MICAL2 Is a Super Enhancer Associated Gene that Promotes Pancreatic Cancer Growth and Metastasis.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest solid cancers and thus identifying more effective therapies is a major unmet need. In this study we characterized the super enhancer (SE) landscape of human PDAC to identify novel, potentially targetable, drivers of the disease. Our analysis revealed that MICAL2 is a super enhancer-associated gene in human PDAC. MICAL2 is a flavin monooxygenase that induces actin depolymerization and indirectly promotes SRF transcription by modulating the availability of serum response factor coactivators myocardin related transcription factors (MRTF-A and MRTF-B). We found that MICAL2 is overexpressed in PDAC and correlates with poor patient prognosis. Transcriptional analysis revealed that MICAL2 upregulates KRAS and EMT signaling pathways, contributing to tumor growth and metastasis. In loss and gain of function experiments in human and mouse PDAC cells, we observed that MICAL2 promotes both ERK1/2 and AKT activation. Consistent with its role in actin depolymerization and KRAS signaling, loss of MICAL2 expression also inhibited macropinocytosis. Through in vitro phenotypic analyses, we show that MICAL2, MRTF-A and MRTF-B influence PDAC cell proliferation, migration and promote cell cycle progression. Importantly, we demonstrate that MICAL2 is essential for in vivo tumor growth and metastasis. Interestingly, we find that MRTF-B, but not MRTF-A, phenocopies MICAL2-driven phenotypes in vivo . This study highlights the multiple ways in which MICAL2 impacts PDAC biology and suggests that its inhibition may impede PDAC progression. Our results provide a foundation for future investigations into the role of MICAL2 in PDAC and its potential as a target for therapeutic intervention.

Genome-wide analysis and visualization of copy number with CNVpytor in igv.js.

SUMMARY: Copy number variation (CNV) and alteration (CNA) analysis is a crucial component in many genomic studies and its applications span from basic research to clinic diagnostics and personalized medicine. CNVpytor is a tool featuring a read depth-based caller and combined read depth and B-allele frequency (BAF) based 2D caller to find CNVs and CNAs. The tool stores processed intermediate data and CNV/CNA calls in a compact HDF5 file-pytor file. Here, we describe a new track in igv.js that utilizes pytor and whole genome variant files as input for on-the-fly read depth and BAF visualization, CNV/CNA calling and analysis. Embedding into HTML pages and Jupiter Notebooks enables convenient remote data access and visualization simplifying interpretation and analysis of omics data. AVAILABILITY: The CNVpytor track is integrated with igv.js and available at https://github.com/igvteam/igv.js. The documentation is available at https://github.com/igvteam/igv.js/wiki/cnvpytor. Usage can be tested in the IGV-Web app at https://igv.org/app and also on https://github.com/abyzovlab/CNVpytor. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Developmental basis of SHH medulloblastoma heterogeneity.

Many genes that drive normal cellular development also contribute to oncogenesis. Medulloblastoma (MB) tumors likely arise from neuronal progenitors in the cerebellum, and we hypothesized that the heterogeneity observed in MBs with sonic hedgehog (SHH) activation could be due to differences in developmental pathways. To investigate this question, here we perform single-nucleus RNA sequencing on highly differentiated SHH MBs with extensively nodular histology and observed malignant cells resembling each stage of canonical granule neuron development. Through innovative computational approaches, we connect these results to published datasets and find that some established molecular subtypes of SHH MB appear arrested at different developmental stages. Additionally, using multiplexed proteomic imaging and MALDI imaging mass spectrometry, we identify distinct histological and metabolic profiles for highly differentiated tumors. Our approaches are applicable to understanding the interplay between heterogeneity and differentiation in other cancers and can provide important insights for the design of targeted therapies.

Circular extrachromosomal DNA promotes tumor heterogeneity in high-risk medulloblastoma.

Circular extrachromosomal DNA (ecDNA) in patient tumors is an important driver of oncogenic gene expression, evolution of drug resistance and poor patient outcomes. Applying computational methods for the detection and reconstruction of ecDNA across a retrospective cohort of 481 medulloblastoma tumors from 465 patients, we identify circular ecDNA in 82 patients (18%). Patients with ecDNA-positive medulloblastoma were more than twice as likely to relapse and three times as likely to die within 5 years of diagnosis. A subset of tumors harbored multiple ecDNA lineages, each containing distinct amplified oncogenes. Multimodal sequencing, imaging and CRISPR inhibition experiments in medulloblastoma models reveal intratumoral heterogeneity of ecDNA copy number per cell and frequent putative 'enhancer rewiring' events on ecDNA. This study reveals the frequency and diversity of ecDNA in medulloblastoma, stratified into molecular subgroups, and suggests copy number heterogeneity and enhancer rewiring as oncogenic features of ecDNA.

