Noah Greenwald

All Publications

• Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nature biotechnology Greenwald, N. F., Miller, G., Moen, E., Kong, A., Kagel, A., Dougherty, T., Fullaway, C. C., McIntosh, B. J., Leow, K. X., Schwartz, M. S., Pavelchek, C., Cui, S., Camplisson, I., Bar-Tal, O., Singh, J., Fong, M., Chaudhry, G., Abraham, Z., Moseley, J., Warshawsky, S., Soon, E., Greenbaum, S., Risom, T., Hollmann, T., Bendall, S. C., Keren, L., Graf, W., Angelo, M., Van Valen, D. 2021

  Abstract

  A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.

  View details for DOI 10.1038/s41587-021-01094-0

  View details for PubMedID 34795433

• Single-Cell Imaging Maps Inflammatory Cell Subsets to Pulmonary Arterial Hypertension Vasculopathy. American journal of respiratory and critical care medicine Ferrian, S., Cao, A., McCaffrey, E. F., Saito, T., Greenwald, N. F., Nicolls, M. R., Bruce, T., Zamanian, R. T., Del Rosario, P., Rabinovitch, M., Angelo, M. 2023

  Abstract

  Rationale: Elucidating the immune landscape within and surrounding pulmonary arteries (PAs) is critical in understanding immune-driven vascular pathology in pulmonary arterial hypertension (PAH). Although more severe vascular pathology is often observed in hereditary (H)PAH patients with BMPR2 mutations, the involvement of specific immune cell subsets remains unclear. Methods: We used cutting-edge multiplexed ion beam imaging by time-of-flight (MIBI-TOF) to compare PAs and adjacent tissue in PAH lungs (idiopathic (I)PAH and HPAH) with unused donor lungs. Measurements: We quantified immune cells' proximity and abundance, focusing on those linked to vascular pathology, and evaluated their impact on pulmonary arterial smooth muscle cells (SMCs) and endothelial cells (ECs). Results: Distinct immune infiltration patterns emerged between PAH subtypes, with intramural involvement independently linked to PA occlusive changes. Notably, we identified monocyte-derived dendritic cells (mo-DCs) within PA subendothelial and adventitial regions, influencing vascular remodeling by promoting SMC proliferation and suppressing endothelial gene expression across PAH subtypes. In HPAH patients, pronounced immune dysregulation encircled PA walls, characterized by heightened perivascular inflammation involving TIM-3+ T cells. This correlated with an expanded DC subset expressing IDO-1, TIM-3, and SAMHD1, alongside increased neutrophils, SMCs, and α-SMA+ECs, reinforcing the severity of pulmonary vascular lesions. Conclusions: This study presents the first architectural map of PAH lungs, connecting immune subsets not only with specific PA lesions but also with heightened severity in HPAH compared to IPAH. Our findings emphasize the therapeutic potential of targeting mo-DCs, neutrophils, cellular interactions, and immune responses to alleviate severe vascular pathology in IPAH and HPAH.

  View details for DOI 10.1164/rccm.202209-1761OC

  View details for PubMedID 37934691

• Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering. Nature communications Liu, C. C., Greenwald, N. F., Kong, A., McCaffrey, E. F., Leow, K. X., Mrdjen, D., Cannon, B. J., Rumberger, J. L., Varra, S. R., Angelo, M. 2023; 14 (1): 4618

  Abstract

  While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we describe a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface.

  View details for DOI 10.1038/s41467-023-40068-5

  View details for PubMedID 37528072

  View details for PubMedCentralID 6086938

• Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP). Nature cell biology Jain, S., Pei, L., Spraggins, J. M., Angelo, M., Carson, J. P., Gehlenborg, N., Ginty, F., Gonçalves, J. P., Hagood, J. S., Hickey, J. W., Kelleher, N. L., Laurent, L. C., Lin, S., Lin, Y., Liu, H., Naba, A., Nakayasu, E. S., Qian, W. J., Radtke, A., Robson, P., Stockwell, B. R., Van de Plas, R., Vlachos, I. S., Zhou, M., Börner, K., Snyder, M. P. 2023

  Abstract

  The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.

  View details for DOI 10.1038/s41556-023-01194-w

  View details for PubMedID 37468756

  View details for PubMedCentralID 8238499

• Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology. Nature communications Bai, Y., Zhu, B., Oliveria, J., Cannon, B. J., Feyaerts, D., Bosse, M., Vijayaragavan, K., Greenwald, N. F., Phillips, D., Schurch, C. M., Naik, S. M., Ganio, E. A., Gaudilliere, B., Rodig, S. J., Miller, M. B., Angelo, M., Bendall, S. C., Rovira-Clave, X., Nolan, G. P., Jiang, S. 2023; 14 (1): 4013

  Abstract

  Cellular organization and functions encompass multiple scales in vivo. Emerging high-plex imaging technologies are limited in resolving subcellular biomolecular features. Expansion Microscopy (ExM) and related techniques physically expand samples for enhanced spatial resolution, but are challenging to be combined with high-plex imaging technologies to enable integrative multiscaled tissue biology insights. Here, we introduce Expand and comPRESS hydrOgels (ExPRESSO), an ExM framework that allows high-plex protein staining, physical expansion, and removal of water, while retaining the lateral tissue expansion. We demonstrate ExPRESSO imaging of archival clinical tissue samples on Multiplexed Ion Beam Imaging and Imaging Mass Cytometry platforms, with detection capabilities of>40 markers. Application of ExPRESSO on archival human lymphoid and brain tissues resolved tissue architecture at the subcellular level, particularly that of the blood-brain barrier. ExPRESSO hence provides a platform for extending the analysis compatibility of hydrogel-expanded biospecimensto mass spectrometry, with minimal modifications to protocols and instrumentation.

  View details for DOI 10.1038/s41467-023-39616-w

  View details for PubMedID 37419873

• A spatially resolved timeline of the human maternal-fetal interface. Nature Greenbaum, S., Averbukh, I., Soon, E., Rizzuto, G., Baranski, A., Greenwald, N. F., Kagel, A., Bosse, M., Jaswa, E. G., Khair, Z., Kwok, S., Warshawsky, S., Piyadasa, H., Goldston, M., Spence, A., Miller, G., Schwartz, M., Graf, W., Van Valen, D., Winn, V. D., Hollmann, T., Keren, L., van de Rijn, M., Angelo, M. 2023; 619 (7970): 595-605

  Abstract

  Beginning in the first trimester, fetally derived extravillous trophoblasts (EVTs) invade the uterus and remodel its spiral arteries, transforming them into large, dilated blood vessels. Several mechanisms have been proposed to explain how EVTs coordinate with the maternal decidua to promote a tissue microenvironment conducive to spiral artery remodelling (SAR)1-3. However, it remains a matter of debate regarding which immune and stromal cells participate in these interactions and how this evolves with respect to gestational age. Here we used a multiomics approach, combining the strengths of spatial proteomics and transcriptomics, to construct a spatiotemporal atlas of the human maternal-fetal interface in the first half of pregnancy. We used multiplexed ion beam imaging by time-of-flight and a 37-plex antibody panel to analyse around 500,000 cells and 588 arteries within intact decidua from 66 individuals between 6 and 20 weeks of gestation, integrating this dataset with co-registered transcriptomics profiles. Gestational age substantially influenced the frequency of maternal immune and stromal cells, with tolerogenic subsets expressing CD206, CD163, TIM-3, galectin-9 and IDO-1 becoming increasingly enriched and colocalized at later time points. By contrast, SAR progression preferentially correlated with EVT invasion and was transcriptionally defined by 78 gene ontology pathways exhibiting distinct monotonic and biphasic trends. Last, we developed an integrated model of SAR whereby invasion is accompanied by the upregulation of pro-angiogenic, immunoregulatory EVT programmes that promote interactions with the vascular endothelium while avoiding the activation of maternal immune cells.

  View details for DOI 10.1038/s41586-023-06298-9

  View details for PubMedID 37468587

  View details for PubMedCentralID PMC10356615

• CLINICAL RESPONSE TO THE PDGFRA/KIT INHIBITOR AVAPRITINIB IN PEDIATRIC AND YOUNG ADULT HIGH-GRADE GLIOMA PATIENTS WITH H3K27M OR PDGFRA GENOMIC ALTERATIONS Trissal, M., Mayr, L., Schwark, K., LaBelle, J., Kong, S., Furtner, J., Weiler-Wichtl, L., Supko, J., Rozowsky, J., Hack, O., Groves, A., Marques, J., Leiss, U., Rosenmayr, V., Dubois, F., Greenwald, N. F., Madlener, S., Guntner, A., Palova, H., Stepien, N., Lotsch-Gojo, D., Dorfer, C., Dieckmann, K., Peyrl, A., Azizi, A. A., Baumgartner, A., Slaby, O., Pokorna, P., Bandopadhayay, P., Beroukhim, R., Ligon, K. L., Kramm, C. M., Bronsema, A., Bailey, S., Stucklin, A., Mueller, S., Jones, D. W., Jager, N., Mullauer, L., Haberler, C., Kumar-Sinha, C., Chinnaiyan, A., Mody, R., Sterba, J., Skrypek, M., Martinez, N., Bowers, D. C., Koschmann, C., Gojo, J., Filbin, M. OXFORD UNIV PRESS INC. 2023

  View details for DOI 10.1093/neuonc/noad073.168

  View details for Web of Science ID 001023504300169

• Single-cell spatial proteomic imaging for human neuropathology. Acta neuropathologica communications Vijayaragavan, K., Cannon, B. J., Tebaykin, D., Bosse, M., Baranski, A., Oliveria, J. P., Bukhari, S. A., Mrdjen, D., Corces, M. R., McCaffrey, E. F., Greenwald, N. F., Sigal, Y., Marquez, D., Khair, Z., Bruce, T., Goldston, M., Bharadwaj, A., Montine, K. S., Angelo, R. M., Montine, T. J., Bendall, S. C. 2022; 10 (1): 158

  Abstract

  Neurodegenerative disorders are characterized by phenotypic changes and hallmark proteopathies. Quantifying these in archival human brain tissues remains indispensable for validating animal models and understanding disease mechanisms. We present a framework for nanometer-scale, spatial proteomics with multiplex ion beam imaging (MIBI) for capturing neuropathological features. MIBI facilitated simultaneous, quantitative imaging of 36 proteins on archival human hippocampus from individuals spanning cognitively normal to dementia. Customized analysis strategies identified cell types and proteopathies in the hippocampus across stages of Alzheimer's disease (AD) neuropathologic change. We show microglia-pathologic tau interactions in hippocampal CA1 subfield in AD dementia. Data driven, sample independent creation of spatial proteomic regions identified persistent neurons in pathologic tau neighborhoods expressing mitochondrial protein MFN2, regardless of cognitive status, suggesting a survival advantage. Our study revealed unique insights from multiplexed imaging and data-driven approaches for neuropathologic analysis and serves broadly as a methodology for spatial proteomic analysis of archival human neuropathology. TEASER: Multiplex Ion beam Imaging enables deep spatial phenotyping of human neuropathology-associated cellular and disease features.

