Background and objective:The role of genetic variants in response to systemic therapy in muscle-invasive bladder cancer (MIBC) is still elusive. We assessed variations in genes involved in DNA damage repair (DDR) before and after cisplatin-based neoadjuvant chemotherapy (NAC) and correlation of alteration patterns with DNA damage and response to therapy. Methods:Matched tissue from 46 patients with MIBC was investigated via Ion Torrent-based next-generation sequencing using a self-designed panel of 30 DDR genes. Phosphorylation of γ-histone 2A.X (H2AX) was analyzed via immunohistochemistry to evaluate DNA damage. Genetic variants were analyzed along with clinical data and quantitative phospho-H2AX data using the Kaplan-Meier method, Cox regression analysis, and factor analysis of mixed data. Key findings and limitations:Twenty-five patients (54%) had a response (<pT2 pN0 cM0) to NAC. Responders had more somatic DDR gene variants in preNAC (53 vs 11; p < 0.001) and postNAC (51 vs 9; p = 0.038) tumor tissue in comparison to nonresponders, as well as significantly greater phosphorylation of H2AX after NAC. ERCC2 was significantly co-mutated with REV3L among responders. Owing to the small cohort, no specific mutation was significantly positively associated with therapy response. However, accumulation of CDK12, NBN, MSH3, MLH1, ATR, BRCA1, BRCA2, REVL3L, and SLX4 variants was observed for responders. Conclusions and clinical implications:Patients with MIBC who responded to cisplatin-based NAC had more somatic DDR gene variants than nonresponders. Moreover, responders exhibited significantly greater DNA damage after NAC. Patient summary:Patients with muscle-invasive bladder cancer who have mutations in genes that are involved in repair of DNA damage are more likely to respond to cisplatin-based chemotherapy. Testing to identify these gene mutations could help in selecting the patients who are most likely to benefit from this treatment.

GeoMx Digital Spatial Profiling (Nanostring) is a commercial spatial transcriptomics method to selectively analyze regions of interest within intact tissue sections. We show that decalcification with ethylene-diamine-tetra-acetic (EDTA) variably attenuates probe counts, while probes that are more resistant to this effect consequently appear overexpressed after quantile normalization. By determining the undisclosed full-length target sequences of probes used in the human whole transcriptome panel, hereby updating target transcripts and genes, we find that the gene-promiscuity of probes is an important factor that determines sensitivity to EDTA incubation.

Ulcerations of the lower extremities are a frequently encountered problem in clinical practice and are of significant interest in public health due to the high prevalence of underlying pathologies, including chronic venous disease, diabetes and peripheral arterial occlusive disease. However, leg ulcers can also present as signs and symptoms of various rare diseases and even as an adverse reaction to drugs. In such cases, correct diagnosis ultimately relies on histopathological examination. Apart from the macroscopic presentation, patient history and anatomic location, which are sometimes indicative, most ulcers have very distinct histopathological features. These features are found in different layers of the skin or even associated vessels. In this narrative review, we discuss and highlight the histopathological differences of several types of leg ulcers that can contribute to efficient and accurate diagnosis.

Current standard-of-care systemic therapy options for locally advanced and metastatic bladder cancer (BC), which are predominantly based on cisplatin-gemcitabine combinations, are limited by significant treatment failure rates and frailty-based patient ineligibility. We previously addressed the urgent clinical need for better-tolerated BC therapeutic strategies using a drug screening approach, which identified outstanding antineoplastic activity of clofarabine in preclinical models of BC. To further assess clofarabine as a potential BC therapy component, we conducted head-to-head comparisons of responses to clofarabine versus gemcitabine in preclinical in vitro and in vivo models of BC, complemented by in silico analyses. In vitro data suggest a distinct correlation between the two antimetabolites, with higher cytotoxicity of gemcitabine, especially against several nonmalignant cell types, including keratinocytes and endothelial cells. Accordingly, tolerance of clofarabine (oral or intraperitoneal application) was distinctly better than for gemcitabine (intraperitoneal) in patient-derived xenograft models of BC. Clofarabine also exhibited distinctly superior anticancer efficacy, even at dosing regimens optimized for gemcitabine. Neither complete remission nor cure, both of which were observed with clofarabine, were achieved with any tolerable gemcitabine regimen. Taken together, our findings demonstrate that clofarabine has a better therapeutic window than gemcitabine, further emphasizing its potential as a candidate for drug repurposing in BC.Patient summaryWe compared the anticancer activity of clofarabine, a drug used for treatment of leukemia but not bladder cancer, and gemcitabine, a drug currently used for chemotherapy against bladder cancer. Using cell cultures and mouse models, we found that clofarabine was better tolerated and more efficacious than gemcitabine, and even cured implanted tumors in mouse models. Our results suggest that clofarabine, alone or in combination schemes, might be superior to gemcitabine for the treatment of bladder cancer.

Introduction:The coronavirus disease-19 (COVID-19) iscaused by the severe acute respiratory syndrome co-ronavirus 2 (SARS-CoV-2). Thevirusisallegedtoenableaproinflammatory state that leads to the activation of thecoagulation and the complement cascade. In this study,we aimed to establish the impact of the COVID-19pandemic on patients with new onset of cTMA/aHUS inthe Vienna TMA cohort and whether COVID-19 or SARS-CoV-2 vaccinations would pose a greater risk of initialmanifestation of cTMA/aHUS.Methods:We used theVienna TMA cohort database to examine the prevalenceof COVID-19-related and of SARS-CoV-2 vaccination-related aHUS/cTMA during thefirst 3 years of the CO-VID-19 pandemic in a large single-centre cohort.Results:Between March 2020 and May 2023, a total of 7patients experienced theirfirst aHUS/cTMA episode. Nopatient experienced a TMA relapse or more than oneepisode during the follow-up period. Three TMA epi-sodes were attributable to either COVID-19 (n= 1; 33%)or SARS-CoV-2 vaccination (n= 2; 66%), respectively. All3 patients had systemic signs of TMA, and TMA wasconfirmed by kidney biopsy in all cases. Among the 7patients, we recordedfive infections that triggered oneTMA episode (20%) and 19 vaccinations triggered twoTMA episodes (10%;p= 0.52, odds ratio 0.47; 95% CI:0.04-8.39).Conclusion:We speculate that both SARS-CoV-2 vaccinations and COVID-19 episodes can repre-sent a triggering factor for aHUS/cTMA episodes in(genetically) vulnerable individuals. However, COVID-19might have a stronger association and might be astronger trigger than the SARS-CoV-2 vaccines. The in-cidence of new aHUS cases did not differ from the pre-pandemic era in a large tertiary care centre cohort.(c) 2024 The Author(s).Published by S. Karger AG, Basel

