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The pathogenetic relationship of marginal zone B-cell lymphoma (MALT lymphoma) of the gastrointestinal (GI) tract and eventually co-existing aggressive B-cell lymphoma and primary aggressive B-cell lymphoma remains to be elucidated. The RNA of laser-microdissected cells was isolated and amplified from small and/or large cell compartments of eight MALT lymphomas (small cell lymphoma, SCL), 14 GI diffuse large B-cell lymphomas (large cell lymphoma, LCL), and ten GI B-cell lymphomas with composite small and large cell compartments (ComL) and expression analyses were performed using cDNA arrays. Hierarchical cluster analysis clearly separated SCL and LCL and the small and large cell compartments of ComL. Likewise, cluster analysis with all samples of SCL, LCL, and ComL yielded two main 'small cell' and 'large cell' branches. Furthermore, 60 genes were differentially expressed between SCL and LCL, and 82 genes between the small and large cell components of ComL; 26 genes were discriminators in both settings. Use of the profiles of ComL as training sets for class prediction resulted in 95% accuracy for the classification of SCL and LCL. Collectively, the data strongly suggest that both secondary and primary aggressive B-cell lymphomas of the GI tract are blastic marginal zone lymphomas. Copyright © 2006 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract Background Cell motility is a critical parameter in many physiological as well as pathophysiological processes. In time-lapse video microscopy, manual cell tracking remains the most common method of analyzing migratory behavior of cell populations. In addition to being labor-intensive, this method is susceptible to user-dependent errors regarding the selection of "representative" subsets of cells and manual determination of precise cell positions. Results We have quantitatively analyzed these error sources, demonstrating that manual cell tracking of pancreatic cancer cells lead to mis-calculation of migration rates of up to 410%. In order to provide for objective measurements of cell migration rates, we have employed multi-target tracking technologies commonly used in radar applications to develop fully automated cell identification and tracking system suitable for high throughput screening of video sequences of unstained living cells. Conclusion We demonstrate that our automatic multi target tracking system identifies cell objects, follows individual cells and computes migration rates with high precision, clearly outperforming manual procedures.

Abstract Background In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer. Results We introduce a multi-core parallelization of the k-means and k-modes cluster algorithms based on the design principles of transactional memory for clustering gene expression microarray type data and categorial SNP data. Our new shared memory parallel algorithms show to be highly efficient. We demonstrate their computational power and show their utility in cluster stability and sensitivity analysis employing repeated runs with slightly changed parameters. Computation speed of our Java based algorithm was increased by a factor of 10 for large data sets while preserving computational accuracy compared to single-core implementations and a recently published network based parallelization. Conclusions Most desktop computers and even notebooks provide at least dual-core processors. Our multi-core algorithms show that using modern algorithmic concepts, parallelization makes it possible to perform even such laborious tasks as cluster sensitivity and cluster number estimation on the laboratory computer.

Abstract Background Microarray experiments generate vast amounts of data. The functional context of differentially expressed genes can be assessed by querying the Gene Ontology (GO) database via GoMiner. Directed acyclic graph representations, which are used to depict GO categories enriched with differentially expressed genes, are difficult to interpret and, depending on the particular analysis, may not be well suited for formulating new hypotheses. Additional graphical methods are therefore needed to augment the GO graphical representation. Results We present an alternative visualization approach, area-proportional Euler diagrams, showing set relationships with semi-quantitative size information in a single diagram to support biological hypothesis formulation. The cardinalities of sets and intersection sets are represented by area-proportional Euler diagrams and their corresponding graphical (circular or polygonal) intersection areas. Optimally proportional representations are obtained using swarm and evolutionary optimization algorithms. Conclusion VennMaster's area-proportional Euler diagrams effectively structure and visualize the results of a GO analysis by indicating to what extent flagged genes are shared by different categories. In addition to reducing the complexity of the output, the visualizations facilitate generation of novel hypotheses from the analysis of seemingly unrelated categories that share differentially expressed genes.

Abstract Background The identification of genomic signatures of colorectal cancer for risk stratification requires the study of large series of cancer patients with an extensive clinical follow-up. Multicentric clinical studies represent an ideal source of well documented archived material for this type of analyses. Methods To verify if this material is technically suitable to perform matrix-CGH, we performed a pilot study using macrodissected 29 formalin-fixed, paraffin-embedded tissue samples collected within the framework of the EORTC-GI/PETACC-2 trial for colorectal cancer. The scientific aim was to identify prognostic genomic signatures differentiating locally restricted (UICC stages II-III) from systemically advanced (UICC stage IV) colorectal tumours. Results The majority of archived tissue samples collected in the different centers was suitable to perform matrix-CGH. 5/7 advanced tumours displayed 13q-gain and 18q-loss. In locally restricted tumours, only 6/12 tumours showed a gain on 13q and 7/12 tumours showed a loss on 18q. Interphase-FISH and high-resolution array-mapping of the gain on 13q confirmed the validity of the array-data and narrowed the chromosomal interval containing potential oncogenes. Conclusion Archival, paraffin-embedded tissue samples collected in multicentric clinical trials are suitable for matrix-CGH analyses and allow the identification of prognostic signatures and aberrations harbouring potential new oncogenes.

