Marieke Kuijjer

• Belova, Tatiana; Osorio, Daniel & Kuijjer, Mariike L. (2026). The evolution of gene regulatory networks. Nature Computational Science. 6(4), p. 320–320. doi: 10.1038/s43588-026-00981-3. Full text in Research Archive
• Svinin, Gleb; Wolf, Jana; Loo, Rebecca Ting Jiin; Kumar, Nikhilesh Vasantha; Murthy, Varsha Venkatesha & Odongo, Dilara Uzuner [Show all 20 contributors for this article] (2026). Ten quick tips for causal analysis of biomedical omics data. PLoS Computational Biology. ISSN 1553-734X. 22(8), p. 1014668–1014668. doi: 10.1371/journal.pcbi.1014668. Full text in Research Archive
• Guebila, Marouen Ben; Saha, Enakshi; Shapoval, Mia; Chen, Chen; Micheletti, Soel & Eicher, Tara D. [Show all 13 contributors for this article] (2026). Deploying a JupyterHub Server for Academic Research Using Netbooks as an Example. Current Protocols. 6(6). doi: 10.1002/cpz1.70396. Full text in Research Archive Show summary

  Abstract Jupyter Notebooks have transformed the way we conduct computational research thanks to a versatile platform that combines code, annotations, plots, and a lightweight format that enables versioning. However, steps for installing dependencies and downloading data for a specific notebook may require significant time and failure to establish identical environments can compromise reproducibility of environments. Deploying a JupyterHub server not only provides a containerized environment for each notebook to ensure fast execution and exact reproducibility but also provides a computational engine that makes these applications available from any device. By providing all the steps for deploying a JupyterHub server, and providing our experience deploying and maintaining Netbooks, a server for academic investigations in network biology, we want to encourage other groups to deploy JupyterHub servers for their own computational work and to recognize their value in improving reproducibility, accelerating manuscript review, and supporting education. © 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol : Setting up a JupyterHub server Support Protocol 1 : Compiling a collection of Jupyter Notebooks Support Protocol 2 : Securing HTTPS access Support Protocol 3 : Adding parameters for JupyterHub Support Protocol 4 : Data management for JupyterHub Support Protocol 5 : Customizing the website homepage Support Protocol 6 : Importing/Exporting conda environments Alternate Protocol : Running a JupyterHub server as a virtual machine

• Belova, Tatiana; Hsieh, Ping-Han; Hovan, Ladislav; Osorio, Daniel & Kuijjer, Marieke L. (2025). Pan-cancer analysis of patient-specific gene regulatory landscapes identifies recurrent PD-1 pathway dysregulation linked to outcome. BioRxiv. ISSN 0362-4331. doi: 10.1101/2025.10.03.680219. Full text in Research Archive Show summary

  Cancer heterogeneity remains a major challenge to the development of broadly effective therapies. Although gene expression-based stratification has advanced our understanding of molecular subtypes, it often overlooks the regulatory mechanisms driving tumor progression and therapeutic resistance. These mechanisms are governed by gene regulatory networks—complex systems of transcription factors and other regulators that control gene expression. To investigate regulatory heterogeneity at the patient level, we inferred individual gene regulatory networks for over 9,000 tumors across 33 cancer types from The Cancer Genome Atlas enabling a comprehensive pancancer analysis of inter-patient regulatory variation. Our analysis uncovered novel regulatory subtypes and revealed recurrent dysregulation of the PD-1 signaling pathway in 23 cancer types. This dysregulation was associated with patient outcomes in 12 cancers and appeared largely independent of established molecular subtypes and immune cell composition, suggesting alternative regulatory mechanisms. We identified both well-characterized (FOS, JUNB, ATF3) and previously unrecognized (RFX7, ELF1, NFYA, NFYC) transcriptional regulators of PD-L1 ( CD274 ), highlighting new candidate targets for modulating immune checkpoint activity across tumor types. Our work demonstrates the power of single-sample gene regulatory networks to uncover hidden layers of tumor biology, improve patient stratification, and inform future research in immunotherapy.

• Newman, Nolan K.; Belova, Tatiana & Kuijjer, Marieke L. (2025). SiSaNA: A Python-based command line interface for Single-Sample Network Analysis. BioRxiv. ISSN 0362-4331. doi: 10.1101/2025.11.06.680212. Full text in Research Archive Show summary

  Abstract Motivation Reconstructing and analyzing single-sample regulatory networks has proven useful for understanding mechanisms of regulatory biology and its disruption in disease, but traditionally required a fair amount of programming experience and work from the user. Results Here, we present SiSaNA ( S ingle- S ample N etwork A nalysis), a workflow that removes the need of prior programming experience and allows for single-sample regulatory network reconstruction and analysis, all via a command line interface. Using example input files, we walk the user through a tutorial on how to run the SiSaNA pipeline, interpret the results, and obtain high-quality figures summarized in a single HTML file as output. Availability SiSaNA is freely available to install with pip via the Python Package Index. It is additionally available on GitHub at https://github.com/kuijjerlab/sisana . Example input files can be found at https://doi.org/10.5281/zenodo.17190642 .

• Lam, Suk Wai; Belova, Tatiana; Franceschini, Natasja; Akker, Brendy van den; IJzendoorn, David G.P. van & Mikkers, Harald M.M. [Show all 11 contributors for this article] (2025). Truncated FOS impairs osteogenic differentiation and induces prostaglandin and NFκB signalling in an in vitro cell-of-origin model for osteoid osteoma and osteoblastoma. Journal of Pathology. ISSN 0022-3417. 268(3), p. 263–275. doi: 10.1002/path.70010. Full text in Research Archive
• Arcos-Encarnación, Bolivar; Cortes-Flores, Eladio; Barón-Mendoza, Isabel; Almazán, Jorge L.; Valle-García, David & León-Guerrero, Sol Díaz de [Show all 15 contributors for this article] (2025). KChIP3 fosters neuroinflammation and synaptic dysfunction in the 5XFAD mouse model of Alzheimer’s disease. Journal of Neuroinflammation. doi: 10.1186/s12974-025-03426-2. Full text in Research Archive Show summary

  Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by β-amyloid (βA) accumulation, neuroinflammation, excessive synaptic pruning, and cognitive decline. Despite extensive research, effective treatments remain elusive. Here, we identify potassium channel-interacting protein 3 (KChIP3) as a key driver of AD pathology using the 5XFAD mouse model. KChIP3 levels were significantly elevated in the hippocampus of 5XFAD mice, correlating with βA burden and neuroinflammation. This upregulation was triggered by inflammatory signaling via the NLRP3 inflammasome and Caspase-1 activation. Notably, genetic deletion of KChIP3 (5XFAD/KChIP3−/−) markedly reduced βA plaque deposition, pro-inflammatory cytokines, reactive gliosis, and expression of inflammation-related proteins (APO, CLU, MDK). Transcriptomic and proteomic analyses revealed restored synaptic markers (CD47, CD200, CACNB4, GDA) and a shift of the disease-associated microglial (DAM-1) phenotype. Mechanistically, we propose that KChIP3 amplifies AD pathology through two key mechanisms: (1) sustaining neuroinflammation by upregulating pro-inflammatory genes and (2) impairing synaptic integrity by repressing genes critical for neuronal function. Consistently, KChIP3 deletion enhanced dendritic complexity, synaptic plasticity, and cognitive performance in 5XFAD mice. These findings position KChIP3 as a potential therapeutic target for mitigating neuroinflammation and synaptic dysfunction in AD and highlight its potential as a biomarker for disease progression. Graphical abstract

