Fast-Track Development and Multi-Institutional Clinical Validation of an Artificial Intelligence Algorithm for Detection of Lymph Node Metastasis in Colorectal Cancer.

Lymph node metastasis (LNM) detection can be automated using artificial intelligence (AI)-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer (CRC). This study aimed to develop of a clinical-grade digital pathology tool for LNM detection in CRC using the original fast-track framework. The training cohort included 432 slides from one department. A segmentation algorithm detecting 8 relevant tissue classes was trained. The test cohorts consisted of materials from 5 pathology departments digitized by 4 different scanning systems. A high-quality, large training data set was generated within 7 days and a minimal amount of annotation work using fast-track principles. The AI tool showed very high accuracy for LNM detection in all cohorts, with sensitivity, negative predictive value, and specificity ranges of 0.980 to 1.000, 0.997 to 1.000, and 0.913 to 0.990, correspondingly. Only 5 of 14,460 analyzed test slides with tumor cells over all cohorts were classified as false negative (3/5 representing clusters of tumor cells in lymphatic vessels). A clinical-grade tool was trained in a short time using fast-track development principles and validated using the largest international, multi-institutional, multiscanner cohort of cases to date, showing very high precision for LNM detection in CRC. We are releasing a part of the test data sets to facilitate academic research.

Trophoblast cell surface antigen-2: a promising new biomarker and potential therapeutic target in penile squamous cell carcinoma.

OBJECTIVE: To evaluate the potential utility of antibody-drug conjugates targeting trophoblast cell surface antigen-2 (TROP-2) in patients with primary penile squamous cell carcinoma (PSCC), patients with recurrence (REC cohort), and patient-matched distant metastases (MET cohort), and to assess the potential use of TROP-2 as a predictive non-invasive biomarker in PSCC. METHODS: A cohort comprising a PRIM (n = 37), REC (n = 5) and MET subcohort (n = 7), with MET including lymph node and lung metastases, was analysed using quantitative real-time PCR, ELISA and immunohistochemical staining with evaluation of H-score. RESULTS: TROP-2 mRNA and serum protein levels were significantly increased in primary and recurrent PSCC compared to cancer-free controls (both P < 0.001). Immunohistochemical analysis revealed that most of the PRIM cohort (n = 34/37, median H-score 260, interquartile range [IQR] 210-300), as well as all patients in the REC (median [IQR] H-score 200 [165-290]) and MET cohorts (median [IQR] H-score 280 [260-300]) exhibited moderate to strong membranous TROP-2 expression. Additionally, The H-score (membranous TROP-2 expression) was positively correlated with TROP-2 mRNA (ρ = 0.69, P < 0.0001, R2 = 0.70) and protein levels (ρ = 0.86, P < 0.0001, R2 = 0.59), indicating its potential as a non-invasive biomarker in PSCC. CONCLUSION: In summary, our results support further studies on TROP-2 as a diagnostic and therapeutic target in primary, recurrent and metastatic PSCC.

Intratumoral Abundance of M2-Macrophages is Associated With Unfavorable Prognosis and Markers of T-Cell Exhaustion in Small Cell Lung Cancer Patients.

Small cell lung cancer (SCLC) accounts for about 10% to 15% of lung cancer cases. Unlike non-SCLC, therapy options for SCLC are limited, reflected by a 5-year survival rate of about 7%. At the same time, the rise of immunotherapeutic approaches in cancer therapy has rationalized to account for inflammatory phenotypes in tumors. However, the composition of the inflammatory microenvironment in human SCLC is poorly understood to date. In our study, we used in-depth image analysis of virtual whole-slide-images of 45 SCLC tumors and evaluated different markers of M2-macrophages (CD163 and CD204) together with global immunologic markers (CD4, CD8, CD68, CD38, FOXP3, and CD20) and characterized their abundance intratumorally using quantitative image analysis, combined with a deep-learning model for tumor segmentation. In addition, independent scoring, blinded to the results of the computational analysis, was performed by an expert pathologist (A.Q.) of both CD163/CD204 and PD-L1. To this end, we evaluated the prognostic relevance of the abundance of these cell types to overall survival. Given a 2-tier threshold of the median of the M2 marker CD163 within the study population, there was a 12-month overall survival rate of 22% (95% CI, 10%-47%) for patients with high CD163 abundance and 41% (95% CI, 25%-68%) for patients with low CD163 counts. Patients with increased CD163 had a median overall survival of 3 months compared to 8.34 months for patients with decreased CD163 counts (P = .039), which could be confirmed by an expert pathologist (A.Q., P = .018). By analyzing cases with increased CD163 cell infiltrates, a trend for higher FOXP3 counts and PD-L1 positive cells, together with increased CD8 T-cell infiltrates, was observed, which could be confirmed using an independent cohort at the transcriptional level. Together, we showed that markers of M2 were associated with unfavorable outcome in our study cohort.

