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PDF file - 38K, Supplemental Table 1. Genes binding both P53 and DACH1 in ChIP Seq.

PDF file - 791K, DACH1 associates with endogenous YB-1 (S1); Phosphorylation determines DACH1 nuclear-cytoplasmic translocation (S2); DACH1 phosphorylation sites govern nuclear translocation of YB-1 (S3); High YB-1 and low DACH1 expression correlates with poor outcome (S4).

PDF file - 293K, Supplemental Figure 1. Reduced DACH1 mRNA in NSCLC. Supplemental Figure 2. DACH1 and p53 co-localized in a nuclear extranucleolar location.Supplemental Figure 3. Western blot analysis of H1299 cells treated with 5-Aza-2'- deoxycytidine (5MM) for 5 days and Trichostatin A (1MM, 8hrs).Supplemental Figure 4. EGFR and DACH1 expression in NSCLC vs. SCLC.

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The epithelial–mesenchymal transition (EMT) enhances cellular invasiveness and confers tumor cells with cancer stem cell–like characteristics, through transcriptional and translational mechanisms. The mechanisms maintaining transcriptional and translational repression of EMT and cellular invasion are poorly understood. Herein, the cell fate determination factor Dachshund (DACH1), suppressed EMT via repression of cytoplasmic translational induction of Snail by inactivating the Y box–binding protein (YB-1). In the nucleus, DACH1 antagonized YB-1–mediated oncogenic transcriptional modules governing cell invasion. DACH1 blocked YB-1–induced mammary tumor growth and EMT in mice. In basal-like breast cancer, the reduced expression of DACH1 and increased YB-1 correlated with poor metastasis-free survival. The loss of DACH1 suppression of both cytoplasmic translational and nuclear transcriptional events governing EMT and tumor invasion may contribute to poor prognosis in basal-like forms of breast cancer, a relatively aggressive disease subtype. Cancer Res; 74(3); 829–39. ©2013 AACR.

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Pancreatic cancer remains a deadly disease due to difficulties hindering its early diagnosis, giving way to metastasis of the tumor and resulting in poor prognosis. While there are many neoplasms of the pancreas, pancreatic invasive ductal adenocarcinoma (PDAC) is the most common, and treatment options are few, with poor overall survival. Recently, several publications have demonstrated improved outcomes with the inclusion of immunotherapy to cytotoxic drug combinations in some patients. However, optimally selecting patients as candidates for immunotherapy-chemotherapy combinations remains a critical challenge. The complexities of the tumor microenvironment (TME) have been implicated in the failure of chemotherapy, radiation therapy, and immunotherapy. The tumor microenvironment of PDAC is especially rich with multiple interactions between pancreatic epithelial/cancer cells, stromal cells, immune cells, and the extracellular matrix (ECM). PDACs are characterized by a complex ECM of desmoplastic reaction consisting of an extensive and dense fibrotic stroma that surrounds and infiltrates clusters of malignant epithelial cells, together with the loss of basement membrane integrity and an abnormal vasculature. PD-L1 is also expressed in PDACs, and its overexpression has been associated with a poor prognosis. In the present study we demonstrate a unique tissue phenotyping workflow combining complementary methods that can unravel the complexity of the tumor microenvironment. We highlight how a workflow combining multiple elements provides utility, robustness, and an ability to derive biological insights in PDAC samples. An InSituPlex® PD-L1 multiplex immunofluorescence assay (4 markers, CD8, CD68, PD-L1, Pan CK/Sox10) was used on whole slides to identify areas of high, medium, and low PD-L1 expression. Imaging Mass Cytometry™ (IMC™, 40 markers) was performed on selected regions of interest from each slide. Advanced tissue and cell segmentation followed by multiplex cellular phenotyping and spatial analyses were performed on both whole-slide InSituPlex and IMC data. These methods combine to give a detailed readout of the location and bio-distribution of specific cell phenotypes in situ in the TME of PDAC. Four-plex imaging and analysis of whole slides gives an overview of the immune status of the section, while 40-plex imaging and analysis gives a comprehensive and multiparametric exploration of cells present. These methods combine to reveal an exceptional view of the PDAC TME at the single-cell level. Citation Format: Andrew Quong, Jordan Nieto, Derek Quong, Amanda Esch, Kirsteen Maclean, Mark Rees, Devan Fleury, Gourab Chatterjee, Keith Wharton, Jeppe Thagaard, Fabian Schneider, Dan Winkowski, James Mansfield. Improved understanding of the biology and pathophysiology of the tumor microenvironment in PDAC samples revealed by InSituPlex, Imaging Mass Cytometry, and advanced image processing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1709.

