Biomedical Informatics for Cancer Research

view, showing that multiple molecular pathways must be affected for cancer to develop, but with different specific proteins in each pathway mutated or differentially expressed in a given tumor (The Cancer Genome Atlas Research Network 2008; Parsons et al. 2008). Different studies demonstrated that while widespread mutations exist in cancer, not all mutations drive cancer development (Lin et al. 2007). This suggests a need to target only a deleterious subset of aberrant proteins, since any tre- ment must aim to improve health to justify its potential side effects. Treatment for cancer must become highly individualized, focusing on the specific aberrant driver proteins in an individual. This drives a need for informatics in cancer far beyond the need in other diseases. For instance, routine treatment with statins has become widespread for minimizing heart disease, with most patients responding to standard doses (Wilt et al. 2004). In contrast, standard treatment for cancer must become tailored to the molecular phenotype of an individual tumor, with each patient receiving a different combination of therapeutics aimed at the specific aberrant proteins driving the cancer. Tracking the aberrations that drive cancers, identifying biomarkers unique to each individual for molecular-level di- nosis and treatment response, monitoring adverse events and complex dosing schedules, and providing annotated molecular data for ongoing research to improve treatments comprise a major biomedical informatics need.

This is an overview of software produced to aid cancer research. It first reviews informatics in cancer research then covers authentication and authorization, data management, data pipelines and annotations, algorithms and models and the NCI caBIG initiative.

view, showing that multiple molecular pathways must be affected for cancer to develop, but with different specific proteins in each pathway mutated or differentially expressed in a given tumor (The Cancer Genome Atlas Research Network 2008; Parsons et al. 2008). Different studies demonstrated that while widespread mutations exist in cancer, not all mutations drive cancer development (Lin et al. 2007). This suggests a need to target only a deleterious subset of aberrant proteins, since any tre- ment must aim to improve health to justify its potential side effects. Treatment for cancer must become highly individualized, focusing on the specific aberrant driver proteins in an individual. This drives a need for informatics in cancer far beyond the need in other diseases. For instance, routine treatment with statins has become widespread for minimizing heart disease, with most patients responding to standard doses (Wilt et al. 2004). In contrast, standard treatment for cancer must become tailored to the molecular phenotype of an individual tumor, with each patient receiving a different combination of therapeutics aimed at the specific aberrant proteins driving the cancer. Tracking the aberrations that drive cancers, identifying biomarkers unique to each individual for molecular-level di- nosis and treatment response, monitoring adverse events and complex dosing schedules, and providing annotated molecular data for ongoing research to improve treatments comprise a major biomedical informatics need.

Section 1: Concepts, Issues, and Approaches 1. Biomedical Informatics for Cancer Research: Introduction Michael F. Ochs, John T. Casagrande, Ramana V. Davuluri 2. Clinical Research Systems and Integration with Medical Systems Joyce C. Niland, Layla Rouse 3. Data Management, Databases and Warehousing Waqas Amin, Hyunseok Peter Kang, Michael J. Becich 4. Middleware Architecture Approaches for Collaborative Cancer Research Tahsin Kurc, Ashish Sharma, Scott Oster, Tony Pan, Shannon Hastings, Stephen Langella, David Ervin, Justin Permar, Daniel Brat, TJ Fitzgerald ,James Purdy, Walter Bosch, Joel Saltz 5. Federated Authentication Frank J. Manion,, William Weems, and William McNamee 6. Genomics Data Analysis Pipelines Michael F. Ochs 7. Mathematical Modeling in Cancer Robert A. Gatenby 8. Reproducible Research Concepts and Tools for Cancer Bioinformatics Vincent J Carey, Victoria Stodden 9. The Cancer Biomedical Informatics Grid (caBIG®): An Evolving Community for Cancer Research J. Robert Beck Section 2: Tools and Applications 10. The caBIG® Clinical Trials Suite John Speakman 11. The CAISIS Research Data System Paul Fearn, Frank Sculli 12. A Common Application Framework that is Extensible: CAF--É Richard Evans, Mark DeTomaso, Reed Comire, Vaibhav Bora, Jeet Poonater, Aarti Vaishnav, Scott Catherall, John T. Casagrande 13. Shared Resource Management (SRM) Matt Stine, Vicki Beal, Nilesh Dosooye, Yingliang Du, Rama Gundapaneni, Andrew Pappas, Srinivas Raghavan, Sundeep Shakya, Roshan Shrestha, Momodou Sanyang, Clayton Naeve 14. The caBIG® Life Science Distribution Juli Klemm, Anand Basu, Ian Fore, Aris Floratos, George Komatsoulis 15. MeV: MultiExperiment Viewer Eleanor Howe, Kristina Holton, Sarita Nair, Daniel Schlauch,Raktim Sinha and John Quackenbush 16. Authentication and Authorization in Cancer Research Systems Stephen Langella, Shannon Hastings, Scott Oster, Philip Payne, Frank Siebenlist 17. Caching and Visualizing Statistical Analyses Roger D. Peng, Duncan Temple Lang 18. Familial Cancer Risk Assessment Using BayesMendel Amanda Blackford, Giovanni Parmigiani 19. Interpreting and Comparing Clustering Experiments through Graph Visualization and Ontology Statistical Enrichment with the ClutrFree Package Ghislain Bidaut 20. Enhanced Dynamic Documents for Reproducible Research Deborah Nolan, Roger D. Peng, Duncan Temple Lang.