The mission statement was last updated on GForge on August 12, 2003. The use cases were last updated on February 8, 2009.
The Mission Statement of ICRi
To identify, understand, and facilitate responses to needs for interoperability between different caBIG applications where at least one of these applications is a product of the ICR workspace.
Describe Use Cases here. Scenario 1: Discovering a Biomarker A scientist is trying to identify a new genetic biomarker for HER2/neu negative stage I breast cancer patients. Using a caGrid-aware client, the scientist queries for HER2/neu negative tissue specimens of Stage I breast cancer patients at LCCC that also have corresponding microarray experiments. Analysis of the microarray experiments identify genes that are significantly over-expressed and under-expressed in a number of cases. The scientist decides that these results are significant, and related literature suggest a hypothesis that gene A may serve as a biomarker in HER2/neu negative Stage I breast cancer. To validate this hypothesis in a significant number of cases the scientist needs a larger data set, so he queries for all the HER2/neu negative specimens of Stage I breast cancer patients with corresponding microarray data and also for appropriate control data from other cancer centers. After retrieving the microarray experiments the scientist analyzes the data for over-expression of genes A. Scenario 2: Finding Biomaterial to Validate a Biomarker In scenario 1, the scientist has validated a biomarker based on available microarray experiments provided by various cancer centers. Now, the scientist would like to request biomaterial in the form of formalin-fixed/paraffin embedded tissue specimens from patients with the appropriate clinical outcomes. The scientist would like to validate the genetic biomarker in a different series of cases, this time using a different technique such as immunohistochemistry. The scientist queries for the presence of appropriate tissue using a caGrid-aware client and for the appropriate contact information of the person(s) responsible for the tissue repository. The scientist contacts the person(s) to begin the protocol for retrieving biomaterials. Scenario 3: Extending the use of a biomarker The scientist would like to check if genes A could also be used as biomarker for other types of cancer. The flow of events will be similar to Scenario 1 with the exception that the specimen query will not be restricted to Stage I breast cancer patients. Scenario 4: Exploring predictive power of gene expression in breast cancer metastasis The scientist would like to explore if gene expression patterns can predict how breast cancer will metastasize. He queries all the specimens of breast cancer patients from other cancer centers where their metastasis sites are liver, bone and brain. The scientist then retrieves the corresponding microarray experiments for these specimens. The scientist analyzes the microarray experiments to explore for a correlation between expression profiles and metastasis sites. Scenario 5: Oncologists in formulating ideas for new clinical studies The oncologist often wants to first find out the answers to questions such as: How many patients have been seen at our institution with disease x"? How does that compare with other institutions? What is the average survival of patients with disease x? How is it different if they are treated with drug x or y? How many patients with disease x and TNM stage y at diagnosis? How many patients with disease x relapse after treatment y? This use case, then, is about enabling oncologists to ask these exploratory questions of their clinical databases as well as those at other institutions accessible on caGrid. Scenario 6: High throughput screen for anti-cancer drugs leads based on robotic microscopy A basic research scientist has developed a high throughput screen for anti-cancer drugs (leads) based on robotic microscopy. The final output of the process is relatively simple; a two by two matrix with the rows being a few million small molecules and the columns being some biological properties of these compounds, e.g. toxicity against several different tumor cell lines, toxicity against several different normal cell lines, ability of the molecule to enter the cell and its intracellular distribution, and impact of the molecule on a number of biological endpoints. However, the process of generating this output presents a number of challenges. The initial output of the robotic microscopy is many thousands of images a day. The raw images must be stored so as to be available for future analytical algorithms and should, for similar reasons, be sharable. The space required to store these images is tens to hundreds of terabytes per year per instrument. Analysis of these images to generate the desired input needs to be automated. Many of the best algorithms are proprietary and are embedded in software which is not caBIG compatible and resides within a community not currently engaged with the caBIG community. Both the raw images and final results, annotated as to how those results were obtained, needs to be made available to appropriate collaborators both within the academic and commercial sphere so that leads identified in these small molecule libraries can be modified into drug candidates which can then be tested