Combination Treatment Options for Castration-Resistant Prostate Cancer

Miller, Dayton T.; Ingersoll, Matthew A.; Teply, Benjamin A.; Lin, Ming‐Fong

doi:10.36255/exonpublications.prostatecancer.combinationtreatment.2021

Cited by 11 publications

(16 citation statements)

References 86 publications

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“…We project that the current ongoing clinical trials will bring more light to the dosimetry of [ 177 Lu]Lu-PSMA, in hopes of optimizing its use so its clinical feasibility will be expanded. There are various studies that have investigated "cocktail therapy" (a combination of drugs used to treat a certain condition) [106], while others explored [ 177 Lu]Lu-PSMA RNT post-exhaustion of standard treatment options. However, there are studies yet to evaluate the optimal sequencing of treatment of options.…”

Section: Discussion and Future Perspectivesmentioning

confidence: 99%

Towards Routine Clinical Use of Dosimetry in [177Lu]Lu-PSMA Prostate Cancer Radionuclide Therapy: Current Efforts and Future Perspectives

et al. 2022

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In light of widely expanding personalized medicine applications and their impact on clinical outcomes, it is naturally befitting to explore all the dimensional aspects of personalized radionuclide therapy (RNT). Adoption of absorbed radiation dose into clinical practice in the field of RNT has been hampered by difficulties such as evidence of dose-effect correlation, technical requirements in quantitative imaging of the radiopharmaceutical, heterogeneity of methods between not only centers, but also across software, hardware and radionuclides used. Additionally, standardized agreed upon definition of outcome measures is being debated whether it be solely related to toxicity, quality of life, survival or other measures. Many clinical RNT activity administrations are still based on empirical/fixed activities, or scaled based on parameters such as body surface area. Although still challenging, a tremendous amount of progress has been made to facilitate routine clinical dosimetry with discussions regarding standardization, harmonization and automated processing techniques. This has also been aided by the development and FDA approval of several companion diagnostics allowing within the theranostic paradigm not only a crude qualitative predictive biomarker but also an objective dosimetry based predictive therapeutic biomarker. This work aims to review the literature of [177Lu]Lu-PSMA RNT, focusing on clinical trials and studies, with the goal to summarize the range of dosimetry techniques and the range of doses calculated to organs and tissues of interest from these techniques. A dosimetry method for [177Lu]Lu-PSMA RNT should be reliable, reproducible and encompassing the knowledge gained from all clinical trials evaluating it. Its translation into clinical routine practice can be achieved with the confirmation that dose calculation represents good clinical efficacy and low treatment-related toxicity. Finally, some future perspectives on the future of [177Lu]Lu-PSMA RNT are made, especially in the rapidly emerging field of artificial intelligence (AI), where deep learning may be able to play a large role in the simplification of dosimetry calculations to aid in their clinical adoption.

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Section: Discussion and Future Perspectivesmentioning

confidence: 99%

Towards Routine Clinical Use of Dosimetry in [177Lu]Lu-PSMA Prostate Cancer Radionuclide Therapy: Current Efforts and Future Perspectives

et al. 2022

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“…c-MYC amplification. The proto-oncogene and regulator gene c-MYC is a transcription factor encoded by the MYC oncogene on 8q24, shown to be constitutively overexpressed in PCa [204][205][206]. Research indicates that c-MYC alters enzyme expressions associated with glycolytic pathways including HK2, PFK1, ENO1, LDHA, and GLUT1 concentrations [207].…”

Section: Metabolomics and Genomicsmentioning

confidence: 99%

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Resurreccion

Fong

2022

Metabolites

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Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

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“…Nevertheless, androgen-deprivation therapy, in general, is not curative. Patients can develop to castration-resistant prostate cancer, which is lethal ( 10 , 11 ). Therefore, timely diagnosis of PCa is of great importance for treatment and prognosis.…”

Section: Introductionmentioning

confidence: 99%

Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis

et al. 2022

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BackgroundProstate cancer (PCa) is one of the most popular cancer types in men. Nevertheless, the pathogenic mechanisms of PCa are poorly understood. Hence, we aimed to identify the potential genetic biomarker of PCa in the present study.MethodsHigh-throughput data set GSE46602 was obtained from the comprehensive gene expression database (GEO) for screening differentially expressed genes (DEGs). The common DEGs were further screened out using The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis includes Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to study related mechanisms. The Cox and Lasso regression analyses were carried out to compress the target genes and construct the high-risk and low-risk gene model. Survival analyses were performed based on the gene risk signature model. The CIBERSORT algorithm was performed to clarify the correlation of the high- and low-risk gene model in risk and infiltration of immune cells in PCa.ResultsA total of 385 common DEGs were obtained. The results of functional enrichment analysis show that common DEGs play an important role in PCa. A three-gene signature model (KCNK3, AK5, and ARHGEF38) was established, and the model was significantly associated with cancer-related pathways, overall survival (OS), and tumor microenvironment (TME)-related immune cells in PCa.ConclusionThis new risk model may contribute to further investigation in the immune-related pathogenesis in progression of PCa.

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Combination Treatment Options for Castration-Resistant Prostate Cancer

Cited by 11 publications

References 86 publications

Towards Routine Clinical Use of Dosimetry in [177Lu]Lu-PSMA Prostate Cancer Radionuclide Therapy: Current Efforts and Future Perspectives

Towards Routine Clinical Use of Dosimetry in [177Lu]Lu-PSMA Prostate Cancer Radionuclide Therapy: Current Efforts and Future Perspectives

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis

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