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Current Oncology (Toronto, Ont.) Jun 2024Interest in the oligometastatic prostate cancer (OMPC) is increasing, and various clinical studies have reported the benefits of metastasis-directed radiation therapy...
BACKGROUND
Interest in the oligometastatic prostate cancer (OMPC) is increasing, and various clinical studies have reported the benefits of metastasis-directed radiation therapy (MDRT) in OMPC. However, the recognition regarding the adopted definitions, methodologies of assessment, and therapeutic approaches is diverse among radiation oncologists. This study aims to evaluate the level of agreement for issues in OMPC among radiation oncologists.
METHODS
We generated 15 key questions (KQs) for OMPC relevant to definition, diagnosis, local therapies, and endpoints. Additionally, three clinical scenarios representing synchronous metastatic prostate cancer (mPC) (case 1), metachronous mPC with visceral metastasis (case 2), and metachronous mPC with castration-resistance and history of polymetastasis (case 3) were developed. The 15 KQs were adapted according to each scenario and transformed into 23 questions with 6-9 per scenario. The survey was distributed to 80 radiation oncologists throughout the Republic of Korea. Answer options with 0.0-29.9%, 30-49.9%, 50-69.9%, 70-79.9%, 80-89.9%, and 90-100% agreements were considered as no, minimal, weak, moderate, strong, and near perfect agreement, respectively.
RESULTS
Forty-five candidates voluntarily participated in this study. Among 23 questions, near perfect ( = 4), strong ( = 3), or moderate ( = 2) agreements were shown in nine. For the case recognized as OMPC with agreements of 93% (case 1), near perfect agreements on the application of definitive radiation therapy (RT) for whole metastatic lesions were achieved. While ≥70% agreements regarding optimal dose-fractionation for metastasis-directed RT (MDRT) has not been achieved, stereotactic body RT (SBRT) is favored by clinicians with higher clinical volume.
CONCLUSION
For the case recognized as OMPC, near perfect agreement for the application of definitive RT for whole metastatic lesions was reached. SBRT was more favored as a MDRT by clinicians with a higher clinical volume.
Topics: Male; Humans; Prostatic Neoplasms; Radiation Oncologists; Republic of Korea; Neoplasm Metastasis; Surveys and Questionnaires; Middle Aged
PubMed: 38920729
DOI: 10.3390/curroncol31060245 -
Cells Jun 2024Prostate cancer (PCa) remains a leading cause of mortality among American men, with metastatic and recurrent disease posing significant therapeutic challenges due to a... (Review)
Review
Prostate cancer (PCa) remains a leading cause of mortality among American men, with metastatic and recurrent disease posing significant therapeutic challenges due to a limited comprehension of the underlying biological processes governing disease initiation, dormancy, and progression. The conventional use of PCa cell lines has proven inadequate in elucidating the intricate molecular mechanisms driving PCa carcinogenesis, hindering the development of effective treatments. To address this gap, patient-derived primary cell cultures have been developed and play a pivotal role in unraveling the pathophysiological intricacies unique to PCa in each individual, offering valuable insights for translational research. This review explores the applications of the conditional reprogramming (CR) cell culture approach, showcasing its capability to rapidly and effectively cultivate patient-derived normal and tumor cells. The CR strategy facilitates the acquisition of stem cell properties by primary cells, precisely recapitulating the human pathophysiology of PCa. This nuanced understanding enables the identification of novel therapeutics. Specifically, our discussion encompasses the utility of CR cells in elucidating PCa initiation and progression, unraveling the molecular pathogenesis of metastatic PCa, addressing health disparities, and advancing personalized medicine. Coupled with the tumor organoid approach and patient-derived xenografts (PDXs), CR cells present a promising avenue for comprehending cancer biology, exploring new treatment modalities, and advancing precision medicine in the context of PCa. These approaches have been used for two NCI initiatives (PDMR: patient-derived model repositories; HCMI: human cancer models initiatives).
Topics: Humans; Prostatic Neoplasms; Male; Cellular Reprogramming; Animals
PubMed: 38920635
DOI: 10.3390/cells13121005 -
The Oncologist Jun 2024Prostate cancer is one of the most prevalent malignancies in men. In the United States, 1 in 8 men will be diagnosed with prostate cancer in their lifetime....