Introduction: A substantial proportion of head and neck squamous cell carcinoma (HNSCC), particularly oropharyngeal squamous cell carcinoma (OPSCC), is associated with human papillomavirus (HPV), resulting in distinct molecular phenotypes. In this study, we investigated differential immune checkpoint molecule (ICM) expression by HPV status using RNA sequencing data to identify additional ICM targets that may complement anti-PD1 antibodies., Material and Methods: RNA sequencing was performed on 51 OPSCC cases and validated using the TCGA HNSCC dataset. Unsupervised clustering and differential gene expression analyses in R were conducted based on HPV status. Additionally, a published single-cell RNA sequencing (scRNA) dataset of tumor-infiltrating lymphocytes (TIL) and peripheral immune cells (PBMC) (GSE139324) was analyzed with a Seurat pipeline grouped by HPV status., Results: Our study identified a significant upregulation of all examined ICM in HPV-positive OPSCC through bulk RNA sequencing, validated by the TCGA cohort. Unsupervised clustering revealed a strong association between HPV-positive/-negative and high/low ICM expression cases respectively, indicating overlap between ICM and HPV status. In scRNA analysis, CD27, PD-1, OX-40, and BTLA were significantly more highly expressed on TILs of HPV-positive OPSCC. Conversely, VSIR was increased in PBMC and TILs of HPV-negative OPSCC, while LAG3 expression on PBMC was reduced in HPV-negative OPSCC., Conclusion: Our study unveils the intricate interplay of ICMs in OPSCC, emphasizing the necessity for personalized therapeutic approaches based on HPV status and immune profiles. The identified ICMs, including PD1, CD27, and CTLA4, are promising candidates for further investigation and may enhance immunotherapeutic interventions in the HPV-dependent treatment strategies for OPSCC., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Simon Laban: Advisory Boards: Merck Sharp & Dohme (MSD), Bristol Myers Squibb (BMS), Astra Zeneca (AZ). Honoraria: MSD, BMS, AZ, Merck Serono. Johannes Döscher: Advisory Boards: Merck Serono. Honoraria: Merck Serono. Thomas K. Hoffmann: Advisory Boards: MSD, BMS. Honoraria: MSD, BMS, Merck Serono. Patrick Schuler: Advisory Boards: BMS. All other authors did not declare a conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The limited availability of suitable animal models and cell lines often impedes experimental cancer research. Wet-laboratory experiments are also time-consuming and cost-intensive. In this review, we present an in silico modeling strategy, namely, Boolean network (BN) models, and demonstrate how it could be applied to streamline experimental design and to focus the effort of experimental read-outs. Boolean network models allow for the dynamic analysis of large molecular signaling pathways and their crosstalks. After establishing and validating a specific tumor model, mechanistic insights into the tumor cell behavior can be gained by studying the trajectories of different tumor phenotypes. Also, tumor driver and drug target screenings can be performed. These automatic screenings can help to identify new intervention targets and putative biomarkers for tumor evolution, hence guiding new wet-laboratory experiments. The goal of this round-up is to demonstrate how to establish, validate, and use BN modeling and its crosstalks in classic wet-laboratory research using a chronic lymphocytic leukemia (CLL) BN model., Competing Interests: Declarations Conflict of interest J. Maier, J.D. Schwab, S.D. Werle, R. Marienfeld, P. Möller, N.T. Gaisa, N. Ikonomi and H.A. Kestler declare that they have no competing interests.For this article no studies with human participants or animals were performed by any of the authors. All studies mentioned were in accordance with the ethical standards indicated in each case.The supplement containing this article is not sponsored by industry., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background and Aims: Timely and accurate detection of tumor recurrence in pancreatic ductal adenocarcinoma (PDAC) patients is an urgent and unmet medical need. This study aimed to develop a noninvasive molecular diagnostic procedure for the detection of recurrence after PDAC resection based on quantification of circulating mRNA and miRNA biomarkers in serum samples., Methods: In a multicentric study, serum samples from a total of 146 patients were prospectively collected after resection. Samples were classified into a "No Evidence of Disease" and a "Recurrence" group based on clinical follow-up data. A multianalyte biomarker panel was composed of mRNAs and miRNA markers and simultaneously analyzed in serum samples using custom microfluidic qPCR arrays (TaqMan array cards). A diagnostic algorithm was developed combining a 7-gene marker signature with CA19-9 data., Results: The best-performing marker combination achieved 90% diagnostic accuracy in predicting the presence of tumor recurrence (98% sensitivity; 84% specificity), clearly outperforming the singular CA 19-9 analysis. Moreover, time series data obtained by analyzing successively collected samples from 5 patients during extended follow-up suggested that molecular diagnosis has the potential to detect recurrence earlier than routine clinical procedures., Conclusions: TaqMan array card measurements were found to be biologically valid and technically reproducible. The BioPac multianalyte marker panel is capable of sensitive and accurate detection of recurrence in patients resected for PDAC using a simple blood test. This could allow a closer follow-up using shorter time intervals than currently used for imaging, thus potentially prompting an earlier work-up with additional modalities to allow for earlier therapeutic intervention. This study provides a promising approach for improved postoperative monitoring of resected PDAC patients, which is an urgent and unmet clinical need., (© 2024 The Author(s). United European Gastroenterology Journal published by Wiley Periodicals LLC on behalf of United European Gastroenterology.)

Background: Understanding the similarities of patients with cancer is essential to advancing personalized medicine, improving patient outcomes, and developing more effective and individualized treatments. It enables researchers to discover important patterns, biomarkers, and treatment strategies that can have a significant impact on cancer research and oncology. In addition, the identification of previously successfully treated patients supports oncologists in making treatment decisions for a new patient who is clinically or molecularly similar to the previous patient., Objective: The planned review aims to systematically summarize, map, and describe existing evidence to understand how patient similarity is defined and used in cancer research and clinical care., Methods: To systematically identify relevant studies and to ensure reproducibility and transparency of the review process, a comprehensive literature search will be conducted in several bibliographic databases, including Web of Science, PubMed, LIVIVIVO, and MEDLINE, covering the period from 1998 to February 2024. After the initial duplicate deletion phase, a study selection phase will be applied using Rayyan, which consists of 3 distinct steps: title and abstract screening, disagreement resolution, and full-text screening. To ensure the integrity and quality of the selection process, each of these steps is preceded by a pilot testing phase. This methodological process will culminate in the presentation of the final research results in a structured form according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. The protocol has been registered in the Journal of Medical Internet Research., Results: This protocol outlines the methodologies used in conducting the scoping review. A search of the specified electronic databases and after removing duplicates resulted in 1183 unique records. As of March 2024, the review process has moved to the full-text evaluation phase. At this stage, data extraction will be conducted using a pretested chart template., Conclusions: The scoping review protocol, centered on these main concepts, aims to systematically map the available evidence on patient similarity among patients with cancer. By defining the types of data sources, approaches, and methods used in the field, and aligning these with the research questions, the review will provide a foundation for future research and clinical application in personalized cancer care. This protocol will guide the literature search, data extraction, and synthesis of findings to achieve the review's objectives., International Registered Report Identifier (irrid): DERR1-10.2196/58705., (©Iryna Manuilova, Jan Bossenz, Annemarie Bianka Weise, Dominik Boehm, Cosima Strantz, Philipp Unberath, Niklas Reimer, Patrick Metzger, Thomas Pauli, Silke D Werle, Susann Schulze, Sonja Hiemer, Arsenij Ustjanzew, Hans A Kestler, Hauke Busch, Benedikt Brors, Jan Christoph. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 04.09.2024.)

