The goal of the DNA Microarray Shared Resource (DMSR) is to facilitate the application of cutting-edge microarray technology and analysis techniques by Cancer Center members. Services provided include the production of catalog and custom spotted microarrays, support of the use of multiple formats of Stanford and commercial microarrays, assistance with experimental design, archiving of array data and bioinformatics tools for the annotation, analysis, visualization and publication of microarray data. DMSR combines the resources of the Stanford Functional Genomics Facility, which develops new technology for genomics, and the Stanford Microarray Database, which is the largest and mostly widely used public database supporting microarray research. DMSR provides added value to Cancer Center members by decreasing the financial barriers associated with microarray experiments and providing the necessary training and expertise needed for the design, execution, analysis and publication of microarray experiments. Our strongly integrated genomics and informatics groups routinely interact with Cancer Center members to ensure that the microarray data generated are high quality and securely archived for analysis and interpretation as well as publication and long-term access. The current success of this Shared Resource is demonstrated by the high-impact publications that have been supported. As of July 2005, 59 cancer studies supported by the Shared Resource have been published, 14 of which were published in 2005. In fiscal year 2005, use of the microarray and data services totaled 15,937, of which 51% are by Cancer Center members from nine out of ten Research Programs. Future plans for this Shared Resource include expansion of the genomics efforts into all forms of microarray-based assays including antibody arrays, protein arrays, and reverse-phase cell lysate arrays. In addition, our bioinformatics capabilities will be significantly expanded by the incorporation of novel approaches for quality assessment, data analysis and annotation of data with cancer-related information.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Center Core Grants (P30)
Project #
5P30CA124435-02
Application #
7623567
Study Section
Subcommittee G - Education (NCI)
Project Start
Project End
Budget Start
2008-06-01
Budget End
2009-05-31
Support Year
2
Fiscal Year
2008
Total Cost
$81,293
Indirect Cost
Name
Stanford University
Department
Type
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Patel, Manali I; Sundaram, Vandana; Desai, Manisha et al. (2018) Effect of a Lay Health Worker Intervention on Goals-of-Care Documentation and on Health Care Use, Costs, and Satisfaction Among Patients With Cancer: A Randomized Clinical Trial. JAMA Oncol 4:1359-1366
Trieu, Vanessa; Pinto, Harlan; Riess, Jonathan W et al. (2018) Weekly Docetaxel, Cisplatin, and Cetuximab in Palliative Treatment of Patients with Squamous Cell Carcinoma of the Head and Neck. Oncologist 23:764-e86
Kuonen, François; Surbeck, Isabelle; Sarin, Kavita Y et al. (2018) TGF?, Fibronectin and Integrin ?5?1 Promote Invasion in Basal Cell Carcinoma. J Invest Dermatol 138:2432-2442
Gee, Marvin H; Han, Arnold; Lofgren, Shane M et al. (2018) Antigen Identification for Orphan T Cell Receptors Expressed on Tumor-Infiltrating Lymphocytes. Cell 172:549-563.e16
Malta, Tathiane M; Sokolov, Artem; Gentles, Andrew J et al. (2018) Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell 173:338-354.e15
Banerjee, Imon; Gensheimer, Michael Francis; Wood, Douglas J et al. (2018) Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives. Sci Rep 8:10037
Thorsson, Vésteinn; Gibbs, David L; Brown, Scott D et al. (2018) The Immune Landscape of Cancer. Immunity 48:812-830.e14
Rogers, Zoë N; McFarland, Christopher D; Winters, Ian P et al. (2018) Mapping the in vivo fitness landscape of lung adenocarcinoma tumor suppression in mice. Nat Genet 50:483-486
Nair, Viswam S; Sundaram, Vandana; Desai, Manisha et al. (2018) Accuracy of Models to Identify Lung Nodule Cancer Risk in the National Lung Screening Trial. Am J Respir Crit Care Med 197:1220-1223
She, Richard; Jarosz, Daniel F (2018) Mapping Causal Variants with Single-Nucleotide Resolution Reveals Biochemical Drivers of Phenotypic Change. Cell 172:478-490.e15

Showing the most recent 10 out of 322 publications