Prostate Cancer Gleason Grade Research Articles

Case presentation of urinary tract infection by <i>StenotrophomonaS maltophilia</i>

Stenotrophomonas maltophilia is an emerging aerobic, non-fermentative, gram-negative multidrug-resistant global opportunistic bacillus. S. maltophilia causes a wide range of infections including respiratory tract infections, blood stream infections and, less commonly, biliary tract infections, skin and soft tissue infections, as well as bone and joint infections. It is increasingly being reported to cause urinary tract infections (UTIs). As for the case report, a 87-year-old male patient visited the Biopathology Laboratory of Nikea Primary Healthcare Center, Piraeus, Greece, for routine examination, being referred by the family doctor (GP). Patient history revealed diabetes mellitus type 2, arterial hypertension, hypercholesterolemia, hypertriglyceridemia, hyperuricemia, chronic obstructive pulmonary disease, diagnosed before 30 years prostate cancer Gleasongrade 6, operated before 15 years, followed by hormone therapy and radiation therapy. Patient history also revealed urinary tract stones with 3 episodes of obstructive pyelonephritis during the last 5 years, followed by hospital admissions and administration of intravenous antibiotic treatment. During the hospital admissions, he had a permanent bladder catheter and received special antimicrobial treatment, for various microorganisms detected in his urine samples, such as Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis and Enterococcus faecalis. Urinary incontinence has been a symptom for 15 years, after the prostate cancer surgery. Moreover, during the last 5 years, the patient faced many difficulties in his daily life because of the urinary incontinence. The situation was managed by bladder catheterisation, which further worsened his condition with recurrent UTIs and new episodes of pyelonephritis with subsequent hospitalization. Urinalysis showed proteinuria, intense pyuria, abundance of micro-organisms and abundance of red blood cells. The urine culture grew monomicrobial Stenotrophomonas maltophilia 105 CFU/ml. The bacterium was identified by the RapID™ REMEL ONE identification system (Thermo Fisher Scientific). Antimicrobial susceptibility testing revealed susceptibility to Trimethoprime/Sulfamethoxazole, Levofloxacin, Ceftriaxone and moderate susceptibility to Ciprofloxacin and Norfloxacin.The patient received treatment with Trimethoprime/Sulfamethoxazole.

Risk factors for Gleason score upgrade from prostate biopsy to radical prostatectomy

Accurate identification of prostate cancer Gleason grade group remains an important component of the initial management of clinically localized disease. However, Gleason score upgrading (GSU) from biopsy to radical prostatectomy can occur in up to a third of patients treated with surgery. Concern for disease undergrading remains a source of diagnostic uncertainty, contributing to both over-treatment of low-risk disease as well as under-treatment of higher-risk prostate cancer. This review examines the published literature concerning risk factors for GSU from time of biopsy to prostatectomy final pathology. Risk factors identified for Gleason upgrading include patient demographic and clinical factors including age, body mass index, race, prostate volume, and biomarker based assays, including prostate-specific antigen (PSA) density, and testosterone values. In addition, prostate magnetic resonance imaging (MRI) findings have also been associated with GSU. Biopsy-specific characteristics associated with GSU include lower number of biopsy cores and lack of targeted methodology, and possibly increasing percent biopsy core positivity. Recognition of risk factors for disease undergrading may prompt confirmatory testing including repeat sampling or imaging. Continued refinements in imaging guided biopsy techniques may also reduce sampling error contributing to undergrading.

External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens.