Substaging of T1 urothelial cancer is associated with tumor progression and its reporting is recommended by international guidelines. However, it has not been integrated in risk stratification tools and there is no agreement on the best method to use for its reporting. We aimed to investigate the applicability, interobserver variability, and prognostic value of histological landmark based and micrometric (aggregate linear length of invasive carcinoma (ALLICA), microscopic vs. extensive system, Rete Oncologica Lombarda (ROL) system) substaging methods. A total of 79 patients with the primary diagnosis of T1 urothelial cancer treated with conventional transurethral resection and adjuvant BCG therapy between 2000 and 2020 at the Medical University of Vienna were included. The anatomical and metrical substaging systems were evaluated using agreement rate, Cohen's kappa, Kendall's tau, and Spearman rank correlation. Prognostic value for high-grade recurrence or T2 progression was evaluated in uni- and multivariable analysis. Applicability and reproducibility were good to moderate and varied between substaging methods. Obstacles are mainly due to fragmentation of samples. Anatomical substaging was associated with progression in univariable and multivariable analysis. In our cohort, we could only identify anatomical landmark-based substaging to be prognostic for T2 progression. A major obstacle for proper pathological assessment is fragmentation of samples due to operational procedure. Avoiding such fragmentation might improve reproducibility and significance of pathological T1 substaging of urothelial cancer.

