Fluorescence confocal microscopy can accelerate diagnosis of cervical lymphadenopathy.

Fluorescence confocal microscopy (FCM) is an optical technique that uses laser light sources of different wavelengths to generate real-time images of fresh, unfixed tissue specimens. Unlike conventional histological evaluation methods, FCM is able to assess fresh tissue samples without the associated cryo artifacts typically observed after frozen sectioning. The purpose of this study was to evaluate the utility of FCM imaging in the differential diagnosis of cervical lymphadenopathy. Twenty-two cervical lymph node specimens from patients with lymphadenopathy of unknown origin were imaged by FCM. Two pathologists independently evaluated the scans for suspicion of malignancy and preliminary diagnosis. Malignancy was reliably excluded or confirmed by both pathologists with a sensitivity of 90.9% for pathologist 1 and 100% for pathologist 2. The specificity was 100% for both pathologists. For the preliminary diagnosis, almost perfect agreement with the final diagnosis was observed for both pathologists (κ= 0.94 for pathologist 1 and κ= 1.00 for pathologist 2). This is the first study to investigate lymph node specimens with different diagnoses, including lymphoma, using FCM. Our results indicate that differential diagnosis of lymph node specimens is feasible in FCM images, thus encouraging further exploration of FCM imaging in lymph node specimens to accelerate diagnosis and open the possibility of digitizing diagnosis on fresh, unfixed tissue.

Heterogeneity of small duct- and large duct-type intrahepatic cholangiocarcinoma.

AIMS: The histological subtype of intrahepatic cholangiocarcinoma (iCCA) is associated with different mutational characteristics that impact clinical management. So far, data are lacking on the presence of small duct iCCA (SD-iCCA) and large duct iCCA (LD-iCCA) in a single patient. The aim of the current study was to determine the presence and degree of intratumoural heterogeneity of SD- and LD-iCCA features in different tumour regions. METHODS AND RESULTS: All patients treated with surgically resected iCCA at Frankfurt University Hospital between December 2005 and March 2023 were retrospectively analysed. Histomorphological features of SD- and LD-iCCA were evaluated by an expert hepatobiliary pathologist. Tissue samples suspicious for subtype heterogeneity were further investigated. Immunohistochemistry for N-cadherin, S100P, MUC5AC, MUC6, TFF1 and AGR2 and mutational profiling with the Illumina TruSight Oncology 500 (TSO500) assay were performed separately for the SD- and LD-iCCA regions. Of 129 patients with surgically resected iCCA, features of either SD- or LD-iCCA were present in 67.4% (n = 87) and 24.8% of the patients (n = 32), respectively; 7.8% (n = 10) had histomorphological features of both SD- and LD-iCCA, seven patients (5.4%) of which had sufficient formalin-fixed, paraffin-embedded tissue for further analysis. Heterogeneity of both subtypes could be confirmed with immunohistochemistry. In five of seven (71.4%) patients, molecular profiling revealed intratumoural differences in genetic alterations between the SD- and LD-iCCA region. In one patient, a BRAF mutation (p.V600E) was found in the SD-iCCA but not in the LD-iCCA region of the tumour. CONCLUSIONS: A marked portion of patients with iCCA exhibits both SD- and LD-iCCA in different tumour regions. In case of the presence of histopathological heterogeneity, mutational profiling should be considered to avoid missing therapeutically relevant genetic alterations.

Does Dual-Energy Computed Tomography Material Decomposition Improve Radiomics Capability to Predict Survival in Head and Neck Squamous Cell Carcinoma Patients? A Preliminary Investigation.

