Prostate Cancer Dataset Research Articles

Investigating the prognostic utility of GSTP1 promoter methylation in prostate cancer

AbstractObjectivesWe aim to determine the prognostic significance of glutathione S‐transferase Pi 1 DNA methylation (mGSTP1) in two independent prostate cancer cohorts with long‐term clinical follow‐up data.Subjects/Patients and MethodsWe first re‐examined a published, in‐house whole genome bisulphite sequencing (WGBS) prostate cancer dataset, derived from radical prostatectomy (RP) tissue (n = 15) with median follow‐up 19.5 years, to confirm and visualise the association between mGSTP1 and patient mortality. To validate prognostic significance, we used a quantitative methylation‐specific head‐loop (MS‐HL) assay to measure mGSTP1 levels in a larger, independent cohort (n = 186), and performed univariable and multivariable Cox survival analysis.ResultsRe‐analysis of WGBS data showed a significant increase in mGSTP1 in RP samples from patients with lethal versus non‐lethal disease. Subsequent analysis in the larger cohort using the MS‐HL assay confirmed that mGSTP1 was detectable in 97% of RP samples, validating the diagnostic potential of mGSTP1. Univariable Cox survival analysis revealed a significant association between mGSTP1 levels and biochemical recurrence and metastatic relapse free survival, with a near‐significant association with prostate cancer specific mortality. Notably, multivariable Cox models demonstrated that mGSTP1 did not add independent prognostic value beyond standard clinicopathological features.ConclusionOur study supports the importance of DNA methylation as a tissue‐based prostate tumour biomarker. GSTP1 methylation is well established as a diagnostic marker, and in this study, we find that GSTP1 methylation levels are also associated with disease prognosis. Further research is required into the clinical utility of prognostic methylation markers and their functional role in disease progression.

Integrative spatial and genomic analysis of tumor heterogeneity with Tumoroscope

Spatial and genomic heterogeneity of tumors are crucial factors influencing cancer progression, treatment, and survival. However, a technology for direct mapping the clones in the tumor tissue based on somatic point mutations is lacking. Here, we propose Tumoroscope, the first probabilistic model that accurately infers cancer clones and their localization in close to single-cell resolution by integrating pathological images, whole exome sequencing, and spatial transcriptomics data. In contrast to previous methods, Tumoroscope explicitly addresses the problem of deconvoluting the proportions of clones in spatial transcriptomics spots. Applied to a reference prostate cancer dataset and a newly generated breast cancer dataset, Tumoroscope reveals spatial patterns of clone colocalization and mutual exclusion in sub-areas of the tumor tissue. We further infer clone-specific gene expression levels and the most highly expressed genes for each clone. In summary, Tumoroscope enables an integrated study of the spatial, genomic, and phenotypic organization of tumors.

Using K-NN Algorithm for Evaluating Feature Selection on High Dimensional Datasets

Data mining is the process of using statistics, mathematics, artificial intelligence and machine learning to identify problems that exist in data so as to produce useful information. Based on its function, data mining is grouped into description, estimation, classification, clustering, and association. K-NN is one of the best data mining methods and is widely used in research. K-NN algorithm was introduced by Fix and Hodges in 1951. K-NN algorithm is a simple algorithm and is often used to cluster supervised data. Feature selection attribute selection is a data mining technique used in the pre-processing stage. This technique works by reducing complex attributes that will be managed at the processing and analysis stage. In this study, the most effective feature selection to improve the accuracy of the K-NN algorithm by increasing accuracy by 95.12% on the breast cancer dataset and 88.75% on the prostate cancer dataset.

Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?

Synthetic data have been proposed as a solution for sharing anonymized versions of sensitive biomedical datasets. Ideally, synthetic data should preserve the structure and statistical properties of the original data, while protecting the privacy of the individual subjects. Differential Privacy (DP) is currently considered the gold standard approach for balancing this trade-off. The aim of this study is to investigate how trustworthy are group differences discovered by independent sample tests from DP-synthetic data. The evaluation is carried out in terms of the tests' Type I and Type II errors. With the former, we can quantify the tests' validity, i.e., whether the probability of false discoveries is indeed below the significance level, and the latter indicates the tests' power in making real discoveries. We evaluate the Mann-Whitney U test, Student's t-test, chi-squared test, and median test on DP-synthetic data. The private synthetic datasets are generated from real-world data, including a prostate cancer dataset (n = 500) and a cardiovascular dataset (n = 70,000), as well as on bivariate and multivariate simulated data. Five different DP-synthetic data generation methods are evaluated, including two basic DP histogram release methods and MWEM, Private-PGM, and DP GAN algorithms. A large portion of the evaluation results expressed dramatically inflated Type I errors, especially at levels of ϵ ≤ 1. This result calls for caution when releasing and analyzing DP-synthetic data: low p-values may be obtained in statistical tests simply as a byproduct of the noise added to protect privacy. A DP Smoothed Histogram-based synthetic data generation method was shown to produce valid Type I error for all privacy levels tested but required a large original dataset size and a modest privacy budget (ϵ ≥ 5) in order to have reasonable Type II error levels.