•PanelCAT enables automatic analysis of DNA target regions of NGS panels from BED files, including optional mask files.•PanelCAT uses RefSeq, ClinVar, and COSMIC Cancer Mutation Census databases to determine protein-coding base and variant/mutation coverage.•Interactive visualization allows intuitive assessment and comparison of panel coverage and saving of graphs as images.•Tables with search function allow researchers to assess specific exons/mutations of interest and save filtered data as text. Multigene next-generation sequencing (NGS) panels have become a routine diagnostic method in the contemporary practice of personalized medicine. To avoid inadequate test choice or interpretation, a detailed understanding of the precise panel target regions is required. However, the necessary bioinformatic expertise is not always available, and publicly accessible and easily interpretable analyses of target regions are scarce. To address this critical knowledge gap, we present the Panel Comparative Analysis Tool (PanelCAT), an open-source application to analyze, visualize, and compare NGS panel DNA target regions. PanelCAT uses Reference Sequence, ClinVar, and Catalogue of Somatic Mutations in Cancer mutation census databases to quantify the exon and mutation coverage of target regions and provides interactive graphical representations and search functions to inspect the results. We demonstrate the utility of PanelCAT by analyzing two large NGS panels (TruSight Oncology 500 and Human Pan Cancer Panel) to validate the advertised target genes, quantify targeted exons and mutations, and identify differences between panels. PanelCAT will enable institutions and researchers to catalog and visualize NGS panel target regions independent of the manufacturer, promote transparency of panel limitations, and share this information with employees and requisitioners. Multigene next-generation sequencing (NGS) panels have become a routine diagnostic method in the contemporary practice of personalized medicine. To avoid inadequate test choice or interpretation, a detailed understanding of the precise panel target regions is required. However, the necessary bioinformatic expertise is not always available, and publicly accessible and easily interpretable analyses of target regions are scarce. To address this critical knowledge gap, we present the Panel Comparative Analysis Tool (PanelCAT), an open-source application to analyze, visualize, and compare NGS panel DNA target regions. PanelCAT uses Reference Sequence, ClinVar, and Catalogue of Somatic Mutations in Cancer mutation census databases to quantify the exon and mutation coverage of target regions and provides interactive graphical representations and search functions to inspect the results. We demonstrate the utility of PanelCAT by analyzing two large NGS panels (TruSight Oncology 500 and Human Pan Cancer Panel) to validate the advertised target genes, quantify targeted exons and mutations, and identify differences between panels. PanelCAT will enable institutions and researchers to catalog and visualize NGS panel target regions independent of the manufacturer, promote transparency of panel limitations, and share this information with employees and requisitioners. Precision oncology routinely involves next-generation sequencing (NGS) of tumor DNA to identify therapeutically actionable targets or diagnostically relevant mutations that critically direct patient management.1Mosele F. Remon J. Mateo J. Westphalen C.B. Barlesi F. Lolkema M.P. Normanno N. Scarpa A. Robson M. Meric-Bernstam F. Wagle N. Stenzinger A. Bonastre J. Bayle A. Michiels S. Bièche I. Rouleau E. Jezdic S. Douillard J.-Y. Reis-Filho J.S. Dienstmann R. André F. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: a report from the ESMO Precision Medicine Working Group.Ann Oncol. 2020; 31: 1491-1505Abstract Full Text Full Text PDF PubMed Scopus (587) Google Scholar Most multigene sequencing panels do not cover entire genes, but only variable portions of genes that are considered most relevant (ie, predominantly protein-coding sequences and tumor mutational hot spots). For this reason, both the choice of an adequate test and its interpretation, especially regarding the certainty of negative findings, crucially depend on detailed knowledge of the portions of genes and genetic alterations that are assessed by a panel. Target regions of commercial NGS panels are typically specified in a panel-specific BED file by a list of chromosome numbers and start and stop coordinates.2Niu J, Denisko D, Hoffman MM. The Browser Extensible Data (BED) Format, n.d.Google Scholar Although this information is an essential part of the test documentation, it is not useful to understand panel target regions in detail without further analysis for several reasons: it does not inform on the nontargeted portions of genes without comparison to a reference genome; the provided information on target genes, transcripts, and exons is not updated alongside the transcript databases [eg, Reference Sequence (RefSeq3O'Leary N.A. Wright M.W. Brister J.R. Ciufo S. Haddad D. McVeigh R. et al.Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation.Nucleic Acids Res. 2016; 44: D733-D745Crossref PubMed Google Scholar)]; the target regions must be systematically compared with mutation/variant databases to determine pathogenic mutations that can be detected; and, last, genomic positions with known high rates of erroneous variant calls are often masked during secondary analysis, but these positions are defined in separate files. To our knowledge, there exists no application that integrates these various sources into high-level, summarized analyses and visualization of NGS panel target regions. Consequently, the lack of detailed publicly available data on precise panel targets, and the barriers to generate it because of the required bioinformatic expertise, portends the risk of inadequate test choice and test misinterpretation. To reduce this risk, we developed the Panel Comparative Analysis Tool (PanelCAT), an application that allows researchers to analyze, visualize, and compare DNA target regions of NGS panels within a user-friendly interface, and provides a platform to clearly communicate this information to others. We demonstrate the use of this tool by analyzing two large multigene NGS panels, the TruSight Oncology 500 (TSO500; Illumina, Hayward, CA) and the Human Pan Cancer Panel (QPC; Qiagen, Hilden, Germany), to provide a more detailed documentation of their targeted genes, exons, known pathogenic mutations, and differences between the panels, than has been available to date. PanelCAT code was generated, and all analyses were performed, in R4R Core TeamR: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria2020Google Scholar version 4.3.0 within RStudio version 2023.03.0. Analysis of genomic ranges (target regions and variant coordinates) was performed using the GenomicFeatures5Lawrence M. Huber W. Pagès H. Aboyoun P. Carlson M. Gentleman R. Morgan M. Carey V. Software for computing and annotating genomic ranges.PLoS Comput Biol. 2013; 9e1003118Crossref PubMed Scopus (2160) Google Scholar package. Graphs were drawn using ggplot26Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, New York, NY2016Crossref Google Scholar and plotly7Sievert C. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC, 2020Crossref Google Scholar packages. A browser-based implementation of the script was generated using ShinyR.8Chang W. Cheng J. Allaire J.J. Sievert C. Schloerke B. Xie Y. Allen J. McPherson J. Dipert A. Borges B. shiny: Web Application Framework for R.2022Google Scholar PanelCAT is provided under the open-source license AGPLv3, and the source code is available (https://github.com/aoszwald/panelcat, last accessed November 11, 2023). The basic procedure of the panel analysis is outlined in the next paragraph. PanelCAT accepts target region files as input (containing columns for chromosome and start and end position of target regions), and optionally the mask region file (also containing chromosome and start and end coordinates). The application first determines the intersection between the panel target regions and RefSeq3O'Leary N.A. Wright M.W. Brister J.R. Ciufo S. Haddad D. McVeigh R. et al.Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation.Nucleic Acids Res. 2016; 44: D733-D745Crossref PubMed Google Scholar exon coordinates to systematically identify target genes, and subsequently all exon ranges of target genes. The exon ranges of each targeted gene are then intersected with the panel target regions to quantify the targeted portion of protein-coding bases per gene. Targeted mutations are then identified by intersection of the panel target regions with the coordinates of mutations in the ClinVar9Landrum M.J. Chitipiralla S. Brown G.R. Chen C. Gu B. Hart J. Hoffman D. Jang W. Kaur K. Liu C. Lyoshin V. Maddipatla Z. Maiti R. Mitchell J. O'Leary N. Riley G.R. Shi W. Zhou G. Schneider V. Maglott D. Holmes J.B. Kattman B.L. ClinVar: improvements to accessing data.Nucleic Acids Res. 2020; 48: D835-D844Crossref PubMed Scopus (385) Google Scholar and Catalogue of Somatic Mutations in Cancer (COSMIC)10Tate J.G. Bamford S. Jubb H.C. Sondka Z. Beare D.M. Bindal N. Boutselakis H. Cole C.G. Creatore C. Dawson E. Fish P. Harsha B. Hathaway C. Jupe S.C. Kok C.Y. Noble K. Ponting L. Ramshaw C.C. Rye C.E. Speedy H.E. Stefancsik R. Thompson S.L. Wang S. Ward S. Campbell P.J. Forbes S.A. COSMIC: the Catalogue of Somatic Mutations in Cancer.Nucleic Acids Res. 2019; 47: D941-D947Crossref PubMed Scopus (2443) Google Scholar databases. Optionally, the mask file is incorporated in the analysis to identify and determine the portion of masked bases and mutations. The summarized output data are combined into lists of items and saved as R data objects. Panels that were previously analyzed and saved in this form are