OBJECTIVE: Our study objective was to explore the additional value of dual-energy CT (DECT) material decomposition for squamous cell carcinoma of the head and neck (SCCHN) survival prognostication. METHODS: A group of 50 SCCHN patients (male, 37; female, 13; mean age, 63.6 ± 10.82 years) with baseline head and neck DECT between September 2014 and August 2020 were retrospectively included. Primary tumors were segmented, radiomics features were extracted, and DECT material decomposition was performed. We used independent train and validation datasets with cross-validation and 100 independent iterations to identify prognostic signatures applying elastic net (EN) and random survival forest (RSF). Features were ranked and intercorrelated according to their prognostic importance. We benchmarked the models against clinical parameters. Intraclass correlation coefficients were used to analyze the interreader variation. RESULTS: The exclusively radiomics-trained models achieved similar ( P = 0.947) prognostic performance of area under the curve (AUC) = 0.784 (95% confidence interval [CI], 0.775-0.812) (EN) and AUC = 0.785 (95% CI, 0.759-0.812) (RSF). The additional application of DECT material decomposition did not improve the model's performance (EN, P = 0.594; RSF, P = 0.198). In the clinical benchmark, the top averaged AUC value of 0.643 (95% CI, 0.611-0.675) was inferior to the quantitative imaging-biomarker models ( P < 0.001). A combined imaging and clinical model did not improve the imaging-based models ( P > 0.101). Shape features revealed high prognostic importance. CONCLUSIONS: Radiomics AI applications may be used for SCCHN survival prognostication, but the spectral information of DECT material decomposition did not improve the model's performance in our preliminary investigation.

The Role of Hypoxia-Inducible Factor 1 Alpha in Acute-on-Chronic Liver Failure.

Acute-on-chronic liver failure (ACLF) is associated with increased mortality. Specific therapy options are limited. Hypoxia-inducible factor 1 alpha (HIF-1α) has been linked to the pathogenesis of chronic liver disease (CLD), but the role of HIF-1α in ACLF is poorly understood. In the current study, different etiologies of CLD and precipitating events triggering ACLF were used in four rodent models. HIF-1α expression and the intracellular pathway of HIF-1α induction were investigated using real-time quantitative PCR. The results were verified by Western blotting and immunohistochemistry for extrahepatic HIF-1α expression using transcriptome analysis. Exploratory immunohistochemical staining was performed to assess HIF-1α in human liver tissue. Intrahepatic HIF-1α expression was significantly increased in all animals with ACLF, regardless of the underlying etiology of CLD or the precipitating event. The induction of HIF-1α was accompanied by the increased mRNA expression of NFkB1 and STAT3 and resulted in a marked elevation of mRNA levels of its downstream genes. Extrahepatic HIF-1α expression was not elevated. In human liver tissue samples, HIF-1α expression was elevated in CLD and ACLF. Increased intrahepatic HIF-1α expression seems to play an important role in the pathogenesis of ACLF, and future studies are pending to investigate the role of therapeutic HIF inhibitors in ACLF.

Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology.

BACKGROUND: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. METHODS: Retrospectively, 106 prostate tissue samples from 48 patients (mean age, [Formula: see text] years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open-source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. RESULTS: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02-0.06 for ERG and PIN-4. CONCLUSIONS: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.

Pathogenetic mechanisms and therapeutic approaches of acute-to-chronic liver failure.

Liver cirrhosis is the end stage of all chronic liver diseases and contributes significantly to overall mortality of 2% globally. The age-standardized mortality from liver cirrhosis in Europe is between 10 and 20% and can be explained by not only the development of liver cancer but also the acute deterioration in the patient's overall condition. The development of complications including accumulation of fluid in the abdomen (ascites), bleeding in the gastrointestinal tract (variceal bleeding), bacterial infections, or a decrease in brain function (hepatic encephalopathy) define an acute decompensation that requires therapy and often leads to acute-on-chronic liver failure (ACLF) by different precipitating events. However, due to its complexity and organ-spanning nature, the pathogenesis of ACLF is poorly understood, and the common underlying mechanisms leading to the development of organ dysfunction or failure in ACLF are still elusive. Apart from general intensive care interventions, there are no specific therapy options for ACLF. Liver transplantation is often not possible in these patients due to contraindications and a lack of prioritization. In this review, we describe the framework of the ACLF-I project consortium funded by the Hessian Ministry of Higher Education, Research and the Arts (HMWK) based on existing findings and will provide answers to these open questions.

Fluorescence confocal microscopy on liver specimens for full digitization of transplant pathology.

