Predicting Prostate Cancer Recurrence Research Articles

Predicting prostate cancer recurrence: Introducing PCRPS, an advanced online web server

BackgroundProstate cancer (PCa) is one of the leading causes of cancer death in men. About 30% of PCa will develop a biochemical recurrence (BCR) following initial treatment, which significantly contributes to prostate cancer-related deaths. In clinical practice, accurate prediction of PCa recurrence is crucial for making informed treatment decisions. However, the development of reliable models and biomarkers for predicting PCa recurrence remains a challenge. In this study, the aim is to establish an effective and reliable tool for predicting the recurrence of PCa. MethodsWe systematically screened and analyzed potential datasets to predict PCa recurrence. Through quality control analysis, low-quality datasets were removed. Using meta-analysis, differential expression analysis, and feature selection, we identified key genes associated with recurrence. We also evaluated 22 previously published signatures for PCa recurrence prediction. To assess prediction performance, we employed nine machine learning algorithms. We compared the predictive capabilities of models constructed using clinical variables, expression data, and their combinations. Subsequently, we implemented these machine learning models into a user-friendly web server freely accessible to all researchers. ResultsBased on transcriptomic data derived from eight multicenter studies consisting of 733 PCa patients, we screened 23 highly influential genes for predicting prostate cancer recurrence. These genes were used to construct the Prostate Cancer Recurrence Prediction Signature (PCRPS). By comparing with 22 published signatures and four important clinicopathological features, the PCRPS exhibited a robust and significantly improved predictive capability. Among the tested algorithms, Random Forest demonstrated the highest AUC value of 0.72 in predicting PCa recurrence in the testing dataset. To facilitate access and usage of these machine learning models by all researchers and clinicians, we also developed an online web server (https://urology1926.shinyapps.io/PCRPS/) where the PCRPS model can be freely utilized. The tool can also be used to (1) predict the PCa recurrence by clinical information or expression data with high accuracy. (2) provide the possibility of PCa recurrence by nine machine learning algorithms. Furthermore, using the PCRPS scores, we predicted the sensitivity of 22 drugs from GDSC2 and 95 drugs from CTRP2 to the samples. These predictions provide valuable insights into potential drug sensitivities related to the PCRPS score groups. ConclusionOverall, our study provides an attractive tool to further guide the clinical management and individualized treatment for PCa.

Abstract 1396: Targeting immune suppressive myeloid cells in prostate cancer recurrence and metastasis

Abstract Metastatic prostate cancer is a leading cause of cancer-related deaths worldwide and therefore an urgent inadequately addressed clinical problem. We have approached this problem primarily by studying immune cells within the tumor microenvironment rather than the tumor cells themselves. One critical cell subset that seems to be contributing to the aggressive behavior of clinically significant prostate cancers is the immune suppressive myeloid cells (MDSC) within the tumor microenvironment. MDSC have been shown to promote tumor metastases by participating in the formation of pre-metastatic niche, promoting angiogenesis, tumor cell invasion, and mediating immune suppression. Understanding the heterogeneity and function of these myeloid cells is an essential step toward developing effective therapeutic strategies to target them and enhance anti-tumor immune responses that have thus far been so elusive. We utilized previously generated scRNAseq data and combined it with publicly available prostate cancer scRNAseq datasets. The analysis included a total of 96 samples at different disease stages, including healthy prostate tissue, adjacent-normal tissue, low-grade tumors, high-grade tumors, as well as local and bone metastatic tumors. In addition, GeoMx spatial profiling was performed on 24 high-grade prostate cancer patients to assess immune cell spatial infiltration. This meta-analysis provided comprehensive insights into the molecular characteristics of prostate cancer. We characterized multiple MDSC subpopulations and revealed the dynamics of tumor associated macrophages (TAMs) during tumor progression and metastasis. We found that a specific type of TAM, known as APOE+ SPP1+TAM, is significantly enriched in metastatic tumors. They are transcriptionally similar to the TAMs found in the bone metastasis niche of prostate cancer. Moreover, we found APOE+ SPP1+TAMs within primary/localized tumors tumors alone could predict prostate cancer recurrence despite curative-intent local therapy. We validated the accumulation of APOE+ SPP1+TAMs in a prostate cancer mice model. We are currently studying the role of the specific subset of TAMs in tumor metastases by focused analysis of the communication between these TAMs, T cells, and tumors. Additionally, we are exploring the therapeutic potential of blocking APOE+ SPP1+TAMs in a mice model of prostate cancer. Our analyses provide a comprehensive picture of myeloid cell linage across different prostate cancer stages, highlighting the potential for therapeutic targets that disrupt the metastatic process via their effects on tumor associated macrophages. Citation Format: Shenglin Mei, Hanyu Zhang, Taghreed Hirz, Nathan Elias Jeffries, Shulin Wu, Chin Lee Wu, Douglas M. Dahl, Philip J. Saylor, David B. Sykes. Targeting immune suppressive myeloid cells in prostate cancer recurrence and metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1396.