Soft-Voting Ensemble Model: An Efficient Learning Approach for Predictive Prostate Cancer Risk

Prostate cancer is increasingly common among men. However, the process of diagnosing malignant disease is relatively complicated and time-consuming. Identifying benign or malignant tumors early can assist medical professionals in choosing appropriate treatment methods. Consequently, we introduce a soft-voting ensemble model comprising several single machine learning models such as Logistic Regression, Random Forest, XGBoost, LGBM, and Support Vector Machine for the classification task with the prostate cancer dataset. The dataset was divided into two parts for training and testing with a ratio of 67:33. The confusion matrix was used to evaluate the performance of both the individual and ensemble models. Experimental results show that ensemble models achieve performance ranging from 87.88% to 96.97%, which is 3% to 9% better than individual models, surpassing recent research. Integrating the strengths of individual models helps minimize errors, resulting in optimal classification with high accuracy and overall performance in the field of machine learning.

Exploration of PVT1 as a biomarker in prostate cancer.

Prostate cancer is a malignant tumor originating from the prostate gland, significantly affecting patients' quality of life and survival rates. Public data was utilized to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis was constructed to classify gene modules. Functional enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes and gene ontology annotations, with results visualized using the Metascape database. Additionally, gene set enrichment analysis evaluated gene expression profiles and related pathways, constructed a protein-protein interaction network to predict core genes, analyzed survival data, plotted heatmaps and radar charts, and predicted microRNAs for core genes through miRTarBase. Two prostate cancer datasets (GSE46602 and GSE55909) were analyzed, identifying 710 DEGs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that DEGs were primarily involved in organic acid metabolism and the P53 signaling pathway. Gene set enrichment analysis and Metascape analyses further confirmed the significance of these pathways. After constructing the weighted gene co-expression network analysis network, 3 core genes (DDX21, NOP56, plasmacytoma variant translocation 1 [PVT1]) were identified. Survival analysis indicated that core genes are closely related to patient prognosis. Through comparative toxicogenomics database and miRNA prediction analysis, PVT1 was considered to play a crucial role in the development of prostate cancer. The PVT1 gene is highly expressed in prostate cancer and has the potential to become a diagnostic biomarker and therapeutic target for prostate cancer.

Comparison of Imputation Methods for Categorical Real-World Prostate Cancer Data with Natural Order.

Missing values (NA) often occur in cancer research, which may be due to reasons such as data protection, data loss, or missing follow-up data. Such incomplete patient information can have an impact on prediction models and other data analyses. Imputation methods are a tool for dealing with NA. Cancer data is often presented in an ordered categorical form, such as tumour grading and staging, which requires special methods. This work compares mode imputation, k nearest neighbour (knn) imputation, and, in the context of Multiple Imputation by Chained Equations (MICE), logistic regression model with proportional odds (mice_polr) and random forest (mice_rf) on a real-world prostate cancer dataset provided by the Cancer Registry of Rhineland-Palatinate in Germany. Our dataset contains relevant information for the risk classification of patients and the time between date of diagnosis and date of death. For the imputation comparison, we use Rubin's (1974) Missing Completely At Random (MCAR) mechanism to remove 10%, 20%, 30%, and 50% observations. The results are evaluated and ranked based on the accuracy per patient. Mice_rf performs significantly best for each percentage of NA, followed by knn, and mice_polr performs significantly worst. Furthermore, our findings indicate that the accuracy of imputation methods increases with a lower number of categories, a relatively even proportion of patients in the categories, or a majority of patients in a particular category.

Monoacylglycerol lipase blockades the senescence-associated secretory phenotype by interfering with NF-κB activation and promotes docetaxel efficacy in prostate cancer.

