Sliding Window Optimal Transport for Open World Artifact Detection in Histopathology.

Histological images are frequently impaired by local artifacts from scanner malfunctions or iatrogenic processes - caused by preparation - impacting the performance of Deep Learning models. Models often struggle with the slightest out-of-distribution shifts, resulting in compromised performance. Detecting artifacts and failure modes of the models is crucial to ensure open-world applicability to whole slide images for tasks like segmentation or diagnosis. We introduce a novel technique for out-of-distribution detection within whole slide images, compatible with any segmentation or classification model. Our approach tiles multi-layer features into sliding window patches and leverages optimal transport to align them with recognized in-distribution samples. We average the optimal transport costs over tiles and layers to detect out-of-distribution samples. Notably, our method excels in identifying failure modes that would harm downstream performance, surpassing contemporary out-of-distribution detection techniques. We evaluate our method for both natural and synthetic artifacts, considering distribution shifts of various sizes and types. The results confirm that our technique outperforms alternative methods for artifact detection. We assess our method components and the ability to negate the impact of artifacts on the downstream tasks. Finally, we demonstrate that our method can mitigate the risk of performance drops in downstream tasks, enhancing reliability by up to 77%. In testing 7 annotated whole slide images with natural artifacts, our method boosted the Dice score by 68%, highlighting its real open-world utility.

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-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer. The aim of this study was to develop of a clinical-grade digital pathology tool for LNM detection in colorectal cancer (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 five pathology departments digitized by four different scanning systems. A high-quality, large training dataset 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 0.980-1.000, 0.997-1.000, and 0.913-0.990, correspondingly. Only 5 of 14460 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, multi-scanner cohort of cases to date, showing very high precision for LNM detection in CRC. We are releasing a part of the test datasets to facilitate academic research.

Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology.

Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on 2603 histological images of prostate tissue stained with hematoxylin and eosin. We analyzed various factors influencing tumor grade discordance between the vPatho system and six human pathologists. Our results demonstrated that vPatho achieved comparable performance in prostate cancer detection and tumor volume estimation, as reported in the literature. The concordance levels between vPatho and human pathologists were examined. Notably, moderate to substantial agreement was observed in identifying complementary histological features such as ductal, cribriform, nerve, blood vessel, and lymphocyte infiltration. However, concordance in tumor grading decreased when applied to prostatectomy specimens (κ = 0.44) compared to biopsy cores (κ = 0.70). Adjusting the decision threshold for the secondary Gleason pattern from 5 to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (κ from 0.44 to 0.64). Potential causes of grade discordance included the vertical extent of tumors toward the prostate boundary and the proportions of slides with prostate cancer. Gleason pattern 4 was particularly associated with this population. Notably, the grade according to vPatho was not specific to any of the six pathologists involved in routine clinical grading. In conclusion, our study highlights the potential utility of AI in developing a digital twin for a pathologist. This approach can help uncover limitations in AI adoption and the practical application of the current grading system for prostate cancer pathology.

Cystic Pelvic Masses in Men: A Presentation of Uncommon Cases and a Literature Review.

Unclear cystic masses in the pelvis in male patients are a rare situation and could be of benign or malignant origin. The underlying diseases demand for specific diagnostic and therapeutic approaches. We present a case series of 3 male patients with different clinical symptoms (perineal pain, urinary retention and a large scrotal cyst) related to cystic lesions in the pelvic region. On all patients initial histopathological workup was unclear. All patients underwent surgery with complete resection of the tumor which revealed a broad spectrum of histopathological findings: unusual form of cystic adenocarcinoma of the prostate, malignant transformation of a dysontogenetic cyst, and finally a very rare diagnosis of a malignant tumor of the Cowper gland. This case series and literature review provide clues for a possible diagnostic and therapeutic approach in the case of unclear pelvic cystic masses and could support urologists during the therapy selection in the future.

Tumour area infiltration and cell count in endoscopic biopsies of therapy-naive upper GI tract carcinomas by QuPath analysis: implications for predictive biomarker testing.