To externally validate the performance of the DeepDx Prostate artificial intelligence (AI) algorithm (Deep Bio Inc., Seoul, South Korea) for Gleason grading on whole-mount prostate histopathology, considering potential variations observed when applying AI models trained on biopsy samples to radical prostatectomy (RP) specimens due to inherent differences in tissue representation and sample size. The commercially available DeepDx Prostate AI algorithm is an automated Gleason grading system that was previously trained using 1133 prostate core biopsy images and validated on 700 biopsy images from two institutions. We assessed the AI algorithm's performance, which outputs Gleason patterns (3, 4, or 5), on 500 1-mm2 tiles created from 150 whole-mount RP specimens from a third institution. These patterns were then grouped into grade groups (GGs) for comparison with expert pathologist assessments. The reference standard was the International Society of Urological Pathology GG as established by two experienced uropathologists with a third expert to adjudicate discordant cases. We defined the main metric as the agreement with the reference standard, using Cohen's kappa. The agreement between the two experienced pathologists in determining GGs at the tile level had a quadratically weighted Cohen's kappa of 0.94. The agreement between the AI algorithm and the reference standard in differentiating cancerous vs non-cancerous tissue had an unweighted Cohen's kappa of 0.91. Additionally, the AI algorithm's agreement with the reference standard in classifying tiles into GGs had a quadratically weighted Cohen's kappa of 0.89. In distinguishing cancerous vs non-cancerous tissue, the AI algorithm achieved a sensitivity of 0.997 and specificity of 0.88; in classifying GG ≥2 vs GG 1 and non-cancerous tissue, it demonstrated a sensitivity of 0.98 and specificity of 0.85. The DeepDx Prostate AI algorithm had excellent agreement with expert uropathologists and performance in cancer identification and grading on RP specimens, despite being trained on biopsy specimens from an entirely different patient population.

Prostate Cancer Gleason Grading: A Review on Deep Learning Approaches for Recognizing

This survey paper is motivated by the imperative need for advanced and precise diagnostic tools in the realm of prostate cancer, where Gleason grading plays a pivotal role in determining the severity and treatment strategy. The aim of this comprehensive review is to explore and assess the diverse spectrum of deep learning approaches applied to prostate cancer Gleason grading, with a specific focus on convolutional neural networks (CNNs), transfer learning, ensemble methods, and emerging techniques. The primary contribution lies in offering a consolidated understanding of the current state-of-the-art methodologies, their architectures, and training strategies, while also addressing challenges and advancements in the integration of deep learning into clinical workflows. Furthermore, the survey discusses recent developments such as the incorporation of multimodal data and explainable AI methods, shedding light on their potential to enhance the interpretability and adoption of deep learning models in the critical domain of prostate cancer diagnosis. Through this, the paper aims to provide a valuable resource for researchers, clinicians, and practitioners, guiding future endeavors toward more accurate and efficient Gleason grading using deep learning techniques.

PI-RADS Version 2.0 Versus Version 2.1: Comparison of Prostate Cancer Gleason Grade Upgrade and Downgrade Rates From MRI-Targeted Biopsy to Radical Prostatectomy.

BACKGROUND. Precise risk stratification through MRI/ultrasound (US) fusion-guided targeted biopsy (TBx) can guide optimal prostate cancer (PCa) management. OBJECTIVE. The purpose of this study was to compare PI-RADS version 2.0 (v2.0) and PI-RADS version 2.1 (v2.1) in terms of the rates of International Society of Urological Pathology (ISUP) grade group (GG) upgrade and downgrade from TBx to radical prostatectomy (RP). METHODS. This study entailed a retrospective post hoc analysis of patients who underwent 3-T prostate MRI at a single institution from May 2015 to March 2023 as part of three prospective clinical trials. Trial participants who underwent MRI followed by MRI/US fusion-guided TBx and RP within a 1-year interval were identified. A single genitourinary radiologist performed clinical interpretations of the MRI examinations using PI-RADS v2.0 from May 2015 to March 2019 and PI-RADS v2.1 from April 2019 to March 2023. Upgrade and downgrade rates from TBx to RP were compared using chi-square tests. Clinically significant cancer was defined as ISUP GG2 or greater. RESULTS. The final analysis included 308 patients (median age, 65 years; median PSA density, 0.16 ng/mL2). The v2.0 group (n = 177) and v2.1 group (n = 131) showed no significant difference in terms of upgrade rate (29% vs 22%, respectively; p = .15), downgrade rate (19% vs 21%, p = .76), clinically significant upgrade rate (14% vs 10%, p = .27), or clinically significant downgrade rate (1% vs 1%, p > .99). The upgrade rate and downgrade rate were also not significantly different between the v2.0 and v2.1 groups when stratifying by index lesion PI-RADS category or index lesion zone, as well as when assessed only in patients without a prior PCa diagnosis (all p > .01). Among patients with GG2 or GG3 at RP (n = 121 for v2.0; n = 103 for v2.1), the concordance rate between TBx and RP was not significantly different between the v2.0 and v2.1 groups (53% vs 57%, p = .51). CONCLUSION. Upgrade and downgrade rates from TBx to RP were not significantly different between patients whose MRI examinations were clinically interpreted using v2.0 or v2.1. CLINICAL IMPACT. Implementation of the most recent PI-RADS update did not improve the incongruence in PCa grade assessment between TBx and surgery.