Multi-Institutional Development and Validation of a Radiomic Model to Predict Prostate Cancer Recurrence Following Radical Prostatectomy

The use of multiparametric magnetic resonance imaging (mpMRI)-derived radiomics has the potential to offer noninvasive, imaging-based biomarkers for the identification of subvisual characteristics indicative of a poor oncologic outcome. The present study, therefore, seeks to develop, validate, and assess the performance of an MRI-derived radiomic model for the prediction of prostate cancer (PC) recurrence following radical prostatectomy (RP) with curative intent. mpMRI imaging was obtained from 251 patients who had undergone an RP for the treatment of localized prostate cancer across two institutions and three surgeons. All patients had a minimum of 2 years follow-up via prostate-specific antigen serum testing. Each prostate mpMRI was individually reviewed, and the prostate was delineated as a single slice (ROI) on axial T2 high-resolution image sets. A total of 924 radiomic features were extracted and tested for stability via intraclass correlation coefficient (ICC) following image normalization via histogram matching. Fourteen important and nonredundant features were found to be predictors of PC recurrence at a mean ± SD of 3.2 ± 2.2 years post-RP. Five-fold, ten-run cross-validation of the model containing these fourteen features yielded an area under the curve (AUC) of 0.89 ± 0.04 in the training set (n = 225). In comparison, the University of California San Fransisco Cancer of the Prostate Risk Assessment score (UCSF-CAPRA) and Memorial Sloan Kettering Cancer Center (MSKCC) Pre-Radical prostatectomy nomograms yielded AUC of 0.66 ± 0.05 and 0.67 ± 0.05, respectively (p < 0.01). When the radiomic model was applied to the test set (n = 26), AUC was 0.78; sensitivity, specificity, positive predictive value, and negative predictive value were 60%, 86%, 52%, and 89%, respectively. Accuracy in predicting PC recurrence was 81%.

Learning to predict prostate cancer recurrence from tissue images

Roughly 30% of men with prostate cancer who undergo radical prostatectomy will suffer biochemical cancer recurrence (BCR). Accurately predicting which patients will experience BCR could identify who would benefit from increased surveillance or adjuvant therapy. Unfortunately, no current method can effectively predict this. We develop and evaluate PathCLR, a novel semi-supervised method that learns a model that can use hematoxylin and eosin (H&E)-stained tissue microarrays (TMAs) to predict prostate cancer recurrence within 5 years after diagnosis. The learning process involves 2 sequential steps: PathCLR (a) first employs self-supervised learning to generate effective feature representations of the input images, then (b) feeds these learned features into a fully supervised neural network classifier to learn a model for predicting BCR. We conducted training and evaluation using 2 large prostate cancer datasets: (1) the Cooperative Prostate Cancer Tissue Resource (CPCTR) with 374 patients, including 189 who experienced BCR, and (2) the Johns Hopkins University (JHU) prostate cancer dataset of 646 patients, with 451 patients having BCR. PathCLR’s (10-fold cross-validation) F1 score was 0.61 for CPCTR and 0.85 for JHU. This was statistically superior (paired t-test with P <.05) to the best-learned model that relied solely on clinicopathological features, including PSA level, primary and secondary Gleason Grade, etc. We attribute the improvement of PathCLR over models using only clinicopathological features to its utilization of both learned latent representations of tissue core images and clinicopathological features. This finding suggests that there is essential predictive information in tissue images at the time of surgery that goes beyond the knowledge obtained from reported clinicopathological features, helping predict the patient’s 5-year outcome.

