Prostate Cancer Gleason Score Research Articles

A rare case of urinary tract infection by Burkholderia cepacia

Burkholderia cepacia is a motile, aerobic, non-fermentative, gram-negative bacillus and has been widely documented as a lung pathogen in patients with cystic fibrosis and chronic granulomatous disease. It is documented as an important emerging cause of multi-drug resistant nosocomial infections, and an important cause of morbidity and mortality. A 64-year-old male patient visited the Nikea Primary Healthcare Center, Piraeus, Greece, referred by the family doctor (GP), for follow-up due a history of prostate cancer (patient on immunosuppression) and recurrent UTIs with subsequent admission to the hospital. Patient history revealed diabetes mellitus type 2, arterial hypertension, hypercholesterolemia, hypertriglyceridemia, history of recurrent UTIs, with 4 hospitalizations in a tertiary hospital during the last 2 years, prostatic hypertrophy, 2 episodes of prostatitis before the diagnosis of prostate cancer Gleason score 6, at the end of 2019, with subsequent total prostatectomy, and radiotherapy. Patient history also revealed dysuria, frequent urination, pain and burning sensation during urination and erectile dysfunction. Urinalysis showed intense pyuria, abundance of micro-organisms and abundance of red blood cells. The urine culture grew monomicrobial Burkholderia cepacia 105 CFU/ml. The bacterium was identified by the RapID™ REMEL ONE identification system (Thermo Fisher Scientific). Antimicrobial susceptibility testing revealed susceptibility to antibiotics such as, Ceftazidime, Ciprofloxacin, Norfloxacin, Levofloxacin and Imipenem.The patient received treatment with Levofloxacin. Burkholderia cepacia infections outside the respiratory system are rare. Moreover, recurrent UTIs with B. cepacia is a rare finding, which highlights the importance of our study. UTIs with B. cepacia have been associated with bladder irrigation or use of contaminated hospital objects and liquids. B. cepacia is one of the most antimicrobial-resistant organisms and treatment options are limited. The patient was treated with Levofloxacin (3rd generation fluoroquinolone — Tavanic) 500 mg daily per os for 2 weeks, due to his history.

A hybrid classification model with radiomics and CNN for high and low grading of prostate cancer Gleason score on mp-MRI

Prostate cancer is a prevalent malignancy among men, and the precise diagnosis and grading of prostate cancer have become key research areas in contemporary medicine. Magnetic resonance imaging is an ideal non-invasive method for the detection and diagnosis of prostate cancer, offering superior spatial resolution compared with invasive biopsy procedures. We developed a hybrid model based on radiomics and deep learning, which performs binary classification on prostate cancer cases at the Gleason score ≤ 3 + 4 and ≥ 4 + 3 cases by utilizing T2-weighted imaging and the apparent diffusion coefficient. In the radiomics model, we incorporated a combined region of interest that included a 5 mm margin around the tumor and employed a mirror comparison method for feature selection. In the deep learning model, we utilized a convolutional neural network framework enhanced by the CPCB and SE-Net modules to improve the discrimination ability of our model. A retrospective study was conducted on 650 cases from two hospitals, and our hybrid model achieved superior performance in terms of accuracy, recall, and F1 score compared with the individual models, with respective accuracy values of 0.907, 0.944, and 0.918.

Abstract 4530: Nuclear export inhibitor cooperates with PARP inhibitor to suppress the growth of metastatic castration resistant prostate cancer in vivo

Abstract Background: Aberrant nuclear protein transport, often observed in cancer, causes mislocalization-dependent inactivation of critical cellular proteins. Earlier we showed that overexpression of nuclear export protein exportin 1 (XPO1) is linked to higher grade and Gleason score in metastatic castration resistant prostate cancer (mCRPC). The network topology computational approach (NTCP) determined a higher synthetic lethal score between XPO1 and poly (ADP-ribose) polymerase (PARP1). We showed that selective inhibitor of nuclear export (SINE) could synergized with PARP inhibitors in mCRPC cell lines. Here we evaluated the efficacy of SINE and PARP inhibitor (PARPi) combination in cell line derived as well as patient derived xenograft (CDX and PDX) models and deciphered the mechanism of synergy. Methods: For CDX model, the 22rv1 mCRPC cells were grown as subcutaneous xenografts in ICR-SCID male mice. For PDX model, tissue was collected from Champions Oncology (CTG-3581) and grown subcutaneously in CEIA/NOG male mice. SINE dosed orally at 10-15 mg/kg twice a week and PARPi dosed orally at 50 mg/kg daily. For in vitro mechanistic study, 22rv1 cells were subjected to RNAseq and proteomic analysis after treatment. Data analysis was performed using iPathwayGuide (advaitabio.com). Results: The CDX and PDX showed pronounced anti-cancer efficacy by this combination compared to single agents without any significant weight loss. Survival analysis in CDX model demonstrated enhanced benefits to the mice of combination group. Immunohistochemistry (IHC) revealed apoptotic cell death in the combination group which is evident from cleaved caspase 3 staining and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL). To decipher the mechanism of synergy we performed transcriptomic (RNAseq) and proteomic analysis in vitro. We observed downregulation of DNA replication related gene minichromosome maintenance complex component 6 (MCM6) and cell division cycle 6 (CDC6) in the combination treatment. Gene set enrichment analysis (GSEA) also showed low enrichment scores for DNA replication. Proteomic analysis revealed a down regulation of DNA replication modulators such as HMGB2 and DNAJC9 which work on DNA coiling and histone respectively. Conclusions: Taken together, this study revealed the therapeutic potential of SINE-PARPi combination via targeting DNA damage response pathway in mCRPC. Further evaluation of molecular synergy in the xenograft models are underway through RNA interference (RNAi) technique and Digital Spatial Profiling (DSP). Citation Format: Md Hafiz Uddin, Amro Aboukameel, Husain Y. Khan, Sahar F. Bannoura, Frank Cackowski, Rafic Beydoun, Gregory Dyson, Seongho Kim, Julie Boerner, Vinod Shidham, Ramzi M. Mohammad, Boris C. Pasche, Asfar S. Azmi, Elisabeth I. Heath. Nuclear export inhibitor cooperates with PARP inhibitor to suppress the growth of metastatic castration resistant prostate cancer in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4530.