Guidelines regulate how many (tumour-bearing) tissue particles should be sampled during gastric cancer biopsy to obtain representative results in predictive biomarker testing. Little is known about how well these guidelines are applied, how the number of tissue particles correlates with the actual tumour-infiltrated area and how many absolute tumour cells are captured. The study included endoscopic biopsies of untreated carcinomas of the upper gastrointestinal (GI)-tract during the 2016-2020 review period. Archival (H&E)-stained histological sections were digitised and the tumour areas were manually annotated. The tumour-bearing tissue area and absolute carcinoma cell count per case were determined by image analysis and compared with a reference primary surgical specimen. Biopsies from 253 patients were analysed. The following mean values were determined: (a) tumour tissue particle number: 6.5 (range: 1-25, standard deviation (SD) = 3.33), (b) number of tumour-bearing tissue particles: 4.7 (range: 1-20, SD = 2.80), (c) tumour-infiltrated area: 7.5 mm2 (range: 0.18-59.46 mm2, SD = 6.67 mm2), (d) absolute tumour cell count: 13,492 (range: 193-92,834, SD = 14,185) and (e) tumour cell count in a primary surgical specimen (tumour size: 6.7 cm): 105,200,176. The guideline-recommended tissue particle count of 10 was not achieved in 208 patients (82.2%) and the required tumour-bearing tissue particle count of 5 was not achieved in 133 patients (52.6%). Tissue particle count, tumour-infiltrated area and tumour cell count were only weakly correlated. Most cases featured an infiltrated area ≥ 4.5 mm2 (156, 61.7%). Cases with more tissue particles showed only a moderate increase in infiltrated area and tumour cells compared to cases with fewer particles. Biopsies are often used to determine predictive biomarkers, particularly Her2/neu and PD-L1. Diagnostic standards to ensure representative material have been suggested in guidelines to reduce false-negative predictions. However, the real-world practice seems to substantially deviate from recommended standards. To the best of our knowledge, this is the first systematic study describing the relationships between endoscopic tissue fragment number, actual infiltrated tumour area and carcinoma cell number. The data question the tissue particle number as a quality assessment parameter. We advocate histopathological reports indicating on which basis statements on therapy-relevant biomarkers were made. Digital pathology has the potential to objectively quantify the tissue for documentation, quality assessment and future clinical studies.

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.

Predicting HPV association using deep learning and regular H&E stains allows granular stratification of oropharyngeal cancer patients.

Human Papilloma Virus (HPV)-associated oropharyngeal squamous cell cancer (OPSCC) represents an OPSCC subgroup with an overall good prognosis with a rising incidence in Western countries. Multiple lines of evidence suggest that HPV-associated tumors are not a homogeneous tumor entity, underlining the need for accurate prognostic biomarkers. In this retrospective, multi-institutional study involving 906 patients from four centers and one database, we developed a deep learning algorithm (OPSCCnet), to analyze standard H&E stains for the calculation of a patient-level score associated with prognosis, comparing it to combined HPV-DNA and p16-status. When comparing OPSCCnet to HPV-status, the algorithm showed a good overall performance with a mean area under the receiver operator curve (AUROC) = 0.83 (95% CI = 0.77-0.9) for the test cohort (n = 639), which could be increased to AUROC = 0.88 by filtering cases using a fixed threshold on the variance of the probability of the HPV-positive class - a potential surrogate marker of HPV-heterogeneity. OPSCCnet could be used as a screening tool, outperforming gold standard HPV testing (OPSCCnet: five-year survival rate: 96% [95% CI = 90-100%]; HPV testing: five-year survival rate: 80% [95% CI = 71-90%]). This could be confirmed using a multivariate analysis of a three-tier threshold (OPSCCnet: high HR = 0.15 [95% CI = 0.05-0.44], intermediate HR = 0.58 [95% CI = 0.34-0.98] p = 0.043, Cox proportional hazards model, n = 211; HPV testing: HR = 0.29 [95% CI = 0.15-0.54] p < 0.001, Cox proportional hazards model, n = 211). Collectively, our findings indicate that by analyzing standard gigapixel hematoxylin and eosin (H&E) histological whole-slide images, OPSCCnet demonstrated superior performance over p16/HPV-DNA testing in various clinical scenarios, particularly in accurately stratifying these patients.

An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading.

Pathologic examination of prostate biopsies is time consuming due to the large number of slides per case. In this retrospective study, we validate a deep learning-based classifier for prostate cancer (PCA) detection and Gleason grading (AI tool) in biopsy samples. Five external cohorts of patients with multifocal prostate biopsy were analyzed from high-volume pathology institutes. A total of 5922 H&E sections representing 7473 biopsy cores from 423 patient cases (digitized using three scanners) were assessed concerning tumor detection. Two tumor-bearing datasets (core n = 227 and 159) were graded by an international group of pathologists including expert urologic pathologists (n = 11) to validate the Gleason grading classifier. The sensitivity, specificity, and NPV for the detection of tumor-bearing biopsies was in a range of 0.971-1.000, 0.875-0.976, and 0.988-1.000, respectively, across the different test cohorts. In several biopsy slides tumor tissue was correctly detected by the AI tool that was initially missed by pathologists. Most false positive misclassifications represented lesions suspicious for carcinoma or cancer mimickers. The quadratically weighted kappa levels for Gleason grading agreement for single pathologists was 0.62-0.80 (0.77 for AI tool) and 0.64-0.76 (0.72 for AI tool) for the two grading datasets, respectively. In cases where consensus for grading was reached among pathologists, kappa levels for AI tool were 0.903 and 0.855. The PCA detection classifier showed high accuracy for PCA detection in biopsy cases during external validation, independent of the institute and scanner used. High levels of agreement for Gleason grading were indistinguishable between experienced genitourinary pathologists and the AI tool.

