Background New therapies for treating Alzheimer’s Disease (AD) are urgently needed. Discovery‐based research approaches have identified a host of promising new AD targets across a spectrum of therapeutic hypotheses. However, progress to validate selected targets and develop new experimental reagents and therapeutics requires efforts beyond the focus of any single research group. We have built the Agora site as an open repository that publicly shares information about new AD targets to facilitate target evaluation by the broader AD research community. Method Agora aggregates information and experimental resources in support of nascent AD targets. We are expanding Agora to catalog evidence and experimental reagents being generated by the Open Drug Discovery Center for AD (Open‐AD). Result Agora is available at https://agora.ampadportal.org. Agora presents a list of nominated targets stemming from the Accelerating Medicines Partnership in AD (AMP‐AD) consortium and the broader AD research community. Agora also hosts interactive visualizations designed to support non‐bioinformaticians in the evaluation of data from RNAseq, proteomic, and metabolomic studies. Updates to Agora in support of the new Open‐AD center include 1) presentation of individual scorecards for nominated targets based on multiple lines of evidence and 2) a new exploration framework in which targets are mapped onto therapeutic hypotheses. Conclusion Agora is a platform to enable the AD research community to unite around promising target hypotheses.

Deconvolution methods infer levels of immune and stromal infiltration from bulk expression of tumor samples. These methods allow projection of characteristics of the tumor microenvironment, known to affect patient outcome and therapeutic response, onto the millions of bulk transcriptional profiles in public databases, many focused on uniquely valuable and clinically-annotated cohorts. Despite the wide development of such methods, a standardized dataset with ground truth to evaluate their performance has been lacking. We generated and sequenced in vitro and in silico admixtures of tumor, immune, and stromal cells and used them as ground truth in a community-wide DREAM Challenge that provided an objective, unbiased assessment of six widely-used published deconvolution methods and of 22 new analytical approaches developed by international teams. Our results demonstrate that existing methods predict many cell types well, while team-contributed methods highlight the potential to resolve functional states of T cells that were either not covered by published reference signatures or estimated poorly by some published methods. Our assessment and the open-source implementations of top-performing methods will allow researchers to apply the deconvolution approach most appropriate to querying their cell type of interest. Further, our publicly-available admixed and purified expression profiles will be a valuable resource to those developing deconvolution methods, including in non-malignant settings involving immune cells.

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Research tools, such as model organisms, cell lines, and antibodies, are essential to designing and executing successful biological experiments. These resources are often shared or made commercially available to support scientific progress. Given the fast pace of research, it can be difficult to keep track of the large number of available tools. Moreover, for those new to a particular disease area, learning about the array of tools available can be a major impediment. Our experience in the neurofibromatosis field has shown that researchers struggle to identify the research tools available to them, determine where tools can be acquired, and understand what tools are most well-suited for which experiments. While a variety of databases exist to help researchers find useful research tools, these databases often a) are specific to one type of research tool while being disease-agnostic, b) provide only high-level information, c) do not contain information about in-development models, and d) do not contain observational data for the research tools. To address these limitations, we created the Neurofibromatosis Research Tools Database, a user-friendly, open-access database and companion portal designed to help the neurofibromatosis type 1 (NF1) research community easily find, obtain, and use NF1-relevant research tools. This prototype database catalogs a wide variety of NF1-relevant research tools using databases such as Cellosaurus, AntibodyRegistry, RRID Portal, among others, as well as information provided in literature and from the NF community. We aggregated and curated metadata for NF1-relevant animal models, cell lines, genetic reagents, antibodies, and biobanks. The database includes core metadata for all tools, e.g., name, type of tool, synonyms, developer, as well as tool type-specific metadata, e.g., for cell lines or animal models, the type of cancer that the model recapitulates. The database is also designed to store observational data contributed directly from the research community. Our companion web portal allows users to search and filter this database interactively and easily explore these tools. This website was built within the NF Data Portal (nf.synapse.org), and is available at tools.nf.synapse.org. Community members can actively contribute to the growth of the database and portal by submitting information about the reliability, biology, usage, and other observations on each research tool. By collating and curating this information and surfacing it in an open-access exploration portal, we anticipate that this database will serve as a valuable resource to help the NF community discover, understand, and use NF1 research tools. Citation Format: Brynn Zalmanek, James Goss, Mialy DeFelice, Jay Hodgson, Ashley Clayton, Stockard Simon, Marco Marasca, Julie Bletz, James A. Eddy, Milen Nikolov, Kevin Boske, Ljubomir Bradic, Jineta Banerjee, Kalyan Vinnakota, Caroline Morin, YooRi Kim, Robert J. Allaway. NF Research Tools Database: A knowledge base of experimental research tools for neurofibromatosis [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 1675.