Metabolic reprogramming and cellular senescence greatly contribute to cancer relapse and recurrence. In aging and treated prostate, persistent accumulating senescence-associated secretory phenotype (SASP) of cancer cells often limits the overall survival of patients. Novel strategic therapy with monoacylglycerol lipase (MGLL) upregulation that counters the cellular and docetaxel induced SASP might overcome this clinical challenge in prostate cancer (PCa). With primary comparative expression and survival analysis screening of fatty acid (FA) metabolism signature genes in the TCGA PCa dataset and our single center cohort, MGLL was detected to be downregulated in malignancy prostate tissues and its low expression predicted worse progression-free and overall survival. Functionally, overexpression of MGLL mainly suppresses NF-κB-driven SASP (N-SASP) which mostly restricts the cancer cell paracrine and autocrine tumorigenic manners and the corresponding cellular senescence. Further investigating metabolites, we determined that MGLL constitutive expression prevents lipid accumulation, decreases metabolites preferably, and consequently downregulates ATP levels. Overexpressed MGLL inhibited IκBα phosphorylation, NF-κB p65 phosphorylation, and NF-κB nuclear translocation to deactivate NF-κB transcriptional activities, and be responsible for the repressed N-SASP, partially through reducing ATP levels. Preclinically, combinational treatment with MGLL overexpression and docetaxel chemotherapy dramatically delays tumor progression in mouse models. Taken together, our findings identify MGLL as a switch for lipase-related N-SASP suppression and provide a potential drug candidate for promoting docetaxel efficacy in PCa.

Multi-Omics Analysis of Primary Prostate Cancer Datasets Reveals Novel Biomarkers.

Prostate cancer (PCa) ranks second in cancer-related deaths in men. Current screenings used in the diagnosis are not sufficient enough in the early stages therefore, more diagnostic biomarker studies are needed. We performed a meta-analysis on the biomarker potential of miRNAs, mRNAs, and methylation for the early stages of PCa by searching available microarrays from the GEO dataset for PCa tissue and benign prostatic hyperplasia (BPH) or normal adjacent to PCa. Target genes of miRNAs were determined using the miRWalk and miRDB datasets. The results were visualized using network analysis. qPCR quantification of potential miRNA and genes was performed in human prostate epithelial cell line (RWPE-1) and human prostate carcinoma epithelial cell line (22RV1). Our meta-analysis of potential biomarkers for the diagnosis of PCa identified several candidates. It was shown that miR-7-5p is overexpressed. CAMKK2, TMEM97 expression were upregulated and CLIP1 expression was downregulated and these genes were shown to be targets of miR-7-5p. CAMKK2, TMEM97, and CLIP1 genes were found to be hypermethylated. Although the changes in the expression levels of miR-7-5p and CAMKK2, TMEM97, and CLIP1 in the two cell lines used in our study were not consistent with the significant expression differences observed in the meta-analysis, our meta-analysis results would be promising in human prostate tissue or different human tumor cell line studies. This highlights the importance of our meta-analysis results in prostate cancer biomarker research, given the difficulty of experimental validation of our large-scale data meta-analysis results using a specific cell line.

Autodelineation of Treatment Target Volume for Radiation Therapy Using Large Language Model-Aided Multimodal Learning

PurposeArtificial intelligence (AI)-aided methods have made significant progress in the auto-delineation of normal tissues. However, these approaches struggle with the auto-contouring of radiotherapy target volume. Our goal is to model the delineation of target volume as a clinical decision-making problem, resolved by leveraging large language model-aided multimodal learning approaches. Methods and MaterialsA vision-language model, termed Medformer, has been developed, employing the hierarchical vision transformer as its backbone, and incorporating large language models to extract text-rich features. The contextually embedded linguistic features are seamlessly integrated into visual features for language-aware visual encoding through the visual language attention module. Metrics, including Dice similarity coefficient (DSC), intersection over union (IOU), and 95th percentile Hausdorff distance (HD95), were used to quantitatively evaluate the performance of our model. The evaluation was conducted on an in-house prostate cancer dataset and a public oropharyngeal carcinoma (OPC) dataset, totaling 668 subjects. ResultsOur Medformer achieved a DSC of 0.81 ± 0.10 versus 0.72 ± 0.10, IOU of 0.73 ± 0.12 versus 0.65 ± 0.09, and HD95 of 9.86 ± 9.77 mm versus 19.13 ± 12.96 mm for delineation of gross tumor volume (GTV) on the prostate cancer dataset. Similarly, on the OPC dataset, it achieved a DSC of 0.77 ± 0.11 versus 0.72 ± 0.09, IOU of 0.70 ± 0.09 versus 0.65 ± 0.07, and HD95 of 7.52 ± 4.8 mm versus 13.63 ± 7.13 mm, representing significant improvements (p < 0.05). For delineating the clinical target volume (CTV), Medformer achieved a DSC of 0.91 ± 0.04, IOU of 0.85 ± 0.05, and HD95 of 2.98 ± 1.60 mm, comparable to other state-of-the-art algorithms. ConclusionsAuto-delineation of the treatment target based on multimodal learning outperforms conventional approaches that rely purely on visual features. Our method could be adopted into routine practice to rapidly contour CTV/GTV.

