Prostate Cancer Dataset Research Articles

Adaptive Tests for Bandedness of High-dimensional Covariance Matrices

Estimation of the high-dimensional banded covariance matrix is widely used in multivariate statistical analysis. To ensure the validity of estimation, we aim to test the hypothesis that the covariance matrix is banded with a certain bandwidth under the high-dimensional framework. Though several testing methods have been proposed in the literature, the existing tests are only powerful for some alternatives with certain sparsity levels, whereas they may not be powerful for alternatives with other sparsity structures. The goal of this paper is to propose a new test for the bandedness of high-dimensional covariance matrix, which is powerful for alternatives with various sparsity levels. The proposed new test also be used for testing the banded structure of covariance matrices of error vectors in high-dimensional factor models. Based on these statistics, a consistent bandwidth estimator is also introduced for a banded high dimensional covariance matrix. Extensive simulation studies and an application to a prostate cancer dataset from protein mass spectroscopy are conducted for evaluating the effectiveness of the proposed adaptive tests blue and bandwidth estimator for the banded covariance matrix.

27-hydroxycholesterol and DNA damage repair: implication in prostate cancer.

We previously reported that cholesterol homeostasis in prostate cancer (PC) is regulated by 27-hydroxycholesterol (27HC) and that CYP27A1, the enzyme that converts cholesterol to 27HC, is frequently lost in PCs. We observed that restoring the CYP27A1/27HC axis inhibited PC growth. In this study, we investigated the mechanism of 27HC-mediated anti-PC effects. We employed in vitro models and human transcriptomics data to investigate 27HC mechanism of action in PC. LNCaP (AR+) and DU145 (AR-) cells were treated with 27HC or vehicle. Transcriptome profiling was performed using the Affymetrix GeneChip™ microarray system. Differential expression was determined, and gene set enrichment analysis was done using the GSEA software with hallmark gene sets from MSigDB. Key changes were validated at mRNA and protein levels. Human PC transcriptomes from six datasets were analyzed to determine the correlation between CYP27A1 and DNA repair gene expression signatures. DNA damage was assessed via comet assays. Transcriptome analysis revealed 27HC treatment downregulated Hallmark pathways related to DNA damage repair, decreased expression of FEN1 and RAD51, and induced "BRCAness" by downregulating genes involved in homologous recombination regulation in LNCaP cells. Consistently, we found a correlation between higher CYP27A1 expression (i.e., higher intracellular 27HC) and decreased expression of DNA repair gene signatures in castration-sensitive PC (CSPC) in human PC datasets. However, such correlation was less clear in metastatic castration-resistant PC (mCRPC). 27HC increased expression of DNA damage repair markers in PC cells, notably in AR+ cells, but no consistent effects in AR- cells and decreased expression in non-neoplastic prostate epithelial cells. While testing the clinical implications of this, we noted that 27HC treatment increased DNA damage in LNCaP cells via comet assays. Effects were reversible by adding back cholesterol, but not androgens. Finally, in combination with olaparib, a PARP inhibitor, we showed additive DNA damage effects. These results suggest 27HC induces "BRCAness", a functional state thought to increase sensitivity to PARP inhibitors, and leads to increased DNA damage, especially in CSPC. Given the emerging appreciation that defective DNA damage repair can drive PC growth, future studies are needed to test whether 27HC creates a synthetic lethality to PARP inhibitors and DNA damaging agents in CSPC.

CAV1 and KRT5 are potential targets for prostate cancer.

