Clinical Implementation of a Noninvasive, Multi-Analyte Droplet Digital PCR Test to Screen for Androgen Receptor Alterations.

Alterations of the androgen receptor (AR) are associated with resistance to AR-directed therapy in prostate cancer. Thus, it is crucial to develop robust detection methods for AR alterations as predictive biomarkers to enable applicability in clinical practice. We designed and validated five multiplex droplet digital PCR assays for reliable detection of 12 AR targets including AR amplification, AR splice variant 7, and 10 AR hotspot mutations, as well as AR and KLK3 gene expression from plasma-derived cell-free DNA and cell-free RNA. The assays demonstrated excellent analytical sensitivity and specificity ranging from 95% to 100% (95% CI, 75% to 100%). Intrarun and interrun variation analyses revealed a high level of repeatability and reproducibility. The developed assays were applied further in peripheral blood samples from 77 patients with advanced prostate cancer to assess their feasibility in a real-world scenario. Optimizing the reverse transcription of RNA increased the yield of plasma-derived cell-free RNA by 30-fold. Among 23 patients with castration-resistant prostate cancer, 6 patients (26.1%) had one or a combination of several AR alterations, whereas only 2 of 54 patients (3.7%) in the hormone-sensitive stage showed AR alterations. These findings were consistent with other studies and suggest that implementation of comprehensive AR status detection in clinical practice is feasible and can support the treatment decision-making process.

Validation of a next-generation sequencing assay for the detection of EGFR mutations in cell-free circulating tumor DNA.

Detection of EGFR mutations from blood plasma represents a gentle, non-invasive alternative to rebiopsy and can therefore be used for therapy monitoring of non-small-cell lung cancer (NSCLC) patients. The aim of this project was to investigate whether the Reveal ctDNA™ 28 NGS assay (ArcherDX, Boulder, CO), has a comparable sensitivity and specificity to droplet digital PCR (ddPCR, gold-standard) and is therefore suitable for therapy monitoring of progressing lung cancer patients. First, we validated the NGS assay with a commercially available reference material (SeraCare, Massachusetts, US). Using an input of 22 ng, a sensitivity of 96% and a specificity of 100% could be achieved for variant allele frequencies (VAF) of 0.5%. For variants at a VAF of 0.1% the sensitivity was substantially reduced. Next, 28 plasma samples from 16 patients were analyzed and results were compared to existing ddPCR data. This comparative analysis of patient samples revealed a concordance of 91% between NGS and ddPCR. These results confirm that the Reveal ctDNA™ 28 NGS assay can be used for therapy monitoring of patients under TKI therapy. However, due to the slightly superior sensitivity of ddPCR, a combination of NGS (with broad coverage of a large number of genomic loci) and ddPCR (with targeted highly sensitive detection of specific mutations) might be the ideal approach.

Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns.

Recent advances in artificial intelligence, particularly in the field of deep learning, have enabled researchers to create compelling algorithms for medical image analysis. Histological slides of basal cell carcinomas (BCCs), the most frequent skin tumor, are accessed by pathologists on a daily basis and are therefore well suited for automated prescreening by neural networks for the identification of cancerous regions and swift tumor classification.In this proof-of-concept study, we implemented an accurate and intuitively interpretable artificial neural network (ANN) for the detection of BCCs in histological whole-slide images (WSIs). Furthermore, we identified and compared differences in the diagnostic histological features and recognition patterns relevant for machine learning algorithms vs. expert pathologists.An attention-ANN was trained with WSIs of BCCs to identify tumor regions (n = 820). The diagnosis-relevant regions used by the ANN were compared to regions of interest for pathologists, detected by eye-tracking techniques.This ANN accurately identified BCC tumor regions on images of histologic slides (area under the ROC curve: 0.993, 95% CI: 0.990-0.995; sensitivity: 0.965, 95% CI: 0.951-0.979; specificity: 0.910, 95% CI: 0.859-0.960). The ANN implicitly calculated a weight matrix, indicating the regions of a histological image that are important for the prediction of the network. Interestingly, compared to pathologists' eye-tracking results, machine learning algorithms rely on significantly different recognition patterns for tumor identification (p < 10-4).To conclude, we found on the example of BCC WSIs, that histopathological images can be efficiently and interpretably analyzed by state-of-the-art machine learning techniques. Neural networks and machine learning algorithms can potentially enhance diagnostic precision in digital pathology and uncover hitherto unused classification patterns.