Combining PSMA-PET and PROMISE to re-define disease stage and risk in patients with prostate cancer: a multicentre retrospective study

Prostate-specific membrane antigen (PSMA)-PET was introduced into clinical practice in 2012 and has since transformed the staging of prostate cancer. Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE) criteria were proposed to standardise PSMA-PET reporting. We aimed to compare the prognostic value of PSMA-PET by PROMISE (PPP) stage with established clinical nomograms in a large prostate cancer dataset with follow-up data for overall survival. In this multicentre retrospective study, we used data from patients of any age with histologically proven prostate cancer who underwent PSMA-PET at the University Hospitals in Essen, Münster, Freiburg, and Dresden, Germany, between Oct 30, 2014, and Dec 27, 2021. We linked a subset of patient hospital records with patient data, including mortality data, from the Cancer Registry North-Rhine Westphalia, Germany. Patients from Essen University Hospital were randomly assigned to the development or internal validation cohorts (2:1). Patients from Münster, Freiburg, and Dresden University Hospitals were included in an external validation cohort. Using the development cohort, we created quantitative and visual PPP nomograms based on Cox regression models, assessing potential PPP predictors for overall survival, with least absolute shrinkage and selection operator penalty for overall survival as the primary endpoint. Performance was measured using Harrell's C-index in the internal and external validation cohorts and compared with established clinical risk scores (International Staging Collaboration for Cancer of the Prostate [STARCAP], European Association of Urology [EAU], and National Comprehensive Cancer Network [NCCN] risk scores) and a previous nomogram defined by Gafita et al (hereafter referred to as GAFITA) using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) estimates. We analysed 2414 male patients (1110 included in the development cohort, 502 in the internal cohort, and 802 in the external validation cohort), among whom 901 (37%) had died as of data cutoff (June 30, 2023; median follow-up of 52·9 months [IQR 33·9-79·0]). Predictors in the quantitative PPP nomogram were locoregional lymph node metastases (molecular imaging N2), distant metastases (extrapelvic nodal metastases, bone metastases [disseminated or diffuse marrow involvement], and organ metastases), tumour volume (in L), and tumour mean standardised uptake value. Predictors in the visual PPP nomogram were distant metastases (extrapelvic nodal metastases, bone metastases [disseminated or diffuse marrow involvement], and organ metastases) and total tumour lesion count. In the internal and external validation cohorts, C-indices were 0·80 (95% CI 0·77-0·84) and 0·77 (0·75-0·78) for the quantitative nomogram, respectively, and 0·78 (0·75-0·82) and 0·77 (0·75-0·78) for the visual nomogram, respectively. In the combined development and internal validation cohort, the quantitative PPP nomogram was superior to STARCAP risk score for patients at initial staging (n=139 with available staging data; AUC 0·73 vs 0·54; p=0·018), EAU risk score at biochemical recurrence (n=412; 0·69 vs 0·52; p<0·0001), and NCCN pan-stage risk score (n=1534; 0·81 vs 0·74; p<0·0001) for the prediction of overall survival, but was similar to GAFITA nomogram for metastatic hormone-sensitive prostate cancer (mHSPC; n=122; 0·76 vs 0·72; p=0·49) and metastatic castration-resistant prostate cancer (mCRPC; n=270; 0·67 vs 0·75; p=0·20). The visual PPP nomogram was superior to EAU at biochemical recurrence (n=414; 0·64 vs 0·52; p=0·0004) and NCCN across all stages (n=1544; 0·79 vs 0·73; p<0·0001), but similar to STARCAP for initial staging (n=140; 0·56 vs 0·53; p=0·74) and GAFITA for mHSPC (n=122; 0·74 vs 0·72; p=0·66) and mCRPC (n=270; 0·71 vs 0·75; p=0·23). Our PPP nomograms accurately stratify high-risk and low-risk groups for overall survival in early and late stages of prostate cancer and yield equal or superior prediction accuracy compared with established clinical risk tools. Validation and improvement of the nomograms with long-term follow-up is ongoing (NCT06320223). Cancer Registry North-Rhine Westphalia.

Towards Unsupervised Learning Driven Intelligence for Prediction of Prostate Cancer

Prostate cancer is a widespread and global disease which affects adult males – it is said that key causes of the cancer include age, family history and ethnicity. In this study, the Kaggle prostate cancer dataset, comprising of data of 100 patients with a mixture that both had cancer and did not have cancer, was used alongside machine learning prediction models for the design of unsupervised and automated intelligent systems for the prediction of prostate cancer. Two intelligent systems were designed and underpinned by unsupervised learning algorithms, namely, fuzzy c-means and agglomerative hierarchical clustering, where the various intelligent systems were able to make a prostate cancer prediction with accuracies of over 80% for the various classification metrics, alongside being able to predict an associated stage of the prostate cancer. Both designed intelligent systems offer a complimentary alternative to each other, and their relative merits are discussed in the paper. ry alternative to each other, and their relative merits are discussed in the paper.

