Stratify Prostate Cancer Patients Research Articles

Abstract LB259: Cell context-dependent role of KDM5D in ATR and CHK1 inhibitor sensitivity in prostate cancer

Abstract Epigenetic regulation plays an important role in prostate cancer progression and metastasis. KDM5D is a male-specific histone-modifying enzyme encoded on the Y chromosome that demethylates di- and tri-methylated forms of lysine 4 in histone H3, resulting in transcriptional repression of certain genes. In this study, we investigated the role of KDM5D in resistance to inhibitors targeting the G2/M cell cycle checkpoint kinase CHK1. Loss of KDM5D expression was associated with reduced cell proliferation and resistance to the CHK1 inhibitor SRA737 in castration-resistant (CRPC) and neuroendocrine (NEPC) prostate cancer cell lines. In KDM5D expressing cells, depletion of KDM5D differentially impacted the proliferation of prostate cancer cell lines. The androgen-sensitive LNCaP cell line was particularly sensitive to the knockdown of KMD5D. On the other hand, in CRPC cell line 22Rv1, the knockdown of KDM5D caused resistance to both inhibitors, indicating a dual role of KDM5D in mediating ATR and CHK1 inhibitor sensitivity and cell proliferation in prostate cancer cells. Further analysis demonstrated a self-regulatory cycle between CHK1 and KDM5D in 22Rv1 cells, where depletion of KMD5D reduced CHK1 mRNA, while decreased CHK1 caused upregulation of KDM5D and overexpression of KDM5D resulted in increased CHK1 expression. The relevance of this regulatory mechanism to ATR and CHK1 inhibitor sensitivity is currently under investigation, along with several potential mediators of the differential responses to these inhibitors. Additional ATR and CHK1 inhibitors will also be examined in this context. This study may uncover novel biomarkers for ATR and CHK1 inhibitors that are currently in clinical trials for various cancers. KDM5D may be used to stratify prostate cancer patients receiving these highly potent anticancer agents. Citation Format: Wenxiao Zheng, Yuzhou Chen, Qiming J. Wang. Cell context-dependent role of KDM5D in ATR and CHK1 inhibitor sensitivity in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB259.

Biomarkers for Pre-Treatment Risk Stratification of Prostate Cancer Patients: A Systematic Review.

Prostate cancer (PCa) is one of the most frequently occurring malignancies. Although most cases are not life-threatening, approximately 20% endure an unfavorable outcome. PSA-based screening reduced mortality but at the cost of an increased overdiagnosis/overtreatment of low-risk (lrPCa) and favorable intermediate-risk (firPCa) PCa. PCa risk-groups are usually identified based on serum Prostate-Specific Antigen (PSA), the Gleason score, and clinical T stage, which have consistent although variable specificity or subjectivity. Thus, more effective and specific tools for risk assessment are needed, ideally making use of minimally invasive methods such as liquid biopsies. In this systematic review we assessed the clinical potential and analytical performance of liquid biopsy-based biomarkers for pre-treatment risk stratification of PCa patients. Studies that assessed PCa pre-treatment risk were retrieved from PubMed, Scopus, and MedLine. PCa risk biomarkers were analyzed, and the studies' quality was assessed using the QUADAS-2 tool. The final analysis comprised 24 full-text articles, in which case-control studies predominated, mostly reporting urine-based biomarkers (54.2%) and biomarker quantification by qPCR (41.7%). Categorization into risk groups was heterogeneous, predominantly making use of the Gleason score. This systematic review unveils the substantial clinical promise of using circulating biomarkers in assessing the risk for prostate cancer patients. However, the standardization of groups, categories, and biomarker validation are mandatory before this technique can be implemented. Circulating biomarkers might represent a viable alternative to currently available tools, obviating the need for tissue biopsies, and allowing for faster and more cost-effective testing, with superior analytical performance, specificity, and reproducibility.

Prostate cancer patient stratification by molecular signatures in the Veterans Precision Oncology Data Commons.