Primary staging with PSMA-PET improves the prediction of prostate cancer recurrence

Primary staging with PSMA-PET improves the prediction of prostate cancer recurrence

A novel, AI-generated morphologic biomarker to predict prostate cancer recurrence in patients with intermediate risk of progression.

e17097 Background: About half of the prostate cancer (PCa) patients within an intermediate-risk group return to the clinic with disease progression after the first line of therapy. While these patients can be stratified into Gleason Grades (GG) 3+4 and 4+3, studies indicate that high-risk patients can still be found in both groups. Using our AI platform, we have developed a morphometric biomarker by analyzing digitized H&amp;E slide images (WSIs), which can accurately predict early biochemical recurrence (within 36 months post-surgery) and the risk of metastatic disease more accurately than GG, TNM staging, and genomic tools. Methods: 125 intermediate-risk (n = 67, 3+4; n = 58, 4+3) samples were collected from the Icahn School of Medicine at Mount Sinai (ISSMS) to form a held-out test set. Seventy-eight patients within the test set had an associated genomic score. A series of deep learning models trained using data from ISMMS, the University of Wisconsin-Madison, and TCGA generated a high dimensional vector for each WSI to provide a numerical representation of observed morphologies, which is then converted into a single score to predict biochemical recurrence within 36 months (BCR) and rank risk of metastasis (MET). Area-under-the-receiver operating characteristic (AROC) was used to measure the accuracy of BCR, and the concordance index (CI) was used to measure the performance of MET. The high and low-risk groups' hazard ratios (HR) for patients within grades 3+4 and 4+3 show that our model can further stratify GG. Results: Our method was significantly better at predicting BCR (AROC: 0.801) and ranking MET (CI: 0.764) relative to GG, TNM, and genomic tool (Table 1). We further sub-stratified the patients into GG 3+4 and 4+3 and identified high-risk patients within each GG. Patients in GG 3+4 with high PATHOMIQ scores had a significantly higher risk of BCR (HR 3.3; 95% CI 1.44 – 7.56; p &lt; 0.005) compared to the low PATHOMIQ score patients. A similar trend was seen in the GG 4+3 group (HR 3.0; 95% CI 1.32 – 6.83; p &lt; 0.01). Conclusions: Our histopathology-based prognostic biomarker significantly improves over standard clinical markers in stratifying patients with intermediate-risk PCa for BCR and MET. Our method overcomes several limitations of genomic testing: We only need digitized WSIs. Our method is non-destructive and preserves the tissue for further interrogation. Moreover, our turn-around time is a few hours. Thus, the PATHOMIQ scoring method may strongly impact the management of intermediate-risk PCa patients and clinical trial patient selection for the successful development of new therapies for early-stage PCa. [Table: see text]

Abstract 5762: New perspective on racial disparities in prostate cancer: identification of new molecular subsets using whole-mount radical prostatectomy

Abstract Introduction: Prostate cancer is a heterogenous multifocal disease. We hypothesize that different tumor foci may harbor distinct driver molecular aberrations, making it a more complex disease and difficult to manage. To avoid overlooking smaller tumor foci with clinical and biological significance, we used an innovative approach to understand the genetic underpinnings of each tumor foci based on the molecular analysis of whole-mount radical prostatectomy specimens rather than a systematic sampling of dominant nodules alone. Our study aimed to identify distinct molecular subsets of prostate cancer, if any, and correlate them with clinical outcomes in Caucasians (CA) and African Americans (AA). Method: We randomly selected 834 whole-mount radical prostatectomy tissues including 463 (56%) CA and 371 (44%) AA. We used combined dual immunohistochemistry (IHC) for ERG and SPINK1 and dual RNA in-situ hybridization (ISH) for ETV1 and ETV4. The racial disparity in aberrant oncogene expression was analyzed by the Chi-squared test. The recurrence-free survival (RFS) of patients with distinct molecular subsets of prostate cancer was examined by the Kaplan-Meier method and cox-ph models. The Gleason’s grades of prostate biopsies were summarized by spaghetti plot and compared by linear mixed models. Results: Patients with localized prostate cancer expressing none, one, two, and three of four oncogenes were 16.4%, 58.4%, 21.7%, and 3.5%, respectively. The expression of ERG and SPINK1 was negatively correlated (odds ratio (OR)=0.38, 95% CI 0.29-0.51, p&amp;lt;.001). Compared with CA, AA had a lower incidence of ERG (38.8% vs 60.3%), a higher incidence of SPINK1 (63.3% vs 35.6%), and similar incidences of ETV1 (9.4% vs 9.3%) and ETV4 (4.6% vs 3.9%). Importantly, ETV1 expression was associated with a worse RFS in CAs (hazard ratio (HR)=2.49, 95% CI 1.15-5.38, p=.02). ETV4 expression was associated with a worse RFS in AA (HR=3.11, 95% CI 1.32-8.04, p=.01). In addition, ETV4 expression was associated with lymph node metastasis in AA (OR=4.0, 95% CI 1.06-12.44, p=.02) but not in CA (OR=0.56, 95% CI 0.03-2.85, p=.57). For those who had multiple biopsies before radical prostatectomy, Gleason’s grade increased with time in AA (0.23 per year, p&amp;lt;.001) but was unchanged in CA. ERG expression was associated with a lower Gleason grade (-0.20, p=.03). ETV4 was associated with a higher Gleason grade (0.50, p=.02). Conclusion: Our findings showed the molecular heterogeneity between CA and AA who had localized prostate cancer, and supported ETV1 and ETV4 as prognostic markers that can be incorporated into clinical practice to better predict prostate cancer recurrence after radical prostatectomy in CA and AA, respectively. Citation Format: Wei Zhao, Pin Li, Shannon Carskadon, Craig Rogers, James Peabody, Mani Menon, Dhananjay Chitale, Sean Williamson, Nilesh Gupta, Nallasivam Palanisamy. New perspective on racial disparities in prostate cancer: identification of new molecular subsets using whole-mount radical prostatectomy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5762.

