SATB1, genomic instability and Gleason grading constitute a novel risk score for prostate cancer.
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ABSTRACT:
SUBMITTER: Dumke C
PROVIDER: S-EPMC8712510 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Screening of differentially expressed genes between benign and prostate tumors with respect to different prostate cancer gleason score 6 and 8 Keywords: disease subtype analysis
2010-03-31 | GSE15484 | GEO
Project description:Despite revisions in 2005 and 2014, the Gleason prostate cancer (PCa) grading system still has major deficiencies. Combining of Gleason scores into a three-tiered grouping (6, 7, 8-10) is used most frequently for prognostic and therapeutic purposes. The lowest score, assigned 6, may be misunderstood as a cancer in the middle of the grading scale, and 3+4=7 and 4+3=7 are often considered the same prognostic group.To verify that a new grading system accurately produces a smaller number of grades with the most significant prognostic differences, using multi-institutional and multimodal therapy data.Between 2005 and 2014, 20,845 consecutive men were treated by radical prostatectomy at five academic institutions; 5501 men were treated with radiotherapy at two academic institutions.Outcome was based on biochemical recurrence (BCR). The log-rank test assessed univariable differences in BCR by Gleason score. Separate univariable and multivariable Cox proportional hazards used four possible categorizations of Gleason scores.In the surgery cohort, we found large differences in recurrence rates between both Gleason 3+4 versus 4+3 and Gleason 8 versus 9. The hazard ratios relative to Gleason score 6 were 1.9, 5.1, 8.0, and 11.7 for Gleason scores 3+4, 4+3, 8, and 9-10, respectively. These differences were attenuated in the radiotherapy cohort as a whole due to increased adjuvant or neoadjuvant hormones for patients with high-grade disease but were clearly seen in patients undergoing radiotherapy only. A five-grade group system had the highest prognostic discrimination for all cohorts on both univariable and multivariable analysis. The major limitation was the unavoidable use of prostate-specific antigen BCR as an end point as opposed to cancer-related death.The new PCa grading system has these benefits: more accurate grade stratification than current systems, simplified grading system of five grades, and lowest grade is 1, as opposed to 6, with the potential to reduce overtreatment of PCa.We looked at outcomes for prostate cancer (PCa) treated with radical prostatectomy or radiation therapy and validated a new grading system with more accurate grade stratification than current systems, including a simplified grading system of five grades and a lowest grade is 1, as opposed to 6, with the potential to reduce overtreatment of PCa.
| S-EPMC5002992 | biostudies-literature
Project description:To investigate potential markers of the prostate cancer (PCa) Gleason score (GS), genetic arrays in 841 PCa patients were conducted followed by functional validation in PCa cell lines. A total of 841 PCa patients who received radical prostatectomy (RP) from November 2003 to July 2019 were enrolled. HumanExome BeadChip 12v1-1 (Illumina, Inc.; San Diego, CA, USA) exomic arrays were performed on RP tissue samples. Unconditional logistic regression was used to calculate odds ratios to generate estimates of the relative risk of pathologic GS (≥8); SNPs with the highest association were selected and validated using PCa cell lines (PC3, LNCaP, 22Rv1 and DU145). Following transfection with target-gene siRNA, assays for cell viability, wound healing, and transwell invasion were performed. Mean age of enrolled subjects was 66.34 years and median PSA was 8.43 ng/mL. After RP, 122 patients (14.5%) had pathological Gleason scores ≥8. The results from genotyping with 242,186 SNPs by exomic array revealed that 4 SNPs (rs200944490, rs117555780, rs34625170, and rs61754877) were significantly associated with high pathological GS (≥8) within cut-off level to p < 10-5. The most highly associated rs200944490 in ARRDC4 (p = 1.39 × 10-6) and rs117555780 in UBXN1 (p = 2.92 × 10-5) were selected for further validation. The knockdown of UBXN1 and ARRDC4 led to significantly reduced cell proliferation and suppressed migration and invasiveness in PCa cell lines. Epithelial mesenchymal transition (EMT) markers were significantly down-regulated in si-ARRDC4 and si-UBXN1-transfected cells. The expression levels of PI3K-phosphorylation and Akt phosphorylation and NF-κB were also suppressed following knockdown of UBXN1 and ARRDC4. The rs200944490 (ARRDC4) and rs117555780 (UBXN1) were identified as candidate markers predictive of PCa Gleason score which is strongly associated with cancer aggressiveness. Additional validation in future studies is warranted.
