Citations

337 Background: Prostate cancer remains the most common solid organ malignancy among males in the United States. Commercial biomarkers and multiparametric MRI have been utilized to better triage men with elevated PSA and determine patients most likely to have clinically significant prostate cancer (csPCa) upon prostate biopsy. We studied the combination of MRI with PSA-based and novel extracellular vesicle (EV)-based biomarkers to determine the optimal pre-biopsy testing to reduce the number of unnecessary biopsies. Methods: Men presenting to our clinic with elevated PSA (≥ 2 ng/ml) were prospectively enrolled from October 2019 to March 2022. All men underwent multiparametric MRI and blood draws to determine prostate health index (PHI) scoring, EV density, and PSA density. MRI-fusion biopsy was performed with both systematic and targeted prostate sampling, with clinicians blinded to prior blood testing results. Blood concentrations of prostate EV (ProsEVs) were measured by nanoscale flow cytometry using antibodies against PSMA and STEAP1. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated, and receiver operator characteristic (ROC) curves were constructed separately for all patients, those without prior diagnosis of PCa, and biopsy-naïve patients. Bootstrapping analysis was performed to provide more accurate assessment of the predictive ability of all combinations. Results: Ultimately, 175 men enrolled and underwent MRI, blood testing, and prostate biopsy. Among the 175 patients included, 101 (58%) had never undergone biopsy, 38 (22%) had a previous negative biopsy, and 36 (21%) were on active surveillance (AS) for GS6 disease. AUC of mpMRI (PI-RADS≥3) to predict clinically significant PCa was 0.66 with a sensitivity, specificity, NPV and PPV of 95.2%, 37.6%, 76.0% and 79.26%. MRI combined with STEAP1-EV density had an AUC of 0.77 with a sensitivity, specificity, NPV and PPV of 90.1%, 46.7%, 64.4% and 80.9%. A multivariable model including MRI, STEAP1-EV density, PSA density and PHI density outperformed other combinations with an AUC, sensitivity, specificity, NPV and PPV of 0.90, 89.2%, 68.7%, 72.8% and 88.1%. Conclusions: Combination of mpMRI with ProsEV and PSA derivatives demonstrated better predictive ability for csPCa on biopsy compared to imaging or serum testing alone. Inclusion of prostate volume with biomarkers further increased predictive ability which could lead to a decrease in number of unnecessary biopsies for men with benign conditions and indolent cancer.

334 Background: Prostate cancer (PCa) screening has relied on prostate-specific antigen (PSA) levels to screen patients for biopsy. However, the limited specificity of serum PSA leads to high rates of negative biopsies and detection of clinically insignificant cancers. Tumor-derived extracellular vesicles, microscopic particles released by tumor cells and ubiquitously found in all bodily fluids, have emerged as novel biomarkers for a diverse set of malignancies. This study explored the utility of urine prostate-specific extracellular vesicles (ProsEVs) identified by unique surface protein markers with novel flow cytometry-based techniques as biomarkers for PCa screening. Methods: This prospective study recruited at a tertiary care center between Oct 2019 and Mar 2022. Patients with PSA≥2 ng/ml underwent prebiopsy mpMRI followed by transperineal prostate biopsy (PI-RADS 1-2) or combined MRI-guided biopsy (PI-RADS ≥ 3). Patients with metastatic PCa or prior systemic therapy were excluded. Urine samples were collected at MRI using Colli-Pee. ProsEVs were labeled using monoclonal antibodies for PSMA and STEAP1 and ProsEV concentrations were determined using nanoscale flow cytometry. EV densities were calculated by normalizing EV counts to prostate volume. Multivariable logistic regression models were constructed and model performance was compared used ROC curve analysis. Results: This study had a total of 179 patients prospectively recruited, 95 were diagnosed with Gleason score (GS) ≥ 7 prostate cancer (csPCa), while the remaining had GS <7 or benign disease. Analysis of urinary ProsEV densities revealed a significant dose-response relationship between benign, GS <7 PCa and GS ≥ 7 PCa groups and ProsEV densities (p=0.01 for PSMA, and <0.001 for STEAP1). The log sum of the PSMA and STEAP1 EV densities exhibited the strongest correlation with other ProsEV variables and was selected for model development. ROC curve analysis showed that the final model including Log Sum ProsEVs, PSA, and PI-RADS outperformed one including PSA and PI-RADS (0.733 vs 0.651, p=0.02). Specificity for detecting csPCa at a sensitivity of 90% was 39.5% in the final model compared to 37% for PSA and PI-RADS (Table). Conclusions: This study highlights the potential of urine ProsEVs as novel biomarkers for improving screening specificity for csPCa detection. Future works should explore analysis of intra-ProsEV content to further improve specificity and advance liquid biopsy techniques. [Table: see text]

Background: Nanoparticles (NPs) have been extensively utilized as a drug delivery system to control the release of therapeutic agents to treat cardiac injuries. However, despite the advantages of utilizing NP-based drug delivery for treating heart diseases, the current delivery system lacks specificity in targeting the cardiac tissue, thus limiting its application. Methods: We created three linear peptides, each consisting of 16–24 amino acids. These peptides were conjugated on the surface of NPs, resulting in the formation of cardiac targeting peptide (CTP)-NPs (designated as CTP-NP1, CTP-NP2, and CTP-NP3). To assess their effectiveness, we compared the binding efficiency of these three CTP-NPs to human and mouse cardiomyocytes. Additionally, we determined their distribution 24 h after injecting the CTP-NPs intravenously into adult C57BL/6J mice. Results: When compared to control NPs without CTP (Con-NPs), all three CTP-NPs exhibited significantly increased binding affinity to both human and mouse cardiomyocytes in vitro and enhanced retention in mouse hearts in vivo. A thorough assessment of the heart sections demonstrated that the binding specificity of CTP-NP3 to cardiomyocytes in vivo was significantly greater than that of Con-NPs. None of the three CTP-NPs were proven to cause cardiomyocyte apoptosis. Conclusions: Biocompatible and safe CTP-NP3 can target the heart via binding to cardiomyocytes. This approach of targeting specific molecules-coated NPs may help in delivering therapeutic compounds to cardiomyocytes for the treatment of heart diseases with high efficacy and low toxicity to other tissues.

