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For more than three decades, concurrent advances in laboratory technologies and computer science have driven the rise of cancer informatics. Today, software tools for cancer research are indispensable to the entire cancer research enterprise., (©2024 The Authors; Published by the American Association for Cancer Research.)

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 and Implementation: 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., (© The Author(s) 2024. Published by Oxford University Press.)

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.

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., (© 2024. The Author(s).)

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., (© 2023. The Author(s).)

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., (© 2023. Springer Nature Limited.)

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 Gα s -coupled GPCRs on exhausted CD8 + T cells. These include EP 2 , EP 4 , A 2A R, β 1 AR and β 2 AR, all of which promote T cell dysfunction. We also developed transgenic mice expressing a chemogenetic CD8-restricted Gα s -DREADD to activate CD8-restricted Gα s signaling and show that a Gα s -PKA signaling axis promotes CD8 + T cell dysfunction and immunotherapy failure. These data indicate that Gα s -GPCRs are druggable immune checkpoints that might be targeted to enhance the response to ICB immunotherapies., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

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., (© 2023. The Author(s).)

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., (© 2023. The Author(s).)

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., (© The Author(s) 2022. Published by Oxford University Press.)

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.

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.

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., (© 2022. The Author(s).)

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., (© 2022. The Author(s).)

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., (©2021 The Authors; Published by the American Association for Cancer Research.)

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 + KIT low 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 + KIT high 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 + KIT low 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 + KIT low 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., (©2021 American Association for Cancer Research.)

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., (©2021 American Association for Cancer Research.)

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)., Competing Interests: DECLARATION OF INTERESTS The authors declare no competing interests.

Background: Glioblastoma (GBM) is the most common primary brain tumor in adults with a median survival of approximately 15 months; therefore, more effective treatment options for GBM are required. To identify new drugs targeting GBMs, we performed a high-throughput drug screen using patient-derived neurospheres cultured to preferentially retain their glioblastoma stem cell (GSC) phenotype., Methods: High-throughput drug screening was performed on GSCs followed by a dose-response assay of the 5 identified original "hits." A PI3K/mTOR dependency to a proteasome inhibitor (carfilzomib), was confirmed by genetic and pharmacologic experiments. Proteasome Inhibition Response Signatures were derived from proteomic and bioinformatic analysis. Molecular mechanism of action was determined using three-dimensional (3D) GBM-organoids and preclinical orthotopic models., Results: We found that GSCs were highly sensitive to proteasome inhibition due to an underlying dependency on an increased protein synthesis rate, and loss of autophagy, associated with PTEN loss and activation of the PI3K/mTOR pathway. In contrast, combinatory inhibition of autophagy and the proteasome resulted in enhanced cytotoxicity specifically in GSCs that did express PTEN. Finally, proteasome inhibition specifically increased cell death markers in 3D GBM-organoids, suppressed tumor growth, and increased survival of mice orthotopically engrafted with GSCs. As perturbations of the PI3K/mTOR pathway occur in nearly 50% of GBMs, these findings suggest that a significant fraction of these tumors could be vulnerable to proteasome inhibition., Conclusions: Proteasome inhibition is a potential synthetic lethal therapeutic strategy for GBM with proteasome addiction due to a high protein synthesis rate and autophagy deficiency., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Here, we describe a protocol combining functional metrics with genomic data to elucidate drivers of within-cell-type heterogeneity via the phenotype-to-genotype link. This technique involves using fluorescence tagging to label and isolate cells grown in 3D culture, enabling high-throughput enrichment of phenotypically defined cell subpopulations by fluorescence-activated cell sorting. We then perform a validated phenotypically supervised single-cell analysis pipeline to reveal unique functional cell states, including genes and pathways that contribute to cellular heterogeneity and were undetectable by unsupervised analysis. For complete details on the use and execution of this protocol, please refer to Chen et al. (2020)., Competing Interests: The authors declare no competing interests., (© 2021 The Author(s).)