Prostate cancer is the most common malignant tumor of male urogenital system that occurs in prostate epithelium. However, relationship between CAV1 and KRT5 and prostate cancer remains unclear. The prostate cancer datasets GSE114740 and GSE200879 were downloaded from Gene Expression Omnibus generated by GPL11154 and GPL32170. De-batch processing was performed, differentially expressed genes (DEGs) were screened, and weighted gene co-expression network analysis. The construction and analysis of protein-protein interaction network, functional enrichment analysis, gene set enrichment analysis. Gene expression heat map was drawn and immune infiltration analysis was performed. Comparative toxicogenomics database analysis were performed to find the disease most related to core gene. In addition, the cell experiment was performed to verify the role of CAV1 and KRT5 by western blot. Divided into 4 groups: control, prostate cancer, prostate cancer-over expression, and prostate cancer- knock out. TargetScan screened miRNAs that regulated central DEGs; 770 DEGs were identified. According to Gene Ontology analysis, they were mainly concentrated in actin binding and G protein coupled receptor binding. In Kyoto Encyclopedia of Gene and Genome analysis, they were mainly concentrated in PI3K-Akt signal pathway, MAPK signal pathway, and ErbB signal pathway. The intersection of enrichment terms of differentially expressed genes and GOKEGG enrichment terms was mainly concentrated in ErbB signaling pathway and MAPK signaling pathway. Three important modules were generated. The protein-protein interaction network obtained 8 core genes (CAV1, BDNF, TGFB3, FGFR1, PRKCA, DLG4, SNAI2, KRT5). Heat map of gene expression showed that core genes (CAV1, TGFB3, FGFR1, SNAI2, KRT5) are highly expressed in prostate cancer tissues and low in normal tissues. Comparative toxicogenomics database analysis showed that core genes (CAV1, TGFB3, FGFR1, SNAI2, KRT5) were associated with prostate tumor, cancer, tumor metastasis, necrosis, and inflammation. CAV1 and KRT5 are up-regulated in prostate cancer. CAV1 and KRT5 are highly expressed in prostate cancer. The higher expression of CAV1 and KRT5, the worse prognosis.

Abstract C041: Transcriptomic analysis of prostate cancer patients living with HIV

Abstract Background: Higher incidence and mortality rates of liver, lung, anal, and head and neck cancers are typically seen in patients living with human immunodeficiency virus (HIV). However, some previous studies have a decreased risk of prostate cancer in HIV+ patients, particularly in men from sexual minorities, with no clear biological or epidemiological explanation. Methods: To identify the specific transcriptomic changes and genetic pathways associated with HIV infection and prostate cancer emergence, we performed bulk RNA-Seq analysis on 18 prostate cancer samples (9 HIV+ and 9 HIV-). We first used kallisto to measure gene-level expression. We then performed principal component analysis (PCA) to determine if HIV status clearly clustered patients based on their gene expression levels. We subsequently used DESeq2 to perform differentially expressed transcriptomic analysis to identify genes that are associated with HIV and prostate cancer phenotypes. Samples were clustered based on expression levels of the top differentially expressed genes across the HIV+ and HIV- phenotypes. Gene set enrichment analysis was performed on the gene signatures from the two phenotypes. Using an existing single cell RNA-Seq prostate cancer dataset, we deconvoluted the 18 samples with CIBERSORT to approximate the cell type composition. Results: PCA highlighted no major separation between samples based on HIV status. We identified 450 significantly upregulated genes in HIV+ patients, including YAP1, MAT2A, KLF4, and ERF. We identified 409 upregulated genes in HIV- patients, including MICA, AQP2, and KRT6C. Upregulated genes found in HIV+ patients were significantly enriched in multiple immune response pathways, which is consistent with previous sequencing studies focused on HIV status. After cell type composition estimation, we found the samples to be similar in their cell type distributions - stromal and tumor cells were the largest populations, while immune cells were not well represented. Conclusions: We identified transcriptomic differences that need future studies that directly interrogate the biological pathways that affect the incidence of prostate cancer in patients living with HIV. Citation Format: Hanbing Song, Michelle Li, Jon Akutagawa, Franklin Huang. Transcriptomic analysis of prostate cancer patients living with HIV [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C041.

Evaluation of the Reliability and the Performance of Magnetic Resonance Imaging Radiomics in the Presence of Randomly Generated Irrelevant Features for Prostate Cancer

Radiomics has the potential to aid prostate cancer (PC) diagnoses and prediction by analyzing and modeling quantitative features extracted from clinical imaging. However, its reliability has been a concern, possibly due to its high-dimensional nature. This study aims to quantitatively investigate the impact of randomly generated irrelevant features on MRI radiomics feature selection, modeling, and performance by progressively adding randomly generated features. Two multiparametric-MRI radiomics PC datasets were used (dataset 1 (n = 260), dataset 2 (n = 100)). The endpoint was to differentiate pathology-confirmed clinically significant (Gleason score (GS) ≥ 7) from insignificant (GS < 7) PC. Random features were generated at 12 levels with a 10% increment from 0% to 100% and an additional 5%. Three feature selection algorithms and two classifiers were used to build the models. The area under the curve and accuracy were used to evaluate the model’s performance. Feature importance was calculated to assess features’ contributions to the models. The metrics of each model were compared using an ANOVA test with a Bonferroni correction. A slight tendency to select more random features with the increasing number of random features introduced to the datasets was observed. However, the performance of the radiomics-built models was not significantly affected, which was partially due to the higher contribution of radiomics features toward the models compared to the random features. These reliability effects also vary among datasets. In conclusion, while the inclusion of additional random features may still slightly impact the performance of the feature selection, it may not have a substantial impact on the MRI radiomics model performance.

