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In Vivo Imaging of Methionine Aminopeptidase II for Prostate Cancer Risk Stratification.

ABSTRACT: Prostate cancer is one of the most common malignancies worldwide, yet limited tools exist for prognostic risk stratification of the disease. Identification of new biomarkers representing intrinsic features of malignant transformation and development of prognostic imaging technologies are critical for improving treatment decisions and patient survival. In this study, we analyzed radical prostatectomy specimens from 422 patients with localized disease to define the expression pattern of methionine aminopeptidase II (MetAP2), a cytosolic metalloprotease that has been identified as a druggable target in cancer. MetAP2 was highly expressed in 54% of low-grade and 59% of high-grade cancers. Elevated levels of MetAP2 at diagnosis were associated with shorter time to recurrence. Controlled self-assembly of a synthetic small molecule enabled design of the first MetAP2-activated PET imaging tracer for monitoring MetAP2 activity in vivo. The nanoparticles assembled upon MetAP2 activation were imaged in single prostate cancer cells with post-click fluorescence labeling. The fluorine-18-labeled tracers successfully differentiated MetAP2 activity in both MetAP2-knockdown and inhibitor-treated human prostate cancer xenografts by micro-PET/CT scanning. This highly sensitive imaging technology may provide a new tool for noninvasive early-risk stratification of prostate cancer and monitoring the therapeutic effect of MetAP2 inhibitors as anticancer drugs. SIGNIFICANCE: This study defines MetAP2 as an early-risk stratifier for molecular imaging of aggressive prostate cancer and describes a MetAP2-activated self-assembly small-molecule PET tracer for imaging MetAP2 activity in vivo.

SUBMITTER: Xie J

PROVIDER: S-EPMC8137584 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Importance:Multiparametric magnetic resonance imaging (MRI) in conjunction with MRI-transrectal ultrasound (TRUS) fusion-guided biopsies have improved the detection of prostate cancer. It is unclear whether MRI itself adds additional value to multivariable prediction models based on clinical parameters. Objective:To determine whether an MRI-based prediction model can reduce unnecessary biopsies in patients with suspected prostate cancer. Design, Setting, and Participants:Patients underwent MRI, MRI-TRUS fusion-guided biopsy, and 12-core systematic biopsy in 1 session. The development cohort used to derive the prediction model consisted of 400 patients from 1 institution enrolled between May 14, 2015, and August 31, 2016, and the validation cohort included 251 patients from 2 independent institutions who underwent biopsies between April 1, 2013, and June 30, 2016, at 1 institution and between July 1, 2015, and October 31, 2016, at the other institution. The MRI model included MRI-derived parameters in addition to clinical variables. Area under the curve of receiver operating characteristic curves and decision curve analysis were performed. Main Outcomes and Measures:Risk of clinically significant prostate cancer on biopsy, defined as a Gleason score of 3?+?4 or higher in at least 1 biopsy core. Results:Overall, 193 (48.3%) of the 400 patients in the development cohort (mean [SD] age at biopsy, 64.3 [7.1] years) and 96 (38.2%) of the 251 patients in the validation cohort (mean [SD] age at biopsy, 64.9 [7.2] years) had clinically significant prostate cancer, defined as a Gleason score greater than or equal to 3?+?4. By applying the model to the external validation cohort, the area under the curve increased from 64% to 84% compared with the baseline model (P?<?.001). At a risk threshold of 20%, the MRI model had a lower false-positive rate than the baseline model (46% [95% CI, 32%-66%] vs 92% [95% CI, 70%-100%]), with only a small reduction in the true-positive rate (89% [95% CI, 85%-96%] vs 99% [95% CI, 89%-100%]). Eighteen of 100 fewer biopsies could have been performed, with no increase in the number of patients with missed clinically significant prostate cancers. Conclusions and Relevance:The inclusion of MRI-derived parameters in a risk model could reduce the number of unnecessary biopsies while maintaining a high rate of diagnosis of clinically significant prostate cancers.

Project description:BackgroundIt is not known which risk stratification system has the best discrimination ability for predicting prostate cancer death.MethodsWe identified patients with non-metastatic primary prostate adenocarcinoma diagnosis between 2004 and 2015 using the Surveillance, Epidemiology, and End Results database. Patients were categorized in different risk groups using the three frequently used risk stratification systems of the National Comprehensive Cancer Network guideline (NCCN-g), American Urological Association guideline (AUA-g), and European Association of Urology guideline (EAU-g), respectively. Associations between risk classification and prostate cancer-specific mortality (PCSM) were determined using Kaplan-Meier analyses and multivariable regression with Cox proportional hazards model. Area under the receiver operating characteristics curve (AUC) analyses were used to test the discrimination ability of the three risk grouping systems.ResultsWe analyzed 310,062 patients with a median follow-up of 61 months. A total of 36,368 deaths occurred, including 6,033 prostate cancer deaths. For all the three risk stratification systems, the risk groups were significantly associated with PCSM. The AUC of the model relying on NCCN-g, AUA-g, and EAU-g risk stratification systems for PCSM at specifically 8 years were 0.818, 0.793, and 0.689 in the entire population; 0.819, 0.795, and 0.691 in Whites; 0.802, 0.777, and 0.681 in Blacks; 0.862, 0.818, and 0.714 in Asians; 0.845, 0.806, and 0.728 in Chinese patients. Regardless of the age, marital status, socioeconomic status, and treatment modality, AUC of the model relying on NCCN-g and AUA-g for PCSM was greater than that relying on EAU-g; AUC of the model relying on NCCN-g system was greater than that of the AUA-g system.ConclusionsThe NCCN-g and AUA-g risk stratification systems perform better in discriminating PCSM compared to the EAU-g system. The discrimination ability of the NCCN-g system was better than that of the AUA-g system. It is recommended to use NCCN-g to evaluate risk groups for prostate cancer patients and then provide more appropriate corresponding treatment recommendations.

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In Vivo Imaging of Methionine Aminopeptidase II for Prostate Cancer Risk Stratification.

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