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https://www.saintluc.be/en/node/2561

An essential reference center in Europe and a leader in French-speaking Belgium that treats all types of adult and childhood cancer. They fight against cancer while giving patients comprehensive and humane care. Their quest for excellence is in three main academic fields: clinical care, research and teaching.

Proper citation: Cliniques Universitaires Saint-Luc Cancer Centre (RRID:SCR_004922) Copy   

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https://array.nci.nih.gov/caarray/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on Sep 18, 2018. Open-source, web and programmatically accessible microarray data management system. caArray guides the annotation and exchange of array data using a federated model of local installations whose results are shareable across the cancer Biomedical Informatics Grid (caBIG). caArray furthers translational cancer research through acquisition, dissemination and aggregation of semantically interoperable array data to support subsequent analysis by tools and services on and off the Grid. As array technology advances and matures, caArray will extend its logical library of assay management.

Proper citation: caArray (RRID:SCR_006053) Copy   

•   Source: SciCrunch Registry

http://www.biogrid.org.au

A federated data sharing platform and infrastructure that provides access to real-time clinical, imaging and biospecimen data across jurisdictions, institutions and diseases. The web-based platform provides a secure infrastructure that advances health research by linking privacy-protected and ethically approved data among a wide network of health collaborators. Access to de-identified health records data is granted to authorized researchers after an application process so patient privacy and intellectual property are protected. BioGrid Australia''s approved researchers are provided access to multiple institutional databases, via the BioGrid interface, preventing gaps in patient records and research analysis. This legal and ethical arrangement with participating collaborators allows BioGrid to connect data through a common platform where data governance and access is managed by a highly skilled team. Data governance, security and ethics are at the core of BioGrid''s federated data sharing platform that securely links patient level clinical, biospecimen, genetic and imaging data sets across multiple sites and diseases for the purpose of medical research. BioGrid''s infrastructure and data management strategies address the increasing need by authorized researchers to dynamically extract and analyze data from multiple sources whilst protecting patient privacy. BioGrid has the capability to link data with other datasets, produce tailored reports for auditing and reporting and provide statistical analysis tools to conduct more advanced research analysis. In the health sector, BioGrid is a trusted independent virtual real-time data repository. Government investment in BioGrid has facilitated a combination of technology, collaboration and ethics approval processes for data sharing that exist nowhere else in the world.

Proper citation: BioGrid Australia (RRID:SCR_006334) Copy   

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https://cansar.icr.ac.uk/

canSAR is an integrated database that brings together biological, chemical, pharmacological (and eventually clinical) data. Its goal is to integrate this data and make it accessible to cancer research scientists from multiple disciplines, in order to help with hypothesis generation in cancer research and support translational research. This cancer research and drug discovery resource was developed to utilize the growing publicly available biological annotation, chemical screening, RNA interference screening, expression, amplification and 3D structural data. Scientists can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets. canSAR has, from the outset, been completely use-case driven which has dramatically influenced the design of the back-end and the functionality provided through the interfaces. The Web interface provides flexible, multipoint entry into canSAR. This allows easy access to the multidisciplinary data within, including target and compound synopses, bioactivity views and expert tools for chemogenomic, expression and protein interaction network data.

Proper citation: canSAR (RRID:SCR_006794) Copy   

•   Source: SciCrunch Registry

http://seer.cancer.gov/

SEER collects cancer incidence data from population-based cancer registries covering approximately 47.9 percent of the U.S. population. The SEER registries collect data on patient demographics, primary tumor site, tumor morphology, stage at diagnosis, and first course of treatment, and they follow up with patients for vital status.There are two data products available: SEER Research and SEER Research Plus. This was motivated because of concerns about the increasing risk of re-identifiability of individuals. The Research Plus databases require more rigorous process for access that includes user authentication through Institutional Account or multiple-step request process for Non-Institutional users.

Proper citation: Surveillance Epidemiology and End Results (RRID:SCR_006902) Copy   

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http://www.ariad.com/

Cambridge, Massachusetts-based biotechnology company focused on cancer. Focus areas are blood cancers and solid tumors. Compounds: ponatinib, AP26113, ridaforolimus and AP1903., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ARIAD (RRID:SCR_008559) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/CTCAE

A coding system for reporting adverse events that occur in the course of cancer therapy. It was derived from the Common Toxicity Criteria (CTC) v2.0 and is maintained by the Cancer Therapy Evaluation Program (CTEP) at the National Cancer Institution (NCI).