preloaded the next time the application is started. The panel analysis output can also be used for analysis outside of PanelCAT; within R, the panel data and listed subitems can be accessed via the $ operator. During this study, individual data were accessed in this way and further processed in R independently of the PanelCAT functions to identify discrepancies between the advertised gene list and the confirmed gene list. BED files indicating target regions of NGS panels and corresponding mask files, including Illumina TSO500 and Qiagen Pan-Cancer Panels, were obtained from the customer support of the manufacturers, or obtained while using a product. The TSO500 mask file was provided by Illumina. These files are not provided as part of PanelCAT. ClinVar (https://www.ncbi.nlm.nih.gov/clinvar, last accessed May 23, 2023) and RefSeq (GRCh37, https://www.ncbi.nlm.nih.gov/refseq, last accessed May 23, 2023) databases are not provided as part of the software download, but will automatically be obtained by PanelCAT from the National Center for Biotechnology Information FTP server. The COSMIC cancer mutation census data version 98 (https://cancer.sanger.ac.uk/cosmic, last accessed May 25, 2023) must be downloaded manually (because they are accessible only after online registration), as outlined by the instructions in the GitHub repository (https://github.com/aoszwald/panelcat). PanelCAT (https://github.com/aoszwald/panelcat and http://panelcat.net, last accessed November 11, 2023) provides functions to automatically distill descriptive information from panel target region files and public databases, and to display these data to facilitate evaluation and comparison of panels. To analyze a panel, PanelCAT is provided with the target region file (typically with a .bed file suffix, but others may be acceptable), and optionally a mask file (indicating regions where variant calls are unreliable and will be filtered out). The application then determines the overlap between target regions and protein-coding bases per gene in RefSeq,3O'Leary N.A. Wright M.W. Brister J.R. Ciufo S. Haddad D. McVeigh R. et al.Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation.Nucleic Acids Res. 2016; 44: D733-D745Crossref PubMed Google Scholar thereby identifying the target genes, and subsequently, the overlap between target regions and known pathogenic and likely pathogenic mutations in ClinVar9Landrum M.J. Chitipiralla S. Brown G.R. Chen C. Gu B. Hart J. Hoffman D. Jang W. Kaur K. Liu C. Lyoshin V. Maddipatla Z. Maiti R. Mitchell J. O'Leary N. Riley G.R. Shi W. Zhou G. Schneider V. Maglott D. Holmes J.B. Kattman B.L. ClinVar: improvements to accessing data.Nucleic Acids Res. 2020; 48: D835-D844Crossref PubMed Scopus (385) Google Scholar and tier 1 to 3 oncogenic mutations in COSMIC10Tate J.G. Bamford S. Jubb H.C. Sondka Z. Beare D.M. Bindal N. Boutselakis H. Cole C.G. Creatore C. Dawson E. Fish P. Harsha B. Hathaway C. Jupe S.C. Kok C.Y. Noble K. Ponting L. Ramshaw C.C. Rye C.E. Speedy H.E. Stefancsik R. Thompson S.L. Wang S. Ward S. Campbell P.J. Forbes S.A. COSMIC: the Catalogue of Somatic Mutations in Cancer.Nucleic Acids Res. 2019; 47: D941-D947Crossref PubMed Scopus (2443) Google Scholar cancer mutation census (CMC) databases. The output data are saved in a compact form that can be used in PanelCAT or explored independently in R Statistics.4R Core TeamR: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria2020Google Scholar The application provides several useful visualization options to analyze and compare panels, briefly outlined here. In a point graph (Gene metrics, X/Y), users can contrast target coverage metrics of RefSeq, ClinVar, and COSMIC databases from all analyzed panels on the level of individual genes. This function can be used to compare between two panels (eg, to determine differences in target gene coverage) (Supplemental Figure S1A) or to compare different metrics within a single panel (eg, to evaluate the relationship between covered protein-coding bases and pathogenic mutations). A panel-wide representation of gene coverage (and masked portions) of protein-coding bases, ClinVar variants, or COSMIC mutations is also provided across multiple panels in a horizontal column plot (Gene metrics, column) (Supplemental Figure S1B). These two options are particularly useful to gain a basic overview of differences between panels. Specific genes of interest can be searched to visualize coverage across panels using a method that was optimized to display many panels simultaneously (Gene metrics, search) (Figure 1 and Supplemental Figure S1C), alongside an indication of which of the searched panels target all genes of interest. The gene-level data underlying the visualizations described in the sentence above can be queried in a customizable and searchable table (Gene metrics, table), where data can also be exported to text and downloaded for later use. The coverage of individual exons by panel target regions is visualized in a horizontal column plot (Exon graph) (Supplemental Figure S1D), where users can choose specific entries of all transcripts of all genes, and select multiple panels for comparison. The underlying exon-level data can also be queried in a table (Exon table) (Supplemental Figure S1E) with search and export function. These visualizations are particularly useful to characterize differences in gene coverage between panels in greater detail. The ClinVar and COSMIC database entries of mutations targeted by individual panels can be inspected in tables (COSMIC table and ClinVar table). The exon, ClinVar, and COSMIC tables can be filtered for each column independently (eg, by specific genes, transcripts, exons, coding or amino acid changes, or genome coordinates), and include hyperlinks that refer a user directly to the respective RefSeq, Clinvar, or COSMIC entry. Last, PanelCAT provides a visualization of the estimated cumulative frequency of COSMIC CMC tier 1 to 3 mutations that are not targeted by panels. The estimate is provided per panel for the actual target genes only, and for all genes, both with and without considering variant masking (Noncovered mutation rate) (Supplemental Figure S1F). In addition to the previous options, these metrics may assist users in estimating the clinical utility of panels with similar sets of target genes. Users can access an online instance of PanelCAT (http://panelcat.net) or implement a local instance of PanelCAT with little effort, as outlined in the online repository (https://github.com/aoszwald/panelcat). When run locally, PanelCAT automatically obtains the current ClinVar (released weekly) and RefSeq databases on first use. These, along with all previously processed panels, can be updated in a single step (in the New Analysis tab); previous versions of databases and panel analyses are stored alongside current files for later reference and documentation. The COSMIC CMC database (updated every several months) requires manual download (because of required online registration) and replacement of the local file. When hosted as a network service (such as http://panelcat.net), analyses of target regions provided by client users are not saved permanently, but can be downloaded and re-uploaded at a later time, as described in the New Analysis tab of the application. In summary, PanelCAT offers multiple useful and intuitive functions to substantially improve the transparency and accessibility of NGS panel target region documentation. The newly developed tool was next used to analyze two large panels currently used in both clinical and research settings; the Illumina TSO500 and the Qiagen QPC panels. Although the target regions can be requested as part of the panel documentation, explicit coverage of genes and mutations is not provided. However, informed clinical use requires detailed information, so the authors used PanelCAT to characterize their target regions in detail and explore their differences. In their respective product documentation, the TSO500 and QPC panels advertise the same 523-gene targets for analysis of small variants (eg, single-nucleotide variations and insertions and deletions). The authors first compared the advertised genes with the target genes identified using PanelCAT. The QPC target regions overlapped with exons of 603 genes, including all advertised genes. By contrast, the TSO500 target regions overlapped with exons of 625 genes, but these included only 521 of the 523 advertised targets. The two remaining target genes (HLA-B and HLA-C) do not overlap with the TSO500 target regions; accordingly, the authors did not find any variant calls in HLA-B or HLA-C in a representative set of 400 samples analyzed with the TSO500 panel (including unfiltered variant call files in 10 samples). Alterations in genes corresponding to major histocompatibility complex class I (eg, HLA-B or HLA-C) have been postulated to promote tumor evasion of immune surveillance (eg, by restricting neoantigen presentation),11Hazini A. Fisher K. Seymour L. Deregulation of HLA-I in cancer and its central importance for immunotherapy.J Immunother Cancer. 