Artificial Intelligence-Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens.

Digital pathology adoption allows for applying computational algorithms to routine pathology tasks. Our study aimed to develop a clinical-grade artificial intelligence (AI) tool for precise multiclass tissue segmentation in colorectal specimens (resections and biopsies) and clinically validate the tool for tumor detection in biopsy specimens. The training data set included 241 precisely manually annotated whole-slide images (WSIs) from multiple institutions. The algorithm was trained for semantic segmentation of 11 tissue classes with an additional module for biopsy WSI classification. Six case cohorts from 5 pathology departments (4 countries) were used for formal and clinical validation, digitized by 4 different scanning systems. The developed algorithm showed high precision of segmentation of different tissue classes in colorectal specimens with composite multiclass Dice score of up to 0.895 and pixel-wise tumor detection specificity and sensitivity of up to 0.958 and 0.987, respectively. In the clinical validation study on multiple external cohorts, the AI tool reached sensitivity of 1.0 and specificity of up to 0.969 for tumor detection in biopsy WSI. The AI tool analyzes most biopsy cases in less than 1 minute, allowing effective integration into clinical routine. We developed and extensively validated a highly accurate, clinical-grade tool for assistive diagnostic processing of colorectal specimens. This tool allows for quantitative deciphering of colorectal cancer tissue for development of prognostic and predictive biomarkers and personalization of oncologic care. This study is a foundation for a SemiCOL computational challenge. We open-source multiple manually annotated and weakly labeled test data sets, representing a significant contribution to the colorectal cancer computational pathology field.

DNA Promoter Methylation and ERG Regulate the Expression of CD24 in Prostate Cancer.

CD24 is overexpressed in many human cancers and is a driver of tumor progression. Herein, molecular mechanisms leading to up-regulation of CD24 in prostate cancer were studied. DNA methylation of the CD24 gene promoter at four loci using quantitative methylation-specific PCR was evaluated. Expression of CD24 in tumor tissues was studied by immunohistochemistry. To corroborate the results in vitro, ERG-inducible LNCaP TMPRSS2:ERG (T2E) cells and luciferase promoter assays were used. DNA methylation of the CD24 promoter was significantly higher in tumors than in benign tissue and was associated with biochemical recurrence-free survival, tumor grade, and stage. CD24 mRNA and protein expression were significantly higher in T2E-positive, ERG-overexpressing, and/or PTEN-deficient cases. Higher levels of CD24 protein expression conferred shorter biochemical recurrence-free survival, and these observations were confirmed using The Cancer Genome Atlas prostate adenocarcinoma data. In silico analysis of the CD24 promoter revealed an ERG binding site in between the DNA methylation sites. ERG overexpression led to a strong induction of CD24 mRNA and protein expression. Luciferase promoter assays using the wild-type and mutated ERG binding site within the CD24 promoter showed ERG-dependent activation. Collectively, our results suggest that promoter DNA methylation of the CD24 gene and T2E fusion status are factors involved in the up-regulation of CD24 in patients with prostate cancer.

N6 -Methyladenosine (m6 A) readers are dysregulated in renal cell carcinoma.