A Comprehensive Review on Deep Learning Approach for Prostate Cancer Gleason Grading

This comprehensive review explores the transformative role of deep learning in revolutionizing the diagnosis of prostate cancer through a refined Gleason grading approach. Prostate cancer diagnosis has significantly benefited from advancements in deep learning techniques, enabling more accurate and precise Gleason grading—a critical component in assessing the severity of prostate tumors. The abstract delves into the latest developments in deep learning algorithms and their application to Gleason grading, highlighting the potential to enhance diagnostic accuracy, improve prognostic predictions, and ultimately contribute to more effective treatment strategies for prostate cancer patients. The synthesis of current research findings in this review underscores the pivotal role that deep learning plays in reshaping the landscape of prostate cancer diagnosis and emphasizes the promising future prospects for integrating these innovative technologies into clinical practice.

Validation of AI-based postoperative nomograms for biochemical recurrence in prostate adenocarcinoma.

e17104 Background: After radical prostatectomy (RP), a steep increase in PSA level is an early sign of the disease progression in prostate cancer, which is known as biochemical recurrence (BCR). The risk of BCR can be evaluated based on a combination of clinicopathological factors, and the patient’s Gleason score plays a significant role. However, this scoring system may have lower consistency due to interobserver reproducibility in classifying Gleason Patterns (GP) as well as in quantifying the amount of each GP. We developed AI-based nomograms, with the aim of investigating their prognostic efficacy. Methods: In this study, digitized whole-slide images (WSIs) of H&E-stained prostatectomy specimens and clinical follow-up information were obtained from two sources: Pusan National University Hospital (PNUH, n = 967, event = 342) from 2010 to 2021 with the median follow-up being 3.7 years, and The Cancer Genome Atlas (TCGA, n = 352, event = 79) from 2000 to 2013 with the median follow-up 2.6 years. We used the DeepDx Prostate - RP, an AI-based prostate cancer Gleason grading model, to compute a pixel-wise probability map of each GP in WSIs. Then, a weighted sum of the probabilities and GPs was calculated for each pixel. The proposed slide-level score (AI score) was then determined by averaging them across all pixels in WSI, resulting in a value ranging from 3 to 5. Also, patients were divided into five groups using AI score thresholds (3.1, 3.5, 3.9, and 4.1), and five binary variables (AI score group 1-5) were generated, where a value of 1 indicates a patient’s categorization into a group. To evaluate predictability we created new nomograms incorporating AI scores based on existing nomograms: MSKCC and CAPRA-S. Unlike the original nomograms, the proposed nomograms do not include the Grade Group (GG) made by pathologists. Then, we fitted a Cox regression model on one of two datasets using the original and newly formed nomograms, and validated on the other dataset reporting the concordance index (c-index). Confidence intervals for the c-index were generated via the non-parametric bootstrap resampling with 9,999 samples. Results: The table shows the prognostic performance of sets of clinicopathological factors and demonstrates that the proposed AI score improved the predictive power. The nomograms with AI score group 1-5 outperformed the original nomograms, as shown in (d). Conclusions: In conclusion, we developed the AI-based nomograms which can improve the accuracy of predicting the biochemical recurrence in prostate cancer compared to the existing nomograms. [Table: see text]

High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade.

To develop and test a Prostate Imaging Stratification Risk (PRISK) tool for precisely assessing the International Society of Urological Pathology Gleason grade (ISUP-GG) of prostate cancer (PCa). This study included 1442 patients with prostate biopsy from two centres (training, n = 672; internal test, n = 231 and external test, n = 539). PRISK is designed to classify ISUP-GG 0 (benign), ISUP-GG 1, ISUP-GG 2, ISUP-GG 3 and ISUP GG 4/5. Clinical indicators and high-throughput MRI features of PCa were integrated and modelled with hybrid stacked-ensemble learning algorithms. PRISK achieved a macro area-under-curve of 0.783, 0.798 and 0.762 for the classification of ISUP-GGs in training, internal and external test data. Permitting error ±1 in grading ISUP-GGs, the overall accuracy of PRISK is nearly comparable to invasive biopsy (train: 85.1% vs 88.7%; internal test: 85.1% vs 90.4%; external test: 90.4% vs 94.2%). PSA ≥ 20 ng/ml (odds ratio [OR], 1.58; p = 0.001) and PRISK ≥ GG 3 (OR, 1.45; p = 0.005) were two independent predictors of biochemical recurrence (BCR)-free survival, with a C-index of 0.76 (95% CI, 0.73-0.79) for BCR-free survival prediction. PRISK might offer a potential alternative to non-invasively assess ISUP-GG of PCa.