Prostate cancer is one of the most prevalent malignancies in men. In the United States, 1 in 8 men will be diagnosed with prostate cancer in their lifetime. Specifically, studies have delved into male subgroups that present a heightened risk for prostate cancer. Despite such high prevalence, prostate cancer can be heterogeneous and carry complexities that manifest differently between individuals. Metastatic hormone-sensitive prostate cancer (mHSPC) often has an abbreviated, aggressive disease course, and can have varying presentations with different molecular profiles that determine response/resistance to the approved treatments targeting the androgen-receptor pathway (eg, enzalutamide, apalutamide, darolutamide, and abiraterone acetate). We present a case of mHSPC quickly progressing to mCRPC, found to have microsatellite instability in mCRPC and excellent response to pembrolizumab, which raises the critical issues of early molecular testing and treatments personalized for the individual patient.
PubMed: 38920278
DOI: 10.1093/oncolo/oyae156 -
International Journal of General... 2024To explore the predictive factors and predictive model construction for the progression of prostate cancer bone metastasis to castration resistance.
Development and Validation of a Clinic Machine Learning Classifier for the Prediction of Risk Stratifications of Prostate Cancer Bone Metastasis Progression to Castration Resistance.
OBJECTIVE
To explore the predictive factors and predictive model construction for the progression of prostate cancer bone metastasis to castration resistance.
METHODS
Clinical data of 286 patients diagnosed with prostate cancer with bone metastasis, initially treated with endocrine therapy, and progressing to metastatic castration resistant prostate cancer (mCRPC) were collected. By comparing the differences in various factors between different groups with fast and slow occurrence of castration-resistant prostate cancer (CRPC). Kaplan-Meier survival analysis and COX multivariate risk proportional regression model were used to compare the differences in the time to progression to CRPC in different groups. The COX multivariate risk proportional regression model was used to evaluate the impact of candidate factors on the time to progression to CRPC and establish a predictive model. The accuracy of the model was then tested using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).
RESULTS
The median time for 286 mCRPC patients to progress to CRPC was 17 (9.5-28.0) months. Multivariate analysis showed that the lowest value of PSA (PSA nadir), the time when PSA dropped to its lowest value (timePSA), and the number of BM, and LDH were independent risk factors for rapid progression to CRPC. Based on the four independent risk factors mentioned above, a prediction model was established, with the optimal prediction model being a random forest with area under curve (AUC) of 0.946[95% CI: 0.901-0.991] and 0.927[95% CI: 0.864-0.990] in the training and validation cohort, respectively.
CONCLUSION
After endocrine therapy, the PSA nadir, timePSA, the number of BM, and LDH are the main risk factors for rapid progression to mCRPC in patients with prostate cancer bone metastases. Establishing a CRPC prediction model is helpful for early clinical intervention decision-making.
PubMed: 38919704
DOI: 10.2147/IJGM.S465031 -
Frontiers in Oncology 2024To develop a semi-automatic model integrating radiomics, deep learning, and clinical features for Bone Metastasis (BM) prediction in prostate cancer (PCa) patients using...
OBJECTIVE
To develop a semi-automatic model integrating radiomics, deep learning, and clinical features for Bone Metastasis (BM) prediction in prostate cancer (PCa) patients using Biparametric MRI (bpMRI) images.
METHODS
A retrospective study included 414 PCa patients (BM, n=136; NO-BM, n=278) from two institutions (Center 1, n=318; Center 2, n=96) between January 2016 and December 2022. MRI scans were confirmed with BM status via PET-CT or ECT pre-treatment. Tumor areas on bpMRI images were delineated as tumor's region of interest (ROI) using auto-delineation tumor models, evaluated with Dice similarity coefficient (DSC). Samples were auto-sketched, refined, and used to train the ResNet BM prediction model. Clinical, radiomics, and deep learning data were synthesized into the ResNet-C model, evaluated using receiver operating characteristic (ROC).
RESULTS
The auto-segmentation model achieved a DSC of 0.607. Clinical BM prediction's internal validation had an accuracy (ACC) of 0.650 and area under the curve (AUC) of 0.713; external cohort had an ACC of 0.668 and AUC of 0.757. The deep learning model yielded an ACC of 0.875 and AUC of 0.907 for the internal, and ACC of 0.833 and AUC of 0.862 for the external cohort. The Radiomics model registered an ACC of 0.819 and AUC of 0.852 internally, and ACC of 0.885 and AUC of 0.903 externally. ResNet-C demonstrated the highest ACC of 0.902 and AUC of 0.934 for the internal, and ACC of 0.885 and AUC of 0.903 for the external cohort.