• Baydar, Damla Övek; Rauluseviciute, Ieva; Aronsen, Dina Ruud; Blanc-Mathieu, Romain; Bonthuis, Ine & de Beukelaer, Herman [Show all 34 contributors for this article] (2025). JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Research (NAR). ISSN 0305-1048. 54(D1), p. D184–D193. doi: 10.1093/nar/gkaf1209. Full text in Research Archive Show summary

  Abstract JASPAR (https://jaspar.elixir.no/) is an open-access database that has provided high-quality, manually curated, and non-redundant DNA binding profiles for transcription factors (TFs) as position frequency matrices (PFMs) for over 20 years. We expanded the CORE (306 new profiles, 12% increase) and UNVALIDATED (433, 60% increase) collections with new PFMs and updated 13 existing profiles. We updated the TF binding site predictions and genome tracks for eight species. TF binding profile clusters and familial TF binding sites were updated accordingly. We integrate the inMOTIFin software to easily simulate regulatory sequences using JASPAR PFMs. To enrich TFs’ annotations, we provide scientific literature-based human TF target information. Notably, this release features a deep learning (DL) collection, providing a paradigm shift in modeling and characterizing TF–DNA interactions with 1259 BPNet models trained on Homo sapiens ENCODE chromatin immunoprecipitation followed by sequencing (ChIP-seq) datasets from 240 TFs and interpreted to reveal predictive motif patterns for the models. The motifs associated with the same TF were clustered to provide a summary of the binding properties, resulting in 240 primary and 113 alternative motif patterns in the DL collection. The JASPAR 2026 collections lay a foundation for future endeavors in genomic research, serving the scientific community in uncovering the mechanisms of gene regulation.

• Hovan, Ladislav & Kuijjer, Marieke (2025). SPONGE: Simple Prior Omics Network GEnerator. Bioinformatics. ISSN 1367-4803. 41(7). doi: 10.1093/bioinformatics/btaf320. Full text in Research Archive Show summary

  Gene regulatory networks modelled from experimental data can be improved through the use of prior biological knowledge, e.g. transcription factor binding. There are several tools that utilize this information. However, the prior networks used with them are often not updated and may fail to reflect the most up-to-date information. Here we present SPONGE, a Python module designed to access information across biological databases, chiefly JASPAR and STRING, to model two types of networks—a prior gene regulatory network mapping transcription factors to genes based on their predicted binding sites, and a prior protein-protein interaction network mapping potential interactions between transcription factors. SPONGE is mainly designed to work with the PANDA algorithm and the corresponding NetZoo family of tools. However, the networks are provided in an easily adaptable format for other tools. SPONGE was designed with ease of use in mind, and it provides sensible default values for all of its parameters while giving the users the freedom to fine-tune them. Availability and implementation The code for the Python module and the documentation can be found in our GitHub repository.

• Luis, Almazán, Jorge; Bolivar, Arcos-Encarnación,; Isabel, Barón-Mendoza,; Nohemi, Camacho-Concha,; Eladio, Cortes-Flores, & Sol, Díaz de León-Guerrero, [Show all 15 contributors for this article] (2025). KChIP3 fosters neuroinflammation and synaptic dysfunction in the 5XFAD mouse model of Alzheimer’s disease | Journal of Neuroinflammation | Full Text. Journal of Neuroinflammation. 22(160). doi: 10.1186/s12974-025-03426-2. Full text in Research Archive Show summary

  Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by β-amyloid (βA) accumulation, neuroinflammation, excessive synaptic pruning, and cognitive decline. Despite extensive research, effective treatments remain elusive. Here, we identify potassium channel-interacting protein 3 (KChIP3) as a key driver of AD pathology using the 5XFAD mouse model. KChIP3 levels were significantly elevated in the hippocampus of 5XFAD mice, correlating with βA burden and neuroinflammation. This upregulation was triggered by inflammatory signaling via the NLRP3 inflammasome and Caspase-1 activation. Notably, genetic deletion of KChIP3 (5XFAD/KChIP3−/−) markedly reduced βA plaque deposition, pro-inflammatory cytokines, reactive gliosis, and expression of inflammation-related proteins (APO, CLU, MDK). Transcriptomic and proteomic analyses revealed restored synaptic markers (CD47, CD200, CACNB4, GDA) and a shift of the disease-associated microglial (DAM-1) phenotype. Mechanistically, we propose that KChIP3 amplifies AD pathology through two key mechanisms: (1) sustaining neuroinflammation by upregulating pro-inflammatory genes and (2) impairing synaptic integrity by repressing genes critical for neuronal function. Consistently, KChIP3 deletion enhanced dendritic complexity, synaptic plasticity, and cognitive performance in 5XFAD mice. These findings position KChIP3 as a potential therapeutic target for mitigating neuroinflammation and synaptic dysfunction in AD and highlight its potential as a biomarker for disease progression.

• Pop, Romana Tabita; Hsieh, Ping-Han; Belova, Tatiana; Mathelier, Anthony & Kuijjer, Marieke (2025). Gene regulatory network integration with multi-omics data enhances survival predictions in cancer. Briefings in Bioinformatics. ISSN 1467-5463. 26(4). doi: 10.1093/bib/bbaf315. Full text in Research Archive Show summary

  The emergence of high-throughput omics technologies has resulted in their wide application to cancer studies, greatly increasing our understanding of the disruptions occurring at different molecular levels. To fully harness these data, integrative approaches have emerged as essential tools, enabling the combination of multiple omics modalities to uncover disease mechanisms. However, many such approaches overlook gene regulatory mechanisms, which play a central role in the development and progression of cancer. Patient-specific gene regulatory networks (GRNs), representing interactions between regulators (such as transcription factors) and their target genes in each individual tumour, offer a powerful framework to bridge this gap and investigate the regulatory landscape of cancer. In this study, we introduce a novel approach for integrating patient-specific GRNs with multi-omic data and assess whether their inclusion in joint dimensionality reduction models improves survival prediction across multiple cancer types. By applying our method on ten cancer datasets from The Cancer Genome Atlas, we demonstrate that incorporating GRNs enhances associations with patient survival in several cancer types. Focusing on liver cancer, with validation in independent data, our methodology identifies potential mechanisms of gene regulatory dysregulation associated with cancer progression. These were linked to dysregulated fatty acid metabolism, and identified JUND as a potential novel transcriptional regulator driving these processes. Our findings highlight the value of network-based multi-omics integration for uncovering clinically relevant regulatory mechanisms and improving our understanding of cancer biology at the patient-specific level.