Veterans are at an increased risk for prostate cancer, a disease with extraordinary clinical and molecular heterogeneity, compared with the general population. However, little is known about the underlying molecular heterogeneity within the veteran population and its impact on patient management and treatment. Using clinical and targeted tumor sequencing data from the National Veterans Affairs health system, we conducted a retrospective cohort study on 45 patients with advanced prostate cancer in the Veterans Precision Oncology Data Commons (VPODC), most of whom were metastatic castration-resistant. We characterized the mutational burden in this cohort and conducted unsupervised clustering analysis to stratify patients by molecular alterations. Veterans with prostate cancer exhibited a mutational landscape broadly similar to prior studies, including KMT2A and NOTCH1 mutations associated with neuroendocrine prostate cancer phenotype, previously reported to be enriched in veterans. We also identified several potential novel mutations in PTEN, MSH6, VHL, SMO, and ABL1 Hierarchical clustering analysis revealed two subgroups containing therapeutically targetable molecular features with novel mutational signatures distinct from those reported in the Catalogue of Somatic Mutations in Cancer database. The clustering approach presented in this study can potentially be used to clinically stratify patients based on their distinct mutational profiles and identify actionable somatic mutations for precision oncology.

Coordinating single-cell and bulk RNA-seq in deciphering the intratumoral immune landscape and prognostic stratification of prostate cancer patients.

Prostate cancer is a common cancer among male population. The aberrant expression of histone modifiers has been identified as a potential driving force in numerous cancer types. However, the mechanism of histone modifiers in the development of prostate cancer remains unknown. Expression profiles and clinical data were obtained from GSE70769, GSE46602, and GSE67980. Seruat R package was utilized to calculate the gene set enrichment of the histone modification pathway and obtain the Histone score. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were employed to identify marker genes with prognostic value. Kaplan-Meier survival analysis was conducted to assess the efficacy of the prognostic model. In addition, microenvironment cell populations counter (MCPcounter), single-sample gene set enrichment analysis (ssGSEA), and xCell algorithms were employed for immune infiltration analysis. Drug sensitivity prediction was performed using oncoPredict R package. We screened differentially expressed genes (DEGs) between Histone-high score (Histone-H) and Histone-low score (Histone-L) groups, which were enriched in RNA splicing and DNA-binding transcription factor binding pathways. We retained four prognostic marker genes, including TACC3, YWHAH, TAF1C and TTLL5. The risk model showed significant efficacy in stratification of the prognosis of prostate cancer patients in both internal and external cohorts (p < .0001 and p = .032, respectively). In addition, prognostic gene YWHAH was infiltrated in abundance of fibroblasts and highly correlated with Entinostat_1593 drug sensitivity score and the value of risk score. We innovatively developed a histone modification-related prognostic model with high prognostic potency and identified YWHAH as possible diagnostic and therapeutic biomarkers for prostate cancer. It provides novel insights to address prostate cancer and enhance clinical outcomes, thereby opening up a new avenue for customized treatment alternatives.

Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients

Background and purposeLate radiation-induced hematuria can develop in prostate cancer patients undergoing radiotherapy and can negatively impact the quality-of-life of survivors. If a genetic component of risk could be modeled, this could potentially be the basis for modifying treatment for high-risk patients. We therefore investigated whether a previously developed machine learning-based modeling method using genome-wide common single nucleotide polymorphisms (SNPs) can stratify patients in terms of the risk of radiation-induced hematuria. Materials and methodsWe applied a two-step machine learning algorithm that we previously developed for genome-wide association studies called pre-conditioned random forest regression (PRFR). PRFR includes a pre-conditioning step, producing adjusted outcomes, followed by random forest regression modeling. Data was from germline genome-wide SNPs for 668 prostate cancer patients treated with radiotherapy. The cohort was stratified only once, at the outset of the modeling process, into two groups: a training set (2/3 of samples) for modeling and a validation set (1/3 of samples). Post-modeling bioinformatics analysis was conducted to identify biological correlates plausibly associated with the risk of hematuria. ResultsThe PRFR method achieved significantly better predictive performance compared to other alternative methods (all p < 0.05). The odds ratio between the high and low risk groups, each of which consisted of 1/3 of samples in the validation set, was 2.87 (p = 0.029), implying a clinically useful level of discrimination. Bioinformatics analysis identified six key proteins encoded by CTNND2, GSK3B, KCNQ2, NEDD4L, PRKAA1, and TXNL1 genes as well as four statistically significant biological process networks previously shown to be associated with the bladder and urinary tract. ConclusionThe risk of hematuria is significantly dependent on common genetic variants. The PRFR algorithm resulted in a stratification of prostate cancer patients at differential risk levels of post-radiotherapy hematuria. Bioinformatics analysis identified important biological processes involved in radiation-induced hematuria.