Abstract 3892: Systematic generation of a clinicogenomic harmonized oncologic real-world dataset (MSK-CHORD)

Abstract Clinical data storage in unstructured notes and siloed datasets present a major challenge for large-scale cancer informatics. Whether natural language processing (NLP) combined with multimodal integration across datasets can produce a mineable resource and improve discovery of relationships between tumor genomics and clinical phenotypes is unknown. We hypothesized that NLP could automatically annotate a pan-cancer corpus of 82,464 patients with tumor genomic sequencing. To develop algorithms to annotate free-text reports, we leveraged the AACR Project GENIE Biopharma Collaborative (BPC), a structured curation of EMR from five cancer types (non-small cell lung (NSCLC), breast, colorectal, prostate, and pancreatic cancer), to train and validate several Transformer and rule based-based NLP models. After automating the generation of NLP annotations alongside medication, demographic, tumor registry, survival, and tumor genomic sequencing data, we tested whether clinicogenomic relationships not apparent in the smaller BPC cohort might be discoverable in the larger cohort. In 5-fold cross-validation, NLP Transformers accurately annotated the presence of cancer (AUC=0.99), cancer progression (AUC=0.97), and sites of disease (AUC=0.99) from radiology reports, and presence of prior outside treatment (AUC=0.98) and hormone receptor (HR) and HER2 receptor status (AUC=0.98, 0.98) from clinician notes. In addition, rule-based models, trained on non-BPC data and validated on the whole BPC cohort, annotated smoking status from clinician notes (ACC=0.95), and Gleason score (ACC=1.0), PD-L1 status (ACC=0.98), and mismatch repair deficiency (ACC=0.98) from histopathology reports. NLP annotations were merged with genomic and other structured clinical data to create a Clinicogenomic, Harmonized Oncologic Real-world Dataset (MSK-CHORD). Finally, we tested if associations not apparent in the BPC might be discoverable in MSK-CHORD. We found positive associations between Gleason score and gene-level alterations in prostate cancer including TP53, PTEN and BRCA2 (q<0.1), none of which were adequately powered for detection in the BPC. We found PD-L1 status was associated with better survival following immunotherapy treatment in NSCLC, but only in the larger MSK-CHORD was this association statistically significant. In breast cancer, NF1 mutations were associated with prior therapy in both cohorts, but this association was only significant in MSK-CHORD. The infrastructure generating MSK-CHORD uses a combination of on-premise and cloud computing resources and open-source development operation applications to automate processes. Once annotations are created, data is imported into a local instance of cBioPortal, where researchers can visualize data and perform analyses. The system generating MSK-CHORD demonstrates how large-scale data delivery and integration can fuel cancer research. Citation Format: Christopher J. Fong, Karl Pichotta, Thinh Tran, Michele Waters, Tom Fu, Mono Pirun, Mirella Altoe, Brooke Mastrogiacomo, Anisha Luthra, Mehnaj Ahmed, Arfath Pasha, Armaan Kohli, Raymond Lim, Tom Pollard, Darin Moore, Benjamin Gross, Avery Wang, Calla Chennault, Ritika Kundra, Ramyasree Madupuri, Ino de Bruijn, Aaron Lisman, Walid K. Chatila, Subhiksha Nandakumar, Anika Begum, Doori Rose, Kenneth L. Kehl, Deborah Schrag, Michael Berger, Jian Carrot-Zhang, Pedram Razavi, Bob Li, Peter Stetson, Nikolaus Schultz, Justin Jee. Systematic generation of a clinicogenomic harmonized oncologic real-world dataset (MSK-CHORD) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3892.

