The discovery of the causative genetic underpinnings of cancer has been a focus of biomedical research for decades. The multigenic nature of cancer has hindered progress in gaining an understanding of the underlying mechanisms that lead to a cancer phenotype. Recent advances in high-throughput technologies, which evaluate tens of thousands of genes or proteins in a single experiment, are providing new methods for determining biochemical determinants of the disease process. To facilitate these advanced technologies, the correlation of specific phenotypes to individual genotypes is key to leveraging the use of model organisms and human subjects in cancer research. Combining these data in useful ways allows cancer researchers to ask complex questions about the mechanism of specific disease manifestations, and to retrieve datasets containing disparate data that can then be analyzed using statistical and data mining methods to reveal new insights that should be further investigated.
While cancer disease diagnostics have traditionally relied on subjective techniques (e.g., TNM classification for tumors), molecular diagnostics has the potential to augment such techniques to more specifically classify the disease and its stage of development, ultimately leading to a more personalized treatment plan for the patient. Transcriptional profiling, genotyping and proteomics methods, for example, have demonstrated promising results. Identifying informative diagnostic markers for cancer and its corresponding stage is critical because they both influence treatment recommendations and patient outcome. As more molecular data are collected from patients and biological samples (human and model organisms, e.g., transgenic mice), the need to combine these data with metadata describing the phenotype and clinical consequences of the disease is essential to identifying useful markers for diagnostics. The identification of informative disease markers may lead to new therapeutics by providing further insight into the biochemical pathways and genetic networks involved in cancer, which may then lead to the discovery of molecules for drug development. This vision can only be achieved by integrating technologies from the fields of bioinformatics, computational biology, biostatistics, mathematics and clinical informatics.
To support this level of investigation, H. Lee Moffitt Cancer Center & Research Institute has established a state-of-the-art biomedical informatics program that builds upon, and extends, Moffitt’s existing computational strengths and resources. The Biomedical Informatics Shared Resource Facility is a key part of this effort by assisting cancer researchers with experimental design and data interpretation for cancer research projects requiring biomedical informatics and bioinformatics resources. Consultations assist researchers by bringing together multidisciplinary expertise, including biostatistics, proteomics, genomics and pathology so that the final research design provides the necessary data for effectively answering the questions being investigated. As the methods and techniques used by the Biomedical Informatics Shared Resource Facility are not familiar to many cancer researchers, members of the Facility work closely with investigators to fully define the needs and requirements for each project. The facility also assists researchers with data mining techniques and data analysis and interpretation. Thus, it enables cancer researchers to explore new areas of research not previously possible by providing informatics expertise and resources.
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