The Anti-proliferative Effect of a Novel Glutaminase Inhibitor IN-3 on Prostate Cancer Cells.

This study aimed to evaluate anti-cancer potential of a novel glutaminase (GLS) inhibitor IN-3 in prostate cancer cells. The cell viability upon IN-3 treatment was examined using crystal violet staining and IC50 values were calculated for cancer cell lines PC-3 and LNCaP and normal fibroblasts CCD1072sk. The expression levels of GLS isoforms were determined by real-time polymerase chain reaction after IN-3 treatment. The metastatic prostate cancer dataset was downloaded from C-Bioportal and the expressions of GLS isoforms were analyzed. The IC50 values of IN-3 for LNCaP, PC-3 and CCD1072sk were 2.13, 6.14 and 15.39 μM respectively. The dose dependent effect of IN-3 was evident even in low concentration with 1 μM in LNCaP and 2 μM in PC-3 and these anti-proliferative effects of IN-3 were highly significant with p-values lower than 0.0001. The treatment of PC-3 cells with 10 μM IN-3 elevated the expression of kidney type GLS isoform of GLS1 but not GLS2. Comparison of metastatic and localized prostate cancer tissues showed that GLS1 was highly expressed not only in primary but also in metastatic prostate cancer tissues. GLS1 expression was significantly higher than GLS2 expression with p-values lower than 0.001. The GLS inhibitor IN-3 may be a potent anti-cancer agent in prostate cancer by demonstrating its differential effect between cancer and normal cells. Further studies are warranted to elucidate its drug potential in prostate cancer.

Relationship between prostate-specific antigen, alkaline phosphatase levels, and time-to-tumor shrinkage: understanding the progression of prostate cancer in a longitudinal study

BackgroundThis study delves into the complex interplay among prostate-specific antigen, alkaline phosphatase, and the temporal dynamics of tumor shrinkage in prostate cancer. By investigating the longitudinal trajectories and time-to-prostate cancer tumor shrinkage, we aim to untangle the intricate patterns of these biomarkers. This understanding is pivotal for gaining profound insights into the multifaceted aspects of prostate cancer progression. The joint model approach serves as a comprehensive framework, facilitating the elucidation of intricate interactions among these pivotal elements within the context of prostate cancer .MethodsA new joint model under a shared parameters strategy is proposed for mixed bivariate longitudinal biomarkers and event time data, for obtaining accurate estimates in the presence of missing covariate data. The primary innovation of our model resides in its effective management of covariates with missing observations. Built upon established frameworks, our joint model extends its capabilities by integrating mixed longitudinal responses and accounting for missingness in covariates, thus confronting this particular challenge. We posit that these enhancements bolster the model’s utility and dependability in real-world contexts characterized by prevalent missing data. The main objective of this research is to provide a model-based approach to get full information from prostate cancer data collected with patients’ baseline characteristics (Age\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Age$$\\end{document}, body mass index (BMI\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$BMI$$\\end{document}), GleasonScore\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Gleason Score$$\\end{document}, Grade\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Grade$$\\end{document}, and Drug\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Drug$$\\end{document}) and two longitudinal endogenous covariates (Platelets\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Platelets$$\\end{document} and Bilirubin\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Bilirubin$$\\end{document}).ResultsThe results reveal a clear association between prostate-specific antigen and alkaline phosphatase biomarkers in the context of time-to-prostate cancer tumor shrinkage. This underscores the interconnected dynamics of these key indicators in gauging disease progression.ConclusionsThe analysis of the prostate cancer dataset, incorporating a joint evaluation of mixed longitudinal prostate-specific antigen and alkaline phosphatase biomarkers alongside tumor status, has provided valuable insights into disease progression. The results demonstrate the effectiveness of the proposed joint model, as evidenced by accurate estimates. The shared variables associated with both longitudinal biomarkers and event times consistently deviate from zero, highlighting the robustness and reliability of the model in capturing the complex dynamics of prostate cancer progression. This approach holds promise for enhancing our understanding and predictive capabilities in the clinical assessment of prostate cancer.