Prostate Cancer Gleason Score Classification Using Transfer Learning Models

This research discusses the application of transfer learning models in the classification of prostate cancer based on Gleason scores. Gleason scoring is crucial in determining the aggressiveness of prostate cancer, guiding treatment decisions. Transfer learning, a technique where knowledge from one task is applied to another, has gained traction in medical image analysis. This study explores the efficacy of transfer learning models, such as convolutional neural networks (CNNs), in accurately classifying Gleason scores from histopathological images. Leveraging pre-trained CNNs like ResNet and VGG, the research demonstrates significant improvements in classification accuracy compared to traditional machine learning approaches. The methodology involves fine-tuning these pre-trained models on a dataset of prostate cancer histopathological images annotated with Gleason scores. Experimental results showcase promising performance metrics, including high accuracy, precision, recall, and F1-score, highlighting the potential of transfer learning in enhancing prostate cancer diagnosis and prognostication. This work contributes to the growing body of research utilizing deep learning techniques for improving cancer classification and personalized treatment strategies.

Identifying High Gleason Score Prostate Cancer by Prostate Fluid Metabolic Fingerprint-Based Multi-Modal Recognition.

Prostate cancer (PCa) is the second most common cancer in males worldwide. The Gleason scoring system, which classifies the pathological growth pattern of cancer, is considered one of the most important prognostic factors for PCa. Compared to indolent PCa, PCa with high Gleason score (h-GS PCa, GS ≥ 8) has greater clinical significance due to its high aggressiveness and poor prognosis. It is crucial to establish a rapid, non-invasive diagnostic modality to decipher patients with h-GS PCa as early as possible. In this study, ferric nanoparticle-assisted laser desorption/ionization mass spectrometry (FeNPALDI-MS) to extract prostate fluid metabolic fingerprint (PSF-MF) is employed and combined with the clinical features of patients, such as prostate-specific antigen (PSA), to establish a multi-modal diagnosis assisted by machine learning. This approach yields an impressive area under the curve (AUC) of 0.87 to diagnose patients with h-GS, surpassing the results of single-modal diagnosis using only PSF-MF or PSA, respectively. Additionally, using various screening methods, six key metabolites that exhibit greater diagnostic efficacy (AUC = 0.96) are identified. These findings also provide insights into related metabolic pathways, which may provide valuable information for further elucidation of the pathological mechanisms underlying h-GS PCa.

A radiomics based method for prediction of prostate cancer Gleason score using enlarged region of interest

BackgroundProstate cancer (PCa) is one of the most common cancers in men worldwide, and its timely diagnosis and treatment are becoming increasingly important. MRI is in increasing use to diagnose cancer and to distinguish between non-clinically significant and clinically significant PCa, leading to more precise diagnosis and treatment. The purpose of this study is to present a radiomics-based method for determining the Gleason score (GS) for PCa using tumour heterogeneity on multiparametric MRI (mp-MRI).MethodsTwenty-six patients with biopsy-proven PCa were included in this study. The quantitative T2 values, apparent diffusion coefficient (ADC) and signal enhancement rates (α) were calculated using multi-echo T2 images, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), for the annotated region of interests (ROI). After texture feature analysis, ROI range expansion and feature filtering was performed. Then obtained data were put into support vector machine (SVM), K-Nearest Neighbor (KNN) and other classifiers for binary classification.ResultsThe highest classification accuracy was 73.96% for distinguishing between clinically significant (Gleason 3 + 4 and above) and non-significant cancers (Gleason 3 + 3) and 83.72% for distinguishing between Gleason 3 + 4 from Gleason 4 + 3 and above, which was achieved using initial ROIs drawn by the radiologists. The accuracy improved when using expanded ROIs to 80.67% using SVM and 88.42% using Bayesian classification for distinguishing between clinically significant and non-significant cancers and Gleason 3 + 4 from Gleason 4 + 3 and above, respectively.ConclusionsOur results indicate the research significance and value of this study for determining the GS for prostate cancer using the expansion of the ROI region.

Optimization and validation of 18F-DCFPyL PET radiomics-based machine learning models in intermediate- to high-risk primary prostate cancer.