Accuracy of grading pancreatic neuroendocrine neoplasms with Ki-67 index in fine-needle aspiration cellblock material.

OBJECTIVE: The aim of this study was to assess the preoperative tumour grade of pancreatic neuroendocrine neoplasms (panNENs) by determining the Ki-67 index in endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) material and to correlate the preoperative tumour grade with the postoperative tumour grade in surgical specimens. METHODS: We performed a retrospective review of the institutional pathology database over a 10-year period (2007-2017) to identify all cases of panNENs with corresponding preoperative EUS-FNA cytological material and surgical specimens. Fifteen cases with adequate EUS-FNA material (more than 400 tumour cells on cellblock) were identified. The cytological and histological samples were graded based on the mitotic rate and the Ki-67 index in accordance with the 2017 World Health Organisation grading system for panNENs. The tumour grades determined on EUS-FNA cellblock material were compared with the histological tumour grades. RESULTS: Mean age at diagnosis was 64.8 ± 12.7 years (range, 38-85 years). The grading scores assigned to the cytological and histological samples were concordant in all 15 (100%) cases. Of those, two (13%) cases were scored as grade 1, nine (60%) cases as grade 2 and four (27%) cases as grade 3 tumours. CONCLUSION: Our study shows that tumour grade in patients with PanNENs can be reliably determined by assessing the Ki-67 index in EUS-FNA specimens based on the 2017 World Health Organisation classification and grading system.

Giant cell arteritis as unusual cause of critical arm ischemia.

Giant cell arteritis is an inflammatory vasculopathy of unknown etiology that typically affects the carotid artery and its branches. Symptomatic involvement of upper extremity arteries is uncommon. We report a case of a 70-year-old woman with polymyalgia rheumatica who presented with critical arm ischemia, constitutional symptoms, and elevated erythrocyte sedimentation rate. Urgent revascularization by a carotid-brachial artery bypass was performed. Histopathologic evaluation of a specimen obtained intraoperatively from the occluded axillary artery confirmed the diagnosis, and corticosteroid therapy was initiated. Large-vessel vasculitis should be considered a rare differential diagnosis in occlusive disease of the upper extremity.

Sinus sternoclavicularis: a congenital cervical sinus.

We try to characterize a previously rather neglected congenital cervical sinus located in the sternoclavicular area in five children. This sinus showed extension to the left sternoclavicular joint in all patients, so we call this congenital lesion "sinus sternoclavicularis." With knowledge of this congenital lesion, diagnosis can easily be established based on case history and clinical examination; no further radiological tests are required. Surgical excision is the treatment of choice. The chance of recurrence seems to be high because of misinterpretation of the lesion.

Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue.

We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.

Brain tumor classification using AFM in combination with data mining techniques.

Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.

Cervical biopsies and cytological smears - a comparison of sample materials in HPV diagnostics.