Background: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations. Methods: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity. Results: 1383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32–1.67]); Black patients (aOR 1.74; 95 CI 1.24–2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70–6.79) and Other (aOR 2.97; 95 CI 1.71–5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83–12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63–3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20–2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66–3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89–22.6]). Hispanic ethnicity, timing, and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort was 9% and 37%, respectively however, it varied according to the BC disease status. Conclusions: Using one of the largest registries on cancer and COVID-19, we identified patient and BC-related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to non-Hispanic White patients. Funding: This study was partly supported by National Cancer Institute grant number P30 CA068485 to Tianyi Sun, Sanjay Mishra, Benjamin French, Jeremy L Warner; P30-CA046592 to Christopher R Friese; P30 CA023100 for Rana R McKay; P30-CA054174 for Pankil K Shah and Dimpy P Shah; KL2 TR002646 for Pankil Shah and the American Cancer Society and Hope Foundation for Cancer Research (MRSG-16-152-01-CCE) and P30-CA054174 for Dimpy P Shah. REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research grant support (UL1 TR000445 from NCATS/NIH). The funding sources had no role in the writing of the manuscript or the decision to submit it for publication. Clinical trial number: CCC19 registry is registered on ClinicalTrials.gov, NCT04354701 .

AbstractBackgroundThe 2018 Leibovich prognostic model for nonmetastatic renal cell carcinoma (RCC) combines clinical, surgical, and pathologic factors to predict progression‐free survival (PFS) and cancer‐specific survival (CSS) for patients with clear cell (ccRCC), papillary (pRCC), and chromophobe (chRCC) histology. Despite high accuracy, <1% of the original cohort was Black. Here, the authors examined this model in a large population with greater Black patient representation.MethodsBy using a prospectively maintained RCC institutional database, patients were assigned Leibovich model risk scores. Survival outcomes included 5‐year and 10‐year PFS and CSS. Prognostic accuracy was determined using area under the curve (AUC) analysis and calibration plots. Black patient subanalyses were conducted.ResultsIn total, 657 (29%) of 2295 patients analyzed identified as Black. Declines in PFS and CSS were observed as scores increased. Discrimination for ccRCC was strong for PFS (AUC: 5‐year PFS, 0.81; 10‐year PFS, 0.78) and for CSS (AUC: 5‐year CSS, 0.82; 10‐year CSS, 0.74). The pRCC AUC for PFS was 0.74 at 5 years and 0.71 at 10 years; and the AUC for CSS was 0.74 at 5 years and 0.70 at 10 years. In chRCC, better performance was observed for CSS (AUC at 5 years, 0.75) than for PFS (AUC: 0.66 at 5 years; 0.55 at 10 years). Black patient subanalysis revealed similar‐to‐improved performance for ccRCC at 5 years (AUC: PFS, 0.79; CSS, 0.87). For pRCC, performance was lower for PFS (AUC at 5 years, 0.63) and was similar for CSS (AUC at 5 years, 0.77). Sample size limited Black patient 10‐year and chRCC analyses.ConclusionsThe authors externally validated the 2018 Leibovich RCC prognostic model and found optimal performance for ccRCC, followed by pRCC, and then chRCC. Importantly, the results were consistent in this large representation of Black patients.Plain Language Summary In 2018, a model to predict survival in patients with renal cell carcinoma (kidney cancer) was introduced by Leibovich et al. This model has performed well; however, Black patients have been under‐represented in examination of its performance. In this study, 657 Black patients (29%) were included, and the results were consistent. This work is important for making sure the model can be applied to all patient populations.

Background: There are different types of grafts for rhinoplasty, each with certain advantages and disadvantages. Fresh frozen cadaveric costal allograft (CCA) provides an alternative to rhinoplasties. The aim of this study was to compare the outcomes of fresh frozen CCA and traditional autologous costal cartilage in cosmetic and reconstructive rhinoplasty procedures. Methods: This is a prospective, single-center, nonrandomized, open-label clinical trial. Objective assessment to evaluate warping, resorption, and displacement of the cartilage was achieved by measuring the differences of standardized values (deviation angle, nasofrontal angle, total facial convexity, nasofacial angle, and nasolabial angle) obtained at 6-months and 12-months postoperative follow-up on standard two-dimensional photographs (Δ = ∣measurement6 − measurement12∣). Subjective assessment was measured by the FACE-Q assessment. Results: Fifty eligible patients between March 2017 and October 2020 were included. The average age was 43.9 ± 16.6 years and the mean follow-up period was 14.8 months. In the control group, the changes (Δ) in the deviation angle and nasolabial angle were greater than in the CCA group (P < 0.05). In the CCA group, the mean score of satisfaction with nose improved at 6 months and 1 year postoperatively (P < 0.05). The mean score of satisfaction with nostrils and overall facial appearance also increased in the CCA group at 6 months postoperatively (P < 0.05). Six patients from the CCA group and 10 patients from the control group experienced postoperative complications. Conclusions: Fresh frozen CCA is a safe and reliable source of rhinoplasty grafts. It is aseptic, readily available, and free of donor site complications.