Prostate Cancer Dataset Research Articles

Alzheimer Gene BIN1 may Simultaneously Influence Dementia Risk and Androgen Deprivation Therapy Dosage in Prostate Cancer.

Androgen deprivation therapy (ADT) is extensively used in prostate cancer. Yet the risk of impaired cognition or Alzheimer disease (AD) in men with prostate cancer receiving ADT is uncertain. Some studies of prostate cancer and ADT suggest that the risk of AD is not increased. But other studies have found an increased risk of AD and cognitive impairment. As the uncertainty about ADT and dementia might relate to the genetics of prostate cancer and AD, the authors used the Cancer Genome Atlas (TCGA) to examine the relationship in men with prostate cancer between genes implicated in AD and genes implicated in prostate cancer. The authors examined the genomics of 492 prostate cancer cases in the Genomic Data Commons (GDC) TCGA Prostate Cancer (PRAD) data set. To access and analyze the data, 2 web-based interfaces were used: (1) the UCSC Xena browser, a web-based visual integration and exploration tool for TCGA data, including clinical and phenotypic annotations; and (2) cBioportal, a web-based interface that enables integrative analysis of complex cancer genomics and clinical profiles. Co-occurrence analysis indicates that alterations in the prostate cancer gene Speckle-type POZ protein (SPOP) significantly co-occur with alterations in the AD gene BIN1 (P<0.001). The presence of somatic mutations (deleterious and missense/in frame) in SPOP deranges BIN1 gene expression. SPOP/BIN1 RNA gene expression in 492 prostate cancer specimens is significantly correlated (P<0.001). Increased expression of SPOP in 492 prostate cancers is associated with reduced survival (P=0.00275). Men receiving pharmacologic therapy had a tumor with a significantly higher Gleason score (P=0.023). Gleason score and BIN1 RNA gene expression, unit log2 (fragments per kilobase of transcript per million mapped reads upper quartile [FPKM-UQ]+1), in 499 prostate cancer specimens were significantly inversely correlated (P<0.001). BIN1 forms part of a network that interacts with the MYC oncogene, activated at the earliest phases of prostate cancer and in its position on chr8q24 linked to disease aggressiveness. Dynamic regulation of the BIN1-Tau interaction is involved in AD. BIN1 loss in AD allows phosphorylated tau to be mis-sorted to synapses, which likely alters the integrity of the postsynapse, alongside reducing the functionally important release of physiological forms of tau. Alzheimer symptoms are usually preceded by a preclinical phase that may be 16 years long. The authors suggest that the ADT dosage reflects the severity of a process that is already underway. The severity is determined by the genetics of the tumor itself, at least in part by BIN1. ADT is not causing new cases of AD. The oncologist treats higher-grade prostate cancer with more ADT, which serves as a surrogate marker for disease severity. Our analysis of TCGA data does not support the idea that ADT causes AD or dementia.

Aspartate β-hydroxylase targeting in castration-resistant prostate cancer modulates the NOTCH/HIF1α/GSK3β crosstalk.

Castration-resistant prostate cancer (CRPC) is an incurable stage of the disease. A multivariate principal component analysis on CRPC in vitro models identified aspartyl (asparaginyl) β hydrolase (ASPH) as the most relevant molecule associated with the CRPC phenotype. ASPH is overexpressed in various malignant neoplasms and catalyzes the hydroxylation of aspartyl and asparaginyl residues in the epidermal growth factor (EGF)-like domains of proteins like NOTCH receptors and ligands, enhancing cell motility, invasion and metastatic spread. Bioinformatics analyses of ASPH in prostate cancer (PCa) and CRPC datasets indicate that ASPH gene alterations have prognostic value both in PCa and CRPC patients. In CRPC cells, inhibition of ASPH expression obtained through specific small interfering RNA or culturing cells in hypoxic conditions, reduced cell proliferation, invasion and cyclin D1 expression through modulation of the NOTCH signaling. ASPH and HIF1α crosstalk, within a hydroxylation-regulated signaling pathway, might be transiently driven by the oxidative stress evidenced inside CRPC cells. In addition, increased phosphorylation of GSK3β by ASPH silencing demonstrates that ASPH regulates GSK3β activity inhibiting its interactions with upstream kinases. These findings demonstrate the critical involvement of ASPH in CRPC development and may represent an attractive molecular target for therapy.