BACKGROUND: Cervical cancer is causally related to cervical infections by oncogenic human papillomavirus (HPV) genotypes. To improve the quality of diagnosis evaluation of screening methods and their HPV type detection rate is an important part for this item. OBJECTIVES: Two different cervical specimens of the same patients were analysed simultaneously with molecular HPV subtyping methods to find the most sensitive sample material for cervical cancer screening. STUDY DESIGN: Biopsy specimens and cytological smears of the cervix of 443 patients were analysed for human papilloma virus (HPV) subtyping by a macroarray from Chipron, Germany, which allows a differentiation of 16 high and 16 low risk types. Results were compared for reliability and differences were studied. RESULTS: Both sample material groups showed HPV conformity of 70%, 23% more subtypes could be detected in smears in contrary to biopsies but only 6% vice versa. 14 biopsies and 7 smears were HPV negative although the concerning second sample type of the patients was HPV positive. HPV 16 as one of the most relevant subtypes in cervical cancer pathogenesis was missed in the biopsies' group with 34.3% out of 35 HPV 16 positive smear cases, whereas only one smear failed to discover this subtype contrariwise. CONCLUSION: Comparison of the examination results shows that subtyping of smear samples is able to detect more subtypes than by biopsy specimens. The probability to underdiagnose HPV 16 and to get a false negative result in bioptic sample material favours smear as method of choice for HPV subtyping.

O6-Methylguanine DNA methyltransferase protein expression in tumor cells predicts outcome of temozolomide therapy in glioblastoma patients.

O(6)-Methylguanine DNA methyltransferase (MGMT) is implicated as a major predictive factor for treatment response to alkylating agents including temozolomide (TMZ) of glioblastoma multiforme (GBM) patients. However, whether the MGMT status in GBM patients should be detected at the level of promoter methylation or protein expression is still a matter of debate. Here, we compared promoter methylation (by methylation-specific polymerase chain reaction) and protein expression (by Western blot) in tumor cell explants with respect to prediction of TMZ response and survival of GBM patients (n = 71). Methylated MGMT gene promoter sequences were detected in 47 of 71 (66%) cases, whereas 37 of 71 (52%) samples were scored positive for MGMT protein expression. Although overall promoter methylation correlated significantly with protein expression (chi(2) test, P < .001), a small subgroup of samples did not follow this association. In the multivariate Cox regression model, a significant interaction between MGMT protein expression, but not promoter methylation, and TMZ therapy was observed (test for interaction, P = .015). In patients treated with TMZ (n = 42), MGMT protein expression predicted a significantly shorter overall survival (OS; hazard ratio [HR] for death 5.53, 95% confidence interval [CI] 1.76-17.37; P = .003), whereas in patients without TMZ therapy (n = 29), no differences in OS were observed (HR for death 1.00, 95% CI 0.45-2.20; P = .99). These data suggest that lack of MGMT protein expression is superior to promoter methylation as a predictive marker for TMZ response in GBM patients.

Dural arachnoid granulations and "giant" arachnoid granulations.

Although arachnoid granulations (AGs) were already described by Antonio Pacchioni more than 300 years ago, two issues draw particular attention: first, the radiological features and differential diagnosis of the so-called giant AGs (GAGs) and second, their possible association with various disease processes. In order to evaluate the frequency, size and normal distribution of GAGs, an anatomical study of the dural sinuses was carried out. It involved all the autopsies performed during the period August 2002-February 2005 and included 651 cases: 306 females and 345 males, aged 13-99 years (mean 69 years). Grossly visible GAGs were identified in 24 cases: 7 females and 17 males, aged 45-92 years (mean 69 years). This is the largest population-based anatomical study on GAGs. It shows that GAGs, in general a rare finding (3.68%), are rather common in the adult population, especially in the elderly (aged >65 years) and that they can reach remarkable size (up to 2.5 cm and more in diameter). Giant AGs should be considered in the radiological differential diagnosis of intradural lesions, particularly those occurring in the transverse sinus of the elderly.

Detailed documentation of one lipolysis treatment: blood values, histology, and ultrasound findings.

This case study on a 54-year-old woman shows the exact progression of one abdominal lipolysis treatment with a phosphatidylcholine compound mixture. The mixture consists of phosphatidylcholine 50 mg/mL, NaCl 0.9% as diluent (50%), buflomedil (in a concentration of 5%), and vitamin B complexes (B2, B3, B6; 1%). The patient's weight, blood values, ultrasound findings, measurement, and histologic findings before the treatment, shortly after treatment, and 3 days, 10 days, 4 weeks, and 8 weeks after treatment are documented.