Radiomics extracted from prostate-specific membrane antigen (PSMA)-PET modeled with machine learning (ML) may be used for prediction of disease risk. However, validation of previously proposed approaches is lacking. We aimed to optimize and validate ML models based on 18F-DCFPyL-PET radiomics for the prediction of lymph-node involvement (LNI), extracapsular extension (ECE), and postoperative Gleason score (GS) in primary prostate cancer (PCa) patients. Patients with intermediate- to high-risk PCa who underwent 18F-DCFPyL-PET/CT before radical prostatectomy with pelvic lymph-node dissection were evaluated. The training dataset included 72 patients, the internal validation dataset 24 patients, and the external validation dataset 27 patients. PSMA-avid intra-prostatic lesions were delineated semi-automatically on PET and 480 radiomics features were extracted. Conventional PET-metrics were derived for comparative analysis. Segmentation, preprocessing, and ML methods were optimized in repeated 5-fold cross-validation (CV) on the training dataset. The trained models were tested on the combined validation dataset. Combat harmonization was applied to external radiomics data. Model performance was assessed using the receiver-operating-characteristics curve (AUC). The CV-AUCs in the training dataset were 0.88, 0.79 and 0.84 for LNI, ECE, and GS, respectively. In the combined validation dataset, the ML models could significantly predict GS with an AUC of 0.78 (p<0.05). However, validation AUCs for LNI and ECE prediction were not significant (0.57 and 0.63, respectively). Conventional PET metrics-based models had comparable AUCs for LNI (0.59, p>0.05) and ECE (0.66, p>0.05), but a lower AUC for GS (0.73, p<0.05). In general, Combat harmonization improved external validation AUCs (-0.03 to +0.18). In internal and external validation, 18F-DCFPyL-PET radiomics-based ML models predicted high postoperative GS but not LNI or ECE in intermediate- to high-risk PCa. Therefore, the clinical benefit seems to be limited. These results underline the need for external and/or multicenter validation of PET radiomics-based ML model analyses to assess their generalizability.

Recurrent Gleason Score 6 Prostate Cancer After Radiotherapy or Ablation: Should We Observe Them All? Results from a Large Multicenter Salvage Radical Prostatectomy Consortium

BackgroundSalvage radical prostatectomy (sRP) yields poor functional outcomes and relatively high complication rates. Gleason score (GS) 6 prostate cancer (PCa) has genetic and clinical features showing little, if not absent, metastatic potential. However, the behavior of GS 6 PCa recurring after previous PCa treatment including radiotherapy and/or ablation has not been investigated. ObjectiveTo evaluate the oncological outcomes of sRP for radio- and/or ablation-recurrent GS 6 PCa. Design, setting, and participantsRetrospective data of sRP for recurrent PCa after local nonsurgical treatment were collected from 14 tertiary referral centers from 2000 to 2021. InterventionProstate biopsy before sRP and sRP. Outcome measurements and statistical analysisA survival analysis was performed for pre-sRP biopsy and sRP-proven GS 6. Concordance between PCa at pre-sRP biopsy and sRP histology was assessed. Results and limitationsWe included GS 6 recurrent PCa at pre-sRP biopsy (n = 142) and at sRP (n = 50), as two cohorts. The majority had primary radiotherapy and/or brachytherapy (83.8% of GS 6 patients at pre-sRP biopsy; 78% of GS 6 patients at sRP) and whole-gland treatments (91% biopsy; 85.1% sRP). Biopsy GS 6 10-yr metastasis, cancer-specific survival (CSS), and overall survival (OS) were 79% (95% confidence interval [CI] 61–89%), 98% (95–99%), and 89% (78–95%), respectively. Upgrading at sRP was 69%, 35.5% had a pT3 stage, and 13.4% had positive nodes. The sRP GS 6 10-yr metastasis-free survival, CSS, and OS were 100%, 100%, and 90% (95% CI 58–98%) respectively; pT3 and pN1 disease were found in 12% and 0%, respectively. Overall complications, high-grade complications, and severe incontinence were experienced by >50%, >10%, and >15% of men, respectively (in both the biopsy and the sRP cohorts). Limitations include the retrospective nature of the study and absence of a centralized pathological review. ConclusionsGS 6 sRP–proven PCa recurring after nonsurgical primary treatment has almost no metastatic potential, while patients experience relevant morbidity of the procedure. However, a significant proportion of GS 6 cases at pre-sRP biopsy are upgraded at sRP. In the idea not to overtreat, efforts should be made to improve the diagnostic accuracy of pre-sRP biopsy. Patient summaryWe investigated the oncological results of salvage radical prostatectomy for recurrent prostate cancer of Gleason score (GS) 6 category. We found a very low malignant potential of GS 6 confirmed at salvage radical prostatectomy despite surgical complications being relatively high. Nonetheless, biopsy GS 6 was frequently upgraded and had less optimal oncological control. Overtreatment for recurrent GS 6 after nonsurgical first-line treatment should be avoided, and efforts should be made to increase the diagnostic accuracy of biopsies for recurrent disease.

Treatment outcomes of our patients with prostate cancer who underwent postoperative radiotherapy

