Sagi Arieh Shpitzer

Background: Metastatic prostate cancer (mPrC), with a median survival of under 2 years, represents an important unmet medical need which may benefit from the development of more effective targeted drug delivery systems. Several cell surface receptors have been identified as candidates for targeted drug delivery to mPrC cells; however, these receptors were selected for their overabundance on PrC cells rather than for their suitability for targeted delivery and uptake of cytotoxic drug payloads. Methods: We describe a novel, unbiased strategy to isolate peptides that fulfill functional criteria required for effective intracellular drug delivery and the specific cytotoxicity of PrC cells without prior knowledge of the targeted receptor. Phage clones displaying 7-mer cyclic peptides were negatively selected in vivo and then positively biopanned through a series of parent and drug-resistant mPrC cells. Peptides from the internalized clones were then subjected to a panel of biochemical and functional tests that led to the selection of several peptide candidates. Results: The selected peptides do not bind PSMA. Peptide-drug conjugates (PDCs) incorporating one of the peptides selectively killed wild-type and drug-resistant PrC cell lines and patient PrC cells but not normal prostate tissue cells in vitro. The PDC also halted the growth of PC3 tumors in a xenograft model. Conclusions: Our study demonstrates that adding unbiased, functional criteria into drug carrier selection protocols can lead to the discovery of novel peptides with appropriate properties required for effective targeted drug delivery into target cancer cells.

Introduction: Current guidelines endorse the use of perioperative chemotherapy (POC) in muscle-invasive bladder cancer (MIBC) to enhance the long-term overall survival (OS) compared to radical cystectomy (RC) alone. This study aims to assess the impact of POC on the OS in frail and morbid (F-M) patients undergoing RC. Methods: A retrospective multicenter study of 291 patients who underwent RC between 2015 and 2019 was performed. Patients with both a Charlson comorbidity index ≥ 4 and Modified Frailty Index ≥ 2 were classified as the F-M cohort. We compared the clinical and pathological characteristics and outcomes of the F-M patients who received POC to those who underwent RC alone. Univariable and multivariable analyses were performed to identify the predictors of the OS. Results: The F-M cohort included 102 patients. POC was administered to 44% of these patients: neoadjuvant (NAC) to 31%, adjuvant (AC) to 19%, and both to 6 (6%). The OS was significantly lower in the F-M cohort compared to in the healthier patients (median OS 42 months, p = 0.02). The F-M patients who received POC were younger, less morbid and had better renal function. Although POC was marginally associated with improved OS in the univariable analysis (p = 0.06), this was not significant in the multivariable analysis (p = 0.50). NAC was associated with improved OS in the univariable analysis (p = 0.004) but not after adjustment for competing factors (p = 1.00). AC was not associated with the OS. Conclusions: POC does not improve the OS in F-M patients undergoing RC. Personalized treatment strategies and further prospective studies are needed to optimize care in this unique vulnerable population.

We thank the commenters for their important insights

Purpose: to evaluate a unique subpopulation of radical prostatectomy (RP) candidates with “negative” prostate ⁶⁸Ga-labeled prostate-specific membrane antigen (PSMA) positron emission tomography (PET) computed tomography (CT) imaging scans and to characterize the clinical implications of misleading findings. Materials and Methods: This case-control retrospective study compared the final histological outcomes of patients with “negative” pre-RP PSMA PET/CT prostate scans (with a prostate maximal standardized uptake value [SUVmax] below the physiologic uptake) to those with an “intense” prostatic tracer uptake (with a SUVmax above the physiologic uptake). The patients underwent an RP between March 2015 and July 2019 in five academic centers. Data on the demographics, comorbidities, prostate-specific antigen (PSA) and rectal exam findings, prior biopsies, imaging results, biopsies, and RP histology results were collected. Results: Ninety-seven of the 392 patients who underwent an RP had PSMA PET/CT imaging preoperatively. Fifty-two (54%) had a “negative” uptake (in the study group), and 45 (46%) had a “positive” uptake (in the control group). Only the lesion size and SUVmax values on the PSMA PET/CT differed between the groups preoperatively. On the histological analysis, only the ISUP score, seminal vesicles invasion, T stage, and positive margin rates differed between the groups (p < 0.05), while 50 (96%) study group patients harbored clinically significant disease (ISUP ≥ 2), with an extra-prostatic disease in 24 (46%), perineural invasion in 35 (67%), and positive lymph nodes in 4 (8%). Conclusions: Disease aggressiveness generally correlated with an intense PSMA uptake on the preoperative PSMA PET/CT, but a subpopulation of patients with clinically significant cancer and aggressive characteristics showed a deceptively weak PSMA uptake. These data raise a concern about the unqualified application of PSMA PET/CT for staging RP candidates.