Identification and verification of an ALYREF-involved 5-methylcytosine based signature for stratification of prostate cancer patients and prediction of clinical outcome and response to therapies

ObjectivesDue to the heterogeneity of PCa, the clinical indicators used for PCa can't satisfy risk prognostication and personalized treatment. It is imperative to develop novel biomarkers for prognosis prediction and therapy response in PCa. Accumulating evidence shows that non-mutational epigenetic reprogramming, independent from genomic instability and mutation, serves as a newly added hallmark in cancer progression.MethodsIn this study, we integrated multi-center cohorts (N > 1300) to develop a RNA 5-methylcytosine regulator-based signature, the m5C score. We performed unsupervised clustering and LASSO regression to identify novel m5C-related subtypes and calculate the m5C score. Then we assessed the role of m5C cluster and m5C score in several clinical aspects such as prognosis in various molecular subtypes, responses to chemotherapy, androgen receptor signaling inhibitor (ARSI) therapy and immunotherapy in PCa. Finally, we validated the cancer-promoting performance of ALYREF through clinical data analysis and experiments in vivo and in vitro.ResultsThe investigation revealed that the m5C score could accurately predict the biochemical recurrence (BCR) in different subtypes (the PAM50 subtypes and immunophenotypes) and the responses to chemotherapy, ARSI therapy, and immunotherapy (PD1/PD-L1). A high m5C score indicated a poor BCR prognosis in every subtype of PCa, unfavorable responses in ARSI therapy and immunotherapy (PD1/PD-L1). Moreover, the m5C reader gene termed ALYREF, yielding the highest weighed coefficient, promoted PCa progression through in silico analysis and experimental validations (in vivo and in vitro).ConclusionsThe m5C signature can function in many aspects of PCa, such as the development and prognosis of the disease, and multiple therapy responses. Further, the m5C reader, ALYREF, was identified as a prognostic biomarker and a potential therapeutic target for PCa. The m5C signature could act as a brand-new tool for predicting the prognosis of patients in different molecular subtypes and patients’ therapy responses and promoting customized treatments.

LSD1 inhibition disrupts super-enhancer driven oncogenic transcriptional programs in castration-resistant prostate cancer.

The lysine demethylase LSD1 (also called KDM1A) plays important roles in promoting multiple malignancies including both hematologic cancers and solid tumors. LSD1 targets histone and non-histone proteins and can function as a transcriptional corepressor and coactivator. LSD1 has been reported to act as a coactivator of androgen receptor (AR) in prostate cancer (PCa) and to regulate the AR cistrome via demethylation of its pioneer factor FOXA1. A deeper understanding of the key oncogenic programs targeted by LSD1 could help stratify PCa patients for treatment with LSD1 inhibitors, which are currently under clinical investigation. In this study, we performed transcriptomic profiling in an array of castration-resistant PCa (CRPC) xenograft models that are sensitive to LSD1 inhibitor treatment. Impaired tumor growth by LSD1 inhibition was attributed to significantly decreased MYC signaling, and MYC was found to be a consistent target of LSD1. Moreover, LSD1 formed a network with BRD4 and FOXA1 and was enriched at super-enhancer (SE) regions exhibiting liquid-liquid phase separation. Combining LSD1 inhibitors with BET inhibitors exhibited strong synergy in disrupting the activities of multiple drivers in CRPC, thereby inducing significant growth repression of tumors. Importantly, the combination treatment showed superior effects than either inhibitor alone in disrupting a subset of newly identified CRPC-specific SEs. These results provide mechanistic and therapeutic insights for co-targeting two key epigenetic factors and could be rapidly translated in the clinic for CRPC patients.

Role of Serum Creatinine Levels in Prognostic Risk Stratification of Prostate Cancer Patients.