Development and validation of a circulating tumor cells-related signature focusing on biochemical recurrence and immunotherapy response in prostate cancer

BackgroundStudies have shown that the circulating tumor cells (CTCs) play a key role for invasion and formation of distant metastases in prostate cancer (PCa). However, few CTCs-related genes (CRGs) have been developed for biochemical recurrence (BCR) prediction and clinical applications of PCa patients. Materials and methodsBioinformatics analysis with public PCa datasets were used to investigate the relationship between the differentially expressed CRGs and BCR. Lasso-COX regression analysis was used to constructed and validated a CRGs-based BCR prediction signature for PCa. Single-cell data were used to validate the expression levels of signature genes in different cell types and then explored the cell-cell communication relationships. Finally, the expression levels of signature genes were verified and the CRGs involved in immunotherapy response were further identified. ResultsThirteen CRGs were differentially expressed and closely associated with BCR in PCa. Then we constructed and validated a BCR prediction signature for PCa patients based on 3 differentially expressed CRGs (EMID1, SPP1 and UBE2C), and the signature was an independent factor to predict BCR for PCa. Single-cell data showed the specific expression patterns of the signature genes, while the SPP1 pathway plays an important role in cell-cell communication. Further analyses suggested UBE2C was highly expressed in BCR group and high expression of UBE2C had a better response for patients who received immunotherapy. Moreover, the expression levels of UBE2C in CTCs were higher than other cells and tissues, indicated that UBE2C may affect the BCR event of PCa patients through CTCs. ConclusionOur findings demonstrated that CRGs were significantly associated with BCR and immunotherapy efficacy in PCa and CRGs may influence the BCR event through CTCs.

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.

Application of Machine Learning and Deep Learning Models in Prostate Cancer Diagnosis Using Medical Images: A Systematic Review

Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, with a higher prevalence and mortality in developing countries specifically. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. Recent advances in technology and algorithms gave rise to the computer-aided diagnosis (CAD) of PCa. With the availability of medical image datasets and emerging trends in state-of-the-art machine and deep learning techniques, there has been a growth in recent related publications. Materials and Methods: In this study, we present a systematic review of PCa diagnosis with medical images using machine learning and deep learning techniques. We conducted a thorough review of the relevant studies indexed in four databases (IEEE, PubMed, Springer and ScienceDirect) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. With well-defined search terms, a total of 608 articles were identified, and 77 met the final inclusion criteria. The key elements in the included papers are presented and conclusions are drawn from them. Results: The findings show that the United States has the most research in PCa diagnosis with machine learning, Magnetic Resonance Images are the most used datasets and transfer learning is the most used method of diagnosing PCa in recent times. In addition, some available PCa datasets and some key considerations for the choice of loss function in the deep learning models are presented. The limitations and lessons learnt are discussed, and some key recommendations are made. Conclusion: The discoveries and the conclusions of this work are organized so as to enable researchers in the same domain to use this work and make crucial implementation decisions.

Persistent organic pollutants promote aggressiveness in prostate cancer.

Increasing evidence points towards a causal link between exposure to persistent organic pollutants (POPs) with increased incidence and aggressivity of various cancers. Among these POPs, dioxin and PCB-153 are widely found in our environment and represent a significant source of contamination. Dioxin exposure has already been linked to cancer such as non-Hodgkin's lymphoma, but remains to be more extensively investigated in other cancers. Potential implications of dioxin and PCB-153 in prostate cancer progression spurred us to challenge both ex vivo and in vivo models with low doses of these POPs. We found that dioxin or PCB-153 exposure increased hallmarks of growth and metastasis of prostate cancer cells ex vivo and in grafted NOD-SCID mice. Exposure induced histopathological carcinoma-like patterns in the Ptenpc-/- mice. We identified up-regulation of Acetyl-CoA Acetyltransferase-1 (ACAT1) involved in ketone bodies pathway as a potential target. Mechanistically, genetic inhibition confirmed that ACAT1 mediated dioxin effect on cell migration. Using public prostate cancer datasets, we confirmed the deregulation of ACAT1 and associated gene encoded ketone bodies pathway enzymes such as OXCT1, BDH1 and HMGCL in advanced prostate cancer. To further explore this link between dioxin and ACAT1 deregulation, we analyzed a unique prostate-tumour tissue collection from the USA veterans exposed to agent orange, known to be highly contaminated by dioxin because of industrial production. We found that ACAT1 histoscore is significantly increased in exposed patients. Our studies reveal the implication of dioxin and PCB-153 to induce a prometastatic programme in prostate tumours and identify ACAT1 deregulation as a key event in this process.

