Stromal Gene Expression is Predictive for Metastatic Primary Prostate Cancer

Identifying Rare Germline Variants Associated with Metastatic Prostate Cancer Through an Extreme Phenotype Study

Background

Studies of germline variants in prostate cancer (PCa) have largely focused on their connections to cancer predisposition. However, an understanding of how heritable factors contribute to cancer progression and metastasis remain limited.

Objective

To identify low frequency to rare germline nonsynonymous variants associated with increased risk for metastatic PCa (mPCa), while providing functional validation.

Design

We assembled an extreme phenotype cohort (EPC) of 52 patients diagnosed with predominantly high-grade (Gleason Score (GS) ≥ 8) PCa and > 7 years of follow-up for which localized treatment naïve tumor tissues were available. In half of the cases, the tumor had metastasized to bone, providing an even distribution of bone mPCa and non mPCa cases. Tumor and matched distant benign DNA samples were exome sequenced and analyzed for germline variants with population-wide minor allelic frequencies ≤ 2%. Findings were validated using two independent PCa germline cohorts, including a closely matched Australian study biased to aggressive disease (n = 53) and Pan Prostate Cancer Group (PPCG, n = 976). Two mPCa-promoting candidate variants in KDM6B and BRCA2 were engineered into cell lines and functionalized.

Results

Germline nonsynonymous rare variants (gnsRVs) identified in 25 DNA Damage Repair (DDR) genes were significantly enriched in the mPCa patients (p=4.57e-06). Conversely, the prevalence of synonymous variants at minor allele frequencies of ≤ 2% were similar between the mPCa and non mPCa patients. The predictive power of variants in 53 non-DDR genes was validated in the Australian cohort (p=0.028) and correlated with high-risk PCa in PPCG (p=0.03). KDM6B K973Q showed functional significance despite being annotated as benign in ClinVar, while BRCA2 I1962T showed sensitivity to Olaparib. In total, six EPC variants related to DNA repair or epigenetics were found to alter enzymatic activity.

Conclusions

EPCs coupled with low frequency/rare variant analyses may advance understanding of interactions between the germline and tumor in PCa. We identified a series of germline variants that were enriched among mPCa patients. Moreover, we showed that one of these variants confers a metastatic phenotype. Our findings suggest that germline testing at diagnosis may improve treatment stratification in PCa.

Patient summary

The presence of specific genetic variants among men with PCa may elevate the risk of mPCa once PCa develops. Knowledge of the variant burden at time of diagnosis may enable accurate stratification of some patients for aggressive therapeutic interventions.

Defining hypoxia in cancer: A landmark evaluation of hypoxia gene expression signatures

Tumor hypoxia drives metabolic shifts, cancer progression, and therapeutic resistance. Challenges in quantifying hypoxia have hindered the exploitation of this potential "Achilles' heel." While gene expression signatures have shown promise as surrogate measures of hypoxia, signature usage is heterogeneous and debated. Here, we present a systematic pan-cancer evaluation of 70 hypoxia signatures and 14 summary scores in 104 cell lines and 5,407 tumor samples using 472 million length-matched random gene signatures. Signature and score choice strongly influenced the prediction of hypoxia in vitro and in vivo. In cell lines, the Tardon signature was highly accurate in both bulk and single-cell data (94% accuracy, interquartile mean). In tumors, the Buffa and Ragnum signatures demonstrated superior performance, with Buffa/mean and Ragnum/interquartile mean emerging as the most promising for prospective clinical trials. This work delivers recommendations for experimental hypoxia detection and patient stratification for hypoxia-targeting therapies, alongside a generalizable framework for signature evaluation.

Deciphering the Tumor Microenvironment in Prostate Cancer: A Focus on the Stromal Component

Prostate cancer progression is significantly affected by its tumor microenvironment, in which mesenchymal cells play a crucial role. Stromal cells are modified by cancer mutations, response to androgens, and lineage plasticity, and in turn, engage with epithelial tumor cells via a complex array of signaling pathways and ligand-receptor interactions, ultimately affecting tumor growth, immune interaction, and response to therapy. The metabolic rewiring and interplay in the microenvironment play an additional role in affecting the growth and progression of prostate cancer. Finally, therapeutic strategies and novel clinical trials with agents that target the stromal microenvironment or disrupt the interaction between cellular compartments are described. This review underscores cancer-associated fibroblasts as essential contributors to prostate cancer biology, emphasizing their potential as prognostic indicators and therapeutic targets.

Two subtle problems with overrepresentation analysis

Motivation

Overrepresentation analysis (ORA) is used widely to assess the enrichment of functional categories in a gene list compared to a background list. ORA is therefore a critical method in the interpretation of 'omics data, relating gene lists to biological functions and themes. Although ORA is hugely popular, we and others have noticed two potentially undesired behaviours of some ORA tools. The first one we call the 'background problem', because it involves the software eliminating large numbers of genes from the background list if they are not annotated as belonging to any category. The second one we call the 'false discovery rate problem', because some tools underestimate the true number of parallel tests conducted.

Results

Here, we demonstrate the impact of these issues on several real RNA-seq datasets and use simulated RNA-seq data to quantify the impact of these problems. We show that the severity of these problems depends on the gene set library, the number of genes in the list, and the degree of noise in the dataset. These problems can be mitigated by changing packages/websites for ORA or by changing to another approach such as functional class scoring.

Availability and implementation

An R/Shiny tool has been provided at https://oratool.ziemann-lab.net/ and the supporting materials are available from Zenodo (https://zenodo.org/records/13823301).

Unraveling molecular characteristics and tumor microenvironment dynamics of neuroendocrine prostate cancer

Prostate cancer (PCa) is the most prevalent malignancy and the second leading cause of cancer-related deaths among men. While adenocarcinoma of the prostate (adeno-PCa) is well-characterized, neuroendocrine prostate cancer (NEPC) remains poorly understood. Generally, NEPC is a rare but highly aggressive histological variant, however its limited patho-physiological understanding leads to insufficient treatment options associated with low survival rates for NEPC patients. Current treatments for NEPC, including platinum-based therapies, offer some efficacy, but there is a significant need for more targeted approaches. This review summarizes the molecular characteristics of NEPC in contrast to adeno-PCa, providing a comprehensive comparison. A significant portion of the discussion is dedicated to the tumor microenvironment (TME), which has recently been identified as a key factor in tumor progression. The TME includes various cells, signaling molecules, and the extracellular matrix surrounding the tumor, all of which play critical roles in cancer development and response to treatment. Understanding the TME's influence on NEPC could uncover new avenues for innovative treatment strategies, potentially improving outcomes for patients with this challenging variant of PCa.

Evidence of the Link between Stroma Remodeling and Prostate Cancer Prognosis

Prostate cancer (PCa), the most commonly diagnosed cancer in men worldwide, is particularly challenging for oncologists when a precise prognosis needs to be established. Indeed, the entire clinical management in PCa has important drawbacks, generating an intense debate concerning the possibility to individuate molecular biomarkers able to avoid overtreatment in patients with pathological indolent cancers. To date, the paradigmatic change in the view of cancer pathogenesis prompts to look for prognostic biomarkers not only in cancer epithelial cells but also in the tumor microenvironment. PCa ecology has been defined with increasing details in the last few years, and a number of promising key markers associated with the reactive stroma are now available. Here, we provide an updated description of the most biologically significant and cited prognosis-oriented microenvironment biomarkers derived from the main reactive processes during PCa pathogenesis: tissue adaptations, inflammatory response and metabolic reprogramming. Proposed biomarkers include factors involved in stromal cell differentiation, cancer-normal cell crosstalk, angiogenesis, extracellular matrix remodeling and energy metabolism.

