The mission for the Oncology Models Forum (OMF) is to align, harness and integrate data and knowledge of cancer mouse models to drive more rapid success in the discovery of diagnostics, therapeutics, and models of disease in cancer. In early June 2014, the National Cancer Institute (NCI) released PAR-14-239 describing the OMF as a comprehensive resource for information to guide generating, validating, and credentialing new models, informing their practical uses, advancing modeling technologies, [and] providing catalogs of available models resources, programs, and services. To maximize the use of available resources, this online site was prescribed to use HUBzero, an existing open-source scientific website framework, avoiding the expensive creation of a new scientific website from scratch. The OMF was designed as a community-building tool, with online discussion forums and an annual meeting. Specific content will be provided from the collaborative R01s funded in parallel to the OMF. Pre-clinical mouse and human-in-mouse models are critical to future translation in cancer. More than 4,300 manuscripts were published in 2013 describing or using cancer mouse models, including xenografts and transplantation models, spontaneous models, inbred mice, and genetically engineered mouse models (GEMM). The NCI Cancer Models Database (caMOD) now lists thousands of mouse models that are prone to develop tumors in one or more sites. But data on these models is scattered across numerous resources. Future success in research using cancer mouse models is now less dependent on the continued creation of new models, and more dependent on the validation of the thousands of currently available models, transparent access to knowledge and data on these models, comparisons of model data with human data, and organization of the research community. We plan to develop the Oncology Models Forum (OMF) to accomplish these goals. Our proposed OMF will integrate structured and unstructured data and knowledge on cancer mouse models, enabling new discoveries and the development of new translational tools.

Public Health Relevance

The mission for the Oncology Models Forum (OMF) is to align, harness and integrate data and knowledge of cancer mouse models to drive more rapid success in the discovery of diagnostics, therapeutics, and models of disease in cancer. Working with the community of researchers developing and using mouse models in cancer, we will build a new website for the OMF using the open-source HUBzero platform for researchers to share what they are learning about these cancer models.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Resource-Related Research Projects--Cooperative Agreements (U24)
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Special Emphasis Panel (ZCA1)
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Boudreau, Nancy
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University of California San Francisco
Schools of Medicine
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Chen, Bin; Wei, Wei; Ma, Li et al. (2017) Computational Discovery of Niclosamide Ethanolamine, a Repurposed Drug Candidate That Reduces Growth of Hepatocellular Carcinoma Cells In Vitro and in Mice by Inhibiting Cell Division Cycle 37 Signaling. Gastroenterology 152:2022-2036
Aran, Dvir; Hu, Zicheng; Butte, Atul J (2017) xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 18:220
Chen, Bin; Ma, Li; Paik, Hyojung et al. (2017) Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets. Nat Commun 8:16022
Meehan, Terrence F; Conte, Nathalie; Goldstein, Theodore et al. (2017) PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models. Cancer Res 77:e62-e66
Lasry, Audrey; Aran, Dvir; Butte, Atul J et al. (2017) Cancer Cell-Autonomous Parainflammation Mimics Immune Cell Infiltration. Cancer Res 77:3740-3744
Aran, Dvir; Camarda, Roman; Odegaard, Justin et al. (2017) Comprehensive analysis of normal adjacent to tumor transcriptomes. Nat Commun 8:1077
Aran, Dvir; Butte, Atul J (2016) Digitally deconvolving the tumor microenvironment. Genome Biol 17:175
Kosti, Idit; Jain, Nishant; Aran, Dvir et al. (2016) Cross-tissue Analysis of Gene and Protein Expression in Normal and Cancer Tissues. Sci Rep 6:24799
Aran, Dvir; Lasry, Audrey; Zinger, Adar et al. (2016) Widespread parainflammation in human cancer. Genome Biol 17:145
Geifman, N; Butte, A J (2016) A patient-level data meta-analysis of standard-of-care treatments from eight prostate cancer clinical trials. Sci Data 3:160027

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