• Struck, Eike Christopher; Belova, Tatiana; Hsieh, Ping-Han; Odeberg, Jacob Olof; Kuijjer, Marieke & Dusart, Philip James [Show all 7 contributors for this article] (2024). Global Transcriptome Analysis Reveals Distinct Phases of the Endothelial Response to TNF. Journal of Immunology. ISSN 0022-1767. 212(1), p. 117–129. doi: 10.4049/jimmunol.2300419. Full text in Research Archive Show summary

  The vascular endothelium acts as a dynamic interface between blood and tissue. TNF-α, a major regulator of inflammation, induces endothelial cell (EC) transcriptional changes, the overall response dynamics of which have not been fully elucidated. In the present study, we conducted an extended time-course analysis of the human EC response to TNF, from 30 min to 72 h. We identified regulated genes and used weighted gene network correlation analysis to decipher coexpression profiles, uncovering two distinct temporal phases: an acute response (between 1 and 4 h) and a later phase (between 12 and 24 h). Sex-based subset analysis revealed that the response was comparable between female and male cells. Several previously uncharacterized genes were strongly regulated during the acute phase, whereas the majority in the later phase were IFN-stimulated genes. A lack of IFN transcription indicated that this IFN-stimulated gene expression was independent of de novo IFN production. We also observed two groups of genes whose transcription was inhibited by TNF: those that resolved toward baseline levels and those that did not. Our study provides insights into the global dynamics of the EC transcriptional response to TNF, highlighting distinct gene expression patterns during the acute and later phases. Data for all coding and noncoding genes is provided on the Web site (http://www.endothelial-response.org/). These findings may be useful in understanding the role of ECs in inflammation and in developing TNF signaling–targeted therapies.

• Osorio, Daniel; Capasso, Anna; Eckhardt, S. Gail; Giri, Uma; Somma, Alexander & Pitts, Todd M. [Show all 14 contributors for this article] (2024). Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data. Nature Computational Science. doi: 10.1038/s43588-024-00597-5. Full text in Research Archive Show summary

  Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION’s scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.

• Ferenc, Katalin Terezia; Rauluseviciute, Ieva; Hovan, Ladislav; Kumar, Vipin; Kuijjer, Marieke & Mathelier, Anthony (2024). Improving bioinformatics software quality through teamwork. Bioinformatics. ISSN 1367-4803. 40(11). doi: 10.1093/bioinformatics/btae632. Full text in Research Archive Show summary

  Since high-throughput techniques became a staple in biological science laboratories, computational algorithms, and scientific software have boomed. However, the development of bioinformatics software usually lacks software development quality standards. The resulting software code is hard to test, reuse, and maintain. We believe that the root of inefficiency in implementing the best software development practices in academic settings is the individualistic approach, which has traditionally been the norm for recognizing scientific achievements and, by extension, for developing specialized software. Software development is a collective effort in most software-heavy endeavors. Indeed, the literature suggests teamwork directly impacts code quality through knowledge sharing, collective software development, and established coding standards. In our computational biology research groups, we sustainably involve all group members in learning, sharing, and discussing software development while maintaining the personal ownership of research projects and related software products. We found that group members involved in this endeavor improved their coding skills, became more efficient bioinformaticians, and obtained detailed knowledge about their peers’ work, triggering new collaborative projects. We strongly advocate for improving software development culture within bioinformatics through collective effort in computational biology groups or institutes with three or more bioinformaticians.

• Meijer, Debora M.; Ruano, Dina; Briaire-de Bruijn, Inge H.; Wijers-Koster, Pauline M.; Sande, Michiel A. J. van de & Gelderblom, Hans [Show all 10 contributors for this article] (2024). The Variable Genomic Landscape During Osteosarcoma Progression: Insights From a Longitudinal WGS Analysis. Genes, Chromosomes and Cancer. ISSN 1045-2257. 63(7). doi: 10.1002/gcc.23253. Full text in Research Archive
• Green, Darrell; Ewijk, Roelof van; Tirtei, Elisa; Andreou, Dimosthenis; Baecklund, Fredrik & Baumhoer, Daniel [Show all 56 contributors for this article] (2024). Biological Sample Collection to Advance Research and Treatment: A Fight Osteosarcoma Through European Research and Euro Ewing Consortium Statement. Clinical Cancer Research. ISSN 1078-0432. 30(16), p. 3395–3406. doi: 10.1158/1078-0432.CCR-24-0101. Full text in Research Archive
• Hsieh, Ping-Han; Hsiao, Ru-Xiu; Ferenc, Katalin; Mathelier, Anthony; Burkholz, Rebekka & Chen, Chien-Yu [Show all 9 contributors for this article] (2024). CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data. arXiv.org. ISSN 2331-8422. doi: 10.48550/arXiv.2405.18655. Full text in Research Archive
• Ünal, Bilal; Kuzu, Omer; Yang, Jin; Osorio, Daniel; Kildal, Wanja & Pradhan, Manohar [Show all 14 contributors for this article] (2024). Targeting IRE1α reprograms the tumor microenvironment and enhances anti-tumor immunity in prostate cancer. Nature Communications. 15(1), p. 1–21. doi: 10.1038/s41467-024-53039-1. Full text in Research Archive Show summary

  Unfolded protein response (UPR) is a central stress response pathway that is hijacked by tumor cells for their survival. Here, we find that IRE1α signaling, one of the canonical UPR arms, is increased in prostate cancer (PCa) patient tumors. Genetic or small molecule inhibition of IRE1α in syngeneic mouse PCa models and an orthotopic model decreases tumor growth. IRE1α ablation in cancer cells potentiates interferon responses and activates immune system related pathways in the tumor microenvironment (TME). Single-cell RNA-sequencing analysis reveals that targeting IRE1α in cancer cells reduces tumor-associated macrophage abundance. Consistently, the small molecule IRE1α inhibitor MKC8866, currently in clinical trials, reprograms the TME and enhances anti-PD-1 therapy. Our findings show that IRE1α signaling not only promotes cancer cell growth and survival but also interferes with anti-tumor immunity in the TME. Thus, targeting IRE1α can be a promising approach for improving anti-PD-1 immunotherapy in PCa.

• Hsieh, Ping-Han; Lopes-Ramos, Camila Miranda; Zucknick, Manuela; Sandve, Geir Kjetil Ferkingstad; Glass, Kimberly & Kuijjer, Marieke (2023). Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data. Bioinformatics. ISSN 1367-4803. 39(10). doi: 10.1093/bioinformatics/btad610. Full text in Research Archive Show summary

  Abstract Motivation Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes. Results We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method’s potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data. Availability and implementation The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.

• Guebila, Marouen Ben; Wang, Tian; Lopes-Ramos, Camila M.; Fanfani, Viola; Weighill, Des & Burkholz, Rebekka [Show all 25 contributors for this article] (2023). The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks. Genome Biology. ISSN 1474-7596. 24(1). doi: 10.1186/s13059-023-02877-1. Full text in Research Archive Show summary

  Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.

• Belova, Tatiana; Biondi, Nicola; Hsieh, Ping-Han; Lutsik, Pavlo; Chudasama, Priya & Kuijjer, Marieke (2023). Heterogeneity in the gene regulatory landscape of leiomyosarcoma. NAR Cancer. 5(3). doi: 10.1093/narcan/zcad037. Full text in Research Archive Show summary

  Abstract Characterizing inter-tumor heterogeneity is crucial for selecting suitable cancer therapy, as the presence of diverse molecular subgroups of patients can be associated with disease outcome or response to treatment. While cancer subtypes are often characterized by differences in gene expression, the mechanisms driving these differences are generally unknown. We set out to model the regulatory mechanisms driving sarcoma heterogeneity based on patient-specific, genome-wide gene regulatory networks. We developed a new computational framework, PORCUPINE, which combines knowledge on biological pathways with permutation-based network analysis to identify pathways that exhibit significant regulatory heterogeneity across a patient population. We applied PORCUPINE to patient-specific leiomyosarcoma networks modeled on data from The Cancer Genome Atlas and validated our results in an independent dataset from the German Cancer Research Center. PORCUPINE identified 37 heterogeneously regulated pathways, including pathways representing potential targets for treatment of subgroups of leiomyosarcoma patients, such as FGFR and CTLA4 inhibitory signaling. We validated the detected regulatory heterogeneity through analysis of networks and chromatin states in leiomyosarcoma cell lines. We showed that the heterogeneity identified with PORCUPINE is not associated with methylation profiles or clinical features, thereby suggesting an independent mechanism of patient heterogeneity driven by the complex landscape of gene regulatory interactions.