Introduction: The human leukocyte antigen complex (HLA) is essential for inducing specific immune responses to cancer by presenting tumor-associated peptides (TAP) to T cells. Overexpressed tumor associated antigens, mainly cancer-testis antigens (CTA), are outlined as essential targets for immunotherapy in oropharyngeal squamous cell carcinoma (OPSCC). This study assessed the degree to which presentation, gene expression, and antibody response (AR) of TAP, mainly CTA, are correlated in OPSCC patients to evaluate their potential as immunotherapy targets., Materials and Methods: Snap-frozen tumor (N Ligand/RNA =40), healthy mucosa (N RNA =6), and healthy tonsils (N Ligand =5) samples were obtained. RNA-Seq was performed using Illumina HiSeq 2500/NovaSeq 6000 and whole exome sequencing (WES) utilizing NextSeq500. HLA ligands were isolated from tumor tissue using immunoaffinity purification, UHPLC, and analyzed by tandem MS. Antibodies were measured in serum (N Ab =27) utilizing the KREX™ CT262 protein array. Data analysis focused on 312 proteins (KREX™ CT262 panel + overexpressed self-proteins)., Results: 183 and 94 of HLA class I and II TAP were identified by comparative profiling with healthy tonsils. Genes from 26 TAP were overexpressed in tumors compared to healthy mucosa (LFC>1; FDR<0.05). Low concordance (r=0.25; p<0.0001) was found between upregulated mRNA and class I TAP. The specific mode of correlation of TAP was found to be dependent on clinical parameters. A lack of correlation was observed both between mRNA and class II TAP, as well as between class II tumor-unique TAP (TAP-U) presentation and antibody response (AR) levels., Discussion: This study demonstrates that focusing exclusively on gene transcript levels fails to capture the full extent of TAP presentation in OPSCC. Furthermore, our findings reveal that although CTA are presented at relatively low levels, a few CTA TAP-U show potential as targets for immunotherapy., Competing Interests: SL: Advisory Boards: Merck Sharp & Dohme MSD, Bristol Myers Squibb BMS, Sanofi Genzyme, Astra Zeneca AZ. Honoraria: MSD, BMS. Travel reimbursement: Merck Serono, Astra Zeneca. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Abou Kors, Meier, Mühlenbruch, Betzler, Oliveri, Bens, Thomas, Kraus, Doescher, von Witzleben, Hofmann, Ezic, Huber, Benckendorff, Barth, Greve, Schuler, Brunner, Blackburn, Hoffmann, Ottensmeier, Kestler, Rammensee, Walz and Laban.)

Deregulated apoptosis signaling is characteristic for many cancers and contributes to leukemogenesis and treatment failure in B-cell precursor acute lymphoblastic leukemia (BCP-ALL). Apoptosis is controlled by different pro- and anti-apoptotic molecules. Inhibition of anti-apoptotic molecules like B-cell lymphoma 2 (BCL-2) has been developed as therapeutic strategy. Venetoclax (VEN), a selective BCL-2 inhibitor has shown clinical activity in different lymphoid malignancies and is currently evaluated in first clinical trials in BCP-ALL. However, insensitivity to VEN has been described constituting a major clinical concern. Here, we addressed and modeled VEN-resistance in BCP-ALL, investigated the underlying mechanisms in cell lines and patient-derived xenograft (PDX) samples and identified potential strategies to overcome VEN-insensitivity. Leukemia lines with VEN-specific resistance were generated in vitro and further characterized using RNA-seq analysis. Interestingly, gene sets annotated to the citric/tricarboxylic acid cycle and the respiratory electron transport chain were significantly enriched and upregulated, indicating increased mitochondrial metabolism in VEN-resistant ALL. Metabolic profiling showed sustained high mitochondrial metabolism in VEN-resistant lines as compared to control lines. Accordingly, primary PDX-ALL samples with intrinsic VEN-insensitivity showed higher oxygen consumption and ATP production rates, further highlighting that increased mitochondrial activity is a characteristic feature of VEN-resistant ALL. VEN-resistant PDX-ALL showed significant higher mitochondrial DNA content and differed in mitochondria morphology with significantly larger and elongated structures, further corroborating our finding of augmented mitochondrial metabolism upon VEN-resistance. Using Oligomycin, an inhibitor of the complex V/ATPase subunit, we found synergistic activity and apoptosis induction in VEN-resistant BCP-ALL cell lines and PDX samples, demonstrating that acquired and intrinsic VEN-insensitivity can be overcome by co-targeting BCL-2 and the OxPhos pathway. These findings of reprogrammed, high mitochondrial metabolism in VEN-resistance and synergistic activity upon co-targeting BCL-2 and oxidative phosphorylation strongly suggest further preclinical and potential clinical evaluation in VEN-resistant BCP-ALL., (© 2024. The Author(s).)