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS.

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.

NMFClustering: Accessible NMF-based clustering utilizing GPU acceleration.

Non-negative Matrix Factorization (NME) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using Cupy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePatten gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelnes on high performance computing (HPC) culsters that enable reproducible in silco research for non-programmers.

The GPCR-Gαs-PKA signaling axis promotes T cell dysfunction and cancer immunotherapy failure.

Immune checkpoint blockade (ICB) targeting PD-1 and CTLA-4 has revolutionized cancer treatment. However, many cancers do not respond to ICB, prompting the search for additional strategies to achieve durable responses. G-protein-coupled receptors (GPCRs) are the most intensively studied drug targets but are underexplored in immuno-oncology. Here, we cross-integrated large singe-cell RNA-sequencing datasets from CD8+ T cells covering 19 distinct cancer types and identified an enrichment of Gαs-coupled GPCRs on exhausted CD8+ T cells. These include EP2, EP4, A2AR, ß1AR and ß2AR, all of which promote T cell dysfunction. We also developed transgenic mice expressing a chemogenetic CD8-restricted Gαs-DREADD to activate CD8-restricted Gαs signaling and show that a Gαs-PKA signaling axis promotes CD8+ T cell dysfunction and immunotherapy failure. These data indicate that Gαs-GPCRs are druggable immune checkpoints that might be targeted to enhance the response to ICB immunotherapies.

Germline modifiers of the tumor immune microenvironment implicate drivers of cancer risk and immunotherapy response.

With the continued promise of immunotherapy for treating cancer, understanding how host genetics contributes to the tumor immune microenvironment (TIME) is essential to tailoring cancer screening and treatment strategies. Here, we study 1084 eQTLs affecting the TIME found through analysis of The Cancer Genome Atlas and literature curation. These TIME eQTLs are enriched in areas of active transcription, and associate with gene expression in specific immune cell subsets, such as macrophages and dendritic cells. Polygenic score models built with TIME eQTLs reproducibly stratify cancer risk, survival and immune checkpoint blockade (ICB) response across independent cohorts. To assess whether an eQTL-informed approach could reveal potential cancer immunotherapy targets, we inhibit CTSS, a gene implicated by cancer risk and ICB response-associated polygenic models; CTSS inhibition results in slowed tumor growth and extended survival in vivo. These results validate the potential of integrating germline variation and TIME characteristics for uncovering potential targets for immunotherapy.

Genomics to Notebook (g2nb): extending the electronic notebook to address the challenges of bioinformatics analysis.

We present Genomics to Notebook (g2nb), an environment that combines the JupyterLab notebook system with widely-used bioinformatics platforms. Galaxy, GenePattern, and the JavaScript versions of IGV and Cytoscape are currently available within g2nb. The analyses and visualizations within those platforms are presented as cells in a notebook, making thousands of genomics methods available within the notebook metaphor and allowing notebooks to contain workflows utilizing multiple software packages on remote servers, all without the need for programming. The g2nb environment is, to our knowledge, the only notebook-based system that incorporates multiple bioinformatics analysis platforms into a notebook interface.

3D genome mapping identifies subgroup-specific chromosome conformations and tumor-dependency genes in ependymoma.

Ependymoma is a tumor of the brain or spinal cord. The two most common and aggressive molecular groups of ependymoma are the supratentorial ZFTA-fusion associated and the posterior fossa ependymoma group A. In both groups, tumors occur mainly in young children and frequently recur after treatment. Although molecular mechanisms underlying these diseases have recently been uncovered, they remain difficult to target and innovative therapeutic approaches are urgently needed. Here, we use genome-wide chromosome conformation capture (Hi-C), complemented with CTCF and H3K27ac ChIP-seq, as well as gene expression and DNA methylation analysis in primary and relapsed ependymoma tumors, to identify chromosomal conformations and regulatory mechanisms associated with aberrant gene expression. In particular, we observe the formation of new topologically associating domains ('neo-TADs') caused by structural variants, group-specific 3D chromatin loops, and the replacement of CTCF insulators by DNA hyper-methylation. Through inhibition experiments, we validate that genes implicated by these 3D genome conformations are essential for the survival of patient-derived ependymoma models in a group-specific manner. Thus, this study extends our ability to reveal tumor-dependency genes by 3D genome conformations even in tumors that lack targetable genetic alterations.

igv.js: an embeddable JavaScript implementation of the Integrative Genomics Viewer (IGV).