Aims: Prostate cancer is one of the most common cancers in men. Surgery, radiotherapy or active surveillance are the treatment options. Treatment is decided according to risk group of the patient. Postoperative radiotherapy is delivered in selected patients to increase the local control rates. The aim of this study is to report the treatment results of patients who received postoperative radiotherapy for localized prostate cancer. Methods: This retrosepctive study included 78 prostate cancer patients who received postoperative radiotherapy between 2011 and 2023. All patients except who had pathologically positive lymph nodes were included into the study. Overall survival, progression free survival, and associated pathological parameters were evaluated by using IBM SPSS programme version 20. Results: The mean follow-up time was 66.7 (IQR 25-75:41,6-89,6) months. Five and 10-year overall survival rates were 93%, and 67.3% respectively; (median (95% CI); NR (not reach): while 5 and 10-year progression-free survival rates were 90.3%, and 50.4, respectively; (median (95% CI); 135.3± (105.9-165.7). Postoperative prostate specific antigen (PSA) and pre-RT PSA values, and the effects of these parameters on progression-free survival was analyzed, median progression-free survival was higher in patients with postoperative PSA values ? 0.185 (135.6±24.4 vs 113.3±6.4, p:0.001). Five and 10 year overall survival and progression-free survival rates of patients with a high gleason score who underwent postoperative radiotherapy was observed lower than the others (86.9% and 46.3%, p=0.006; 86.9% and 33.8%, p=0.009 respectively). Conclusion: Postoperative radiotherapy is generated best results in progression free survival and overall survival in patients with low pre-rt psa and high gleason score prostate cancer cases

Role of DWI/ADC in the Assessment of Prostate Cancer Aggressiveness and Its Correlation to Gleason Score

Background Cancer prostate represents a major health problem. it is one of the most common solid malignancies among male population. Prostate cancer (PCa) is considered one of the most common cancers among male population. Its incidence increases with age, ranging from 34% in the fifth decade, to 70% over the 80 years. Aim of the Work The objective of the study is to correlate and compare the DWI and ADC values of prostate cancer with the histopathological grading via Gleason score. Patients and Methods During a period of 6 months duration thirty patients were enrolled in the study. All patients with elevated PSA values greater than 4 ng/ml underwent sextant TRUS guided biopsies. MRI examination was done either prior to the TRUS biopsy or at least 3 weeks after the TRUS biopsy. Results In our study, we found that the ADC values correlated significantly with Gleason score of prostate cancer based on TRUS biopsy with significant p value &amp;lt; 0.001 and were significantly predictive of high-grade prostate cancer. This outcome agrees with recent reports that found an inverse correlation between ADC and Gleason score. They confrmed the correlation between ADC derived DWI and tumor alteration. ADC analysis seems to show a high capability in defining the tumor aggressiveness, as in monitoring radiation therapy effect or surgical recurrence. Conclusion ADC values showed a negative correlation with GS. This study suggests that ADC values may allow the noninvasive assessment of biological aggressiveness of prostate cancer, which may contribute in targeting biopsy to MRI-positive foci using a fusion of MRI and transrectal ultrasound. Incorporating the GS on targeted biopsy with the ADC value will further improve the detection of highgrade cancer foci also plays a role in planning initial treatment strategies.

Expression and clinical value of NLRP1 and NLRC4 inflammasomes in prostate cancer.

The present study explored the clinical value of the protein expression levels of nucleotide binding oligomerization-like receptor family pyrin domain containing 1 (NLRP1) and nucleotide-binding oligomerization domain leucine-rich repeat and caspase recruitment domain-containing 4 (NLRC4) inflammasomes in the diagnosis and treatment of prostate cancer. A total of 54 patients with prostatic hyperplasia and 58 patients with prostate cancer were recruited at The First People's Hospital of Pinghu between January and May 2022. Immunohistochemical staining was used to determine the protein expression levels of the NLRP1 and NLRC4 inflammasomes in addition to the proinflammatory cytokines IL-18 and IL-1β in the two groups of patients. The protein expression levels of NLRP1 and NLRC4 inflammasome were significantly increased in patients with prostate cancer compared with patients with prostate hyperplasia. The differences in expression of NLRP1 and NLRC4 inflammatory vesicles in prostate cancer of different stages were also compared based on data from The Cancer Genome Atlas. The protein expression level of NLRP1 demonstrated a significant positive correlation with IL-1β and IL-18 expression, and the protein expression level of the NLRC4 inflammasome was significantly positively correlated with IL-18 expression. The protein expression levels of both NLRP1 and NLRC4 demonstrated a significant positive correlation with the Gleason score of prostate cancer. The expression of NLRP1 in tumor (T)3/T4 was significantly higher compared with T1 and expression of the NLRC4 inflammasome in T2 and T3/T4 was significantly higher compared with T1. Expression of the NLRP1 and NLRC4 inflammasomes was significantly higher in patients with prostate cancer, compared with patients with prostatic hyperplasia. Therefore, expression of NLRP1 and NLRC4 may promote tumorigenesis by promoting the maturation and release of proinflammatory cytokines IL-1β and IL-18. Expression of the NLRP1 and NLRC4 inflammasomes demonstrated a significant positive correlation with the risk of prostate cancer. Expression of the NLRP1 and NLRC4 inflammasomes in middle- and advanced-stage tumors was higher compared with early-stage tumors. These results suggested that inflammasome expression may serve a significant role in the progression of tumors and could provide a fixed value for the risk assessment and prognosis prediction of prostate cancer.