The tumor-stroma ratio in giant cell tumor of bone: associations with the immune microenvironment and responsiveness to denosumab treatment

BACKGROUND

Currently, there is limited understanding regarding the clinical significance of the tumor-stroma ratio (TSR) in giant cell tumor of bone (GCTB). Hence, we aimed to investigate the distribution of TSR in GCTB and explore its correlation with various clinicopathologic factors, immune microenvironment, survival prognosis, and denosumab treatment responsiveness.

METHODS

We conducted a multicenter cohort study comprising 426 GCTB patients treated at four centers. TSR was evaluated on hematoxylin and eosin-stained and immunofluorescent sections of tumor specimens. Immunohistochemistry was performed to assess CD3+, CD4+, CD8+, CD20+, PD-1+, PD-L1+, and FoxP3+ TIL subtypes as well as Ki-67 expression levels in 426 tissue specimens. These parameters were then analyzed for their correlations with patient outcomes [local recurrence-free survival (LRFS) and overall survival (OS)], clinicopathological features, and denosumab treatment responsiveness.

RESULTS

Low TSR was significantly associated with poor LRFS and OS in both cohorts. Furthermore, TSR was also correlated with multiple clinicopathological features, TIL subtype expression, and denosumab treatment responsiveness. TSR demonstrated similar predictive capabilities as the conventional Campanacci staging system for predicting patients' LRFS and OS.

CONCLUSION

The results of this study provide evidence supporting the use of TSR as a reliable prognostic tool in GCTB and as a predictor of denosumab treatment responsiveness. These findings may aid in developing individualized treatment strategies for GCTB patients in the future.

Sensitivity of magnetic resonance elastography to extracellular matrix and cell motility in human prostate cancer cell line-derived xenograft models

Prostate cancer (PCa) is a significant health problem in the male population of the Western world. Magnetic resonance elastography (MRE), an emerging medical imaging technique sensitive to mechanical properties of biological tissues, detects PCa based on abnormally high stiffness and viscosity values. Yet, the origin of these changes in tissue properties and how they correlate with histopathological markers and tumor aggressiveness are largely unknown, hindering the use of tumor biomechanical properties for establishing a noninvasive PCa staging system. To infer the contributions of extracellular matrix (ECM) components and cell motility, we investigated fresh tissue specimens from two PCa xenograft mouse models, PC3 and LNCaP, using magnetic resonance elastography (MRE), diffusion-weighted imaging (DWI), quantitative histology, and nuclear shape analysis. Increased tumor stiffness and impaired water diffusion were observed to be associated with collagen and elastin accumulation and decreased cell motility. Overall, LNCaP, while more representative of clinical PCa than PC3, accumulated fewer ECM components, induced less restriction of water diffusion, and exhibited increased cell motility, resulting in overall softer and less viscous properties. Taken together, our results suggest that prostate tumor stiffness increases with ECM accumulation and cell adhesion - characteristics that influence critical biological processes of cancer development. MRE paired with DWI provides a powerful set of imaging markers that can potentially predict prostate tumor development from benign masses to aggressive malignancies in patients. STATEMENT OF SIGNIFICANCE: Xenograft models of human prostate tumor cell lines, allowing correlation of microstructure-sensitive biophysical imaging parameters with quantitative histological methods, can be investigated to identify hallmarks of cancer.

Stroma-specific gene expression signature identifies prostate cancer subtype with high recurrence risk

Current prognostic tools cannot clearly distinguish indolent and aggressive prostate cancer (PC). We hypothesized that analyzing individual contributions of epithelial and stromal components in localized PC (LPC) could improve risk stratification, as stromal subtypes may have been overlooked due to the emphasis on malignant epithelial cells. Hence, we derived molecular subtypes of PC using gene expression analysis of LPC samples from prostatectomy patients (cohort 1, n = 127) and validated these subtypes in two independent prostatectomy cohorts (cohort 2, n = 406, cohort 3, n = 126). Stroma and epithelium-specific signatures were established from laser-capture microdissection data and non-negative matrix factorization was used to identify subtypes based on these signatures. Subtypes were functionally characterized by gene set and cell type enrichment analyses, and survival analysis was conducted. Three epithelial (E1-E3) and three stromal (S1-S3) PC subtypes were identified. While subtyping based on epithelial signatures showed inconsistent associations to biochemical recurrence (BCR), subtyping by stromal signatures was significantly associated with BCR in all three cohorts, with subtype S3 indicating high BCR risk. Subtype S3 exhibited distinct features, including significantly decreased cell-polarity and myogenesis, significantly increased infiltration of M2-polarized macrophages and CD8 + T-cells compared to subtype S1. For patients clinically classified as CAPRA-S intermediate risk, S3 improved prediction of BCR. This study demonstrates the potential of stromal signatures in identification of clinically relevant PC subtypes, and further indicated that stromal characterization may enhance risk stratification in LPC and may be particularly promising in cases with high prognostic ambiguity based on clinical parameters.

Prostate Cancer and the Mevalonate Pathway

Antineoplastic therapies for prostate cancer (PCa) have traditionally centered around the androgen receptor (AR) pathway, which has demonstrated a significant role in oncogenesis. Nevertheless, it is becoming progressively apparent that therapeutic strategies must diversify their focus due to the emergence of resistance mechanisms that the tumor employs when subjected to monomolecular treatments. This review illustrates how the dysregulation of the lipid metabolic pathway constitutes a survival strategy adopted by tumors to evade eradication efforts. Integrating this aspect into oncological management could prove valuable in combating PCa.

Distinct mesenchymal cell states mediate prostate cancer progression

In the complex tumor microenvironment (TME), mesenchymal cells are key players, yet their specific roles in prostate cancer (PCa) progression remain to be fully deciphered. This study employs single-cell RNA sequencing to delineate molecular changes in tumor stroma that influence PCa progression and metastasis. Analyzing mesenchymal cells from four genetically engineered mouse models (GEMMs) and correlating these findings with human tumors, we identify eight stromal cell populations with distinct transcriptional identities consistent across both species. Notably, stromal signatures in advanced mouse disease reflect those in human bone metastases, highlighting periostin's role in invasion and differentiation. From these insights, we derive a gene signature that predicts metastatic progression in localized disease beyond traditional Gleason scores. Our results illuminate the critical influence of stromal dynamics on PCa progression, suggesting new prognostic tools and therapeutic targets.

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for 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.

Diabetes mellitus and prostate cancer risk: A mendelian randomization analysis

BACKGROUND

Some studies have directed towards an association between diabetes mellitus (DM) and prostate cancer (PCa); however, this specific relationship remains inconclusive. In recent years, Mendelian randomization (MR) has become a widely used analytical method for inferring epidemiological causes.

AIM

To investigated the potential relationship between DM and PCa using MR.