A Personalized Clinical Dynamic Prediction Model to Characterize Prognosis for Patients With Localized Prostate Cancer: Analysis of the CHHiP Phase 3 Trial

The CHHiP trial assessed moderately hypofractionated radiotherapy in localised prostate cancer. We utilised longitudinal prostate-specific antigen (PSA) measurements collected over time to evaluate and characterise patient prognosis. We developed a clinical dynamic prediction joint model to predict the risk of biochemical or clinical recurrence. Modelling included repeated PSA values and adjusted for baseline prognostic risk factors of age, tumour characteristics and treatment received. We included 3,071 trial participants for model development using a mixed-effect submodel for the longitudinal PSAs, and a time-to-event hazard submodel for predicting recurrence of prostate cancer. We evaluated how baseline prognostic factor subgroups impacted on the nonlinear PSA levels over time and quantify the association of PSA on time-to-recurrence. We assessed bootstrapped optimism-adjusted predictive performance on calibration and discrimination. Additionally, we performed comparative dynamic predictions on patients with contrasting prognostic factors and investigated PSA thresholds over landmark times to correlate with prognosis. Patients that developed recurrence had generally higher baseline and overall PSA values during follow-up and had an exponentially rising PSA in the two-years before recurrence. Additionally, most baseline risk factors were significant in the mixed-effect- and relative risk submodels. PSA value- and rate-of-change was predictive of recurrence. Predictive performance of the model was good across different prediction times over an 8-year period, with an overall mean AUC of 0.70, mean Brier score of 0.10, and mean integrated calibration index of 0.048; these were further improved for predictions after 5 years of accrued longitudinal post-treatment PSA assessments. PSA thresholds less than 0.23ng/mL after 3 years were indicative of a minimal risk of recurrence by 8 years. We successfully developed a joint statistical model to predict prostate cancer recurrence, evaluating prognostic factors and longitudinal PSA. We showed dynamically updated PSA information can improve prognostication, which can be used to guide follow-up and treatment management options.

A machine learning framework develops a DNA replication stress model for predicting clinical outcomes and therapeutic vulnerability in primary prostate cancer

