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Combination therapies in metastatic hormone-sensitive prostate cancer (mHSPC), which include the addition of an androgen receptor signaling inhibitor and/or docetaxel to androgen deprivation therapy, have been a game changer in the management of this disease stage. However, these therapies come with their fair share of toxicities and side effects. The goal of this observational study is to report drug-related adverse events (AEs), which are correlated with systemic combination therapies for mHSPC. Determining the optimal treatment option requires large cohorts to estimate the tolerability and AEs of these combination therapies in "real-life" patients with mHSPC, as provided in this study. We use a network of databases that includes population-based registries, electronic health records, and insurance claims, containing the overall target population and subgroups of patients defined by unique certain characteristics, demographics, and comorbidities, to compute the incidence of common AEs associated with systemic therapies in the setting of mHSPC. These data sources are standardised using the Observational Medical Outcomes Partnership Common Data Model. We perform the descriptive statistics as well as calculate the AE incidence rate separately for each treatment group, stratified by age groups and index year. The time until the first event is estimated using the Kaplan-Meier method within each age group. In the case of episodic events, the anticipated mean cumulative counts of events are calculated. Our study will allow clinicians to tailor optimal therapies for mHSPC patients, and they will serve as a basis for comparative method studies.
RESUMO
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
RESUMO
Prostate Cancer Diagnosis and Treatment Enhancement Through the Power of Big Data in Europe (PIONEER) is a European network of excellence for big data in prostate cancer, consisting of 32 private and public stakeholders from 9 countries across Europe. Launched by the Innovative Medicines Initiative 2 and part of the Big Data for Better Outcomes Programme (BD4BO), the overarching goal of PIONEER is to provide high-quality evidence on prostate cancer management by unlocking the potential of big data. The project has identified critical evidence gaps in prostate cancer care, via a detailed prioritization exercise including all key stakeholders. By standardizing and integrating existing high-quality and multidisciplinary data sources from patients with prostate cancer across different stages of the disease, the resulting big data will be assembled into a single innovative data platform for research. Based on a unique set of methodologies, PIONEER aims to advance the field of prostate cancer care with a particular focus on improving prostate-cancer-related outcomes, health system efficiency by streamlining patient management, and the quality of health and social care delivered to all men with prostate cancer and their families worldwide.
Assuntos
Big Data , Pesquisa Biomédica , Neoplasias da Próstata , Humanos , MasculinoRESUMO
RNA interference is a powerful tool for studying gene function and for drug target discovery in diverse organisms and cell types. In mammalian systems, small interfering RNAs (siRNAs), or DNA plasmids expressing these siRNAs, have been used to down-modulate gene expression. However, inefficient transfection protocols, in particular, for primary cell types, have hampered the use of these tools in disease-relevant cellular assays. To be able to use this technology for genome-wide function screening, a more robust transduction protocol, resulting in a longer duration of the knock-down effect, is required. Here, we describe the validation of adenoviral vectors that express hairpin RNAs that are further processed to siRNAs. Infection of cell lines, or primary human cells, with these viruses leads to an efficient, sequence-specific, and prolonged reduction of the corresponding target mRNA, resulting in a reduction of the encoded protein level in the cell. For knock-down of one of the targets, GalphaS, we have measured inhibition of ligand-dependent, G-protein-coupled signaling. It is expected that this technology will prove to be of great value in target validation and target discovery efforts.