• DeMarzio, Margherita; Glass, Kimberly & Kuijjer, Marieke (2023). Single-sample network modeling on omics data. BMC Biology. doi: 10.1186/s12915-023-01783-z. Full text in Research Archive Show summary

  Modeling networks for single samples can facilitate discoveries in biology that go beyond those found when analyzing an “aggregate” network, i.e., one that merges information across all the samples in an omics data set. In this commentary article, we discuss the history, current state, and future directions of the field of single-sample network modeling using omics data.

• Guebila, Marouen Ben; Morgan, Daniel C.; Glass, Kimberly; Kuijjer, Marieke; Demeo, Dawn L. & Quackenbush, John (2022). gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit. NAR Genomics and Bioinformatics. 4(1). doi: 10.1093/nargab/lqac002. Full text in Research Archive Show summary

  Abstract Gene regulatory network inference allows for the modeling of genome-scale regulatory processes that are altered during development, in disease, and in response to perturbations. Our group has developed a collection of tools to model various regulatory processes, including transcriptional (PANDA, SPIDER) and post-transcriptional (PUMA) gene regulation, as well as gene regulation in individual samples (LIONESS). These methods work by postulating a network structure and then optimizing that structure to be consistent with multiple lines of biological evidence through repeated operations on data matrices. Although our methods are widely used, the corresponding computational complexity, and the associated costs and run times, do limit some applications. To improve the cost/time performance of these algorithms, we developed gpuZoo which implements GPU-accelerated calculations, dramatically improving the performance of these algorithms. The runtime of the gpuZoo implementation in MATLAB and Python is up to 61 times faster and 28 times less expensive than multi-core CPU implementation of the same methods. gpuZoo is available in MATLAB through the netZooM package https://github.com/netZoo/netZooM and in Python through the netZooPy package https://github.com/netZoo/netZooPy.

• Birkeälv, Sofia; Harland, Mark; Matsuyama, Larissa Satiko Alcantara Sekim; Rashid, Mamun; Mehta, Ishan & Laye, Jonathan P [Show all 18 contributors for this article] (2022). Mutually exclusive genetic interactions and gene essentiality shape the genomic landscape of primary melanoma. Journal of Pathology. ISSN 0022-3417. doi: 10.1002/path.6019. Full text in Research Archive Show summary

  Melanoma is a heterogenous malignancy with an unpredictable clinical course. Most patients who present in the clinic are diagnosed with primary melanoma, yet large-scale sequencing efforts have focused primarily on metastatic disease. In this study we sequence-profiled 524 American Joint Committee on Cancer Stage I–III primary tumours. Our analysis of these data reveals recurrent driver mutations, mutually exclusive genetic interactions, where two genes were never or rarely co-mutated, and an absence of co-occurring genetic events. Further, we intersected copy number calls from our primary melanoma data with whole-genome CRISPR screening data to identify the transcription factor interferon regulatory factor 4 (IRF4) as a melanoma-associated dependency.

• Stahlberg, Eric A; Abdel-Rahman, Mohamed; Aguilar, Boris; Asadpoure, Alireza; Beckman, Robert A & Borkon, Lynn L [Show all 33 contributors for this article] (2022). Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation. Frontiers in Digital Health. 4. doi: 10.3389/fdgth.2022.1007784. Full text in Research Archive Show summary

  We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins. Several diverse pilot projects were launched to provide key insights into important features of this emerging landscape and to determine the requirements for the development and adoption of cancer patient digital twins. Projects included exploring approaches to using a large cohort of digital twins to perform deep phenotyping and plan treatments at the individual level, prototyping self-learning digital twin platforms, using adaptive digital twin approaches to monitor treatment response and resistance, developing methods to integrate and fuse data and observations across multiple scales, and personalizing treatment based on cancer type. Collectively these efforts have yielded increased insights into the opportunities and challenges facing cancer patient digital twin approaches and helped define a path forward. Given the rapidly growing interest in patient digital twins, this manuscript provides a valuable early progress report of several CPDT pilot projects commenced in common, their overall aims, early progress, lessons learned and future directions that will increasingly involve the broader research community.

• Weber, Cédric R.; Rubio, Teresa; Wang, Longlong; Zhang, Wei; Robert, Philippe Paul Auguste & Akbar, Rahmad [Show all 15 contributors for this article] (2022). Reference-based comparison of adaptive immune receptor repertoires. Cell Reports Methods. 2(8). doi: 10.1016/j.crmeth.2022.100269. Full text in Research Archive Show summary

  B and T cell receptor (immune) repertoires can represent an individual’s immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters. Here, we introduce immuneREF: a quantitative multidimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2,400 datasets from individuals with varying immune states (healthy, [autoimmune] disease, and infection). We discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF enables the population-wide study of adaptive immune response similarity across immune states.

• Guebila, Marouen Ben; Lopes-Ramos, Camila; Weighill, Deborah; Sonawane, Abhijeet; Burkholz, Rebekka & Shamsaei, Behrouz [Show all 10 contributors for this article] (2022). GRAND: a database of gene regulatory network models across human conditions. Nucleic Acids Research (NAR). ISSN 0305-1048. 50, p. D610–D621. doi: 10.1093/nar/gkab778. Full text in Research Archive Show summary

  Abstract Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties.

• Lopes-Ramos, Camila; Belova, Tatiana; Brunner, Tess; Guebila, Marouen Ben; Osorio, Daniel & Quackenbush, John [Show all 7 contributors for this article] (2021). Regulatory network of PD1 signaling is associated with prognosis in glioblastoma multiforme. Cancer Research. ISSN 0008-5472. 81(21), p. 5401–5412. doi: 10.1158/0008-5472.CAN-21-0730. Full text in Research Archive Show summary

  Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma ‘omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in gene expression, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms associated with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas. We performed comparative network analysis between patients with long- and short-term survival. Seven pathways were identified as associated with survival, all of them involved in immune signaling; differential regulation of PD1 signaling was validated to correspond with outcome in an independent dataset from the German Glioma Network. In this pathway, transcriptional repression of genes for which treatment options are available was lost in short-term survivors; this was independent of mutational burden and only weakly associated with T-cell infiltration. Collectively, these results provide a new way to stratify patients with glioblastoma that uses network features as biomarkers to predict survival. They also identify new potential therapeutic interventions, underscoring the value of analyzing gene regulatory networks in individual patients with cancer.