SUMMARY: igv.js is an embeddable JavaScript implementation of the Integrative Genomics Viewer (IGV). It can be easily dropped into any web page with a single line of code and has no external dependencies. The viewer runs completely in the web browser, with no backend server and no data pre-processing required. AVAILABILITY AND IMPLEMENTATION: The igv.js JavaScript component can be installed from NPM at https://www.npmjs.com/package/igv. The source code is available at https://github.com/igvteam/igv.js under the MIT open-source license. IGV-Web, the end-user application built around igv.js, is available at https://igv.org/app. The source code is available at https://github.com/igvteam/igv-webapp under the MIT open-source license. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

NMF Clustering: Accessible NMF-based Clustering Utilizing GPU Acceleration.

Non-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using CuPy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePattern gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelines on high performance computing (HPC) clusters that enable reproducible in silico research for non-programmers.

Subgroup-Enriched Pathways and Kinase Signatures in Medulloblastoma Patient-Derived Xenografts.

Medulloblastoma (MB) is the most common malignant pediatric brain tumor. MB is classified into four primary molecular subgroups: wingless (WNT), sonic hedgehog (SHH), Group 3 (G3), and Group 4 (G4), and further genomic and proteomic subtypes have been reported. Subgroup heterogeneity and few actionable mutations have hindered the development of targeted therapies, especially for G3 MB, which has a particularly poor prognosis. To identify novel therapeutic targets for MB, we performed mass spectrometry-based deep expression proteomics and phosphoproteomics in 20 orthotopic patient-derived xenograft (PDX) models of MB comprising SHH, G3, and G4 subgroups. We found that the proteomic profiles of MB PDX tumors are closely aligned with those of primary human MB tumors illustrating the utility of PDX models. SHH PDXs were enriched for NFκB and p38 MAPK signaling, while G3 PDXs were characterized by MYC activity. Additionally, we found a significant association between actinomycin D sensitivity and increased abundance of MYC and MYC target genes. Our results highlight several candidate pathways that may serve as targets for new MB therapies. Mass spectrometry data are available via ProteomeXchange with identifier PXD035070.

Lymphatic-preserving treatment sequencing with immune checkpoint inhibition unleashes cDC1-dependent antitumor immunity in HNSCC.

Despite the promise of immune checkpoint inhibition (ICI), therapeutic responses remain limited. This raises the possibility that standard of care treatments delivered in concert may compromise the tumor response. To address this, we employ tobacco-signature head and neck squamous cell carcinoma murine models in which we map tumor-draining lymphatics and develop models for regional lymphablation with surgery or radiation. We find that lymphablation eliminates the tumor ICI response, worsening overall survival and repolarizing the tumor- and peripheral-immune compartments. Mechanistically, within tumor-draining lymphatics, we observe an upregulation of conventional type I dendritic cells and type I interferon signaling and show that both are necessary for the ICI response and lost with lymphablation. Ultimately, we provide a mechanistic understanding of how standard oncologic therapies targeting regional lymphatics impact the tumor response to immune-oncology therapy in order to define rational, lymphatic-preserving treatment sequences that mobilize systemic antitumor immunity, achieve optimal tumor responses, control regional metastatic disease, and confer durable antitumor immunity.

Single-cell characterization of step-wise acquisition of carboplatin resistance in ovarian cancer.

The molecular underpinnings of acquired resistance to carboplatin are poorly understood and often inconsistent between in vitro modeling studies. After sequential treatment cycles, multiple isogenic clones reached similar levels of resistance, but significant transcriptional heterogeneity. Gene-expression based virtual synchronization of 26,772 single cells from 2 treatment steps and 4 resistant clones was used to evaluate the activity of Hallmark gene sets in proliferative (P) and quiescent (Q) phases. Two behaviors were associated with resistance: (1) broad repression in the P phase observed in all clones in early resistant steps and (2) prevalent induction in Q phase observed in the late treatment step of one clone. Furthermore, the induction of IFNα response in P phase or Wnt-signaling in Q phase were observed in distinct resistant clones. These observations suggest a model of resistance hysteresis, where functional alterations of the P and Q phase states affect the dynamics of the successive transitions between drug exposure and recovery, and prompts for a precise monitoring of single-cell states to develop more effective schedules for, or combination of, chemotherapy treatments.

A FZD7-specific Antibody-Drug Conjugate Induces Ovarian Tumor Regression in Preclinical Models.