| S-EPMC8533922 | biostudies-literature
Project description:Adenocarcinomas of the prostate can be categorized into tumor grades based on the extent to which the cancers histologically resemble normal prostate glands. Because grades are surrogates of intrinsic tumor behavior, characterizing the molecular phenotype of grade is of potential clinical importance. To identify molecular alterations underlying prostate cancer grades, we used microdissection to obtain specific cohorts of cancer cells corresponding to the most common Gleason patterns (patterns 3, 4, and 5) from 29 radical prostatectomy samples. We paired each cancer sample with matched benign lumenal prostate epithelial cells and profiled transcript abundance levels by microarray analysis. We identified an 86-gene model capable of distinguishing low-grade (pattern 3) from high-grade (patterns 4 and 5) cancers. This model performed with 76% accuracy when applied to an independent set of 30 primary prostate carcinomas. Using tissue microarrays comprising >800 prostate samples, we confirmed a significant association between high levels of monoamine oxidase A expression and poorly differentiated cancers by immunohistochemistry. We also confirmed grade-associated levels of defender against death (DAD1) protein and HSD17 beta4 transcripts by immunohistochemistry and quantitative RT-PCR, respectively. The altered expression of these genes provides functional insights into grade-associated features of therapy resistance and tissue invasion. Furthermore, in identifying a profile of 86 genes that distinguish high- from low-grade carcinomas, we have generated a set of potential targets for modulating the development and progression of the lethal prostate cancer phenotype.
| S-EPMC1544162 | biostudies-literature
Project description:The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen's quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model's Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.
| S-EPMC6089889 | biostudies-literature
Project description:BackgroundGleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether and to what extent A.I. grading translates to better prognostication.MethodsIn this study, we developed a system to predict prostate cancer-specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2807 prostatectomy cases from a single European center with 5-25 years of follow-up (median: 13, interquartile range 9-17).ResultsHere, we show that the A.I.'s risk scores produced a C-index of 0.84 (95% CI 0.80-0.87) for prostate cancer-specific mortality. Upon discretizing these risk scores into risk groups analogous to pathologist Grade Groups (GG), the A.I. has a C-index of 0.82 (95% CI 0.78-0.85). On the subset of cases with a GG provided in the original pathology report (n = 1517), the A.I.'s C-indices are 0.87 and 0.85 for continuous and discrete grading, respectively, compared to 0.79 (95% CI 0.71-0.86) for GG obtained from the reports. These represent improvements of 0.08 (95% CI 0.01-0.15) and 0.07 (95% CI 0.00-0.14), respectively.ConclusionsOur results suggest that A.I.-based Gleason grading can lead to effective risk stratification, and warrants further evaluation for improving disease management.
| S-EPMC9053226 | biostudies-literature
Project description:The identification of novel oncogenic and druggable targets in patient subgroups with poor prognosis may help to develop corresponding targeted therapy approaches. Microarray data analyses of 59 prostate cancer and 39 benign tissue samples revealed major transcriptional differences. More than 5.000 genes were identified to be differentially expressed between matched tumor and benign samples. In the prostate cancer samples we identified 144 differentially expressed associated with Gleason pattern. Illumina microarray experiments were done from of 59 prostate cancer and 39 matched benign tissue samples. We analyzed for differentially expressed genes between tumor and benign tissues, and between tumors with higher Gleason patter (4+3 and higher) against lower Gleason patter (3+4 and lower).