Inorganic pyrophosphatase, a human prostate cancer activator

Prostate cancer (PCa) represents a common tumor in men. Inorganic pyrophosphatase (PPA1) can promote inorganic pyrophosphate (PPi) hydrolysis. However, whether PPA1 participates in castration-resistant prostate cancer (CRPC) is unclear. PPA1 expression was herein determined by immunohistochemistry along with analysis of cell behaviors by flow cytometry and CCK-8 assay. Cell invasion was measured by BD Matrigel matrix. PPA1 was found to be significantly upregulated and positively correlated with Gleason grade of PCa. PPA1 expression was significantly higher in the PC-3 and DU145 cells than LNCaP cells and Benign prostatic hyperplasia (BPH)-1 cells. Additionally, stable overexpression of AR reduced PPA1 expression, implying the important roles of PPA1 in CRPC pathogenesis. Moreover, knockdown of PPA1 inhibited cell activities and behaviors. Furthermore, silencing of PPA1 reduced the expression of cell proliferation markers Ki-67 and PCNA, cell cycle protein cyclin D1, cyclin E, c-Myc, and MMP-2/9. Moreover, it also inhibited epithelial to mesenchymal transition (EMT) by upregulating E-cadherin and ZO-1, but downregulating N-cadherin and ZEB-2. Mechanistically, silencing of PPA1 inhibited NF-κB activation through reduction of IκBα and p65 phosphorylation. Our findings provide a critical link between PPA1 and CRPC.

Automatic diagnosis and grading of Prostate Cancer with weakly supervised learning on whole slide images

Background:The workflow of prostate cancer diagnosis and grading is cumbersome and the results suffer from substantial inter-observer variability. Recent trials have shown potential in using machine learning to develop automated systems to address this challenge. Most automated deep learning systems for prostate cancer Gleason grading focused on supervised learning requiring demanding fine-grained pixel-level annotations. Methods:A weakly-supervised deep learning model with slide-level labels is presented in this study for the diagnosis and grading of prostate cancer with whole slide image (WSI). WSIs are first cropped into small patches and then processed with a deep learning model to extract patch-level features. A graph convolution network (GCN) is used to aggregate the features for classifications. Throughout the training process, the noisy labels are progressively filtered out to reduce inter-observer variations in clinical reports. Finally, multi-center independent test cohorts with 6,174 slides are collected to evaluate the prostate cancer diagnosis and grading performance of our model. Results:The cancer diagnosis (2-level classification) results on two external test sets (n= 4,675, n= 844) show an area under the receiver operating characteristic curve (AUC) of 0.985 and 0.986. The Gleason grading (6-level classification) results reach 0.931 quadratic weighted kappa on the internal test set (n= 531). It generalizes well on the external test dataset (n= 844) with 0.801 quadratic weighted kappa with the reference standard set independently. The model enables pathological meaningful interpretability by visualizing the most attended lesions which are highly consistent with expert annotations. Conclusion:The proposed model incorporates a graph network in weakly supervised learning with only slide-level reports. A robust learning strategy is also employed to correct the label noise. It is highly accurate (>0.985 AUC for diagnosis) and also interpretable with intuitive heatmap visualization. It can be unified with a digital pathology pipeline to deliver prostate cancer metrics for a pathology report.

A deep learning network for Gleason grading of prostate biopsies using EfficientNet.

The most crucial part in the diagnosis of cancer is severity grading. Gleason's score is a widely used grading system for prostate cancer. Manual examination of the microscopic images and grading them is tiresome and consumes a lot of time. Hence to automate the Gleason grading process, a novel deep learning network is proposed in this work. In this work, a deep learning network for Gleason grading of prostate cancer is proposed based on EfficientNet architecture. It applies a compound scaling method to balance the dimensions of the underlying network. Also, an additional attention branch is added to EfficientNet-B7 for precise feature weighting. To the best of our knowledge, this is the first work that integrates an additional attention branch with EfficientNet architecture for Gleason grading. The proposed models were trained using H&E-stained samples from prostate cancer Tissue Microarrays (TMAs) in the Harvard Dataverse dataset. The proposed network was able to outperform the existing methods and it achieved an Kappa score of 0.5775.

