellular plasticity and impairment of cellular differentiation.This article has an associated First Person interview with the first author of the paper.

Enzalutamide therapy for advanced prostate cancer: efficacy, resistance and beyond

The androgen receptor drives the growth of metastatic castration-resistant prostate cancer. This has led to the development of multiple novel drugs targeting this hormone-regulated transcription factor, such as enzalutamide – a potent androgen receptor antagonist. Despite the plethora of possible treatment options, the absolute survival benefit of each treatment separately is limited to a few months. Therefore, current research efforts are directed to determine the optimal sequence of therapies, discover novel drugs effective in metastatic castration-resistant prostate cancer and define patient subpopulations that ultimately benefit from these treatments. Molecular studies provide evidence on which pathways mediate treatment resistance and may lead to improved treatment for metastatic castration-resistant prostate cancer. This review provides, firstly a concise overview of the clinical development, use and effectiveness of enzalutamide in the treatment of advanced prostate cancer, secondly it describes translational research addressing enzalutamide response vs resistance and lastly highlights novel potential treatment strategies in the enzalutamide-resistant setting.

Genetics and biology of prostate cancer

Despite the high long-term survival in localized prostate cancer, metastatic prostate cancer remains largely incurable even after intensive multimodal therapy. The lethality of advanced disease is driven by the lack of therapeutic regimens capable of generating durable responses in the setting of extreme tumor heterogeneity on the genetic and cell biological levels. Here, we review available prostate cancer model systems, the prostate cancer genome atlas, cellular and functional heterogeneity in the tumor microenvironment, tumor-intrinsic and tumor-extrinsic mechanisms underlying therapeutic resistance, and technological advances focused on disease detection and management. These advances, along with an improved understanding of the adaptive responses to conventional cancer therapies, anti-androgen therapy, and immunotherapy, are catalyzing development of more effective therapeutic strategies for advanced disease. In particular, knowledge of the heterotypic interactions between and coevolution of cancer and host cells in the tumor microenvironment has illuminated novel therapeutic combinations with a strong potential for more durable therapeutic responses and eventual cures for advanced disease. Improved disease management will also benefit from artificial intelligence-based expert decision support systems for proper standard of care, prognostic determinant biomarkers to minimize overtreatment of localized disease, and new standards of care accelerated by next-generation adaptive clinical trials.

Computer-aided classification of prostate cancer grade groups from MRI images using texture features and stacked sparse autoencoder

A novel method to determine the Grade Group (GG) in prostate cancer (PCa) using multi-parametric magnetic resonance imaging (mpMRI) biomarkers is investigated in this paper. In this method, high-level features are extracted from hand-crafted texture features using a deep network of stacked sparse autoencoders (SSAE) and classified them using a softmax classifier (SMC). Transaxial T2 Weighted (T2W), Apparent Diffusion Coefficient (ADC) and high B-Value Diffusion-Weighted (BVAL) images obtained from PROSTATEx-2 2017 challenge dataset are used in this technique. The method was evaluated on the challenge dataset composed of a training set of 112 lesions and a test set of 70 lesions. It achieved a quadratic-weighted Kappa score of 0.2772 on evaluation using test dataset of the challenge. It also reached a Positive Predictive Value (PPV) of 80% in predicting PCa with GG > 1. The method achieved first place in the challenge, winning over 43 methods submitted by 21 groups. A 3-fold cross-validation using training data of the challenge was further performed and the method achieved a quadratic-weighted kappa score of 0.2326 and Positive Predictive Value (PPV) of 80.26% in predicting PCa with GG > 1. Even though the training dataset is a highly imbalanced one, the method was able to achieve a fair kappa score. Being one of the pioneer methods which attempted to classify prostate cancer into 5 grade groups from MRI images, it could serve as a base method for further investigations and improvements.

Prostate cancer: unmet clinical needs and RAD9 as a candidate biomarker for patient management