The incidence and risk factors predictive of bone metastases at initial diagnosis of malignancy.

e18816 Background: Bone metastases are associated with increased morbidity and decreased quality of life in patients with solid tumors. We report here the incidence and predictive factors for bone metastases at initial diagnosis in a large population-based dataset. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify patients 18 years-old or older and with solid cancers of all stages diagnosed from 2010 to 2019. We calculated the incidence and predictive factors for bone metastases according to demographic and tumor characteristics. Results: Among the 1,132,154 patients identified, 1,075,069 (94.96%) had known bone metastasis status and were eligible for the study. Bone metastases were detected in 55,903 (4.9%) patients. Among patients with bone metastases, the most common primary tumors were lung (44.4%), prostate (19.3%), breast (12.4%), kidney (4.0%), colorectal (2.24%) and pancreas (2.23%). The percentage of bone metastases in patients with non-small cell lung cancer (NSCLC), prostate adenocarcinoma, infiltrating ductal carcinoma of the breast (IDC), and colorectal adenocarcinoma (CRC) was 18.04% (18,123 out of 100,447), 5.92% (9,071 out of 153,325), 2.9% (4,490 out of 154,626), and 1.17% (1,043 out of 89,510) respectively. In addition to T and N stage, independent risk factors by multivariable analysis for bone metastases included female sex and non-squamous histology in NSCLC, black race, hormonal and HER2 status in breast cancer, Gleason score and PSA in prostate cancer, male sex, black race, rectal location, liver metastases and lung metastases in CRC. In univariate analysis, the strongest predictors for bone metastases were N2/3 (28.3%) and T3/4 (24.9%) in NSCLC, T3/4 (15.8%), N2/3 (10.6%), N1 (5.9%) and HER2 positive (4.9%) in IDC, N1 (38.1%), Gleason score 9/10 (22.8%), PSA 98 or higher (19.5%) and PSA 20-97 (16.8%) in prostate cancer, and rectal or rectosigmoid location (1.43% vs 1.05% in colon cancer, p &lt; 0.001) in CRC. Conclusions: Approximately 5% of patients with solid tumors have bone metastases at presentation. In addition to T and N stage, there are several risk factors for bone metastases according to the primary tumor site.

Investigating the association (assoc) between mental health illness (MHI) and development of prostate cancer (PC) in a nationwide matched cohort of &gt;5,000,000 US Veterans.

e17113 Background: Previous reports suggest that men with MHI who subsequently develop PC have worse outcomes. However, it has not been established whether a history of MHI is associated with a PC diagnosis (dx) or more aggressive PC. The objective of this study was to (i) investigate the assoc between MHI and development of PC, and (ii) in a subset of men with available Gleason Score (GS) data, assess the assoc between MHI and aggressive PC as measured by GS ≥7 PC at dx. Methods: This was a retrospective matched-cohort analysis to assess the assoc between MHI and time to PC dx in the Veterans Affairs (VA) Health Care System. Men with an ICD code for MHI dx between 2000-2020 were matched 1:1 to men with no MHI dx in the same time frame. The MHI dx date of the exposed (exp) male was assigned as the index date of the unexp male. Variables matched on included race, age at MHI exp (+/- 3 years), census region, and median household income. Uni- and multivariable competing risks models were used to test the assoc between MHI and time to PC using death from other causes as competing risk. In a subset of men with GS data available, logistic regression was used to test the assoc between MHI and GS ≥7 at PC dx. All multivariable models were adjusted for age, race, census region, income, Charlson Comorbidity Index, year of MHI index, and year of VA entry. Results: There were 2,597,810 MHI-exp men matched 1:1 to unexp men. During a median (Q1, Q3) follow up of 128 (65, 192) months, 390,977 PC diagnoses were observed (172,442 MHI-exp vs. 218,535 unexp). MHI-exp men were significantly less likely to be diagnosed with PC than unexp men in both uni- (HR: 0.798, 95% CI: 0.793-0.803) and multivariable (HR: 0.763, 95% CI: 0.758-0.768) analysis. Cumulative incidence estimates at 3, 5, 10, and 20 years were 2.1%, 3.1%, 5.2%, and 8.5%, respectively, for MHI-exp men vs 2.9%, 4.2%, 6.9%, and 11.7%, respectively, for unexp men. Among men with PC and available GS data (n = 51,404), 32,645 had GS ≥7 PC, of which 20,749 were MHI-exp vs 11,896 unexp. MHI-exp men had significantly higher odds of GS ≥7 PC in both uni- (OR: 2.38, 95% CI: 2.32-2.43) and multivariable (OR: 1.97, 95% CI: 1.92-2.02) analysis. Conclusions: Men with MHI are 20% less likely to be diagnosed with PC, however when diagnosed, are nearly 2 times more likely to have aggressive PC compared to non-MHI men. Although speculative based on the nature of the study, this may be due to poorer access to preventative health services resulting in delayed diagnoses.