2021; 9e002899Crossref PubMed Scopus (58) Google Scholar,12Fangazio M. Ladewig E. Gomez K. Garcia-Ibanez L. Kumar R. Teruya-Feldstein J. Rossi D. Filip I. Pan-Hammarström Q. Inghirami G. Boldorini R. Ott G. Staiger A.M. Chapuy B. Gaidano G. Bhagat G. Basso K. Rabadan R. Pasqualucci L. Dalla-Favera R. Genetic mechanisms of HLA-I loss and immune escape in diffuse large B cell lymphoma.Proc Natl Acad Sci U S A. 2021; 118e2104504118Crossref Scopus (32) Google Scholar although no guideline recommendations to test HLA genes exist momentarily. The authors next identified the targeted exon-coding bases of each gene. They first searched for genes with the greatest coverage and found that in the TSO500, 20 genes had exon coverage >95% (including NAB2, TERC, CD74, TFE3, KIF5B, EML4, EWSR1, FLI1, ETV1, ETV5, and PAX3, all >99%), whereas in the QPC, it was only six genes (TERC, ZRSR2, ATR, POLD1, KMT2B, and RECQL4). The authors found a strong direct correlation between the base coverage of TSO500 and QPC panels (Pearson r = 0.81, P < 2e-16), and no significant difference in mean exon base coverage per gene (TSO500 versus QPC, 50.3% versus 48.4%; P = 0.23) (Figure 2A). The authors identified target genes where relative coverage was considerably greater in the TSO500 than in the QPC, including NTRK2, ETV1, AKT3, ERG, and PAX7, but only a few genes with greater coverage in the QPC panel, notably PMS2, TERT, HLA-B, and HLA-C (Figure 2B). Importantly, four targeted genes (HLA-A, KMT2B, KMT2C, and KMT2D) showed total masking of all target regions in the TSO500 panel (but not in the QPC, which does not use a mask file). Accordingly, the authors did not find any variant calls in these genes in a representative set of 400 samples analyzed with the TSO500 panel. ClinVar (last accessed May 23, 2023) lists approximately 50,000 known variants labeled pathogenic or likely pathogenic in the advertised TSO500 and QPC target genes. Although the TSO500 targeted 92.5% of these variants (94.8% without variant masking), the QPC targeted 97.4%, despite lower exon coverage. Consequently, targeting of all pathogenic variants was achieved for 182 genes in the TSO500 panel (200 without masking), and 223 genes in the QPC panel. In the TSO500 panel, no pathogenic variants were targeted in eight genes (of which six were due to variant masking), whereas in the QPC, it was only three. In both panels, most (QPC, 51%; TSO500, 64%) nontargeted variants occurred in two similar sets of only 10 genes, in both cases including NF1 and the DNA repair genes MLH1, MSH2, BRCA1, BRCA2, and ATM (Supplemental Table S1). Most differences between panels were attributable to either greater exon coverage in the QPC (PMS2 and TERT) (Supplemental Table S1) or extensive masking in the TSO500 (KMT2B, KMT2C, and KMT2D) (Figure 2, C and D, and Supplemental Table S2). The COSMIC CMC database version 98 (last accessed May 23, 2023) lists approximately 43,000 unique mutations occurring in genes targeted by the TSO500 and QPC panels, of which most (93.4%, or 99.5% without variant masking) are targeted by the TSO500, and all by the QPC (100%) (Figure 2, E and F). Because of masking, no mutations are targeted by the TSO500 in HLA-A, KMT2C, and KMT2D. Independent of masking, the QPC panel more extensively targeted mutations in NF1, TERT, and PMS2 than the TSO500. The authors estimated the frequency of samples to harbor nontargeted mutations by calculating the positive sample proportion of unique mutations in the CMC data set, and cumulating the frequency all nontargeted mutations. The rate of noncovered CMC tier 1 to 3 mutations in targeted genes per sample was lower in the QPC (0.005) than in the TSO500 (0.14, 0.02 without masking), suggesting that 1 in 8 (TSO500, or 1 in 50 without masking) or 1 in 200 (QPC) samples would harbor potentially oncogenic mutations (ie, tier 1 to 3 COSMIC census mutations) that cannot be detected with the panels, in one of the panel target genes. Detailed knowledge of the target regions of NGS panels is important for the correct choice and interpretation of molecular tests, but it is not typically well illustrated by the test manufacturer, and usually requires bioinformatic analysis to acquire. In this study, we present PanelCAT, a novel open-source tool that can be used by laboratories or NGS panel distributors to analyze NGS target regions and share this information to enable more informed decisions. A limitation of PanelCAT is that it does not assess fusion or copy number events detected by panels, but respective features may be implemented in the future. PanelCAT enables rapid assessment and rich visualization of the designed target regions of NGS panels without bioinformatic expertise. As an example, we expanded in detail on the existing and incomplete documentation of two large NGS tests (Illumina TSO500 and Qiagen QPC). PanelCAT quantified precise exon coverage and identified genes with poor coverage, extensive variant masking, differences between panels, and even discrepancies to the advertised gene list. Thus, we found that unlike the QPC, the TSO500 does not target HLA-B and HLA-C; that KMT2B, KMT2C, and KMT2D are extensively masked in the TSO500 and will not yield variant calls after filtering; and that exon sequences of PMS2 and TERT are substantially better covered in the QPC panel independent of variant masking. In addition, we used PanelCAT to describe the different coverage of individual exons of PMS2 in the panels. PanelCAT offers unique and specialized functions that distinguish it from other software with more general use. Theoretically, the target regions of an NGS panel can also be visualized in a linear manner using software, such as the Integrated Genomic Viewer,13Robinson J.T. Thorvaldsdóttir H. Wenger A.M. Zehir A. Mesirov J.P. Variant review with the integrative genomics viewer.Cancer Res. 2017; 77: e31-e34Crossref PubMed Scopus (609) Google Scholar but obtaining summarized data and comparisons between large panels would be extremely tedious. To our knowledge, there is no software dedicated to the purpose of comparing NGS target regions. Some commercial NGS reagent providers offer online software tools (eg, GeneGlobe and Qiagen) to search and design panels, typically with basic feedback on the portion of the desired target regions that are covered by a produced panel design. However, the feedback does not inform of coverage of individual genes and exons, and comparisons or visualizations of panels are not supported. PanelCAT is also different from the Panel Informativity Optimizer (PIO) method, previously demonstrated to assist in optimizing NGS panel design.14Alcazer V. Sujobert P. Panel Informativity Optimizer: an R package to improve cancer next-generation sequencing panel informativity.J Mol Diagn. 2022; 24: 697-709Abstract Full Text Full Text PDF PubMed Google Scholar Because PanelCAT does not generate new target regions, it only indirectly assists in panel design by highlighting deficits in exon or mutation coverage in particular genes of interest. Compared with PIO, PanelCAT provides superior functions to analyze and compare existing (or proposed) panels. Crucially, PIO cannot process complex target regions or conventional BED-format files, only lists of complete genes or exons. Most panels target incomplete genes or exons, and would thus be inaccurately represented using PIO. Consequently, the limited panel benchmarking functions of PIO cannot inform on precise exon coverage, whereas PanelCAT provides detailed information on the level of genes, exons, and individual mutations. In contrast to PIO, which provides a linear data pipeline from input to output, PanelCAT is a platform to collect panel analyses and visualize them for frequent inspection in a routine clinical setting. In summary, PanelCAT provides opportunities that have not yet been demonstrated with previous methods, albeit with features designed more for panel end users than panel developers. In contrast to PIO, PanelCAT does not use a variety of mutation databases to account for the heterogeneity of mutation frequencies across different disease entities. Although panels are often designed for specific disease entities or groups thereof, many widely used panels (eg, Thermo Fisher Oncomine Focus or Illumina TSO500) were designed to cover a wide range of disease entities, and we therefore also chose a disease-agnostic approach for PanelCAT. However, the variant databases used by PanelCAT can be preprocessed by users to focus the analysis entirely on mutations that are relevant in a specific disease context. PanelCAT was designed for users with limited or no information technology support, and no more than basic computational expertise. We have aimed to make installation of the software as simple as possible and provide a guide for this process in the online repository (https://github.com/aoszwald/panelcat). The software functions on a local device (without installation of software besides R statistics and R Studio); however, users may also choose to analyze and visualize their panels using the online instance (http://panelcat.net). Similarly, PanelCAT can be hosted as a private network service with the use of ShinyServer (also outlined in the repository). PanelCAT reference databases can be easily updated, and stored panel analyses can be managed within the operating system's file system without database experience. Although multigene NGS panels are currently the standard procedure in many institutions, routine whole-exome sequencing of tumor specimens is being increasingly performed. Because of the high performance of the underlying packages,5Lawrence M. Huber W. Pagès H. Aboyoun P. Carlson M. Gentleman R. Morgan M. Carey V. Software for computing and annotating genomic ranges.PLoS Comput Biol. 2013; 9e1003118Crossref PubMed Scopus (2160) Google Scholar PanelCAT could be used to analyze target regions of whole-exome panels. However, the increased rendering time of some of the implemented visualization methods could be impractical. Nevertheless, the PanelCAT output data, saved as R objects, could be used outside of PanelCAT to plot custom graphs demanding less computation. In conclusion, we present PanelCAT as a powerful solution to current shortcomings in the presentation, analysis, and awareness of NGS panel target regions. We believe this software will improve the transparency of NGS panels and facilitate more informed decisions in test choice and interpretation, thus constituting a valuable addition to the expanding repertoire of available tools. None declared. Download .xlsx (.02 MB) Help with xlsx files Supplemental Table S1 Download .xlsx (.14 MB) Help with xlsx files Supplemental Table S2