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 for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study.

BACKGROUND: Oesophageal adenocarcinoma and adenocarcinoma of the oesophagogastric junction are among the most common malignant epithelial tumours. Most patients receive neoadjuvant therapy before complete tumour resection. Histological assessment after resection includes identification of residual tumour tissue and areas of regressive tumour, data which are used to calculate a clinically relevant regression score. We developed an artificial intelligence (AI) algorithm for tumour tissue detection and tumour regression grading in surgical specimens from patients with oesophageal adenocarcinoma or adenocarcinoma of the oesophagogastric junction. METHODS: We used one training cohort and four independent test cohorts to develop, train, and validate a deep learning tool. The material consisted of histological slides from surgically resected specimens from patients with oesophageal adenocarcinoma and adenocarcinoma of the oesophagogastric junction from three pathology institutes (two in Germany, one in Austria) and oesophageal cancer cohort of The Cancer Genome Atlas (TCGA). All slides were from neoadjuvantly treated patients except for those from the TCGA cohort, who were neoadjuvant-therapy naive. Data from training cohort and test cohort cases were extensively manually annotated for 11 tissue classes. A convolutional neural network was trained on the data using a supervised principle. First, the tool was formally validated using manually annotated test datasets. Next, tumour regression grading was assessed in a retrospective cohort of post-neoadjuvant therapy surgical specimens. The grading of the algorithm was compared with that of a group of 12 board-certified pathologists from one department. To further validate the tool, three pathologists processed whole resection cases with and without AI assistance. FINDINGS: Of the four test cohorts, one included 22 manually annotated histological slides (n=20 patients), one included 62 sides (n=15), one included 214 slides (n=69), and the final one included 22 manually annotated histological slides (n=22). In the independent test cohorts the AI tool had high patch-level accuracy for identifying both tumour and regression tissue. When we validated the concordance of the AI tool against analyses by a group of pathologists (n=12), agreement was 63·6% (quadratic kappa 0·749; p<0·0001) at case level. The AI-based regression grading triggered true reclassification of resected tumour slides in seven cases (including six cases who had small tumour regions that were initially missed by pathologists). Use of the AI tool by three pathologists increased interobserver agreement and substantially reduced diagnostic time per case compared with working without AI assistance. INTERPRETATION: Use of our AI tool in the diagnostics of oesophageal adenocarcinoma resection specimens by pathologists increased diagnostic accuracy, interobserver concordance, and significantly reduced assessment time. Prospective validation of the tool is required. FUNDING: North Rhine-Westphalia state, Federal Ministry of Education and Research of Germany, and the Wilhelm Sander Foundation.

Androgen Receptor Splice Variants Contribute to the Upregulation of DNA Repair in Prostate Cancer.

BACKGROUND: Canonical androgen receptor (AR) signaling regulates a network of DNA repair genes in prostate cancer (PCA). Experimental and clinical evidence indicates that androgen deprivation not only suppresses DNA repair activity but is often synthetically lethal in combination with PARP inhibition. The present study aimed to elucidate the impact of AR splice variants (AR-Vs), occurring in advanced or late-stage PCA, on DNA repair machinery. METHODS: Two hundred and seventy-three tissue samples were analyzed, including primary hormone-naïve PCA, primary metastases, hormone-sensitive PCA on androgen deprivation therapy (ADT) and castration refractory PCA (CRPC group). The transcript levels of the target genes were profiled using the nCounter platform. Experimental support for the findings was gained in AR/AR-V7-expressing LNCaP cells subjected to ionizing radiation. RESULTS: AR-Vs were present in half of hormone-sensitive PCAs on androgen deprivation therapy (ADT) and two-thirds of CRPC samples. The presence of AR-Vs is highly correlated with increased activity in the AR pathway and DNA repair gene expression. In AR-V-expressing CRPC, the DNA repair score increased by 2.5-fold as compared to AR-V-negative samples. Enhanced DNA repair and the deregulation of DNA repair genes by AR-V7 supported the clinical data in a cell line model. CONCLUSIONS: The expression of AR splice variants such as AR-V7 in PCA patients following ADT might be a reason for reduced or absent therapy effects in patients on additional PARP inhibition due to the modulation of DNA repair gene expression. Consequently, AR-Vs should be further studied as predictive biomarkers for therapy response in this setting.