Abstract B078: FOXA1 genetic alterations in Whites versus Blacks or African Americans in breast and prostate cancer

Abstract Forkhead box A1 (FOXA1) is a pioneer factor for the nuclear hormone receptors: estrogen receptor and androgen receptor. FOXA1 plays a major role inducing endocrine resistance in breast cancer (BC) and prostate cancer (PC), the two most prevalent cancers in the United States. In this study, we investigated FOXA1 gene alterations across different race and ethnicity using the TCGA PanCan Atlas dataset for BC and PC patients. The BC and PC dataset included 1084 and 494 patient samples, respectively, profiled for copy number alterations (CNA), gene expression, and mutations. In the BC dataset, the samples were from 877 non-Hispanic (81%), 38 Hispanic (3.5%) and 169 patients with no ethnicity data (15.5%). Among them, the majority of patients were White (n=751, 69.3%) followed by Black/African American (AA) (n=182, 16.8%), Asian (n=60, 5.5%), and American Indian or Alaska Native (n=1, 0.09%). Ninety BC patients (8.3%) had no race information. The PC dataset included 152 non-Hispanic (30.8%), 0 Hispanic, and 342 patients with no ethnicity data (69.2%). The samples obtained for the PC study were from White (n=147, 29.8%), Black/AA (n=7, 1.4%), and Asian (n=2, 0.4%) patients. Majority of the PC patients had no racial information (n=338, 68.4%) recorded. In the BC dataset, the incidence of FOXA1 alterations was 16/1070 (1.5%) CNA 24/1082 (2.2%) high mRNA expression, and 31/1066 (2.9%) mutations. Only amplifications were found within the BC patients. In the PC dataset, there were 15/489 (3.1%) CNA, 16/493 (3.2%) high mRNA expression, and 28/494 (5.7%) mutations reported in FOXA1. Deep deletion was found in one of the PC patients while the rest had amplifications. Due to insufficient numbers of Hispanic patients in the datasets, we compared the incidence of various FOXA1 alterations in White vs. Black/AA population using Fisher’s exact test. Only FOXA1 mutation rate was significantly higher (p =0.03) in Blacks/AA (2/7, 28.6%) compared to Whites (5/147, 3.4%) in PC, but not in the BC dataset. Comparing the separate results of the FOXA1 CNA and gene overexpression White vs. Black/AA patients were statistically significant in both BC and PC datasets. A majority of FOXA1 mutations were missense mutations with a few frame shifts in BC and PC. The missense mutation reported in both BC and PC datasets were D226G, D226N, and H247Y. Additional studies are necessary to understand the functional significance of these mutations on the development of cancer. Complete and larger datasets that include the race and ethnicity information from diverse group of patients as well as tumor molecular subtyping are also needed for the assessment of the mechanism of health disparity in BC and PC in minority population in the United States. Citation Format: Jennifer Torres, Hariprasad Thangavel, Xiaoyong Fu, Rachel Schiff, Meghana V. Trivedi. FOXA1 genetic alterations in Whites versus Blacks or African Americans in breast and prostate cancer [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr B078.

Abstract C023: Annexin A2 expression level correlates with adverse pathology and disease progression in prostate cancer

Abstract Background: Annexin A2 (ANXA2) is absent or expressed focally in the prostate epithelium of moderately differentiated tumors but is highly expressed in a subset of poorly differentiated tumors. We observed earlier that ANXA2 transcription is negatively regulated by ERG in prostate cancer (CaP) cells. Here we examined whether Annexin A2 expression in prostate tumors correlates with aggressive CaP or disease progression, whether prostate tumors of Caucasian American (CA) and African American (AA) patients have distinct Annexin A2 expression profiles, and if the levels of Annexin A2 in sera is correlated to its expression in tumor tissues. We also speculated on the underlying mechanism likely to regulate ANXA2 expression in the absence of ERG. Methods: We evaluated Annexin A2 and ERG expression in index tumors on whole-mounted prostate sections and tissue microarrays derived from radical prostatectomies of 176 patients by immunohistochemistry. Both cohorts were matched for post RP follow-up, pathologic stage, and race. ERG and Annexin A2 expression status in tumors were correlated to Gleason score, biochemical recurrence and metastasis. We measured, by enzyme-linked immunosorbent assay (ELISA), Annexin A2 proteins levels in sera of 94 controls and 222 patients, matched for race and Gleason sum. Correlation between promoter methylation and gene expression profiles of ANXA2 from TCGA studies were also examined. Results: A significant association was found between ANXA2(+) index tumors and higher Gleason Grade Group (GG) 4 and 5, and worse pathologic stage (pT 3-4). Moreover, ERG(-)/ANXA2(+) tumors were found to be associated with Gleason GG 4 and 5, and ERG(+)/ANXA2(-) tumors with Gleason GG 1 and 2. However, unlike ERG expression, which is more prevalent in prostate tumors of CA men, Annexin A2 expression was not associated with race. Trends toward earlier occurrence of BCR and worse metastasis-free survival were observed among ANXA2(+) cases. Significantly higher levels of secreted Annexin A2 were detected in the sera healthy controls than in CaP patients. Analysis of TCGA prostate cancer dataset revealed an inverse association between methylation of the ANXA2 promoter region and ANXA2 expression in prostate tumor tissues. Conclusion: The association between high Annexin A2 expression in prostate tumors with higher grade and stage suggests a potential for its use as a biomarker for aggressive CaP and to stratify patients after radical prostatectomy. Citation Format: Shyh-Han Tan, Denise Young, Wei Huang, Amina Ali, Lakshmi Ravindranath, Kurt Christen, Kevin Babcock, Huai-Ching Kuo, Yongmei Chen, Jacob Kagan, Sudhir Srivastava, Albert Dobi, Srinivasan Alagarsamy, Gyorgy Petrovics, David G. McLeod, Jennifer Cullen, Inger L. Rosner, Isabell A. Sesterhenn, Shiv Srivastava. Annexin A2 expression level correlates with adverse pathology and disease progression in prostate cancer [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr C023.