METHODS

We downloaded relevant data on "diabetes" and "PCa" from the IEU OpenGWAS project database, performed three different methods to conduct MR, and carried out sensitivity analysis for verification.

RESULTS

The results indicated that DM was an independent risk factor for PCa. The odds ratio (OR) values obtained using the inverse variance weighted method in this study were as follows: OR = 1.018 (95% confidence interval: 1.004-1.032), P = 0.014.

CONCLUSION

We found that DM could increase the incidence rate of PCa.

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.

Mendelian randomization study on the causal relationship between leukocyte telomere length and prostate cancer

BACKGROUND

Leukocyte telomere length (LTL) is related to prostate cancer (PCa). However, the causal relationship between them remains unknown. This study was aimed at identifying the causal direction between LTL and PCa with Mendelian randomization (MR).

METHODS

Single-nucleotide polymorphisms associated with LTL were identified from a genome-wide association study (GWAS) involving 472,174 individuals. Summary-level data of PCa-related GWAS were extracted from four cohorts comprising 456,717 individuals. An inverse-variance-weighted (IVW) algorithm was used for MR. Sensitivity analyses were performed with MR-Egger regression, IVW regression, leave-one-out test, and MR-Pleiotropy Residual Sum and Outlier analyses. A meta-analysis was also performed to compute the average genetically determined effect of LTL on PCa.

RESULTS

A long LTL was associated with an increased risk of PCa in all cohorts, with odds ratios of 1.368 (95% confidence interval [CI]: 1.247 to 1.500, P = 2.84×10-11), 1.503 (95% CI: 1.243 to 1.816, P = 2.57×10-5), 1.722 (95% CI: 1.427 to 2.077, P = 1.48×10-8), and 1.358 (95% CI: 1.242 to 1.484, P = 1.73×10-11) in the IVW analysis. Sensitivity analyses showed that the genetically determined effect of LTL on PCa was stable and reliable. The meta-analysis showed that the genetically determined per 1-standard deviation rise in LTL correlated significantly with an average 40.6% increase in the PCa risk, with an average odds ratio of 1.406 (95% CI: 1.327 to 1.489, P < 0.001).

CONCLUSION

The results of this study supported the causal hypothesis that the genetically determined longer LTL was associated with a higher risk of PCa. This finding could serve as a basis for therapeutic strategies for PCa.

The future of patient-derived xenografts in prostate cancer research

Patient-derived xenografts (PDXs) are generated by engrafting human tumours into mice. Serially transplantable PDXs are used to study tumour biology and test therapeutics, linking the laboratory to the clinic. Although few prostate cancer PDXs are available in large repositories, over 330 prostate cancer PDXs have been established, spanning broad clinical stages, genotypes and phenotypes. Nevertheless, more PDXs are needed to reflect patient diversity, and to study new treatments and emerging mechanisms of resistance. We can maximize the use of PDXs by exchanging models and datasets, and by depositing PDXs into biorepositories, but we must address the impediments to accessing PDXs, such as institutional, ethical and legal agreements. Through collaboration, researchers will gain greater access to PDXs representing diverse features of prostate cancer.

Distinct mesenchymal cell states mediate prostate cancer progression

Alterations in tumor stroma influence prostate cancer progression and metastatic potential. However, the molecular underpinnings of this stromal-epithelial crosstalk are largely unknown. Here, we compare mesenchymal cells from four genetically engineered mouse models (GEMMs) of prostate cancer representing different stages of the disease to their wild-type (WT) counterparts by single-cell RNA sequencing (scRNA-seq) and, ultimately, to human tumors with comparable genotypes. We identified 8 transcriptionally and functionally distinct stromal populations responsible for common and GEMM-specific transcriptional programs. We show that stromal responses are conserved in mouse models and human prostate cancers with the same genomic alterations. We noted striking similarities between the transcriptional profiles of the stroma of murine models of advanced disease and those of of human prostate cancer bone metastases. These profiles were then used to build a robust gene signature that can predict metastatic progression in prostate cancer patients with localized disease and is also associated with progression-free survival independent of Gleason score. Taken together, this offers new evidence that stromal microenvironment mediates prostate cancer progression, further identifying tissue-based biomarkers and potential therapeutic targets of aggressive and metastatic disease.

WNT1 Inducible Signaling Pathway Protein 1 Is a Stroma-Specific Secreting Protein Inducing a Fibroblast Contraction and Carcinoma Cell Growth in the Human Prostate

The WNT1 inducible signaling pathway protein 1 (WISP1), a member of the connective tissue growth factor family, plays a crucial role in several important cellular functions in a highly tissue-specific manner. Results of a RT-qPCR indicated that WISP1 expressed only in cells of the human prostate fibroblasts, HPrF and WPMY-1, but not the prostate carcinoma cells in vitro. Two major isoforms (WISP1v1 and WISP1v2) were identified in the HPrF cells determined by RT-PCR and immunoblot assays. The knock-down of a WISP1 blocked cell proliferation and contraction, while treating respectively with the conditioned medium from the ectopic WISP1v1- and WISPv2-overexpressed 293T cells enhanced the migration of HPrF cells. The TNFα induced WISP1 secretion and cell contraction while the knock-down of WISP1 attenuated these effects, although TNFα did not affect the proliferation of the HPrF cells. The ectopic overexpression of WISP1v1 but not WISP1v2 downregulated the N-myc downstream regulated 1 (NDRG1) while upregulating N-cadherin, slug, snail, and vimentin gene expressions which induced not only the cell proliferation and invasion in vitro but also tumor growth of prostate carcinoma cells in vivo. The results confirmed that WISP1 is a stroma-specific secreting protein, enhancing the cell migration and contraction of prostate fibroblasts, as well as the proliferation, invasion, and tumor growth of prostate carcinoma cells.

Prostate Cancer Secretome and Membrane Proteome from Pten Conditional Knockout Mice Identify Potential Biomarkers for Disease Progression

Prostate cancer (PCa) is the second most common cause of mortality among men. Tumor secretome is a promising strategy for understanding the biology of tumor cells and providing markers for disease progression and patient outcomes. Here, transcriptomic-based secretome analysis was performed on the PCa tumor transcriptome of Genetically Engineered Mouse Model (GEMM) Pb-Cre4/Pten^f/f mice to identify potentially secreted and membrane proteins—PSPs and PMPs. We combined a selection of transcripts from the GSE 94574 dataset and a list of protein-coding genes of the secretome and membrane proteome datasets using the Human Protein Atlas Secretome. Notably, nine deregulated PMPs and PSPs were identified in PCa (DMPK, PLN, KCNQ5, KCNQ4, MYOC, WIF1, BMP7, F3, and MUC1). We verified the gene expression patterns of Differentially Expressed Genes (DEGs) in normal and tumoral human samples using the GEPIA tool. DMPK, KCNQ4, and WIF1 targets were downregulated in PCa samples and in the GSE dataset. A significant association between shorter survival and KCNQ4, PLN, WIF1, and F3 expression was detected in the MSKCC dataset. We further identified six validated miRNAs (mmu-miR-6962-3p, mmu-miR- 6989-3p, mmu-miR-6998-3p, mmu-miR-5627-5p, mmu-miR-15a-3p, and mmu-miR-6922-3p) interactions that target MYOC, KCNQ5, MUC1, and F3. We have characterized the PCa secretome and membrane proteome and have spotted new dysregulated target candidates in PCa.