Retrospective review of 250 gallium-68 PSMA scans in prostate cancer patients in Health Canada Special Access Program.

e17035 Background: Prostate Cancer is the most common malignancy in men, and until recently has been staged using standard imaging tests, such as ultrasound, CT, bone scan, and MRI. A novel imaging test targeting the membrane receptor, Prostate Specific Membrane Antigen (PSMA) using the PET tracer Ga-68 PSMA-11 has recently been approved in several countries for staging and assessing potential for radionuclide therapy. This agent was only recently approved in Canada, but has been available through the Health Canada Special Access (SA) Program for over a year, until it was approved (Illuccix, Telix Pharmaceuticals). Methods: In the past year, 275 prostate cancer patients were scanned at our facility (INITIO Medical Imaging, Burnaby BC) through Health Canada SA. All patients were required to have positive prostate cancer biopsies, with negative or indeterminate CT and bone scan, and rising PSA (prostate specific antigen) blood test in order to qualify for SA, or high risk Gleason Score prostate cancer. Results: Of the 275 patients, 71 had high risk cancers and 28/71 had positive PSMA studies (39%). 185 of the patients had biochemical evidence of recurrence and negative /indeterminate conventional imaging tests, and 101/185 or 55% had positive PSMA scans. All other patients were scanned for metastatic castrate resistant prostate cancer (MCRPC). Conclusions: Ga-68 PSMA scans are highly sensitive for upstaging patients at high risk of prostate cancer metastasis or recurrence, even with negative conventional imaging studies.

Goodness-of-fit two-phase sampling designs for time-to-event outcomes: a simulation study based on New York University Women’s Health Study for breast cancer

BackgroundSub-cohort sampling designs such as a case-cohort study play a key role in studying biomarker-disease associations due to their cost effectiveness. Time-to-event outcome is often the focus in cohort studies, and the research goal is to assess the association between the event risk and risk factors. In this paper, we propose a novel goodness-of-fit two-phase sampling design for time-to-event outcomes when some covariates (e.g., biomarkers) can only be measured on a subgroup of study subjects.MethodsAssuming that an external model, which can be the well-established risk models such as the Gail model for breast cancer, Gleason score for prostate cancer, and Framingham risk models for heart diseases, or built from preliminary data, is available to relate the outcome and complete covariates, we propose to oversample subjects with worse goodness-of-fit (GOF) based on an external survival model and time-to-event. With the cases and controls sampled using the GOF two-phase design, the inverse sampling probability weighting method is used to estimate the log hazard ratio of both incomplete and complete covariates. We conducted extensive simulations to evaluate the efficiency gain of our proposed GOF two-phase sampling designs over case-cohort study designs.ResultsThrough extensive simulations based on a dataset from the New York University Women’s Health Study, we showed that the proposed GOF two-phase sampling designs were unbiased and generally had higher efficiency compared to the standard case-cohort study designs.ConclusionIn cohort studies with rare outcomes, an important design question is how to select informative subjects to reduce sampling costs while maintaining statistical efficiency. Our proposed goodness-of-fit two-phase design provides efficient alternatives to standard case-cohort designs for assessing the association between time-to-event outcome and risk factors. This method is conveniently implemented in standard software.

PO-2112 Radiomics of diffusion-MRI for predicting Gleason Score in Prostate Cancer treated with radiotherapy

PO-2112 Radiomics of diffusion-MRI for predicting Gleason Score in Prostate Cancer treated with radiotherapy

HP1α promotes the progression of prostate cancer.

Patients who have been diagnosed with prostate cancer (PCa) typically have a dismal outlook and few therapeutic choices available to them, because the precise pathogenesis of the disease is not yet fully understood. The presence of HP1α, also known as the heterochromatin protein 1α, is required for the creation of higher-order chromatin structures. However, little is known about HP1α that serves roles in the pathogenesis of PCa. The primary purpose of our research was to investigate alterations in the levels of HP1α expression and to plan a series of tests to validate the function of HP1α in PCa. Information on HP1α expression in PCa and benign prostatic hyperplasia (BPH) tissues were gathered using the Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) databases. RT-qPCR, western blotting, and immunohistochemistry (IHC) were used to assess HP1α mRNA and protein expression in several human PCa tissues and cell lines. The CCK8 assay, clone formation assay, and transwell assay were used to examine biological activities including cell proliferation, migration, and invasion. The expression of proteins connected to apoptosis and the epithelial-mesenchymal transition (EMT) was examined using Western blot. The tumorigenic effect of HP1α was also verified by in vivo experiments. HP1α expression was much higher in PCa than in BPH tissues and cells, and was positively correlated with the Gleason score of PCa. In vitro experiments showed that HP1α knockdown could inhibit the ability of proliferation, invasion, and migration of PC3 and LNCaP cells, and promote cell apoptosis and EMT. In vivo experiments showed that HP1α knockdown inhibited tumorigenesis in mice. Our findings indicate that HP1α expression promotes PCa development and might be a novel therapeutic target for the diagnosis or treatment of PCa.