2012-10-19 | E-GEOD-32571 | biostudies-arrayexpress
Project description:Prostate cancer (PCa) is common in aging males, diagnosed via the Gleason grading system. The study explores the unexamined prognostic value of cuprotosis, a distinct cell death type, alongside Gleason grades in PCa. We explored Cuprotosis-related genes (CRGs) in prostate cancer (PCa), using NMF on TCGA-PRAD data for patient classification and WGCNA to link genes with Gleason scores and prognosis. A risk model was crafted via LASSO Cox regression. STX3 knockdown in PC-3 cells, analyzed for effects on cell behaviors and tumor growth in mice, highlighted its potential therapeutic impact. We identified five genes crucial for a prognostic risk model, with higher risk scores indicating worse prognosis. Survival analysis and ROC curves confirmed the model's predictive accuracy in TCGA-PRAD and GSE70769 datasets. STX3 was a key adverse prognostic factor, with its knockdown significantly reducing mRNA and protein levels, impairing PC-3 cell functions. In vivo, STX3 knockdown in PC-3 cells led to significantly smaller tumors in nude mice, underscoring its potential therapeutic value. Our prognostic model, using five genes linked to Gleason scores, effectively predicts prostate cancer outcomes, offering a novel treatment strategy angle.
| S-EPMC11291559 | biostudies-literature
Project description:The identification of novel oncogenic and druggable targets in patient subgroups with poor prognosis may help to develop corresponding targeted therapy approaches. Microarray data analyses of 59 prostate cancer and 39 benign tissue samples revealed major transcriptional differences. More than 5.000 genes were identified to be differentially expressed between matched tumor and benign samples. In the prostate cancer samples we identified 144 differentially expressed associated with Gleason pattern.
2012-10-19 | GSE32571 | GEO
Project description:BackgroundProstate tumor volume predicts biochemical recurrence, metastases, and tumor proliferation. A recent study showed that prostate tumor eccentricity (elongation or roundness) correlated with Gleason score. No studies examined the relationship among the prostate tumor's shape, volume, and potential aggressiveness.MethodsOf the 26 patients that were analyzed, 18 had volumes >1 cc for the histology-based study, and 25 took up contrast material for the MRI portion of this study. This retrospective study quantitatively compared tumor eccentricity and volume measurements from pathology assessment sectioned wholemount prostates and multi-parametric MRI to Gleason scores. Multi-parametric MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Pixel-based blobbing, and labeling were applied to digitized pathology slides and threshold ACE images. Tumor volumes were measured by counting voxels within the blob. Eccentricity calculation used moments of inertia from the blobs.ResultsFrom wholemount prostatectomy slides, fitting two sets of independent variables, prostate tumor eccentricity (largest blob eccentricity, weighted eccentricity, filtered weighted eccentricity) and tumor volume (largest blob volume, average blob volume, filtered average blob volume) to Gleason score in a multivariate analysis, yields correlation coefficient R=0.798 to 0.879 with P<0.01. The eccentricity t-statistic exceeded the volume t-statistic. Fitting histology-based total prostate tumor volume against Gleason score yields R=0.498, P=0.0098. From multi-parametric MRI, the correlation coefficient R between the Gleason score and the largest blob eccentricity for varying thresholds (0.30 to 0.55) ranged from -0.51 to -0.672 (P<0.01). For varying thresholds (0.60 to 0.80) for MRI detection, the R between the largest blob volume eccentricity against the Gleason score ranged from 0.46 to 0.50 (P<0.03). Combining tumor eccentricity and tumor volume in multivariate analysis failed to increase Gleason score prediction.ConclusionsProstate tumor eccentricity, determined by histology or MRI, more accurately predicted Gleason score than prostate tumor volume. Combining tumor eccentricity with volume from histology-based analysis enhanced Gleason score prediction, unlike MRI.
| S-EPMC8739117 | biostudies-literature