A Cluster of Metabolic-Related Genes Serve as Potential Prognostic Biomarkers for Renal Cell Carcinoma

Renal cell carcinoma (RCC) is the most common type of renal cancer, characterized by the dysregulation of metabolic pathways. RCC is the second highest cause of death among patients with urologic cancers and those with cancer cell metastases have a 5-years survival rate of only 10–15%. Thus, reliable prognostic biomarkers are essential tools to predict RCC patient outcomes. This study identified differentially expressed genes (DEGs) in the gene expression omnibus (GEO) database that are associated with pre-and post-metastases in clear cell renal cell carcinoma (ccRCC) patients and intersected these with metabolism-related genes in the Kyoto encyclopedia of genes and genomes (KEGG) database to identify metabolism-related DEGs (DEMGs). GOplot and ggplot packages for gene ontology (GO) and KEGG pathway enrichment analysis of DEMGs with log (foldchange) (logFC) were used to identify metabolic pathways associated with DEMG. Upregulated risk genes and downregulated protective genes among the DEMGs and seven independent metabolic genes, RRM2, MTHFD2, AGXT2, ALDH6A1, GLDC, HOGA1, and ETNK2, were found using univariate and multivariate Cox regression analysis, intersection, and Lasso-Cox regression analysis to establish a metabolic risk score signature (MRSS). Kaplan-Meier survival curve of Overall Survival (OS) showed that the low-risk group had a significantly better prognosis than the high-risk group in both the training cohort (p < 0.001; HR = 2.73, 95% CI = 1.97–3.79) and the validation cohort (p = 0.001; HR = 2.84, 95% CI = 1.50–5.38). The nomogram combined with multiple clinical information and MRSS was more effective at predicting patient outcomes than a single independent prognostic factor. The impact of metabolism on ccRCC was also assessed, and seven metabolism-related genes were established and validated as biomarkers to predict patient outcomes effectively.

Modeling Androgen Deprivation Therapy-Induced Prostate Cancer Dormancy and Its Clinical Implications

Treatment-induced tumor dormancy is a state in cancer progression where residual disease is present but remains asymptomatic. Dormant cancer cells are treatment-resistant and responsible for cancer recurrence and metastasis. Prostate cancer treated with androgen-deprivation therapy (ADT) often enters a dormant state. ADT-induced prostate cancer dormancy remains poorly understood due to the challenge in acquiring clinical dormant prostate cancer cells and the lack of representative models. In this study, we aimed to develop clinically relevant models for studying ADT-induced prostate cancer dormancy. Dormant prostate cancer models were established by castrating mice bearing patient-derived xenografts (PDX) of hormonal naïve or sensitive prostate cancer. Dormancy status and tumor relapse were monitored and evaluated. Paired pre- and postcastration (dormant) PDX tissues were subjected to morphologic and transcriptome profiling analyses. As a result, we established eleven ADT-induced dormant prostate cancer models that closely mimicked the clinical courses of ADT-treated prostate cancer. We identified two ADT-induced dormancy subtypes that differed in morphology, gene expression, and relapse rates. We discovered transcriptomic differences in precastration PDXs that predisposed the dormancy response to ADT. We further developed a dormancy subtype-based, predisposed gene signature that was significantly associated with ADT response in hormonal naïve prostate cancer and clinical outcome in castration-resistant prostate cancer treated with ADT or androgen-receptor pathway inhibitors.

IMPLICATIONS

We have established highly clinically relevant PDXs of ADT-induced dormant prostate cancer and identified two dormancy subtypes, leading to the development of a novel predicative gene signature that allows robust risk stratification of patients with prostate cancer to ADT or androgen-receptor pathway inhibitors.

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.

Patient-derived xenograft models in hepatopancreatobiliary cancer

Animal models are crucial tools for evaluating the biological progress of human cancers and for the preclinical investigation of anticancer drugs and cancer prevention. Various animals are widely used in hepatopancreatobiliary cancer research, and mouse models are the most popular. Generally, genetic tools, graft transplantation, and chemical and physical measures are adopted to generate sundry mouse models of hepatopancreatobiliary cancer. Graft transplantation is commonly used to study tumour progression. Over the past few decades, subcutaneous or orthotopic cell-derived tumour xenograft models (CDX models) have been developed to simulate distinct tumours in patients. However, two major limitations exist in CDX models. One model poorly simulates the microenvironment of tumours in humans, such as the vascular, lymphatic and immune environments. The other model loses genetic heterogeneity compared with the corresponding primary tumour. Increased efforts have focused on developing better models for hepatopancreatobiliary cancer research. Hepatopancreatobiliary cancer is considered a tumour with high molecular heterogeneity, making precision medicine challenging in cancer treatment. Developing a new animal model that can better mimic tumour tissue and more accurately predict the efficacy of anticancer treatments is urgent. For the past several years, the patient-derived xenograft model (PDX model) has emerged as a promising tool for translational research. It can retain the genetic and histological stability of their originating tumour at limited passages and shed light on precision cancer medicine. In this review, we summarize the methodology, advantages/disadvantages and applications of PDX models in hepatopancreatobiliary cancer research.

Stroma-derived extracellular vesicle mRNA signatures inform histological nature of prostate cancer

Histological assessment of prostate cancer is the key diagnostic test and can predict disease outcome. This is however an invasive procedure that carries associated risks, hence non-invasive assays to support the diagnostic pathway are much needed. A key feature of disease progression, and subsequent poor prognosis, is the presence of an altered stroma. Here we explored the utility of prostate stromal cell-derived vesicles as indicators of an altered tumour environment. We compared vesicles from six donor-matched pairs of adjacent-normal versus disease-associated primary stromal cultures. We identified 19 differentially expressed transcripts that discriminate disease from normal stromal extracellular vesicles (EVs). EVs isolated from patient serum were investigated for these putative disease-discriminating mRNA. A set of transcripts including Caveolin-1 (CAV1), TMP2, THBS1, and CTGF were found to be successful in discriminating clinically insignificant (Gleason = 6) disease from clinically significant (Gleason > 8) prostate cancer. Furthermore, correlation between transcript expression and progression-free survival suggests that levels of these mRNA may predict disease outcome. Informed by a machine learning approach, combining measures of the five most informative EV-associated mRNAs with PSA was shown to significantly improve assay sensitivity and specificity. An in-silico model was produced, showcasing the superiority of this multi-modal liquid biopsy compared to needle biopsy for predicting disease progression. This proof of concept highlights the utility of serum EV analytics as a companion diagnostic test with prognostic utility, which may obviate the need for biopsy.

Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression

Cancer diagnosis using machine learning algorithms is one of the main topics of research in computer-based medical science. Prostate cancer is considered one of the reasons that are leading to deaths worldwide. Data analysis of gene expression from microarray using machine learning and soft computing algorithms is a useful tool for detecting prostate cancer in medical diagnosis. Even though traditional machine learning methods have been successfully applied for detecting prostate cancer, the large number of attributes with a small sample size of microarray data is still a challenge that limits their ability for effective medical diagnosis. Selecting a subset of relevant features from all features and choosing an appropriate machine learning method can exploit the information of microarray data to improve the accuracy rate of detection. In this paper, we propose to use a correlation feature selection (CFS) method with random committee (RC) ensemble learning to detect prostate cancer from microarray data of gene expression. A set of experiments are conducted on a public benchmark dataset using 10-fold cross-validation technique to evaluate the proposed approach. The experimental results revealed that the proposed approach attains 95.098% accuracy, which is higher than related work methods on the same dataset.