Fluorescence confocal microscopy (FCM) is a rapidly evolving tool that provides real-time virtual HE images of native tissue. Data about the potential of FCM as an alternative to frozen sections for the evaluation of donor liver specimens are lacking so far. The aim of the current study was to determine the value of FCM in liver specimens according to the criteria of the German Society for Organ Procurement. In this prospective study, conventional histology and FCM scans of 50 liver specimens (60% liver biopsies, 26% surgical specimens, and 14% donor samples) were evaluated according to the German Society for Organ Procurement. A comparison of FCM scans and conventional frozen sections revealed almost perfect levels of agreement for cholangitis (κ = 0.877), fibrosis (κ = 0.843), and malignancy (κ = 0.815). Substantial levels of agreement could be obtained for macrovesicular steatosis (κ = 0.775), inflammation (κ = 0.763), necrosis (κ = 0.643), and steatohepatitis (κ = 0.643). Levels of agreement were moderate for microvesicular steatosis (κ = 0.563). The strength of agreement between frozen sections and FCM was superior to the comparison of conventional HE and FCM imaging. We introduce FCM as a potential alternative to the frozen section that may represent a novel approach to liver transplant pathology where timely feedback is crucial and the deployment of human resources is becoming increasingly difficult.

Tumor-associated macrophages and Tregs influence and represent immune cell infiltration of muscle-invasive bladder cancer and predict prognosis.

INTRODUCTION AND OBJECTIVE: Muscle-invasive urothelial bladder cancer (MIBC) is associated with limited response rates to systemic therapy, risk of recurrence and death. Tumor infiltrating immune cells have been associated with outcome and response to chemo-and immunotherapy in MIBC. We aimed to profile the immune cells in the tumor microenvironment (TME) to predict prognosis in MIBC and responses to adjuvant chemotherapy. METHODS: We performed multiplex immunohistochemistry (IHC) profiling and quantification of immune and stromal cells (CD3, CD4, CD8, CD163, FoxP3, PD-1, and CD45, Vimentin, αSMA, PD-L1, Pan-Cytokeratin, Ki67) in 101 patients with MIBC receiving radical cystectomy. We used uni- and multivariate survival analyses to identify cell types predicting prognosis. Samples were subdivided using K-means clustering for Treg and macrophage infiltration resulting in 3 clusters, Cluster 1: Treg high, cluster 2: macrophage high, cluster 3: Treg and macrophage low. Routine CD68 and CD163 IHC were analyzed with QuPath in an extended cohort of 141 MIBC. RESULTS: High concentrations of macrophages were associated with increased risk of death (HR 10.9, 95% CI 2.8-40.5; p < 0.001) and high concentrations of Tregs were associated with decreased risk of death (HR 0.1, 95% CI 0.01-0.7; p = 0.03) in the multivariate Cox-regression model adjusting for adjuvant chemotherapy, tumor and lymph node stage. Patients in the macrophage rich cluster (2) showed the worst OS with and without adjuvant chemotherapy. The Treg rich cluster (1) showed high levels of effector and proliferating immune cells and had the best survival. Cluster 1 and 2 both were rich in PD-1 and PD-L1 expression on tumor and immune cells. CONCLUSION: Treg and macrophage concentrations in MIBC are independent predictors of prognosis and are important players in the TME. Standard IHC with CD163 for macrophages is feasible to predict prognosis but validation to use immune-cell infiltration, especially to predict response to systemic therapies, is required.

Optimizing identification of consensus molecular subtypes in muscle-invasive bladder cancer: a comparison of two sequencing methods and gene sets using FFPE specimens.

BACKGROUND: Molecular subtypes predict prognosis in muscle-invasive bladder cancer (MIBC) and are explored as predictive markers. To provide a common base for molecular subtyping and facilitate clinical applications, a consensus classification has been developed. However, methods to determine consensus molecular subtypes require validation, particularly when FFPE specimens are used. Here, we aimed to evaluate two gene expression analysis methods on FFPE samples and to compare reduced gene sets to classify tumors into molecular subtypes. METHODS: RNA was isolated from FFPE blocks of 15 MIBC patients. Massive analysis of 3' cDNA ends (MACE) and the HTG transcriptome panel (HTP) were used to retrieve gene expression. We used normalized, log2-transformed data to call consensus and TCGA subtypes with the consensusMIBC package for R using all available genes, a 68-gene panel (ESSEN1), and a 48-gene panel (ESSEN2). RESULTS: Fifteen MACE-samples and 14 HTP-samples were available for molecular subtyping. The 14 samples were classified as Ba/Sq in 7 (50%), LumP in 2 (14.3%), LumU in 1 (7.1%), LumNS in 1 (7.1%), stroma-rich in 2 (14.3%) and NE-like in 1 (7.1%) case based on MACE- or HTP-derived transcriptome data. Consensus subtypes were concordant in 71% (10/14) of cases when comparing MACE with HTP data. Four cases with aberrant subtypes had a stroma-rich molecular subtype with either method. The overlap of the molecular consensus subtypes with the reduced ESSEN1 and ESSEN2 panels were 86% and 100%, respectively, with HTP data and 86% with MACE data. CONCLUSION: Determination of consensus molecular subtypes of MIBC from FFPE samples is feasible using various RNA sequencing methods. Inconsistent classification mainly involves the stroma-rich molecular subtype, which may be the consequence of sample heterogeneity with (stroma)-cell sampling bias and highlights the limitations of bulk RNA-based subclassification. Classification is still reliable when analysis is reduced to selected genes.