CONCLUSION
The ResNet-C model, utilizing bpMRI scanning strategy, accurately assesses bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients, facilitating precise treatment planning and improving patient prognoses.
PubMed: 38919538
DOI: 10.3389/fonc.2024.1298516 -
Iranian Journal of Public Health Mar 2024Cadmium, a toxic heavy metal, experienced a surge in production during the 20th century due to the rise of nickel-cadmium batteries, metal plating, and plastic... (Review)
Review
BACKGROUND
Cadmium, a toxic heavy metal, experienced a surge in production during the 20th century due to the rise of nickel-cadmium batteries, metal plating, and plastic stabilizers. Exposure to cadmium primarily occurs through the consumption of contaminated food, such as vegetables and grains, as well as drinking water or inhaling polluted air. The objective of this study was to investigate the relationship between cadmium exposure and the incidence of prostate cancer using a systematic review and meta-analysis approach.
METHODS
This research involved searching and retrieving observational and experimental studies conducted until May 2022 from various databases, including ISI Web of Science, Cochrane, Science Direct, Scopus, Pub-Med, and Google Scholar. Data analysis was performed using Stata 15 statistical software.
RESULTS
The initial search yielded 794 articles, which were subsequently reduced to 427 articles after eliminating duplicates. Following the application of inclusion and exclusion criteria, a total of 16 studies were included in the meta-analysis. The odds ratio of prostate cancer compared to the first quartile of exposure in the second quartile was 1.03 (0.95-1.12), in the third quartile it was 1.12 (0.99-1.26) and in the fourth quartile of exposure was equal to 1.16 (0.79-1.70). Regarding the investigation of the probability of the occurrence of publication bias, the results of Begg's and Egger's tests were not statistically significant.
CONCLUSION
Although exposure to cadmium leads to an increase in the chance of prostate cancer, this chance increase was not statistically significant.
PubMed: 38919294
DOI: 10.18502/ijph.v53i3.15136 -
Frontiers in Pharmacology 2024Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find...
Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show DR approaches and the gap between these strategies and their ultimate application in oncology. The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.
PubMed: 38919258
DOI: 10.3389/fphar.2024.1400029 -
Biology Direct Jun 2024Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause of death among patients....
BACKGROUND
Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause of death among patients. Identifying novel and effective biomarkers is essential for understanding the mechanisms of metastasis in PCa patients and developing successful interventions.
METHODS
Using the GSE8511 and GSE27616 data sets, 21 metastasis-related genes were identified through the weighted gene co-expression network analysis (WGCNA) method. Subsequent functional analysis of these genes was conducted on the gene set cancer analysis (GSCA) website. Cluster analysis was utilized to explore the relationship between these genes, immune infiltration in PCa, and the efficacy of targeted drug IC50 scores. Machine learning algorithms were then employed to construct diagnostic and prognostic models, assessing their predictive accuracy. Additionally, multivariate COX regression analysis highlighted the significant role of POLD1 and examined its association with DNA methylation. Finally, molecular docking and immunohistochemistry experiments were carried out to assess the binding affinity of POLD1 to PCa drugs and its impact on PCa prognosis.
RESULTS
The study identified 21 metastasis-related genes using the WGCNA method, which were found to be associated with DNA damage, hormone AR activation, and inhibition of the RTK pathway. Cluster analysis confirmed a significant correlation between these genes and PCa metastasis, particularly in the context of immunotherapy and targeted therapy drugs. A diagnostic model combining multiple machine learning algorithms showed strong predictive capabilities for PCa diagnosis, while a transfer model using the LASSO algorithm also yielded promising results. POLD1 emerged as a key prognostic gene among the metastatic genes, showing associations with DNA methylation. Molecular docking experiments supported its high affinity with PCa-targeted drugs. Immunohistochemistry experiments further validated that increased POLD1 expression is linked to poor prognosis in PCa patients.
CONCLUSIONS
The developed diagnostic and metastasis models provide substantial value for patients with prostate cancer. The discovery of POLD1 as a novel biomarker related to prostate cancer metastasis offers a promising avenue for enhancing treatment of prostate cancer metastasis.
Topics: Humans; Male; Prostatic Neoplasms; Machine Learning; Immunotherapy; Neoplasm Metastasis; Biomarkers, Tumor; Prognosis; Molecular Docking Simulation; Gene Expression Regulation, Neoplastic
PubMed: 38918844
DOI: 10.1186/s13062-024-00494-x -
Clinical Proteomics Jun 2024Tumorigenesis and progression of prostate cancer (PCa) are indispensably dependent on androgen receptor (AR). Antiandrogen treatment is the principal preference for...