Proper citation: Common Terminology Criteria for Adverse Events (RRID:SCR_010296) Copy   

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http://purl.bioontology.org/ontology/CANONT

Upper-level ontology for cancer.

Proper citation: Upper-Level Cancer Ontology (RRID:SCR_010443) Copy   

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http://www.zbh.uni-hamburg.de/?id=292

A web-based software tool for the integrative analysis of cancer genomics data. It stores different kinds of downstream processed data from multiple samples in a single database. A powerful search interface allows to dynamically filter the data to be displayed with respect to different criteria. The combination of AJAX technology and a fast visualization engine facilitates a highly dynamic visualization for large amounts of data. FISH Oracle 2 is able to simultaneously display different data sets, thus simplifying their comparison. Filter and display options can be changed on the fly. High quality image export enables the life scientist to easily communicate the results, e.g. in presentations or publications. A comprehensive data administration assures to keep track of the data stored in the database.

Proper citation: FISH Oracle (RRID:SCR_010927) Copy   

•   Source: SciCrunch Registry

https://humantumoratlas.org

HTAN is National Cancer Institute funded Cancer Moonshot initiative to construct 3-dimensional atlases of dynamic cellular, morphological, and molecular features of human cancers as they evolve from precancerous lesions to advanced disease.Provides three dimensional atlases of cancer transitions for diverse set of tumor types. Efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at single point in time. Data portal for Human Tumor Atlas Network. Data available on HTAN Portal is open access. Certain data types with potential for re-identification are available in restricted access through dbGAP.

Proper citation: Human Tumor Atlas Network (RRID:SCR_023364) Copy   

•   Source: SciCrunch Registry

https://www.cancermodels.org/

Cancer research platform that aggregates clinical, genomic and functional data from various types of patient derived cancer models, xenographs, organoids and cell lines. Open catalog of harmonised patient-derived cancer models. Standardises, harmonises and integrates clinical metadata, molecular and treatment-based data from academic and commercial providers worldwide. Data is FAIR and underpins generation and testing of new hypotheses in cancer mechanisms and personalised medicine development. PDCM Finder have expanded to organoids and cell lines and is now called CancerModels.Org. PDCM Finder was launched in April 2022 as successor of PDX Finder portal, which focused solely on patient-derived xenograft models.

Proper citation: CancerModels.Org (RRID:SCR_023931) Copy   

•   Source: SciCrunch Registry

https://ccsp.hms.harvard.edu/

Center includes studies for responsiveness and resistance to anti cancer drugs. Committed to training students and postdocs, promoting junior faculty and ensuring that data and software are reproducible, reliable and publicly accessible. Member of National Cancer Institute’s Cancer Systems Biology Consortium.

Proper citation: Harvard Medical School Center for Cancer Systems Pharmacology (RRID:SCR_022831) Copy   

•   Source: SciCrunch Registry

https://www.mitochondriasci.com/cancer.html

Creative Biogene provides comprehensive range of services and products to assist researchers in cancer related mitochondria studies. Offers tests and services with advantage of cell based and animal based models.

Proper citation: Creative Biogene Mitochondrial Gene Mutations (RRID:SCR_022082) Copy   

•   Source: SciCrunch Registry

http://magi.cs.brown.edu/

A tool for annotating, exploring, and analyzing gene sets that may be associated with cancer.

Proper citation: Mutation Annotation and Genomic Interpretation (RRID:SCR_002800) Copy   

•   Source: SciCrunch Registry

http://omniBiomarker.bme.gatech.edu

omniBiomarker is a web-application for analysis of high-throughput -omic data. Its primary function is to identify differentially expressed biomarkers that may be used for diagnostic or prognostic clinical prediction. Currently, omniBiomarker allows users to analyze their data with many different ranking methods simultaneously using a high-performance compute cluster. The next release of omniBiomarker will automatically select the most biologically relevant ranking method based on user input regarding prior knowledge. The omniBiomarker workflow * Data: Gene Expression * Algorithms: Knowledge-Driven Gene Ranking * Differentially expressed Genes * Clinical / Biological Validation * Knowledge: NCI Thesaurus of Cancer, Cancer Gene Index * back to Algorithms

Proper citation: omniBiomarker (RRID:SCR_005750) Copy   

•   Source: SciCrunch Registry

http://ranchobiosciences.com/gse4922/

Curated data set of a study that investigated the expression profiles of 347 primary invasive breast tumors on Affymetrix microarrays. Three separate breast cancer cohorts were analyzed: 1) Uppsala (n=249), 2) Stockholm (n=58), 3) Singapore (n=40). The Uppsala and Singapore data can be accessed in GSE4922. The Stockholm cohort data can be accessed at GEO Series GSE1456.