PD59-02 HIGH DIMENSIONAL PROFILING OF THE IMMUNE MICROENVIRONMENT IN PROSTATE CANCER REVEALED NOVEL TUMOR-DRIVEN ALTERATIONS IN INFILTRATING SUBSETS

You have accessJournal of UrologyProstate Cancer: Basic Research & Pathophysiology III (PD59)1 Apr 2020PD59-02 HIGH DIMENSIONAL PROFILING OF THE IMMUNE MICROENVIRONMENT IN PROSTATE CANCER REVEALED NOVEL TUMOR-DRIVEN ALTERATIONS IN INFILTRATING SUBSETS Massimo Lazzeri*, Michela Masetti, Federica Portale, Roberta Carriero, Bianca Partini, Andrea Ponzetta, Sebastien Jaillon, Piergiuseppe Colombo, Grazia Elefante, Alberto Saita, Paolo Casale, Clelia Peano, Paolo Kunderfranco, Javier Cibella, Giorgio Guazzoni, and Diletta Di Mitri Massimo Lazzeri*Massimo Lazzeri* More articles by this author , Michela MasettiMichela Masetti More articles by this author , Federica PortaleFederica Portale More articles by this author , Roberta CarrieroRoberta Carriero More articles by this author , Bianca PartiniBianca Partini More articles by this author , Andrea PonzettaAndrea Ponzetta More articles by this author , Sebastien JaillonSebastien Jaillon More articles by this author , Piergiuseppe ColomboPiergiuseppe Colombo More articles by this author , Grazia ElefanteGrazia Elefante More articles by this author , Alberto SaitaAlberto Saita More articles by this author , Paolo CasalePaolo Casale More articles by this author , Clelia PeanoClelia Peano More articles by this author , Paolo KunderfrancoPaolo Kunderfranco More articles by this author , Javier CibellaJavier Cibella More articles by this author , Giorgio GuazzoniGiorgio Guazzoni More articles by this author , and Diletta Di MitriDiletta Di Mitri More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000969.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate adenocarcinoma is one of the most common cancers in the male world population and the incidence of this type of tumor has undergone a significant increase in the last decades. In recent years, research in this field has led to a greater awareness of the clinical and molecular characteristics of this tumor type. As a result, new therapeutic options have emerged to date, mainly aimed at manipulating the immune system. Here we investigated the profile of the immune composition in PCa by single cell analysis. The primary endpoint was to obtain a comprehensive profiling of the immune composition in PCa patients. Secondary endpoint was the identification of tumor-driven immune changes in the immune microenvironment that may be used in the diagnosis and prediction of progression of prostate cancer. METHODS: We conduct a single cell immune profiling strategy based on single cell RNA sequencing (scRNAseq) and multiparametric flow cytometry (FACS) in order to dissect the immune landscape of prostate cancer, both at tumor site and periphery. CD45+ immune cells infiltrating tumoral and adjacent tissues were isolated from patients with PCa and analyzed by single cell sequencing. In addition both peripheral blood and fresh tumoral tissue were collected and analyzed by FACS. RESULTS: For the scRNAseq the study included 3 patients with clinically significant prostate cancer who underwent prostatectomy (GS 4+5). Flow cytometry analysis was then extended to 45 patients bearing prostate cancer at different progression stage. Our analysis revealed a significant modulation in the abundance of several immune subsets infiltrating the tumoral tissue when compared to the non-tumoral. Furthermore we identified a subset of tumor infiltrating macrophages showing an altered gene expression profile when compared with macrophages infiltrating the non-tumoral tissue. Importantly we derived a genetic signature from this cluster of TAMs that resulted to be associated with cancer progression in human prostate cancer datasets. CONCLUSIONS: The results of our study provided a comprehensive analysis of the immune landscape in prostate cancer patients and identified tumor-driven immune changes that may be useful to develop new therapeutic strategies and for risk-stratification and decision-making process of patients. Source of Funding: AIRC foundation © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1205-e1205 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Massimo Lazzeri* More articles by this author Michela Masetti More articles by this author Federica Portale More articles by this author Roberta Carriero More articles by this author Bianca Partini More articles by this author Andrea Ponzetta More articles by this author Sebastien Jaillon More articles by this author Piergiuseppe Colombo More articles by this author Grazia Elefante More articles by this author Alberto Saita More articles by this author Paolo Casale More articles by this author Clelia Peano More articles by this author Paolo Kunderfranco More articles by this author Javier Cibella More articles by this author Giorgio Guazzoni More articles by this author Diletta Di Mitri More articles by this author Expand All Advertisement PDF downloadLoading ...