Medulloblastoma is among the most common malignant brain tumors in children. Recent studies have identified at least four subgroups of the disease that differ in terms of molecular characteristics and patient outcomes. Despite this heterogeneity, most patients with medulloblastoma receive similar therapies, including surgery, radiation, and intensive chemotherapy. Although these treatments prolong survival, many patients still die from the disease and survivors suffer severe long-term side effects from therapy. We hypothesize that each patient with medulloblastoma is sensitive to different therapies and that tailoring therapy based on the molecular and cellular characteristics of patients' tumors will improve outcomes. To test this, we assembled a panel of orthotopic patient-derived xenografts (PDX) and subjected them to DNA sequencing, gene expression profiling, and high-throughput drug screening. Analysis of DNA sequencing revealed that most medulloblastomas do not have actionable mutations that point to effective therapies. In contrast, gene expression and drug response data provided valuable information about potential therapies for every tumor. For example, drug screening demonstrated that actinomycin D, which is used for treatment of sarcoma but rarely for medulloblastoma, was active against PDXs representing Group 3 medulloblastoma, the most aggressive form of the disease. Functional analysis of tumor cells was successfully used in a clinical setting to identify more treatment options than sequencing alone. These studies suggest that it should be possible to move away from a one-size-fits-all approach and begin to treat each patient with therapies that are effective against their specific tumor. SIGNIFICANCE: These findings show that high-throughput drug screening identifies therapies for medulloblastoma that cannot be predicted by genomic or transcriptomic analysis., (©2020 American Association for Cancer Research.)

Purpose: The availability of increasing volumes of multiomics, imaging, and clinical data in complex diseases such as cancer opens opportunities for the formulation and development of computational imaging genomics methods that can link multiomics, imaging, and clinical data., Methods: Here, we present the Imaging-AMARETTO algorithms and software tools to systematically interrogate regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes., Results: To demonstrate its utility, we applied Imaging-AMARETTO to integrate three patient studies of brain tumors, specifically, multiomics with radiography imaging data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and low-grade glioma (LGG) cohorts and transcriptomics with histopathology imaging data from the Ivy Glioblastoma Atlas Project (IvyGAP) GBM cohort. Our results show that Imaging-AMARETTO recapitulates known key drivers of tumor-associated microglia and macrophage mechanisms, mediated by STAT3 , AHR , and CCR2 , and neurodevelopmental and stemness mechanisms, mediated by OLIG2 . Imaging-AMARETTO provides interpretation of their underlying molecular mechanisms in light of imaging biomarkers of clinical outcomes and uncovers novel master drivers, THBS1 and MAP2 , that establish relationships across these distinct mechanisms., Conclusion: Our network-based imaging genomics tools serve as hypothesis generators that facilitate the interrogation of known and uncovering of novel hypotheses for follow-up with experimental validation studies. We anticipate that our Imaging-AMARETTO imaging genomics tools will be useful to the community of biomedical researchers for applications to similar studies of cancer and other complex diseases with available multiomics, imaging, and clinical data.

Dependence on the 26S proteasome is an Achilles' heel for triple-negative breast cancer (TNBC) and multiple myeloma (MM). The therapeutic proteasome inhibitor, bortezomib, successfully targets MM but often leads to drug-resistant disease relapse and fails in breast cancer. Here we show that a 26S proteasome-regulating kinase, DYRK2, is a therapeutic target for both MM and TNBC. Genome editing or small-molecule mediated inhibition of DYRK2 significantly reduces 26S proteasome activity, bypasses bortezomib resistance, and dramatically delays in vivo tumor growth in MM and TNBC thereby promoting survival. We further characterized the ability of LDN192960, a potent and selective DYRK2-inhibitor, to alleviate tumor burden in vivo. The drug docks into the active site of DYRK2 and partially inhibits all 3 core peptidase activities of the proteasome. Our results suggest that targeting 26S proteasome regulators will pave the way for therapeutic strategies in MM and TNBC., Competing Interests: The authors declare no competing interest., (Copyright © 2019 the Author(s). Published by PNAS.)

Genome sequencing was performed on matched normal and tumor tissue from a 6.5-yr-old boy with a diagnosis of recurrent medulloblastoma. A pathogenic heterozygous c.432+1G>A canonical splice donor site variant in GNAS was detected on analysis of blood DNA. Analysis of tumor DNA showed the same splice variant along with copy-neutral loss of heterozygosity on Chromosome 20 encompassing GNAS , consistent with predicted biallelic loss of GNAS in the tumor specimen. This case strengthens the evidence implicating GNAS as a tumor-suppressor gene in medulloblastoma and highlights a scenario in which therapeutics targeting the cAMP pathway may be of great utility., (© 2019 Tokita et al.; Published by Cold Spring Harbor Laboratory Press.)