Disulfidptosis-related genes serve as potential prognostic biomarkers and indicate tumor microenvironment characteristics and immunotherapy response in prostate cancer

Disulfidptosis, a newly identified programmed cell death pathway in prostate cancer (PCa), is closely associated with intracellular disulfide stress and glycolysis. This study aims to elucidate the roles of disulfidptosis-related genes (DRGs) in the pathogenesis and progression of PCa, with the goal of improving diagnostic and therapeutic approaches. We analyzed PCa datasets and normal tissue transcriptome data from TCGA, GEO, and MSKCC. Using consensus clustering analysis and LASSO regression, we developed a risk scoring model, which was validated in an independent cohort. The model's predictive accuracy was confirmed through Kaplan–Meier curves, receiver operating characteristic (ROC) curves, and nomograms. Additionally, we explored the relationship between the risk score and immune cell infiltration, and examined the tumor microenvironment and somatic mutations across different risk groups. We also investigated responses to immunotherapy and drug sensitivity. Our analysis identified two disulfidosis subtypes with significant differences in survival, immune environments, and treatment responses. According to our risk score, the high-risk group exhibited poorer progression-free survival (PFS) and higher tumor mutational burden (TMB), associated with increased immune suppression. Functional enrichment analysis linked high-risk features to key cancer pathways, including the IL-17 signaling pathway. Moreover, drug sensitivity analysis revealed varied responses to chemotherapy, suggesting the potential for disulfidosis-based personalized treatment strategies. Notably, we identified PROK1 as a crucial prognostic marker in PCa, with its reduced expression correlating with disease progression. In summary, our study comprehensively assessed the clinical implications of DRGs in PCa progression and prognosis, offering vital insights for tailored precision medicine approaches.

Abstract 5648: Decoding the non-canonical functions of HO-1 in prostate cancer: A nuclear perspective and its association with a neuroendocrine signature

Abstract Previous studies have demonstrated the non-canonical anti-tumor effect of heme-oxygenase 1 (HO-1) in prostate cancer (PCa). Although HO-1 is crucial for free heme degradation, its nuclear expression unveils non-canonical functions beyond its enzymatic function. Understanding the specifics of its non-canonical role remains a critical unmet need. In this study, we identified nuclear interactors of HO-1 and assessed their association with PCa.PCa cells were treated with hemin (80 µM, 24 h), a specific pharmacological inducer of HO-1. Nuclear HO-1 immunoprecipitation and LC-ESI MS/MS analysis identified 11 differential nuclear associated-HO-1 proteins between control and hemin-treated PCa cells (ILF3, ILF2, BCLAF1, SAFB, DDX17, SLC25A5, CASP14, PRDX1, BRIX1, CCDC175, and GPATCH1). Next, we performed an Ingenuity Pathway Analysis (QIAGEN) showing that ILF3 appears as a master regulator of this signature. To assess the clinical relevance of these factors in PCa, we analyzed overall survival (OS), progression-free survival (PFS), relapse free survival (RFS) in multiple PCa datasets (GSE34312, GSE35988, GSE3933, GSE46602, GSE6956, GSE70768, TCGA-PRAD, GSE70770, GSE16560, GSE24136; n=1064). We performed univariable and multivariable analyses for these factors and identified the ones that significantly and independently affected the OS, RFS, PFS. Next, a risk score model was built based on the expression of 9 genes (∑ni=1(Coefi×Expri)) for the GSE70770 dataset using these factors identifying a subpopulation of PCa patients with high-risk of RFS (HR: 7.39 High vs Low score, p&amp;lt;0.0001); ascertaining the critical role of this signature in PCa. To elucidate the association of these factors with the aggressive phenotype of PCa, we analyzed RNA-seq expression data from the MDA-PCa-PDXs series (PCa Patient Derived Xenografts Program; MD Anderson Cancer Center), capturing PCa heterogeneity. Unsupervised clustering analysis revealed that samples with high expression of HO-1 nuclear interactors corresponded to neuroendocrine tumors, negative for androgen receptor staining. Principal Component Analysis identified ILF3 as the most relevant HO-1 interactor driving PDXs’ samples variance, correlating with a significant decrease in relapse-free survival of PCa patients. Further, we used ChIP-Atlas to assess the epigenomic landscapes for these nuclear HO-1 interactors. Results indicated that 40% of HO-1 nuclear interactors were potential transcription regulators. Strikingly, KEGG pathways analyses revealed that their regulomes were significantly associated with neurodegenerative disorders, highlighting their relevance in neural processes. In conclusion, our findings suggest that HO-1 and its nuclear interactors may play a pivotal role in neuroendocrine PCa, shedding light on potential therapeutic targets for this aggressive form of the disease. Citation Format: Seniuk A. Rocio, Agustina Sabater, Pablo Sanchis, Juan Bizzotto, Gastón Pascual, Estefania Labanca, Nicolas Anselmino, Nora Navone, Elba Vazquez, Pia Valacco, Javier Cotignola, Ayelén Toro, Geraldine Gueron. Decoding the non-canonical functions of HO-1 in prostate cancer: A nuclear perspective and its association with a neuroendocrine signature [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 5648.