N6 -Methyladenosine (m6 A) is the most common modification of messenger RNA (mRNA) in mammals. It critically influences RNA metabolism and plays an essential role in virtually all types of bioprocesses including gene expression, tissue development, self-renewal and differentiation of stem cells, stress response and circadian clock control. It plays a crucial role in carcinogenesis and could be used as a prognostic and a diagnostic tool and as a target for new anticancer therapies. m6 A modification is dynamically and reversibly regulated by three types of proteins. Methyltransferases, so-called "writers" add a methyl group to the adenosine, which can be removed by demethylases, also called "erasers." m6 A-specific RNA-binding proteins, from here on referred to as "readers," preferentially bind to the m6 A site and mediate biological functions, such as translation, splicing or decay of RNA. In this study, we examined the expression of the six m6 A readers HNRNPA2B1, HNRNPC, YTHDC1 and YTHDF1-3 in clear cell renal carcinoma (ccRCC). We show that on mRNA level the expression of all six m6 A readers is significantly downregulated compared to normal renal tissue and on protein level five out of six readers are dysregulated. Lower levels of some m6 A readers are correlated with advanced stage and grade as well as associated with a shorter overall, progression-free and cancer-specific survival. In summary, we could show that m6 A readers are dysregulated in ccRCC and might therefore act as a tumor marker, could give further information on the individual prognosis and be a target of innovative cancer therapy.

Quality control stress test for deep learning-based diagnostic model in digital pathology.

Digital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections' thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts' influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of diagnostic model accuracy losses in the context of artifacts are warranted. Stress-testing of diagnostic models using synthetically generated artifacts might be an essential step during clinical validation of deep learning-based algorithms.

Targeted Therapy in Patients with Metastatic Male Germ Cell Tumors.

Ten to fifteen percent of patients with metastatic testis cancer (mGCT) will develop chemorefractory disease of which about 50% will die. We report on the integration of next generation sequencing in daily clinical practice to identify druggable mutations in metastatic lesions of 3 patients with mGCT. Mutational analysis revealed KIT D820G, TP53, and NPM1 mutations as well as mismatch repair deficiency with loss of MSH2 and MSH6 proteins so that targeted therapy with sunitinib (n = 2) or pembrolizumab (n = 1) was initiated resulting in remarkable partial remissions for 9, 12+, and 15 months.

Otoferlin is a prognostic biomarker in patients with clear cell renal cell carcinoma: A systematic expression analysis.

OBJECTIVES: To comprehensively investigate the role of otoferlin as a prognostic and diagnostic biomarker in clear cell renal cell carcinoma. METHODS: Three independent cohorts were used to study otoferlin in clear cell renal cell carcinoma: The Cancer Genome Atlas cohort (messenger ribonucleic acid expression; clear cell renal cell carcinoma n = 514, normal renal tissue n = 81); study validation cohort (messenger ribonucleic acid expression; clear cell renal cell carcinoma n = 79, normal renal tissue n = 44); and immunohistochemistry cohort (protein expression; clear cell renal cell carcinoma n = 142, normal renal tissue n = 30). Otoferlin gene expressions were extracted from The Cancer Genome Atlas database or determined using quantitative real-time polymerase chain reaction, respectively. Protein expression was assessed using immunohistochemistry staining against otoferlin on tissue microarrays. Correlations between otoferlin messenger ribonucleic acid/protein expression and clinicopathological data/patient survival were statistically tested. RESULTS: Otoferlin messenger ribonucleic acid expression was significantly upregulated in clear cell renal cell carcinoma compared with normal renal tissue. High expression levels correlated with advanced stage, higher grade and metastatic tumors, accompanied by independent prognostic significance for overall and cancer-specific survival. In contrast, otoferlin protein expression was downregulated in tumor tissue. Although, high otoferlin expression in clear cell renal cell carcinoma was positively correlated with histological grading and independently predictive of a shortened progression-free survival. CONCLUSION: Our data suggest otoferlin as an indicator of tumor aggressiveness and as a prognostic biomarker for patients with clear cell renal cell carcinoma, leading to the conclusion that otoferlin could promote the malignancy of clear cell renal cell carcinoma.

Integrative clinical transcriptome analysis reveals TMPRSS2-ERG dependency of prognostic biomarkers in prostate adenocarcinoma.

