The Shared Resources Request for Applications was introduced in 2008, to fund the purchase of shared resources that will bring novel technologies to the research laboratories of Memorial Sloan Kettering Cancer Center. Since its introduction twelve Memorial Sloan Kettering investigators have been awarded Geoffrey Beene Cancer Research Center shared resources funds.
The following investigators were awarded Geoffrey Beene Cancer Research Center shared resources funds:
Antitumor Assessment Core Facility
Project Abstract: To initiate first-in-human clinical trials, the FDA requires that animal safety studies be conducted in accordance with federal regulations for Good Laboratory Practices (GLP). In the past five years alone, Memorial Sloan Kettering investigators have developed more than 20 investigational new drugs (INDs), including biologics, radiopharmaceuticals, and small molecule drugs, which have been cleared by the FDA for clinical trials at Memorial Sloan Kettering The Antitumor Assessment Facility conducted IND-enabling safety studies for more than half of these studies in a GLP-like manner. However, similar future studies will have to be conducted under strict GLP conditions. Currently, no facility exists for conducting GLP-compliant studies at Memorial Sloan Kettering; therefore, the only option available for investigators developing novel agents is to contract GLP safety studies to an outside vendor (CRO). Drug development efforts with CROs are extremely costly, frequently delayed, and often suffer difficulties in method development, technology transfer, and initial characterization of novel agents. Thus, to maintain a competitive translational research program, Memorial Sloan Kettering needs to develop internal resources to conduct GLP studies in a more cost- and time-effective way. The proposed resource will enable investigators to conduct GLP-compliant animal safety studies onsite at Memorial Sloan Kettering, thereby streamlining preclinical development and reducing costs.
Project Abstract: The rapid increase in speed and decrease in cost of DNA sequencing have started a revolution in genomics. While data generation is now quick and cost-effective, data transfer and storage issues currently prevent the effective use of many new data-sequencing resources . At present, researchers may lose precious time, bandwidth, and disk space by downloading and storing large-scale and commonly utilized data resources. The establishment of a one-petabyte (1,000,000-gigabytes) local data storage server will solve these issues for many Memorial Sloan Kettering investigators across departments by providing fast and convenient access to insight-enabling data. The resources we have targeted include The Cancer Genome Atlas, ENCODE, The 1000 Genomes Project, The Human Microbiome Project, and others.
Geoffrey Beene Translational Oncology Core Facility
Project: Beckman Coulter SPRIworks HT Fragment Library System
Project Abstract: Next-generation deep sequencing of human tumors is revolutionizing the cancer genomics field by facilitating the correlation of genomics data with clinical data, which aims to inform diagnosis and risk stratification, and ultimately result in individualized treatment. Regardless of the platform used, libraries of template genomic DNA, cDNA, or amplicons have to be prepared prior to sequencing. The manual preparation and quality control of libraries is labor intensive and is the main bottleneck in the efficient production of sequence data. The use of automation will reduce the time burden for technicians, eliminate human error, and increase the volume and efficiency of library construction. We have received a Shared Equipment Grant to purchase the Beckman Coulter SPRIworks HT Fragment Library System. This instrument comes with a validated suite of methods, for the various library construction steps, and its software is flexible, permitting, for example, the creation of new methods for target enrichment. The purchase of this equipment will benefit many departments and investigators across Memorial Sloan Kettering Cancer Center.
Moritz Kircher, MD, PhD
Department of Radiology
Shared Resource: Raman Microscope for Label-Free Tissue Characterization and High-Sensitivity Detection of Raman Contrast Agents
The instrument purchased with this grant is a Renishaw InVia Raman microscope with Streamline upgrade. It allows rapid acquisition of Raman spectra and Raman imaging maps of materials, cells, and tissue sections as well as of small animals in vivo.
Jason T. Huse, MD, PhD
Department of Pathology
Human Oncology and Pathogenesis Program
Shared Resource: High-Throughput Immunohistochemistry
We have recently acquired a state-of-the-art immunostainer that will considerably improve our ability to detect proteins of interest directly on tissue slides obtained from patient tumors. The device can hold 30 slides at any one time, is fully automated, and can complete staining runs in six hours. Our lab, along with Ingo Mellinghoff’s and Timothy Chan’s labs, are already using it extensively.
Gabriela Chiosis, MA, PhD
Breast Cancer Medicine Service, Department of Medicine
Molecular Pharmacy and Chemistry Program, Sloan Kettering Institute
Shared Resource: LC/MS/MS instrument for use in translational research and drug discovery and development research at MSK
Marcel van den Brink, MD, PhD
Head, Division of Hematologic Oncology, Department of Medicine
Immunology Program, Sloan Kettering Institute
Shared Resource: Human Tissue Procurement, Tissue Bank, Clinical Database for Hematologic Oncology Division
Hakim Djaballah, PhD
Molecular Pharmacology and Chemistry Program, Sloan Kettering Institute
Shared Resource: Expansion of the Functional Genomics Platform at the High-Throughput Screening Core Facility
Peter Kijewski, PhD
Department of Radiology
Imaging and Spectroscopic Physics Service, Department of Medical Physics
Shared Resource: System for Research Image Management, Storage, and Processing
Paul Tempst, PhD
Molecular Biology Program, Sloan Kettering Institute
Shared Resource: Capillary LC-MS/MS System for High-complexity, High-sensitivity, Quantitative Proteomics