Imaging Mass Cytometry™ (IMC™) is a vital tool to deeply characterize the complexity and diversity of any tissue without disrupting spatial context. The Hyperion™ Imaging System utilizes IMC, based on CyTOF® technology, to simultaneously assess up to 40 individual structural and functional markers in tissues, providing unprecedented insight into the organization and function of tissue microenvironment. We have previously demonstrated the application of IMC in combination with Maxpar® panel kits to highlight cellular composition of human tissues. Here, we showcase the recently released Maxpar OnDemand Antibodies for IMC application on mouse tissue. We introduced 11 additional biomarkers to our existing mouse antibody catalog, providing the basis for the use of high-multiplex imaging in preclinical investigations. To demonstrate the IMC workflow on mouse tissue, we analyzed a normal mouse tissue microarray using IMC spatial proteomic analysis. Tissues were stained with a 20-marker panel designed to highlight tissue architecture and major immune lineage markers combined with our IMC Cell Segmentation Kit*. The IMC Cell Segmentation Kit facilitates identification of cellular borders using plasma membrane markers that lead to improved nucleus and plasma membrane demarcation. We generated a detailed spatial map of the heterogeneous tissue architecture and successfully identified immune, epithelial, and stromal cell populations in various mouse tissues. Additionally, we classified the activation state of immune cell populations, adhesion state of epithelial cells, and molecular composition of the extracellular matrix.Overall, this work demonstrates the capability of IMC to identify subcellular localization of cellular and structural markers in the mouse tissue microenvironment. Information gained from IMC studies will enable in-depth high-throughput phenotypic characterization of the tissue microenvironment in various mouse models of development and disease, and thus accelerate preclinical discoveries. *The IMC Cell Segmentation Kit is part of the Innovative Solutions menu of custom-made reagents and workflows developed and tested by Fluidigm scientists to give faster access to new cutting-edge solutions for high-multiplex single-cell analysis. Innovative Solutions are not part of the Maxpar catalog. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Qanber Raza, Michael Cohen, Smriti Kala, Liang Lim, Geneve Awong, Andrew Quong, Christina Loh. Imaging mass cytometry identifies structural and cellular composition of the mouse tissue microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2035.

Imaging Mass Cytometry™ (IMC™) is a vital tool to deeply characterize the complexity and diversity of any tissue without disrupting spatial context. The Hyperion™ Imaging System utilizes IMC, based on CyTOF® technology, to assess up to 40 individual structural and functional markers in tissues, providing unprecedented insight into the organization and function of tissue microenvironment. We have previously demonstrated the application of IMC in combination with Maxpar® panel kits on human tissues. Here, we showcase Maxpar OnDemand Antibodies for IMC application including 11 new highly relevant markers to construe cellular and molecular composition of mouse tissues. We analyzed a normal mouse tissue microarray using IMC spatial proteomic analysis. Tissues were stained with a 20-marker panel designed to highlight tissue architecture and major immune lineage markers. We generated a detailed spatial map of the diverse tissue architecture and successfully identified immune, epithelial, and stromal cell populations in various mouse tissues. Additionally, we classified the activation state of immune cell populations, adhesion state of epithelial cells, and molecular composition of the extracellular matrix. This work demonstrates the capability of IMC to identify subcellular localization of cellular and structural markers in the mouse tissue microenvironment. Future studies utilizing IMC in combination with Maxpar OnDemand Antibodies will enable in-depth phenotypic characterization of the tissue microenvironment in various mouse models of development and disease, and thus provide the basis for the use of high-multiplex imaging in preclinical investigations. For Research Use Only. Not for use in diagnostic procedures.