In prostate adenocarcinoma (PCa), distinction between indolent and aggressive disease is challenging. Around 50% of PCa are characterized by TMPRSS2-ERG (T2E)-fusion oncoproteins defining two molecular subtypes (T2E-positive/negative). However, current prognostic tests do not differ between both molecular subtypes, which might affect outcome prediction. To investigate gene-signatures associated with metastasis in T2E-positive and T2E-negative PCa independently, we integrated tumor transcriptomes and clinicopathological data of two cohorts (total n = 783), and analyzed metastasis-associated gene-signatures regarding the T2E-status. Here, we show that the prognostic value of biomarkers in PCa critically depends on the T2E-status. Using gene-set enrichment analyses, we uncovered that metastatic T2E-positive and T2E-negative PCa are characterized by distinct gene-signatures. In addition, by testing genes shared by several functional gene-signatures for their association with event-free survival in a validation cohort (n = 272), we identified five genes (ASPN, BGN, COL1A1, RRM2 and TYMS)-three of which are included in commercially available prognostic tests-whose high expression was significantly associated with worse outcome exclusively in T2E-negative PCa. Among these genes, RRM2 and TYMS were validated by immunohistochemistry in another validation cohort (n = 135), and several of them proved to add prognostic information to current clinicopathological predictors, such as Gleason score, exclusively for T2E-negative patients. No prognostic biomarkers were identified exclusively for T2E-positive tumors. Collectively, our study discovers that the T2E-status, which is per se not a strong prognostic biomarker, crucially determines the prognostic value of other biomarkers. Our data suggest that the molecular subtype needs to be considered when applying prognostic biomarkers for outcome prediction in PCa.

The N6 -methyladenosine (m6 A) erasers alkylation repair homologue 5 (ALKBH5) and fat mass and obesity-associated protein (FTO) are prognostic biomarkers in patients with clear cell renal carcinoma.

OBJECTIVES: To comprehensively investigate the role of the N6 -methyladenosine (m6 A) erasers ALKBH5 and FTO in clear cell renal cell carcinoma (ccRCC), other RCC subtypes, and oncocytoma with respect to prognostic value and biomarker potential. PATIENTS AND METHODS: The collection of tissue samples was performed within the framework of the Biobank at the Centre for Integrated Oncology Cologne-Bonn. The gene expressions of alkylation repair homologue 5 (ALKBH5) and fat mass and obesity-associated protein (FTO) were determined using quantitative real-time polymerase chain reaction. ALKBH5 and FTO expressions were further investigated in ccRCC, papillary RCC, chromophobe RCC, sarcomatoid RCC, oncocytoma, and benign renal tissue using tissue microarrays. RESULTS: ALKBH5 mRNA, as well as ALKBH5 and FTO protein expressions, was significantly downregulated in ccRCC compared to normal tissue and most of the other studied tumour entities. Decreased mRNA levels of ALKBH5 and FTO correlated with a shortened overall and cancer-specific survival following nephrectomy. CONCLUSIONS: Taken together, our present data indicate that the m6 A-demethylases ALKBH5 and FTO are dysregulated in ccRCC and could be used as prognostic biomarkers.

Apelin and apelin receptor expression in renal cell carcinoma.

BACKGROUND: The APLNR (apelin receptor) has been shown to be an essential gene for cancer immunotherapy, with deficiency in APLNR leading to immunotherapy failure. The aim of this study is to investigate the expression of APLN (apelin) and APLNR in patients with renal cell carcinoma (RCC), and its association with clinicopathological parameters and survival. METHODS: Three well-characterised patient cohorts with RCC were used: Study cohort 1 (clear-cell RCC; APLN/APLNR mRNA expression; n = 166); TCGA validation cohort (clear-cell RCC; APLN/APLNR mRNA expression; n = 481); Study cohort 2 (all RCC subtypes; APLNR protein expression/immunohistochemistry; n = 300). Associations between mRNA/protein expression and clinicopathological variables/patients' survival were tested statistically. RESULTS: While APLN showed only very weak association with tumour histological grade (TCGA cohort), APLNR/mRNA protein expression correlate significantly with ccRCC aggressiveness. APLNR is expressed in tumour vasculature and tumour cells at different levels, and these expression levels associate with tumour aggressiveness in opposing directions. APLNR expression was negatively correlated with PD-L1 expression by tumour cells in a subset of patients with ccRCC. APLNR expression in either compartment is an independent prognostic factor for survival of patients with ccRCC. CONCLUSION: The APLNR/APLN-system appears to play an important role in ccRCC, warranting further clinical investigation.