Glucocorticoid receptor-induced non-muscle caldesmon regulates metastasis in castration-resistant prostate cancer

Lethal prostate cancer (PCa) is characterized by the presence of metastases and development of resistance to therapies. Metastases form in a multi-step process enabled by dynamic cytoskeleton remodeling. An actin cytoskeleton regulating gene, CALD1, encodes a protein caldesmon (CaD). Its isoform, low-molecular-weight CaD (l-CaD), operates in non-muscle cells, supporting the function of filaments involved in force production and mechanosensing. Several factors, including glucocorticoid receptor (GR), have been identified as regulators of l-CaD in different cell types, but the regulation of l-CaD in PCa has not been defined. PCa develops resistance in response to therapeutic inhibition of androgen signaling by multiple strategies. Known strategies include androgen receptor (AR) alterations, modified steroid synthesis, and bypassing AR signaling, for example, by GR upregulation. Here, we report that in vitro downregulation of l-CaD promotes epithelial phenotype and reduces spheroid growth in 3D, which is reflected in vivo in reduced formation of metastases in zebrafish PCa xenografts. In accordance, CALD1 mRNA expression correlates with epithelial-to-mesenchymal transition (EMT) transcripts in PCa patients. We also show that CALD1 is highly co-expressed with GR in multiple PCa data sets, and GR activation upregulates l-CaD in vitro. Moreover, GR upregulation associates with increased l-CaD expression after the development of resistance to antiandrogen therapy in PCa xenograft mouse models. In summary, GR-regulated l-CaD plays a role in forming PCa metastases, being clinically relevant when antiandrogen resistance is attained by the means of bypassing AR signaling by GR upregulation.

A new type of generalized information criterion for regularization parameter selection in penalized regression with application to treatment process data

ABSTRACT We propose a new approach to select the regularization parameter using a new version of the generalized information criterion ( GIC ) in the subject of penalized regression. We prove the identifiability of bridge regression model as a prerequisite of statistical modeling. Then, we propose asymptotically efficient generalized information criterion ( AGIC ) and prove that it has asymptotic loss efficiency. Also, we verified the better performance of AGIC in comparison to the older versions of GIC . Furthermore, we propose MSE search paths to order the selected features by lasso regression based on numerical studies. The MSE search paths provide a way to cover the lack of feature ordering in lasso regression model. The performance of AGIC with other types of GIC is compared using MSE and model utility in simulation study. We exert AGIC and other criteria to analyze breast and prostate cancer and Parkinson disease datasets. The results confirm the superiority of AGIC in almost all situations.

Deep Learning Based Localisation and Segmentation of Prostate Cancer from mp-MRI Images

Prostate Cancer (PCa) is one of the most common diseases in adult males. Currently, mp-MRI imaging represents the most promising technique for screening, diagnosing, and managing this cancer. However, the multiple mp-MRI sequences' visual interpretation is not straightforward and may present crucial inter-reader variability in the diagnosis, especially when the images contradict each other. In this work, we propose a computer-aided diagnostic system to assist the radiologist inlocating and segmenting prostate lesions. As fully convolutional neural networks (UNet) have proved themselves the leading algorithm for biomedical image segmentation, we investigate their use to find PCa lesions and segment for accurate lesions contours jointly. We offer a fully automatic system via MultiResUNet, initially proposed to segment skin cancer. We trained and validated an altered version of the MultiResUnet model using an augmented Radboudumc prostate cancer dataset and obtained encouraging results. An accuracy of 98.34\% is achieved, outperforming the concurrent system based on deep architecture.

Abstract B027: Elevated mitochondrial reactive oxygen species dysregulate the tumor microenvironment in prostate cancer of African American men