• Grad, Iwona; Hanes, Robert; Ayuda-Duran, Maria del Pilar; Kuijjer, Marieke Lydia; Enserink, Jorrit & Meza, Leonardo Zepeda [Show all 7 contributors for this article] (2021). Discovery of novel candidates for anti-liposarcoma therapies by medium-scale high-throughput drug screening. PLOS ONE. 16(3), p. 1–23. doi: 10.1371/journal.pone.0248140. Full text in Research Archive Show summary

  Sarcomas are a heterogeneous group of mesenchymal orphan cancers and new treatment alternatives beyond traditional chemotherapeutic regimes are much needed. So far, tumor mutation analysis has not led to significant treatment advances, and we have attempted to bypass this limitation by performing direct drug testing of a library of 353 anti-cancer compounds that are either FDA-approved, in clinical trial, or in advanced stages of preclinical development on a panel of 13 liposarcoma cell lines. We identified and validated six drugs, targeting different mechanisms and with good efficiency across the cell lines: MLN2238 –a proteasome inhibitor, GSK2126458 –a PI3K/mTOR inhibitor, JNJ-26481585 –a histone deacetylase inhibitor, triptolide–a multi-target drug, YM155 –a survivin inhibitor, and APO866 (FK866)–a nicotinamide phosphoribosyl transferase inhibitor. GR50s for those drugs were mostly in the nanomolar range, and in many cases below 10 nM. These drugs had long-lasting effect upon drug withdrawal, limited toxicity to normal cells and good efficacy also against tumor explants. Finally, we identified potential genomic biomarkers of their efficacy. Being approved or in clinical trials, these drugs are promising candidates for liposarcoma treatment.

• Pavlović, Milena; Scheffer, Lonneke; Motwani, Keshav; Kanduri, Chakravarthi; Kompova, Radmila & Vazov, Nikolay Aleksandrov [Show all 42 contributors for this article] (2021). The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nature Machine Intelligence. 3(11), p. 936–944. doi: 10.1038/s42256-021-00413-z. Full text in Research Archive
• Osorio, Daniel; Kuijjer, Marieke & Cai, James (2021). rPanglaoDB: an R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database. Bioinformatics. ISSN 1367-4803. doi: 10.1093/bioinformatics/btab549. Full text in Research Archive Show summary

  Abstract Motivation Characterizing cells with rare molecular phenotypes is one of the promises of high throughput single-cell RNA sequencing (scRNA-seq) techniques. However, collecting enough cells with the desired molecular phenotype in a single experiment is challenging, requiring several samples preprocessing steps to filter and collect the desired cells experimentally before sequencing. Data integration of multiple public single-cell experiments stands as a solution for this problem, allowing the collection of enough cells exhibiting the desired molecular signatures. By increasing the sample size of the desired cell type, this approach enables a robust cell type transcriptome characterization. Results Here, we introduce rPanglaoDB, an R package to download and merge the uniformly processed and annotated scRNA-seq data provided by the PanglaoDB database. To show the potential of rPanglaoDB for collecting rare cell types by integrating multiple public datasets, we present a biological application collecting and characterizing a set of 157 fibrocytes. Fibrocytes are a rare monocyte-derived cell type, that exhibits both the inflammatory features of macrophages and the tissue remodeling properties of fibroblasts. This constitutes the first fibrocytes’ unbiased transcriptome profile report. We compared the transcriptomic profile of the fibrocytes against the fibroblasts collected from the same tissue samples and confirm their associated relationship with healing processes in tissue damage and infection through the activation of the prostaglandin biosynthesis and regulation pathway. Availability and implementation rPanglaoDB is implemented as an R package available through the CRAN repositories https://CRAN.R-project.org/package=rPanglaoDB.

• Oost, Siddh van; Meijer, Debora; Kuijjer, Marieke; Bovée, Judith V.M.G. & Miranda, Noel F.C.C. de (2021). Linking Immunity with Genomics in Sarcomas: Is Genomic Complexity an Immunogenic Trigger? Biomedicines. doi: 10.3390/biomedicines9081048. Full text in Research Archive Show summary

  Sarcomas comprise a collection of highly heterogeneous malignancies that can be grossly grouped in the categories of sarcomas with simple or complex genomes. Since the outcome for most sarcoma patients has barely improved in the last decades, there is an urgent need for improved therapies. Immunotherapy, and especially T cell checkpoint blockade, has recently been a game-changer in cancer therapy as it produced significant and durable treatment responses in several cancer types. Currently, only a small fraction of sarcoma patients benefit from immunotherapy, supposedly due to a general lack of somatically mutated antigens (neoantigens) and spontaneous T cell immunity in most cancers. However, genomic events resulting from chromosomal instability are frequent in sarcomas with complex genomes and could drive immunity in those tumors. Improving our understanding of the mechanisms that shape the immune landscape of sarcomas will be crucial to overcoming the current challenges of sarcoma immunotherapy. This review focuses on what is currently known about the tumor microenvironment in sarcomas and how this relates to their genomic features. Moreover, we discuss novel therapeutic strategies that leverage the tumor microenvironment to increase the clinical efficacy of immunotherapy, and which could provide new avenues for the treatment of sarcomas.

• Garcia, Genís Calderer i & Kuijjer, Marieke (2021). Community Detection in Large-Scale Bipartite Biological Networks. Frontiers in Genetics. doi: 10.3389/fgene.2021.649440. Full text in Research Archive Show summary

  Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

• Andreeva, Olga; Li, Wei; Ding, Wei; Kuijjer, Marieke Lydia; Quackenbush, John & Chen, Ping (2020). Catalysis Clustering with GAN by Incorporating Domain Knowledge. In Gupta, Rajesh & Liu, Yan (Ed.), KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery (ACM). ISSN 9781450379984. p. 1344–1352. doi: 10.1145/3394486.3403187. Full text in Research Archive
• Qiang, Jipeng; Ding, Wei; Kuijjer, Marieke Lydia; Quackenbush, John & Chen, Ping (2020). Clustering Sparse Data with Feature Correlation with Application to Discover Subtypes in Cancer. IEEE Access. 8, p. 67775–67789. doi: 10.1109/ACCESS.2020.2982569. Full text in Research Archive Show summary

  In this paper, given data with high-dimensional features, we study this problem of how to calculate the similarity between two samples by considering feature interaction network, where a feature interaction network represents the relationship between features. This is different from some traditional methods, those of which learn similarities based on a sample network that represents the relationship between samples. Therefore, we propose a novel network-based similarity metric for computing the similarity between samples, which incorporates the knowledge of feature interaction network, in order to overcome the data sparseness problem. Our similarity metric uses a new Feature Alignment Similarity measure, which does not directly compute the similarities among samples, but projects each sample into a feature interaction network and measures the similarities between two samples using the similarities between the vertices of the samples in the network. As such, when two samples do not share any common features, they are likely to have higher similarity values when their features share the similar network regions. For ensuring that the metric is useful in a real-world application, we apply our metric to discover subtypes in tumor mutational data by incorporating the information of the gene interaction network. Our experimental results from using synthetic data and real-world tumor mutational data show that our approach outperforms the top competitors in cancer subtype discovery. Furthermore, our approach can identify cancer subtypes that cannot be detected by other clustering algorithms in real cancer data.