An overview of translational prostate cancer cohorts for prognostic and predictive studies.

The aim of this review is to describe the characteristics of patient cohorts commonly used for translational biomarker research in prostate cancer and to outline the most prominent contemporary cohorts which serve as a source of prognostic and predictive biomarkers. A non-systematic review of the literature was performed to identify and summarise well-characterized translational prostate cancer cohorts that provide state-of-the-art characterization of (i) primary and (ii) metastatic and castration-resistant prostate cancer. The main advantages and features of these cohorts are a substantial number of patients, unique patient groups, comprehensive genetic characterisation of tumours using multi-omics/next-generation sequencing approaches, high-quality control standards and fully or partially open data for the research community. This overview includes the contemporary cohorts which serve as a rich source of new targets for prognostic and predictive biomarkers as well as a reference database for validation of known biomarkers, therefore representing the cohorts whose impact extends over the current state of biomarker research into the near future (5-10 years).

Mitochondrial PIWI-interacting RNAs are novel biomarkers for clear cell renal cell carcinoma.

PURPOSE: PIWI-interacting RNAs (piRNAs) have been suggested to serve as biomarkers in cancer. In this study, we validated the expression profile of two piRNAs derived from mitochondria, piR-34536 and piR-51810, in tissue and serum of a cohort of clear cell renal cell carcinoma (ccRCC) patients. METHODS: Tissue and serum samples of patients with ccRCC were collected prospectively in our biobank. Total RNA was isolated from 118 ccRCC tissues, 75 normal renal tissues as well as 30 serum samples from patients with ccRCC, and 15 serum samples from patients with non-malignant diseases. The expression of piRNAs was determined using quantitative real-time PCR. RESULTS: Both piR-34536 and piR-51810 were downregulated in ccRCC compared to non-malignant renal tissue. Decreased tissue piRNA levels were significant and independent predictors of shortened progression-free, cancer-specific and overall survival of ccRCC patients. The piRNA levels in serum did not differ in ccRCC patients and control subjects. CONCLUSIONS: The expression of piR-34536 and piR-51810 in ccRCC tissues may be used as prognostic biomarkers in ccRCC.

Is high-grade prostatic intraepithelial neoplasia (HGPIN) a reliable precursor for prostate carcinoma? Implications for clonal evolution and early detection strategies.

High-grade prostatic intraepithelial neoplasia (HGPIN) is a documented putative precursor lesion for invasive prostate adenocarcinoma. However, the precise mechanisms of the carcinoma's development from HGPIN are unclear. Many studies have attempted a comparative molecular genetic characterisation of HGPIN and its corresponding carcinoma to study this transformation. However, to date, some HGPIN mimickers, such as intraductal carcinoma, which can engage in retrograde colonisation of the prostatic acini in an HGPIN-like manner, have been described. In this work, we hypothesise that the lesion formerly known as HGPIN adjacent to invasive carcinoma does not necessarily represent its respective precursor lesion. This hypothesis stems from recent morphological, experimental, and theoretical evidence on the development of tumour clonality, as well as recent studies outlining the three-dimensional architecture of prostate adenocarcinomas (most importantly, their interconnection with the tumoural glandular system). Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers.