Signaling pathways are orchestrated by post-translational modifications (PTMs) such as phosphorylation. However, pathway analysis of PTM data sets generated by mass spectrometry (MS)-based proteomics is typically performed at a gene-centric level because of the lack of appropriately curated PTM signature databases and bioinformatic tools that leverage PTM site-specific information. Here we present the first version of PTMsigDB, a database of modification site-specific signatures of perturbations, kinase activities and signaling pathways curated from more than 2,500 publications. We adapted the widely used single sample Gene Set Enrichment Analysis approach to utilize PTMsigDB, enabling PTMS ignature E nrichment A nalysis (PTM-SEA) of quantitative MS data. We used a well-characterized data set of epidermal growth factor (EGF)-perturbed cancer cells to evaluate our approach and demonstrated better representation of signaling events compared with gene-centric methods. We then applied PTM-SEA to analyze the phosphoproteomes of cancer cells treated with cell-cycle inhibitors and detected mechanism-of-action specific signatures of cell cycle kinases. We also applied our methods to analyze the phosphoproteomes of PI3K-inhibited human breast cancer cells and detected signatures of compounds inhibiting PI3K as well as targets downstream of PI3K (AKT, MAPK/ERK) covering a substantial fraction of the PI3K pathway. PTMsigDB and PTM-SEA can be freely accessed at https://github.com/broadinstitute/ssGSEA2.0., (© 2019 Krug et al.)

For the 2019 Pacific Symposium on Biocomputing's session on precision medicine, we present new research on computational techniques in range of areas including data curation, whole genome analysis, transcriptomics, microbiome profiling, EHR data-mining, and histological image processing.

Illumina Infinium DNA methylation arrays are a cost-effective technology to measure DNA methylation at CpG sites genome-wide and across cohorts of normal and cancer samples. While copy number alterations are commonly inferred from array-CGH, SNP arrays, or whole-genome DNA sequencing, Illumina Infinium DNA methylation arrays have been shown to detect copy number alterations at comparable sensitivity. Here we present an accessible, interactive GenePattern notebook for the analysis of copy number variation using Illumina Infinium DNA methylation arrays. The notebook provides a graphical user interface to a workflow using the R/Bioconductor packages minfi and conumee . The environment allows analysis to be performed without the installation of the R software environment, the packages and dependencies, and without the need to write or manipulate code., Competing Interests: No competing interests were disclosed.

There is a pressing need to identify therapeutic targets in tumors with low mutation rates such as the malignant pediatric brain tumor medulloblastoma. To address this challenge, we quantitatively profiled global proteomes and phospho-proteomes of 45 medulloblastoma samples. Integrated analyses revealed that tumors with similar RNA expression vary extensively at the post-transcriptional and post-translational levels. We identified distinct pathways associated with two subsets of SHH tumors, and found post-translational modifications of MYC that are associated with poor outcomes in group 3 tumors. We found kinases associated with subtypes and showed that inhibiting PRKDC sensitizes MYC-driven cells to radiation. Our study shows that proteomics enables a more comprehensive, functional readout, providing a foundation for future therapeutic strategies., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Single-cell RNA sequencing (scRNA-seq) has emerged as a popular method to profile gene expression at the resolution of individual cells. While there have been methods and software specifically developed to analyze scRNA-seq data, they are most accessible to users who program. We have created a scRNA-seq clustering analysis GenePattern Notebook that provides an interactive, easy-to-use interface for data analysis and exploration of scRNA-Seq data, without the need to write or view any code. The notebook provides a standard scRNA-seq analysis workflow for pre-processing data, identification of sub-populations of cells by clustering, and exploration of biomarkers to characterize heterogeneous cell populations and delineate cell types., Competing Interests: No competing interests were disclosed.