first preclinically and then in clinical trials. Scenario 7: Translational Research Use Case - Multi-Center Ancillary Study in the context of a Consortium Clinical Trial (extension from Enterprise Use Cases) • Within a consortium of cooperating institutions an investigator conducts a search across the consortiums’ clinical data repositories to investigate the feasibility of a potential clinical research idea. • Within the consortium, the research question is circulated to gauge interest. • Members of the consortium discuss the research question and approve it as viable. • The research question is formalized by the coordinating center into a clinical research ancillary protocol for validation of a biomarker as predictive of tumor shrinkage in the context of treatment using an investigational agent and posts to the consortium for consideration (e.g. Do patients with a particular marker respond better to treatment with the agent?). • Consortium member sites choose to join the protocol and agree to accrue patients on to it, collect bio samples from each participant and ship a defined set of bio samples to Central Pathology. • Participating consortium sites each submit common consent forms, case report forms, and boilerplate Material Transfer Agreements to the appropriate local regulatory offices. • The protocol meta data, case report forms, standard operating procedures, MTA documents, etc are finalized and disseminated to each participating site. • Participants are screened on the basis of eligibility by study coordinator at each site. • Patients are accrued (by physician or patient self referral) by local staff onto the protocol at each site, and the accrual event is reported to the coordinating center. • Bio samples are collected and relevant clinical annotations including tumor measurements are collected at the appropriate time points as indicated in the protocol (these are for calculating your primary end point – tumor shrinkage). • Follow-up appointments are scheduled as specified in the protocol. • Bio samples are periodically sent to Central Pathology. • Central Pathology re-labels the samples to hide the source and identity. • Central Pathology sends out batches of collated bio samples to each of the participating biomarker assay labs. • Basic scientist at biomarker lab submits the result of biomarker assays. • Patients are followed for 3 years from primary treatment date. Annual follow-up visit occurs and blood sample is taken. Additional clinical annotations are collected. • The trial closes, and all the data are made accessible to the statisticians. • Statistician communicates the clinical significance of and evidence for biomarker response prediction. • Clinical researcher, basic scientist and statistician write a scientific paper reporting the results. • Data are made available according to funding agencies requirements. Scenario 8: Overlay of protein array data on the regulatory pathways with links to patient and cell culture data. A clinical research scientist wants to be able to predict the efficacy of tyrosine kinase inhibitors as cancer chemotherapeutic agents. The fact that many oncogenes are tyrosine kinases would predict that such agents should be effective, but several have been synthesized and tested in clinical trials, and the results have been disappointing in the extreme, with more cases of tumor growth stimulation than inhibition. The clinician hypothesizes that these unexpected effects are the result of regulatory feedback loops. To test this hypothesis, he requires software tools for modeling regulatory pathways. In addition, he needs to determine the state of such pathways in different patients by measuring the state of phosphorylation of the elements (proteins) of these pathways using reverse phase protein arrays. Because the consequences of treating the wrong patient with the wrong agent are so severe, the response of the tumor to the inhibitors will be tested in vitro, on cell cultures established from tumor biopsies. However, biospecimens and data from those patients who participated in clinical trials of these reagents before their ineffectiveness was appreciated is also available. Outputs measured on these cultures and biospecimens will include growth rate (determined by flow cytometry or by visually 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 in giving the correct drugs to the correct patients, data from the protein arrays will be overlayed on the regulatory pathways and linked to patient and cell culture data. Scenario 9: Animal model use case · Bench scientist chooses candidate glioblastoma genes using human GWAS (eg, TCGA). · Scientist also utilizes pathway analysis to postulate how multiple "hits" may be involved in tumorigenesis, to direct design of genetically altered mice. · Utilizes targeted gene transfer to deliver mutated genes to inbred mice. o Using inbred mice provides uniform genetic background in which researcher can also investigate mutated candidate genes in conjunction with other gene knockouts. · Scientist finds mutated gene x expressed in gene y knockout mouse results in glioblastoma development that parallels human pathology. · Scientist validates that model reacts in similar way to current therapeutic treatments. · Scientist uses mouse model to test new therapeutic treatments, including combinations of drugs