Colonization with multidrug-resistant organisms is associated with impaired survival of patients with surgically resected cholangiocarcinoma.

BACKGROUND & AIMS: Colonization with multidrug-resistant organisms (MDRO) has been shown to impair survival in patients with various malignancies. Despite the increasing spread of MDRO, its impact on patients with cholangiocarcinoma (CCA) is unclear. Aim of this study was to analyse the impact of MDRO-colonization on overall prognosis in CCA patients. METHODS: All patients with surgically resected CCA diagnosed between August 2005 and November 2021 at the University Hospital Frankfurt were screened for MDRO. CCA patients with a positive MDRO screening before or within the first 90 days after diagnosis of CCA were defined as colonized. Patients with a negative MDRO screening were defined as non-colonized. RESULTS: Hundred and sixty nine patients were included. 32% (n = 54) were screened MDRO positive, while 68% (115) were non-colonized. Median overall survival (OS) for colonized patients was 17.1 months (95% CI = 9-25.2 months) compared to 50 months (95% CI = 37.1-62.8) for MDRO-negative patients (p ≤ .001). Non-cancer-related mortality (p ≤ .001) and infectious-related death (p ≤ .001) was significantly higher in the MDRO-colonized group. In multivariate analysis, MDRO colonization (HR = 2.1, 95% CI = 1.4-3.3, p = .001), ECOG 1 (HR = 2.5, 95% CI = 1.6-4, p ≤ .001) and N1 status (HR = 1.7, 95% CI = 1.1-2.6, p = .017) were independent risk factors for OS. CONCLUSION: MDRO-colonization contributes to poor survival in patients with surgically resected CCA. MDRO surveillance is necessary to optimize clinical management of infections and to potentially reduce mortality in this critical population.

Deep learning based on hematoxylin-eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma.

In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the TCGA. In clinical practice, however, not every patient undergoes molecular testing. Many laboratories have used well-implemented in situ techniques (IHC and EBER-ISH) to determine the subclasses in their cohorts. Although multiple stains are used, we show that a staining approach is unable to correctly discriminate all subclasses. As an alternative, we trained an ensemble convolutional neuronal network using bagging that can predict the molecular subclass directly from hematoxylin-eosin histology. We also identified patients with predicted intra-tumoral heterogeneity or with features from multiple subclasses, which challenges the postulated TCGA-based decision tree for GC subtyping. In the future, deep learning may enable targeted testing for molecular subtypes and targeted therapy for a broader group of GC patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Metachronous Osteosarcoma, A Differential Diagnosis to be Considered in Children With Osteosarcoma: A Review of Literature and a Case From Our Center.

Metachronous osteosarcomas (MOS) are currently defined as tumors that arise in a way and site unusual for typical metastasis. In this article, we reviewed the recent literature on the occurrence of metachronous osteosarcoma and presented a case from our center. Our patient, a 10-year-old girl, presented with metachronous osteoblastic osteosarcoma of the left distal femur ∼5 years after the successful treatment for osteosarcoma of the right distal femur. Even after several relapses, complete remission (CR) was achieved after the first osteosarcoma and after the metachronous osteosarcoma. The literature research revealed that metachronous osteosarcoma occurs in 3.4 to 5.4% of osteosarcoma patients. The time interval between the diagnosis of the initial osteosarcoma and the metachronous tumor ranged from 0.2 to 14.3 years (median 2.5 y). MOS appears to have differences in localization and metastatic spread, as well as a different survival pattern compared with primary osteosarcoma and osteosarcoma recurrence. Survival (median 4.3 y, range 0 to 24.6 y) appears to be associated with the time interval to diagnosis of MOS. In particular, early MOS (<24 mo after primary diagnosis) seem to have a poorer prognosis. Therefore, the occurrence of MOS at oncological unusual sites should be considered as a differential diagnosis in osteosarcoma survivors.

