Preclinical evaluation of (111)In-DTPA-INCA-X anti-Ku70/Ku80 monoclonal antibody in prostate cancer.

The aim of this investigation was to assess the Ku70/Ku80 complex as a potential target for antibody imaging of prostate cancer. We evaluated the in vivo and ex vivo tumor targeting and biodistribution of the (111)In-labeled human internalizing antibody, INCA-X ((111)In-DTPA-INCA-X antibody), in NMRI-nude mice bearing human PC-3, PC-3M-Lu2 or DU145 xenografts. DTPA-conjugated, non-labeled antibody was pre-administered at different time-points followed by a single intravenous injection of (111)In-DTPA-INCA-X. At 48, 72 and 96 h post-injection, tissues were harvested, and the antibody distribution was determined by measuring radioactivity. Preclinical SPECT/CT imaging of mice with and without the predose was performed at 48 hours post-injection of labeled DTPA-INCA-X. Biodistribution of the labeled antibody showed enriched activity in tumor, spleen and liver. Animals pre-administered with DTPA-INCA-X showed increased tumor uptake and blood content of (111)In-DTPA-INCA-X with reduced splenic and liver uptake. The in vitro and in vivo data presented show that the (111)In-labeled INCA-X antibody is internalized into prostate cancer cells and by pre-administering non-labeled DTPA-INCA-X, we were able to significantly reduce the off target binding and increase the (111)In-DTPA-INCA-X mAb uptake in PC-3, PC-3M-Lu2 and DU145 xenografts. The results are encouraging and identifying the Ku70/Ku80 antigen as a target is worth further investigation for functional imaging of prostate cancer

Unanswered questions in prostate cancer : Findings of an international multi-stakeholder consensus by the PIONEER Consortium

Acknowledgements PIONEER is funded through the IMI2 Joint Undertaking and is listed under grant agreement No. 777492. This joint under- taking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.Peer reviewedPostprin

How Can We Improve Patient-Clinician Communication for Men Diagnosed with Prostate Cancer?

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Predictive Models for Assessing Patients’ Response to Treatment in Metastatic Prostate Cancer:A Systematic Review

Background and objective: The treatment landscape of metastatic prostate cancer (mPCa) has evolved significantly over the past two decades. Despite this, the optimal therapy for patients with mPCa has not been determined. This systematic review identifies available predictive models that assess mPCa patients’ response to treatment. Methods: We critically reviewed MEDLINE and CENTRAL in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. Only quantitative studies in English were included with no time restrictions. The quality of the included studies was assessed using the PROBAST tool. Data were extracted following the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews criteria. Key findings and limitations: The search identified 616 citations, of which 15 studies were included in our review. Nine of the included studies were validated internally or externally. Only one study had a low risk of bias and a low risk concerning applicability. Many studies failed to detail model performance adequately, resulting in a high risk of bias. Where reported, the models indicated good or excellent performance. Conclusions and clinical implications: Most of the identified predictive models require additional evaluation and validation in properly designed studies before these can be implemented in clinical practice to assist with treatment decision-making for men with mPCa. Patient summary: In this review, we evaluate studies that predict which treatments will work best for which metastatic prostate cancer patients. We found that existing studies need further improvement before these can be used by health care professionals.</p

Predictive Models for Assessing Patients’ Response to Treatment in Metastatic Prostate Cancer:A Systematic Review

Background and objective: The treatment landscape of metastatic prostate cancer (mPCa) has evolved significantly over the past two decades. Despite this, the optimal therapy for patients with mPCa has not been determined. This systematic review identifies available predictive models that assess mPCa patients’ response to treatment. Methods: We critically reviewed MEDLINE and CENTRAL in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. Only quantitative studies in English were included with no time restrictions. The quality of the included studies was assessed using the PROBAST tool. Data were extracted following the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews criteria. Key findings and limitations: The search identified 616 citations, of which 15 studies were included in our review. Nine of the included studies were validated internally or externally. Only one study had a low risk of bias and a low risk concerning applicability. Many studies failed to detail model performance adequately, resulting in a high risk of bias. Where reported, the models indicated good or excellent performance. Conclusions and clinical implications: Most of the identified predictive models require additional evaluation and validation in properly designed studies before these can be implemented in clinical practice to assist with treatment decision-making for men with mPCa. Patient summary: In this review, we evaluate studies that predict which treatments will work best for which metastatic prostate cancer patients. We found that existing studies need further improvement before these can be used by health care professionals.</p

How Well do Polygenic Risk Scores Identify Men at High Risk for Prostate Cancer? : Systematic Review and Meta-Analysis

OBJECTIVES: Genome-wide association studies have revealed over 200 genetic susceptibility loci for prostate cancer (PCa). By combining them, polygenic risk scores (PRS) can be generated to predict risk of PCa. We summarize the published evidence and conduct meta-analyses of PRS as a predictor of PCa risk in Caucasian men. PATIENTS AND METHODS: Data were extracted from 59 studies, with 16 studies including 17 separate analyses used in the main meta-analysis with a total of 20,786 cases and 69,106 controls identified through a systematic search of ten databases. Random effects meta-analysis was used to obtain pooled estimates of area under the receiver-operating characteristic curve (AUC). Meta-regression was used to assess the impact of number of single-nucleotide polymorphisms (SNPs) incorporated in PRS on AUC. Heterogeneity is expressed as I2 scores. Publication bias was evaluated using funnel plots and Egger tests. RESULTS: The ability of PRS to identify men with PCa was modest (pooled AUC 0.63, 95% CI 0.62-0.64) with moderate consistency (I2 64%). Combining PRS with clinical variables increased the pooled AUC to 0.74 (0.68-0.81). Meta-regression showed only negligible increase in AUC for adding incremental SNPs. Despite moderate heterogeneity, publication bias was not evident. CONCLUSION: Typically, PRS accuracy is comparable to PSA or family history with a pooled AUC value 0.63 indicating mediocre performance for PRS alone.publishedVersionPeer reviewe

Predictive Models for Assessing Patients’ Response to Treatment in Metastatic Prostate Cancer : A Systematic Review

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PIONEER Big Data Platform for Prostate Cancer : Lessons for Advancing Future Real-World Evidence Research

This manuscript has been made open access under a Creative Commons Attribution (CC BY) licence under the terms of the University of Aberdeen Research Publications Policy. https://creativecommons.org/licenses/by/4.0/Peer reviewe

AGREE II Quality assessment of National and International Clinical Practice Guidelines on Prostate Cancer Management by the OPTIMA Consortium

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