Active vitamin D induces gene-specific hypomethylation in prostate cancer cells developing vitamin D resistance.

Prostate cancer (PCa) is a leading cause of cancer death in men. Despite the antiproliferative effects of 1α,25-dihydroxyvitamin D3 [1,25(OH)2D3] on PCa, accumulating evidence indicates that 1,25(OH)2D3 promotes cancer progression by increasing genome plasticity. Our investigation of epigenetic changes associated with vitamin D insensitivity found that 1,25(OH)2D3 treatment reduced the expression levels and activities of DNA methyltransferases 1 and 3B (DNMT1 and DNMT3B, respectively). In silico analysis and reporter assay confirmed that 1,25(OH)2D3 downregulated transcriptional activation of the DNMT3B promoter and upregulated microRNAs targeting the 3'-untranslated regions of DNMT3B. We then profiled DNA methylation in the vitamin D-resistant PC-3 cells and a resistant PCa cell model generated by long-term 1,25(OH)2D3 exposure. Several candidate genes were found to be hypomethylated and overexpressed in vitamin D-resistant PCa cells compared with vitamin D-sensitive cells. Most of the identified genes were associated with mammalian target of rapamycin (mTOR) signaling activation, which is known to promote cancer progression. Among them, we found that inhibition of ribosomal protein S6 kinase A1 (RPS6KA1) promoted vitamin D sensitivity in PC-3 cells. Furthermore, The Cancer Genome Atlas (TCGA) prostate cancer data set demonstrated that midline 1 (MID1) expression is positively correlated with tumor stage. Overall, our study reveals an inhibitory mechanism of 1,25(OH)2D3 on DNMT3B, which may contribute to vitamin D resistance in PCa.

Ensemble-based active learning using fuzzy-rough approach for cancer sample classification

Background and Objective: Classification of cancer from gene expression data is one of the major research areas in the field of machine learning and medical science. Generally, conventional supervised methods are not able to produce desired classification accuracy due to inadequate training samples present in gene expression data to train the system. Ensemble-based active learning technique in this situation can be effective as it determines few informative samples by all the base classifiers and ensemble the decisions of all the base classifiers to get the most informative samples. Most informative samples are labeled by the subject experts and those are added to the training set, which can improve the classification accuracy.Method: We propose a novel ensemble-based active learning using fuzzy-rough approach for cancer sample classification from microarray gene expression data. The proposed method is able to deal with the uncertainty, overlap and indiscernibility usually present in the subtype classes of the gene expression data and can improve the accuracy of the individual base classifier in presence of limited training samples.Results: The proposed method is validated using eight microarray gene expression datasets. The performance of the proposed method in terms of classification accuracy, precision, recall, F1-measures and kappa is compared with six other methods. The improvements in accuracy achieved by the proposed method compared to its nearest competitive methods are 2.96%, 9.34%, 0.93%, 3.69%, 7.2% and 4.53% respectively for Colon cancer, Prostate cancer, SRBCT, Ovarian cancer, DLBCL and Central nervous system datasets. Results of the paired t-test justify the statistical relevance of the results in favor of the proposed method for most of the datasets.Conclusion: The proposed method is an effective general purpose ensemble-based active learning adopting the fuzzy-rough concept and therefore can be applied for other classification problem in future.