Although WNT signaling is frequently dysregulated in solid tumors, drugging this pathway has been challenging due to off-tumor effects. Current clinical pan-WNT inhibitors are nonspecific and lead to adverse effects, highlighting the urgent need for more specific WNT pathway-targeting strategies. We identified elevated expression of the WNT receptor Frizzled class receptor 7 (FZD7) in multiple solid cancers in The Cancer Genome Atlas, particularly in the mesenchymal and proliferative subtypes of ovarian serous cystadenocarcinoma, which correlate with poorer median patient survival. Moreover, we observed increased FZD7 protein expression in ovarian tumors compared with normal ovarian tissue, indicating that FZD7 may be a tumor-specific antigen. We therefore developed a novel antibody-drug conjugate, septuximab vedotin (F7-ADC), which is composed of a chimeric human-mouse antibody to human FZD7 conjugated to the microtubule-inhibiting drug monomethyl auristatin E (MMAE). F7-ADC selectively binds human FZD7, potently kills ovarian cancer cells in vitro, and induces regression of ovarian tumor xenografts in murine models. To evaluate F7-ADC toxicity in vivo, we generated mice harboring a modified Fzd7 gene where the resulting Fzd7 protein is reactive with the human-targeting F7-ADC. F7-ADC treatment of these mice did not induce acute toxicities, indicating a potentially favorable safety profile in patients. Overall, our data suggest that the antibody-drug conjugate approach may be a powerful strategy to combat FZD7-expressing ovarian cancers in the clinic.

Model-based assessment of mammalian cell metabolic functionalities using omics data.

Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie).

KITlow Cells Mediate Imatinib Resistance in Gastrointestinal Stromal Tumor.

Gastrointestinal stromal tumor (GIST) is commonly driven by oncogenic KIT mutations that are effectively targeted by imatinib (IM), a tyrosine kinase inhibitor (TKI). However, IM does not cure GIST, and adjuvant therapy only delays recurrence in high-risk tumors. We hypothesized that GIST contains cells with primary IM resistance that may represent a reservoir for disease persistence. Here, we report a subpopulation of CD34+KITlow human GIST cells that have intrinsic IM resistance. These cells possess cancer stem cell-like expression profiles and behavior, including self-renewal and differentiation into CD34+KIThigh progeny that are sensitive to IM treatment. We also found that TKI treatment of GIST cell lines led to induction of stem cell-associated transcription factors (OCT4 and NANOG) and concomitant enrichment of the CD34+KITlow cell population. Using a data-driven approach, we constructed a transcriptomic-oncogenic map (Onco-GPS) based on the gene expression of 134 GIST samples to define pathway activation during GIST tumorigenesis. Tumors with low KIT expression had overexpression of cancer stem cell gene signatures consistent with our in vitro findings. Additionally, these tumors had activation of the Gas6/AXL pathway and NF-κB signaling gene signatures. We evaluated these targets in vitro and found that primary IM-resistant GIST cells were effectively targeted with either single-agent bemcentinib (AXL inhibitor) or bardoxolone (NF-κB inhibitor), as well as with either agent in combination with IM. Collectively, these findings suggest that CD34+KITlow cells represent a distinct, but targetable, subpopulation in human GIST that may represent a novel mechanism of primary TKI resistance, as well as a target for overcoming disease persistence following TKI therapy.

Location of Gastrointestinal Stromal Tumor (GIST) in the Stomach Predicts Tumor Mutation Profile and Drug Sensitivity.

PURPOSE: Gastrointestinal stromal tumors (GIST) commonly arise in different regions of the stomach and are driven by various mutations (most often in KIT, PDGFRA, and SDHx). We hypothesized that the anatomic location of gastric GIST is associated with unique genomic profiles and distinct driver mutations. EXPERIMENTAL DESIGN: We compared KIT versus non-KIT status with tumor location within the National Cancer Database (NCDB) for 2,418 patients with primary gastric GIST. Additionally, we compiled an international cohort (TransAtlantic GIST Collaborative, TAGC) of 236 patients and reviewed sequencing results, cross-sectional imaging, and operative reports. Subgroup analyses were performed for tumors located proximally versus distally. Risk factors for KIT versus non-KIT tumors were identified using multivariate regression analysis. A random forest machine learning model was then developed to determine feature importance. RESULTS: Within the NCDB cohort, non-KIT mutants dominated distal tumor locations (P < 0.03). Proximal GIST were almost exclusively KIT mutant (96%) in the TAGC cohort, whereas 100% of PDGFRA and SDH-mutant GIST occurred in the distal stomach. On multivariate regression analysis, tumor location was associated with KIT versus non-KIT mutations. Using random forest machine learning analysis, stomach location was the most important feature for predicting mutation status. CONCLUSIONS: We provide the first evidence that the mutational landscape of gastric GIST is related to tumor location. Proximal gastric GIST are overwhelmingly KIT mutant, irrespective of morphology or age, whereas distal tumors display non-KIT genomic diversity. Anatomic location of gastric GIST may therefore provide immediate guidance for clinical treatment decisions and selective confirmatory genomic testing when resources are limited.