BACKGROUND
Tumorigenesis and progression of prostate cancer (PCa) are indispensably dependent on androgen receptor (AR). Antiandrogen treatment is the principal preference for patients with advanced PCa. However, the molecular characteristics of PCa with antiandrogen intervention have not yet been fully uncovered.
METHODS
We first performed proteome analysis with 32 PCa tumor samples and 10 adjacent tissues using data-independent acquisition (DIA)- parallel accumulation serial fragmentation (PASEF) proteomics. Then label-free quantification (LFQ) mass spectrometry was employed to analyze protein profiles in LNCaP and PC3 cells.
RESULTS
M-type creatine kinase CKM and cartilage oligomeric matrix protein COMP were demonstrated to have the potential to be diagnostic biomarkers for PCa at both mRNA and protein levels. Several E3 ubiquitin ligases and deubiquitinating enzymes (DUBs) were significantly altered in PCa and PCa cells under enzalutamide treatment, and these proteins might reprogram proteostasis at protein levels in PCa. Finally, we discovered 127 significantly varied proteins in PCa samples with antiandrogen therapy and further uncovered 4 proteins in LNCaP cells upon enzalutamide treatment.
CONCLUSIONS
Our research reveals new potential diagnostic biomarkers for prostate cancer and might help resensitize resistance to antiandrogen therapy.
PubMed: 38918720
DOI: 10.1186/s12014-024-09490-9 -
Asian Pacific Journal of Cancer... Jun 2024The alterations of EGFR and HER2/neu as growth factor receptors and the cytoplasmic signal transduction proteins of RAS/RAF/MAP kinases including its end effector...
Evaluation of the Expression EGFR, HER2/NEU and the End Effector ERK of the RAS/RAF/MAP Kinase Pathway in Prostatic Adenocarcinoma for a Possible Role as New Target Therapy.
UNLABELLED
The alterations of EGFR and HER2/neu as growth factor receptors and the cytoplasmic signal transduction proteins of RAS/RAF/MAP kinases including its end effector molecule (ERK) are important in the carcinogenesis of many tumors. The activation of these protooncogenes in prostate cancer is still under investigation. The aim of this work was to study EGFR, HER2- neu, inactive (non-phosphorylated) and active (phosphorylated) ERK expression in prostatic adenocarcinomas in correlation to the clinical and pathological parameters.
METHODS
Immunohistochemistry- using tissue microarrays- for EGFR, HER2/neu, non-phosphorylated, and phosphor-ERK, was performed on tissues from 166 patients- with primary prostatic adenocarcinoma with no prior treatment-. The results of different markers expression were correlated with the clinical and pathological parameters and were analyzed statistically.
RESULTS
The prostatic tissue showed EGFR, HER2 neu, phosphorylated and non-phosphorylated ERK expression in 8.4%, 1.4%, 78.2%, and 83.4% respectively whether low (patchy) or high expression (diffuse). There were no significant correlations found between patient characteristics and expression of the tested markers. The negative immune reactivity for non-phosphorylated ERK and EGFR- was significantly correlated with high tumor stage (p values 0.03 and 0.01, respectively).
CONCLUSION
EGFR and HER2/neu may play a limited role in prostatic adenocarcinoma as they showed positive expression in a limited number of the examined tissues specifically HER2neu. The expression of non-phosphorylated ERK (mostly weak to moderate) and phosphorylated ERK (mostly moderate to strong)- was appreciated in most cases. Thus, we suggest that anti-EGFR drugs may have a limited role in the treatment of castrate-resistant prostate cancer, but anti-MEK/ERK drugs may have more promising role as a target therapy. It is recommended to perform further molecular testing to elucidate the exact mechanism and significance of these markers.
Topics: Humans; Male; Prostatic Neoplasms; ErbB Receptors; Receptor, ErbB-2; Adenocarcinoma; Biomarkers, Tumor; Aged; Middle Aged; Prognosis; Phosphorylation; raf Kinases; Follow-Up Studies; MAP Kinase Signaling System; ras Proteins; Aged, 80 and over; Extracellular Signal-Regulated MAP Kinases; Signal Transduction
PubMed: 38918683
DOI: 10.31557/APJCP.2024.25.6.2193