  View details for DOI 10.1186/s40478-022-01465-x

  View details for PubMedID 36333818

• Spatial epitope barcoding reveals clonal tumor patch behaviors. Cancer cell Rovira-Clave, X., Drainas, A. P., Jiang, S., Bai, Y., Baron, M., Zhu, B., Dallas, A. E., Lee, M. C., Chu, T. P., Holzem, A., Ayyagari, R., Bhattacharya, D., McCaffrey, E. F., Greenwald, N. F., Markovic, M., Coles, G. L., Angelo, M., Bassik, M. C., Sage, J., Nolan, G. P. 2022

  Abstract

  Intratumoral heterogeneity is a seminal feature of human tumors contributing to tumor progression and response to treatment. Current technologies are still largely unsuitable to accurately track phenotypes and clonal evolution within tumors, especially in response to genetic manipulations. Here, we developed epitopes for imaging using combinatorial tagging (EpicTags), which we coupled to multiplexed ion beam imaging (EpicMIBI) for in situ tracking of barcodes within tissue microenvironments. Using EpicMIBI, we dissected the spatial component of cell lineages and phenotypes in xenograft models of small cell lung cancer. We observed emergent properties from mixed clones leading to the preferential expansion of clonal patches for both neuroendocrine and non-neuroendocrine cancer cell states in these models. In a tumor model harboring a fraction of PTEN-deficient cancer cells, we observed a non-autonomous increase of clonal patch size in PTEN wild-type cancer cells. EpicMIBI facilitates in situ interrogation of cell-intrinsic and cell-extrinsic processes involved in intratumoral heterogeneity.

  View details for DOI 10.1016/j.ccell.2022.09.014

  View details for PubMedID 36240778

• Structural variants shape driver combinations and outcomes in pediatric high-grade glioma NATURE CANCER Dubois, F. B., Shapira, O., Greenwald, N. F., Zack, T., Wala, J., Tsai, J. W., Crane, A., Baguette, A., Hadjadj, D., Harutyunyan, A. S., Kumar, K. H., Blattner-Johnson, M., Vogelzang, J., Sousa, C., Kang, K., Sinai, C., Wang, D. K., Khadka, P., Lewis, K., Nguyen, L., Malkin, H., Ho, P., O'Rourke, R., Zhang, S., Gold, R., Deng, D., Serrano, J., Snuderl, M., Jones, C., Wright, K. D., Chi, S. N., Grill, J., Kleinman, C. L., Goumnerova, L. C., Jabado, N., Jones, D. W., Kieran, M. W., Ligon, K. L., Beroukhim, R., Bandopadhayay, P. 2022

  Abstract

  We analyzed the contributions of structural variants (SVs) to gliomagenesis across 179 pediatric high-grade gliomas (pHGGs). The most recurrent SVs targeted MYC isoforms and receptor tyrosine kinases (RTKs), including an SV amplifying a MYC enhancer in 12% of diffuse midline gliomas (DMG), indicating an underappreciated role for MYC in pHGG. SV signature analysis revealed that tumors with simple signatures were TP53 wild type (TP53WT) but showed alterations in TP53 pathway members PPM1D and MDM4. Complex signatures were associated with direct aberrations in TP53, CDKN2A and RB1 early in tumor evolution and with later-occurring extrachromosomal amplicons. All pHGGs exhibited at least one simple-SV signature, but complex-SV signatures were primarily restricted to subsets of H3.3K27M DMGs and hemispheric pHGGs. Importantly, DMGs with complex-SV signatures were associated with shorter overall survival independent of histone mutation and TP53 status. These data provide insight into the impact of SVs on gliomagenesis and the mechanisms that shape them.

  View details for DOI 10.1038/s43018-022-00403-z

  View details for Web of Science ID 000820583800002

  View details for PubMedID 35788723

• Combined protein and nucleic acid imaging reveals virus-dependent B cell and macrophage immunosuppression of tissue microenvironments. Immunity Jiang, S., Chan, C. N., Rovira-Clave, X., Chen, H., Bai, Y., Zhu, B., McCaffrey, E., Greenwald, N. F., Liu, C., Barlow, G. L., Weirather, J. L., Oliveria, J. P., Nakayama, T., Lee, I. T., Matter, M. S., Carlisle, A. E., Philips, D., Vazquez, G., Mukherjee, N., Busman-Sahay, K., Nekorchuk, M., Terry, M., Younger, S., Bosse, M., Demeter, J., Rodig, S. J., Tzankov, A., Goltsev, Y., McIlwain, D. R., Angelo, M., Estes, J. D., Nolan, G. P. 2022

  Abstract

  Understanding the mechanisms of HIV tissue persistence necessitates the ability to visualize tissue microenvironments where infected cells reside; however, technological barriers limit our ability to dissect the cellular components of these HIV reservoirs. Here, we developed protein and nucleic acid in situ imaging (PANINI) to simultaneously quantify DNA, RNA, and protein levels within these tissue compartments. By coupling PANINI with multiplexed ion beam imaging (MIBI), we measured over 30 parameters simultaneously across archival lymphoid tissues from healthy or simian immunodeficiency virus (SIV)-infected nonhuman primates. PANINI enabled the spatial dissection of cellular phenotypes, functional markers, and viral events resulting from infection. SIV infection induced IL-10 expression in lymphoid B cells, which correlated with local macrophage M2 polarization. This highlights a potential viral mechanism for conditioning an immunosuppressive tissue environment for virion production. The spatial multimodal framework here can be extended to decipher tissue responses in other infectious diseases and tumor biology.

  View details for DOI 10.1016/j.immuni.2022.03.020

  View details for PubMedID 35447093

• Deep Learning-Inferred Multiplex ImmunoFluorescence for Immunohistochemical Image Quantification. Nature machine intelligence Ghahremani, P., Li, Y., Kaufman, A., Vanguri, R., Greenwald, N., Angelo, M., Hollmann, T. J., Nadeem, S. 2022; 4 (4): 401-412

  Abstract

  Reporting biomarkers assessed by routine immunohistochemical (IHC) staining of tissue is broadly used in diagnostic pathology laboratories for patient care. To date, clinical reporting is predominantly qualitative or semi-quantitative. By creating a multitask deep learning framework referred to as DeepLIIF, we present a single-step solution to stain deconvolution/separation, cell segmentation, and quantitative single-cell IHC scoring. Leveraging a unique de novo dataset of co-registered IHC and multiplex immunofluorescence (mpIF) staining of the same slides, we segment and translate low-cost and prevalent IHC slides to more expensive-yet-informative mpIF images, while simultaneously providing the essential ground truth for the superimposed brightfield IHC channels. Moreover, a new nuclear-envelop stain, LAP2beta, with high (>95%) cell coverage is introduced to improve cell delineation/segmentation and protein expression quantification on IHC slides. By simultaneously translating input IHC images to clean/separated mpIF channels and performing cell segmentation/classification, we show that our model trained on clean IHC Ki67 data can generalize to more noisy and artifact-ridden images as well as other nuclear and non-nuclear markers such as CD3, CD8, BCL2, BCL6, MYC, MUM1, CD10, and TP53. We thoroughly evaluate our method on publicly available benchmark datasets as well as against pathologists' semi-quantitative scoring. The code, the pre-trained models, along with easy-to-run containerized docker files as well as Google CoLab project are available at https://github.com/nadeemlab/deepliif.

  View details for DOI 10.1038/s42256-022-00471-x

  View details for PubMedID 36118303

  View details for PubMedCentralID PMC9477216

• PPM1D mutations are oncogenic drivers of de novo diffuse midline glioma formation. Nature communications Khadka, P., Reitman, Z. J., Lu, S., Buchan, G., Gionet, G., Dubois, F., Carvalho, D. M., Shih, J., Zhang, S., Greenwald, N. F., Zack, T., Shapira, O., Pelton, K., Hartley, R., Bear, H., Georgis, Y., Jarmale, S., Melanson, R., Bonanno, K., Schoolcraft, K., Miller, P. G., Condurat, A. L., Gonzalez, E. M., Qian, K., Morin, E., Langhnoja, J., Lupien, L. E., Rendo, V., Digiacomo, J., Wang, D., Zhou, K., Kumbhani, R., Guerra Garcia, M. E., Sinai, C. E., Becker, S., Schneider, R., Vogelzang, J., Krug, K., Goodale, A., Abid, T., Kalani, Z., Piccioni, F., Beroukhim, R., Persky, N. S., Root, D. E., Carcaboso, A. M., Ebert, B. L., Fuller, C., Babur, O., Kieran, M. W., Jones, C., Keshishian, H., Ligon, K. L., Carr, S. A., Phoenix, T. N., Bandopadhayay, P. 1800; 13 (1): 604

  Abstract

  The role of PPM1D mutations in de novo gliomagenesis has not been systematically explored. Here we analyze whole genome sequences of 170 pediatric high-grade gliomas and find that truncating mutations in PPM1D that increase the stability of its phosphatase are clonal driver events in 11% of Diffuse Midline Gliomas (DMGs) and are enriched in primary pontine tumors. Through the development of DMG mouse models, we show that PPM1D mutations potentiate gliomagenesis and that PPM1D phosphatase activity is required for in vivo oncogenesis. Finally, we apply integrative phosphoproteomic and functional genomics assays and find that oncogenic effects of PPM1D truncation converge on regulators of cell cycle, DNA damage response, and p53 pathways, revealing therapeutic vulnerabilities including MDM2 inhibition.

  View details for DOI 10.1038/s41467-022-28198-8

  View details for PubMedID 35105861

• Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma. Cell Risom, T., Glass, D. R., Averbukh, I., Liu, C. C., Baranski, A., Kagel, A., McCaffrey, E. F., Greenwald, N. F., Rivero-Gutiérrez, B., Strand, S. H., Varma, S., Kong, A., Keren, L., Srivastava, S., Zhu, C., Khair, Z., Veis, D. J., Deschryver, K., Vennam, S., Maley, C., Hwang, E. S., Marks, J. R., Bendall, S. C., Colditz, G. A., West, R. B., Angelo, M. 2022; 185 (2): 299-310.e18

  Abstract

  Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes.