Contact mapping experiments such as Hi-C explore how genomes fold in 3D. Here, we introduce Juicebox.js, a cloud-based web application for exploring the resulting datasets. Like the original Juicebox application, Juicebox.js allows users to zoom in and out of such datasets using an interface similar to Google Earth. Juicebox.js also has many features designed to facilitate data reproducibility and sharing. Furthermore, Juicebox.js encodes the exact state of the browser in a shareable URL. Creating a public browser for a new Hi-C dataset does not require coding and can be accomplished in under a minute. The web app also makes it possible to create interactive figures online that can complement or replace ordinary journal figures. When combined with Juicer, this makes the entire process of data analysis transparent, insofar as every step from raw reads to published figure is publicly available as open source code., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Alteration of corticostriatal glutamatergic function is an early pathophysiological change associated with Huntington's disease (HD). The factors that regulate the maintenance of corticostriatal glutamatergic synapses post-developmentally are not well understood. Recently, the striatum-enriched transcription factor Foxp2 was implicated in the development of these synapses. Here, we show that, in mice, overexpression of Foxp2 in the adult striatum of two models of HD leads to rescue of HD-associated behaviors, while knockdown of Foxp2 in wild-type mice leads to development of HD-associated behaviors. We note that Foxp2 encodes the longest polyglutamine repeat protein in the human reference genome, and we show that it can be sequestered into aggregates with polyglutamine-expanded mutant Huntingtin protein (mHTT). Foxp2 overexpression in HD model mice leads to altered expression of several genes associated with synaptic function, genes that present additional targets for normalization of corticostriatal dysfunction in HD., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Manual review of aligned reads for confirmation and interpretation of variant calls is an important step in many variant calling pipelines for next-generation sequencing (NGS) data. Visual inspection can greatly increase the confidence in calls, reduce the risk of false positives, and help characterize complex events. The Integrative Genomics Viewer (IGV) was one of the first tools to provide NGS data visualization, and it currently provides a rich set of tools for inspection, validation, and interpretation of NGS datasets, as well as other types of genomic data. Here, we present a short overview of IGV's variant review features for both single-nucleotide variants and structural variants, with examples from both cancer and germline datasets. IGV is freely available at https://www.igv.org Cancer Res; 77(21); e31-34. ©2017 AACR ., (©2017 American Association for Cancer Research.)

Interactive analysis notebook environments promise to streamline genomics research through interleaving text, multimedia, and executable code into unified, sharable, reproducible "research narratives." However, current notebook systems require programming knowledge, limiting their wider adoption by the research community. We have developed the GenePattern Notebook environment (http://www.genepattern-notebook.org), to our knowledge the first system to integrate the dynamic capabilities of notebook systems with an investigator-focused, easy-to-use interface that provides access to hundreds of genomic tools without the need to write code., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The systematic sequencing of the cancer genome has led to the identification of numerous genetic alterations in cancer. However, a deeper understanding of the functional consequences of these alterations is necessary to guide appropriate therapeutic strategies. Here, we describe Onco-GPS (OncoGenic Positioning System), a data-driven analysis framework to organize individual tumor samples with shared oncogenic alterations onto a reference map defined by their underlying cellular states. We applied the methodology to the RAS pathway and identified nine distinct components that reflect transcriptional activities downstream of RAS and defined several functional states associated with patterns of transcriptional component activation that associates with genomic hallmarks and response to genetic and pharmacological perturbations. These results show that the Onco-GPS is an effective approach to explore the complex landscape of oncogenic cellular states across cancers, and an analytic framework to summarize knowledge, establish relationships, and generate more effective disease models for research or as part of individualized precision medicine paradigms., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Plasticity of the cell state has been proposed to drive resistance to multiple classes of cancer therapies, thereby limiting their effectiveness. A high-mesenchymal cell state observed in human tumours and cancer cell lines has been associated with resistance to multiple treatment modalities across diverse cancer lineages, but the mechanistic underpinning for this state has remained incompletely understood. Here we molecularly characterize this therapy-resistant high-mesenchymal cell state in human cancer cell lines and organoids and show that it depends on a druggable lipid-peroxidase pathway that protects against ferroptosis, a non-apoptotic form of cell death induced by the build-up of toxic lipid peroxides. We show that this cell state is characterized by activity of enzymes that promote the synthesis of polyunsaturated lipids. These lipids are the substrates for lipid peroxidation by lipoxygenase enzymes. This lipid metabolism creates a dependency on pathways converging on the phospholipid glutathione peroxidase (GPX4), a selenocysteine-containing enzyme that dissipates lipid peroxides and thereby prevents the iron-mediated reactions of peroxides that induce ferroptotic cell death. Dependency on GPX4 was found to exist across diverse therapy-resistant states characterized by high expression of ZEB1, including epithelial-mesenchymal transition in epithelial-derived carcinomas, TGFβ-mediated therapy-resistance in melanoma, treatment-induced neuroendocrine transdifferentiation in prostate cancer, and sarcomas, which are fixed in a mesenchymal state owing to their cells of origin. We identify vulnerability to ferroptic cell death induced by inhibition of a lipid peroxidase pathway as a feature of therapy-resistant cancer cells across diverse mesenchymal cell-state contexts.