Objective With widespread and sophisticated imaging techniques, micro papillary thyroid cancers (PTCs) may be discovered prior to developing the classic pathological determinants of aggressiveness, such as extrathyroidal extent (ETE). Many studies have suggested that the V600E B-Raf proto-oncogene (BRAF) mutation can be used as a marker for aggressive disease. One objective of this study is to determine what prognostic value this mutation holds. However, nearly all of current studies have focused solely on classically aggressive tumors, not classically nonaggressive samples. This study also seeks to determine the BRAF mutation status in both the groups of tumors. Materials and methods Sixty-six PTC samples were tested for the V600E BRAF mutation using competitive allele-specific TaqMan probes in real-time PCR (Applied Biosystems/Life Technologies). Testing demonstrates that this assay has at least a Results The V600E BRAF mutation was found in 27 of the 45 aggressive samples (60.0%) and 5 of the 21 nonaggressive samples. The Fisher exact test resulted in a correlation between aggressiveness and BRAF mutation as well as correlations between ETE, LNM and the BRAF mutation. When using the BRAF mutation as a predictor of prognosis based on the pathological features of aggressiveness, there was 60% sensitivity and 80% specificity. Conclusion The V600E BRAF mutation is correlated with pathological aggressive features, but may lack sufficient specificity or sensitivity to be used as a marker to predict outcome. How to cite this article Quong A, Wynne C, Curry J, Scott K, Rosen D, Cognetti D, Pribitkin E, Duddy E. BRAF Mutation Correlates with Aggressive Features, Little Predictive Value. Int J Head Neck Surg 2014;5(3):130-134.

The Frederick National Laboratory for Cancer Research hosts a Data Coordinating Center and Toolset (the Metadata Designer plus Validator) hereafter referred to as the DCC that embodies a scalable, next-generation biological and cancer research data repository that is flexible, intuitive, and adaptive. The DCC provides integrated management of datasets across all deposited projects making its data more accessible and easily reusable by the cancer research community. The DCC stores and manages access to data, enabling researchers or data depositors to grant controlled access only to specific collaborators while maintaining a user-specified embargo on deposited datasets. The DCC enables a data access and sharing capability aimed to facilitate the development of new biological insights. The DCC implements the ISA (Investigation-Study-Assay) paradigm. The ISA framework provides a rich description of experimental metadata that is agnostic and irrespective of sample characteristic, technology or measurement type. It provides clear and simple sample-to-data relationships that enables resulting data and discoveries to be reproducible and reusable. These data are in the standard Investigation-Study-Assay tab-delimited format (ISA-TAB) format, which describes a scientific investigation, its study or studies, and each study's assay(s). The DCC portal is a public repository of experiment-related information describing cancer and biomedical research investigations. The portal can be used to browse, search, and access data from uploaded datasets. Through its stand-alone Toolset (Metadata Designer plus Validator), the DCC offers data depositors and researchers a simple interface to create and validate ISA-Tab compatible metadata associated with data generated in their research. Deriving new insights from aggregates of datasets and the need for reproducible research has never been more apparent. However, a fundamental requirement for these is a comprehensive metadata annotation and documentation of the research processes - an art that is elusive to researchers. Beyond the resultant publication is the data and metadata. These two basic ingredients are building blocks for a successful reproducible research project. In addition, appropriately curated data and metadata are invaluable resources for meta-analyses of seemingly disparate research studies. Further emphasizing the importance of these basic elements, are the principles of Findability, Accessibility, Interoperability, and Reusability (FAIR) - to guide data producers and publishers around obstacles and help maximize added-value gained from published studies. The DCC provides a much-needed resource to the research community for simplifying the sharing of data sets that meet these principles. The DCC portal is located at https://cssi-dcc.nci.nih.gov/cssiportal/ Citation Format: Paul Aiyetan, Paul Donovan, David Mott, Matthew Starr, Rajani Kuchipudi, Mahesh Yelisetti, Debra Hope, Corinne Zeitler, Uma Mudunuri, Andrew Quong. Enabling data access, sharing, collaborative and reproducible research: The Frederick National Laboratory for Cancer Research (FNLCR) data coordinating center [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2489.