  View details for DOI 10.1016/j.cell.2021.12.023

  View details for PubMedID 35063072

• The immunoregulatory landscape of human tuberculosis granulomas. Nature immunology McCaffrey, E. F., Donato, M., Keren, L., Chen, Z., Delmastro, A., Fitzpatrick, M. B., Gupta, S., Greenwald, N. F., Baranski, A., Graf, W., Kumar, R., Bosse, M., Fullaway, C. C., Ramdial, P. K., Forgó, E., Jojic, V., Van Valen, D., Mehra, S., Khader, S. A., Bendall, S. C., van de Rijn, M., Kalman, D., Kaushal, D., Hunter, R. L., Banaei, N., Steyn, A. J., Khatri, P., Angelo, M. 2022

  Abstract

  Tuberculosis (TB) in humans is characterized by formation of immune-rich granulomas in infected tissues, the architecture and composition of which are thought to affect disease outcome. However, our understanding of the spatial relationships that control human granulomas is limited. Here, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) to image 37 proteins in tissues from patients with active TB. We constructed a comprehensive atlas that maps 19 cell subsets across 8 spatial microenvironments. This atlas shows an IFN-γ-depleted microenvironment enriched for TGF-β, regulatory T cells and IDO1+ PD-L1+ myeloid cells. In a further transcriptomic meta-analysis of peripheral blood from patients with TB, immunoregulatory trends mirror those identified by granuloma imaging. Notably, PD-L1 expression is associated with progression to active TB and treatment response. These data indicate that in TB granulomas, there are local spatially coordinated immunoregulatory programs with systemic manifestations that define active TB.

  View details for DOI 10.1038/s41590-021-01121-x

  View details for PubMedID 35058616

• Multiplexed Ion Beam Imaging: Insights into Pathobiology. Annual review of pathology Liu, C. C., McCaffrey, E. F., Greenwald, N. F., Soon, E., Risom, T., Vijayaragavan, K., Oliveria, J., Mrdjen, D., Bosse, M., Tebaykin, D., Bendall, S. C., Angelo, M. 2021

  Abstract

  Next-generation tools for multiplexed imaging have driven a new wave of innovation in understanding how single-cell function and tissue structure are interrelated. In previous work, we developed multiplexed ion beam imaging by time of flight, a highly multiplexed platform that uses secondary ion mass spectrometry to image dozens of antibodies tagged with metal reporters. As instrument throughput has increased, the breadth and depth of imaging data have increased as well. To extract meaningful information from these data, we have developed tools for cell identification, cell classification, and spatial analysis. In this review, we discuss these tools and provide examples of their application in various contexts, including ductal carcinoma in situ, tuberculosis, and Alzheimer's disease. We hope the synergy between multiplexed imaging and automated image analysis will drive a new era in anatomic pathology and personalized medicine wherein quantitative spatial signatures are used routinely for more accurate diagnosis, prognosis, and therapeutic selection. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease, Volume 17 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

  View details for DOI 10.1146/annurev-pathmechdis-030321-091459

  View details for PubMedID 34752710

• Single cell biology-a Keystone Symposia report. Annals of the New York Academy of Sciences Cable, J., Elowitz, M. B., Domingos, A. I., Habib, N., Itzkovitz, S., Hamidzada, H., Balzer, M. S., Yanai, I., Liberali, P., Whited, J., Streets, A., Cai, L., Stergachis, A. B., Hong, C. K., Keren, L., Guilliams, M., Alon, U., Shalek, A. K., Hamel, R., Pfau, S. J., Raj, A., Quake, S. R., Zhang, N. R., Fan, J., Trapnell, C., Wang, B., Greenwald, N. F., Vento-Tormo, R., Santos, S. D., Spencer, S. L., Garcia, H. G., Arekatla, G., Gaiti, F., Arbel-Goren, R., Rulands, S., Junker, J. P., Klein, A. M., Morris, S. A., Murray, J. I., Galloway, K. E., Ratz, M., Romeike, M. 2021

  Abstract

  Single cell biology has the potential to elucidate many critical biological processes and diseases, from development and regeneration to cancer. Single cell analyses are uncovering the molecular diversity of cells, revealing a clearer picture of the variation among and between different cell types. New techniques are beginning to unravel how differences in cell state-transcriptional, epigenetic, and other characteristics-can lead to different cell fates among genetically identical cells, which underlies complex processes such as embryonic development, drug resistance, response to injury, and cellular reprogramming. Single cell technologies also pose significant challenges relating to processing and analyzing vast amounts of data collected. To realize the potential of single cell technologies, new computational approaches are needed. On March 17-19, 2021, experts in single cell biology met virtually for the Keystone eSymposium "Single Cell Biology" to discuss advances both in single cell applications and technologies.

  View details for DOI 10.1111/nyas.14692

  View details for PubMedID 34605044

• DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes. Nature methods Bannon, D., Moen, E., Schwartz, M., Borba, E., Kudo, T., Greenwald, N., Vijayakumar, V., Chang, B., Pao, E., Osterman, E., Graf, W., Van Valen, D. 2021; 18 (1): 43–45

  Abstract

  Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 106 1-megapixel images in ~5.5h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console ; a persistent deployment is available at https://deepcell.org/ .

  View details for DOI 10.1038/s41592-020-01023-0

  View details for PubMedID 33398191

• A Molecularly Integrated Grade for Meningioma. Neuro-oncology Driver, J., Hoffman, S. E., Tavakol, S., Woodward, E., Maury, E. A., Bhave, V., Greenwald, N. F., Nassiri, F., Aldape, K., Zadeh, G., Choudhury, A., Vasudevan, H. N., Magill, S. T., Raleigh, D. R., Abedalthagafi, M., Aizer, A. A., Alexander, B. M., Ligon, K. L., Reardon, D. A., Wen, P. Y., Al-Mefty, O., Ligon, A. H., Dubuc, A. M., Beroukhim, R., Claus, E. B., Dunn, I. F., Santagata, S., Bi, W. L. 2021

  Abstract

  Meningiomas are the most common primary intracranial tumor in adults. Clinical care is currently guided by the World Health Organization (WHO) grade assigned to meningiomas, a three-tiered grading system based on histopathology features, as well as extent of surgical resection. Clinical behavior, however, often fails to conform to the WHO grade. Additional prognostic information is needed to optimize patient management.We evaluated whether chromosomal copy-number data improved prediction of time to recurrence for patients with meningioma who were treated with surgery, relative to the WHO schema. The models were developed using Cox proportional hazards, random survival forest, and gradient boosting in a discovery cohort of 527 meningioma patients and validated in two independent cohorts of 172 meningioma patients characterized by orthogonal genomic platforms.We developed a three-tiered grading scheme (Integrated Grades 1-3), which incorporated mitotic count and loss of chromosome 1p, 3p, 4, 6, 10, 14q, 18, 19, or CDKN2A. 32% of meningiomas reclassified to either a lower-risk or higher-risk Integrated Grade compared to their assigned WHO grade. The Integrated Grade more accurately identified meningioma patients at risk for recurrence, relative to the WHO grade, as determined by time-dependent AUC, average precision, and the Brier score.We propose a molecularly integrated grading scheme for meningiomas that significantly improves upon the current WHO grading system in prediction of progression-free survival. This framework can be broadly adopted by clinicians with relative ease using widely available genomic technologies and presents an advance in the care of meningioma patients.

  View details for DOI 10.1093/neuonc/noab213

  View details for PubMedID 34508644

• Evaluation of Geuenich et al.: Targeting a crucial bottleneck for analyzing single-cell multiplexed imaging data. Cell systems Averbukh, I., Greenwald, N. F., Liu, C. C., Angelo, M. 2021; 12 (12): 1121-1123

  Abstract

  One snapshot of the peer review process for "Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data" (Geuenich et al., 2021).

  View details for DOI 10.1016/j.cels.2021.11.003

  View details for PubMedID 34914901

• Single-cell metabolic profiling of human cytotoxic T cells. Nature biotechnology Hartmann, F. J., Mrdjen, D. n., McCaffrey, E. n., Glass, D. R., Greenwald, N. F., Bharadwaj, A. n., Khair, Z. n., Verberk, S. G., Baranski, A. n., Baskar, R. n., Graf, W. n., Van Valen, D. n., Van den Bossche, J. n., Angelo, M. n., Bendall, S. C. 2020

  Abstract

  Cellular metabolism regulates immune cell activation, differentiation and effector functions, but current metabolic approaches lack single-cell resolution and simultaneous characterization of cellular phenotype. In this study, we developed an approach to characterize the metabolic regulome of single cells together with their phenotypic identity. The method, termed single-cell metabolic regulome profiling (scMEP), quantifies proteins that regulate metabolic pathway activity using high-dimensional antibody-based technologies. We employed mass cytometry (cytometry by time of flight, CyTOF) to benchmark scMEP against bulk metabolic assays by reconstructing the metabolic remodeling of in vitro-activated naive and memory CD8+ T cells. We applied the approach to clinical samples and identified tissue-restricted, metabolically repressed cytotoxic T cells in human colorectal carcinoma. Combining our method with multiplexed ion beam imaging by time of flight (MIBI-TOF), we uncovered the spatial organization of metabolic programs in human tissues, which indicated exclusion of metabolically repressed immune cells from the tumor-immune boundary. Overall, our approach enables robust approximation of metabolic and functional states in individual cells.

  View details for DOI 10.1038/s41587-020-0651-8

  View details for PubMedID 32868913

• The Society for Immunotherapy in Cancer statement on best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) staining and validation. Journal for immunotherapy of cancer Taube, J. M., Akturk, G. n., Angelo, M. n., Engle, E. L., Gnjatic, S. n., Greenbaum, S. n., Greenwald, N. F., Hedvat, C. V., Hollmann, T. J., Juco, J. n., Parra, E. R., Rebelatto, M. C., Rimm, D. L., Rodriguez-Canales, J. n., Schalper, K. A., Stack, E. C., Ferreira, C. S., Korski, K. n., Lako, A. n., Rodig, S. J., Schenck, E. n., Steele, K. E., Surace, M. J., Tetzlaff, M. T., von Loga, K. n., Wistuba, I. I., Bifulco, C. B. 2020; 8 (1)

  Abstract

  The interaction between the immune system and tumor cells is an important feature for the prognosis and treatment of cancer. Multiplex immunohistochemistry (mIHC) and multiplex immunofluorescence (mIF) analyses are emerging technologies that can be used to help quantify immune cell subsets, their functional state, and their spatial arrangement within the tumor microenvironment.The Society for Immunotherapy of Cancer (SITC) convened a task force of pathologists and laboratory leaders from academic centers as well as experts from pharmaceutical and diagnostic companies to develop best practice guidelines for the optimization and validation of mIHC/mIF assays across platforms.Representative outputs and the advantages and disadvantages of mIHC/mIF approaches, such as multiplexed chromogenic IHC, multiplexed immunohistochemical consecutive staining on single slide, mIF (including multispectral approaches), tissue-based mass spectrometry, and digital spatial profiling are discussed.mIHC/mIF technologies are becoming standard tools for biomarker studies and are likely to enter routine clinical practice in the near future. Careful assay optimization and validation will help ensure outputs are robust and comparable across laboratories as well as potentially across mIHC/mIF platforms. Quantitative image analysis of mIHC/mIF output and data management considerations will be addressed in a complementary manuscript from this task force.

