Page History

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• Participating consortium sites each submit common consent forms, case report forms, and boilerplate Material Transfer Agreements (MTAMTAs) to the appropriate local regulatory offices.

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Outputs measured on these cultures and biospecimens will include growth rate (determined by flow cytometry or by visually visual counting of cells at different time points, extent of cell death determined similarly, photomicrographs, reports of microscopic observations by trained investigators, rate of DNA synthesis measured by radioisotope or fluorescent labeled precursor uptake and incorporation, and staining with various immune reagents followed by high throughput robotic microscopy and automated image analysis. To develop an understanding that will resulting result in giving the correct drugs to the correct patients, data from the protein arrays will be overlaid on the regulatory pathways and linked to patient and cell culture data.

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• A bench scientist chooses candidate glioblastoma genes using human Genome-Wide Association Studies (GWAS), for example, The Cancer Genome Atlas (TCGA).

• The scientist also uses pathway analysis to postulate how multiple "hits" may be involved in tumorigenesis, to direct design of genetically altered mice.

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• Clinical scientists use mouse model results to design clinical trials to treat glioblastoma, incorporating genomic information on patients.

An outside researcher requests access to a consortium's Prostate SPOREs Federated Biorepositories, eleven instances of caTissue Suite independently maintained and managed

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• A Research Fellow at a University has been working at identifying SNPs that might be related to aggressive forms of Prostate Cancer. The Fellow has narrowed the search down to 21 SNPs, and discusses the results with the mentor.

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• The Fellow uses this information to request tissue from four institutions to build the tissue microarray (TMA).

High

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throughput sequencing using DNA sequencing to exhaustively identify tumor associated mutations

This is a basic research use case that easily becomes translational when the output of this use case is used, for example, to identify targets for biomarker studies or drug candidates for clinical trials.

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Version A is "Sequencing of selected genes via Maxim Gilbert Capillary (“First Generation”) sequencing." Nature. 2008 Sep 4 - Epub ahead of print (posted on GForge for the a workgroup).

1. Develop a list of 2000 to 3000 genes thought to be likely targets for cancer causing mutations.
2. As a preliminary (lower cost) test, pick the most promising 600 genes from this list.
3. Develop a gene model for each of these genes.
4. Hand modify that gene model, for example, to merge small exons into a single amplicon.
5. Design primers for PCR amplification for each of these genes.
6. Order Primers for each exon of each of the genes.
7. Test Primers.
8. In parallel with steps 1-7, identify match matched pairs of tumor samples and normal tissue from the same individual for the tumors of interest.
9. Have pathologists confirm that the tumor samples are what they claim to be and that they consist of a high percentage of tumor tissue.
10. Make DNA from the tumor samples, confirming for each tumor that quantity and quality of the DNA are adequate.
11. PCR amplify each of the genes.
12. Sequence each of the exons of each of the genes for each tumor and normal pair of DNA samples.
13. Find all the differences between the tumor sequence and normal sequence.
14. Confirm that these differences are real using custom arrays, the seqenome (Mass Spec) technology and biotage or both. (A biotage is pyrosequencing-based technology directed specifically at looking for SNP-like changes.)
15. Identify changes that are seen at a higher frequency than what would occur by chance.
16. Relate the genes in which these changes are seen to known signaling pathways.

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1) None; a completely manual process. 2) None; a completely manual process. 3) Data is uploaded from the UCSC Genome Browser to Genboree which has modules for all of the required tasks. 4) Same as 3. 5) Primer3 embedded into a local pipeline developed at the HGSC that keeps primers away from repeats and SNPs. Gaps where this pipeline is unable to create primers are filled in by hand. 6) Manual process. 7) Manual process. 8) It is not known how this was done by the HGSC, but caTissue and similar products can be used here. 9) Manual process. The pathology imaging initiative of Tissue Banks and Pathology Tools (TBPT) might fit in here. 10) Manual process. 11) Manual process. Could a Laboratory Information Management System (LIMS system ) help here? 12) Software provided as part of the ABI sequencer. 13) Combination of custom, ad-hoc software and manual processes. 14) Manual process. 15) Combination of custom, ad-hoc software and manual processes. 16) Manual process. This should not be a manual process, but almost always is, or it is of low quality.)

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Version B. As above, except globally sequence all genes. Science 321: 1807-1812 (2008) (posted on GForge for the a workgroup). Delete steps 1 and 2 and replace step 3 with: 3) Develop a gene model for each of the genes in the Human genome.

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Version C. Whole genome sequencing using second generation sequencers. Hypothetical (posted on GForge for the a workgroup).

1. Identify matched pairs of tumor samples and normal tissue from the same individual for the tumors of interest.
2. Have pathologists confirm that the tumor samples are what they claim to be and that they consist of a high percentage of tumor tissue.
3. Make DNA from the tumor samples, confirming for each tumor that the quantity and quality of the DNA are adequate.
4. Sequence each of the sample pairs to the required fold coverage (7.5 to 35-fold, depending on the technology and read length).
5. Map the individual reads to the canonical human genome sequence.
6. Find all the differences between the tumor sequence and normal sequence.
7. Confirm that these differences are real using custom arrays, the seqenome (Mass Spec) technology or biotage or both. (Biotage is a pyrosequencing-based technology directed specifically at looking for SNP-like changes).
8. Identify changes that are seen at a higher frequency than what would occur by chance.
9. Relate the genes in which these changes are seen to known signaling pathways.