BackgroundStudies on the relationship between serum creatinine and the prognosis of prostate cancer have been very limited. The aim of this study was to investigate the role of serum creatinine in the prognostic risk stratification of patients with prostate cancer.Material/MethodsWe identified 1134 eligible patients from the “Prostate Cancer Data Set” in the National Clinical Medical Science Data Center. Patients with prostate cancer were divided high- and low-risk prognostic groups according to prostate-specific antigen levels and Gleason scores and were divided into 5 groups according to serum creatinine quintile: Q1 (<70.1 umol/L), Q2 (70.1–76.8 umol/L), Q3 (76.8–83.4 umol/L), Q4 (83.4–92.1 umol/L), and Q5 (>92.1 umol/L). Multivariate logistic regression and a multiple restricted cubic spline method were used to evaluate the relationship between serum creatinine level and the level of prostate cancer prognostic risk.ResultsOf the 1134 patients with prostate cancer, 134 (11.8%) had a high-risk prognosis. Compared with the Q2 group (the reference group), the lowest serum creatinine levels in the Q1 group and the highest serum creatinine levels in groups Q5, Q3, and Q4 were associated with a high-risk prognosis, and this association remained significant after adjusting for confounders. The multiple restricted cubic spline regression model showed the relationship between serum creatinine level and high-risk prognosis was U-shaped.ConclusionsSerum creatinine level was an independent predictor of high-risk prognosis. Controlling serum creatinine levels between 70.1 and 76.8 umol/L in patients with prostatic cancer may benefit the prognosis of patients with prostatic cancer.

Disassembly of α6β4-mediated hemidesmosomal adhesions promotes tumorigenesis in PTEN-negative prostate cancer by targeting plectin to focal adhesions

Loss of α6β4-dependent hemidesmosomal adhesions has been observed during prostate cancer progression. However, the significance and underlying mechanisms by which aberrant hemidesmosome assembly may modulate tumorigenesis remain elusive. Using an extensive CRISPR/Cas9-mediated genetic engineering approaches in different prostate cancer cell lines combined with in vivo tumorigenesis studies in mice, bone marrow-on-chip assays and bioinformatics, as well as histological analysis of prostate cancer patient cohorts, we demonstrated that simultaneous loss of PTEN and hemidesmosomal adhesions induced several tumorigenic properties including proliferation, migration, resistance to anoikis, apoptosis, and drug treatment in vitro, and increased metastatic capacity in vivo. These effects were plectin-depended and plectin was associated with actin-rich adhesions upon hemidesmosome disruption in PTEN-negative prostate cancer cells leading to activation of EGFR/PI3K/Akt- and FAK/Src-pathways. These results suggest that analysis of PTEN and hemidesmosomal proteins may have diagnostic value helping to stratify prostate cancer patients with high risk for development of aggressive disease and highlight actin-associated plectin as a potential therapeutic target specifically in PTEN/hemidesmosome dual-negative prostate cancer.

Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer

BackgroundNon-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development.MethodsUsing Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients.ResultsMachine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence.ConclusionUsing MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.

Stratification of prostate cancer patients into low- and high-grade groups using multiparametric magnetic resonance radiomics with dynamic contrast-enhanced image joint histograms.

This study aimed to investigate the potential of stratification of prostate cancer patients into low- and high-grade groups (GGs) using multiparametric magnetic resonance (mpMR) radiomics in conjunction with two-dimensional (2D) joint histograms computed with dynamic contrast-enhanced (DCE) images. A total of 101 prostate cancer regions extracted from the MR images of 44 patients were identified and divided into training (n = 31 with 72 cancer regions) and test datasets (n = 13 with 29 cancer regions). Each dataset included low-grade tumors (International Society of Urological Pathology [ISUP] GG ≤ 2) and high-grade tumors (ISUP GG ≥ 3). A total of 137,970 features consisted of mpMR image(16 types of images in four sequences)-based and joint histogram (DCE images at 10 phases)-based features for each cancer region. Joint histogram features can visualize temporally changing perfusion patterns in prostate cancer based on the joint histograms between different phases or subtraction phases of DCE images. Nine signatures (a set of significant features related to GGs) were determined using the best combinations of features selected using the least absolute shrinkage and selection operator. Further, support vector machinemodels with the nine signatures were built based on a leave-one-out cross-validation for the training dataset and evaluated with receiver operating characteristic (ROC) curve analysis. The signature showing the best performance was constructed using six features derived from the joint histograms, DCE original images, and apparent diffusion coefficient maps. The areas under the ROC curves for the training and test datasets were 1.00 and 0.985, respectively. This study suggests that the proposed approach with mpMR radiomics in conjunction with 2D joint histogram computed with DCE images could have the potential to stratify prostate cancer patients into low- and high-GGs.

Small Extracellular Vesicle-Derived microRNAs Stratify Prostate Cancer Patients According to Gleason Score, Race and Associate with Survival of African American and Caucasian Men.