Cancer-Associated Fibroblasts and T Cells: From Mechanisms to Outcomes

Over the past decade, T cell immunotherapy has changed the face of cancer treatment, providing robust treatment options for several previously intractable cancers. Unfortunately, many epithelial tumors with high mortality rates respond poorly to immunotherapy, and an understanding of the key impediments is urgently required. Cancer-associated fibroblasts (CAFs) comprise the most frequent nonneoplastic cellular component in most solid tumors. Far from an inert scaffold, CAFs significantly influence tumor neogenesis, persistence, and metastasis and are emerging as a key player in immunotherapy resistance. In this review, we discuss the physical and chemical barriers that CAFs place between effector T cells and their tumor cell targets, and the therapies poised to target them.

The potential of tumour microenvironment markers to stratify the risk of recurrence in prostate cancer patients

The tumour micro-environment (TME) plays a crucial role in the onset and progression of prostate cancer (PCa). Here we studied the potential of a selected panel of TME-markers to predict clinical recurrence (CLR) in PCa. Patient cohorts were matched for the presence or absence of CLR 5 years post-prostatectomy. Tissue micro-arrays (TMA) were composed with both prostate non-tumour (PNT) and PCa tissue and subsequently processed for immunohistochemistry (IHC). The IHC panel included markers for cancer activated fibroblasts (CAFs), blood vessels and steroid hormone receptors ((SHR): androgen receptor (AR), progesterone receptor (PR) and estrogen receptor (ER)). Stained slides were digitalised, selectively annotated and analysed for percentage of marker expression with standardized and validated image analysis algorithms. A univariable analysis identified several TME markers with significant impact on CR: expression of CD31 (vascular marker) in PNT stroma, expression of alpha smooth muscle actin (αSMA) in PCa stroma, and PR expression ratio between PCa stroma and PNT stroma. A multivariable model, which included CD31 expression (vascular marker) in PNT stroma and PR expression ratio between PCa stroma and PNT stroma, could significantly stratify patients for CLR, with the identification of a low risk and high-risk subgroup. If validated and confirmed in an independent prospective series, this subgroup might have clinical potential for PCa patient stratification.

Stroma Transcriptomic and Proteomic Profile of Prostate Cancer Metastasis Xenograft Models Reveals Prognostic Value of Stroma Signatures

Resistance acquisition to androgen deprivation treatment and metastasis progression are a major clinical issue associated with prostate cancer (PCa). The role of stroma during disease progression is insufficiently defined. Using transcriptomic and proteomic analyses on differentially aggressive patient-derived xenografts (PDXs), we investigated whether PCa tumors predispose their microenvironment (stroma) to a metastatic gene expression pattern. RNA sequencing was performed on the PCa PDXs BM18 (castration-sensitive) and LAPC9 (castration-resistant), representing different disease stages. Using organism-specific reference databases, the human-specific transcriptome (tumor) was identified and separated from the mouse-specific transcriptome (stroma). To identify proteomic changes in the tumor (human) versus the stroma (mouse), we performed human/mouse cell separation and subjected protein lysates to quantitative Tandem Mass Tag labeling and mass spectrometry. Tenascin C (TNC) was among the most abundant stromal genes, modulated by androgen levels in vivo and highly expressed in castration-resistant LAPC9 PDX. The tissue microarray of primary PCa samples (n = 210) showed that TNC is a negative prognostic marker of the clinical progression to recurrence or metastasis. Stroma markers of osteoblastic PCa bone metastases seven-up signature were induced in the stroma by the host organism in metastatic xenografts, indicating conserved mechanisms of tumor cells to induce a stromal premetastatic signature. A 50-gene list stroma signature was identified based on androgen-dependent responses, which shows a linear association with the Gleason score, metastasis progression and progression-free survival. Our data show that metastatic PCa PDXs, which differ in androgen sensitivity, trigger differential stroma responses, which show the metastasis risk stratification and prognostic biomarker potential.

Integrating Tumor Stroma Biomarkers With Clinical Indicators for Colon Cancer Survival Stratification

The tumor stroma plays an important role in tumor progression and chemotherapeutic resistance; however, its role in colon cancer (CC) survival prognosis remains to be investigated. Here, we identified tumor stroma biomarkers and evaluated their role in CC prognosis stratification. Four independent datasets containing a total of 1,313 patients were included in this study and were divided into training and testing sets. Stromal scores calculated using the estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) algorithm were used to assess the tumor stroma level. Kaplan-Meier curves and the log-rank test were used to identify relationships between stromal score and prognosis. Tumor stroma biomarkers were identified by cross-validation of multiple datasets and bioinformatics methods. Cox proportional hazards regression models were constructed using four prognosis factors (age, tumor stage, the ESTIMATE stromal score, and the biomarker stromal score) in different combinations for prognosis prediction and compared. Patients with high stromal scores had a lower overall survival rate (p = 0.00016), higher risk of recurrence (p < 0.0001), and higher probability of chemotherapeutic resistance (p < 0.0001) than those with low scores. We identified 16 tumor stroma biomarkers and generated a new prognosis indicator termed the biomarker stromal score (ranging from 0 to 16) based on their expression levels. Its addition to an age/tumor stage-based model significantly improved prognosis prediction accuracy. In conclusion, the tumor stromal score is significantly negatively associated with CC survival prognosis, and the new tumor stroma indicator can improve CC prognosis stratification.

Current experimental human tissue-derived models for prostate cancer research

Scientists engaged in prostate cancer research have been conducting experiments using two-dimensional cultures of prostate cancer cell lines for decades. However, these experiments fail to reproduce and reflect the clinical course of individual patients with prostate cancer, or the molecular and genetic characteristics of prostate cancer, the basic requirement for most of the preclinical studies on prostate cancer. The use of human prostate cancer tissues in experiments has enabled the collection and verification of clinically relevant data, including chemical reactions, changes in proteins, and specific gene expression. Tissue recombination models have been employed for studying prostate development, the initiation and progression of prostate cancer, and the tumor microenvironment. Notably, the epithelial-stromal interaction, which might play a critical role in prostate cancer pathogenesis, can be reproduced in this model. Patient-derived xenograft models have been developed as powerful avatars comprising patient-derived prostate cancer tissues implanted in immunocompromised mice and could serve as a precision medicine approach for each prostate cancer patient. Spheroid and organoid assays, representative of modern three-dimensional cultures, can replicate the conditions in human prostate tumors and the prostate organ itself as a miniature model. Although an intact immune system against the tumor is missing from the models aimed at investigating immuno-oncological reagents in various malignancies, all these experimental models can help researchers in developing new drugs and selecting appropriate treatment strategies for prostate cancer patients.

