Part IV – Future Steps and Challenges
Igor Jurisica and Dennis A. Wigle
Medical information science involves using system-analytic tools to develop algorithms for management, process control, decision-making, and scientific analysis of medical knowledge. Medical informatics comprises the theoretical and practical aspects of information process- ing and communication, based on knowledge and experience derived from processes in medicine and health care. This is achieved by devel- oping and assessing methods and systems to support the acquisition, processing and interpretation of patient data with the help of knowledge that is obtained from basic and clinical research.
Medical practitioners have been treating patients by integrat- ing knowledge and best practices, personal experience and clinical observation since the days of Hippocrates. However, exponential increases in the body of knowledge applicable to patient care have resulted in ever increasing niche specialization in large, academic, tertiary care medical centers. In modern cancer care, the days of the
“generalist” are long gone. While further specialization in areas of expertise is possible, the need for computational approaches to knowledge discovery, information management, and decision sup- port continue to increase. While expertise is essential, advancing available “tools” and methods has the potential to revolutionize many aspects of healthcare delivery. Recently, we have witnessed an accelerated understanding of complex diseases at the molecular level. Cancer informatics provides both a methodology and tools to handle such information on a patient-centered level. Although many challenges remain ahead, this progress toward information-based medicine has the potential to increase healthcare quality and ena- ble innovative approaches in a true personalized manner. Key
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challenges include the development of comprehensive electronic patient records and biobank repositories, data integration and shar- ing, and seamless integration and translation of research into clinical care.
Advancing from histopathologically based disease classifica- tions to true molecular staging based on genomic and proteomic pro- files will require ongoing development of novel computational tools for clinical correlation. Many of the tools developed to date repre- sent a major step forward in cancer informatics, but further devel- the molecular heterogeneity of cancer, this is an obvious area to inte- grate and analyze diverse data sets for their ability to provide addi- tional information. These integrated analyses of multidimensional data will reveal markers that enhance existing clinical approaches to diagnosis, prognosis and treatment planning in cancer. The devel- opment of cancer profiles could potentially lead to new cancer treatments as well as techniques for early diagnosis. The long-term goal of these collective strategies is information-based individual- ized patient care. There are already low-throughput examples of
Measuring the genomic expression profile in cell cultures and accumulating a set of characteristic profiles as a background information base can assess the effect of known toxic compounds.
Patient progress can be assessed by detailed measurements of thou- sands of molecular indicators from bodily fluids, biopsies, such as RNA expression, protein expression, protein modification, or con- centration of metabolites. However, the current medical practice is primarily reactive – frequently, we only treat disease after symptoms appear, which for cancer usually means an advanced stage with dismal prognosis and limited treatment options. Even when the treat- ments are available, we may not deliver them optimally for an indi- vidual patient. The FDA’s Center for Drug Evaluation and Research estimates that approximately 2 million of the 2.8 billion prescriptions filled annually in the United States will result in adverse drug reac- problems, we have to further:
Cancer Informatics in the Post Genomic Era
genotyping for genetic markers (e.g. cystic fibrosis) and profiling opment will be required to enable routine clinical application. Given
for disease markers (e.g. prostate-specific antigen).
tions, leading to about 100,000 deaths per year. To diminish these
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1. Accelerate the molecular understanding of cancer by systems biology approaches to investigate the underlying basis of disease.
2. Extend and apply cancer informatics to support the acquisition, integration, analysis, visualization, interpretation, and dissemi- nation of integrated molecular and clinical data for decision support.
One of the current goals from amassing large databases of protein structural information is the ability to compute reliable struc- tural predictions of proteins based on amino acid sequence. The attainment of this goal would greatly facilitate the design of syn- thetic organic compounds in medicinal chemistry and dramatically accelerate the pace of rational drug design. Speeding up this process of lead target to drug candidate is a critical step in translating the volume of high-throughput data being generated in disease models to clinical utility.
True understanding of biological systems will require the in- tegration of data across multiple high-throughput platforms. Our ability to derive true knowledge from the current data being gener- ated on SNPs, gene expression, protein abundance and interaction, and mutational information will necessitate the creation of novel methods to store, analyze, and visualize this information. The advent of genomic and proteomic technologies has ushered forth the era of genomic medicine. The promise of these advances is true “personal- ized medicine” where treatment strategies can be individually tai- lored based on combinations of SNPs, gene expression, and protein expression levels in biological samples. Translating these advances to the improvement of objective outcomes such as prolonged sur- vival and increased quality of life is eagerly awaited by patients with cancer and their healthcare providers.
Future Steps and Challenges