Background: Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab., Patients and Methods: 15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM)., Results: Using random forests, we developed a predictive machine learning model that discriminated between the situations of "no progress within 100 days before radiological progress" and "progress within 100 days before radiological progress". We could further identify a combination of ten out of the 102 CAF markers, which fulfilled this task with 78.2% accuracy, 71.8% sensitivity, and 82.5% specificity., Conclusions: We identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress., Competing Interests: “•Thomas Seufferlein reports research funding to the institution from Sanofi during the conduct of the study; honoraria from Lilly, Pierre Fabre, BMS, MSD; advisory/consultancy roles with Amgen, Lilly, BMS, MSD, Pierre Fabre, Servier, Immodulon, Biontech, Mirati, Boehringer; Travel expenses from Takeda • Stefan Kasper discloses honoraria (self) from Merck, Amgen, Roche, Sanofi-Aventis, Servier, and Lilly; honoraria (institution) from Merck, Amgen, Roche, and Lilly; advisory/consultancy roles with Merck, BMS, Amgen, Roche, MSD, Sanofi-Aventis, Servier, and Lilly; research grants/funding (self) from Merck, Roche, BMS and Lilly; research grants/funding (institution) from Merck, Roche, BMS and Lilly; travel/accommodation expenses from Merck, Amgen, BMS, Roche, Sanofi, Aventis, Servier, and Lilly. • Thomas J. Ettrich reports honoraria from Roche, Sanofi; advisory/consultancy roles with Roche, Sanofi; research funding from Servier. • All other authors declare to have no conflicts of interest. This does not alter our adherence to PLOS ONE policies on sharing data and materials”., (Copyright: © 2024 Seufferlein et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: The usage of digital information and communication technologies in European healthcare is growing. Unlike numerous technological possibilities, the present use of these technologies and perspectives towards them in relation to otolaryngology care have so far been of less interest. This study evaluates the utilisation of and attitudes towards digital information and communication technologies in cross-sectoral otolaryngology care among German patients., Methods: A structured interview-based study was conducted at the outpatient facility of a tertiary hospital in Germany. It focused on chief complaints, current use of digital technologies, estimated benefits of increased digital technology use in otolaryngology care, and sociodemographic data. The detailed statistical analysis employed Chi-squared tests and multivariate logistic regression., Results: A total of 208 otolaryngology patients completed the interview. Digital communication technologies exhibited a high penetration rate (91.8%) and were regularly used in daily life (78.7%) and for health reasons (73.3%). Younger age ( p  ≤ 0.003) and higher education levels ( p  ≤ 0.008) were significantly correlated with the increased digital communication technology use. The overall potential of eHealth technologies was rated significantly higher by younger patients ( p  ≤ 0.001). The patients' chief complaints showed no significant influence on the current and potential use of these technologies for cross-sectoral otolaryngology care., Conclusion: Regardless of their chief complaints, German otolaryngology patients regularly use digital information and communication technologies for health reasons and express interest in their further use for cross-sectoral care. To enhance digital patient communication in otolaryngology, attention should be given to treatment quality, usability, data security and availability and financial remuneration for service providers., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

The successful integration of neural networks in a clinical setting is still uncommon despite major successes achieved by artificial intelligence in other domains. This is mainly due to the black box characteristic of most optimized models and the undetermined generalization ability of the trained architectures. The current work tackles both issues in the radiology domain by focusing on developing an effective and interpretable cardiomegaly detection architecture based on segmentation models. The architecture consists of two distinct neural networks performing the segmentation of both cardiac and thoracic areas of a radiograph. The respective segmentation outputs are subsequently used to estimate the cardiothoracic ratio, and the corresponding radiograph is classified as a case of cardiomegaly based on a given threshold. Due to the scarcity of pixel-level labeled chest radiographs, both segmentation models are optimized in a semi-supervised manner. This results in a significant reduction in the costs of manual annotation. The resulting segmentation outputs significantly improve the interpretability of the architecture's final classification results. The generalization ability of the architecture is assessed in a cross-domain setting. The assessment shows the effectiveness of the semi-supervised optimization of the segmentation models and the robustness of the ensuing classification architecture., (© 2024. The Author(s).)

Introduction: In Multiple Sclerosis (MS), patients´ characteristics and (bio)markers that reliably predict the individual disease prognosis at disease onset are lacking. Cohort studies allow a close follow-up of MS histories and a thorough phenotyping of patients. Therefore, a multicenter cohort study was initiated to implement a wide spectrum of data and (bio)markers in newly diagnosed patients., Methods: ProVal-MS (Prospective study to validate a multidimensional decision score that predicts treatment outcome at 24 months in untreated patients with clinically isolated syndrome or early Relapsing-Remitting-MS) is a prospective cohort study in patients with clinically isolated syndrome (CIS) or Relapsing-Remitting (RR)-MS (McDonald 2017 criteria), diagnosed within the last two years, conducted at five academic centers in Southern Germany. The collection of clinical, laboratory, imaging, and paraclinical data as well as biosamples is harmonized across centers. The primary goal is to validate (discrimination and calibration) the previously published DIFUTURE MS-Treatment Decision score (MS-TDS). The score supports clinical decision-making regarding the options of early (within 6 months after study baseline) platform medication (Interferon beta, glatiramer acetate, dimethyl/diroximel fumarate, teriflunomide), or no immediate treatment (> 6 months after baseline) of patients with early RR-MS and CIS by predicting the probability of new or enlarging lesions in cerebral magnetic resonance images (MRIs) between 6 and 24 months. Further objectives are refining the MS-TDS score and providing data to identify new markers reflecting disease course and severity. The project also provides a technical evaluation of the ProVal-MS cohort within the IT-infrastructure of the DIFUTURE consortium (Data Integration for Future Medicine) and assesses the efficacy of the data sharing techniques developed., Perspective: Clinical cohorts provide the infrastructure to discover and to validate relevant disease-specific findings. A successful validation of the MS-TDS will add a new clinical decision tool to the armamentarium of practicing MS neurologists from which newly diagnosed MS patients may take advantage. Trial registration ProVal-MS has been registered in the German Clinical Trials Register, `Deutsches Register Klinischer Studien` (DRKS)-ID: DRKS00014034, date of registration: 21 December 2018; https://drks.de/search/en/trial/DRKS00014034., (© 2024. The Author(s).)