Over the years, our understanding of cribriform and intraductal prostate cancer (PCa) has evolved significantly, leading to substantial changes in their classification and clinical management. This review discusses the histopathological disparities between intraductal and cribriform PCa from a diagnostic perspective, aiming to aid pathologists in achieving accurate diagnoses. Furthermore, it discusses the ongoing debate surrounding the different recommendations between ISUP and GUPS, which pose challenges for practicing pathologists and complicates consensus among them. Recent studies have shown promising results in integrating these pathological features into clinical decision-making tools, improving predictions of PCa recurrence, cancer spread, and mortality. Future research efforts should focus on further unraveling the biological backgrounds of these entities and their implications for clinical management to ultimately improve PCa patient outcomes.

Multi-gene next-generation sequencing (NGS) panels have become a routine diagnostic method in the contemporary practice of personalised medicine. To avoid inadequate test choice or interpretation, a detailed understanding of the precise panel target regions is required. However, the necessary bioinformatic expertise is not always available, and publicly accessible and easily interpretable analyses of target regions are scarce. To address this critical knowledge gap, we present the Panel Comparative Analysis Tool (PanelCAT) an open-source application to analyze, visualize and compare NGS panel DNA target regions. PanelCat uses RefSeq, ClinVar and COSMIC cancer mutation census databases to quantify the exon and mutation coverage of target regions and provides interactive graphical representations and search functions to inspect the results. We demonstrate the utility of PanelCAT by analyzing two large NGS panels (Illumina TSO500 and Qiagen pan-cancer panel) to validate the advertized target genes, quantify targeted exons and mutations, and identify differences between panels. PanelCat will enable institutions and researchers to catalogue and visualize NGS panel target regions independent of the manufacturer, promote transparency of panel limitations, and share this information with employees and requisitioners.