  View details for DOI 10.1136/jitc-2019-000155

  View details for PubMedID 32414858

• Large expert-curated database for benchmarking document similarity detection in biomedical literature search DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION Brown, P., Tan, A., El-Esawi, M. A., Liehr, T., Blanck, O., Gladue, D. P., Almeida, G. F., Cernava, T., Sorzano, C. O., Yeung, A. K., Engel, M. S., Chandrasekaran, A., Muth, T., Staege, M. S., Daulatabad, S. V., Widera, D., Zhang, J., Meule, A., Honjo, K., Pourret, O., Yin, C., Zhang, Z., Cascella, M., Flegel, W. A., Goodyear, C. S., van Raaij, M. J., Bukowy-Bieryllo, Z., Campana, L. G., Kurniawan, N. A., Lalaouna, D., Huttner, F. J., Ammerman, B. A., Ehret, F., Cobine, P. A., Tan, E., Han, H., Xia, W., McCrum, C., Dings, R. M., Marinello, F., Nilsson, H., Nixon, B., Voskarides, K., Yang, L., Costa, V. D., Bengtsson-Palme, J., Bradshaw, W., Grimm, D. G., Kumar, N., Martis, E., Prieto, D., Sabnis, S. C., Amer, S. R., Liew, A. C., Perco, P., Rahimi, F., Riva, G., Zhang, C., Devkota, H. P., Ogami, K., Basharat, Z., Fierz, W., Siebers, R., Tan, K., Boehme, K. A., Brenneisen, P., Brown, J. L., Dalrymple, B. P., Harvey, D. J., Ng, G., Werten, S., Bleackley, M., Dai, Z., Dhariwal, R., Gelfer, Y., Hartmann, M. D., Miotla, P., Tamaian, R., Govender, P., Gurney-Champion, O. J., Kauppila, J. H., Zhang, X., Echeverria, N., Subhash, S., Sallmon, H., Tofani, M., Bae, T., Bosch, O., Cuiv, P. O., Danchin, A., Diouf, B., Eerola, T., Evangelou, E., Filipp, F. V., Klump, H., Kurgan, L., Smith, S. S., Terrier, O., Tuttle, N., Ascher, D. B., Janga, S. C., Schulte, L. N., Becker, D., Browngardt, C., Bush, S. J., Gaullier, G., Ide, K., Meseko, C., Werner, G. A., Zaucha, J., Al-Farha, A. A., Greenwald, N. F., Popoola, S. I., Rahman, M., Xu, J., Yang, S. Y., Hiroi, N., Alper, O. M., Baker, C. I., Bitzer, M., Chacko, G., Debrabant, B., Dixon, R., Forano, E., Gilliham, M., Kelly, S., Klempnauer, K., Lidbury, B. A., Lin, M. Z., Lynch, I., Ma, W., Maibach, E. W., Mather, D. E., Nandakumar, K. S., Ohgami, R. S., Parchi, P., Tressoldi, P., Xue, Y., Armitage, C., Barraud, P., Chatzitheochari, S., Coelho, L. P., Diao, J., Doxey, A. C., Gobet, A., Hu, P., Kaiser, S., Mitchell, K. M., Salama, M. F., Shabalin, I. G., Song, H., Stevanovic, D., Yadollahpour, A., Zeng, E., Zinke, K., Alimba, C. G., Beyene, T. J., Cao, Z., Chan, S. S., Gatchell, M., Kleppe, A., Piotrowski, M., Torga, G., Woldesemayat, A. A., Cosacak, M. I., Haston, S., Ross, S. A., Williams, R., Wong, A., Abramowitz, M. K., Effiong, A., Lee, S., Abid, M., Agarabi, C., Alaux, C., Albrecht, D. R., Atkins, G. J., Beck, C. R., Bonvin, A. J., Bourke, E., Brand, T., Braun, R. J., Bull, J. A., Cardoso, P., Carter, D., Delahay, R. M., Ducommun, B., Duijf, P. G., Epp, T., Eskelinen, E., Fallah, M., Farber, D. B., Fernandez-Triana, J., Feyerabend, F., Florio, T., Friebe, M., Furuta, S., Gabrielsen, M., Gruber, J., Grybos, M., Han, Q., Heinrich, M., Helantera, H., Huber, M., Jeltsch, A., Jiang, F., Josse, C., Jurman, G., Kamiya, H., de Keersmaecker, K., Kristiansson, E., de Leeuw, F., Li, J., Liang, S., Lopez-Escamez, J. A., Lopez-Ruiz, F. J., Marchbank, K. J., Marschalek, R., Martin, C. S., Miele, A. E., Montagutelli, X., Morcillo, E., Nicoletti, R., Niehof, M., O'Toole, R., Ohtomo, T., Oster, H., Palma, J., Paterson, R., Peifer, M., Portilla, M., Portillo, M. C., Pritchard, A. L., Pusch, S., Raghava, G. S., Roberts, N. J., Ross, K., Schuele, B., Sergeant, K., Shen, J., Stella, A., Sukocheva, O., Uversky, V. N., Vanneste, S., Villet, M. H., Viveiros, M., Vorholt, J. A., Weinstock, C., Yamato, M., Zabetakis, I., Zhao, X., Ziegler, A., Aizat, W. M., Atlas, L., Bridges, K. M., Chakraborty, S., Deschodt, M., Domingues, H. S., Esfahlani, S. S., Falk, S., Guisado, J. L., Kane, N. C., Kueberuwa, G., Lau, C. L., Liang, D., Liu, E., Luu, A. M., Ma, C., Ma, L., Moyer, R., Norris, A. D., Panthee, S., Parsons, J. R., Peng, Y., Pinto, I., Reschke, C. R., Sillanpaa, E., Stewart, C. J., Uhle, F., Yang, H., Zhou, K., Zhu, S., Ashry, M., Bergsland, N., Berthold, M., Chen, C., Colella, V., Cuypers, M., Eskew, E. A., Fan, X., Gajda, M., Gonzalezlez-Prendes, R., Goodin, A., Graham, E. B., Groen, E. N., Gutierrez-Sacristan, A., Habes, M., Heffler, E., Higginbottom, D. B., Janzen, T., Jayaraman, J., Jibb, L. A., Jongen, S., Kinyanjui, T., Koleva-Kolarova, R. G., Li, Z., Liu, Y., Lund, B. A., Lussier, A. A., Ma, L., Mier, P., Moore, M. D., Nagler, K., Orme, M. W., Pearson, J. A., Prajapati, A. S., Saito, Y., Troder, S. E., Uchendu, F., Verloh, N., Voutchkova, D. D., Abu-Zaid, A., Bakkach, J., Baumert, P., Dono, M., Hanson, J., Herbelet, S., Hobbs, E., Kulkarni, A., Kumar, N., Liu, S., Loft, N. D., Reddan, T., Senghore, T., Vindin, H., Xu, H., Bannon, R., Chen, B., Cheung, J. K., Cooper, J., Esnakul, A. K., Feghali, K. A., Ghelardi, E., Gnasso, A., Horbar, J., Lai, H. M., Li, J., Ma, L., Ma, R., Pan, Z., Peres, M. A., Pranata, R., Seow, E., Sydes, M., Testoni, I., Westermair, A. L., Yang, Y., Afnan, M., Albiol, J., Albuquerque, L. G., Amiya, E., Amorim, R. M., An, Q., Andersen, S. U., Aplin, J. D., Argyropoulos, C., Asmann, Y. W., Assaeed, A. M., Atanasov, A. G., Atchison, D. A., Avery, S. V., Avillach, P., Baade, P. D., Backman, L., Badie, C., Baldi, A., Ball, E., Bardot, O., Barnett, A. G., Basner, M., Batra, J., Bazanova, O. M., Beale, A., Beddoe, T., Bell, M. L., Berezikov, E., Berners-Price, S., Bernhardt, P., Berry, E., Bessa, T. B., Billington, C., Birch, J., Blakely, R. D., Blaskovich, M. T., Blum, R., Boelaert, M., Bogdanos, D., Bosch, C., Bourgoin, T., Bouvard, D., Boykin, L. M., Bradley, G., Braun, D., Brownlie, J., Bruhl, A., Burt, A., Butler, L. M., Byrareddy, S. N., Byrne, H. J., Cabantous, S., Calatayud, S., Candal, E., Carlson, K., Casillas, S., Castelvetro, V., Caswell, P. T., Cavalli, G., Cerovsky, V., Chagoyen, M., Chen, C., Chen, D. F., Chen, H., Chen, H., Chen, J., Chen, Y., Cheng, C., Cheng, J., Chinapaw, M., Chinopoulos, C., Cho, W. S., Chong, L., Chowdhury, D., Chwalibog, A., Ciresi, A., Cockcroft, S., Conesa, A., Cook, P. A., Cooper, D. N., Coqueret, O., Corea, E. M., Costa, E., Coupland, C., Crawford, S. Y., Cruz, A. D., Cui, H., Cui, Q., Culver, D. C., D'Angiulli, A., Dahms, T. S., Daigle, F., Dalgleish, R., Danielsen, H. E., Darras, S., Davidson, S. M., Day, D. A., Degirmenci, V., Demaison, L., Devriendt, K., Ding, J., Dogan, Y., Dong, X. C., Donner, C. F., Dressick, W., Drevon, C. A., Duan, H., Ducho, C., Dumaz, N., Dwarakanath, B. S., Ebell, M. H., Eisenhardt, S., Elkum, N., Engel, N., Erickson, T. B., Fairhead, M., Faville, M. J., Fejzo, M. S., Festa, F., Feteira, A., Flood-Page, P., Forsayeth, J., Fox, S. A., Franks, S. J., Frentiu, F. D., Frilander, M. J., Fu, X., Fujita, S., Galea, I., Galluzzi, L., Gani, F., Ganpule, A. P., Garcia-Alix, A., Gedye, K., Giordano, M., Giunta, C., Gleeson, P. A., Goarant, C., Gong, H., Gora, D., Gough, M. J., Goyal, R., Graham, K. E., Grande-Perez, A., Graves, P. M., Greidanus, H., Grice, D., Grunau, C., Gumulya, Y., Guo, Y., Gurevich, V. V., Gusev, O., Hacker, E., Hage, S. R., Hagen, G., Hahn, S., Haller, D. M., Hammerschmidt, S., Han, J., Han, R., Handfield, M., Hapuarachchi, H. C., Harder, T., Hardingham, J. E., Heck, M., Heers, M., Hew, K. F., Higuchi, Y., St Hilaire, C., Hilton, R., Hodzic, E., Hone, A., Hongoh, Y., Hu, G., Huber, H. P., Hueso, L. E., Huirne, J., Hurt, L., Idborg, H., Ikeo, K., Ingley, E., Jakeman, P. M., Jensen, A., Jia, H., Jia, H., Jia, S., Jiang, J., Jiang, X., Jin, Y., Jo, D., Johnson, A. M., Johnston, M., Jonscher, K. R., Jorens, P. G., Jorgensen, J. L., Joubert, J. W., Jung, S., Junior, A. M., Kahan, T., Kamboj, S. K., Kang, Y., Karamanos, Y., Karp, N. A., Kelly, R., Kenna, R., Kennedy, J., Kersten, B., Khalaf, R. A., Khalid, J. M., Khatlani, T., Khider, T., Kijanka, G. S., King, S. B., Kluz, T., Knox, P., Kobayashi, T., Koch, K., Kohonen-Corish, M. J., Kong, X., Konkle-Parker, D., Korpela, K. M., Kostrikis, L. G., Kraiczy, P., Kratz, H., Krause, G., Krebsbach, P. H., Kristensen, S. R., Kumari, P., Kunimatsu, A., Kurdak, H., Kwon, Y. D., Lachat, C., Lagisz, M., Laky, B., Lammerding, J., Lange, M., Larrosa, M., Laslett, A. L., LeClair, E. E., Lee, K., Lee, M., Lee, M., Li, G., Li, J., Lieb, K., Lim, Y. Y., Lindsey, M. L., Line, P., Liu, D., Liu, F., Liu, H., Liu, H., Lloyd, V. K., Lo, T., Locci, E., Loidl, J., Lorenzen, J., Lorkowski, S., Lovell, N. H., Lu, H., Lu, W., Lu, Z., Luengo, G. S., Lundh, L., Lysy, P. A., Mabb, A., Mack, H. G., Mackey, D. A., Mahdavi, S. R., Maher, P., Maher, T., Maity, S. N., Malgrange, B., Mamoulakis, C., Mangoni, A. A., Manke, T., Manstead, A. R., Mantalaris, A., Marsal, J., Marschall, H., Martin, F. L., Martinez-Raga, J., Martinez-Salas, E., Mathieu, D., Matsui, Y., Maza, E., McCutcheon, J. E., Mckay, G. J., McMillan, B., McMillan, N., Meads, C., Medina, L., Merrick, B., Metzger, D. W., Meunier, F. A., Michaelis, M., Micheau, O., Mihara, H., Mintz, E. M., Mizukami, T., Moalic, Y., Mohapatra, D. P., Monteiro, A., Montes, M., Moran, J. V., Morozov, S. Y., Mort, M., Murai, N., Murphy, D. J., Murphy, S. K., Murray, S. A., Naganawa, S., Nammi, S., Nasios, G., Natoli, R. M., Nguyen, F., Nicol, C., van Nieuwerburgh, F., Nilsen, E. B., Nobile, C. J., O'Mahony, M., Ohlsson, S., Olatunbosun, O., Olofsson, P., Ortiz, A., Ostrikov, K., Otto, S., Outeiro, T. F., Ouyang, S., Paganoni, S., Page, A., Palm, C., Paradies, Y., Parsons, M. H., Parsons, N., Pascal, P., Paul, E., Peckham, M., Pedemonte, N., Pellizzon, M. A., Petrelli, M., Pichugin, A., Pinto, C. C., Plevris, J. N., Pollesello, P., Polz, M., Ponti, G., Porcelli, P., Prince, M., Quinn, G. P., Quinn, T. J., Ramula, S., Rappsilber, J., Rehfeldt, F., Reiling, J. H., Remacle, C., Rezaei, M., Riddick, E. W., Ritter, U., Roach, N. W., Roberts, D. D., Robles, G., Rodrigues, T., Rodriguez, C., Roislien, J., Roobol, M. J., Rowe, J., Ruepp, A., van Ruitenbeek, J., Rust, P., Saad, S., Sack, G. H., Santos, M., Saudemont, A., Sava, G., Schrading, S., Schramm, A., Schreiber, M., Schuler, S., Schymkowitz, J., Sczyrba, A., Seib, K. L., Shi, H., Shimada, T., Shin, J., Shortt, C., Silveyra, P., Skinner, D., Small, I., Smeets, P. M., So, P., Solano, F., Sonenshine, D. E., Song, J., Southall, T., Speakman, J. R., Srinivasan, M. V., Stabile, L. P., Stasiak, A., Steadman, K. J., Stein, N., Stephens, A. W., Stewart, D. I., Stine, K., Storlazzi, C., Stoynova, N. V., Strzalka, W., Suarez, O. M., Sultana, T., Sumant, A. V., Summers, M. J., Sun, G., Tacon, P., Tanaka, K., Tang, H., Tanino, Y., Targett-Adams, P., Tayebi, M., Tayyem, R., Tebbe, C. C., Telfer, E. E., Tempel, W., Teodorczyk-Injeyan, J. A., Thijs, G., Thorne, S., Thrift, A. G., Tiffon, C., Tinnefeld, P., Tjahjono, D. H., Tolle, F., Toth, E., del