Abstract 737: Variation in prognostic value of Gleason score for lethal prostate cancer using time-varying area under the curve

Abstract Background: Gleason score is one of the strongest prognostic factors for prostate cancer survival in patients with primary prostate cancer. However, its predictive accuracy is less clear during extended follow-up periods. Methods: We included men diagnosed with prostate cancer in the Physicians’ Health Study (PHS) and Health Professionals Follow-up Study (HPFS) between 1982 and 2009. We included individuals with available radical prostatectomy tissue specimens and undertook a central standardized histopathologic re-review of H&amp;E slides including Gleason score according to the 2014 International Society of Urological Pathology Consensus. Comparing pathologic Gleason score, we estimated hazard ratios (HR) and 95% confidence intervals (CIs) for lethal prostate cancer-free survival from the time of diagnosis using the Cox proportional hazards regression through 2018. Lethal prostate cancer was defined as development of distant metastases or prostate cancer-specific death. We assessed the time-dependent predictive accuracy of Gleason score over 10, 15, 20, and 25-years post-diagnosis with time-varying area under the curve (tAUC) using the Uno estimator. Results: Among the 1,404 prostate cancer patients, 113 developed lethal prostate cancer during a follow-up of up to 30 years (median 17.1 years). There were 11% men with Gleason score of 9-10 (grade group 5), 7.1% with 8 (grade group 4), 23% with 4+3 (grade group 3), 36% with 3+4 (grade group 2), and 23% with 6 (grade group 1). Compared to Gleason score 6, the HRs for lethal disease were 5.6 (95% CI: 1.3 to 24) for score 3+4, 13 (95% CI: 3.1 to 55) for 4+3, 17 (95% CI: 3.8 to 75) for 8, and 27 (95% CI: 6.5 to 117) for 9-10, adjusting for pathological tumor stage, age at diagnosis, and prostate-specific antigen (PSA) level at diagnosis. The overall AUC over the entire follow-up was 0.71 (95% CI: 0.63 to 0.79) for Gleason score alone and 0.76 (95% CI: 0.68 to 0.83) with the addition of stage, PSA, and age. Discrimination for lethal prostate cancer based on Gleason score alone varied over follow-up, peaking at a tAUC of 0.81 (95% CI: 0.77 to 0.85) at 10 years, and slowly declined to 0.78 (95% CI: 0.74 to 0.83) at 15 years, 0.78 (95% CI: 0.74 to 0.81) at 20 years, and 0.74 (95% CI: 0.69 to 0.80) at 25 years post-diagnosis. The tAUC had a similar trend with the addition of stage, PSA, and age, with higher point estimates at each time point. Conclusions: The prognostic value of Gleason score for lethal prostate cancer after prostatectomy, based on genitourinary pathologist re-review, remains high for decades after diagnosis, with only a modest decline over time. Given the long natural history of prostate cancer, an understanding of the factors that contribute to remaining metastasis-free is needed to inform clinical decisions. Citation Format: Zhike Lin, Konrad H. Stopsack, Michelangelo Fiorentino, Jane B. Vaselkiv, Travis Gerke, Andreas Pettersson, Sinead Flanagan, Kathryn L. Penney, Stephen P. Finn, Massimo Loda, Lorelei A. Mucci. Variation in prognostic value of Gleason score for lethal prostate cancer using time-varying area under the curve [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 737.

Diagnosis of Prostate Cancer through the Multi-Ligand Binding of Prostate-Derived Extracellular Vesicles and miRNA Analysis.

The development of new non-invasive markers for prostate cancer (PC) diagnosis, prognosis, and management is an important issue that needs to be addressed to decrease PC mortality. Small extracellular vesicles (SEVs) secreted by prostate gland or prostate cancer cells into the plasma are considered next-generation diagnostic tools because their chemical composition might reflect the PC development. The population of plasma vesicles is extremely heterogeneous. The study aimed to explore a new approach for prostate-derived SEV isolation followed by vesicular miRNA analysis. We used superparamagnetic particles functionalized by five types of DNA-aptamers binding the surface markers of prostate cells. Specificity of binding was assayed by AuNP-aptasensor. Prostate-derived SEVs were isolated from the plasma of 36 PC patients and 18 healthy donors and used for the assessment of twelve PC-associated miRNAs. The amplification ratio (amp-ratio) value was obtained for all pairs of miRNAs, and the diagnostic significance of these parameters was evaluated. The multi-ligand binding approach doubled the efficiency of prostate-derived SEVs' isolation and made it possible to purify a sufficient amount of vesicular RNA. The neighbor clusterization, using three pairs of microRNAs (miR-205/miR-375, miR-26b/miR375, and miR-20a/miR-375), allowed us to distinguish PC patients and donors with sensitivity-94%, specificity-76%, and accuracy-87%. Moreover, the amp-ratios of other miRNAs pairs reflected such parameters as plasma PSA level, prostate volume, and Gleason score of PC. Multi-ligand isolation of prostate-derived vesicles followed by vesicular miRNA analysis is a promising method for PC diagnosis and monitoring.