Potential biomarkers and risk assessment models to enhance the tumor-node-metastasis (TNM) staging classification of urologic cancers

INTRODUCTION

The anatomic-based TNM classification is considered the benchmark in cancer staging and has been regularly updated since its inception. In the current era of precision medicine, the added intention for future TNM modifications is to heighten its impact in the more 'personalized' level of cancer care. In urologic cancers, this goal may be achieved by incorporating 'non-anatomic' factors into TNM, such as biomarkers (e.g. gene alterations, molecular subtypes, genomic classifiers) and risk assessment models (e.g. nomogram, look-up table), while maintaining the anatomic extent as the foundation of staging. These different prognosticators can be combined and integrated, may serve as substratifiers for T, N, or M categories, and perhaps, incorporated as elements in TNM stage groupings to enhance their prognostic capability in urologic cancers.

AREAS COVERED

This review highlights candidate biomarkers and risk assessment models that can be explored to potentially improve TNM prognostication of bladder, prostate, kidney, and testicular cancers.

EXPERT OPINION

Recent advances in molecular analysis have increased the understanding of the genomic, transcriptomic, and epigenetic features for biomarker use in prognostication of urologic cancers, which together with the available risk assessment models, may complement and overcome the limitations of the traditional TNM staging.

PDX: Moving Beyond Drug Screening to Versatile Models for Research Discovery

Patient-derived xenografts (PDXs) are tools of the trade for many researchers from all disciplines and medical specialties. Most endocrinologists, and especially those working in oncology, commonly use PDXs for preclinical drug testing and development, and over the last decade large collections of PDXs have emerged across all tumor streams. In this review, we examine how the field has evolved to include PDXs as versatile resources for research discoveries, providing evidence for guidelines and changes in clinical practice.

Cancer-associated fibroblasts stimulate primary tumor growth and metastatic spread in an orthotopic prostate cancer xenograft model

The unique microenvironment of the prostate plays a crucial role in the development and progression of prostate cancer (PCa). We examined the effects of cancer-associated fibroblasts (CAFs) on PCa progression using patient-derived fibroblast primary cultures in a representative orthotopic xenograft model. Primary cultures of CAFs, non-cancer-associated fibroblasts (NCAFs) and benign prostate hyperplasia-associated fibroblasts (BPHFs) were generated from patient-derived tissue specimens. These fibroblasts were coinjected together with cancer cells (LuCaP136 spheroids or LNCaP cells) in orthotopic PCa xenografts to investigate their effects on local and systemic tumor progression. Primary tumor growth as well as metastatic spread to lymph nodes and lungs were significantly stimulated by CAF coinjection in LuCaP136 xenografts. When NCAFs or BPHFs were coinjected, tumor progression was similar to injection of tumor cells alone. In LNCaP xenografts, all three fibroblast types significantly stimulated primary tumor progression compared to injection of LNCaP cells alone. CAF coinjection further increased the frequency of lymph node and lung metastases. This is the first study using an orthotopic spheroid culture xenograft model to demonstrate a stimulatory effect of patient-derived CAFs on PCa progression. The established experimental setup will provide a valuable tool to further unravel the interacting mechanisms between PCa cells and their microenvironment.

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.

The Role of Cancer-Associated Fibroblasts in Prostate Cancer Tumorigenesis

Tumors strongly depend on their surrounding tumor microenvironment (TME) for growth and progression, since stromal elements are required to generate the optimal conditions for cancer cell proliferation, invasion, and possibly metastasis. Prostate cancer (PCa), though easily curable during primary stages, represents a clinical challenge in advanced stages because of the acquisition of resistance to anti-cancer treatments, especially androgen-deprivation therapies (ADT), which possibly lead to uncurable metastases such as those affecting the bone. An increasing number of studies is giving evidence that prostate TME components, especially cancer-associated fibroblasts (CAFs), which are the most abundant cell type, play a causal role in PCa since the very early disease stages, influencing therapy resistance and metastatic progression. This is highlighted by the prognostic value of the analysis of stromal markers, which may predict disease recurrence and metastasis. However, further investigations on the molecular mechanisms of tumor-stroma interactions are still needed to develop novel therapeutic approaches targeting stromal components. In this review, we report the current knowledge of the characteristics and functions of the stroma in prostate tumorigenesis, including relevant discussion of normal prostate homeostasis, chronic inflammatory conditions, pre-neoplastic lesions, and primary and metastatic tumors. Specifically, we focus on the role of CAFs, to point out their prognostic and therapeutic potential in PCa.

The Relationship Between Tumor-Stroma Ratio, the Immune Microenvironment, and Survival in Patients With Spinal Chordoma

BACKGROUND

Currently, little is known about the clinical relevance of tumor-stroma ratio (TSR) in chordoma and data discussing the relationship between TSR and immune status of chordoma are lacking.

OBJECTIVE

To characterize TSR distribution in spinal chordoma, and investigated its correlation with clinicopathologic or immunological features of patients and outcome.

METHODS

TSR was assessed visually on hematoxylin and eosin-stained sections from 54 tumor specimens by 2 independent pathologists. Multiplex immunofluorescence was used to quantify the expression levels of microvessel density, Ki-67, Brachyury, and tumor as well as stromal PD-L1. Tumor immunity status including the Immunoscore and densities of tumor-infiltrating lymphocytes (TILs) subtypes were obtained from our published data and reanalyzed.

RESULTS

Bland-Altman plot showed no difference between mean TSR derived from the two observers. TSR was positively associated with stromal PD-L1 expression, the Immunoscore and CD3+ as well as CD4+ TILs density, but negatively correlated with tumor microvessel density, Ki-67 index, surrounding muscle invasion by tumor and number of Foxp3+ and PD-1+ TILs. Low TSR independently predicted poor local recurrence-free survival and overall survival. Moreover, patients with low TSR and low Immunoscore chordoma phenotype were associated with the worst survival. More importantly, combined TSR and Immunoscore accurately reflected prognosis and enhanced the ability of TSR or Immunoscore alone for outcome prediction.

CONCLUSION

These data reveal the significant impact of TSR on tumor progression and immunological response of patients. Subsequent use of agents targeting the stroma compartment may be an effective strategy to treat chordoma especially in combination with immune-based drugs.

STAT3-dependent analysis reveals PDK4 as independent predictor of recurrence in prostate cancer

Prostate cancer (PCa) has a broad spectrum of clinical behavior; hence, biomarkers are urgently needed for risk stratification. Here, we aim to find potential biomarkers for risk stratification, by utilizing a gene co-expression network of transcriptomics data in addition to laser-microdissected proteomics from human and murine prostate FFPE samples. We show up-regulation of oxidative phosphorylation (OXPHOS) in PCa on the transcriptomic level and up-regulation of the TCA cycle/OXPHOS on the proteomic level, which is inversely correlated to STAT3 expression. We hereby identify gene expression of pyruvate dehydrogenase kinase 4 (PDK4), a key regulator of the TCA cycle, as a promising independent prognostic marker in PCa. PDK4 predicts disease recurrence independent of diagnostic risk factors such as grading, staging, and PSA level. Therefore, low PDK4 is a promising marker for PCa with dismal prognosis.