Tredici, A. L., Tsapas, A., Tsirigotis, K., Turak, A., Tzotzos, G., Udo, E. E., Utsumi, T., Vaidyanathan, S., Vaillant, M., Valsesia, A., Vandenbroucke, R. E., Veiga, F. H., Vendrell, M., Vesk, P. A., Vickers, P., Victor, V. M., Villemur, R., Vohl, M., Voolstra, C. R., Vuillemin, A., Wakelin, S., Waldron, L., Walsh, L. J., Wang, A. Y., Wang, F., Wang, Y., Watanabe, Y., Weigert, A., Wen, J., Wham, C., White, E. P., Wiener, J., Wilharm, G., Wilkinson, S., Willmann, R., Wilson, C., Wirth, B., Wojan, T. R., Wolff, M., Wong, B. M., Wu, T., Wuerbel, H., Xiao, X., Xu, D., Xu, J. W., Xu, J., Xue, B., Yalcin, S., Yan, H., Yang, E., Yang, S., Yang, W., Ye, Y., Ye, Z., Yli-Kauhaluoma, J., Yoneyama, H., Yu, Y., Yuan, G., Yuh, C., Zaccolo, M., Zeng, C., Zevnik, B., Zhang, C., Zhang, L., Zhang, L., Zhang, Y., Zhang, Y., Zhang, Z., Zhang, Z., Zhao, Y., Zhou, M., Zuberbier, T., Aanei, C. M., Ahmad, R., Al-Lawama, M., Alanio, A., Allardyce, J., Alonso-Caneiro, D., Atack, J. M., Baier, D., Bansal, A., Benezeth, Y., Berbesque, C., Berrevoet, F., Biedermann, P. W., Bijleveld, E., Bittner, F., Blombach, F., Van den Bos, W., Boudreau, S. A., Bramoweth, A. D., Braubach, O., Cai, Y., Campbell, M., Cao, Z., Catry, T., Chen, X., Cheng, S., Chung, H., Chavez-Fumagalli, M. A., Conway, A., Costa, B. M., Cyr, N., Dean, L. T., Denzel, M. S., Dlamini, S. V., Dudley, K. J., Dufies, M., Ecke, T., Eckweiler, D., Eixarch, E., El-Adawy, H., Emmrich, J. V., Eustace, A. J., Falter-Wagner, C. M., Fuss, J., Gao, J., Gill, M. R., Gloyn, L., Goggs, R., Govinden, U., Greene, G., Greiff, V., Grundle, D. S., Gruneberg, P., Gumede, N., Haore, G., Harrison, P., Hoenner, X., Hojsgaard, D., Hori, H., Ikonomopoulou, M. P., Jeurissen, P., Johnson, D. M., Kabra, D., Kamagata, K., Karmakar, C., Kasian, O., Kaye, L. K., Khan, M. M., Kim, Y., Kish, J. K., Kobold, S., Kohanbash, G., Kohls, G., Kugler, J., Kumar, G., Lacy-Colson, J., Latif, A., Lauschke, V. M., Li, B., Lim, C. J., Liu, F., Liu, X., Lu, J., Lu, Q., Mahavadi, P., Marzocchi, U., McGarrigle, C. A., van Meerten, T., Min, R., Moal, I., Molari, M., Molleman, L., Mondal, S. R., Van de Mortel, T., Moss, W. N., Moultos, O. A., Mukherjee, M., Nakayama, K., Narayan, E., Navaratnarajah, Neumann, P., Nie, J., Nie, Y., Niemeyer, F., Fiona, Nwaiwu, O., Oldenmenger, W. H., Olumayede, E., Ou, J., Pallebage-Gamarallage, M., Pearce, S. P., Pelkonen, T., Pelleri, M. C., Pereira, J. L., Pheko, M., Pinto, K. A., Piovesan, A., Pluess, M., Podolsky, I. M., Prescott, J., Qi, D., Qi, X., Raikou, V. D., Ranft, A., Rhodes, J., Rotge, J., Rowe, A. D., Saggar, M., Schuon, R. A., Shahid, S., Shalchyan, V., Shirvalkar, P., Shiryayev, O., Singh, J., Smout, M. J., Soares, A., Song, C., Srivastava, K., Srivastava, R. K., Sun, J., Szabo, A., Szymanski, W., Tai, C. P., Takeuchi, H., Tanadini-Lang, S., Tang, F., Tao, W., Theron, G., Tian, C. F., Tian, Y., Tuttle, L. M., Valenti, A., Verlot, P., Walker, M., Wang, J., Welter, D., Winslade, M., Wu, D., Wu, Y., Xiao, H., Xu, B., Xu, J., Xu, Z., Yang, D., Yang, M., Yankilevich, P., You, Y., Yu, C., Zhan, J., Zhang, G., Zhang, K., Zhang, T., Zhang, Y., Zhao, G., Zhao, J., Zhou, X., Zhu, Z., Ajani, P. A., Anazodo, U. C., Bagloee, S. A., Bail, K., Bar, I., Bathelt, J., Benkeser, D., Bernier, M. L., Blanchard, A. M., Boakye, D. W., Bonatsos, V., Boon, M. H., Bouboulis, G., Bromfield, E., Brown, J., Bul, K. M., Burton, K. J., Butkowski, E. G., Carroll, G., Chao, F., Charrier, E. E., Chen, X., Chen, Y., Chenguang, Choi, J. R., Christoffersen, T., Comel, J. C., Cosse, C., Cui, Y., van Dessel, P., Dhaval, Diodato, D., Duffey, M., Dutt, A., Egea, L. G., El-Said, M., Faye, M., Fernandez-Fernandez, B., Foley, K. G., Founou, L. L., Fu, F., Gadelkareem, R. A., Galimov, E., Garip, G., Gemmill, A., Gouil, Q., Grey, J., Gridneva, Z., Grothe, M. J., Grebert, T., Guerrero, F., Guignard, L., Haenssgen, M. J., Hasler, D., Holgate, J. Y., Huang, A., Hulse-Kemp, A. M., Jean-Quartier, C., Jeon, S., Jia, Y., Jutzeler, C., Kalatzis, P., Karim, M., Karsay, K., Keitel, A., Kempe, A., Keown, J. R., Khoo, C. M., Khwaja, N., Kievit, R. A., Kosanic, A., Koutoukidis, D. A., Kramer, P., Kumar, D., Kirag, N., Lanza, G., Le, T. D., Leem, J. W., Leightley, D., Leite, A., Lercher, L., Li, Y., Lim, R., Lima, L. A., Lin, L., Ling, T., Liu, Y., Liu, Z., Lu, Y., Lum, F. M., Luo, H., Machhi, J., Macleod, A., Macwan, I., Madala, H. R., Madani, N., de Maio, N., Makowiecki, K., Mallinson, D. J., Margelyte, R., Maria, C., Markonis, Y., Marsili, L., Mavoa, S., McWilliams, L., Megersa, M., Mendes, C. M., Menichetti, J., Mercieca-Bebber, R., Miller, J. J., Minde, D. M., Minges, A., Mishra, E., Mishra, V. R., Moores, C., Morrice, N., Moskalensky, A. E., Navarin, N., Negera, E., Nolet, P., Nordberg, A., Norden, R., Nowicki, J. P., Olova, N., Olszewski, P., Onzima, R., Pan, C., Park, C., Park, D., Park, S., Patil, C. D., Pedro, S. A., Perry, S. R., Peter, J., Peterson, B. M., Pezzuolo, A., Pozdnyakov, I., Qian, S., Qin, L., Rafe, A., Raote, I., Raza, A., Rebl, H., Refai, O., Regan, T., Richa, T., Richardson, M. F., Robinson, K. R., Rossoni, L., Rouet, R., Safaei, S., Schneeberger, P. H., Schwotzer, D., Sebastian, A., Selinski, J., Seltmann, S., Sha, F., Shalev, N., Shang, J., Singer, J., Singh, M., Smith, T., Solomon-Moore, E., Song, L., Soraggi, S., Stanley, R., Steckhan, N., Strobl, F., Subissi, L., Supriyanto, I., Surve, C. R., Suzuki, T., Syme, C., Sorelius, K., Tang, Y., Tantawy, M., Tennakoon, S., Teseo, S., Toelzer, C., Tomov, N., Tovar, M., Tran, L., Tripathi, S., Tuladhar, A. M., Ukubuiwe, A. C., Ung, C. L., Valgepea, K., Vatanparast, H., Vidal, A., Wang, F., Wang, Q., Watari, R., Webster, R., Webster, R., Wei, J., Wibowo, D., Wingenbach, T. H., Xavier, R. M., Xiao, S., Xiong, P., Xu, S., Xu, S., Yao, R., Yao, W., Yin, Q., Yu, Y., Zaitsu, M., Zeineb, Z., Zhan, X., Zhang, J., Zhang, R., Zhang, W., Zhang, X., Zheng, S., Zhou, B., Zhou, X., Ahmad, H., Akinwumi, S. A., Albery, G. F., Alhowimel, A., Ali, J., Alshehri, M., Alsuhaibani, M., Anikin, A., Azubuike, S. O., Bach-Mortensen, A., Baltiansky, L., Bartas, M., Belachew, K. Y., Bhardwaj, V., Binder, K., Bland, N. S., Boah, M., Bullen, B., Calabro, G. E., Callahan, T. J., Cao, B., Chalmers, K., Chang, W., Che, Z., Chen, A. Y., Chen, H., Chen, H., Chen, Y., Chen, Z., Choi, Y., Chowdhury, M. K., Christensen, M. R., Cooke, R. C., Cottini, M., Covington, N. V., Cunningham, C., Delarocque, J., Devos, L., Dhar, A. R., Ding, K., Dong, K., Dong, Z., Dreyer, N., Ekstrand, C., Fardet, T., Feleke, B. E., Feurer, T., Freitas, A., Gao, T., Asefa, N. G., Giganti, F., Grabowski, P., Guerra-Mora, J. R., Guo, C., Guo, X., Gupta, H., He, S., Heijne, M., Heinemann, S., Hogrebe, A., Huang, Z., Iskander-Rizk, S., Iyer, L. M., Jahan, Y., James, A. S., Joel, E., Joffroy, B., Jegousse, C., Kambondo, G., Karnati, P., Kaya, C., Ke, A., Kelly, D., Kickert, R., Kidibule, P. E., Kieselmann, J. P., Kim, H. J., Kitazawa, T., Lamberts, A., Li, Y., Liang, H., Linn, S. N., Litfin, T., Liusuo, W., Lygirou, V., Mahato, A. K., Mai, Z., Major, R. W., Mali, S., Mallis, P., Mao, W., Mao, W., Marvin-Dowle, K., Marvin-Dowle, K., Mason, L. D., Merideth, B., Merino-Plaza, M. J., Merlaen, B., Messina, R., Mishra, A. K., Muhammad, J., Musinguzi, C., Nanou, A., Naqash, A., Nguyen, J. T., Nguyen, T. H., Ni, D., Nida, Notcovich, S., Ohst, B., Ollivier, Q. R., Osses, D. F., Peng, X., Plantinga, A., Pulia, M., Rafiq, M., Raman, A., Raucher-Chene, D., Rawski, R., Ray, A., Razak, L. A., Rudolf, K., Rusch, P., Sadoine, M. L., Schmidt, A., Schurr, R., Searles, S., Sharma, S., Sheehan, B., Shi, C., Shohayeb, B., Sommerlad, A., Strehlow, J., Sun, X., Sundar, R., Taherzadeh, G., Tahir, N. M., Tang, J., Testa, J., Tian, Z., Tingting, Q., Verheijen, G. P., Vickstrom, C., Wang, T., Wang, X., Wang, Z., Wei, P., Wilson, A., Wyart, Yassine, A., Yousefzadeh, A., Zare, A., Zeng, Z., Zhang, C., Zhang, H., Zhang, L., Zhang, T., Zhang, W., Zhang, Z., Zhou, J., Zhu, D., Adamo, V., Adeyemo, A. A., Aggelidou, M., Al-Owaifeer, A. M., Al-Riyami, A. Z., Alzghari, S. K., Andersen, V., Angus, K., Asaduzzaman, M., Asady, H., Ato, D., Bai, X., Baines, R. L., Ballantyne, M., Ban, B., Beck, J., Ben-Nafa, W., Black, E., Blancher, A., Blankstein, R., Bodagh, N., Borges, P. V., Brooks, A., Brox-Ponce, J., Brunetti, A., Canham, C. D., Carninci, P., Carvajal, R., Chang, S. C., Chao, J., Chatterjee, P., Chen, H., Chen, Y., Chhatriwalla, A. K., Chikowe, I., Chuang, T., Collevatti, R. G., Valera-Cornejo, D. A., Cuenda, A., Dao, M., Dauga, D., Deng, Z., Devkota, K., Doan, L. V., Elewa, Y. A., Fan, D., Faruk, M., Feifei, S., Ferguson, T. S., Fleres, F., Foster, E. J., Foster, C., Furer, T., Gao, Y., Garcia-Rivera, E. J., Gazdar, A., George, R. B., Ghosh, S., Gianchecchi, E., Gleason, J. M., Hackshaw, A., Hall, A., Hall, R., Harper, P., Hogg, W. E., Huang, G., Hunter, K. E., IJzerman, A. P., Jesus, C., Jian, G., Lewis, J. S., Kanj, S. S., Kaur, H., Kelly, S., Kheir, F., Kichatova, V. S., Kiyani, M., Klein, R., Kovesi, T., Kraschnewski, J. L., Kumar, A. P., Labutin, D., Lazo-Langner, A., Leclercq, G., Li, M., Li, Q., Li, T., Li, Y., Liao, W., Liao, Z., Lin, J., Lizer, J., Lobreglio, G., Lowies, C., Lu, C., Majeed, H., Martin, A., Martinez-Sobrido, L., Meresh, E., Middelveen, M., Mohebbi, A., Mota, J., Mozaheb, Z., Muyaya, L., Nandhakumar, A., Ng, S. X., Obeidat, M., Oh, D., Owais, M., Pace-Asciak, P., Panwar, A., Park, C., Patterson, C., Penagos-Tabaree, F., Pianosi, P. T., Pinzi, V., Pridans, C., Psaroulaki, A., Pujala, R., Pulido-Arjona, L., Qi, P., Rahman, P., Rai, N. K., Rassaf, T., Refardt, J., Ricciardi, W., Riess, O., Rovas, A., Sacks, F. M., Saleh, S., Sampson, C., Schmutz, A., Sepanski, R., Sharma, N., Singh, M., Spearman, P., Subramaniapillai, M., Swali, R., Tan, C. M., Tellechea, J. I., Thomas, L., Tong, X., Veys, R., Vitriol, V., Wang, H., Wang, J., Wang, J., Waugh, J., Webb, S. A., Williams, B. A., Workman, A. D., Xiang, T., Xie, L., Xu, J., Xu, T., Yang, C., Yoon, J. G., Yuan, C. M., Zaritsky, A., Zhang, Y., Zhao, H., Zuckerman, H., Lyu, R., Pullan, W., Zhou, Y., RELISH Consortium 2019

