Personalized medicine has been a goal for a growing number of biomedical researchers over the last twenty years. Considering the fact that biomedical research literature on personalized medicine has grown from 5-10 articles/year in the 1980’s to over 2500 articles per year since 2013, incredible progress has been made towards this extremely challenging goal. For personalized medicine to happen, at least two elements are necessary: 1. A means of acquiring personalized data is needed, and 2. A means of integrating, analyzing, and applying that data. The explosive improvements in the amount, quality, efficiency, and cost-effectiveness of obtaining personalized data , creates a huge challenge in the integrating, analysis, and application of that exponentially growing body of data available. Thus, the challenge of personalized medicine primarily lies in the integration and analysis of ‘Big Data.’ Yes, there is always room for improvement in data acquisition, but all the growing data is problematic if it cannot be effectively utilized. Researchers from all over the world have been working on analysis tools in order to better extract useful information from the growing available sets of omics data, and [Warning: shameless plug alert] The Su lab’s Omics Pipe is one attempt to automate the best practice multi-omics data analysis pipelines. [End shameless plug]
Barbour Analytics published a fascinating post on the N-of-1 problem in Big Genomics. It is a great read for anyone interested in personalized medicine, rare diseases, big data, and bioinformatics. Here is a teaser to encourage you to take a look at the original post:
How do we assess the impact of a single novel mutation, or a set of novel mutations, unique to an individual? This is the N of 1 problem in Big Genomics. Statistics, and statistical genetics rely on summary, on binning the patterns of populations of individuals into categories of adequate size that we can compare groups using standard metrics like mean, median, mode, standard error, and in more elaborate frameworks use more sophisticated metrics like moments, edges, vectors and ridges.
The N of 1 problem in Big Genomics will require modeling approaches, to construct models of the genome, and make projections on the likely function of single de novo mutations, and suites of these private mutations. Robust modeling efforts in this area will be a major challenge in the era of genomic medicine, and personalized medicine. At present we are effectively constrained to study mutations that have recurred throughout evolution. As our population grows, as the number of persons under care, and participating in genomic medicine increases, we will need to address the private mutation issue head on.
We can look to cancer genomics for some guidance. Cancers are a genomic disease, with both inherited and de novo elements, and direct sequencing of genomes often reveal unique mutations that lead to unique cancer profiles. This field has the advantage of seeing a clear disease manifestation in the form of tumor growth, often restricted to a tissue or cell type. This helps make more direct inferences about the likely function of the novel mutation.
That said, we face the inherent limitation that a mutation may be unique, or at least rare, and for this reason it is difficult to use traditional statistical approaches, approaches that rely on summary, on the behavior of groups of instances. While the genotype information may be limited to one person in these instances, we can assist ourselves in this effort by capturing more information about clinical and biological phenotype. Detailed phenotypic characterizations of a tumor or affected tissue – extending to the transcriptome, kinome, metabolome, cytokine profiles, cell morphology and indeed clinical status itself, can help us perform a sort of reverse interpolation to infer the function of the single N of 1 de novo mutation. While the mutation may be unique or rare, the disease manifestation itself may be common, or at least share key features with other maladies.
Now go read the original post here: ‘Private Mutations’: The N of 1 in Big Genomics — Barbour Analytics.