Alterations in the methylome of the stromal tumour microenvironment signal the presence and severity of prostate cancer

Background

Prostate cancer changes the phenotype of cells within the stromal microenvironment, including fibroblasts, which in turn promote tumour progression. Functional changes in prostate cancer-associated fibroblasts (CAFs) coincide with alterations in DNA methylation levels at loci-specific regulatory regions. Yet, it is not clear how these methylation changes compare across CAFs from different patients. Therefore, we examined the consistency and prognostic significance of genome-wide DNA methylation profiles between CAFs from patients with different grades of primary prostate cancer.

Results

We used Infinium MethylationEPIC BeadChips to evaluate genome-wide DNA methylation profiles from 18 matched CAFs and non-malignant prostate tissue fibroblasts (NPFs) from men with moderate to high grade prostate cancer, as well as five unmatched benign prostate tissue fibroblasts (BPFs) from men with benign prostatic hyperplasia. We identified two sets of differentially methylated regions (DMRs) in patient CAFs. One set of DMRs reproducibly differed between CAFs and fibroblasts from non-malignant tissue (NPFs and BPFs). Indeed, more than 1200 DMRs consistently changed in CAFs from every patient, regardless of tumour grade. The second set of DMRs varied between CAFs according to the severity of the tumour. Notably, hypomethylation of the EDARADD promoter occurred specifically in CAFs from high-grade tumours and correlated with increased transcript abundance and increased EDARADD staining in patient tissue. Across multiple cohorts, tumours with low EDARADD DNA methylation and high EDARADD mRNA expression were consistently associated with adverse clinical features and shorter recurrence free survival.

Conclusions

We identified a large set of DMRs that are commonly shared across CAFs regardless of tumour grade and outcome, demonstrating highly consistent epigenome changes in the prostate tumour microenvironment. Additionally, we found that CAFs from aggressive prostate cancers have discrete methylation differences compared to CAFs from moderate risk prostate cancer. Together, our data demonstrates that the methylome of the tumour microenvironment reflects both the presence and the severity of the prostate cancer and, therefore, may provide diagnostic and prognostic potential.

Complexity of Neural Component of Tumor Microenvironment in Prostate Cancer

The tumor microenvironment (TME) plays an essential role in the development and progression of neoplasms. TME consists of the extracellular matrix and numerous specialized cells interacting with cancer cells by paracrine and autocrine mechanisms. Tumor axonogenesis and neoneurogenesis constitute a developing area of investigation. Prostate cancer (PC) is one of the most common malignancies in men worldwide. During the past years, more and more studies have shown that mechanisms leading to the development of PC are not confined only to the epithelial cancer cell, but also involve the tumor stroma. Different nerve types and neurotransmitters present within the TME are thought to be important factors in PC biology. Moreover, perineural invasion, which is a common way of PC spreading, in parallel creates the neural niche for malignant cells. Cancer neurobiology seems to have become a new discipline to explore the contribution of neoplastic cell interactions with the nervous system and the neural TME component, also to search for potential therapeutic targets in malignant tumors such as PC.

Transient Sox9 Expression Facilitates Resistance to Androgen-Targeted Therapy in Prostate Cancer

PURPOSE

Patients with metastatic prostate cancer are increasingly presenting with treatment-resistant, androgen receptor-negative/low (AR^-/Low) tumors, with or without neuroendocrine characteristics, in processes attributed to tumor cell plasticity. This plasticity has been modeled by Rb1/p53 knockdown/knockout and is accompanied by overexpression of the pluripotency factor, Sox2. Here, we explore the role of the developmental transcription factor Sox9 in the process of prostate cancer therapy response and tumor progression.

EXPERIMENTAL DESIGN

Unique prostate cancer cell models that capture AR^-/Low stem cell-like intermediates were analyzed for features of plasticity and the functional role of Sox9. Human prostate cancer xenografts and tissue microarrays were evaluated for temporal alterations in Sox9 expression. The role of NF-κB pathway activity in Sox9 overexpression was explored.

RESULTS

Prostate cancer stem cell-like intermediates have reduced Rb1 and p53 protein expression and overexpress Sox2 as well as Sox9. Sox9 was required for spheroid growth, and overexpression increased invasiveness and neural features of prostate cancer cells. Sox9 was transiently upregulated in castration-induced progression of prostate cancer xenografts and was specifically overexpressed in neoadjuvant hormone therapy (NHT)-treated patient tumors. High Sox9 expression in NHT-treated patients predicts biochemical recurrence. Finally, we link Sox9 induction to NF-κB dimer activation in prostate cancer cells.

CONCLUSIONS

Developmentally reprogrammed prostate cancer cell models recapitulate features of clinically advanced prostate tumors, including downregulated Rb1/p53 and overexpression of Sox2 with Sox9. Sox9 is a marker of a transitional state that identifies prostate cancer cells under the stress of therapeutic assault and facilitates progression to therapy resistance. Its expression may index the relative activity of the NF-κB pathway.

Targeting of the Cancer-Associated Fibroblast-T-Cell Axis in Solid Malignancies

The introduction of a wide range of immunotherapies in clinical practice has revolutionized the treatment of cancer in the last decade. The majority of these therapeutic modalities are centered on reinvigorating a tumor-reactive cytotoxic T-cell response. While impressive clinical successes are obtained, the majority of cancer patients still fail to show a clinical response, despite the fact that their tumors express antigens that can be recognized by the immune system. This is due to a series of other cellular actors, present in or attracted towards the tumor microenvironment, including regulatory T-cells, myeloid-derived suppressor cells and cancer-associated fibroblasts (CAFs). As the main cellular constituent of the tumor-associated stroma, CAFs form a heterogeneous group of cells which can drive cancer cell invasion but can also impair the migration and activation of T-cells through direct and indirect mechanisms. This singles CAFs out as an important next target for further optimization of T-cell based immunotherapies. Here, we review the recent literature on the role of CAFs in orchestrating T-cell activation and migration within the tumor microenvironment and discuss potential avenues for targeting the interactions between fibroblasts and T-cells.

Fibroblast as a critical stromal cell type determining prognosis in prostate cancer

BACKGROUND

Tumor stroma associates with prostate cancer (PCa) progression, but its specific cellular composition and association to patient survival outcome have not been characterized.

METHODS

We analyzed stromal composition in human PCa using multiplex immunohistochemistry and quantitative, high-resolution image analysis in two retrospective, formalin-fixed paraffin embedded observational clinical cohorts (Cohort I, n = 117; Cohort II, n = 340) using PCa-specific mortality as outcome measurement.

RESULTS

A high proportion of fibroblasts associated with aggressive disease and castration-resistant prostate cancer (CRPC). In a multivariate analysis, increase in fibroblast proportion predicted poor cancer-specific outcome independently in the two clinical cohorts studied.

CONCLUSIONS

Fibroblasts were the most important cell type in determining prognosis in PCa and associated with CRPC. Thus, the stromal composition could be critically important in developing diagnostic and therapeutic approaches to aggressive prostate cancer.