  View details for DOI 10.1093/database/baz085

  View details for Web of Science ID 000494411700001

• MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure. Science advances Keren, L., Bosse, M., Thompson, S., Risom, T., Vijayaragavan, K., McCaffrey, E., Marquez, D., Angoshtari, R., Greenwald, N. F., Fienberg, H., Wang, J., Kambham, N., Kirkwood, D., Nolan, G., Montine, T. J., Galli, S. J., West, R., Bendall, S. C., Angelo, M. 2019; 5 (10): eaax5851

  Abstract

  Understanding tissue structure and function requires tools that quantify the expression of multiple proteins while preserving spatial information. Here, we describe MIBI-TOF (multiplexed ion beam imaging by time of flight), an instrument that uses bright ion sources and orthogonal time-of-flight mass spectrometry to image metal-tagged antibodies at subcellular resolution in clinical tissue sections. We demonstrate quantitative, full periodic table coverage across a five-log dynamic range, imaging 36 labeled antibodies simultaneously with histochemical stains and endogenous elements. We image fields of view up to 800 mum * 800 mum at resolutions down to 260 nm with sensitivities approaching single-molecule detection. We leverage these properties to interrogate intrapatient heterogeneity in tumor organization in triple-negative breast cancer, revealing regional variability in tumor cell phenotypes in contrast to a structured immune response. Given its versatility and sample back-compatibility, MIBI-TOF is positioned to leverage existing annotated, archival tissue cohorts to explore emerging questions in cancer, immunology, and neurobiology.