Abstract 7430: Development and demonstration of a user-friendly application to explore single-cell atlas for prostate cancer (PCa) and analyze newly generated single-cell and spatial PCa datasets

Abstract Background: Single-cell atlases provide a high-resolution and tissue-specific depiction of cellular state at the transcriptomic level. Newer technologies like spatial transcriptomics, MALDI, and long-read sequencing introduce orthogonal modalities to single-cell data, making the analyses of large atlases even more complex. RShiny package in R is used to develop user-friendly GUI applications that could enable users to use analysis tools in an interactive manner, thus minimizing the requirement of prior programming experience. Methods: Publicly available scRNA-seq datasets were collected for a wide range of prostate-related pathologies. The datasets were evaluated for their quality based on cell count and availability of the appropriate markers in each compartment. Finally, 11 datasets out of 16 datasets were selected for building integrated datasets. These datasets were filtered for cell types with &amp;lt;10% mitochondrial RNA, and cells with &amp;lt;500 genes were removed from the datasets. The datasets were then clustered to evaluate the effects of cell cycle, dissociation-induced stress, and the presence of doublets. The quality-controlled datasets were batch-corrected using the Harmony package and the harmonized principal components were used in Seurat for clustering the integrated data. Tools used for the analysis of single-cell datasets, beginning from raw counts, have been developed in R with a focus on modularity. These tools are packaged into a simple and intuitive RShiny application which follows the workflow shown in Figure 1a. Results: The atlas obtained include samples from healthy prostate, benign prostate hyperplasia, primary prostate tumor, ICC/IDC, castration-resistant prostate cancer (CRPC), and Neuroendocrine prostate cancer (NEPC). The dataset includes 72 patients and ~320k cells with a median number of ~1600 genes expressed. We were able to distinctly identify Luminal, Hillock, Club, Basal, Neuroendocrine, Endothelial, Fibroblast, Smooth Muscle, Myeloid, T, B, NK, Mast, and Plasma cells in the integrated along with some unknown cellular subtypes in the epithelial compartment. The integrated data was manually annotated using cell type-specific markers. The single-cell atlas obtained was weaved with the workflow tools into an RShiny app. Conclusions: Besides being a useful reference for PCa, the atlas would find extensive use in understanding the transcriptomic landscape of PCa biology. The integrated dataset will also prove crucial in developing bulk RNA sequencing deconvolution and single-cell imputation algorithms. Finally, we aim to provide a user-friendly interface to the prostate cancer community for using the single-cell PCa atlas and analysis tools with minimal computational requirements. Citation Format: Shivang Sharma, Eugene Shenderov. Development and demonstration of a user-friendly application to explore single-cell atlas for prostate cancer (PCa) and analyze newly generated single-cell and spatial PCa datasets [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 7430.

DAX-Net: A dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images

Background and ObjectiveMulti-class cancer classification has been extensively studied in digital and computational pathology due to its importance in clinical decision-making. Numerous computational tools have been proposed for various types of cancer classification. Many of them are built based on convolutional neural networks. Recently, Transformer-style networks have shown to be effective for cancer classification. Herein, we present a hybrid design that leverages both convolutional neural networks and transformer architecture to obtain superior performance in cancer classification. MethodsWe propose a dual-branch dual-task adaptive cross-weight feature fusion network, called DAX-Net, which exploits heterogeneous feature representations from the convolutional neural network and Transformer network, adaptively combines them to boost their representation power, and conducts cancer classification as categorical classification and ordinal classification. For an efficient and effective optimization of the proposed model, we introduce two loss functions that are tailored to the two classification tasks. ResultsTo evaluate the proposed method, we employed colorectal and prostate cancer datasets, of which each contains both in-domain and out-of-domain test sets. For colorectal cancer, the proposed method obtained an accuracy of 88.4%, a quadratic kappa score of 0.945, and an F1 score of 0.831 for the in-domain test set, and 84.4%, 0.910, and 0.768 for the out-of-domain test set. For prostate cancer, it achieved an accuracy of 71.6%, a kappa score of 0.635, and an F1 score of 0.655 for the in-domain test set, 79.2% accuracy, 0.721 kappa score, and 0.686 F1 score for the first out-of-domain test set, and 58.1% accuracy, 0.564 kappa score, and 0.493 F1 score for the second out-of-domain test set. It is worth noting that the performance of the proposed method outperformed other competitors by significant margins, in particular, with respect to the out-of-domain test sets. ConclusionsThe experimental results demonstrate that the proposed method is not only accurate but also robust to varying conditions of the test sets in comparison to several, related methods. These results suggest that the proposed method can facilitate automated cancer classification in various clinical settings.

Effect of variants in the KLK3 gene on prostate cancer survival and time to metastases in a large biobank prostate cancer dataset.