BACKGROUND: The identification of appropriate biomarkers is essential to support important clinical decisions in patients with prostate cancer. The aim of our study was a systematic bioinformatical analysis of the mRNA expression of all genes available for the prostate adenocarcinoma cohort of The Cancer Genome Atlas (TCGA), regarding their potential prognostic and diagnostic role. METHODS: The study cohort comprises 499 patients (TCGA prostate cancer cohort). mRNA expression data were available for approx. 20,000 genes. The bioinformatical statistical pipeline addressed gene expression differences in tumor vs. benign prostate tissue (including gene set enrichment analysis, GSEA) in samples from tumors with different aggressivenesses (Gleason score), as well as prognostic values in multistep survival analyses. RESULTS: Among all genes analyzed, 1754 were significantly downregulated and 1553 genes were significantly upregulated in tumor tissue. In GSEA, 16 of 30 top enriched biological processes were alterations of epigenetic regulation at different levels. Significant correlation with Gleason Score was evident for 8724 genes (range of Pearson r-values 0.09-0.43; all p < 0.05). In univariate Cox regression analyses, mRNA expression of 3571 genes showed statistically significant association with biochemical recurrence-free survival with a range of hazard ratios 0.3-3.8 (p-value 7.4e- 07 to 0.05). Among these, 571 genes were independently associated with biochemical recurrence in multivariate analysis. Access to the full database including results is provided as supplement. CONCLUSIONS: In our systematic analysis we found a big number of genes of potential diagnostic and prognostic value, many of which have not been studied in prostate cancer to date. Due to the comprehensive nature of this analysis and free access to the results, this study represents a reference database for prostate cancer researchers which can be used as a powerful tool for validation purposes and planning of new studies.

Pathogenic and targetable genetic alterations in 70 urachal adenocarcinomas.

Urachal cancer (UrC) is a rare but aggressive malignancy often diagnosed in advanced stages requiring systemic treatment. Although cytotoxic chemotherapy is of limited effectiveness, prospective clinical studies can hardly be conducted. Targeted therapeutic treatment approaches and potentially immunotherapy based on a biological rationale may provide an alternative strategy. We therefore subjected 70 urachal adenocarcinomas to targeted next-generation sequencing, conducted in situ and immunohistochemical analyses (including PD-L1 and DNA mismatch repair proteins [MMR]) and evaluated the microsatellite instability (MSI) status. The analytical findings were correlated with clinicopathological and outcome data and Kaplan-Meier and univariable/multivariable Cox regression analyses were performed. The patients had a mean age of 50 years, 66% were male and a 5-year overall survival (OS) of 58% and recurrence-free survival (RFS) of 45% was detected. Sequence variations were observed in TP53 (66%), KRAS (21%), BRAF (4%), PIK3CA (4%), FGFR1 (1%), MET (1%), NRAS (1%), and PDGFRA (1%). Gene amplifications were found in EGFR (5%), ERBB2 (2%), and MET (2%). We detected no evidence of MMR-deficiency (MMR-d)/MSI-high (MSI-h), whereas 10 of 63 cases (16%) expressed PD-L1. Therefore, anti-PD-1/PD-L1 immunotherapy approaches might be tested in UrC. Importantly, we found aberrations in intracellular signal transduction pathways (RAS/RAF/PI3K) in 31% of UrCs with potential implications for anti-EGFR therapy. Less frequent potentially actionable genetic alterations were additionally detected in ERBB2 (HER2), MET, FGFR1, and PDGFRA. The molecular profile strengthens the notion that UrC is a distinct entity on the genomic level with closer resemblance to colorectal than to bladder cancer.

Prostate-specific membrane antigen in breast cancer: a comprehensive evaluation of expression and a case report of radionuclide therapy.

PURPOSE: Prostate-specific membrane antigen (PSMA), a protein product of the folate hydrolase 1 (FOLH1) gene, is gaining increasing acceptance as a target for positron emission tomography/computer tomography (PET/CT) imaging in patients with several cancer types, including breast cancer. So far, PSMA expression in breast cancer endothelia has not been sufficiently characterized. METHODS: This study comprised 315 cases of invasive carcinoma of no special type (NST) and lobular breast cancer (median follow-up time 9.0 years). PSMA expression on tumor endothelia was detected by immunohistochemistry. Further, vascular mRNA expression of the FOLH1 gene (PSMA) was investigated in a cohort of patients with invasive breast cancer provided by The Cancer Genome Atlas (TCGA). RESULTS: Sixty percent of breast cancer cases exhibited PSMA-positive endothelia with higher expression rates in tumors of higher grade, NST subtype with Her2-positivity, and lack of hormone receptors. These findings were confirmed on mRNA expression levels. The highest PSMA rates were observed in triple-negative carcinomas (4.5 × higher than in other tumors). Further, a case of a patient with metastatic breast cancer showing PSMA expression in PET/CT imaging and undergoing PSMA radionuclide therapy is discussed in detail. CONCLUSIONS: This study provides a rationale for the further development of PSMA-targeted imaging in breast cancer, especially in triple-negative tumors.