Simple SummaryNovel biomarkers are needed to guide prognosis and treatment of aggressive forms of prostate cancer (PCa). In this study, small extracellular vesicles (sEVs)-derived microRNAs (miRs) are used to predict aggressive phenotypes and ancestral background of PCa patients. Two cohorts was used to study the diagnostic and prognostic utility of sEV-associated miRs in predicting aggressive forms of PCa in African American (AA) and Caucasian (CA) men. In training cohort, miR profiling was performed and top-ranked sEV-associated miRs were then validated in two independent confirmatory cohorts comprising 150 plasma samples. Results revealed that sEV-associated miR-6068 and miR-1915-3p were enriched in PCa patients compared to healthy subjects. sEV-associated miR-6716-5p and miR-3692-3p distinguished AA from CA men and low from high Gleason score. However, miR-1915-3p was the only studied miR associated with longer recurrence-free survival as independent prognostic marker. The utility of small extracellular vesicles (sEVs)-derived microRNAs (miRs) to segregate prostate cancer (PCa) patients according to tumor aggressiveness and ancestral background has not been fully investigated. Thus, we aimed to determine the diagnostic and prognostic utility of sEV-associated miRs in identifying aggressive PCa in African American (AA) and Caucasian (CA) men. Using a training cohort, miR profiling was performed on sEVs isolated from plasma of PCa patients. Top-ranked sEV-associated miRs were then validated in 150 plasma samples (75 AA and 75 CA) collected from two independent cohorts; NIH (n = 90) and Washington University (n = 60) cohorts. Receiver operating characteristic (ROC) curve, Kaplan–Meier and Cox proportional hazards regression were used to assess these miRs as clinical biomarkers. Among nine top-ranked sEV-associated miRs, miR-6068 and miR-1915-3p were enriched in sEVs collected from PCa patients compared to healthy volunteers. Moreover, miR-6716-5p and miR-3692-3p segregated AA from CA men and low from high Gleason score (GS), respectively. Upregulation of sEV-associated miR-1915-3p, miR-3692-3p and miR-5001-5p was associated with improved survival time, and only miR-1915-3p was associated with longer recurrence-free survival (RFS) as an independent prognostic marker. Taken together, we identified novel sEV-associated miRs that can differentiate PCa patients from normal, AA from CA and high from low GS and predicts RFS.

Identification of a Costimulatory Molecule-Related Signature for Predicting Prognostic Risk in Prostate Cancer.

Costimulatory molecules have been proven to enhance antitumor immune responses, but their roles in prostate cancer (PCa) remain unexplored. In this study, we aimed to explore the gene expression profiles of costimulatory molecule genes in PCa and construct a prognostic signature to improve treatment decision making and clinical outcomes. Five prognosis-related costimulatory molecule genes (RELT, TNFRSF25, EDA2R, TNFSF18, and TNFSF10) were identified, and a prognostic signature was constructed based on these five genes. This signature was an independent prognostic factor according to multivariate Cox regression analysis; it could stratify PCa patients into two subgroups with different prognoses and was highly associated with clinical features. The prognostic significance of the signature was well validated in four different independent external datasets. Moreover, patients identified as high risk based on our prognostic signature exhibited a high mutation frequency, a high level of immune cell infiltration and an immunosuppressive microenvironment. Therefore, our signature could provide clinicians with prognosis predictions and help guide treatment for PCa patients.

(Invited) Controlling Synthesis and Functionalization of Anisotropic Gold Nanoparticles for Applications in Biology

Anisotropic gold nanoparticles, such as, for instance, nanorods and nanostars, have been integrated in many areas of biomedical research, including, but not limited to, imaging, sensing, and therapy. While the use of gold has remained relatively consistent due to, primarily, its biocompatibility, the choice of morphology to employ has depended on the intended application. Regardless of the application, attention must be paid toward the definition of best practices for the synthesis, functionalization, and characterization of nanoparticles, especially when they need to be interfaced with the biological milieu.In my talk, I will report on our recent observations concerning the design of gold nanostars to target the biologically transparent window of the electromagnetic spectrum, the optimization of synthetic monodispersity to improve single cell applicability, and the careful control of relevant bioconjugation steps that need to be implemented to realize functional intracellular biosensors. I will focus in particular on the synthesis of highly monodispersed gold nanostars with resonant bands extending up to 2000 nm, on the use of surfactant free nanostars for the design of surface enhanced Raman spectroscopy (SERS) nanoprobes for the stratification of prostate cancer patients, and the use of tunable silica shell to increase stability of the nanostars while also enhancing their SERS response. I will also report on the effect of the protein corona on the performance of fluorescent flares and SERS beacons for the identification and quantification of influenza A virus (IAV) RNA both in buffer and in individual intact cells.Despite the broad applicability of anisotropic gold nanoparticles, often a complete understanding of their properties and the steps needed to reproducibly and accurately achieve them is lacking. Due to the intrinsic complexity of the biological milieu, the consistency of the probes used to understand it is even more imperative and thus requires even more synthetic control. I will therefore, in conclusion, present some tips and guidelines that will hopefully stimulate an interactive conversation on the topic.