Medulloblastoma is the most frequent malignant pediatric brain tumor and is divided into at least four subgroups known as WNT, SHH, Group 3, and Group 4. Here, we characterized gene regulation mechanisms in the most aggressive subtype, Group 3 tumors, through genome-wide chromatin and expression profiling. Our results show that most active distal sites in these tumors are occupied by the transcription factor OTX2. Highly active OTX2-bound enhancers are often arranged as clusters of adjacent peaks and are also bound by the transcription factor NEUROD1. These sites are responsive to OTX2 and NEUROD1 knockdown and could also be generated de novo upon ectopic OTX2 expression in primary cells, showing that OTX2 cooperates with NEUROD1 and plays a major role in maintaining and possibly establishing regulatory elements as a pioneer factor. Among OTX2 target genes, we identified the kinase NEK2, whose knockdown and pharmacologic inhibition decreased cell viability. Our studies thus show that OTX2 controls the regulatory landscape of Group 3 medulloblastoma through cooperative activity at enhancer elements and contributes to the expression of critical target genes. Significance: The gene regulation mechanisms that drive medulloblastoma are not well understood. Using chromatin profiling, we find that the transcription factor OTX2 acts as a pioneer factor and, in cooperation with NEUROD1, controls the Group 3 medulloblastoma active enhancer landscape. OTX2 itself or its target genes, including the mitotic kinase NEK2, represent attractive targets for future therapies. Cancer Discov; 7(3); 288-301. ©2017 AACR. This article is highlighted in the In This Issue feature, p. 235 ., (©2017 American Association for Cancer Research.)

Background: Pediatric acute respiratory distress in tropical settings is very common. Bacterial pneumonia is a major contributor to morbidity and mortality rates and requires adequate diagnosis for correct treatment. A rapid test that could identify bacterial (vs other) infections would have great clinical utility., Methods and Results: We performed RNA (RNA-seq) sequencing and analyzed the transcriptomes of 68 pediatric patients with well-characterized clinical phenotype to identify transcriptional features associated with each disease class. We refined the features to predictive models (support vector machine, elastic net) and validated those models in an independent test set of 37 patients (80%-85% accuracy)., Conclusions: We have identified sets of genes that are differentially expressed in pediatric patients with pneumonia syndrome attributable to different infections and requiring different therapeutic interventions. Findings of this study demonstrate that human transcription signatures in infected patients recapitulate the underlying biology and provide models for predicting a bacterial diagnosis to inform treatment., (© The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.)

Prevention is an essential component of cancer eradication. Next-generation sequencing of cancer genomes and epigenomes has defined large numbers of driver mutations and molecular subgroups, leading to therapeutic advances. By comparison, there is a relative paucity of such knowledge in premalignant neoplasia, which inherently limits the potential to develop precision prevention strategies. Studies on the interplay between germ-line and somatic events have elucidated genetic processes underlying premalignant progression and preventive targets. Emerging data hint at the immune system's ability to intercept premalignancy and prevent cancer. Genetically engineered mouse models have identified mechanisms by which genetic drivers and other somatic alterations recruit inflammatory cells and induce changes in normal cells to create and interact with the premalignant tumor microenvironment to promote oncogenesis and immune evasion. These studies are currently limited to only a few lesion types and patients. In this Perspective, we advocate a large-scale collaborative effort to systematically map the biology of premalignancy and the surrounding cellular response. By bringing together scientists from diverse disciplines (e.g., biochemistry, omics, and computational biology; microbiology, immunology, and medical genetics; engineering, imaging, and synthetic chemistry; and implementation science), we can drive a concerted effort focused on cancer vaccines to reprogram the immune response to prevent, detect, and reject premalignancy. Lynch syndrome, clonal hematopoiesis, and cervical intraepithelial neoplasia which also serve as models for inherited syndromes, blood, and viral premalignancies, are ideal scenarios in which to launch this initiative., Competing Interests: The authors declare no conflict of interest.

