European urology focus最新文献

Background and objective: The role of cytoreductive nephrectomy (CN) in metastatic renal cell carcinoma (mRCC) in the era of immune checkpoint inhibitors (ICIs) is controversial. We aimed to investigate the survival benefit of CN in patients with mRCC treated with ICIs.

Methods: We searched the EMBASE, MEDLINE, and Web of Science databases up to August 26, 2023 to identify studies comparing overall survival (OS) for patients with mRCC treated with ICIs with or without CN. We reconstructed individual patient data using published Kaplan-Meier curves and performed one- and two-stage meta-analyses using 6-mo and 12-mo landmarks to control for immortal time bias. We also performed subgroup analyses for patients treated with first-line ICI or upfront CN.

Key findings and limitations: We identified eight eligible studies involving a total of 2319 patients. There were statistically significant differences in baseline characteristics (age, clear cell histology, International mRCC Database Consortium scores) between the ICI + CN and ICI-alone groups. Combined CN + ICI therapy was associated with superior OS in the primary analysis (hazard ratio [HR] 0.45, 95% confidence interval [CI] 0.37-0.54) and secondary analyses, and in subgroup analyses for patients receiving first-line ICI therapy (HR 0.39, 95% CI 0.30-0.48) and upfront CN (HR 0.52, 95% CI 0.40-0.69).

Conclusions and clinical implications: CN combined with ICI therapy in mRCC may be associated with superior OS. Further studies are needed to confirm this finding and identify the patients most likely to benefit from CN in this setting.

Patient summary: We compared outcomes after immune checkpoint inhibitor (ICI) therapy, which boosts the immune system to fight cancer, with or without nephrectomy (surgical removal of the kidney) in patients with metastatic kidney cancer. We found that the combination of nephrectomy and ICI therapy was associated with better survival than just ICI therapy.

Background and objective: Prostate cancer (PC) heterogeneity can result in sampling discrepancies during biopsy, leading to inaccurate molecular classifications that affect treatment decisions. We evaluated transcriptomic profile variability between multiparametric magnetic resonance imaging (mpMRI)-targeted biopsy (TBx) and systematic biopsy (SBx) methods using the Decipher GRID platform.

Methods: The study included 205 men from the MAST trial. We analyzed 408 biopsy samples, of which 149 were TBx and 259 were SBx samples. Three prognostic signatures-the Decipher genomic classifier (DGC), cell cycle progression (CCP), and Genomic Prostate Score-were assessed in relation to grade group (GG) and MRI phenotype. Multivariable linear regression was conducted to adjust for the confounding effects of GG and tumor purity.

Key findings and limitations: Unpaired analysis revealed that TBx samples had higher derived GPS and CCP scores than SBx samples (p < 0.05), but the difference was no longer significant after multiple-test adjustment. There was no significant difference in scores between SBx and TBx samples in the subgroup with GG 1 disease. For TBx cores, higher genomic scores were associated with higher Prostate Imaging-Reporting and Data System (PI-RADS) scores in the overall cohort, but not in the GG 1 subgroup. Multivariable analysis revealed significant associations between DGC and CCP scores and PI-RADS scores (p < 0.01). Higher DGC score concordance between TBx and SBx lesions was observed in the low-risk subgroup. A limitation of the study is the small sample size, so further validation is required.

Conclusions and clinical implications: TBx samples yield higher genomic scores than SBx samples, with grade influencing the association between PI-RADS score and genomic risk. For the GG 1 subgroup, there was no correlation between PI-RADS and genomic scores. These findings need further validation to assess the impact of TBx on genomic risk assessment in active surveillance.

Patient summary: We examined the effectiveness of two different biopsy methods in assessing the risk of prostate cancer (PC) progression. We found that while biopsy samples guided by MRI (magnetic resonance imaging) scans often showed higher genetic risk scores than biopsy samples without MRI guidance, the difference was not significant for men with lower-grade PC. Our findings suggest that MRI targeting for biopsy might not always provide additional information about cancer aggressiveness for patients with low-risk PC.

Background and objective: Lenvatinib is a multitargeted tyrosine kinase inhibitor (TKI) used in the upfront and refractory settings for metastatic renal cell carcinoma (mRCC). However, there are limited data on the efficacy of subsequent TKI therapies after lenvatinib. We investigated the activity of TKI therapies after lenvatinib in patients with mRCC.

Methods: We conducted a retrospective analysis of data from the International Metastatic RCC Database Consortium (IMDC). Patients who received post-lenvatinib treatment were divided into two cohorts: a second-line cohort after first-line lenvatinib; and a third-line cohort after second-line lenvatinib. The primary endpoint was the objective response rate (ORR). Secondary endpoints included the time to treatment failure (TTF).

Key findings and limitations: Of the 168 patients included, 122 (73%) had clear-cell histology. In the second-line cohort (n = 20), all patients received first-line pembrolizumab + lenvatinib. The ORR was 50% and median TTF was 19.7 mo for first-line treatment. Median follow-up from initiation of second-line treatment was 4.9 mo. The ORR to second-line treatment was 5% (95% confidence interval [CI] 0.2-25%) and median TTF was 5.8 mo (95% CI 1.9-14.9). In the third-line cohort (n = 34), most patients received second-line everolimus + lenvatinib (97%). The ORR was 31% and median TTF was 9.2 mo for second-line therapy. Median follow-up from initiation of third-line treatment was 14.9 mo. The ORR to third-line treatment was 12% (95% CI 3.3-27%) and median TTF was 2.8 mo (95% CI 1.9-7.4).