Correlation between immunohistochemical expression of PSA and PTHrP in different Gleason grades of prostate cancer

Correlation between immunohistochemical expression of PSA and PTHrP in different Gleason grades of prostate cancer

Re: Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer: The PANDA Challenge

Re: Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer: The PANDA Challenge

Interlaboratory Gleason grading variation affects treatment: a Dutch historic cohort study in 30 509 patients with prostate cancer

AimSubstantial variation in Gleason grading (GG) of prostate cancer (PCa) exists between Dutch pathology laboratories. This study investigates its impact on treatment strategies.MethodsPathology reports of prostate needle biopsies and clinical...

Abstract 2270: Hyperlipidemia promotes aggressive variant prostate cancer via RNA-binding protein Quaking

Abstract Introduction: Hyperlipidemia has been associated with increased risk of advanced stage and high Gleason grade prostate cancer (PCa), yet the underlying mechanism is not completely understood. Quaking (QKI) is an RNA-binding protein (RBP) that regulate lipid metabolism. For PCa, QKI overexpression has been associated with increased chance of metastatic recurrence. Method: We performed RNA sequencing of prostate cancer tissues of hyperlipidemia (n = 12) and normolipidemia (n=32). Tissue samples were acquired by MRI-US fusion targeted biopsy. DESeq2 and GSEA was used to identify differently expressed genes and enriched genesets. A web-based RBP motif screening tool TRANSITE was utilized to identify active RBPs in hyperlipidemia-associated PCa. Results: PCa samples of hyperlipidemia patients exhibited poorer histology than normolipidemia PCA. RNA sequencing found that hyperlipidemia-associated PCa is enriched of Myc-regulated genes as well as interferon or innate immunity-associated genes. Interestingly, hyperlipidemia-associated PCa gene expression signature identified PCa samples devoid of common PCa genetic alterations - TMPRSS2-ERG fusion and PTEN deletion/mutation. RBP motif screening found QKI as one of the top enriched RBP in hyperlipidemia-associated PCa transcriptome. In a separate a consecutive radical prostatectomy series (n=190), we found that QKI-high (immunohistochemistry staining score 2-3) tumors had significantly higher serum cholesterol than QKI-low (score 0-1) tumors. Overexpression and knockdown experiments proved that QKI is responsible of rapid cell proliferation and interferon-related inflammatory gene expressions, In silico analysis predicted QKI overexpressing cancer cells are susceptive to stain-mediated ferroptosis-like cell death. Mechanistically, QKI regulates expression of acyl-CoA synthetase long-chain family member 4 (ACSL4), which dictates the sensitivity of QKI overexpressing cells to ferroptosis. Conclusion: Hyperlipidemia was associated with aggressive pathologic characteristics of PCa without TMPRSS2-ERG fusion or PTEN deletion/mutation, and high tissue expression of QKI, an RNA-binding protein. QKI overexpression in PCa increased sensitivity to ACSL4-mediated ferroptosis by statins. This explains the epidemiological associations between hyperlipidemia and PCa, and will guide molecular subtype-driven drug repurposing studies. Citation Format: Hyun Ho Han, Jin Sol Sung, Dong Wook Song, Cheol Keun Park, Nam Hoon Cho, Young Deuk Choi, Woo Jin Ko. Hyperlipidemia promotes aggressive variant prostate cancer via RNA-binding protein Quaking [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2270.

PathAL: An Active Learning Framework for Histopathology Image Analysis.