Flat urothelial lesions are common and recognition is important for patient management. Over- and undertreatment can be a consequence of misdiagnosis. Reporting the right diagnosis is also important for the follow-up. We describe the most frequent entities with a focus on clinical meaning. Several of these described lesions do not figure in the WHO 2022 classification; therefore knowledge of them is important. We also discuss benign, precursor and malignant lesions and suggest the latest nomenclatures given by international societies. Molecular data, as far as currently known, are included where possible. The aim is to give a practical and precise overview of this complicated topic.

Tumor staging of prostate cancer is a fundamental principle in management and therapy, with a hallmark being tumor growth beyond the organ boundary. Often, this is referred to as “capsule penetration”, suggesting the existence of a true prostatic capsule that would facilitate the determination of tumor penetration. In fact, the prostate does not have a true capsule and, depending on the anatomic area, it blends with the surrounding fibrous, adipose and muscular tissue. This makes it sometimes difficult or impossible to unequivocally identify extraprostatic tumor extension. It is necessary to appreciate this difficulty in order to better understand the significance of extraprostatic tumor extension.

Pre-clinical studies from the recent past have indicated that senescent cells can negatively affect health and contribute to premature aging. Targeted eradication of these cells has been shown to improve the health of aged experimental animals, leading to a clinical interest in finding compounds that selectively eliminate senescent cells while sparing non-senescent ones. In our study, we identified a senolytic capacity of statins, which are lipid-lowering drugs prescribed to patients at high risk of cardiovascular events. Using two different models of senescence in human vascular endothelial cells (HUVECs), we found that statins preferentially eliminated senescent cells, while leaving non-senescent cells unharmed. We observed that the senolytic effect of statins could be negated with the co-administration of mevalonic acid and that statins induced cell detachment leading to anoikis-like apoptosis, as evidenced by real-time visualization of caspase-3/7 activation. Our findings suggest that statins possess a senolytic property, possibly also contributing to their described beneficial cardiovascular effects. Further studies are needed to explore the potential of short-term, high-dose statin treatment as a candidate senolytic therapy.

PURPOSE OF REVIEW:This review provides a summary of recent developments in classification of renal oncocytic neoplasms that were incorporated in the fifth edition WHO classification of renal tumors, released in 2022.RECENT FINDINGS:Besides the distinct entities of renal oncocytoma and chromophobe renal cell carcinoma, the WHO now acknowledges a heterogeneous group of oncocytic tumors of the kidney that can be reported as 'oncocytic renal neoplasms of low malignant potential'. Case series by multiple institutions have revealed recurrent patterns of morphological features, protein marker expression, and genetic alterations within these neoplasms that may permit further subclassification in the future.SUMMARY:The new classification system provides pathologists with the opportunity to simplify the diagnostic workup and reporting of morphologically equivocal oncocytic neoplasms.

TFEB-altered renal cell carcinomas are rare tumours. Here, we report the exceptional case of such a tumour in the setting of solid organ transplantation and with already metastatic disease at the time of diagnosis. The primary tumour occurred in the native kidney and only focally showed biphasic morphology whereas the metastasis, among others to the transplant kidney, showed nonspecific, albeit different morphology, but both had consistent TFEB translocation. Treatment with the immune checkpoint inhibitor pembrolizumab together with the multi-kinase inhibitor lenvatinib achieved partial response 14 months after diagnosis.

Introduction: Infectious diseases and vaccinations are trigger factors for thrombotic microangiopathy. Consequently, the COVID-19 pandemic could have an effect on disease manifestation or relapse in patients with atypical hemolytic syndrome/complement-mediated thrombotic microangiopathy (aHUS/cTMA).Methods: We employed the Vienna TMA cohort database to examine the incidence of COVID-19 related and of SARS-CoV-2 vaccination-related relapse of aHUS/cTMA among patients previously diagnosed with aHUS/cTMA during the first 2.5 years of the COVID-19 pandemic. We calculated incidence rates, including respective confidence intervals (CIs) and used Cox proportional hazard models for comparison of aHUS/ cTMA episodes following infection or vaccination.Results: Among 27 patients with aHUS/cTMA, 13 infections triggered 3 (23%) TMA episodes, whereas 70 vaccinations triggered 1 TMA episode (1%; odds ratio 0.04; 95% CI 0.003-0.37, P = 0.01). In total, the incidence of TMA after COVID-19 or SARS-CoV-2 vaccination was 6 cases per 100 patient years (95% CI 0.017-0.164) (4.5/100 patient years for COVID-19 and 1.5/100 patient years for SARS-CoV-2 vaccination). The mean follow-up time was 2.31 10.26 years (total amount: 22,118 days; 62.5 years) to either the end of the follow-up or TMA relapse (outcome). Between 2012 and 2022 we did not find a significant increase in the incidence of aHUS/cTMA.Conclusion: COVID-19 is associated with a higher risk for aHUS/cTMA recurrence when compared to SARS-CoV-2 vaccination. Overall, the incidence of aHUS/cTMA after COVID-19 infection or SARS-CoV-2 vaccination is low and comparable to that described in the literature.

