IDENTIFYING BLADDER UROTHELIAL CARCINOMAS USING FLUORESCENT SIGNAL INTENSITY OF PH LOW INSERTING PEPTIDES

You have accessJournal of UrologyCME1 May 2022PD53-10 IDENTIFYING BLADDER UROTHELIAL CARCINOMAS USING FLUORESCENT SIGNAL INTENSITY OF PH LOW INSERTING PEPTIDES Borivoj Golijanin, Michael DuPont, Ali Amin, Anna Moshnikova, Ohad Kott, Yana Reshetnyak, Oleg Andreev, and Dragan Golijanin Borivoj GolijaninBorivoj Golijanin More articles by this author , Michael DuPontMichael DuPont More articles by this author , Ali AminAli Amin More articles by this author , Anna MoshnikovaAnna Moshnikova More articles by this author , Ohad KottOhad Kott More articles by this author , Yana ReshetnyakYana Reshetnyak More articles by this author , Oleg AndreevOleg Andreev More articles by this author , and Dragan GolijaninDragan Golijanin More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002630.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Urothelial carcinomas (UC) are heterogenous malignancy with an acidic microenvironment. pH low insertion peptides (pHLIPs) are a class of pH specific transmembrane peptides that target the acidic microenvironment of cancer cells. pHLIP variant-3 (Var3-pHLIP) was conjugated to a near infrared fluorescent (NIRF) dye to evaluate the specificity and sensitivity as a UC targeting molecular probe. METHODS: After incubation for 15 minutes with Var3-pHLIP conjugated to indocyaninie green (ICG) or IRDye® 800CW, 38 ex-vivo bladder specimens from patients undergoing robotic assisted laparoscopic radical cystectomy for bladder cancer were placed under NIRF capable laser to excite the fluorophore. Peak signal intensity and the corrected mean intensity of malignant and nonmalignant cases were analyzed. The number of lesions visible under NIRF was compared with the number identified under white light. Paired-samples t-test was used to analyzed these data. Expression of NIRF signal and identification of lesions under white light by urologic oncology pathologist were compared to histopathology for specificity and sensitivity calculations. RESULTS: Of 58 lesions processed for histopathology, 47 (81%) were seen under white light and 57 (98.3%) were seen with Var3-pHLIP, representing an improved diagnosis by 17.3% (p=0.003). In NIRF, Var3-pHLIP demonstrated an average peak signal intensity of 116.4 relative fluorescent units (RFU) in malignant cases, and nonmalignant cases demonstrated an average of 44.3 RFU (p <0.001). Corrected average signal intensity of malignant cases demonstrated an average of 52.9 RFU/μm2 and nonmalignant cases demonstrated an average of 25 RFU/μm2 (p <0.001). Var3-pHLIP demonstrated 98% sensitivity and 100% specificity in identification of UC. CONCLUSIONS: NIRF of Var3-pHLIP enhanced visualization and improved diagnosis of UC. Categorizing into the borader categories of malignant and nonmalignant, the unique Var3-pHLIP NIRF signal can be used to differentiate the two groups on both peak signal intensity and mean intensity per unit area of lesion. NIRF of Var3-pHLIP identified UC irrespective of subtype, previous treatment, and stage. All CIS cases missed by white light cystoscopy were diagnosed using Var3-pHLIP NIRF imaging. Additional work into digital, automated analysis of Var3-pHLIP NIRF signal and development of a fluorescent signal analysis capable cystoscope can lead to diagnosis of bladder cancer at the time of cystoscopy. Source of Funding: This study was funded by internal departmental funds allocated to DG, OA, and YK © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e913 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Borivoj Golijanin More articles by this author Michael DuPont More articles by this author Ali Amin More articles by this author Anna Moshnikova More articles by this author Ohad Kott More articles by this author Yana Reshetnyak More articles by this author Oleg Andreev More articles by this author Dragan Golijanin More articles by this author Expand All Advertisement PDF downloadLoading ...