Radiomics for therapy-specific head and neck squamous cell carcinoma survival prognostication (part I).

BACKGROUND: Treatment plans for squamous cell carcinoma of the head and neck (SCCHN) are individually decided in tumor board meetings but some treatment decision-steps lack objective prognostic estimates. Our purpose was to explore the potential of radiomics for SCCHN therapy-specific survival prognostication and to increase the models' interpretability by ranking the features based on their predictive importance. METHODS: We included 157 SCCHN patients (male, 119; female, 38; mean age, 64.39 ± 10.71 years) with baseline head and neck CT between 09/2014 and 08/2020 in this retrospective study. Patients were stratified according to their treatment. Using independent training and test datasets with cross-validation and 100 iterations, we identified, ranked and inter-correlated prognostic signatures using elastic net (EN) and random survival forest (RSF). We benchmarked the models against clinical parameters. Inter-reader variation was analyzed using intraclass-correlation coefficients (ICC). RESULTS: EN and RSF achieved top prognostication performances of AUC = 0.795 (95% CI 0.767-0.822) and AUC = 0.811 (95% CI 0.782-0.839). RSF prognostication slightly outperformed the EN for the complete (ΔAUC 0.035, p = 0.002) and radiochemotherapy (ΔAUC 0.092, p < 0.001) cohort. RSF was superior to most clinical benchmarking (p ≤ 0.006). The inter-reader correlation was moderate or high for all features classes (ICC ≥ 0.77 (± 0.19)). Shape features had the highest prognostic importance, followed by texture features. CONCLUSIONS: EN and RSF built on radiomics features may be used for survival prognostication. The prognostically leading features may vary between treatment subgroups. This warrants further validation to potentially aid clinical treatment decision making in the future.

Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer.

BACKGROUND: Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development. METHODS: Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients. RESULTS: Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence. CONCLUSION: Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.

Increased HRD score in cisplatin resistant penile cancer cells.

BACKGROUND/INTRODUCTION: Penile cancer is a rare disease in demand for new therapeutic options. Frequently used combination chemotherapy with 5 fluorouracil (5-FU) and cisplatin (CDDP) in patients with metastatic penile cancer mostly results in the development of acquired drug resistance. Availability of cell culture models with acquired resistance against standard therapy could help to understand molecular mechanisms underlying chemotherapy resistance and to identify candidate treatments for an efficient second line therapy. METHODS: We generated a cell line from a humanpapilloma virus (HPV) negative penile squamous cell carcinoma (UKF-PEC-1). This cell line was subject to chronic exposure to chemotherapy with CDDP and / or 5-FU to induce acquired resistance in the newly established chemo-resistant sublines (PEC-1rCDDP2500, adapted to 2500 ng/ml CDDP; UKF-PEC-1r5-FU500, adapted to 500 ng/ml 5- FU; UKF-PEC1rCDDP2500/r5-FU500, adapted to 2500 ng/ml CDDP and 500 ng/ml 5 -FU). Afterwards cell line pellets were formalin-fixed, paraffin embedded and subject to sequencing as well as testing for homologous recombination deficiency (HRD). Additionally, exemplary immunohistochemical stainings for p53 and gammaH2AX were applied for verification purposes. Finally, UKF-PEC-1rCDDP2500, UKF-PEC-1r5-FU500, UKF-PEC1rCDDP2500/r5-FU500, and UKF-PEC-3 (an alternative penis cancer cell line) were tested for sensitivity to paclitaxel, docetaxel, olaparib, and rucaparib. RESULTS AND CONCLUSIONS: The chemo-resistant sublines differed in their mutational landscapes. UKF-PEC-1rCDDP2500 was characterized by an increased HRD score, which is supposed to be associated with increased PARP inhibitor and immune checkpoint inhibitor sensitivity in cancer. However, UKF-PEC-1rCDDP2500 did not display sensitivity to PARP inhibitors.

The routine use of LCD-Array hybridisation technique for HPV subtyping in the diagnosis of penile carcinoma compared to other methods.

