Informatics Needs and Challenges in Cancer Research Workshop Summary (2012) / Chapter Skim
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3 Informatics and Personalized Medicine
3 Informatics and Personalized Medicine
Pages 31-42
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From page 31... ...
· User-, domain-, and data-driven analytic informatics tools will allow researchers to decipher the billions of data points col lected for an individual and use them to define and understand individual wellness and disease. · Together, systems biology and advanced informatics tools applied to disease and wellness provide the foundation for a personalized approach to medicine, allowing health care to be more predictive, preventive, personalized, and participatory.
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From page 32... ...
social and business networks that evolve from the knowledge gained from big datasets, and (3) digital personalized devices that will lead to the generation of a "quantized self." Hood predicted that in 10 years, each individual will be surrounded by a virtual data cloud of billions of data points from many different types of networks (e.g., genome, proteome, transcriptome, epigenome, phenome, single cells, transactional, telehealth, social media)
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From page 33... ...
Simply stated, systems biology is a holistic and integrative approach to studying biological complexity, where frontier biology drives new technologies, which in turn catalyze novel domain-driven and data-driven computational tools; systems medicine is the application of the strategies, technologies, and computational tools of systems biology to disease and wellness; and P4 medicine is the clinical application of systems medicine to patients. An integrated systems approach to disease is essential for dealing with complexity, Hood said, and he elaborated on the five pillars of that philosophy: 1.
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From page 34... ...
Validation may also entail assessing an assay's measurement performance characteristics to determine the range of conditions under which the assay will give reproducible and accurate data (analytical validation) , as well as assessing a test's ability to accurately and reliably predict the clinically defined disorder or phenotype of interest (clinical/biological validation)
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From page 35... ...
, and integration of the different datasets from the system. Hood noted that large datasets are subject to two types of noise: technical noise resulting from how the data are acquired, managed, and analyzed and biological noise that arises as a natural consequence.
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From page 36... ...
Briefly, inbred mice were injected with infectious prion particles and followed for changes in the transcriptome of their brains relative to the brains of normal littermates. Surprisingly, Hood and colleagues observed that 7,400 genes, or one-third of the mouse genome, were differentially expressed.
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From page 37... ...
Hood described four technology-driven projects with potential commercial applications on which the Institute for Systems Biology is currently working: 1.Complete genome sequencing of families -- integrating genomics and genetics to find disease genes 2.The Human Proteome Project -- selected reaction monitoring (SRM) mass spectrometry assays for all human proteins 3.Clinical assays for patients -- allowing new dimensions of data space to be explored 4.The Second Human Genome Project -- mining all complete human genomes and associated phenotypic or clinical data for the predictive medicine of the future
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From page 38... ...
. By sequencing the genomes of a family of four and applying the principles of Mendelian genetics, one can identify 70 percent of the sequencing errors in the family, identify rare variants, determine the chromosomal haplotypes, determine the intergenerational mutation rate, and identify candidate genes for simple Mendelian diseases.
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From page 39... ...
One ongoing project that Hood described involves differentiating iPS cells from healthy and diseased individuals into neurons, exposing them to environmental signals, and then using global and single-cell -omics analysis to try to understand the relative contributions of the digital genome and the environmental signals. Another example of the use of iPS cells is the stratification of complex genetic diseases such as Alzheimer's disease.
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From page 40... ...
. Among the technology and informatics challenges Hood listed were the lack of standards for electronic medical information; how to handle conventional medical records and histories as well as molecular, cellular, and phenotypic data; how to identify the actionable gene variants in individual genome sequences; and how to handle the comparative and subtractive analyses of billions of genomes and associated phenotypic data.
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From page 41... ...
Participatory · he patient will become the center of the P4 health care net T work, and patient-driven social networks for disease and well ness will be a driving force for change. · S ociety should be able to access patient data after de identification and make them available to biologists for pioneer ing the predictive medicine approaches of the future.
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From page 42... ...
. The digital revolution will generate enormous amounts of useful personal data including, for example, imaging data, longitudinal data, and social network data, existing together in a dynamic "network of networks," Hood concluded.
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