CNV Radar: an improved method for somatic copy number alteration characterization in oncology

BackgroundCancer associated copy number variation (CNV) events provide important information for identifying patient subgroups and suggesting treatment strategies. Technical and logistical issues, however, make it challenging to accurately detect abnormal copy number events in a cost-effective manner in clinical studies.ResultsHere we present CNV Radar, a software tool that utilizes next-generation sequencing read depth information and variant allele frequency patterns, to infer the true copy number status of genes and genomic regions from whole exome sequencing data. Evaluation of CNV Radar in a public multiple myeloma dataset demonstrated that CNV Radar was able to detect a variety of CNVs associated with risk of progression, and we observed > 70% concordance with fluorescence in situ hybridization (FISH) results. Compared to other CNV callers, CNV Radar showed high sensitivity and specificity. Similar results were observed when comparing CNV Radar calls to single nucleotide polymorphism array results from acute myeloid leukemia and prostate cancer datasets available on TCGA. More importantly, CNV Radar demonstrated its utility in the clinical trial setting: in POLLUX and CASTOR, two phase 3 studies in patients with relapsed or refractory multiple myeloma, we observed a high concordance rate with FISH for del17p, a risk defining CNV event (88% in POLLUX and 90% in CASTOR), therefore allowing for efficacy assessments in clinically relevant disease subgroups. Our case studies also showed that CNV Radar is capable of detecting abnormalities such as copy-neutral loss of heterozygosity that elude other approaches.ConclusionsWe demonstrated that CNV Radar is more sensitive than other CNV detection methods, accurately detects clinically important cytogenetic events, and allows for further interrogation of novel disease biology. Overall, CNV Radar exhibited high concordance with standard methods such as FISH, and its success in the POLLUX and CASTOR clinical trials demonstrated its potential utility for informing clinical and therapeutic decisions.

Mutational and transcriptomic landscapes of a rare human prostate basal cell carcinoma.

As a rare subtype of prostate carcinoma, basal cell carcinoma (BCC) has not been studied extensively and thus lacks systematic molecular characterization. Here, we applied single-cell genomic amplification and RNA-Seq to a specimen of human prostate BCC (CK34βE12+ /P63+ /PAP- /PSA- ). The mutational landscape was obtained via whole exome sequencing of the amplification mixture of 49 single cells, and the transcriptomes of 69 single cells were also obtained. The five putative driver genes mutated in BCC are CASC5, NUTM1, PTPRC, KMT2C, and TBX3, and the top three nucleotide substitutions are C>T, T>C, and C>A, similar to common prostate cancer. The distribution of the variant allele frequency values indicated that these single cells are from the same tumor clone. The 69 single cells were clustered into tumor, stromal, and immune cells based on their global transcriptomic profiles. The tumor cells specifically express basal cell markers like KRT5, KRT14, and KRT23 and epithelial markers EPCAM, CDH1, and CD24. The transcription factor covariance network analysis showed that the BCC tumor cells have distinct regulatory networks. By comparison with current prostate cancer datasets, we found that some of the bulk samples exhibit basal cell signatures. Interestingly, at single-cell resolution the gene expression patterns of prostate BCC tumor cells show uniqueness compared with that of common prostate cancer-derived circulating tumor cells. This study, for the first time, discloses the comprehensive mutational and transcriptomic landscapes of prostate BCC, which lays a foundation for the understanding of its tumorigenesis mechanism and provides new insights into prostate cancers in general.

Least squares support vector machines with fast leave-one-out AUC optimization on imbalanced prostate cancer data

Quite often, the available pre-biopsy data for early prostate cancer detection are imbalanced. When the least squares support vector machines (LS-SVMs) are applied to such scenarios, it becomes naturally desirable for us to introduce the well-known AUC performance index into the LS-SVMs framework to avoid bias towards majority classes. However, this may result in high computational complexity for the minimal leave-one-out error. In this paper, by introducing the parameter $$\lambda $$ , a generalized Area under the ROC curve (AUC) performance index $$R_{AUCLS}$$ is developed to theoretically guarantee that $$R_{AUCLS}$$ linearly depends on the classical AUC performance index $$R_{AUC}$$ . Based on both $$R_{AUCLS}$$ and the classical LS-SVM, a new AUC-based least squares support vector machine called AUC-LS-SVMs is proposed for directly and effectively classifying imbalanced prostate cancer data. The distinctive advantage of the proposed classifier AUC-LS-SVMs exists in that it can achieve the minimal leave-one-out error by quickly optimizing the parameter $$\lambda $$ in $$R_{AUCLS}$$ using the proposed fast leave-one-out cross validation (LOOCV) strategy. The proposed classifier is first evaluated using generic public datasets. Further experiments are then conducted on a real-world prostate cancer dataset to demonstrate the efficacy of our proposed classifier for early prostate cancer detection.