BACKGROUND: Routine human papillomavirus (HPV) testing is performed in cervival cancer and is required for classification of some head and neck cancers. In penile cancer a statement on HPV association of the carcinoma is required. In most cases p16 immunohistochemistry as a surrogate marker is applied in this setting. Since differing clinical outcomes for HPV positive and HPV negative tumors are described we await HPV testing to be requested more frequently by clinicians, also in the context of HPV vaccination, where other HPV subtypes are expected to emerge. METHOD: Therefore, a cohort of archived, formalin-fixed paraffin embedded (FFPE) penile neoplasias was stained for p16 and thereafter tested for HPV infection status via PCR based methods. Additionally to Sanger sequencing, we chose LCD-Array technique (HPV 3.5 LCD-Array Kit, Chipron; LCD-Array) for the detection of HPV in our probes expecting a less time consuming and sensitive HPV test for our probes. RESULTS: We found that LCD-Array is a sensitive and feasible method for HPV testing in routine diagnostics applicable to FFPE material in our cohort. Our cohort of penile carcinomas and carcinomas in situ was associated with HPV infection in 61% of cases. We detected no significant association between HPV infection status and histomorphological tumor characteristics as well as overall survival. CONCLUSIONS: We showed usability of molecular HPV testing on a cohort of archived penile carcinomas. To the best of our knowledge, this is the first study investigating LCD-Array technique on a cohort of penile neoplasias.

[Histopathological evaluation of nonalcoholic fatty liver disease : Automated evaluation of liver biopsies]. / Histopathologische Evaluierung der nichtalkoholischen Fettleber : Automatisierte Auswertung von Leberstanzen.

Nonalcoholic fatty liver disease (NAFLD) is one of the most frequent chronic liver diseases with a rising incidence in industrial countries. This is accompanied by an increased prevalence for NAFLD-associated liver cirrhosis and an increased risk for developing hepatocellular carcinoma. The current gold standard in the diagnostics is a liver biopsy. The histopathological evaluation is performed through semiquantitative scoring. To optimize the standardization and quantification of the existing scoring systems, in the coming years procedures with artificial intelligence, such as deep learning models could be used. Fields of application could be the supplementation of conventional histopathological diagnostics, the identification of new predictive parameters for estimating the prognosis and the prediction of a possible response to treatment.

A Targeted Mass Spectrometry Strategy for Developing Proteomic Biomarkers: A Case Study of Epithelial Ovarian Cancer.

Protein biomarkers for epithelial ovarian cancer are critical for the early detection of the cancer to improve patient prognosis and for the clinical management of the disease to monitor treatment response and to detect recurrences. Unfortunately, the discovery of protein biomarkers is hampered by the limited availability of reliable and sensitive assays needed for the reproducible quantification of proteins in complex biological matrices such as blood plasma. In recent years, targeted mass spectrometry, exemplified by selected reaction monitoring (SRM) has emerged as a method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing plasma-based protein biomarkers for epithelial ovarian cancer and illustrate how the SRM platform, when combined with rigorous experimental design and statistical analysis, can result in detection of predictive analytes.Our biomarker development strategy first involved a discovery-driven proteomic effort to derive potential N-glycoprotein biomarker candidates for plasma-based detection of human ovarian cancer from a genetically engineered mouse model of endometrioid ovarian cancer, which accurately recapitulates the human disease. Next, 65 candidate markers selected from proteins of different abundance in the discovery dataset were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive a 5-protein signature for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion.

Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features.

OBJECTIVES: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). METHODS: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. RESULTS: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. CONCLUSIONS: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance. KEY POINTS: • Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. • Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. • Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone.

[Importance of tumour mutation burden testing]. / Stellenwert der Testung der Tumormutationslast.

Although therapy with immune checkpoint inhibitors (ICIs) for patients with non-small-cell lung carcinoma (NSCLC), which has recently become available, does offer a survival advantage compared with chemotherapy, the overall response rate is only around 20 %. Biomarkers are increasingly important in identifying patients who would benefit from ICI therapy. Expression of PD-L1 was the first predictive biomarker to be developed, but was unable to sufficiently predict the efficacy of ICI. Another biomarker, tumour mutation burden (TMB), is defined as the number of mutations per megabase of DNA analysed. Microsatellite instability also acts as a predictive marker for ICI therapy response. Many tumour entities demonstrate a high correlation between MSI and high TMB. Studies show a benefit of progression-free survival for patients with NSCLC and a TMB of at least 10 mutations per megabase.