Recent studies have identified DNA replication stress as an important feature of advanced prostate cancer (PCa). The identification of biomarkers for DNA replication stress could therefore facilitate risk stratification and help inform treatment options for PCa. Here, we designed a robust machine learning-based framework to comprehensively explore the impact of DNA replication stress on prognosis and treatment in 5 PCa bulk transcriptomic cohorts with a total of 905 patients. Bootstrap resampling-based univariate Cox regression and Boruta algorithm were applied to select a subset of DNA replication stress genes that were more clinically relevant. Next, we benchmarked 7 survival-related machine-learning algorithms for PCa recurrence using nested cross-validation. Multi-omic and drug sensitivity data were also utilized to characterize PCa with various DNA replication stress. We found that the hyperparameter-tuned eXtreme Gradient Boosting model outperformed other tuned models and was therefore used to establish a robust replication stress signature (RSS). RSS demonstrated superior performance over most clinical features and other PCa signatures in predicting PCa recurrence across cohorts. Lower RSS was characterized by enriched metabolism pathways, high androgen activity, and a favorable prognosis. In contrast, higher RSS was significantly associated with TP53, RB1, and PTEN deletion, exhibited increased proliferation and DNA replication stress, and was more immune-suppressive with a higher chance of immunotherapy response. In silico screening identified 13 potential targets (e.g. TOP2A, CDK9, and RRM2) from 2249 druggable targets, and 2 therapeutic agents (irinotecan and topotecan) for RSS-high patients. Additionally, RSS-high patients were more responsive to taxane-based chemotherapy and Poly (ADP-ribose) polymerase inhibitors, whereas RSS-low patients were more sensitive to androgen deprivation therapy. In conclusion, a robust machine-learning framework was used to reveal the great potential of RSS for personalized risk stratification and therapeutic implications in PCa.

Prediction of prostate cancer recurrence after radiation therapy using multiparametric magnetic resonance imaging and spectroscopy: assessment of prognostic factors on pretreatment imaging.

To assess whether data from pre-therapeutic multiparametric magnetic resonance imaging (mpMRI) combined with three-dimensional magnetic resonance spectroscopy (3D MRS) provide prognostic factors of biochemical relapse in patients with localized prostate cancer treated by external radiotherapy or brachytherapy. In our single institution observational retrospective study we included a cohort of 230 patients treated by external radiotherapy or brachytherapy who had an initial mpMRI with 3D MRS from January 2008 to December 2015 for newly diagnosed localized prostatic cancer, proven histologically. Three trained radiologists recorded tumor characteristics, MRI T-stage and metabolic abnormalities from 3D MRS data. Univariate and multivariate Cox analyzes explored the relationship between clinical and imaging variables to highlight prognostic factors for recurrence, using biochemical relapse as the primary endpoint. mpMRI data analysis allowed to reclassify 21.7% of the patients in a MRI National Comprehensive Cancer Network (NCCN) group higher than their initial clinical T-stage, but also to detect a lesion in 78% of the patients considered as clinically T1c. After a median of follow-up of 8.7 years (IQR, 6.6-10.1) following cancer diagnosis, 36 (16%) patients developed a biochemical relapse. The multivariate Cox analysis demonstrated the existence of 3 independent factors for prediction of biochemical recurrence: extracapsular extension (ECE) (HR =3.33; 95% CI: 1.93-5.73; P<0.01), choline/citrate ratio in healthy tissue in the transition zone (TZ) (HR =2.96; 95% CI: 1.06-8.28; P=0.04) and the NCCN Magnetic Resonance Imaging classification (intermediate versus low-risk, HR =3.06; 95% CI: 1.13-8.30; P<0.01). Combination of mpMRI and 3DMRS could aid in the prognostic stratification of localized prostate cancer treated by radiotherapy or brachytherapy, by combining accurate evaluation of tumor extension, and quantification of prostate metabolism.

HLA-BAT1 alters migration, invasion and pro-inflammatory cytokines in prostate cancer.

Prostate cancer (PCa) accounts for more than 1 in 5 diagnoses and is the second cause of cancer-related deaths in men. Although PCa may be successfully treated, patients may undergo cancer recurrence and there is a need for new biomarkers to improve the prediction of prostate cancer recurrence and improve treatment. Our laboratory demonstrated that HLA-B-associated transcript 1 (BAT1) was differentially expressed in patients with high Gleason scores when compared to low Gleason scores. BAT1 is an anti-inflammatory gene but its role in PCa has not been identified. The objective of this study is to understand the role of BAT1 in prostate cancer. In vitro studies showed that BAT1 down-regulation increased cell migration and invasion. In contrast, BAT1 overexpression decreased cell migration and invasion. RT-PCR analysis showed differential expression of pro-inflammatory cytokines (TNF-α and IL-6) and cell adhesion and migration genes (MMP10, MMP13, and TIMPs) in BAT1 overexpressed cells when compared to BAT1 siRNA cells. Our in vivo studies demonstrated up-regulation of TNF-α, IL-6, and MMP10 in tumors developed from transfected BAT1 shRNA cells when compared to tumors developed from BAT1 cDNA cells. These findings indicate that BAT1 down-regulation modulates TNF-α and IL-6 expression which may lead to the secretion of MMP-10 and inhibition of TIMP2.