219 Background: Prostate Specific Antigen (PSA), a serine protease, is the most widely used clinical biomarker to detect prostate cancer. It is a product of the KLK3 gene. Several SNPs in KLK3 gene have previously been implicated in prostate cancer aggressiveness and PSA levels, however, the effect of KLK3 SNPs on survival and metastatic disease is not clear. We surveyed common and rare variants within 10-Kb of the KLK3 gene to estimate their effect on prostate cancer survival and time to metastasis in a large biobank data set. Methods: We analyzed a cohort of 5,287 men who developed prostate cancer after enrollment in the Million Veterans Program (MVP), 619 of whom developed metastatic disease. We analyzed 1,583 imputed SNPs (Info-R2 &gt; 0.3) that occurred within 10-Kb of the KLK3 gene, regardless of minor allele frequency. We conducted two separate analyses, the first concerned survival times of prostate cancer specific mortality and the second analysis concerned time to metastatic disease using the Survival R package. For each analysis, variants were collectively assessed for an effect on survival using SKAT-O (metaSeq R package). We also performed a logistic regression to test each SNP for association with Gleason score 8-10 at diagnosis. Results: We identified seven rare SNPs that significantly affected prostate cancer survival (Bonferroni corrected p&lt;3.16e-5) within 10-Kb of KLK3, 3 of which occurred within the gene boundaries (SKAT-O p=1e-4). We also identified 11 other SNPs that significantly affected time to metastasis (SKAT-O p=7e-3). The logistic regression of each SNP on Gleason score 8-10 at diagnosis identified 20 significant SNPs. The SNPs emerging from these analyses were all rare (minor allele frequency &lt; 1e-4), and no SNPs were in linkage disequilibrium with each other, indicating that they comprise independent signals. Conclusions: Understanding the mechanism and role of rare SNPs in the KLK3 gene in prostate cancer detection has important clinical implications, especially in an era when patients’ genetics is being used to inform PSA screening. These finding may identify patients who should undergo prostate biopsy at lower PSA thresholds and/or for whom PSA biochemical recurrence is a less reliable predictor of disease progression. Ongoing prospective and retrospective analyses of Veterans with these variants both prior to and absent any prostate cancer diagnoses and progression.

Survival Outcomes of APA as a Starting treatment: Impact in real-world patients with mCSPC (OASIS).

65 Background: Over the last decade,androgen receptor signaling inhibitors (ARSIs; apalutamide [APA], enzalutamide [ENZ], abiraterone acetate + prednisone [AAP]) in combination with androgen-deprivation therapy (ADT) have emerged as more effective life-prolonging treatment options for mCSPC. We examined the impact of ARSI starting therapy in mCSPC on long-term clinical outcomes in real-world clinical practice in the US. Methods: This retrospective, observational cohort study evaluated clinical outcomes in adult patients with mCSPC using the ConcertAI RWD 360 prostate cancer dataset. All patients with newly diagnosed mCSPC from 1 Jan 2018 to 30 Sept 2022 were enrolled and followed up until 31 Mar 2023. Among other outcomes, PSA50 and PSA90 (PSA response defined as a ≥50% and ≥90% decline from baseline, respectively) and undetectable PSA (PSA ≤0.2 ng/mL) were assessed using the Kaplan-Meier method. Relative risks of onset of castration resistance (CR) and death were estimated using multivariate Cox proportional hazard models adjusted for potential confounding factors (age, comorbidities, BMI, baseline PSA). Results: 165 patients with mCSPC started on APA + ADT, 643 started on ENZ + ADT, 1064 on AAP +ADT, 293 on docetaxel (DTX) + ADT, and 543 on ADT alone (Table). A higher proportion of patients initiating APA achieved PSA50, PSA90, or undetectable PSA compared with other treatments. CR-free survival was significantly longer in patients first treated with an ARSI + ADT than those treated with DTX + ADT or ADT alone. Reduced risk of developing CR was observed in patients starting with APA + ADT compared with ADT alone (adjusted hazard ratio [aHR] 0.36, 95% CI 0.19, 0.67; p=0.0016). OS was longer in patients started on ARSIs compared to ADT alone. Patients starting with APA + ADT were observed to have a lower risk of death compared with ENZ + ADT (aHR 0.48, 95% CI 0.23, 0.99; p&lt;0.05) or AAP + ADT (aHR 0.43, 95% CI 0.21, 0.90; p&lt;0.05). Conclusions: ADT alone continues to be used widely despite the availability of newer and more effective life prolonging therapies. Use of APA + ADT as starting treatment for mCSPC demonstrated significantly better clinical outcomes than other ARSIs, DXT + ADT, or ADT alone in real-world clinical practice in the US. [Table: see text]