Deep neural networks, in particular convolutional networks, have rapidly become a popular choice for analyzing histopathology images. However, training these models relies heavily on a large number of samples manually annotated by experts, which is cumbersome and expensive. In addition, it is difficult to obtain a perfect set of labels due to the variability between expert annotations. This paper presents a novel active learning (AL) framework for histopathology image analysis, named PathAL. To reduce the required number of expert annotations, PathAL selects two groups of unlabeled data in each training iteration: one "informative" sample that requires additional expert annotation, and one "confident predictive" sample that is automatically added to the training set using the model's pseudo-labels. To reduce the impact of the noisy-labeled samples in the training set, PathAL systematically identifies noisy samples and excludes them to improve the generalization of the model. Our model advances the existing AL method for medical image analysis in two ways. First, we present a selection strategy to improve classification performance with fewer manual annotations. Unlike traditional methods focusing only on finding the most uncertain samples with low prediction confidence, we discover a large number of high confidence samples from the unlabeled set and automatically add them for training with assigned pseudo-labels. Second, we design a method to distinguish between noisy samples and hard samples using a heuristic approach. We exclude the noisy samples while preserving the hard samples to improve model performance. Extensive experiments demonstrate that our proposed PathAL framework achieves promising results on a prostate cancer Gleason grading task, obtaining similar performance with 40% fewer annotations compared to the fully supervised learning scenario. An ablation study is provided to analyze the effectiveness of each component in PathAL, and a pathologist reader study is conducted to validate our proposed algorithm.

A0611 - Crowdsourcing of artificial intelligence algorithms for diagnosis and Gleason grading of prostate cancer in biopsies

A0611 - Crowdsourcing of artificial intelligence algorithms for diagnosis and Gleason grading of prostate cancer in biopsies

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge—the largest histopathology competition to date, joined by 1,290 developers—to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840–0.884) and 0.868 (95% CI, 0.835–0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.

Abstract PR-17: The role of HMGA2 and CLK3 in prostate cancer health disparities

Abstract African American men (AA) suffer disproportionately from prostate cancer (PCa) displaying higher incidence and mortality rates when compared to Caucasian-American men (CA). Recent studies have shown that high mobility group A2 (HMGA2), a non-histone chromatin binding protein, plays a critical role in promoting metastasis. HMGA2 full-length/wild-type and truncated (lacking the 3'UTR) isoforms are overexpressed in several cancers, however, their distinct roles in PCa have not been reported. Literature has previously revealed a splicing factor, CDC-like kinase 3 (CLK3), that promotes splicing of wild-type HMGA2 to create the truncated HMGA2 isoform. We hypothesize that HMGA2 isoforms may play differential roles to promote PCa progression according to ethnicity. qPCR analysis of patient tissue of varying PCa stages was conducted to look at the mRNA expression of HMGA2 isoforms and CLK3. RNA-Seq was performed on LNCaP prostate cancer cell lines stably overexpressing wild-type or truncated HMGA2 and compared to Neo control. Both wild-type and truncated HMGA2 increased with increased PCa gleason grade. AA when compared to CA, displayed higher levels of truncated HMGA2 and its splicing factor, CLK3. RNA-Seq analysis revealed distinct gene sets regulated by truncated HMGA2 compared to wild-type HMGA2, including upregulation of a distinct set of genes involved in androgen signaling. These results indicate that HMGA2 isoforms are disproportionately expressed in AA and CA and increase with PCa progression. The regulation of differential genes by HMGA2 isoforms suggest that they may use distinct pathways to promote PCa. Citation Format: Taaliah Campbell, Ohuod Hawsawi, Valerie Odero-Marah. The role of HMGA2 and CLK3 in prostate cancer health disparities [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PR-17.

The Association Of Body Mass Index And Prostatic Cancer Histopathological Grade

Background: Obesity, a global public health concern, has been repeatedly linked to the development of different cancers in epidemiologic and basic research studies, Prostate cancer is the most frequently encountered solid tissue cancer in men. Gleason score of the prostate cancer is one of the most important parameter, which provides the most important data about biological behavior of the cancer and affect on the selection of the treatment and its outcomes. Therefore, accuracy of Gleason score, based on histopathological analysis of the biopsy material, has a critical importance Patients and Methods: A cross-sectional study with retrospective analytical elements, among prostate cancer patients who were diagnosed based on histopathology of prostate gland ,then classified accorrding to G.S (gleasson score) and body mass index. Aim of this study: To evaluate the association between high body mass index and Gleason grade of prostate cancer. Results: The results of this study showed that patients with high body mass index Significantly associated with high grade of prostate cancer, as ( 46.4% and 62.2% of overweight and obese patients respectively had high Gleason score above 7 while 12.5% of overweight and 8.1% of obese patients had low Gleason score and the p Value was (0.002). Conclusion: This study found that high body mass index associated with increase high grade of prostate cancer