Development of a predictive model for stromal content in prostate cancer samples to improve signature performance

Prostate cancer is heterogeneous in both cellular composition and patient outcome, and development of biomarker signatures to distinguish indolent from aggressive tumours is a high priority. Stroma plays an important role during prostate cancer progression and undergoes histological and transcriptional changes associated with disease. However, identification and validation of stromal markers is limited by a lack of datasets with defined stromal/tumour ratio. We have developed a prostate‐selective signature to estimate the stromal content in cancer samples of mixed cellular composition. We identified stromal‐specific markers from transcriptomic datasets of developmental prostate mesenchyme and prostate cancer stroma. These were experimentally validated in cell lines, datasets of known stromal content, and by immunohistochemistry in tissue samples to verify stromal‐specific expression. Linear models based upon six transcripts were able to infer the stromal content and estimate stromal composition in mixed tissues. The best model had a coefficient of determination R² of 0.67. Application of our stromal content estimation model in various prostate cancer datasets led to improved performance of stromal predictive signatures for disease progression and metastasis. The stromal content of prostate tumours varies considerably; consequently, deconvolution of stromal proportion may yield better results than tumour cell deconvolution. We suggest that adjusting expression data for cell composition will improve stromal signature performance and lead to better prognosis and stratification of men with prostate cancer. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Epigenetic Control of Gene Expression in the Normal and Malignant Human Prostate: A Rapid Response Which Promotes Therapeutic Resistance

A successful prostate cancer must be capable of changing its phenotype in response to a variety of microenvironmental influences, such as adaptation to treatment or successful proliferation at a particular metastatic site. New cell phenotypes emerge by selection from the large, genotypically heterogeneous pool of candidate cells present within any tumor mass, including a distinct stem cell-like population. In such a multicellular model of human prostate cancer, flexible responses are primarily governed not only by de novo mutations but appear to be dominated by a combination of epigenetic controls, whose application results in treatment resistance and tumor relapse. Detailed studies of these individual cell populations have resulted in an epigenetic model for epithelial cell differentiation, which is also instructive in explaining the reported high and inevitable relapse rates of human prostate cancers to a multitude of treatment types.

Human Periprostatic Adipose Tissue: Secretome from Patients With Prostate Cancer or Benign Prostate Hyperplasia

BACKGROUND/AIM

Periprostatic adipose tissue (PPAT) directs tumour behaviour. Microenvironment secretome provides information related to its biology. This study was performed to identify secreted proteins by PPAT, from both prostate cancer and benign prostate hyperplasia (BPH) patients.

PATIENTS AND METHODS

Liquid chromatography-mass spectrometry-based proteomic analysis was performed in PPAT-conditioned media (CM) from patients with prostate cancer (CMs-T) (stage T3: CM-T3, stage T2: CM-T2) or benign disease (CM-BPH).

RESULTS

The highest number and diversity of proteins was identified in CM-T3. Locomotion was the biological process mainly associated to CMs-T and reproduction to CM-T3. Immune responses were enriched in CMs-T. Extracellular matrix and structural proteins were associated to CMs-T. CM-T3 was enriched in proteins with catalytic activity and CM-T2 in proteins with defense/immunity activity. Metabolism and energy pathways were enriched in CM-T3 and those with immune system functions in CMs-T. Transport proteins were enriched in CM-T2 and CM-BPH.

CONCLUSION

Proteins and pathways reported in this study could be useful to distinguish stages of disease and may become targets for novel therapies.

A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer

The five-year survival rate of epithelial ovarian cancer (EOC) is approximately 35-40% despite maximal treatment efforts, highlighting a need for stratification biomarkers for personalized treatment. Here we extract 657 quantitative mathematical descriptors from the preoperative CT images of 364 EOC patients at their initial presentation. Using machine learning, we derive a non-invasive summary-statistic of the primary ovarian tumor based on 4 descriptors, which we name "Radiomic Prognostic Vector" (RPV). RPV reliably identifies the 5% of patients with median overall survival less than 2 years, significantly improves established prognostic methods, and is validated in two independent, multi-center cohorts. Furthermore, genetic, transcriptomic and proteomic analysis from two independent datasets elucidate that stromal phenotype and DNA damage response pathways are activated in RPV-stratified tumors. RPV and its associated analysis platform could be exploited to guide personalized therapy of EOC and is potentially transferrable to other cancer types.

Application of Prostate Cancer Models for Preclinical Study: Advantages and Limitations of Cell Lines, Patient-Derived Xenografts, and Three-Dimensional Culture of Patient-Derived Cells

Various preclinical models have been developed to clarify the pathophysiology of prostate cancer (PCa). Traditional PCa cell lines from clinical metastatic lesions, as exemplified by DU-145, PC-3, and LNCaP cells, are useful tools to define mechanisms underlying tumorigenesis and drug resistance. Cell line-based experiments, however, have limitations for preclinical studies because those cells are basically adapted to 2-dimensional monolayer culture conditions, in which the majority of primary PCa cells cannot survive. Recent tissue engineering enables generation of PCa patient-derived xenografts (PDXs) from both primary and metastatic lesions. Compared with fresh PCa tissue transplantation in athymic mice, co-injection of PCa tissues with extracellular matrix in highly immunodeficient mice has remarkably improved the success rate of PDX generation. PDX models have advantages to appropriately recapitulate the molecular diversity, cellular heterogeneity, and histology of original patient tumors. In contrast to PDX models, patient-derived organoid and spheroid PCa models in 3-dimensional culture are more feasible tools for in vitro studies for retaining the characteristics of patient tumors. In this article, we review PCa preclinical model cell lines and their sublines, PDXs, and patient-derived organoid and spheroid models. These PCa models will be applied to the development of new strategies for cancer precision medicine.

Development and Characterization of a Spontaneously Metastatic Patient-Derived Xenograft Model of Human Prostate Cancer

Here we describe the establishment and characterization of an AR+, PSMA+, ERG+, PTEN^-/-, CHD1^+/- patient-derived xenograft (PDX) model termed 'C5', which has been developed from a 60 years old patient suffering from castration-resistant prostate cancer (CRPC). The patient underwent radical prostatectomy, showed early tumor marker PSA recurrence and, one year after surgery, abiraterone resistance. Subcutaneous C5 tumors can be serially transplanted between mice and grow within ~90 days to 1.5-2 cm³ tumors in SCID Balb/c mice (take rate 100%), NOD-scid IL2Rg^null (NSG) mice (100%) and C57BL/6 pfp^-/-/rag2^-/- mice (66%). In contrast, no tumor growth is observed in female mice. C5 tumors can be cryopreserved and show the same growth characteristics in vivo afterwards. C5 tumor cells do not grow stably in vitro, neither under two- nor three-dimensional cell culture conditions. Upon serial transplantation, some C5 tumors spontaneously disseminated to distant sites with an observable trend towards higher metastatic cell loads in scid compared to NSG mice. Lung metastases could be verified by histology by means of anti-PSMA immunohistochemistry, exclusively demonstrating single disseminated tumor cells (DTCs) and micro-metastases. Upon surgical resection of the primary tumors, such pulmonary foci rarely grew out to multi-cellular metastatic colonies despite doubled overall survival span. In the brain and bone marrow, the metastatic cell load present at surgery even disappeared during the post-surgical period. We provide shallow whole genome sequencing and whole exome sequencing data of C5 tumors demonstrating the copy number aberration/ mutation status of this PCa model and proving genomic stability over several passages. Moreover, we analyzed genomic and transcriptomic alterations during metastatic progression achieved by serial transplantation. This study describes a novel PCa PDX model that enables future research on several aspects of metastatic PCa, particularly for the AR+ , ERG+ , PTEN^-/- PCa subtype.

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.