chosen to inhibit multiple pathways. · Clinical scientists utilize mouse model results to design clinical trials to treat glioblastoma, incorporating genomic information on patients. Scenario 10: Outside researcher requesting access to a consortium's (Prostate SPOREs) Federated Biorepositories—11 instances of caTissue Suite independently maintained and managed. • A Research Fellow at a University has been working at identifying SNPs that might be related to aggressive forms of Prostate Cancer. She has narrowed her search down to 21 SNPs, and she discusses her results with her mentor. • Her mentor just returned from a Bio repository presentation where he learned of the Consortium's federated biobanks built on caTissue, and he mentions that this might be a valuable resource that could aid her research. He suggests that she contact a colleague of his at MSKCC who is a member of the Consortium. • The Research Fellow drafts an email briefly explaining her research and sends it to the MSKCC cancer center member. She asks about the possibility for searching across the Consortium's federated biobanks for cases that have X and Y and at least 3 years of outcome data. Her goal is to collect enough tissue to construct a Tissue Microarray. • The MSKCC member responds and directs the Fellow to the consortium hub web site where there are details on the policies and procedures for requesting an account to be able to submit a query that would search each of the 11 instances of caTissue Suite. He agrees to be her sponsor. • The Fellow completes an online form that requests and abstract of the research, the name of her institution, the non-profit status of her institution, the name of the sponsoring Consortium member and that person’s instition, and a required checkbox indicating that she has read and agrees to the terms of use. • The Oversight Committee (OC) of the Consortium Biorepositories has a standing regularly scheduled telephone conference call during which they review requests to query the federated biorespositories. After each regular call a new set of primary reviewers are elected who will be responsible for thoroughly reading new requests and presenting them to the other members of the OC for a vote. • The OC has authored a set of appropriate use policy documents against which all requests are measured. The current primary reviewers read the Fellow’s application and report to the other members of the OC. • The OC votes to approve the Fellow’s request. • The Fellow is notified of the OC’s decision, and she is supplied with an account to the MSKCC instance of caTissue, since her application was sponsored by a member at MSKCC. • The Fellow logs into caTissue Suite at MSKCC as a researcher, and she formulates the parameters of her query. She submits the query and after a period of time she sees a results set that span 8 of the 11 instances of caTissue Suite at the Consortium sites. • The Fellow uses this information to request tissue from 4 institutions to build her TMA. Scenario 11: High Throughput Sequencing Using DNA Sequencing to Exhaustively Identify Tumor Associated Mutations (Use cases courtesy of David Wheeler and Kim Worley of BCM’s Human Genome Sequencing Center (HGSC).) 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. Version A: Sequencing of selected genes via Maxim Gilbert Capillary (“First Generation”) sequencing. Nature. 2008 Sep 4 - Epub ahead of print? 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 e.g. 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 pairs of tumor samples/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/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/or biotage (a 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. Existing Tools: 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) I don’t know how this was done by the HGSC, but clearly caTissue and similar products can be used here. 9) Manual process. The pathology imaging initiative of TBPT might fit in here. 10) Manual process. 11) Manual process. Could a 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 DEFINITELY should not be a manual process, but almost always is, or else it is of low quality.) Variants of This Use Case Version B. As above, except globally sequence all genes Science 321: 1807-1812 (2008)? Delete steps 1 and 2 and replace step 3 with: 3) Develop a gene model for each of the genes in the Human genome Version C. Whole genome sequencing using second generation sequencers Hypothetical? 1) Identify match pairs of tumor samples/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 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/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 and/or biotage (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. Existing Tools: 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 DEFINITELY should not be a manual process, but almost always is, or else it is of low quality.) Scenario 12 A 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.A scientist interested in targeting this drug to GBM does research on possible nanoparticle-delivery systems that have the following properties: • Biocompatibility • Sufficiently long intravascular half-life to allow for repeated passage through and interactions with the activated endothelium • The ability to have ligands and proteins conjugated on the surface