Abstract African American men (AAM) are more than twice as likely to die of prostate cancer (PCa) compared to European American men (EAM). While socioeconomics, access to optimal care and life-style factors contribute towards these disparities, underlying biologic differences in AAM remain important drivers of PCa disparities. In the current work, we aimed to explore the interplay between oxidative stress and inflammatory signaling in promoting progression of PCa in AAM. Oxidative stress results from excessive reactive oxygen species (ROS) generated mainly via membrane bound oxidases, the mitochondrial electron transport chain (ETC) or peroxisomes. We performed a robust comparative genomics to determine whether there is a race dependent expression of genes related to ROS in publicly available PCa datasets. We then prospectively examined the differential gene expression of these genesets in PCa from clinically matched AAM and EAM in VANDAAM, a validation study of Decipher genomic testing in 243 men with localized PCa. We then combined computational and experimental approaches to explore the molecular mechanisms and consequences of exacerbated oxidative stress. We found that ETC complex I, III related genes are the most differentially expressed in AAM compared with EAM. To examine whether AAM vs EAM cell lines exhibits differential sensitivity to ETC modulation, we blocked key ETC enzymes using complex I inhibitor, rotenone, and complex III inhibitor, antimycin A. We used 2 types of mitochondria-specific labels that distinguish respiring and ROS-generating mitochondria. While the relative levels of respiring mitochondria remain constant, we found that antimycin A induced higher level of mitochondrial ROS in AAM PCa cell lines compared with EAM PCa cell lines. To understand the interplay between mitochondrial ETC complex I &amp; III and immune microenvironment, we computed the correlational discordance of ETC complex I &amp; III expression scores and expression of &amp;gt;1200 immune-related genes between AAM vs EAM in the VANDAAM cohort. These analyses suggest that ETC complex I &amp; III correlate with several immunosuppressive genes including the macrophage markers CD68 and CD163 and T cell exhaustion markers. In addition, intersecting discordant genes from several datasets suggests enrichment of inflammatory cytokines and type I interferon signature which we then validated using ex vivo culture of PCa tissues. In summary, we discovered a role of mitochondrial electron transport chain in regulating inflammatory circuits in PCa of AAM. Unraveling roles of mitochondria in promoting immune suppression will have significant impact on understanding the intersection between social pressures and biological stress responses in minority populations. Citation Format: Asmaa El-Kenawi, Ryan Putney, Shivanshu Awasthi, Amparo Serna, Corrado Caslini, Jasreman Dhillon, Kosj Yamoah. Elevated mitochondrial reactive oxygen species dysregulate the tumor microenvironment in prostate cancer of African American men [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr B027.

Abstract A024: Glucocorticoid receptor-induced non-muscle caldesmon regulates growth and metastasis in castration-resistant prostate cancer

Abstract Lethal prostate cancer (PCa) is characterized by the presence of metastases and the development of resistance to therapies. Metastases form in a multi-step process enabled by dynamic cytoskeleton remodeling. An actin cytoskeleton regulating gene, CALD1, encodes a protein caldesmon (CaD). Its isoform, low-molecular-weight CaD (l-CaD), operates in non-muscle cells, supporting the function of filaments involved in force production and mechanosensing. Several factors, including glucocorticoid receptor (GR), have been identified as regulators of l-CaD in different cell types, but the regulation of l-CaD in PCa has not been defined. PCa develops resistance in response to therapeutic inhibition of androgen signaling by multiple strategies. Known strategies include androgen receptor (AR) alterations, modified steroid synthesis, and bypassing AR signaling, for example, by GR upregulation. The goal of our study was to characterize the potential role of l-CaD in PCa metastases and antiandrogen therapy resistance. In this study, we extracted co-expression data of l-CaD from the largest public PCa patient data sets and identified the common transcripts between the sets. The common co-expression hits were used to recognize biological processes associated with l-CaD in the PCa context. Next, we used in vitro techniques, including 3D culture, cell viability assays, immunofluorescence staining, and Western blotting, to study the role of l-CaD in PCa with a focus on cancer hallmarks and the recognized associated biological processes. Finally, we used in vivo zebrafish and mouse xenograft models to verify the findings observed in silico in PCa patient data and in vitro in PC3, DU145, and VCaP cell lines. Here, we report that in vitro downregulation of l-CaD promotes the epithelial phenotype and reduces the spheroid growth in 3D, which is reflected in vivo in the reduced formation of metastases in a zebrafish PCa xenograft model. In accordance, CALD1 mRNA expression correlates with epithelial-to-mesenchymal transition transcripts in PCa patients. We also show that CALD1 is highly co-expressed with GR in multiple PCa data sets and that GR activation upregulates l-CaD in vitro. Moreover, GR upregulation associates with increased l-CaD expression after the development of resistance to antiandrogen therapy in PCa xenograft mouse models. In summary, GR-regulated l-CaD plays a role in forming PCa metastases, being clinically relevant when antiandrogen resistance is attained by the means of bypassing AR signaling by GR upregulation. Citation Format: Verneri Virtanen, Kreetta Paunu, Antti Kukkula, Saana Niva, Ylva Junila, Mervi Toriseva, Terhi Jokilehto, Sari Mäkelä, Riikka Huhtaniemi, Matti Poutanen, Ilkka Paatero, Maria Sundvall. Glucocorticoid receptor-induced non-muscle caldesmon regulates growth and metastasis in castration-resistant prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A024.