• Fagny, Maud; Kuijjer, Marieke Lydia; Stam, Maike; Joets, Johann; Turc, Olivier & Rozière, Julien [Show all 9 contributors for this article] (2020). Identification of Key Tissue-Specific, Biological Processes by Integrating Enhancer Information in Maize Gene Regulatory Networks. Frontiers in Genetics. 11. doi: 10.3389/fgene.2020.606285. Full text in Research Archive Show summary

  Enhancers are key players in the spatio-temporal coordination of gene expression during numerous crucial processes, including tissue differentiation across development. Characterizing the transcription factors (TFs) and genes they connect, and the molecular functions underpinned is important to better characterize developmental processes. In plants, the recent molecular characterization of enhancers revealed their capacity to activate the expression of several target genes. Nevertheless, identifying these target genes at a genome-wide level is challenging, particularly for large-genome species, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to plant regulatory networks remains poorly understood. Here, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage (V2-IST) and husks (bracts) at flowering. Using systems biology, we integrate genomic, epigenomic, and transcriptomic data to model the regulatory relationships between TFs and their potential target genes, and identify regulatory modules specific to husk and V2-IST. We show that leaves at the V2-IST stage are characterized by the response to hormones and macromolecules biogenesis and assembly, which are regulated by the BBR/BPC and AP2/ERF TF families, respectively. In contrast, husks are characterized by cell wall modification and response to abiotic stresses, which are, respectively, orchestrated by the C2C2/DOF and AP2/EREB families. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped part of the regulatory network in each tissue, and that MITEs have provided potential new TF binding sites involved in husk tissue-specificity.

• Kuijjer, Marieke Lydia; Fagny, Maud; Marin, Alessandro; Quackenbush, John & Glass, Kimberly (2020). PUMA: PANDA Using MicroRNA Associations. Bioinformatics. ISSN 1367-4803. doi: 10.1093/bioinformatics/btaa571. Full text in Research Archive Show summary

  Abstract Motivation Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA–target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https://kuijjer.shinyapps.io/puma_gtex/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue. Availability and implementation PUMA is available in C++, MATLAB and Python on GitHub (https://github.com/kuijjerlab and https://netzoo.github.io/). Supplementary information Supplementary data are available at Bioinformatics online.

• Kuijjer, Marieke Lydia (2020). Predicting Cancer Evolution Using Cell State Dynamics. Cancer Research. ISSN 0008-5472. 80(15). doi: 10.1158/0008-5472.CAN-20-1878. Full text in Research Archive
• Lopes-ramos, Camila; Chen, Cho-Yi; Kuijjer, Marieke Lydia; Paulson, Joseph N.; Sonawane, Abhijeet & Fagny, Maud [Show all 10 contributors for this article] (2020). Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues. Cell reports. 31(12). doi: 10.1016/j.celrep.2020.107795. Full text in Research Archive Show summary

  Sex differences manifest in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sex differences, we evaluated sex-biased gene regulation by constructing sample-specific gene regulatory networks in 29 human healthy tissues using 8,279 whole-genome expression profiles from the Genotype-Tissue Expression (GTEx) project. We find sex-biased regulatory network structures in each tissue. Even though most transcription factors (TFs) are not differentially expressed between males and females, many have sex-biased regulatory targeting patterns. In each tissue, genes that are differentially targeted by TFs between the sexes are enriched for tissue-related functions and diseases. In brain tissue, for example, genes associated with Parkinson’s disease and Alzheimer’s disease are targeted by different sets of TFs in each sex. Our systems-based analysis identifies a repertoire of TFs that play important roles in sex-specific architecture of gene regulatory networks, and it underlines sex-specific regulatory processes in both health and disease.

• Kuijjer, Marieke Lydia; Tung, Matthew George; Yuan, GuoCheng; Quackenbush, John & Glass, Kimberly (2019). Estimating Sample-Specific Regulatory Networks. iScience. 14, p. 226–240. doi: 10.1016/j.isci.2019.03.021. Full text in Research Archive Show summary

  Biological systems are driven by intricate interactions among molecules. Many methods have been developed that draw on large numbers of expression samples to infer connections between genes (or their products). The result is an aggregate network representing a single estimate for the likelihood of each interaction, or “edge,” in the network. Although informative, aggregate models fail to capture population heterogeneity. Here we propose a method to reverse engineer sample-specific networks from aggregate networks. We demonstrate our approach in several contexts, including simulated, yeast microarray, and human lymphoblastoid cell line RNA sequencing data. We use these sample-specific networks to study changes in network topology across time and to characterize shifts in gene regulation that were not apparent in the expression data. We believe that generating sample-specific networks will greatly facilitate the application of network methods to large, complex, and heterogeneous multi-omic datasets, supporting the emerging field of precision network medicine.

• Kuijjer, Marieke Lydia; Hsieh, Ping-Han; Quackenbush, John & Glass, Kimberly (2019). lionessR: single sample network inference in R. BMC Cancer. 19(1003). doi: 10.1186/s12885-019-6235-7. Full text in Research Archive Show summary

  Background In biomedical research, network inference algorithms are typically used to infer complex association patterns between biological entities, such as between genes or proteins, using data from a population. This resulting aggregate network, in essence, averages over the networks of those individuals in the population. LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) is a method that can be used together with a network inference algorithm to extract networks for individual samples in a population. The method’s key characteristic is that, by modeling networks for individual samples in a data set, it can capture network heterogeneity in a population. LIONESS was originally made available as a function within the PANDA (Passing Attributes between Networks for Data Assimilation) regulatory network reconstruction framework. However, the LIONESS algorithm is generalizable and can be used to model single sample networks based on a wide range of network inference algorithms. Results In this software article, we describe lionessR, an R implementation of LIONESS that can be applied to any network inference method in R that outputs a complete, weighted adjacency matrix. As an example, we provide a vignette of an application of lionessR to model single sample networks based on correlated gene expression in a bone cancer dataset. We show how the tool can be used to identify differential patterns of correlation between two groups of patients. Conclusions We developed lionessR, an open source R package to model single sample networks. We show how lionessR can be used to inform us on potential precision medicine applications in cancer. The lionessR package is a user-friendly tool to perform such analyses. The package, which includes a vignette describing the application, is freely available at: https://github.com/kuijjerlab/lionessR and at: http://bioconductor.org/packages/lionessR .

• Wong, Kim; Weyden, Louise van der; Schott, Courtney R.; Foote, Alastair; Constantino-Casas, Fernando & Smith, Sionagh [Show all 23 contributors for this article] (2019). Cross-species genomic landscape comparison of human mucosal melanoma with canine oral and equine melanoma. Nature Communications. 10(353), p. 1–14. doi: 10.1038/s41467-018-08081-1. Full text in Research Archive Show summary

  Mucosal melanoma is a rare and poorly characterized subtype of human melanoma. Here we perform a cross-species analysis by sequencing tumor-germline pairs from 46 primary human muscosal, 65 primary canine oral and 28 primary equine melanoma cases from mucosal sites. Analysis of these data reveals recurrently mutated driver genes shared between species such as NRAS, FAT4, PTPRJ, TP53 and PTEN, and pathogenic germline alleles of BRCA1, BRCA2 and TP53. We identify a UV mutation signature in a small number of samples, including human cases from the lip and nasal mucosa. A cross-species comparative analysis of recurrent copy number alterations identifies several candidate drivers including MDM2, B2M, KNSTRN and BUB1B. Comparison of somatic mutations in recurrences and metastases to those in the primary tumor suggests pervasive intra-tumor heterogeneity. Collectively, these studies suggest a convergence of some genetic changes in mucosal melanomas between species but also distinctly different paths to tumorigenesis.