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.

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.

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.

Prognostic role of TSPAN1, KIAA1324 and ESRP1 in prostate cancer.

The aim of this study was to validate prostate cancer-associated genes on transcript level and to assess the prognostic value of the most promising markers by immunohistochemistry. Based on differentially expressed genes found in a previous study, 84 genes were further validated using mRNA expression data and follow-up information from the Cancer Genome Atlas (TCGA) prostate cancer cohort (n = 497). Immunohistochemistry was used for validation of three genes in an independent, clinically annotated prostatectomy patient cohort (n = 175) with biochemical relapse as endpoint. Also, associations with clinicopathological variables were evaluated. Eleven protein-coding genes from the list of 84 genes were associated with biochemical recurrence-free survival on mRNA expression level in multivariate Cox-analyses. Three of these genes (TSPAN1, ESRP1 and KIAA1324) were immunohistochemically validated using an independent cohort of prostatectomy patients. Both ESRP1 and KIAA1324 were independently associated with biochemical recurrence-free survival. TSPAN1 was univariately prognostic but failed significance on multivariate analysis, probably due to its strong correlation with high Gleason scores. Multistep filtering using the publicly available TCGA cohort, data of an earlier expression profiling study which profiled 3023 cancer-associated transcripts in 42 primary prostate cancer cases, identified two novel candidate prognostic markers (ESRP1 and KIAA1324) of primary prostate cancer for further study.

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.

Systematic expression analysis of m6A RNA methyltransferases in clear cell renal cell carcinoma.

Objectives: To investigate the regulation of the N-6-methyladenosine (m6A) methyltransferases METTL3, METTL14, WTAP, KIAA1429, and METTL4, referred to as "m6A writers," in clear cell renal cell carcinoma (ccRCC), and other RCC subtypes in respect of the potential prognostic value. Patients and methods: Tissue samples were collected within the framework of the Biobank at the Center for Integrated Oncology Bonn. The expression of the methyltransferases was systematically determined in clear cell renal carcinoma (ccRCC) on the RNA (real-time PCR) and protein level (immunohistochemistry). Additionally, protein expression of the m6A writers was further investigated in papillary RCC, chromophobe RCC, sarcomatoid RCC, oncocytoma, and normal renal tissue (immunohistochemistry). Results: The expression of all m6A-methyltransferases was significantly downregulated in ccRCC compared to benign renal tissue. Low m6A-methyltransferase levels were correlated with higher histological grade, advanced pT-stage, pN-stage, and metastatic disease. Reduced m6A-methyltransferase expression was associated with shorter overall survival. Conclusion: In conclusion, m6A-methyltransferases are dysregulated in ccRCC and might act as tumor suppressor genes, which could be of particular importance for future diagnostic and therapeutic options.

Mitophagy-associated genes PINK1 and PARK2 are independent prognostic markers of survival in papillary renal cell carcinoma and associated with aggressive tumor behavior.

The aim of this study was to investigate the mitophagy-related genes PINK1 and PARK2 in papillary renal cell carcinoma and their association with prognosis. In silico data of PINK1 and PARK2 were analyzed in TCGA cohorts of papillary renal cell carcinoma comprising 290 tumors and 33 corresponding non-neoplastic renal tissues. Protein expression data from a cohort of 95 papillary renal cell carcinoma patients were analyzed and associated with clinical-pathological parameters including survival. PINK1 and PARK2 were significantly downregulated in papillary renal cell carcinoma at transcript and protein levels. Reduced transcript levels of PINK1 and PARK2 were negatively associated with overall survival (p < 0.05). At the protein level, PARK2 and PINK1 expression were positively correlated (correlation coefficient 0.286, p = 0.04) and reduced PINK1 protein expression was prognostic for shorter survival. Lower PINK1 protein levels were found in tumors with metastases at presentation and in tumors of higher pT-stages. The multivariate analysis revealed mRNA expression of PINK1 and PARK2 as well as PINK1 protein expression as independent prognostic factors for shorter overall survival. The downregulation of PINK1 is a strong predictor of poor survival in papillary renal cell carcinoma. Immunohistochemical PINK1 expression in resected pRCC should be considered as an additional prognostic marker for routine practice.