Background: The COVID-19 outbreak required prompt action by health authorities around the world in response to a novel threat. With enormous amounts of information originating in sources with uncertain degree of validation and accuracy, it is essential to provide executive-level decision-makers with the most actionable, pertinent, and updated data analysis to enable them to adapt their strategy swiftly and competently. Objective: We report here the origination of a COVID-19 dedicated response in the Israel Defense Forces with the assembly of an operational Data Center for the Campaign against Coronavirus. Methods: Spearheaded by directors with clinical, operational, and data analytics orientation, a multidisciplinary team utilized existing and newly developed platforms to collect and analyze large amounts of information on an individual level in the context of SARS-CoV-2 contraction and infection. Results: Nearly 300,000 responses to daily questionnaires were recorded and were merged with other data sets to form a unified data lake. By using basic as well as advanced analytic tools ranging from simple aggregation and display of trends to data science application, we provided commanders and clinicians with access to trusted, accurate, and personalized information and tools that were designed to foster operational changes and mitigate the propagation of the pandemic. The developed tools aided in the in the identification of high-risk individuals for severe disease and resulted in a 30% decline in their attendance to their units. Moreover, the queue for laboratory examination for COVID-19 was optimized using a predictive model and resulted in a high true-positive rate of 20%, which is more than twice as high as the baseline rate (2.28%, 95% CI 1.63%-3.19%). Conclusions: In times of ambiguity and uncertainty, along with an unprecedented flux of information, health organizations may find multidisciplinary teams working to provide intelligence from diverse and rich data a key factor in providing executives relevant and actionable support for decision-making.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The COVID-19 outbreak required prompt action by health authorities around the world in response to a novel threat. With enormous amounts of information originating in sources with uncertain degree of validation and accuracy, it is essential to provide executive-level decision-makers with the most actionable, pertinent, and updated data analysis to enable them to adapt their strategy swiftly and competently. Objective: We report here the origination of a COVID-19 dedicated response in the Israel Defense Forces with the assembly of an operational Data Center for the Campaign against Coronavirus. Methods: Spearheaded by directors with clinical, operational, and data analytics orientation, a multidisciplinary team utilized existing and newly developed platforms to collect and analyze large amounts of information on an individual level in the context of SARS-CoV-2 contraction and infection. Results: Nearly 300,000 responses to daily questionnaires were recorded and were merged with other data sets to form a unified data lake. By using basic as well as advanced analytic tools ranging from simple aggregation and display of trends to data science application, we provided commanders and clinicians with access to trusted, accurate, and personalized information and tools that were designed to foster operational changes and mitigate the propagation of the pandemic. The developed tools aided in the in the identification of high-risk individuals for severe disease and resulted in a 30% decline in their attendance to their units. Moreover, the queue for laboratory examination for COVID-19 was optimized using a predictive model and resulted in a high true-positive rate of 20%, which is more than twice as high as the baseline rate (2.28%, 95% CI 1.63%-3.19%). Conclusions: In times of ambiguity and uncertainty, along with an unprecedented flux of information, health organizations may find multidisciplinary teams working to provide intelligence from diverse and rich data a key factor in providing executives relevant and actionable support for decision-making.