• Barnett, Ian; Nishant, Malik; Kuijjer, Marieke Lydia; Mucha, Peter & Onnela, Jukka-Pekka (2019). Feature-based classification of networks. Network Science. ISSN 2050-1242. 7(3), p. 438–444. doi: 10.1017/nws.2019.21. Full text in Research Archive
• Fagny, Maud; Platig, John; Kuijjer, Marieke Lydia; Lin, Xihong & Quackenbush, John (2019). Nongenic cancer-risk SNPs affect oncogenes, tumour-suppressor genes, and immune function. British Journal of Cancer. ISSN 0007-0920. doi: 10.1038/s41416-019-0614-3. Full text in Research Archive Show summary

  Background Genome-wide association studies (GWASes) have identified many noncoding germline single-nucleotide polymorphisms (SNPs) that are associated with an increased risk of developing cancer. However, how these SNPs affect cancer risk is still largely unknown. Methods We used a systems biology approach to analyse the regulatory role of cancer-risk SNPs in thirteen tissues. By using data from the Genotype-Tissue Expression (GTEx) project, we performed an expression quantitative trait locus (eQTL) analysis. We represented both significant cis- and trans-eQTLs as edges in tissue-specific eQTL bipartite networks. Results Each tissue-specific eQTL network is organised into communities that group sets of SNPs and functionally related genes. When mapping cancer-risk SNPs to these networks, we find that in each tissue, these SNPs are significantly overrepresented in communities enriched for immune response processes, as well as tissue-specific functions. Moreover, cancer-risk SNPs are more likely to be ‘cores’ of their communities, influencing the expression of many genes within the same biological processes. Finally, cancer-risk SNPs preferentially target oncogenes and tumour-suppressor genes, suggesting that they may alter the expression of these key cancer genes. Conclusions This approach provides a new way of understanding genetic effects on cancer risk and provides a biological context for interpreting the results of GWAS cancer studies.

• IJzendoorn, David G.P. van; Szuhai, Karoly; Bruijn, Inge H Briaire-De; Kostine, Marie; Kuijjer, Marieke Lydia & Bovée, Judith V.M.G. (2019). Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas. PLoS Computational Biology. ISSN 1553-734X. 15(2), p. 1–19. doi: 10.1371/journal.pcbi.1006826. Full text in Research Archive Show summary

  Based on morphology it is often challenging to distinguish between the many different soft tissue sarcoma subtypes. Moreover, outcome of disease is highly variable even between patients with the same disease. Machine learning on transcriptome sequencing data could be a valuable new tool to understand differences between and within entities. Here we used machine learning analysis to identify novel diagnostic and prognostic markers and therapeutic targets for soft tissue sarcomas. Gene expression data was used from the Cancer Genome Atlas, the Genotype-Tissue Expression project and the French Sarcoma Group. We identified three groups of tumors that overlap in their molecular profiles as seen with unsupervised t-Distributed Stochastic Neighbor Embedding clustering and a deep neural network. The three groups corresponded to subtypes that are morphologically overlapping. Using a random forest algorithm, we identified novel diagnostic markers for soft tissue sarcoma that distinguished between synovial sarcoma and MPNST, and that we validated using qRT-PCR in an independent series. Next, we identified prognostic genes that are strong predictors of disease outcome when used in a k-nearest neighbor algorithm. The prognostic genes were further validated in expression data from the French Sarcoma Group. One of these, HMMR, was validated in an independent series of leiomyosarcomas using immunohistochemistry on tissue micro array as a prognostic gene for disease-free interval. Furthermore, reconstruction of regulatory networks combined with data from the Connectivity Map showed, amongst others, that HDAC inhibitors could be a potential effective therapy for multiple soft tissue sarcoma subtypes. A viability assay with two HDAC inhibitors confirmed that both leiomyosarcoma and synovial sarcoma are sensitive to HDAC inhibition. In this study we identified novel diagnostic markers, prognostic markers and therapeutic leads from multiple soft tissue sarcoma gene expression datasets. Thus, machine learning algorithms are powerful new tools to improve our understanding of rare tumor entities.