Assessment of inherited DNA repair defects in African-American and Caucasian men with prostate cancer.

185 Background: Pathogenic inherited DNA repair defects are well described in Caucasian American (CA) men with prostate cancer (PCa) but comparative data with large subsets of African-American (AA) men are limited. Methods: Herein we examine germline pathogenic/likely pathogenic defects (P/LP) in 14 well annotated genes involved in DNA repair pathways in a large Invitae Corporation (San Francisco) lab-derived dataset of men with PCa. Frequency of P/LP alteration were directly compared in AA men and CA men. Reliable data on metastatic rates, family history, and Gleason score are missing. These data are derived from “real-world” experiences using both academic and private practice sites in accordance with physician ordering practices. The number of men assessed for individual genes aberrations varied. The 14 genes were assessed in 148-213 AA men and 1760-2488 CA men. Results: Comparative data using Invitae assays have lower levels of P/LP DNA repair alterations (as a whole) in the assays from AA men as compared to CA men (7.5% vs 13.9%, P=0.008). The P/LP variants in AA men were BRCA2 (6/214, 2.8%), BRCA1 (3/213, 1.4%), PALB2 (2/182, 1.1%), ATM (2/206, 1.0%), RAD51C (1/148, 0.68%), CHEK2 (1/207, 0.48%), and PMS2 (1/212, 0.47%). No AA men in this data set had P/LP mutations in BARD1, BRIP1, MLH1, MSH2, MSH6, NBN, or RAD51D. Lower frequency of P/LP findings in non-BRCA alterations were notably different in AA men as compared to CA men (0.48% vs 3.11%, P=0.03). The frequency of BRCA P/LP variants was not distinct between the AA and CA men. Additional comparisons show that Invitae CA data set is similar to a well cited previously published metastatic PCa data set. Conclusions: Taken together these data from comparable commercial assays indicate that AA men with PCa in this large dataset have lower rates of germline DNA repair P/LP alterations as compared to CA men. These findings are driven by lower rates of P/LP alterations in the non-BRCA genes.

Fraction genome altered (FGA) to regulate both cell autonomous and non-cell autonomous functions in prostate cancer and its effect on prostate cancer aggressiveness.

347 Background: Our ability to distinguish lethal from non-lethal forms of prostate cancer (PC) is limited. Given prostate tumors’ genetic heterogeneity, is it unlikely that a single somatic variant is prognostic. Herein we investigated fraction of genome altered (FGA; percentage of copy number altered chromosome regions out of measured regions; cBioportal) and tumor mutational count (TMC; number of mutational events per case) harbored by the primary tumor as two tumor-specific factors posited to influence disease aggressiveness or responsiveness to certain therapeutic agents. Methods: We used the TCGA data (n= 490 primary PC) and MSKCC-IMPACT (n=717, Zehir et al 2017) PC datasets to analyze the correlation between FGA and TMC in PC. GSEA was performed with transcriptomes used to identify signaling pathways associated with these two measures. We then categorized 490 primary PC patients from TCGA dataset into 4 groups based on FGA and TMC levels (based on the median values) to assess associations with outcomes. Results: Primary PC patients who harbor FGAhighTMClow exhibited shorter disease-free survival (High Risk). We observed attenuation of the androgen signaling pathway and induction of cell proliferation pathways associated with this aggressive form of disease. We used results from CIBERSORT algorithm and deep learning methods of TCGA data and observed that quantities of tumor infiltrating lymphocytes was higher in the FGAhighTMClow group (p=0.038). However, we also observed significantly reduced immune effector signaling-pathway signaling in this high-risk FGAhighTMClow group suggesting the presence of immune-suppressive networks in primary disease associated with a high risk of progression. Conclusions: A greater understanding of molecular features of aggressive primary PC (FGA/TMC) will be important in developing management strategies. Based on our preliminary analyses, we hypothesize that patients whose primary PC harbors FGAhighTMClow have a higher likelihood of aggressive disease due to their impact on PC cell proliferation and dedifferentiation (cell autonomous), and subdued immune responses (non-cell-autonomous).

