The cost of mapping an individual human genome may break the $1,000 barrier by the end of the year, thanks to Life Technologies, Jonathan Rothberg and the Ion Proton Sequencer. This achievement, more than a decade in the making, will help to usher in a new era of personalized medicine – an era in which individuals’ genetic information will help guide diagnosis, treatment and prevention.
Larger availability of complete genomic data will have a profound near-term impact on cancer research. The ability to rapidly and economically sequence individual patient tumors will help us to better understand the biological mechanisms of cancer and will facilitate data-driven patient stratification. This, in turn, will facilitate more effective clinical trials and speed the development of new therapies.
The significant near-term growth of rich genomic data will impact the patient care side too. Companies like Foundation Medicine use this data to perform molecular analysis of tumors that will assist in pinpointing the optimal treatment strategies for individuals with cancer.
Genomic data alone is not a silver bullet. Rather, it is just one of multitude of data sources that will help us unravel the still largely mysterious workings of the human body and make the leap from genotype to phenotype. The true power of genomic data becomes unlocked when it is integrated with other data types, including other molecular data, imaging and laboratory results, data from apps and mobile devices, and Real World data such as electronic medical records and medical claims. These integrated data sets then need to be paired with analytical methods that can extract actionable knowledge from the data, and do it at scale.
Genomic data is big, but it’s not nearly as big as the data sets that are being routinely analyzed in the context of ecommerce today, data sets like those used by Amazon’s recommendation engine. The infrastructure that will allow us to analyze $1,000 genomes is already in place.
Eric Green, director of the NHGRI, told the Wall Street Journal that “we can sequence the genome for dirt cheap, but we don’t know how to deal with the data.” I disagree – we absolutely know what to do with the data. We now know how to combine genetic data with data from other modalities to discover what works in healthcare and for whom. Scientists, mathematicians, and computer engineers during the last couple of decades have developed new analytic tools that go beyond traditional statistics — tools that can extract knowledge at scale and make novel predictions based on data.
GNS Healthcare is part of a group of stakeholders patiently waiting for the data to become widely available. This group includes industry thought leaders Eric Schadt, Director of the Institute for Genomics and Multi-scale Biology at the Mount Sinai School of Medicine, Stephen Friend of Sage Bionetworks and Daphne Koller of the Stanford AI Lab.
The infrastructure is already in place. The analytical tools have been developed. We are ready to go.