Conclusions and clinical implications: Our data demonstrate modest activity of TKI-based therapy after exposure to lenvatinib. The results highlight the need for better treatment options for patients who experience progression on lenvatinib-based therapies.

Patient summary: Lenvatinib is a type of drug called a tyrosine kinase inhibitor (TKI). It is used to treat metastatic kidney cancer either when first diagnosed or after progression on a previous treatment. There is limited information on how patients respond to a different TKI after receiving lenvatinib. Our results show that other TKIs have modest clinical activity after patients have received lenvatinib.

The treatment landscape for metastatic hormone-sensitive prostate cancer (mHSPC) has been extended by another phase 3 randomized control trial (ARANOTE) demonstrating favorable outcomes of a doublet therapy combining the androgen receptor pathway inhibitor (ARPI) darolutamide with androgen deprivation therapy (ADT) over ADT monotherapy. Owing to differences in trial designs, patient enrollment, and most notably different control treatment regimens, we hereby present an updated network meta-analysis (NMA) embedding the doublet therapy with darolutamide within the current treatment regimens. In NMA-derived ranking, darolutamide and ADT showed similar oncological efficacy to the already known doublet therapies for progression-free survival (p = 0.49). These findings were consistent when solely doublet treatments, including apalutamide, enzalutamide, or darolutamide, were stratified according to disease volume. Overall survival (OS) data in ARANOTE are very immature, with up to date no significant differences in OS between the doublet regimen and the control group (hazard ratio: 0.81; 95% confidence interval: 0.59-1.12). The combination of darolutamide and ADT is likely-with the requirement of additional follow-up-to become another standard of care regimen for mHSPC following approval in the future. PATIENT SUMMARY: The phase 3 ARANOTE trial has shown the combination of darolutamide and androgen deprivation therapy (ADT) to have favorable outcomes for metastatic hormone-sensitive prostate cancer (mHSPC) over ADT monotherapy. This combination therapy was as effective as the already known doublet therapies for progression free-survival, and had a very favorable safety and toxicity profile. Following approval, the combination of darolutamide and ADT may become another standard of care regimen for mHSPC.

Background: Defining optimal therapeutic sequencing strategies in prostate cancer (PC) is challenging and may be assisted by artificial intelligence (AI)-based tools for an analysis of the medical literature.

Objective: To demonstrate that INSIDE PC can help clinicians query the literature on therapeutic sequencing in PC and to develop previously unestablished practices for evaluating the outputs of AI-based support platforms.

Design, setting, and participants: INSIDE PC was developed by customizing PubMed Bidirectional Encoder Representations from Transformers. Publications were ranked and aggregated for relevance using data visualization and analytics. Publications returned by INSIDE PC and PubMed were given normalized discounted cumulative gain (nDCG) scores by PC experts reflecting ranking and relevance.

Intervention: INSIDE PC for AI-based semantic literature analysis.

Outcome measurements and statistical analysis: INSIDE PC was evaluated for relevance and accuracy for three test questions on the efficacy of therapeutic sequencing of systemic therapies in PC.

Results and limitations: In this initial evaluation, INSIDE PC outperformed PubMed for question 1 (novel hormonal therapy [NHT] followed by NHT) for the top five, ten, and 20 publications (nDCG score, +43, +33, and +30 percentage points [pps], respectively). For question 2 (NHT followed by poly [adenosine diphosphate ribose] polymerase inhibitors [PARPi]), INSIDE PC and PubMed performed similarly. For question 3 (NHT or PARPi followed by ¹⁷⁷Lu-prostate-specific membrane antigen-617), INSIDE PC outperformed PubMed for the top five, ten, and 20 publications (+16, +4, and +5 pps, respectively).

Conclusions: We applied INSIDE PC to develop standards for evaluating the performance of AI-based tools for literature extraction. INSIDE PC performed competitively with PubMed and can assist clinicians with therapeutic sequencing in PC.

Patient summary: The medical literature is often very difficult for doctors and patients to search. In this report, we describe INSIDE PC-an artificial intelligence (AI) system created to help search articles published in medical journals and determine the best order of treatments for advanced prostate cancer in a much better time frame. We found that INSIDE PC works as well as another search tool, PubMed, a widely used resource for searching and retrieving articles published in medical journals. Our work with INSIDE PC shows new ways in which AI can be used to search published articles in medical journals and how these systems might be evaluated to support shared decision-making.

From bacillus Calmette-Guérin to cutting-edge research, the microbiome has played an integral role in urology treatments and will continue to do so for many generations to come.

Recent preclinical and clinical research has established that the microbiome affects response to immunotherapy, and other work has shown that our diet has strong and rapid effects on the microbiome. Together, these findings have generated strong enthusiasm for the development of therapeutic approaches to exploit these effects. However, inconsistencies in sample collection and data analyses have made it challenging to evaluate the true impact of these interventions. We suggest that the research community needs to develop technical best practices before the true potential of therapeutic microbiome modulation can be realized.