Inverse Stage Migration in Radical Prostatectomy-A Sustaining Phenomenon.

Objective: To investigate temporal trends in prostate cancer (PCa) radical prostatectomy (RP) candidates.Materials and Methods: Patients who underwent RP for PCa between January 2014 and December 2019 were identified form our institutional database. Trend analysis and logistic regression models assessed RP trends after stratification of PCa patients according to D'Amico classification and Gleason score. Patients with neoadjuvant androgen deprivation or radiotherapy prior to RP were excluded from the analysis.Results: Overall, 528 PCa patients that underwent RP were identified. Temporal trend analysis revealed a significant decrease in low-risk PCa patients from 17 to 9% (EAPC: −14.6%, p < 0.05) and GS6 PCa patients from 30 to 14% (EAPC: −17.6%, p < 0.01). This remained significant even after multivariable adjustment [low-risk PCa: (OR): 0.85, p < 0.05 and GS6 PCa: (OR): 0.79, p < 0.001]. Furthermore, a trend toward a higher proportion of intermediate-risk PCa undergoing RP was recorded.Conclusion: Our results confirm that inverse stage migration represents an ongoing phenomenon in a contemporary RP cohort in a European tertiary care PCa center. Our results demonstrate a significant decrease in the proportion of low-risk and GS6 PCa undergoing RP and a trend toward a higher proportion of intermediate-risk PCa patients undergoing RP. This indicates a more precise patient selection when it comes to selecting suitable candidates for definite surgical treatment with RP.

Long Non-Coding RNA Landscape in Prostate Cancer Molecular Subtypes: A Feature Selection Approach.

Prostate cancer is one of the most common malignancies in men. It is characterized by a high molecular genomic heterogeneity and, thus, molecular subtypes, that, to date, have not been used in clinical practice. In the present paper, we aimed to better stratify prostate cancer patients through the selection of robust long non-coding RNAs. To fulfill the purpose of the study, a bioinformatic approach focused on feature selection applied to a TCGA dataset was used. In such a way, LINC00668 and long non-coding(lnc)-SAYSD1-1, able to discriminate ERG/not-ERG subtypes, were demonstrated to be positive prognostic biomarkers in ERG-positive patients. Furthermore, we performed a comparison between mutated prostate cancer, identified as “classified”, and a group of patients with no peculiar genomic alteration, named “not-classified”. Moreover, LINC00920 lncRNA overexpression has been linked to a better outcome of the hormone regimen. Through the feature selection approach, it was found that the overexpression of lnc-ZMAT3-3 is related to low-grade patients, and three lncRNAs: lnc-SNX10-87, lnc-AP1S2-2, and ADPGK-AS1 showed, through a co-expression analysis, significant correlation values with potentially druggable pathways. In conclusion, the data mining of publicly available data and robust bioinformatic analyses are able to explore the unknown biology of malignancies.

PSA density of the lesion: a mathematical formula that uses clinical and pathological data to predict biochemical recurrence in prostate cancer patients.

A main challenge in the clinical management of prostate cancer is to identify which tumor is aggressive and needs invasive treatment. Thus, being able to predict which cancer will progress to biochemical recurrence is a great strategy to stratify prostate cancer patients. With that in mind, we created a mathematical formula that takes into account the patients clinical and pathological data resulting in a quantitative variable, called PSA density of the lesion, which has the potential to predict biochemical recurrence. To test if our variable is able to predict biochemical recurrence, we use a cohort of 219 prostate cancer patients, associating our new variable and classic parameters of prostate cancer with biochemical recurrence. Total PSA, lesion weight, volume and classic PSA density were positively associated with biochemical recurrence (p<0.05). ISUP score was also associated with biochemical recurrence in both biopsy and surgical specimen (p<0.001). The increase of PSA density of the lesion was significantly associated with the biochemical recurrence (p=0.03). Variables derived from the formula, PSA 15% and PSA 152, were also positive associated with the biochemical recurrence (p=0.01 and p=0.002 respectively). Logistic regression analysis shows that classic PSA density, PSA density of the lesion and total PSA, together, can explain up to 13% of cases of biochemical recurrence. PSA density of the lesion alone would have the ability to explain up to 7% of cases of biochemical recurrence. In conclusion, this new mathematical approach could be a useful tool to predict disease recurrence in prostate cancer.