Key Points Pauci-immune focal necrotizing glomerulonephritis (piFNGN) entails heterogeneous glomerular lesions in different stages of evolution. Spatial profiling of glomeruli in piFNGN identifies protein and mRNA signatures that correlate with morphologically distinct lesions. Profiling of individual glomeruli provides insights into the pathogenesis of piFNGN and may identify therapeutic targets or biomarkers. Pauci-immune focal necrotizing glomerulonephritis (piFNGN) involves asynchronous onset and progression of injurious lesions in biopsies. Pathologists can describe this heterogeneity within a biopsy, but translating the information into prognostic or expression analyses is challenging. Understanding the underlying molecular processes could improve treatment; however, bulk or single-cell transcriptomic analyses of dissociated tissue disregard the heterogeneity of glomerular injury. We characterize protein and mRNA expression of individual glomeruli in 20 biopsies from 18 patients with antineutrophil cytoplasmic antibody-associated piFNGN using the NanoString digital spatial profiling (DSP) platform. For this purpose, circular annotations of glomeruli were analyzed using protein, immuno-oncology RNA, and Cancer Transcriptome Atlas panels ( n =120, 72, and 48 glomeruli, respectively). Histologic evaluation of glomerular patterns of injury was performed in adjacent serial sections. Expression data were processed by log 2 transformation, quantile normalization, and batch adjustment. DSP revealed distinct but overlapping gene expression profiles relating to the morphological evolution of injurious lesions, including dynamic expression of various immune checkpoint regulators. Enrichment analysis indicated deregulated pathways that underline known and highlight novel potential mechanisms of disease. Moreover, by capturing individual glomeruli, DSP describes heterogeneity between and within biopsies. We demonstrate the benefit of spatial profiling for characterization of heterogeneous glomerular injury, indicating novel molecular correlates of glomerular injury in piFNGN.

Purpose of review Nonmuscle-invasive bladder cancer (NMIBC) is the most frequent bladder cancer and represents around 75% of bladder cancers. This review will discuss known challenges and recent advances in staging, grading and treatment stratification based on pathology. Recent findings Pathological staging and grading in NMIBC remains challenging and different techniques exist. Substaging has been shown to be of prognostic relevance and to help predict treatment response in patients receiving Bacillus Calmette-Guérin (BCG) therapy, which is the treatment of choice for high-grade NMIBC. Recent advances in molecular classification and artificial intelligence were also able to show promising results in the stratification of patients. Summary Many challenges in the diagnosis of NMIBC are still unresolved and ask for more prospective research. New technologies, molecular insights and AI will help in the upcoming years to better stratify and manage these patients.

The reporting recommendations on "flat and papillary urothelial neoplasia," published in 2 position articles by the Genitourinary Pathology Society in July 2021, was a collective contribution of 38 multidisciplinary experts aiming to clarify nomenclature, classification of flat and papillary urothelial neoplasia and controversial issues. In this review, we discuss some of these recommendations including nomenclature, practical approaches, and their importance for clinical practice.

Purpose of review To highlight the latest changes in prostate cancer (PCa), urothelial carcinoma, upper tract urothelial carcinoma (UTUC) and renal cell carcinoma (RCC) diagnosis and the impact of genetics in this field. Recent findings Breast cancer1/2 mutations start to play a major role in PCa treatment with regard to personalized medicine. In urothelial carcinoma an overlap between histological pathological and molecular findings exists, fibroblast growth factor receptor alteration are starting to play a major role, programmed death-ligand 1 although problematic is still important in the treatment setting. UTUC is rare, but genetically different from urothelial carcinoma. In the development of RCC, different genetic pathways such as Von Hippel–Lindau, but also tuberous sclerosis 1/2 and others play a major role in tumor development. Summary Over the last years, genetics has become increasingly important role in the diagnosis and the treatment of patients with urological malignancies. The upcoming 5th edition (1) of the WHO still considers conventional surgical pathology as the diagnostic gold standard, but molecular pathology is gaining importance not only for diagnosis, but also in personalized treatment, of prostate, kidney cancer and urothelial carcinomas. Therefore, a close collaboration between surgical urology, pathology and oncology departments is mandatory. In this review, we will discuss the latest evolutions in PCa, urothelial carcinoma, upper urinary tract carcinomas and RCC s in the field of genetics in urology.

Squamous cell carcinoma of the penis (PSC) is a rare disease with limited information on the molecular events leading to malignant transformation. In a third of PSC cases, presence of human papilloma virus (HPV) is found. The APOBEC3 family of proteins is known to play a significant role in defense against HPV infection, but their role in PSC is largely unknown. In this study, we aim to assess mRNA expression levels of APOBEC3 family members in HPV+ and HPV− PSC to get insight into their association with clinicopathological features and to evaluate their prognostic impact. Expression levels of six APOBEC3 family members in tissue from 50 patients with PSC were determined by RT-PCR and correlated with clinical and histopathological features. Lower expression of APOBEC3A, APOBEC3B, and APOBEC3C was observed in advanced PSC stages. Except for APOBEC3D, HPV+ samples showed higher expression of APOBEC3s compared to HPV− samples. In univariate analyses, APOBEC3A and APOBEC3C expression tended to be associated with disease-free survival and APOBEC3A expression with overall survival; however, multivariable analyses failed to confirm these associations with outcome. More extensive external validation and functional laboratory studies are needed to evaluate further their role in PSC development and progression.