• Kang, Tianyu; Zarringhalam, Kourosh; Kuijjer, Marieke Lydia; Chen, Ping; Quackenbush, John & Ding, Wei (2018). Clustering on Sparse Data in Non-Overlapping Feature Space with Applications toCancer Subtyping. IEEE International Conference on Data Mining. Proceedings. ISSN 1550-4786. doi: 10.1109/ICDM.2018.00138. Full text in Research Archive
• Kuijjer, Marieke; Paulson, Joseph N.; Salzman, Peter; Ding, Wei & Quackenbush, John (2018). Cancer subtype identification using somatic mutation data. British Journal of Cancer. ISSN 0007-0920. 118(11), p. 1492–1501. doi: 10.1038/s41416-018-0109-7. Full text in Research Archive
• Lopes-Ramos, Camila; Kuijjer, Marieke; Ogino, Shuji; Fuchs, Charles; DeMeo, Dawn L & Glass, Kimberly [Show all 7 contributors for this article] (2018). Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism. Cancer Research. ISSN 0008-5472. 78(19), p. 5538–5547. doi: 10.1158/0008-5472.CAN-18-0454. Full text in Research Archive
• Sonawane, Abhijeet; Platig, John; Fagny, Maud; Chen, Cho-Yi; Paulson, Joseph N. & DeMeo, Dawn L [Show all 9 contributors for this article] (2017). Understanding Tissue-Specific Gene Regulation. Cell reports. 21(4). doi: 10.1016/j.celrep.2017.10.001. Full text in Research Archive
• Lopes-ramos, Camila; Paulson, Joseph N.; Chen, Cho-Yi; Kuijjer, Marieke; Fagny, Maud & Platig, John [Show all 10 contributors for this article] (2017). Regulatory network changes between cell lines and their tissues of origin. BMC Genomics. doi: 10.1186/s12864-017-4111-x. Full text in Research Archive
• Paulson, Joseph N.; Chen, Cho-Yi; Lopes-Ramos, Camila; Kuijjer, Marieke; Platig, John & Sonawane, Abhijeet [Show all 9 contributors for this article] (2017). Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data. BMC Bioinformatics. doi: 10.1186/s12859-017-1847-x. Full text in Research Archive
• Fagny, Maud; Paulson, Joseph N.; Kuijjer, Marieke; Sonawane, Abhijeet; Chen, Cho-Yi & Lopes-ramos, Camila [Show all 9 contributors for this article] (2017). Exploring regulation in tissues with eQTL networks. Proceedings of the National Academy of Sciences of the United States of America (PNAS). ISSN 0027-8424. doi: 10.1073/pnas.1707375114. Full text in Research Archive
• Hill, Katherine; Kelly, Andrew; Kuijjer, Marieke; Barry, William; Rattani, Ahmed & Garbutt, Cassandra [Show all 19 contributors for this article] (2017). An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets. Journal of Hematology & Oncology. doi: 10.1186/s13045-017-0465-4. Full text in Research Archive
• IJzendoorn, David G.P. van; Glass, Kimberly; Quackenbush, John & Kuijjer, Marieke (2016). PyPanda: a Python package for gene regulatory network reconstruction. Bioinformatics. ISSN 1367-4803. doi: 10.1093/bioinformatics/btw422. Full text in Research Archive
• Wouters, Maartje C.A.; Dijkgraaf, Eveline M.; Kuijjer, Marieke; Jordanova, Ekaterina S.; Hollema, H. & Welters, M.J.P. [Show all 11 contributors for this article] (2015). Interleukin-6 receptor and its ligand interleukin-6 are opposite markers for survival and infiltration with mature myeloid cells in ovarian cancer. Oncoimmunology. ISSN 2162-4011. doi: 10.4161/21624011.2014.962397. Full text in Research Archive
• Buddingh, Emmeline P.; Ruslan, S. Eriaty N.; Reijnders, Christianne M.A.; Szuhai, Karoly; Kuijjer, Marieke & Roelofs, Helene [Show all 10 contributors for this article] (2015). Mesenchymal stromal cells of osteosarcoma patients do not show evidence of neoplastic changes during long‑term culture. Clinical Sarcoma Research. doi: 10.1186/s13569-015-0031-1. Full text in Research Archive
• Baranski, Zuzanna; Booij, Tijmen H.; Kuijjer, Marieke; Jong, Yvonne de; Cleton-Jansen, Anne-Marie & Price, Leo S. [Show all 10 contributors for this article] (2015). MEK inhibition induces apoptosis in osteosarcoma cells with constitutive ERK1/2 phosphorylation. Genes & cancer. ISSN 1947-6019. 6. Full text in Research Archive
• Pahl, Jens H.W.; Santos, Susy J.; Kuijjer, Marieke; Boerman, Gerharda H.; Sand, Laurens G.L. & Szuhai, Karoly [Show all 11 contributors for this article] (2015). Expression of the immune regulation antigen CD70 in osteosarcoma. Cancer Cell International. doi: 10.1186/s12935-015-0181-5. Full text in Research Archive
• Pahl, Jens H.W.; Kwappenberg, Kitty M.C.; Varypataki, Eleni M.; Santos, Susy J.; Kuijjer, Marieke & Mohamed, Susan [Show all 13 contributors for this article] (2014). Macrophages inhibit human osteosarcoma cell growth after activation with the bacterial cell wall derivative liposomal muramyl tripeptide in combination with interferon-γ. Journal of Experimental and Clinical Cancer Research. ISSN 0392-9078. doi: 10.1186/1756-9966-33-27. Full text in Research Archive
• Kuijjer, Marieke; Akker, Brendy E.W.M. van den; Hilhorst, Riet; Mommersteeg, Monique; Buddingh, Emilie P. & Serra, Massimo [Show all 9 contributors for this article] (2014). Kinome and mRNA expression profiling of high-grade osteosarcoma cell lines implies Akt signaling as possible target for therapy. BMC Medical Genomics. doi: 10.1186/1755-8794-7-4. Full text in Research Archive
• Kuijjer, Marieke; Hogendoorn, Pancras C.W. & Cleton-Jansen, Anne-Marie (2013). Genome-wide analyses on high-grade osteosarcoma: making sense of a genomically most unstable tumor. International Journal of Cancer. ISSN 0020-7136. doi: 10.1002/ijc.28124. Full text in Research Archive
• Kansara, Maya; Leong, Huei San; Lin, Dan Mei; Popkiss, Sophie; Pang, Puiyi & Garsed, Dale W. [Show all 16 contributors for this article] (2013). Immune response to RB1-regulated senescence limits radiation-induced osteosarcoma formation. Journal of Clinical Investigation. ISSN 0021-9738. doi: 10.1172/JCI70559. Full text in Research Archive
• Steenwijk, Peggy J. de Vos van; Ramwadhdoebe, T.H.; Goedemans, R.; Doorduijn, E.M.; Ham, J.J. van & Gorter, A. [Show all 11 contributors for this article] (2013). Tumor-infiltrating CD14-positive myeloid cells and CD8- positive T-cells prolong survival in patients with cervical carcinoma. International Journal of Cancer. ISSN 0020-7136. doi: 10.1002/ijc.28309. Full text in Research Archive
• Lenos, Kristiaan; Grawenda, Anna M.; Lodder, Kirsten; Kuijjer, Marieke; Teunisse, Amina F.A.S. & Repapi, Emmanouela [Show all 14 contributors for this article] (2012). Alternate Splicing of the p53 Inhibitor HDMX Offers a Superior Prognostic Biomarker than p53 Mutation in Human Cancer. Cancer Research. ISSN 0008-5472. doi: 10.1158/0008-5472.CAN-12-0215. Full text in Research Archive
• Mohseny, Alexander B.; Cai, Yongping; Kuijjer, Marieke; Xiao, Wei; Akker, Brendy E.W.M. van den & Andrea, Carlos E. de [Show all 10 contributors for this article] (2012). The activities of Smad and Gli mediated signalling pathways in high-grade conventional osteosarcoma. European Journal of Cancer. ISSN 0959-8049. doi: 10.1016/j.ejca.2012.06.018. Full text in Research Archive
• Pansuriya, Twinkal C.; Eijk, Ronald van; D'Adamo, Pio; Ruler, Maayke A.J.H. van; Kuijjer, Marieke & Oosting, Jan [Show all 27 contributors for this article] (2011). Somatic mosaic IDH1 and IDH2 mutations are associated with enchondroma and spindle cell hemangioma in Ollier disease and Maffucci syndrome. Nature Genetics. ISSN 1061-4036. doi: 10.1038/ng.1004. Full text in Research Archive

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• Osorio, Daniel & Kuijjer, Marieke (2024). Enabling comparative gene regulatory network analysis on single-cell data with SCORPION. Nature Computational Science. doi: 10.1038/s43588-024-00615-6. Full text in Research Archive
• Sajib, Saikat Das; Florholmen-Kjær, Åse; Brun, Vegard; Mortensen, Elin Synnøve; Hovd, Aud-Malin Karlsson & Tekpli, Xavier [Show all 10 contributors for this article] (2023). Epithelial-to-mesenchymal transition (EMT) is a major contributor to intratumor heterogeneity in lymph node metastasis in breast cancer. Full text in Research Archive
• Kuijjer, Marieke (2022). Gene regulatory networks in precision medicine. Full text in Research Archive
• Kuijjer, Marieke (2022). A network approach to personalized medicine. Full text in Research Archive
• Kuijjer, Marieke (2022). Modeling regulatory network rewiring in cancer. Full text in Research Archive
• Kuijjer, Marieke (2022). Modeling genome-wide regulatory network rewiring in healthy tissues and cancer. Full text in Research Archive
• Guebila, Marouen Ben; Weighill, Deborah; Lopes-Ramos, Camila M.; Burkholz, Rebekka; Pop, Romana-Tabita & Palepu, Kalyan [Show all 14 contributors for this article] (2022). An online notebook resource for reproducible inference, analysis and publication of gene regulatory networks. Nature Methods. ISSN 1548-7091. 19, p. 511–513. doi: 10.1038/s41592-022-01479-2. Full text in Research Archive
• Fagny, Maud; Glass, Kimberly & Kuijjer, Marieke (2022). Editorial: Applications and Methods in Genomic Networks. Frontiers in Genetics. 13. doi: 10.3389/fgene.2022.936015. Full text in Research Archive
• Kuijjer, Marieke (2021). Modeling gene regulatory network rewiring in healthy tissues and cancer. Full text in Research Archive
• Kuijjer, Marieke Lydia; Paulson, Joseph N. & Quackenbush, John (2020). Expression analysis. In Baxevanis, Andreas D.; Bader, Gary D. & Wishart, David S. (Ed.), Bioinformatics. John Wiley & Sons. ISSN 9781119335580. p. 279–314. Full text in Research Archive
• Lopes-ramos, Camila; Kuijjer, Marieke Lydia; Ogino, Shuji; Fuchs, Charles S.; DeMeo, Dawn L. & Glass, Kimberly [Show all 7 contributors for this article] (2019). Correction: Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism. Cancer Research. ISSN 0008-5472. Full text in Research Archive

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