  View details for DOI 10.1126/sciadv.aax5851

  View details for PubMedID 31633026

• Neuronal differentiation and cell-cycle programs mediate response to BET-bromodomain inhibition in MYC-driven medulloblastoma NATURE COMMUNICATIONS Bandopadhayay, P., Piccioni, F., O'Rourke, R., Ho, P., Gonzalez, E. M., Buchan, G., Qian, K., Gionet, G., Girard, E., Coxon, M., Rees, M. G., Brenan, L., Dubois, F., Shapira, O., Greenwald, N. F., Pages, M., Iniguez, A., Paolella, B. R., Meng, A., Sinai, C., Roti, G., Dharia, N. V., Creech, A., Tanenbaum, B., Khadka, P., Tracy, A., Tiv, H. L., Hong, A. L., Coy, S., Rashid, R., Lin, J., Cowley, G. S., Lam, F. C., Goodale, A., Lee, Y., Schoolcraft, K., Vazquez, F., Hahn, W. C., Tsherniak, A., Bradner, J. E., Yaffe, M. B., Milde, T., Pfister, S. M., Qi, J., Schenone, M., Carr, S. A., Ligon, K. L., Kieran, M. W., Santagata, S., Olson, J. M., Gokhale, P. C., Jaffe, J. D., Root, D. E., Stegmaier, K., Johannessen, C. M., Beroukhim, R. 2019; 10: 2400

  Abstract

  BET-bromodomain inhibition (BETi) has shown pre-clinical promise for MYC-amplified medulloblastoma. However, the mechanisms for its action, and ultimately for resistance, have not been fully defined. Here, using a combination of expression profiling, genome-scale CRISPR/Cas9-mediated loss of function and ORF/cDNA driven rescue screens, and cell-based models of spontaneous resistance, we identify bHLH/homeobox transcription factors and cell-cycle regulators as key genes mediating BETi's response and resistance. Cells that acquire drug tolerance exhibit a more neuronally differentiated cell-state and expression of lineage-specific bHLH/homeobox transcription factors. However, they do not terminally differentiate, maintain expression of CCND2, and continue to cycle through S-phase. Moreover, CDK4/CDK6 inhibition delays acquisition of resistance. Therefore, our data provide insights about the mechanisms underlying BETi effects and the appearance of resistance and support the therapeutic use of combined cell-cycle inhibitors with BETi in MYC-amplified medulloblastoma.

  View details for DOI 10.1038/s41467-019-10307-9

  View details for Web of Science ID 000469909500002

  View details for PubMedID 31160565

  View details for PubMedCentralID PMC6546744

• SvABA: genome-wide detection of structural variants and indels by local assembly GENOME RESEARCH Wala, J. A., Bandopadhayay, P., Greenwald, N. F., O'Rourke, R., Sharpe, T., Stewart, C., Schumacher, S., Li, Y., Weischenfeldt, J., Yao, X., Nusbaum, C., Campbell, P., Getz, G., Meyerson, M., Zhang, C., Imielinski, M., Beroukhim, R. 2018; 28 (4): 581–91

  Abstract

  Structural variants (SVs), including small insertion and deletion variants (indels), are challenging to detect through standard alignment-based variant calling methods. Sequence assembly offers a powerful approach to identifying SVs, but is difficult to apply at scale genome-wide for SV detection due to its computational complexity and the difficulty of extracting SVs from assembly contigs. We describe SvABA, an efficient and accurate method for detecting SVs from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. We evaluated SvABA's performance on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs and substantially improves detection performance for variants in the 20-300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (<1000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types and found that short templated-sequence insertions occur in ∼4% of all somatic rearrangements. Finally, we demonstrate that SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized (50-300 bp) SVs.

  View details for DOI 10.1101/gr.221028.117

  View details for Web of Science ID 000428993500014

  View details for PubMedID 29535149

  View details for PubMedCentralID PMC5880247

• Radiographic prediction of meningioma grade by semantic and radiomic features PLOS ONE Coroller, T. P., Bi, W., Huynh, E., Abedalthagafi, M., Aizer, A. A., Greenwald, N. F., Parmar, C., Narayan, V., Wu, W. W., de Moura, S., Gupta, S., Beroukhim, R., Wen, P. Y., Al-Mefty, O., Dunn, I. F., Santagata, S., Alexander, B. M., Huang, R. Y., Aerts, H. L. 2017; 12 (11): e0187908

  Abstract

  The clinical management of meningioma is guided by tumor grade and biological behavior. Currently, the assessment of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor grade may enhance clinical decision-making.A total of 175 meningioma patients (103 low-grade and 72 high-grade) with pre-operative contrast-enhanced T1-MRI were included. Fifteen radiomic (quantitative) and 10 semantic (qualitative) features were applied to quantify the imaging phenotype. Area under the curve (AUC) and odd ratios (OR) were computed with multiple-hypothesis correction. Random-forest classifiers were developed and validated on an independent dataset (n = 44).Twelve radiographic features (eight radiomic and four semantic) were significantly associated with meningioma grade. High-grade tumors exhibited necrosis/hemorrhage (ORsem = 6.6, AUCrad = 0.62-0.68), intratumoral heterogeneity (ORsem = 7.9, AUCrad = 0.65), non-spherical shape (AUCrad = 0.61), and larger volumes (AUCrad = 0.69) compared to low-grade tumors. Radiomic and sematic classifiers could significantly predict meningioma grade (AUCsem = 0.76 and AUCrad = 0.78). Furthermore, combining them increased the classification power (AUCradio = 0.86). Clinical variables alone did not effectively predict tumor grade (AUCclin = 0.65) or show complementary value with imaging data (AUCcomb = 0.84).We found a strong association between imaging features of meningioma and histopathologic grade, with ready application to clinical management. Combining qualitative and quantitative radiographic features significantly improved classification power.

  View details for DOI 10.1371/journal.pone.0187908

  View details for Web of Science ID 000415378800037

  View details for PubMedID 29145421

  View details for PubMedCentralID PMC5690632

• Patient-derived xenografts undergo mouse-specific tumor evolution NATURE GENETICS Ben-David, U., Ha, G., Tseng, Y., Greenwald, N. F., Oh, C., Shih, J., McFarland, J. M., Wong, B., Boehm, J. S., Beroukhim, R., Golub, T. R. 2017; 49 (11): 1567-+

  Abstract

  Patient-derived xenografts (PDXs) have become a prominent cancer model system, as they are presumed to faithfully represent the genomic features of primary tumors. Here we monitored the dynamics of copy number alterations (CNAs) in 1,110 PDX samples across 24 cancer types. We observed rapid accumulation of CNAs during PDX passaging, often due to selection of preexisting minor clones. CNA acquisition in PDXs was correlated with the tissue-specific levels of aneuploidy and genetic heterogeneity observed in primary tumors. However, the particular CNAs acquired during PDX passaging differed from those acquired during tumor evolution in patients. Several CNAs recurrently observed in primary tumors gradually disappeared in PDXs, indicating that events undergoing positive selection in humans can become dispensable during propagation in mice. Notably, the genomic stability of PDXs was associated with their response to chemotherapy and targeted drugs. These findings have major implications for PDX-based modeling of human cancer.

  View details for DOI 10.1038/ng.3967

  View details for Web of Science ID 000413909800005

  View details for PubMedID 28991255

  View details for PubMedCentralID PMC5659952

• Osteoglycin promotes meningioma development through downregulation of NF2 and activation of mTOR signaling CELL COMMUNICATION AND SIGNALING Mei, Y., Du, Z., Hu, C., Greenwald, N. F., Abedalthagafi, M., Agar, N. R., Dunn, G. P., Bi, W., Santagata, S., Dunn, I. F. 2017; 15: 34

  Abstract

  Meningiomas are the most common primary intracranial tumors in adults. While a majority of meningiomas are slow growing neoplasms that may cured by surgical resection, a subset demonstrates more aggressive behavior and insidiously recurs despite surgery and radiation, without effective alternative treatment options. Elucidation of critical mitogenic pathways in meningioma oncogenesis may offer new therapeutic strategies. We performed an integrated genomic and molecular analysis to characterize the expression and function of osteoglycin (OGN) in meningiomas and explored possible therapeutic approaches for OGN-expressing meningiomas.OGN mRNA expression in human meningiomas was assessed by RNA microarray and RNAscope. The impact of OGN on cell proliferation, colony formation, and mitogenic signaling cascades was assessed in a human meningioma cell line (IOMM-Lee) with stable overexpression of OGN. Furthermore, the functional consequences of introducing an AKT inhibitor in OGN-overexpressing meningioma cells were assessed.OGN mRNA expression was dramatically increased in meningiomas compared to a spectrum of other brain tumors and normal brain. OGN-overexpressing meningioma cells demonstrated an elevated rate of cell proliferation, cell cycle activation, and colony formation as compared with cells transfected with control vector. In addition, NF2 mRNA and protein expression were both attenuated in OGN-overexpressing cells. Conversely, mTOR pathway and AKT activation increased in OGN-overexpressing cells compared to control cells. Lastly, introduction of an AKT inhibitor reduced OGN expression in meningioma cells and resulted in increased cell death and autophagy, suggestive of a reciprocal relationship between OGN and AKT.We identify OGN as a novel oncogene in meningioma proliferation. AKT inhibition reduces OGN protein levels in meningioma cells, with a concomitant increase in cell death, which provides a promising treatment option for meningiomas with OGN overexpression.