Patients with oropharyngeal squamous cell carcinoma (OPSCC) caused by human papilloma virus (HPV) exhibit a better prognosis than those with HPV-negative OPSCC. This study investigated the distinct molecular pathways that delineate HPV-negative from HPV-positive OPSCC to identify biologically relevant therapeutic targets. Bulk mRNA from 23 HPV-negative and 39 HPV-positive OPSCC tumors (n = 62) was sequenced to uncover the transcriptomic profiles. Differential expression followed by gene set enrichment analysis was performed to outline the top enriched biological process in the HPV-negative compared with HPV-positive entity. INHBA, the highest overexpressed gene in the HPV-negative tumor, was knocked down. Functional assays (migration, proliferation, cell death, stemness) were conducted to confirm the target's oncogenic role. Correlation analyses to reveal its impact on the tumor microenvironment were performed. We revealed that epithelial-to-mesenchymal transition (EMT) is the most enriched process in HPV-negative compared with HPV-positive OPSCC, with INHBA (inhibin beta A subunit) being the top upregulated gene. INHBA knockdown downregulated the expression of EMT transcription factors and attenuated migration, proliferation, stemness, and cell death resistance of OPSCC cells. We uncovered that INHBA associates with a pro-tumor microenvironment by negatively correlating with antitumor CD8+ T and B cells while positively correlating with pro-tumor M1 macrophages. We identified three miRNAs that are putatively involved in repressing INHBA expression. Our results indicate that the upregulation of INHBA is tumor-promoting. We propose INHBA as an attractive therapeutic target for the treatment of INHBA-enriched tumors in patients with HPV-negative OPSCC to ameliorate prognosis., Significance: Patients with HPV-negative OPSCC have a poorer prognosis due to distinct molecular pathways. This study reveals significant transcriptomic differences between HPV-negative and HPV-positive OPSCC, identifying INHBA as a key upregulated gene in HPV-negative OPSCC's oncogenic pathways. INHBA is crucial in promoting EMT, cell proliferation, and an immunosuppressive tumor environment, suggesting its potential as a therapeutic target for HPV-negative OPSCC., (© 2024 The Authors; Published by the American Association for Cancer Research.)

Motivation: Boolean networks can serve as straightforward models for understanding processes such as gene regulation, and employing logical rules. These rules can either be derived from existing literature or by data-driven approaches. However, in the context of large networks, the exhaustive search for intervention targets becomes challenging due to the exponential expansion of a Boolean network's state space and the multitude of potential target candidates, along with their various combinations. Instead, we can employ the logical rules and resultant interaction graph as a means to identify targets of specific interest within larger-scale models. This approach not only facilitates the screening process but also serves as a preliminary filtering step, enabling the focused investigation of candidates that hold promise for more profound dynamic analysis. However, applying this method requires a working knowledge of R, thus restricting the range of potential users. We, therefore, aim to provide an application that makes this method accessible to a broader scientific community., Results: Here, we introduce GatekeepR, a graphical, web-based R Shiny application that enables scientists to screen Boolean network models for possible intervention targets whose perturbation is likely to have a large impact on the system's dynamics. This application does not require a local installation or knowledge of R and provides the suggested targets along with additional network information and visualizations in an intuitive, easy-to-use manner. The Supplementary Material describes the underlying method for identifying these nodes along with an example application in a network modeling pancreatic cancer., Availability and Implementation: https://www.github.com/sysbio-bioinf/GatekeepR https://abel.informatik.uni-ulm.de/shiny/GatekeepR/., (© The Author(s) 2024. Published by Oxford University Press.)

Personalized health care can be optimized by including patient-reported outcomes. Standardized and disease-specific questionnaires have been developed and are routinely used. These patient-reported outcome questionnaires can be simple paper forms given to the patient to fill out with a pen or embedded in digital devices. Regardless of the format used, they provide a snapshot of the patient's feelings and indicate when therapies need to be adjusted. The advantage of digitizing these questionnaires is that they can be automatically analyzed, and patients can be monitored independently of doctor visits. Although the questions of most clinical patient-reported outcome questionnaires follow defined standards and are evaluated by clinical trials, these standards do not exist for data processing. Interoperable data formats and structures would benefit multilingual and cross-study data exchange. Linking questionnaires to standardized terminologies such as the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Logical Observation Identifiers, Names, and Codes (LOINC) would improve this interoperability. However, linking clinically validated patient-reported outcome questionnaires to clinical terms available in SNOMED CT or LOINC is not as straightforward as it sounds. Here, we report our approach to link patient-reported outcomes from health applications to SNOMED CT or LOINC codes. We highlight current difficulties in this process and outline ways to minimize them., (©Julian D Schwab, Silke D Werle, Rolf Hühne, Hannah Spohn, Udo X Kaisers, Hans A Kestler. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 22.12.2023.)