Abstract A048: The importance of time: Circadian rhythm disruption and prostate cancer

Abstract Circadian rhythms are evolutionarily created cycles programmed to integrate human behavior, physiology, and metabolism with the surrounding environment. The core clock machinery is composed of an autoregulatory network. The positive components of the loop are CLOCK and BMAL transcription factors, which heterodimerize to regulate expression of key circadian genes through E-box elements. CLOCK/BMAL regulate expression of cryptochrome (CRY1 and CRY2) and period (PER1, PER2, and PER3) genes, which are the negative components of the circadian loop. CRYs and PERs repress CLOCK/BMAL activity, thus conferring daily rhythmicity of cellular, metabolic, and physiological functions for homeostatic maintenance. Disruption to these rhythms, via nighttime light exposure or other forms of desynchronization, has been linked to poor health outcomes. Specifically, circadian disruption is designated as a probable carcinogen by the World Health Organization. Subsequent epidemiology research identified pilots and other shift workers have an increased risk of prostate cancer (PCa). Additional genomic studies identified multiple core clock genes (CCGs) with associated upregulation and/or downregulation in prostatic tumor tissues compared to benign samples. CCGs have also been found to impact several key oncogenic pathways including cell cycle control, apoptosis, DNA repair, and metabolic regulation. In order to further clarify the risk of an altered circadian genome, we analyzed multiple publicly available human PCa datasets to identify clinical correlates in both primary and metastatic disease. Intriguingly, we found genomic alterations in circadian rhythm were associated with increased Gleason score, cancer staging, and positive node status in primary disease. Furthermore, within a subset of patients with castrate-resistant prostate cancer (CRPC), those with concurrent altered CCGs had a decreased survival compared to patients without altered CCGs. This data further emphasizes the importance of circadian rhythm consideration in the context of malignancy and preventative medicine. With so many unknown questions and a high potential to identify subtypes of tumors with altered CCGs and aggressive disease, future studies are needed to improve therapeutic efficacy. Analysis of circadian genomic alterations with subsequent comparison of demographics including ethnicity, occupation, and sleep habits would expand the understanding of everyday variables on circadian disruption. Additionally, the comparison of short-term versus long-term circadian synchronizations should be analyzed to identify higher-risk populations. Lastly, the long-term goal should be to discover potential circadian targeted treatments or to identify ways of involvement of circadian rhythmicity in cancer treatment, including chronotherapy (i.e., time of day of treatment) to optimize the impact of cancer therapeutics to enhance patient outcome. Citation Format: Sierra T. Pence, Ayesha A. Shafi. The importance of time: Circadian rhythm disruption and prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A048.

Abstract A060: Genetic alterations induce distinct histone post-translational modifications during the transition to castration-resistant prostate cancer