Identification and validation of a signature involving voltage-gated chloride ion channel genes for prediction of prostate cancer recurrence.

Voltage-gated chloride ion channels (CLCs) are transmembrane proteins that maintain chloride ion homeostasis in various cells. Accumulating studies indicated CLCs were related to cell growth, proliferation, and cell cycle. Nevertheless, the role of CLCs in prostate cancer (PCa) has not been systematically profiled. The purpose of this study was to investigate the expression profiles and biofunctions of CLCs genes, and construct a novel risk signature to predict biochemical recurrence (BCR) of PCa patients. We identified five differentially expressed CLCs genes in our cohort and then constructed a signature composed of CLCN2 and CLCN6 through Lasso-Cox regression analysis in the training cohort from the Cancer Genome Atlas (TCGA). The testing and entire cohorts from TCGA and the GSE21034 from the Gene Expression Omnibus (GEO) were used as internal and independent external validation datasets. This signature could divide PCa patients into the high and low risk groups with different prognoses, was apparently correlated with clinical features, and was an independent excellent prognostic indicator. Enrichment analysis indicated our signature was primarily concentrated in cellular process and metabolic process. The expression patterns of CLCN2 and CLCN6 were detected in our own cohort based immunohistochemistry staining, and we found CLCN2 and CLCN6 were highly expressed in PCa tissues compared with benign tissues and positively associated with higher Gleason score and shorter BCR-free time. Functional experiments revealed that CLCN2 and CLCN6 downregulation inhibited cell proliferation, colony formation, invasion, and migration, but prolonged cell cycle and promoted apoptosis. Furthermore, Seahorse assay showed that silencing CLCN2 or CLCN6 exerted potential inhibitory effects on energy metabolism in PCa. Collectively, our signature could provide a novel and robust strategy for the prognostic evaluation and improve treatment decision making for PCa patients.

Up-regulation of secretory leukocyte protease inhibitor in human samples might have a potential role of predicting prostate cancer recurrence and progression after surgery and hormonal therapy.

Using new castration-resistant prostate cancer (CRPC) cell lines developed from LNCaP cells as a model for CRPC, we searched for novel biomarkers by analyzing the proteins secreted in culture supernatants. The results showed that the levels of secretory leukocyte protease inhibitor (SLPI) in these cell lines were 4.7-6.7 times higher than those secreted in parental LNCaP. Patients with localized prostate cancer (PC) and who expressed SLPI had a significantly lower prostate-specific antigen (PSA) progression-free survival rate than those who did not. Multivariate analysis revealed that SLPI expression was an independent risk factor for PSA recurrence. By contrast, when immunostaining of SLPI was performed on consecutive prostate tissue samples obtained from 11 patients, both in hormone naive (HN) and castration resistant (CR) conditions, only one patient expressed SLPI in the HNPC state; however, four of the 11 patients expressed SLPI in the CRPC state. In addition, two of these four patients were resistant to enzalutamide, and there was a discrepancy between their serum PSA levels and radiographic progression of the disease. These results suggest that SLPI can be a predictor of prognosis in patients with localized PC and disease progression in CRPC patients.

Identification of Novel Pyroptosis-Related Gene Signatures to Predict Prostate Cancer Recurrence.

Prostate cancer (PCa) is a common malignant type of urogenital tract tumor with poor prognosis. Despite therapeutic advances, the recurrence and mortality rates of PCa have continued to increase with poor prognoses. Pyroptosis, also known as inflammatory cell necrosis, is a recently identified type of programmed cell death that can regulate the invasiveness, differentiation, proliferation, and metastasis of tumor cells; thus, it has a profound effect on the prognosis of patients with tumors. However, the relationship between pyroptosis and PCa remains unclear. We first identified 25 pyroptosis-related genes (PRGs) that were differentially expressed between PCa tissues and matched normal tissues in The Cancer Genome Atlas (TCGA) cohort. Based on the expression levels of 25 PRGs, PCa patients were clearly divided into two clusters and 17 PRGs were found to be significantly different between the two clusters, suggesting probable roles for these genes in the progression and recurrence of PCa. Therefore, the GSE40272 dataset with recurrence follow-up information was used to verify their value. Univariate analysis suggested that 5/17 genes were associated with recurrence, the number of genes did not decrease after least absolute shrinkage and selection operator (LASSO) regression analysis, and 5 PRGs constituted the risk score formula. Low-risk and high-risk subgroups identified using the recurrence model showed different disease-free survival (DFS) times (P<0.001) and the risk score of five PRGs was a factor of independence for recurrence in patients with PCa. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses suggested that these pathways, and comprising PRGs might be closely related to carcinogenesis and invasion of tumors, tumor microenvironment, and immune response. In conclusion, the expression signatures of PRGs play an important role in predicting PCa recurrence.