in multivalent configuration to increase the affinity and avidity of interactions with endothelial receptors • The ability to have functional groups for high-affinity surface metal chelation or radio-labeling for imaging • The ability to encapsulate drugs • The capability to have both imaging and therapeutic agents loaded on the same vehicle Furthermore, the scientist looks for information on nanoparticles that could potentially target the GBM. Integrin-targeted nanoparticles are identified. Synthesis involves ultraviolet (UV) cross-linking of an αv β3-integrin-targeting ligand attached to diacetylene phospholipids and a cationic lipid. These are sonicated to form polymerized vesicles and the αv β3-targeted NP can serve as a scaffold for the attachment of therapeutic agents for imaging and therapy. The physical characteristics have been determined. These include size, zeta potential, and the relevant IC50. In a cell adhesion assay, the 10 of 19 effect of multivalency on IC50 is also measured. Selectivity was also demonstrated in a receptor-binding assay and it is also shown that the αv β3-targeted NP is not rapidly cleared from the target tissue. Previous studies have shown this particle to be highly stable, have no measurable toxicity and to specifically target tumor associated vasculature in GBM when conjugated to GFP. Furthermore the particle has been used as an imaging agent when conjugated with Gd3+ or Indium2+. The αv β3-targeted NPsorafenib is synthesized. Sorafenib absorption characteristics are available and the concentration of the drug in the system is determined via spectroscopy methods. Other physical properties are characterized. Scenario 12 B. Extended scenario 12 The scientist investigates what data sets are available on 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, 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) is monitored. Finally efficacy, dosing, and side effects of the current dosing protocol are compared with targeted nanoparticle delivery of sorafenib. Scenario 13 A 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. Scenario 13 B Extended scenario 13 How can an investigator use the dataset described above (and others created in similar ways) to make choices about nanoparticle design to optimize the chance that it would have a favorable in vivo activity? A scientist wants to maximize the circulating half-life of a nanomaterial. One material that has a long half-life is known and the scientist wonders if other nanomaterial compositions have similarly long half-lives. 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 her 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. She queries for nanoparticles that cluster closest to the starting nanoparticle with a long halflife, based on their behavior in the cell-based assays She then tests the hypothesis that the cluster neighbors will also have long half-lives in vivo. Scenario 14 A 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 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/classes. The apparent concentration of binding or uptake of each nanoparticle to the different cell classes is measured. Metrics for differential binding to target vs. background cells are calculated, and statistical significance is calculated by permutation. (These calculations employ analysis modules available through GenePattern?, a caBIG®-supported project.)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. Scenario 14 B Extended Scenario 14 Customizing cell lines to identify nanoparticle probes Varying the cell lines chosen for the study can help to generate analogous datasets. A scientist wants to find a nanoparticle that targets cancer cells bearing a specific oncogene mutation. Cell assays are performed in multiple cell lines that either do (target) or do not (background) bear this oncogene mutation. The data are analyzed as above to find particles that discriminate between the presence and absence of the mutation. The scientist then tries to validate these probes using independent tumor samples, or in mice genetically engineered to bear tumors that either do or do not express the mutation under study. Scenario 14 C Extended scenario 14 Analyzing existing datasets to identify nanoparticle probes When many nanoparticles have been screened for their uptake in many different cell lines across many cancer centers, a scientist imports all the datasets that involve nanoparticle binding or uptake to cells. The cell lines are reclassified into target or background cells based on a set of criteria (tissue type, presence or absence of a oncogene mutation, etc.) and an analogous analysis is performed to identify nanoparticles that exhibit differential binding/uptake to different classes of cell lines. Scenario 15 Scenario based on evaluating and enriching the NanoParticle? Ontology The NanoParticle? Ontology (NPO) is an ontology which is being developed at Washington University in St. Louis to serve as a reference source of controlled vocabularies / 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 would include field names and/or field entries of the data model. NPO represents the knowledge for supporting unambiguous annotation and semantic interpretation of data in a database or in the literature. To expedite the development of 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.