The PTEN Conundrum: How to Target PTEN-Deficient Prostate Cancer.

Loss of the tumor suppressor phosphatase and tensin homologue deleted on chromosome 10 (PTEN), which negatively regulates the PI3K–AKT–mTOR pathway, is strongly linked to advanced prostate cancer progression and poor clinical outcome. Accordingly, several therapeutic approaches are currently being explored to combat PTEN-deficient tumors. These include classical inhibition of the PI3K–AKT–mTOR signaling network, as well as new approaches that restore PTEN function, or target PTEN regulation of chromosome stability, DNA damage repair and the tumor microenvironment. While targeting PTEN-deficient prostate cancer remains a clinical challenge, new advances in the field of precision medicine indicate that PTEN loss provides a valuable biomarker to stratify prostate cancer patients for treatments, which may improve overall outcome. Here, we discuss the clinical implications of PTEN loss in the management of prostate cancer and review recent therapeutic advances in targeting PTEN-deficient prostate cancer. Deepening our understanding of how PTEN loss contributes to prostate cancer growth and therapeutic resistance will inform the design of future clinical studies and precision-medicine strategies that will ultimately improve patient care.

Identification and Validation of Leucine-rich α-2-glycoprotein 1 as a Noninvasive Biomarker for Improved Precision in Prostate Cancer Risk Stratification

BackgroundMore accurate risk assessments are needed to improve prostate cancer management. ObjectiveTo identify blood-based protein biomarkers that provided prognostic information for risk stratification. Design, setting, and participantsMass spectrometry was used to identify biomarker candidates from blood, and validation studies were performed in four independent cohorts retrospectively collected between 1988 and 2015. Outcome measurements and statistical analysisThe primary outcome objectives were progression-free survival, prostate cancer–specific survival (PCSS), and overall survival. Statistical analyses to assess survival and model performance were performed. Results and limitationSerum leucine-rich α-2-glycoprotein 1 (LRG1) was found to be elevated in fatal prostate cancer. LRG1 provided prognostic information independent of metastasis and increased the accuracy in predicting PCSS, particularly in the first 3 yr. A high LRG1 level is associated with an average of two-fold higher risk of disease-progression and mortality in both high-risk and metastatic patients. However, our study design, with a retrospective analysis of samples spanning several decades back, limits the assessment of the clinical utility of LRG1 in today’s clinical practice. Thus, independent prospective studies are needed to establish LRG1 as a clinically useful biomarker for patient management. ConclusionsHigh blood levels of LRG1 are unfavourable in newly diagnosed high-risk and metastatic prostate cancer, and LRG1 increased the accuracy of risk stratification of prostate cancer patients. Patient summaryHigh blood levels of leucine-rich α-2-glycoprotein 1 are unfavourable in newly diagnosed high-risk and metastatic prostate cancer.

Incorporating Prognostic Biomarkers into Risk Assessment Models and TNM Staging for Prostate Cancer.

In current practice, prostate cancer staging alone is not sufficient to adequately assess the patient’s prognosis and plan the management strategies. Multiple clinicopathological parameters and risk tools for prostate cancer have been developed over the past decades to better characterize the disease and provide an enhanced assessment of prognosis. Herein, we review novel prognostic biomarkers and their integration into risk assessment models for prostate cancer focusing on their capability to help avoid unnecessary imaging studies, biopsies and diagnosis of low risk prostate cancers, to help in the decision-making process between active surveillance and treatment intervention, and to predict recurrence after radical prostatectomy. There is an imperative need of reliable biomarkers to stratify prostate cancer patients that may benefit from different management approaches. The integration of biomarkers panel with risk assessment models appears to improve prostate cancer diagnosis and management. However, integration of novel genomic biomarkers in future prognostic models requires further validation in their clinical efficacy, standardization, and cost-effectiveness in routine application.