Identification of Potential Predictor of Biochemical Recurrence in Prostate Cancer.

BackgroundProstate cancer is a common malignancy in men. Radical prostatectomy is one of the primary treatment modalities for patients with prostate cancer. However, early identification of biochemical recurrence is a major challenge for post-radical prostatectomy surveillance. There is a lack of reliable predictors of biochemical recurrence. The purpose of this study was to explore potential biochemical recurrence indicators for prostate cancer.Materials and MethodsWe analyzed transcriptomic data of cases with biochemical recurrence in The Cancer Genome Atlas (TCGA). Then, we performed integrative bioinformatics analyses to establish a biochemical recurrence predictor model of prostate cancer.ResultsThere were 146 differentially expressed genes (DEGs) between prostate cancer and normal prostate, including 12 upregulated and 134 downregulated genes. Comprehensive pathway enrichment analyses revealed that these DEGs were associated with multiple cellular metabolic pathways. Subsequently, according to the random assignment principle, 208 patients were assigned to the training cohort and 205 patients to the validation cohort. Univariate Cox regression analysis showed that 7 genes were significantly associated with the biochemical recurrence of prostate cancer. A model consisting of 5 genes was constructed using LASSO regression and multivariate Cox regression to predict biochemical recurrence of prostate cancer. Expression of PAH and AOC1 decreased with an increasing incidence of prostate cancer, whereas expression of DDC, LINC01436 and ORM1 increased with increasing incidence of prostate cancer. Kaplan–Meier curves and receiver operator characteristic (ROC) curves indicated that the 5-gene model had reliable utility in identifying the risk of biochemical recurrence of prostate cancer.ConclusionThis study provides a model for predicting prostate cancer recurrence after surgery, which may be an optional indicator for postoperative follow-up.

A Metabolism-Related Gene Landscape Predicts Prostate Cancer Recurrence and Treatment Response.

BackgroundProstate cancer (PCa) is the most common malignant tumor in men. Although clinical treatments of PCa have made great progress in recent decades, once tolerance to treatments occurs, the disease progresses rapidly after recurrence. PCa exhibits a unique metabolic rewriting that changes from initial neoplasia to advanced neoplasia. However, systematic and comprehensive studies on the relationship of changes in the metabolic landscape of PCa with tumor recurrence and treatment response are lacking. We aimed to construct a metabolism-related gene landscape that predicts PCa recurrence and treatment response.MethodsIn the present study, we used differentially expressed gene analysis, protein–protein interaction (PPI) networks, univariate and multivariate Cox regression, and least absolute shrinkage and selection operator (LASSO) regression to construct and verify a metabolism-related risk model (MRM) to predict the disease-free survival (DFS) and response to treatment for PCa patients.ResultsThe MRM predicted patient survival more accurately than the current clinical prognostic indicators. By using two independent PCa datasets (International Cancer Genome Consortium (ICGC) PCa and Taylor) and actual patients to test the model, we also confirmed that the metabolism-related risk score (MRS) was strongly related to PCa progression. Notably, patients in different MRS subgroups had significant differences in metabolic activity, mutant landscape, immune microenvironment, and drug sensitivity. Patients in the high-MRS group were more sensitive to immunotherapy and endocrine therapy, while patients in the low-MRS group were more sensitive to chemotherapy.ConclusionsWe developed an MRM, which might act as a clinical feature to more accurately assess prognosis and guide the selection of appropriate treatment for PCa patients. It is promising for further application in clinical practice.

Predictive Value of Circulating Tumor Cells Detected by ISET® in Patients with Non-Metastatic Prostate Cancer Undergoing Radical Prostatectomy.