Abstract Background: Epigenetic alterations can impact gene expression and have been implicated in tumorigenesis. One major process is the post-translational modification (PTM) of histone proteins governed by histone-modifying enzyme (HME) activity. There is growing evidence that the dysregulation of HMEs plays an essential role in cancer initiation and progression. In this study, we hypothesized that alterations in histone PTMs and their associated enzymes drive the development of castration-resistant prostate cancer (CRPC) from hormone-sensitive prostate cancer (HSPC). To address this, we performed an unbiased screen of histone PTMs in a series of prostate cancer (PC) PDXs before and after the transition to CRPC. Methods: Histone PTM levels were analyzed in 10 paired HSPC and CRPC PDXs using mass spectrometry. Histone PTM patterns were subcategorized based on common genetic alterations found in PC. RNA-sequencing data for the PDXs and publicly available human PC datasets were used to further characterize HMEs. Co-expression of CHD1, NSD2, and other HMEs was examined in human PC tissue microarrays (TMAs). CHD1 KO clones were generated using CRISPR and levels of histone PTMs and HMEs were assessed and quantified. Gene set enrichment analysis (GSEA) was performed to determine the pathways altered with CHD1 in CRPC PDXs. Results: A comparison of all PDX tumors revealed increased H3K14 and H4 acetylation in CRPC tumors relative to paired HSPC derivatives. We also observed distinct histone PTM patterns associated with common genetic alterations. When compared to tumors with CHD1 WT, tumors with CHD1 loss develop reduced histone PTM H3.3K36me2. CHD1 is a chromatin remodeler commonly deleted in ~ 15% of primary PCs. Screening the differential expression and activity of HMEs in these PDXs reveals that HME NSD2 correlates with CHD1. The human CRPC SU2C/PCF dataset shows a strong positive correlation between CHD1 and NSD2 mRNA (r=0.44). Human CRPC TMAs show decreased protein expression of NSD2 and its histone mark H3K36me2 is associated with reduced CHD1 levels. CHD1 KO in DU145 results in a loss of H3.3K36me2 and a 0.5-fold decrease in NSD2 expression. GSEA revealed that a reduction in the DNA repair pathway is associated with CHD1 loss. The CRPC PDXs (n=24) and human dataset SU2C/PCF 2019 showed a strong positive correlation between CHD1, NSD2, and DNA damage repair genes. Conclusions: We have identified for the first time that genetic alterations alter histone marks during the transition to castration-resistant cancer. Reduced histone H3.3K36 methylation is associated with the genetic loss of CHD1 during the transition to CRPC. The expression of NSD2 is altered in the CRPC state in a CHD1 status-dependent manner. The CHD1-NSD2 phenotype may be the target of novel therapeutic drug combinations – the subject of ongoing research. Citation Format: Tanaya A. Purohit, Joseph Gawdzik, Bing Yang, Eric A. Armstrong, Peter W. Lewis, John M. Denu, David F. Jarrard. Genetic alterations induce distinct histone post-translational modifications during the transition to castration-resistant prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A060.

Abstract A008: Elucidating the effects of the Alzheimer's disease associated gene BIN1 on cancer tumorigenesis

Abstract Purpose- BIN1 is in deleted in ~5% of all prostate cancer (PC) patients and interestingly is highly enriched in the Speckle-type POZ protein (SPOP) mutant subclass (15%) of PC. BIN1 was originally identified as a repressor of cMYC activity and our lab has shown that patients with SPOPmut;MYChigh PC have worse clinical outcomes. Our observation of BIN1 deletion in the SPOP mutant subclass leads us to hypothesize that BIN1 deletion leads to enhanced AR signaling and more aggressive PC. Experimental Procedures- We analyzed baseline protein and mRNA levels of BIN1 in 13 PC cell lines via quantitative polymerase chain reaction (qPCR) and immunoblot. To understand the role of BIN1 in PC cells, we overexpressed BIN1 and evaluated changes in cell proliferation, migration, and AR target gene expression. To elucidate the role of BIN1 in vivo prostate development, we also generated a prostate-specific knockout of BIN1 and investigated changes in prostate development and AR signaling axis over time in this mouse model. Results- Our results show that none of the 13 PC cell lines analyzed had strong protein expression at baseline, but all had detectable mRNA levels. Interestingly, the three cell lines with the highest mRNA expression were AR negative (DU-145), AR independent (LN95) or castration resistant (MDVR). We also saw a decrease in downstream AR target genes (KLK3 and NKX3.1) measured using qPCR after BIN1 overexpression in 22Rv1 cells. We further corroborated this finding by measuring KLK3 expression using a KLK3-luc reporter assay after BIN1 overexpression. BIN1 overexpression also reduced invasive activity of 22Rv1 measured using a Matrigel invasion assay system. Since BIN1 deletion was enriched in SPOP mutant PC clinical data, we evaluated changes in cell proliferation in the wildtype and mutant SPOP cell lines with and without BIN1OE and found that BIN1OEhindered growth of SPOP mutant PC cells. Analysis of the TCGA primary PC dataset illustrate that PC patients harboring BIN1 deletion had higher AR activity compared to PC patients expressing wildtype BIN1. Importantly, mice with prostate specific BIN1 deletion had larger prostate mass at 2 months of age compared to age matched controls and several mice at the 6&amp;9 month had severely enlarged prostates. Conclusion- Our findings show for the first time that BIN1 functions as an inhibitor of the AR signaling axis and deletion of BIN1 leads to higher levels of AR signaling. We also show that BIN1 deletion in mice affects prostate development and leads to enlarged prostates. These novel findings illustrate the clinical significance of BIN1 deletion in PC patients and highlights the AR-signaling axis as a potential target for PC patients with BIN1 deletion Citation Format: Collin McColl, Darlene Skapura, Elisa Echartea, Jenny Deng, Cristian Coarfa, Salma Kaochar, Brian Simons, Aleksandra Rusin. Elucidating the effects of the Alzheimer's disease associated gene BIN1 on cancer tumorigenesis [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A008.