Proper citation: GSE4922 (RRID:SCR_003557) Copy   

•   Source: SciCrunch Registry

http://www.yale.edu/herzongroup/Herzon_Lab/Home.html

My laboratory has created a family of natural product-inspired anticancer agents. We have evaluated our compounds at the Yale Center for Chemical Genomics, and they are exhibiting IC50 values in the low nM range against K562, HeLa, LnCAP, and HCT-116 lines. Their mechanism of action is unknown, although the natural products have been shown to cleave DNA. An evaluation of the natural products at the NCI has shown that they have a toxicity profile that is distinct from other known DNA damaging agents. We can readily access gram-quantities of these agents for further studies. We are looking for researchers who might find these compounds useful in medicinal applications, for example, for treatment of a specific cancer. We are capable of synthesizing new analogs, such as those incorporating a specific recognition or targeting element, and would be excited to pursue this avenue of research.

Proper citation: Herzon Lab (RRID:SCR_008850) Copy   

•   Source: SciCrunch Registry

http://sharedresources.fredhutch.org/core-facilities/bioinformatics

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on July 27,2022. Core provides bioinformatics specialists available to assist researchers with processing, exploring, and understanding genomics data.

Proper citation: Fred Hutchinson Cancer Research Center Co-operative Center for Excellence in Hematology Bioinformatics Resource (RRID:SCR_015324) Copy   

•   Source: SciCrunch Registry

http://gepia.cancer-pku.cn

Web server for cancer and normal gene expression profiling and interactive analyses. Interactive web server for analyzing RNA sequencing expression data of tumors and normal samples from TCGA and GTEx projects, using standard processing pipeline. Provides customizable functions such as tumor or normal differential expression analysis, profiling according to cancer types or pathological stages, patient survival analysis, similar gene detection, correlation analysis and dimensionality reduction analysis.

Proper citation: Gene Expression Profiling Interactive Analysis (RRID:SCR_018294) Copy   

•   Source: SciCrunch Registry

https://omictools.com/l2l-tool

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented on August 26, 2019.

Database of published microarray gene expression data, and a software tool for comparing that published data to a user''''s own microarray results. It is very simple to use - all you need is a web browser and a list of the probes that went up or down in your experiment. If you find L2L useful please consider contributing your published data to the L2L Microarray Database in the form of list files. L2L finds true biological patterns in gene expression data by systematically comparing your own list of genes to lists of genes that have been experimentally determined to be co-expressed in response to a particular stimulus - in other words, published lists of microarray results. The patterns it finds can point to the underlying disease process or affected molecular function that actually generated the observed changed in gene expression. Its insights are far more systematic than critical gene analyses, and more biologically relevant than pure Gene Ontology-based analyses. The publications included in the L2L MDB initially reflected topics thought to be related to Cockayne syndrome: aging, cancer, and DNA damage. Since then, the scope of the publications included has expanded considerably, to include chromatin structure, immune and inflammatory mediators, the hypoxic response, adipogenesis, growth factors, hormones, cell cycle regulators, and others. Despite the parochial origins of the database, the wide range of topics covered will make L2L of general interest to any investigator using microarrays to study human biology. In addition to the L2L Microarray Database, L2L contains three sets of lists derived from Gene Ontology categories: Biological Process, Cellular Component, and Molecular Function. As with the L2L MDB, each GO sub-category is represented by a text file that contains annotation information and a list of the HUGO symbols of the genes assigned to that sub-category or any of its descendants. You don''''t need to download L2L to use it to analyze your microarray data. There is an easy-to-use web-based analysis tool, and you have the option of downloading your results so you can view them at any time on your own computer, using any web browser. However, if you prefer, the entire L2L project, and all of its components, can be downloaded from the download page. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: L2L Microarray Analysis Tool (RRID:SCR_013440) Copy   

•   Source: SciCrunch Registry