Optimized Feature Selection and Classification in Microarray Gene Expression Cancer Data

Cancer classification can be performed by Microarray Gene Expression data which comprises of thousands of genes and small number of samples. Gene expression data is efficient method for finding which gene causes cancer in human being. In this work, formulate hybrid model containing filter approach, the wrapper approach and partial least square method that used to select the optimized features form the high dimensional dataset. Filter approach uses mutual information, wrapper approach uses genetic algorithm and partial least square method uses t-score estimation for feature selection mechanism. With the reduced dimension of features, classification is performed on the reduced data set to classify the samples into normal or abnormal. To attain the improved classification accuracy both the feature selection and the dimension reduction is performed. By using feature selection technique most possibly cancer related genes from huge microarray gene expression data are selected. The trained classifier model is tested with benchmark cancer dataset which consists of colon cancer dataset comprises 62 samples, 40 of which are tumor and 22 are normal with 2000 genes and the prostate cancer dataset comprises 136 samples, 59 of which is normal and 75 are tumor with 12,600 genes. The proposed model achieves accuracy of 92.7% for wrapper approach with optimal features and also outperforms other two approaches with respect to accuracy and time complexity

Sulfiredoxin as a Potential Therapeutic Target for Advanced and Metastatic Prostate Cancer.

The incidence of prostate cancer (PCa) is increasing, and it is currently the second most frequent cause of death by cancer in men. Despite advancements in cancer therapies, new therapeutic approaches are still needed for treatment-refractory advanced metastatic PCa. Cross-species analysis presents a robust strategy for the discovery of new potential therapeutic targets. This strategy involves the integration of genomic data from genetically engineered mouse models (GEMMs) and human PCa datasets. Considering the role of antioxidant pathways in tumor initiation and progression, we searched oxidative stress-related genes for a potential therapeutic target for PCa. First, we analyzed RNA-sequencing data from Pb-Cre4; Ptenf/f mice and discovered an increase in sulfiredoxin (Srxn1) mRNA expression in high-grade prostatic intraepithelial neoplasia (PIN), well-differentiated adenocarcinoma (medium-stage tumors), and poor-differentiated adenocarcinoma (advanced-stage prostate tumors). The increase of SRXN1 protein expression was confirmed by immunohistochemistry in mouse prostate tumor paraffin samples. Analyses of human databases and prostate tissue microarrays demonstrated that SRXN1 is overexpressed in a subset of high-grade prostate tumors and correlates with aggressive PCa with worse prognosis and decreased survival. Analyses in vitro showed that SRXN1 expression is also higher in most PCa cell lines compared to normal cell lines. Furthermore, siRNA-mediated downregulation of SRXN1 led to decreased viability of PCa cells LNCaP. In conclusion, we identified the antioxidant enzyme SRXN1 as a potential therapeutic target for PCa. Our results suggest that the use of specific SRXN1 inhibitors may be an effective strategy for the adjuvant treatment of castration-resistant PCa with SRXN1 overexpression.

Kesirli Polinomlar ile Cox Regresyon Modeli: Prostat Kanseri Veri Kümesi Üzerine Bir Uygulama

Kesirli Polinomlar ile Cox Regresyon Modeli: Prostat Kanseri Veri Kümesi Üzerine Bir Uygulama

Co-occurrent Alterations of Alzheimer's Genes and Prostate Cancer Genes in Prostate Cancer.

Androgen deprivation therapy (ADT) is extensively employed in treatment of prostate cancer. Some studies have found increased risk of Alzheimer's disease and cognitive impairment in patients treated with ADT. Since the uncertainty about ADT and dementia might relate to the genetics of prostate cancer and Alzheimer's disease, we used the Cancer Genome Atlas (TCGA) to examine the relationship between genes implicated in Alzheimer's disease and genes implicated in prostate cancer in men with prostate cancer. The genomics of 492 prostate cancer cases in the Genomic Data Commons TCGA Prostate Cancer data set were examined. Alterations (mutation, amplification or deletion) in prostate cancer gene speckle-type POZ protein (SPOP) significantly co-occurred with alterations in Alzheimer's disease gene bridging integrator-1 (BIN1). Alterations in prostate cancer gene spectrin alpha 1 (SPTA1) significantly co-occurred with alterations in Alzheimer's disease gene CD2-associated protein (CD2AP) (p<0.001). The presence of somatic mutations (deleterious and missense/in frame) in SPOP disturbs BIN1 gene expression. SPOP and BIN1 RNA expression in 492 prostate cancer specimens was significantly positively correlated (p<0.001). Increased expression of SPOP in 492 cases of prostate cancer was associated with reduced survival (p=0.00275). BIN1 forms part of a network that interacts with the MYC oncogene, which is activated at the earliest phases of prostate cancer and is linked to disease aggressiveness. Men receiving ADT had tumor with a significantly higher Gleason score (p=0.023). Gleason score and BIN1 RNA expression in 499 prostate cancer specimens were significantly correlated (p<0.001). The severity of prostate cancer is determined by the genetics of the tumor itself, possibly at least in part by the interactions of SPOP/BIN1, MYC/BIN1 and SPTA1/CD2AP. Oncologists treats higher grade prostate cancer with more ADT, which serves as a surrogate marker for disease severity. A weakness of our study is that we did not examine Alzheimer's disease or dementia at all in patients with cancer, only co-occurrence of genetic alterations. Nevertheless, our analysis of TCGA data does not support the idea that ADT causes Alzheimer's disease or dementia.