Prostate cancer is a complex disease, with multiple subtypes and clinical presentations. Much progress has been made in recent years to understand the underlying genetic basis that drives prostate cancer. Such mechanistic information is useful for development of novel therapeutic targets, to identify biomarkers for early detection or to distinguish between aggressive and indolent disease, and to predict treatment outcome. Multiple tests have become available in recent years to address these clinical needs for prostate cancer. We describe several of these assays, summarizing test details, performance characteristics, and acknowledging their limitations. There is a pressing unmet need for novel biomarkers that can demonstrate improvement in these areas. We introduce one such candidate biomarker, RAD9, describe its functions in the DNA damage response, and detail why it can potentially fill this void. RAD9 has multiple roles in prostate carcinogenesis, making it potentially useful as a clinical tool for men with prostate cancer. RAD9 was originally identified as a radioresistance gene, and subsequent investigations revealed several key functions in the response of cells to DNA damage, including involvement in cell cycle checkpoint control, at least five DNA repair pathways, and apoptosis. Further studies indicated aberrant overexpression in approximately 45% of prostate tumors, with a strong correlation between RAD9 abundance and cancer stage. A causal relationship between RAD9 and prostate cancer was first demonstrated using a mouse model, where tumorigenicity of human prostate cancer cells after subcutaneous injection into nude mice was diminished when RNA interference was used to reduce the normally high levels of the protein. In addition to activity needed for the initial development of tumors, cell culture studies indicated roles for RAD9 in promoting prostate cancer progression by controlling cell migration and invasion through regulation of ITGB1 protein levels, and anoikis resistance by modulating AKT activation. Furthermore, RAD9 enhances the resistance of human prostate cancer cells to radiation in part by regulating ITGB1 protein abundance. RAD9 binds androgen receptor and inhibits androgen-induced androgen receptor's activity as a transcription factor. Moreover, RAD9 also acts as a gene-specific transcription factor, through binding p53 consensus sequences at target gene promoters, and this likely contributes to its oncogenic activity. Given these diverse and extensive activities, RAD9 plays important roles in the initiation and progression of prostate cancer and can potentially serve as a valuable biomarker useful in the management of patients with this disease.

Biomarkers of aggressiveness in genitourinary tumors with emphasis on kidney, bladder, and prostate cancer

INTRODUCTION

Over the last decade, the improvement in molecular techniques and the acquisition of genomic information has transformed and increased the quality of patient care and our knowledge of diseases. Areas covered: Protein expression levels in immunohistochemistry and molecular biomarkers are reported for their ability to predict recurrence, progression, development of metastases, or patient survival. In particular, for renal cell carcinoma, we take into consideration the biomarkers applicable to immunohistochemistry and with molecular and genetic analyses. In urothelial carcinoma, there is great interest in the possibility of distinguishing the basal vs. luminal subtypes and to acquire deeper insight into the tumor biology through examining exosomes in urine and biomarkers in the serum. In prostate cancer, single gene expression and multiple gene expression classifiers are reviewed as a tool to distinguish indolent vs. aggressive disease. Expert commentary: The genomic information along with the application of ancillary techniques allow the definition of a neoplasia not only by its morphology but also by its biological signature. This continuous increase in knowledge will result in a better comprehension of oncogenesis, development of targeted therapies and optimizing decision-making processes related to patient care.

Development and Validation of a 28-gene Hypoxia-related Prognostic Signature for Localized Prostate Cancer

BACKGROUND

Hypoxia is associated with a poor prognosis in prostate cancer. This work aimed to derive and validate a hypoxia-related mRNA signature for localized prostate cancer.

METHOD

Hypoxia genes were identified in vitro via RNA-sequencing and combined with in vivo gene co-expression analysis to generate a signature. The signature was independently validated in eleven prostate cancer cohorts and a bladder cancer phase III randomized trial of radiotherapy alone or with carbogen and nicotinamide (CON).

RESULTS

A 28-gene signature was derived. Patients with high signature scores had poorer biochemical recurrence free survivals in six of eight independent cohorts of prostatectomy-treated patients (Log rank test P < .05), with borderline significances achieved in the other two (P < .1). The signature also predicted biochemical recurrence in patients receiving post-prostatectomy radiotherapy (n = 130, P = .007) or definitive radiotherapy alone (n = 248, P = .035). Lastly, the signature predicted metastasis events in a pooled cohort (n = 631, P = .002). Prognostic significance remained after adjusting for clinic-pathological factors and commercially available prognostic signatures. The signature predicted benefit from hypoxia-modifying therapy in bladder cancer patients (intervention-by-signature interaction test P = .0026), where carbogen and nicotinamide was associated with improved survival only in hypoxic tumours.

CONCLUSION

A 28-gene hypoxia signature has strong and independent prognostic value for prostate cancer patients.

The Non-Coding Transcriptome of Prostate Cancer: Implications for Clinical Practice

Prostate cancer (PCa) is the most common type of cancer and the second leading cause of cancer-related death in men. Despite extensive research, the molecular mechanisms underlying PCa initiation and progression remain unclear, and there is increasing need of better biomarkers that can distinguish indolent from aggressive and life-threatening disease. With the advent of advanced genomic technologies in the last decade, it became apparent that the human genome encodes tens of thousands non-protein-coding RNAs (ncRNAs) with yet to be discovered function. It is clear now that the majority of ncRNAs exhibit highly specific expression patterns restricted to certain tissues and organs or developmental stages and that the expression of many ncRNAs is altered in disease and cancer, including cancer of the prostate. Such ncRNAs can serve as important biomarkers for PCa diagnosis, prognosis, or prediction of therapy response. In this review, we give an overview of the different types of ncRNAs and their function, describe ncRNAs relevant for the diagnosis and prognosis of PCa, and present emerging new aspects of ncRNA research that may contribute to the future utilization of ncRNAs as clinically useful therapeutic targets.