Objective Bayesian analysis of Marshall-Olkin bivariate Weibull distribution with partial information

ABSTRACT In competing risks problem, a subset of risks is needed more attention for inferential purposes. In the objective Bayesian paradigm, reference priors enable to achieve such inferential objectives. In this article, the Marshall-Olkin bivariate Weibull distribution is considered to model the competing risks data. In the availability of partial information for some of the parameters, the reference priors are derived as per the importance of the parameters. The Dirichlet prior is taken as a conditional subjective prior and the marginal reference prior has been derived. Also, the propriety of the resulting posterior density has been proved. The Bayesian estimates of the parameters are obtained under squared error and linear-exponential loss functions. Further, the derived reference prior is used for the computation of Bayes factors or posterior odds in testing the hypothesis that the competing risks are identical. The performance of established Bayesian estimators is illustrated using the Diabetic Retinopathy Study (DRS) and Prostate Cancer data sets. Finally, the model compatibility is done for the considered data sets under Bayesian Paradigm.

A gravity inspired clustering algorithm for gene selection from high-dimensional microarray data

ABSTRACT The problem of selecting a subset of genes relevant to a particular disease classification task from high-dimensional gene microarray data has been an area of active research in the scientific community. A wide variety of methods ranging from assigning scores to genes based on their interactions with the output to the Gravity inspired clustering algorithm with the hybrid approach have been proposed. The feature sets selected by this approach have been evaluated by using the Support Vector Machine (SVM) with Leave One Out Cross Validation (LOOCV). The method has been evaluated on twelve different publicly available microarray cancer datasets and the proposed method provided good accuracy of 90.16% to the colon dataset, 98.59% to the ALL-AML dataset, 97.03% to the lung cancer dataset, 93.07% to the prostate cancer dataset, 98.41% to the meningioma dataset, 83.72% to the brain tumour dataset and has been shown to compete with existing methods.

Abstract 1448: The YAP1/TEAD and NF-Kappa B transcription factors may cooperate to mediate prostate cancer progression

Abstract Metastatic castration-resistant prostate cancer is morbid and lethal. The transcriptional coregulator YAP1 (Yes-associated protein 1) is a nuclear effector of the Hippo pathway. The Hippo pathway regulates cell growth, motility, and carcinogenesis. Here, we investigated biochemical and functional interactions between YAP1 and the nuclear factor (NF) kappa B/RELA subunit in prostate cancer cell models. We demonstrated that endogenous YAP1 and RELA form protein complexes in the cell, as revealed by co-immunoprecipitation and western blotting. Compared with control, we found that combined treatment of cells with androgen and SDF1a (stromal cell-derived factor-1 alpha) or RANKL (receptor activator of NF-kappa Β ligand) enhanced the protein-protein interaction between YAP1 and RELA, as showed by proximity ligation assay. Our confocal microscopy experiment further showed that combined SDF1a and androgen treatment augmented the YAP1 and RELA colocalization relative to single-agent. Moreover, our promoter-reporter and RNAi experiments showed that genetic silencing YAP1 or TEAD protein, a key mediator of the YAP1 transcription, significantly reduced the NF-Kappa B promoter-reporter gene activity. Also, disruption of YAP1 activity by genetic and small molecule attenuated endogenous protein-protein interaction between TEAD and RELA. Furthermore, controlled expression of MST1/STK4, a potent inhibitor of YAP1, suppressed the NF-Kappa B-promoter reporter activity. Additionally, our unbiased bioinformatics analysis of the exiting ChIP-seq (chromatin immunoprecipitation-sequencing) data identified several genes likely co-regulated by the TEAD1-4 and NF-Kappa B transcription factors. Besides, our computational analysis of the TCGA and Stand Up To Cancer (SU2C) prostate cancer data sets indicated that expression of YAP/TEAD and NF-kappa B/RELA correlates in clinical cases. These findings suggest that cooperative androgen and cytokine signaling regulates Hippo/YAP and NF-Kappa B interaction and their functions. These may have critical roles in advanced prostate cancer downstream of the Hippo pathway. Citation Format: Bekir Cinar, Marwah M. Al-Mathkour, Abdulrahman M. Dwead, Ava M. Boston, Michael S. Lewis, Carlos S. Moreno. The YAP1/TEAD and NF-Kappa B transcription factors may cooperate to mediate prostate cancer progression [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 1448.