Motivation: Personalized decision-making for cancer therapy relies on molecular profiling from sequencing data in combination with database evidence and expert knowledge. Molecular tumor boards (MTBs) bring together clinicians and scientists with diverse expertise and are increasingly established in the clinical routine for therapeutic interventions. However, the analysis and documentation of patients data are still time-consuming and difficult to manage for MTBs, especially as few tools are available for the amount of information required., Results: To overcome these limitations, we developed an interactive web application AMBAR (Alteration annotations for Molecular tumor BoARds), for therapeutic decision-making support in MTBs. AMBAR is an R shiny-based application that allows customization, interactive filtering, visualization, adding expert knowledge, and export to clinical systems of annotated mutations., Availability: AMBAR is dockerized, open source and available at https://sysbio.uni-ulm.de/?Software:Ambar Contact:hans.kestler@uni-ulm.de., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Boolean networks are commonly used in systems biology to dynamically model gene regulatory interactions. Here, we present a protocol for implementing Boolean network dynamics as quantum circuits. We describe steps for accessing cloud-based quantum processing units offered by IBM and IonQ and downloading and parsing logic for gene regulatory networks. We then detail procedures for performing simulations of quantum circuits on local devices and visualizing measurement results. For complete details on the use and execution of this protocol, please refer to Weidner et al. 1 ., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Mutations in a broad variety of genes can provoke the severe childhood disorder trichothiodystrophy (TTD) that is classified as a DNA repair disease or a transcription syndrome of RNA polymerase II. In an attempt to identify the common underlying pathomechanism of TTD we performed a knockout/knockdown of the two unrelated TTD factors TTDN1 and RNF113A and investigated the consequences on ribosomal biogenesis and performance. Interestingly, interference with these TTD factors created a nearly uniform impact on RNA polymerase I transcription with downregulation of UBF, disturbed rRNA processing and reduction of the backbone of the small ribosomal subunit rRNA 18S. This was accompanied by a reduced quality of decoding in protein translation and the accumulation of misfolded and carbonylated proteins, indicating a loss of protein homeostasis (proteostasis). As the loss of proteostasis by the ribosome has been identified in the other forms of TTD, here we postulate that ribosomal dysfunction is a common underlying pathomechanism of TTD.

Introduction: Geriatric co-management is known to improve treatment of older adults in various clinical settings, however, widespread application of the concept is limited due to restricted resources. Digitalization may offer options to overcome these shortages by providing structured, relevant information and decision support tools for medical professionals. We present the SURGE-Ahead project (Supporting SURgery with GEriatric co-management and Artificial Intelligence) addressing this challenge., Methods: A digital application with a dashboard-style user interface will be developed, displaying 1) evidence-based recommendations for geriatric co-management and 2) artificial intelligence-enhanced suggestions for continuity of care (COC) decisions. The development and implementation of the SURGE-Ahead application (SAA) will follow the Medical research council framework for complex medical interventions. In the development phase a minimum geriatric data set (MGDS) will be defined that combines parametrized information from the hospital information system with a concise assessment battery and sensor data. Two literature reviews will be conducted to create an evidence base for co-management and COC suggestions that will be used to display guideline-compliant recommendations. Principles of machine learning will be used for further data processing and COC proposals for the postoperative course. In an observational and AI-development study, data will be collected in three surgical departments of a University Hospital (trauma surgery, general and visceral surgery, urology) for AI-training, feasibility testing of the MGDS and identification of co-management needs. Usability will be tested in a workshop with potential users. During a subsequent project phase, the SAA will be tested and evaluated in clinical routine, allowing its further improvement through an iterative process., Discussion: The outline offers insights into a novel and comprehensive project that combines geriatric co-management with digital support tools to improve inpatient surgical care and continuity of care of older adults., Trial Registration: German clinical trials registry (Deutsches Register für klinische Studien, DRKS00030684), registered on 21st November 2022., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Leinert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Pancreatic neuroendocrine tumors (PanNETs) are a rare tumor entity with largely unpredictable progression and increasing incidence in developed countries. Molecular pathways involved in PanNETs development are still not elucidated, and specific biomarkers are missing. Moreover, the heterogeneity of PanNETs makes their treatment challenging and most approved targeted therapeutic options for PanNETs lack objective responses. Here, we applied a systems biology approach integrating dynamic modeling strategies, foreign classifier tailored approaches, and patient expression profiles to predict PanNETs progression as well as resistance mechanisms to clinically approved treatments such as the mammalian target of rapamycin complex 1 (mTORC1) inhibitors. We set up a model able to represent frequently reported PanNETs drivers in patient cohorts, such as Menin-1 (MEN1), Death domain associated protein (DAXX), Tuberous Sclerosis (TSC), as well as wild-type tumors. Model-based simulations suggested drivers of cancer progression as both first and second hits after MEN1 loss. In addition, we could predict the benefit of mTORC1 inhibitors on differentially mutated cohorts and hypothesize resistance mechanisms. Our approach sheds light on a more personalized prediction and treatment of PanNET mutant phenotypes., (© 2023. The Author(s).)

At the beginning of the COVID-19 pandemic, with a lack of knowledge about the novel virus and a lack of widely available tests, getting first feedback about being infected was not easy. To support all citizens in this respect, we developed the mobile health app Corona Check. Based on a self-reported questionnaire about symptoms and contact history, users get first feedback about a possible corona infection and advice on what to do. We developed Corona Check based on our existing software framework and released the app on Google Play and the Apple App Store on April 4, 2020. Until October 30, 2021, we collected 51,323 assessments from 35,118 users with explicit agreement of the users that their anonymized data may be used for research purposes. For 70.6% of the assessments, the users additionally shared their coarse geolocation with us. To the best of our knowledge, we are the first to report about such a large-scale study in this context of COVID-19 mHealth systems. Although users from some countries reported more symptoms on average than users from other countries, we did not find any statistically significant differences between symptom distributions (regarding country, age, and sex). Overall, the Corona Check app provided easily accessible information on corona symptoms and showed the potential to help overburdened corona telephone hotlines, especially during the beginning of the pandemic. Corona Check thus was able to support fighting the spread of the novel coronavirus. mHealth apps further prove to be valuable tools for longitudinal health data collection.