Purpose: We used human stem and progenitor cells to develop a genetically accurate novel model of MYC-driven Group 3 medulloblastoma. We also developed a new informatics method, Disease-model Signature versus Compound-Variety Enriched Response ("DiSCoVER"), to identify novel therapeutics that target this specific disease subtype., Experimental Design: Human neural stem and progenitor cells derived from the cerebellar anlage were transduced with oncogenic elements associated with aggressive medulloblastoma. An in silico analysis method for screening drug sensitivity databases (DiSCoVER) was used in multiple drug sensitivity datasets. We validated the top hits from this analysis in vitro and in vivo, Results: Human neural stem and progenitor cells transformed with c-MYC, dominant-negative p53, constitutively active AKT and hTERT formed tumors in mice that recapitulated Group 3 medulloblastoma in terms of pathology and expression profile. DiSCoVER analysis predicted that aggressive MYC-driven Group 3 medulloblastoma would be sensitive to cyclin-dependent kinase (CDK) inhibitors. The CDK 4/6 inhibitor palbociclib decreased proliferation, increased apoptosis, and significantly extended the survival of mice with orthotopic medulloblastoma xenografts., Conclusions: We present a new method to generate genetically accurate models of rare tumors, and a companion computational methodology to find therapeutic interventions that target them. We validated our human neural stem cell model of MYC-driven Group 3 medulloblastoma and showed that CDK 4/6 inhibitors are active against this subgroup. Our results suggest that palbociclib is a potential effective treatment for poor prognosis MYC-driven Group 3 medulloblastoma tumors in carefully selected patients. Clin Cancer Res; 22(15); 3903-14. ©2016 AACR., Competing Interests: The authors report no conflict of interest, (©2016 American Association for Cancer Research.)

Kataegis is a mutational process observed in ∼55% of breast tumors that results in hypermutation in localized genomic regions. Using whole-genome sequence data of 97 tumors, we examined the distribution of kataegis loci, showing that these somatic mutations are over-represented on chromosomes 8, 17, and 22 and enriched in genic regions and active chromatin elements. We show that tumors harboring kataegis are associated with transcriptome-wide expression changes consistent with low invasive potential. We exploit the kataegis expression signature to predict kataegis status in 412 breast cancers with transcriptome but not whole-genome sequence data and show that kataegis loci are enriched in high-grade, HER2(+) tumors in patients diagnosed with breast cancer at an older age and who have a later age at death. Our study demonstrates that kataegis loci are associated with important clinical features in breast cancer and may serve as a marker of good prognosis., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Hi-C experiments study how genomes fold in 3D, generating contact maps containing features as small as 20 bp and as large as 200 Mb. Here we introduce Juicebox, a tool for exploring Hi-C and other contact map data. Juicebox allows users to zoom in and out of Hi-C maps interactively, just as a user of Google Earth might zoom in and out of a geographic map. Maps can be compared to one another, or to 1D tracks or 2D feature sets., (Copyright © 2016 Elsevier Inc. All rights reserved.)

RNA has the intrinsic property to base pair, forming complex structures fundamental to its diverse functions. Here, we develop PARIS, a method based on reversible psoralen crosslinking for global mapping of RNA duplexes with near base-pair resolution in living cells. PARIS analysis in three human and mouse cell types reveals frequent long-range structures, higher-order architectures, and RNA-RNA interactions in trans across the transcriptome. PARIS determines base-pairing interactions on an individual-molecule level, revealing pervasive alternative conformations. We used PARIS-determined helices to guide phylogenetic analysis of RNA structures and discovered conserved long-range and alternative structures. XIST, a long noncoding RNA (lncRNA) essential for X chromosome inactivation, folds into evolutionarily conserved RNA structural domains that span many kilobases. XIST A-repeat forms complex inter-repeat duplexes that nucleate higher-order assembly of the key epigenetic silencing protein SPEN. PARIS is a generally applicable and versatile method that provides novel insights into the RNA structurome and interactome. VIDEO ABSTRACT., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes.

Complex biomedical analyses require the use of multiple software tools in concert and remain challenging for much of the biomedical research community. We introduce GenomeSpace (http://www.genomespace.org), a cloud-based, cooperative community resource that currently supports the streamlined interaction of 20 bioinformatics tools and data resources. To facilitate integrative analysis by non-programmers, it offers a growing set of 'recipes', short workflows to guide investigators through high-utility analysis tasks.