There is an unmet need for reliable biomarkers to predict prostate cancer recurrence after prostatectomy in order to better guide the choice of surgical treatment. We have evaluated the predictive value of the preoperative detection of Circulating Tumor Cells (CTC) for prostate cancer recurrence after surgery. A cohort of 108 patients with non-metastatic prostate adenocarcinoma undergoing radical prostatectomy was tested for the presence of CTC before prostatectomy using ISET®. Disease recurrence was assessed by the increase in serum PSA level after prostatectomy. The following factors were assessed for statistical association with prostate cancer recurrence: the presence of CTC, serum PSA, Gleason score, and pT stage using univariate and multivariate analyses, with a mean follow-up of 34.9 months. Prostate cancer recurrence was significantly associated with the presence of at least 1 CTC at the preoperative time point (p < 0.001; Predictive value = 0.83). Conversely, the absence of prostate cancer recurrence was significantly associated with the lack of CTC detection at diagnosis (Predictive value = 1). Our multivariate analysis shows that only CTC presence is an independent risk factor associated with prostate cancer recurrence after prostatectomy (p < 0.001). Our results suggest that CTC detection by ISET® before surgery is an interesting candidate predictive marker for cancer recurrence in patients with non-metastatic PCa.

Development and validation of novel inflammatory response-related gene signature to predict prostate cancer recurrence and response to immune checkpoint therapy.

The aim of this study is to construct an inflammatory response-related genes (IRRGs) signature to monitor biochemical recurrence (BCR) and treatment effects in prostate cancer patients (PCa). A gene signature for inflammatory responses was constructed on the basis of the data from the Cancer Genome Atlas (TCGA) database, and validated in external datasets. It was analyzed using receiver operating characteristic curve, BCR-free survival, Cox regression, and nomogram. Distribution analysis and external model comparison were utilized. Then, enrichment analysis, tumor mutation burden, tumor immune microenvironment, and immune cell infiltration signatures were investigated. The role of the signature in immunotherapy was evaluated. The expression patterns of core genes were verified by RNA sequencing. We identified an IRRGs signature in the TCGA-PRAD cohort and verified it well in two other independent external datasets. The signature was a robust and independent prognostic index for predicting the BCR of PCa. The high-risk group of our signature predicted a shortened BCR time and an aggressive disease progression. A nomogram was constructed to predict BCR-free time in clinical practices. Neutrophils and CD8+ T cells were in higher abundance among the low-risk individuals. Immune functions varied significantly between the two groups and immune checkpoint therapy worked better for the low-risk patients. The expression of four IRRGs showed significant differences between PCa and surrounding benign tissues, and were validated in BPH-1 and DU145 cell lines by RNA sequencing. Our signature served as a reliable and promising biomarker for predicting the prognosis and evaluating the efficacy of immunotherapy, facilitating a better outcome for PCa patients.

In Situ Analysis of N-Linked Glycans as Potential Biomarkers of Clinical Course in Human Prostate Cancer.

Prostate cancer is the most common cancer in men worldwide. Despite its prevalence, there is a critical knowledge gap in understanding factors driving disparities in survival among different cohorts of patients with prostate cancer. Identifying molecular features separating disparate populations is an important first step in prostate cancer research that could lead to fundamental hypotheses in prostate biology, predictive biomarker discovery, and personalized therapy. N-linked glycosylation is a cotranslational event during protein folding that modulates a myriad of cellular processes. Recently, aberrant N-linked glycosylation has been reported in prostate cancers. However, the full clinical implications of dysregulated glycosylation in prostate cancer has yet to be explored. Herein, we performed direct on-tissue analysis of N-linked glycans using matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) from tissue microarrays of over 100 patient tumors with over 10 years of follow-up metadata. We successfully identified a panel of N-glycans that are unique between benign and prostate tumor tissue. Specifically, high-mannose as well as tri-and tetra-antennary N-glycans were more abundant in tumor tissue and increase proportionally with tumor grade. Further, we expanded our analyses to examine the N-glycan profiles of Black and Appalachian patients and have identified unique glycan signatures that correlate with recurrence in each population. Our study highlights the potential applications of MALDI-MSI for digital pathology and biomarker discovery for prostate cancer. IMPLICATIONS: MALDI-MSI identifies N-glycan perturbations in prostate tumors compared with benign tissue. This method can be utilized to predict prostate cancer recurrence and study prostate cancer disparities.

Факторы прогноза локорегионального рецидива рака после радикальной простатэктомии

An analysis of domestic and foreign literature on the predictors of prostate cancer recurrence are presented in the article. A deep analysis of both pathological and histological risk factors for progression was provided, as well as of laboratory and clinical predictors.