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1) caTissue or similar product. 2) caTissue or similar product pathology imaging tools to be developed by TBPT. 3) caTissue or similar product. 4) Combination of custom, ad-hoc software and manual processes. 5) Proprietary, platform-dependent software, a wide variety of non-caBIG-compatible software packages: Solexa Mapper, Mosaic, 454 Mapper, Velvet Mapper, Solid Mapper (uses a non-standard sequence representation model), Mac. 6) Combination of custom, ad-hoc software and manual processes. 7) Manual process. 8) Combination of custom, ad-hoc software and manual processes. 9) Manual process. This should not be a manual process, but almost always is, or it is of low quality.)

Scenario 12

This is a Scenario scenario based on finding a nanoparticle delivery system to target a drug which in its free form causes significant side effects. Sorafenib is a Raf kinase inhibitor that disrupts the key Ras/Raf/MEK/ERK cellular pathway that is up-regulated in renal cell carcinoma, glioblastoma multiforme (GBM), and stomach cancer. The drug has significant side effects and a scientist hypothesizes that nanoparticle-assisted targeted delivery of the drug will reduce the required dosing and its side effects.

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This is Scenario 12 extended. The scientist investigates what data sets are available for in vivo use of the drug. A breast cancer xenograph subcutaneous model is found and cell lines from this system are also available. However, toxicity data for the drug in animal models are not publicly available. The scientist contacts the drug manufacturer and begins in vitro testing. PK/PD in vitro tests, including drug uptake, toxicity and effectiveness, are performed in the model system cell lines, and related and control cell lines by comparing the effects of drug alone, nanoparticle alone, and the combination. Next is in vivo testing with three established animal tumor models. The drug alone, nanoparticle alone, and the combination are administered and tumor size (and other parameters) are monitored. Finally efficacy, dosing, and side effects of the current dosing protocol are compared with targeted nanoparticle delivery of sorafenib.

Scenario 13

This is a Scenario scenario based on in vitro profiling of nanomaterial activity. A scientist has created a library of surface-modified nanoparticles with potential as in vivo imaging agents. The scientist would like to use an in vitro approach to gain insight on potential toxicity of these nanoparticles, and exclude those that might be problematic prior to using costly and time-intensive in vivo methods. The mode of administration is considered in selecting a variety of cell types to use in the in vitro assays. Cell cultures are started. Each nanoparticle is added to cultures of each cell type at multiple biologically-relevant concentrations. Multiple cell-based activity assays are used to test each combination of nanoparticle type and cell type, resulting in each nanoparticle being tested in all conditions. Hierarchical clustering algorithms are used to group the nanoparticles based on their activity profiles. Class predictions can be made and verified. Understanding of structure-activity relationships increases, and in vivo correlations among nanoparticles can be tested, and compared with in vitro correlations.

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The scientist would like to look at all available datasets, to see which nanomaterials act similarly to the known agent with a 11 of 19 long half-life. The scientist first queries across cancer center datasets to identify other nanoparticles with the best half-life. Initially, those data sets that use the same experimental protocol and a similar or better half-life are retrieved and compared. Next, the scientist wishes to broaden the search to include data sets that do not explicitly measure half-life, but a common set of cell-based assays. The data sets are normalized and combined. Hierarchical clustering algorithms are used to group the nanoparticles based on their activity profiles across the various cell-based assays. The scientist queries for nanoparticles that cluster closest to the starting nanoparticle with a long halflife, based on their behavior in the cell-based assays. The scientist then tests the hypothesis that the cluster neighbors will also have long half-lives in vivo.

Scenario 14

This is a Scenario scenario based on identifying in vivo imaging probes using in vitro cell binding data. The scientist in the previous scenario would like to increase the imaging potential of candidate nanoparticles by modifying them and looking for cell type-specific binding capabilities.

The scientist submits a protocol to the institutional review board (IRB) and begins work upon approval. Libraries of surface-modified nanoparticles with appropriate pharmacokinetic and toxicity profiles are selected and screened for cell binding in vitro using cell cultures of “background” and “target” cell types /or classes. The apparent concentration of binding or uptake of each nanoparticle to the different cell classes is measured. Metrics for differential binding to target versus background cells are calculated, and statistical significance is calculated by permutation. (These calculations employ analysis modules available through GenePattern (posted on GForge for the a workgroup).

To validate the increased specificity for binding target cells, those that provide the best discrimination are further tested ex vivo. Under IRB approval, anatomically intact human tissue specimens containing target and background cells are collected. The tissues are incubated with nanoparticles and evaluated for nanoparticle localization using microscopy. Further validation is conducted in vivo using an animal model. Animals are injected with the nanoparticle and another tissue specific probe and intravital microscopy is used to determine the extent of co-localization. The scientist contacts the tech transfer office to pursue next steps.

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This is a scenario based on evaluating and enriching the NanoParticle Ontology (NPO) (posted on GForge for the a workgroup). The NanoParticle Ontology (posted on GForge for the a workgroup) is an ontology which is being developed at Washington University in St. Louis to serve as a reference source of controlled vocabularies and terminologies in cancer nanotechnology research. Concepts in the NPO have their instances in the data represented in a database or in literature. In a database, these instances include field names, field entries, or both for the data model. The NPO represents the knowledge supporting unambiguous annotation and semantic interpretation of data in a database or in the literature. To expedite the development of the NPO, object models must be developed to capture the concepts and inter-concept relationships from the literature. Minimum information standards should provide guidelines for developing these object models, so the minimum information is also captured for representation in the NPO.

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