Background: The immune peptidome of OPSCC has not previously been studied. Cancer-antigen specific vaccination may improve clinical outcome and efficacy of immune checkpoint inhibitors such as PD1/PD-L1 antibodies., Methods: Mapping of the OPSCC HLA ligandome was performed by mass spectrometry (MS) based analysis of naturally presented HLA ligands isolated from tumour tissue samples (n = 40) using immunoaffinity purification. The cohort included 22 HPV-positive (primarily HPV-16) and 18 HPV-negative samples. A benign reference dataset comprised of the HLA ligandomes of benign haematological and tissue datasets was used to identify tumour-associated antigens., Results: MS analysis led to the identification of naturally HLA-presented peptides in OPSCC tumour tissue. In total, 22,769 peptides from 9485 source proteins were detected on HLA class I. For HLA class II, 15,203 peptides from 4634 source proteins were discovered. By comparative profiling against the benign HLA ligandomic datasets, 29 OPSCC-associated HLA class I ligands covering 11 different HLA allotypes and nine HLA class II ligands were selected to create a peptide warehouse., Conclusion: Tumour-associated peptides are HLA-presented on the cell surfaces of OPSCCs. The established warehouse of OPSCC-associated peptides can be used for downstream immunogenicity testing and peptide-based immunotherapy in (semi)personalised strategies., (© 2023. The Author(s).)

The dynamics of cellular mechanisms can be investigated through the analysis of networks. One of the simplest but most popular modeling strategies involves logic-based models. However, these models still face exponential growth in simulation complexity compared with a linear increase in nodes. We transfer this modeling approach to quantum computing and use the upcoming technique in the field to simulate the resulting networks. Leveraging logic modeling in quantum computing has many benefits, including complexity reduction and quantum algorithms for systems biology tasks. To showcase the applicability of our approach to systems biology tasks, we implemented a model of mammalian cortical development. Here, we applied a quantum algorithm to estimate the tendency of the model to reach particular stable conditions and further revert dynamics. Results from two actual quantum processing units and a noisy simulator are presented, and current technical challenges are discussed., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

In recent years, several deep learning approaches have been successfully applied in the field of medical image analysis. More specifically, different deep neural network architectures have been proposed and assessed for the detection of various pathologies based on chest X-ray images. While the performed assessments have shown very promising results, most of them consist in training and evaluating the performance of the proposed approaches on a single data set. However, the generalization of such models is quite limited in a cross-domain setting, since a significant performance degradation can be observed when these models are evaluated on data sets stemming from different medical centers or recorded under different protocols. The performance degradation is mostly caused by the domain shift between the training set and the evaluation set. To alleviate this problem, different unsupervised domain adaptation approaches are proposed and evaluated in the current work, for the detection of cardiomegaly based on chest X-ray images, in a cross-domain setting. The proposed approaches generate domain invariant feature representations by adapting the parameters of a model optimized on a large set of labeled samples, to a set of unlabeled images stemming from a different data set. The performed evaluation points to the effectiveness of the proposed approaches, since the adapted models outperform optimized models which are directly applied to the evaluation sets without any form of domain adaptation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Thiam, Lausser, Kloth, Blaich, Liebold, Beer and Kestler.)

In clinical research as well as patient care, structured documentation of findings is an important task. In many cases, this is achieved by means of electronic case report forms (eCRF) using corresponding information technology systems. To avoid double data entry, eCRF systems can be integrated with electronic health records (EHR). However, when researchers from different institutions collaborate in collecting data, they often use a single joint eCRF system on the Internet. In this case, integration with EHR systems is not possible in most cases due to information security and data protection restrictions. To overcome this shortcoming, we propose a novel architecture for a federated electronic data capture system (fEDC). Four key requirements were identified for fEDC: Definitions of forms have to be available in a reliable and controlled fashion, integration with electronic health record systems must be possible, patient data should be under full local control until they are explicitly transferred for joint analysis, and the system must support data sharing principles accepted by the scientific community for both data model and data captured. With our approach, sites participating in a joint study can run their own instance of an fEDC system that complies with local standards (such as being behind a network firewall) while also being able to benefit from using identical form definitions by sharing metadata in the Operational Data Model (ODM) format published by the Clinical Data Interchange Standards Consortium (CDISC) throughout the collaboration. The fEDC architecture was validated with a working open-source prototype at five German university hospitals. The fEDC architecture provides a novel approach with the potential to significantly improve collaborative data capture: Efforts for data entry are reduced and at the same time, data quality is increased since barriers for integrating with local electronic health record systems are lowered. Further, metadata are shared and patient privacy is ensured at a high level., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Although of high individual and socioeconomic relevance, a reliable prediction model for the prognosis of juvenile stroke (18-55 years) is missing. Therefore, the study presented in this protocol aims to prospectively validate the discriminatory power of a prediction score for the 3 months functional outcome after juvenile stroke or transient ischemic attack (TIA) that has been derived from an independent retrospective study using standard clinical workup data., Methods: PREDICT-Juvenile-Stroke is a multi-centre (n = 4) prospective observational cohort study collecting standard clinical workup data and data on treatment success at 3 months after acute ischemic stroke or TIA that aims to validate a new prediction score for juvenile stroke. The prediction score has been developed upon single center retrospective analysis of 340 juvenile stroke patients. The score determines the patient's individual probability for treatment success defined by a modified Rankin Scale (mRS) 0-2 or return to pre-stroke baseline mRS 3 months after stroke or TIA. This probability will be compared to the observed clinical outcome at 3 months using the area under the receiver operating characteristic curve. The primary endpoint is to validate the clinical potential of the new prediction score for a favourable outcome 3 months after juvenile stroke or TIA. Secondary outcomes are to determine to what extent predictive factors in juvenile stroke or TIA patients differ from those in older patients and to determine the predictive accuracy of the juvenile stroke prediction score on other clinical and paraclinical endpoints. A minimum of 430 juvenile patients (< 55 years) with acute ischemic stroke or TIA, and the same number of older patients will be enrolled for the prospective validation study., Discussion: The juvenile stroke prediction score has the potential to enable personalisation of counselling, provision of appropriate information regarding the prognosis and identification of patients who benefit from specific treatments., Trial Registration: The study has been registered at https://drks.de on March 31, 2022 ( DRKS00024407 )., (© 2023. The Author(s).)