Usage of ensemble model and genetic algorithm in pipeline for feature selection from cancer microarray data

This paper proposes an ensemble of feature selection techniques with genetic algorithm (GA) in pipeline for selecting features from microarray data. The ensemble is a combination of filter and wrapper-based feature selection methods. In addition, GA in pipeline has been used for refinement of ensemble output to produce a non-local set of robust feature subset. An extensive computational experiment has been carried out on a prostate cancer dataset for validation of the method and comparison with group genetic algorithm (GGA). Finally, the resultant feature subsets of GA, GGA, and other constituents of the ensemble in standalone mode have been used for uncovering frequent patterns based on Apriori and FP-growth. The experimental study confirms that the proposed method gives classification accuracy of 100%, 98.34%, 98.02%, and 97% based on an ensemble of classifiers w. r. t. 5, 10, 15, and 20 features, respectively, vis-a-vis 92.34%, 90.34%, 86.54%, and 87.21% of GGA.

Usage of ensemble model and genetic algorithm in pipeline for feature selection from cancer microarray data

This paper proposes an ensemble of feature selection techniques with genetic algorithm (GA) in pipeline for selecting features from microarray data. The ensemble is a combination of filter and wrapper-based feature selection methods. In addition, GA in pipeline has been used for refinement of ensemble output to produce a non-local set of robust feature subset. An extensive computational experiment has been carried out on a prostate cancer dataset for validation of the method and comparison with group genetic algorithm (GGA). Finally, the resultant feature subsets of GA, GGA, and other constituents of the ensemble in standalone mode have been used for uncovering frequent patterns based on Apriori and FP-growth. The experimental study confirms that the proposed method gives classification accuracy of 100%, 98.34%, 98.02%, and 97% based on an ensemble of classifiers w. r. t. 5, 10, 15, and 20 features, respectively, vis-à-vis 92.34%, 90.34%, 86.54%, and 87.21% of GGA.

Cluster validity indices for mixture hazards regression models.

In the analysis of survival data, the problems of competing risks arise frequently in medical applications where individuals fail from multiple causes. Semiparametric mixture regression models have become a prominent approach in competing risks analysis due to their flexibility and easy interpretation of resultant estimates. The literature presents several semiparametric methods on the estimations for mixture Cox proportional hazards models, but fewer works appear on the determination of the number of model components and the estimation of baseline hazard functions using kernel approaches. These two issues are important because both incorrect number of components and inappropriate baseline functions can lead to insufficient estimates of mixture Cox hazard models. This research thus proposes four validity indices to select the optimal number of model components based on the posterior probabilities and residuals resulting from the application of an EM-based algorithm on a mixture Cox regression model. We also introduce a kernel approach to produce a smooth estimate of the baseline hazard function in a mixture model. The effectiveness and the preference of the proposed cluster indices are demonstrated through a simulation study. An analysis on a prostate cancer dataset illustrates the practical use of the proposed method.

PGC1α Suppresses Prostate Cancer Cell Invasion through ERRα Transcriptional Control.

The PPARγ coactivator 1 alpha (PGC1α) is a prostate tumor suppressor that controls the balance between anabolism and catabolism. PGC1A downregulation in prostate cancer is causally associated with the development of metastasis. Here we show that the transcriptional complex formed by PGC1α and estrogen-related receptor 1 alpha (ERRα) controls the aggressive properties of prostate cancer cells. PGC1α expression significantly decreased migration and invasion of various prostate cancer cell lines. This phenotype was consistent with remarkable cytoskeletal remodeling and inhibition of integrin alpha 1 and beta 4 expression, both in vitro and in vivo. CRISPR/Cas9-based deletion of ERRα suppressed PGC1α regulation of cytoskeletal organization and invasiveness. Mechanistically, PGC1α expression decreased MYC levels and activity prior to inhibition of invasiveness. In addition, PGC1α and ERRα associated at the MYC promoter, supporting the inhibitory activity PGC1α. The inverse correlation between PGC1α-ERRα activity and MYC levels was corroborated in multiple prostate cancer datasets. Altogether, these results support that PGC1α-ERRα functions as a tumor-suppressive transcriptional complex through the regulation of metabolic and signaling events. SIGNIFICANCE: These findings describe how downregulation of the prostate tumor suppressor PGC1 drives invasiveness and migration of prostate cancer cells.