Ovarian serous borderline tumors (SBTs) have a generally favorable prognosis. Although the risk of progression to low-grade serous carcinoma is well documented, progression to high-grade carcinoma is rare. We report the clinicopathologic features of seven SBTs, each associated with the presence of a morphologically unique high-grade component with an extremely dismal prognosis. All of the SBTs exhibited typical hierarchical branching and scattered eosinophilic cells, whereas the high-grade component consisted of a profuse proliferation of epithelioid cells with abundant dense, eosinophilic cytoplasm, variable nuclear pleomorphism, and evident loss of WT1, estrogen receptor, and p16 positivity. In most cases, the SBT demonstrated an abrupt transition to the high-grade component, but one patient initially presented with the usual SBT and developed a recurrent disease that was composed entirely of the high-grade component. Targeted next-generation sequencing revealed identical driver mutations in both the SBT and high-grade components ( BRAF in 3, KRAS in 1), confirming clonality. Three cases, in addition, harbored telomerase reverse transcriptase promoter mutations in both components. One case, despite insufficient material for sequencing, was BRAF V600E-positive by immunohistochemistry. Most patients with available follow-up data died within 9 months of diagnosis. This study confirms prior reports of ovarian SBT transformation to high-grade carcinoma and further characterizes a distinct subset with abundant dense eosinophilic cytoplasm and an extremely dismal prognosis. The presence of BRAF mutations in a major subset of these tumors questions the notion that BRAF is associated with senescent eosinophilic cells and improved outcomes in SBT. The role of the additional telomerase reverse transcriptase promoter mutations merits further investigation.
<p>Summary of GSEA analysis on Sik-targeted tumors using gene sets from previous publications centered on transcriptional profiling of the Lkb1-deficient state.</p>
Significance Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is a label-free molecular imaging technique that provides a window into the biochemical processes present in benign and malignant prostate tissue. This is important both in improving the understanding of tissue biology and in achieving rapid cancer diagnosis. We applied DESI-MSI to record lipid, carbohydrate, and most importantly, small metabolite images from 54 normal and malignant prostate tissue specimens. Several Krebs cycle intermediates were present at different concentrations in prostate cancer compared with normal tissue. Statistical calculations identified panels of metabolites that could readily distinguish prostate cancer from normal tissue with nearly 90% accuracy in a validation set. The results also indicated that the ratio of glucose to citrate ion signals could be used to accurately identify prostate cancer.
A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.
Abstract Prostate cancer is the most common noncutaneous malignancy among men in the United States and a leading cause of cancer death worldwide. The major clinical challenges in prostate cancer diagnosis include accurate staging, localizing tumors within the prostate, and developing imaging prognostic biomarkers. PET is routinely used in the clinical management of many cancers dues to its high sensitivity (10-15 M), spatial resolution (2-4 mm3), rapid whole-body scan times (<20 min), noninvasiveness, and its ability to provide both anatomic and molecular information when combined with computed tomography (PET/CT). Several PET tracers (e.g., 18F-FDG, 18F-NaF, 11C-choline, 11C-acetate, 18F-fluciclovine, etc.) have been studied for early stage prostate cancer, bone metastases, and recurrent disease; nevertheless, all of these tracers rely on cell uptake and their applications are often limited by the low metabolic activity and a low growth rate of prostate cancer. In this project, we aim to develop a novel enzymatic activity-based PET tracer for in vivo imaging of prostate cancer by targeting methionine aminopeptidase II (MetAP2), a cytosolic metalloprotease that catalyzes the cotranslational removal of the N-terminus initiator methionine residue from nascent proteins. We hypothesize that MetAP2 can serve as a novel diagnostic biomarker of prostate cancer. A MetAP2 activatable molecular imaging probe that self-aggregates in situ can be utilized to image and define this disease. To test our hypothesis, we first measured the expression level of MetAP2 in primary benign human prostate epithelial cells (BS403) and prostate tumor cell lines by Western blot. BS403 cells express significantly lower levels of MetAP2 than PC3 (by 4-fold), DU145 (3-fold), and 22Rv1 (3-fold). The expression of MetAP2 in DU145 tumor xenografts is 12-fold higher and for 22Rv1 14-fold higher, compared to BS403 cells. Immunohistologic analysis of tissues derived from DU145 and 22Rv1 xenografts confirms strong staining of MetAP2. We performed immunohistochemistry on a prostate cancer tissue microarray containing 225 independent patient samples represented in quadruplicate. Positive staining for MetAP2 was found in nearly all of the cancer samples. Encouraged by these data, we designed and synthesized a nonradioactive MetAP2 sensitive nano-aggregation probe called M-SNAP. Incubation of M-SNAP with recombinant MetAP2 enzyme confirmed the formation of the cyclized and aggregated product M-SNAP-cycl by HPLC, TEM (transmission electron microscopy), and DLS (dynamic light scattering). Treatment of PC3 and DU145 cells with or without M-SNAP and the MetAP2 specific inhibitor TNP470 demonstrated MetAP2 dependent intracellular probe aggregation/signal retention using post fluorescent labeling of the cyclized and aggregated products. In future work, we will prepare and study an MetAP2 activatable radioisotope-labeled probe in vitro for sensing and imaging and will evaluate the PET probe in different human prostate xenograft models. MetAP2 sensing probes could provide a highly sensitive and noninvasive PET imaging approach for prostate cancer detection that could be useful in prostate cancer detection, staging, and monitoring of therapeutic response. Citation Format: Jinghang Xie, Meghan Rice, Yunfeng Cheng, Guosheng Song, Christian Kunder, James D. Brooks, Tanya Stoyanova, Jianghong Rao. Methionine aminopeptidase II (MetAP2) activated in situ self-assembly of small-molecule probes for imaging prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B068.