Aim: Recently, a tumor cell-platelet interaction was identified in different tumor entities, resulting in a transfer of tumor-derived RNA into platelets, named further "tumor-educated platelets (TEP)". The present pilot study aims to investigate whether such a tumor-platelet transfer of RNA occurs also in patients suffering from head and neck squamous cell carcinoma (HNSCC)., Methods: Sequencing analysis of RNA derived from platelets of tumor patients (TPs) and healthy donors (HDs) were performed. Subsequently, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used for verification of differentially expressed genes in platelets from TPs and HDs in a second cohort of patients and HDs. Data were analyzed by applying bioinformatic tools., Results: Sequencing of RNA derived from the tumor as well as from platelets of TPs and HDs revealed 426 significantly differentially existing RNA, at which 406 RNA were more and 20 RNA less abundant in platelets from TPs in comparison to that of HDs. In TPs' platelets, abundantly existing RNA coding for 49 genes were detected, characteristically expressed in epithelial cells and RNA, the products of which are involved in tumor progression. Applying bioinformatic tools and verification on a second TP/HD cohort, collagen type I alpha 1 chain (COL1A1) and zinc finger protein 750 (ZNF750) were identified as the strongest potentially platelet-RNA-sequencing (RNA-seq)-based biomarkers for HNSCC., Conclusions: These results indicate a transfer of tumor-derived messenger RNA (mRNA) into platelets of HNSCC patients. Therefore, analyses of a patient's platelet RNA could be an efficient option for liquid biopsy in order to diagnose HNSCC or to monitor tumorigenesis as well as therapeutic responses at any time and in real time., Competing Interests: The authors declare that they have no conflicts of interest., (© The Author(s) 2023.)

As the Corona Disease 2019 (COVID-19) caused by SARS-CoV-2 persists, vaccination is one of the key measures to contain the spread. Side effects (SE) from vaccination are one of the reasons for reluctance to vaccinate. We systematically investigated self-reported SE after the first, second, and booster vaccinations. The data were collected during the TüSeRe: exact study (Tübinger Monitoring Studie zur exakten Analyse der Immunantwort nach Vakzinierung). Employees of health and research institutions were invited to participate. Study participants were asked to fill out an online questionnaire and report their SE after each dose of SARS-CoV-2 vaccination. A total of 1046 participants (mean age: 44 ± 12.9 years; female, n = 815 (78%); male, n = 231 (22%)) were included in the analysis. Local and systemic SE were more frequent after receiving the vector-based vaccine ChAdOx1 nCoV-19 in the first vaccination. However, local and systemic SE were more common after receiving mRNA vaccines (BNT162b2, mRNA-1273) in the second vaccination. Compared to the BNT162b2 vaccine, more SE have been observed after receiving the mRNA-1273 vaccine in the booster vaccination. In multivariate analysis, local and systemic side effects were associated with vaccine type, age and gender. Local and systemic SE are common after SARS-CoV-2 vaccines. The frequency of self-reported local and systemic SE differ significantly between mRNA and vector-based vaccines.

Sparse and robust classification models have the potential for revealing common predictive patterns that not only allow for categorizing objects into classes but also for generating mechanistic hypotheses. Identifying a small and informative subset of features is their main ingredient. However, the exponential search space of feature subsets and the heuristic nature of selection algorithms limit the coverage of these analyses, even for low-dimensional datasets. We present methods for reducing the computational complexity of feature selection criteria allowing for higher efficiency and coverage of screenings. We achieve this by reducing the preparation costs of high-dimensional subsets [Formula: see text] to those of one-dimensional ones [Formula: see text]. Our methods are based on a tight interaction between a parallelizable cross-validation traversal strategy and distance-based classification algorithms and can be used with any product distance or kernel. We evaluate the traversal strategy exemplarily in exhaustive feature subset selection experiments (perfect coverage). Its runtime, fitness landscape, and predictive performance are analyzed on publicly available datasets. Even in low-dimensional settings, we achieve approximately a 15-fold increase in exhaustively generating distance matrices for feature combinations bringing a new level of evaluations into reach., (© 2022. The Author(s).)

Semantic segmentation of electron microscopy images using deep learning methods is a valuable tool for the detailed analysis of organelles and cell structures. However, these methods require a large amount of labeled ground truth data that is often unavailable. To address this limitation, we present a weighted average ensemble model that can automatically segment biological structures in electron microscopy images when trained with only a small dataset. Thus, we exploit the fact that a combination of diverse base-learners is able to outperform one single segmentation model. Our experiments with seven different biological electron microscopy datasets demonstrate quantitative and qualitative improvements. We show that the Grad-CAM method can be used to interpret and verify the prediction of our model. Compared with a standard U-Net, the performance of our method is superior for all tested datasets. Furthermore, our model leverages a limited number of labeled training data to segment the electron microscopy images and therefore has a high potential for automated biological applications., (© 2022. The Author(s).)

Motivation: Biological processes are complex systems with distinct behaviour. Despite the growing amount of available data, knowledge is sparse and often insufficient to investigate the complex regulatory behaviour of these systems. Moreover, different cellular phenotypes are possible under varying conditions. Mathematical models attempt to unravel these mechanisms by investigating the dynamics of regulatory networks. Therefore, a major challenge is to combine regulations and phenotypical information as well as the underlying mechanisms. To predict regulatory links in these models, we established an approach called CANTATA to support the integration of information into regulatory networks and retrieve potential underlying regulations. This is achieved by optimizing both static and dynamic properties of these networks., Results: Initial results show that the algorithm predicts missing interactions by recapitulating the known phenotypes while preserving the original topology and optimizing the robustness of the model. The resulting models allow for hypothesizing about the biological impact of certain regulatory dependencies., Availability and Implementation: Source code of the application, example files and results are available at https://github.com/sysbio-bioinf/Cantata., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)