  View details for DOI 10.1186/s12964-017-0189-7

  View details for Web of Science ID 000411347100002

  View details for PubMedID 28923059

  View details for PubMedCentralID PMC5604305

• Artificial intelligence in research SCIENCE Musib, M. 2017; 357 (6346): 28

  View details for DOI 10.1126/science.357.6346.28

  View details for Web of Science ID 000404854100021

  View details for PubMedID 28684488

• Clinical Identification of Oncogenic Drivers and Copy-Number Alterations in Pituitary Tumors ENDOCRINOLOGY Bi, W., Greenwald, N. F., Ramkissoon, S. H., Abedalthagafi, M., Coy, S. M., Ligon, K. L., Mei, Y., MacConaill, L., Ducar, M., Min, L., Santagata, S., Kaiser, U. B., Beroukhim, R., Laws, E. R., Dunn, I. F. 2017; 158 (7): 2284–91

  Abstract

  Pituitary tumors are the second most common adult primary brain tumor, with a variable clinical course. Recent work has identified a number of genetic determinants of pituitary tumor subtypes, which may augment traditional histopathologic classification schemes. We sought to determine whether pituitary tumors could be stratified based on objective molecular characteristics using a clinical genomics assay. We performed a retrospective analysis of patients operated on at the Brigham and Women's Hospital from 2012 to 2016 whose pituitary tumors were profiled using multiplexed next-generation sequencing. We analyzed 127 pituitary tumors, including 114 adenomas, 5 craniopharyngiomas, and 8 tumors of other histologies. We observed recurrent BRAFV600E mutations in papillary craniopharyngiomas, CTNNB1 mutations in adamantinomatous craniopharyngiomas, and activating GNAS mutations in growth hormone-secreting adenomas. Furthermore, we validated the presence of two distinct genomic subclasses in adenomas (i.e., those with disrupted or quiet copy-number profiles) and the significant association of disruption with functional hormone status (P < 0.05). We report the clinical implementation of next-generation sequencing of pituitary tumors. We confirmed previously identified molecular subclasses for these tumors and show that routine screening as part of clinical practice is both feasible and informative. This large-scale proof-of-principle study may help to guide future institutional efforts for pituitary tumor classification as well as the incorporation of such techniques into prospective analysis as part of clinical trials.

  View details for DOI 10.1210/en.2016-1967

  View details for Web of Science ID 000405105100030

  View details for PubMedID 28486603

  View details for PubMedCentralID PMC5505210

• Clinical targeted exome-based sequencing in combination with genome-wide copy number profiling: precision medicine analysis of 203 pediatric brain tumors NEURO-ONCOLOGY Ramkissoon, S. H., Bandopadhayay, P., Hwang, J., Ramkissoon, L. A., Greenwald, N. F., Schumacher, S. E., O'Rourke, R., Pinches, N., Ho, P., Malkin, H., Sinai, C., Filbin, M., Plant, A., Bi, W., Chang, M. S., Yang, E., Wright, K. D., Manley, P. E., Ducar, M., Alexandrescu, S., Lidov, H., Delalle, I., Goumnerova, L. C., Church, A. J., Janeway, K. A., Harris, M. H., MacConaill, L. E., Folkerth, R. D., Lindeman, N. I., Stiles, C. D., Kieran, M. W., Ligon, A. H., Santagata, S., Dubuc, A. M., Chi, S. N., Beroukhim, R., Ligon, K. L. 2017; 19 (7): 986–96

  Abstract

  Clinical genomics platforms are needed to identify targetable alterations, but implementation of these technologies and best practices in routine clinical pediatric oncology practice are not yet well established.Profile is an institution-wide prospective clinical research initiative that uses targeted sequencing to identify targetable alterations in tumors. OncoPanel, a multiplexed targeted exome-sequencing platform that includes 300 cancer-causing genes, was used to assess single nucleotide variants and rearrangements/indels. Alterations were annotated (Tiers 1-4) based on clinical significance, with Tier 1 alterations having well-established clinical utility. OncoCopy, a clinical genome-wide array comparative genomic hybridization (aCGH) assay, was also performed to evaluate copy number alterations and better define rearrangement breakpoints.Cancer genomes of 203 pediatric brain tumors were profiled across histological subtypes, including 117 samples analyzed by OncoPanel, 146 by OncoCopy, and 60 tumors subjected to both methodologies. OncoPanel revealed clinically relevant alterations in 56% of patients (44 cancer mutations and 20 rearrangements), including BRAF alterations that directed the use of targeted inhibitors. Rearrangements in MYB-QKI, MYBL1, BRAF, and FGFR1 were also detected. Furthermore, while copy number profiles differed across histologies, the combined use of OncoPanel and OncoCopy identified subgroup-specific alterations in 89% (17/19) of medulloblastomas.The combination of OncoPanel and OncoCopy multiplex genomic assays can identify critical diagnostic, prognostic, and treatment-relevant alterations and represents an effective precision medicine approach for clinical evaluation of pediatric brain tumors.

  View details for DOI 10.1093/neuonc/now294

  View details for Web of Science ID 000403446300018

  View details for PubMedID 28104717

  View details for PubMedCentralID PMC5570190

• Genomic profile of human meningioma cell lines PLOS ONE Mei, Y., Bi, W., Greenwald, N. F., Agar, N. Y., Beroukhim, R., Dunn, G. P., Dunn, I. F. 2017; 12 (5): e0178322

  Abstract

  Meningiomas, derived from arachnoid cap cells, are the most common intracranial tumor. High-grade meningiomas, as well as those located at the skull base or near venous sinuses, frequently recur and are challenging to manage. Next-generation sequencing is identifying novel pharmacologic targets in meningiomas to complement surgery and radiation. However, due to the lack of in vitro models, the importance and implications of these genetic variants in meningioma pathogenesis and therapy remain unclear. We performed whole exome sequencing to assess single nucleotide variants and somatic copy number variants in four human meningioma cell lines, including two benign lines (HBL-52 and Ben-Men-1) and two malignant lines (IOMM-Lee and CH157-MN). The two malignant cell lines harbored an elevated rate of mutations and copy number alterations compared to the benign lines, consistent with the genetic profiles of high-grade meningiomas. In addition, these cell lines also harbored known meningioma driver mutations in neurofibromin 2 (NF2) and TNF receptor-associated factor 7 (TRAF7). These findings demonstrate the relevance of meningioma cell lines as a model system, especially as tools to investigate the signaling pathways of, and subsequent resistance to, therapeutics currently in clinical trials.

  View details for DOI 10.1371/journal.pone.0178322

  View details for Web of Science ID 000402063000035

  View details for PubMedID 28552950

  View details for PubMedCentralID PMC5446134

• Genomic landscape of high-grade meningiomas NPJ GENOMIC MEDICINE Bi, W., Greenwald, N. F., Abedalthagafi, M., Wala, J., Gibson, W. J., Agarwalla, P. K., Horowitz, P., Schumacher, S. E., Esaulova, E., Mei, Y., Chevalier, A., Ducar, M. A., Thorner, A. R., van Hummelen, P., Stemmer-Rachamimov, A. O., Artyomov, M., Al-Mefty, O., Dunn, G. P., Santagata, S., Dunn, I. F., Beroukhim, R. 2017; 2

  View details for DOI 10.1038/s41525-017-0014-7

  View details for Web of Science ID 000413247200001

• Landscape of Genomic Alterations in Pituitary Adenomas CLINICAL CANCER RESEARCH Bi, W., Horowitz, P., Greenwald, N. F., Abedalthagafi, M., Agarwalla, P. K., Gibson, W. J., Mei, Y., Schumacher, S. E., Ben-David, U., Chevalier, A., Carter, S., Tiao, G., Brastianos, P. K., Ligon, A. H., Ducar, M., MacConaill, L., Laws, E. R., Santagata, S., Beroukhim, R., Dunn, I. F. 2017; 23 (7): 1841–51

  Abstract

  Purpose: Pituitary adenomas are the second most common primary brain tumor, yet their genetic profiles are incompletely understood.Experimental Design: We performed whole-exome sequencing of 42 pituitary macroadenomas and matched normal DNA. These adenomas included hormonally active and inactive tumors, ones with typical or atypical histology, and ones that were primary or recurrent.Results: We identified mutations, insertions/deletions, and copy-number alterations. Nearly one-third of samples (29%) had chromosome arm-level copy-number alterations across large fractions of the genome. Despite such widespread genomic disruption, these tumors had few focal events, which is unusual among highly disrupted cancers. The other 71% of tumors formed a distinct molecular class, with somatic copy number alterations involving less than 6% of the genome. Among the highly disrupted group, 75% were functional adenomas or atypical null-cell adenomas, whereas 87% of the less-disrupted group were nonfunctional adenomas. We confirmed this association between functional subtype and disruption in a validation dataset of 87 pituitary adenomas. Analysis of previously published expression data from an additional 50 adenomas showed that arm-level alterations significantly impacted transcript levels, and that the disrupted samples were characterized by expression changes associated with poor outcome in other cancers. Arm-level losses of chromosomes 1, 2, 11, and 18 were significantly recurrent. No significantly recurrent mutations were identified, suggesting no genes are altered by exonic mutations across large fractions of pituitary macroadenomas.Conclusions: These data indicate that sporadic pituitary adenomas have distinct copy-number profiles that associate with hormonal and histologic subtypes and influence gene expression. Clin Cancer Res; 23(7); 1841-51. ©2016 AACR.

  View details for DOI 10.1158/1078-0432.CCR-16-0790

  View details for Web of Science ID 000398262700023

  View details for PubMedID 27707790

  View details for PubMedCentralID PMC5380512

• Increased expression of programmed death ligand 1 (PD-L1) in human pituitary tumors ONCOTARGET Mei, Y., Bi, W., Greenwald, N. F., Du, Z., Agar, N. R., Kaiser, U. B., Woodmansee, W. W., Reardon, D. A., Freeman, G. J., Fecci, P. E., Laws, E. R., Santagata, S., Dunn, G. P., Dunn, I. F. 2016; 7 (47): 76565–76

  Abstract

  Subsets of pituitary tumors exhibit an aggressive clinical courses and recur despite surgery, radiation, and chemotherapy. Because modulation of the immune response through inhibition of T-cell checkpoints has led to durable clinical responses in multiple malignancies, we explored whether pituitary adenomas express immune-related biomarkers that could suggest suitability for immunotherapy. Specifically, programmed death ligand 1 (PD-L1) has emerged as a potential biomarker whose expression may portend more favorable responses to immune checkpoint blockade therapies. We thus investigated the expression of PD-L1 in pituitary adenomas.PD-L1 RNA and protein expression were evaluated in 48 pituitary tumors, including functioning and non-functioning adenomas as well as atypical and recurrent tumors. Tumor infiltrating lymphocyte populations were also assessed by immunohistochemistry.Pituitary tumors express variable levels of PD-L1 transcript and protein. PD-L1 RNA and protein expression were significantly increased in functioning (growth hormone and prolactin-expressing) pituitary adenomas compared to non-functioning (null cell and silent gonadotroph) adenomas. Moreover, primary pituitary adenomas harbored higher levels of PD-L1 mRNA compared to recurrent tumors. Tumor infiltrating lymphocytes were observed in all pituitary tumors and were positively correlated with increased PD-L1 expression, particularly in the functional subtypes.Human pituitary adenomas harbor PD-L1 across subtypes, with significantly higher expression in functioning adenomas compared to non-functioning adenomas. This expression is accompanied by the presence of tumor infiltrating lymphocytes. These findings suggest the existence of an immune response to pituitary tumors and raise the possibility of considering checkpoint blockade immunotherapy in cases refractory to conventional management.

  View details for DOI 10.18632/oncotarget.12088

  View details for Web of Science ID 000389633400018

  View details for PubMedID 27655724

  View details for PubMedCentralID PMC5363530