5'-tRNA Halves are Dysregulated in Clear Cell Renal Cell Carcinoma.

PURPOSE: In various malignancies RNA fragments are dysregulated. Our study was designed to determine the expression of 4, 5'-tRNA halves in the tissue and serum of patients with clear cell renal cell carcinoma. MATERIALS AND METHODS: Tissue and serum samples of patients with clear cell renal cell carcinoma and nonmalignant disease were collected prospectively in our biobank. We isolated total RNA from 95 clear cell renal cell carcinomas and 50 normal renal tissues as well as serum RNA from 27 patients with clear cell renal cell carcinoma and 13 with nonmalignant urological disease. To specifically determine the expression of 5'-tRNA halves we dephosphorylated and ligated an adaptor nucleotide to the 3' end of the tRNA halves. The expression levels of 4, 5'-tRNA halves (5'-tRNA-Arg-CCT, 5'-tRNA-Glu-CTC, 5'-tRNA-Leu-CAG and 5'-tRNA-Lys-TTT) were then measured by TaqMan® based quantitative reverse transcription-polymerase chain reaction. RESULTS: All studied 5'-tRNA halves were down-regulated in clear cell renal cell carcinoma tissues, indicating a potential role as a tumor suppressor. Furthermore, we noted decreased expression of 5'-tRNA halves in patients with adverse clinicopathological parameters. All 5'-tRNA halves were expressed at lower levels in nonorgan confined clear cell renal cell carcinoma. The 5'-tRNA-Lys-TTT halves inversely correlated with ISUP (International Society of Urological Pathology) grade. In patients with clear cell renal cell carcinoma 5'-tRNA-Arg-CCT, 5'-tRNA-Glu-CTC and 5'-tRNA-Lys-TTT halves circulated at lower levels than in control subjects, indicating relevance as noninvasive biomarkers. CONCLUSIONS: In patients with clear cell renal cell carcinoma 5'-tRNA halves have potential as diagnostic and prognostic biomarkers. The 5'-tRNA halves may act in a tumor suppressive manner, which requires further research to confirm.

Comprehensive Evaluation of Prostate Specific Membrane Antigen Expression in the Vasculature of Renal Tumors: Implications for Imaging Studies and Prognostic Role.

PURPOSE: Prostate specific membrane antigen is expressed by the endothelium of many tumors. The aim of the study was to find a rationale for prostate specific membrane antigen based imaging and investigate the prognostic role of vascular prostate specific membrane antigen expression in patients with renal cell carcinoma. MATERIALS AND METHODS: A total of 257 patients with renal cell carcinoma were included in study with a median followup exceeding 10.0 years. Prostate specific membrane antigen expression on tumor vessels was detected by immunohistochemistry. Vascular expression of FOLH1 gene (prostate specific membrane antigen) mRNA was investigated in clear cell carcinoma and papillary renal cell carcinoma using TCGA (The Cancer Genome Atlas) data. RESULTS: Endothelial prostate specific membrane antigen protein expression was higher in clear cell than in papillary and chromophobe renal cell carcinoma. Higher grade and stage, metastatic and lethal clear cell renal cell carcinoma showed higher prostate specific membrane antigen expression in tumor vessels. On univariate and multivariate analysis the intensity of positive vs negative endothelial prostate specific membrane antigen protein expression was significantly associated with overall survival. TCGA based analyses confirmed the prognostic role of vascular expression of FOLH1 mRNA. The analyses also supported the usefulness of prostate specific membrane antigen based imaging in cases of clear cell but not papillary renal cell carcinoma. CONCLUSIONS: We provide a rationale for further development of prostate specific membrane antigen targeted imaging in patients with clear cell renal cell carcinoma. The prognostic role of prostate specific membrane antigen was determined at the protein level in clear cell renal cell carcinoma and at the mRNA level in clear cell and papillary renal cell carcinoma.