The Promise of Genomic Medicine: Are We There Yet?

Popularly referred to as next-generation sequencing (NGS), or high-throughput sequencing, NGS is the catch-all term used to describe a number of different modern sequencing technologies including Illumina (Solexa), Roche 454, Ion Torrent (Proton/PGM), and SOLiD. This has allowed us to sequence DNA and RNA much faster and cheaper than the previously used Sanger sequencing, and has revolutionized the study of genomics and molecular biology.

The cost of genomic sequencing has also come a long way. From $3 billion to sequence the first human genome, it cost about $100 million per genome in 2001, and as of January 2014, the cost is about $1,000. Compared to Moore’s law that observes computing doubles every two years, the cost of sequencing a genome is falling five to 10 times annually.

The issue now is computing power to analyze this data. Newer sequencers are now producing four times the data in half the time. Intel® technologies like Xeon® and Xeon® Phi®, SSDs, 10/40 GbE networking solutions, Omni-Path fabric interconnect, Intel Enterprise Edition for Lustre (IEEL), along with partners like Cloudera and Amazon Web Services, are helping to cut down the time for secondary analysis from weeks to hours.

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Genomic information is now catalogued and used for advancing precision medicine. For example, genomic information from TCGA (The Cancer Genome Atlas) has led to developments and FDA approval for certain cancer treatments. Currently, there are about 34 FDA-approved targeted therapies like Gleevec that treat gastrointestinal stromal tumors by blocking tyrosine kinase enzymes. Though approved by the FDA in 2001, it was further granted efficacy to treat 10 more types of cancers in 2011.

Technical Challenges

Sequencers are now producing four times more data in 50 percent less time at about 0.5TB/device/day. This is a lot of data. Newer modalities like 4-D imaging are now producing 2 TB/device/day. The majority of the software used for informatics and analytics is open sourced and the market is very fragmented.

Once the data is generated, the burden of storing, managing, sharing, ingesting, and moving it has its own set of challenges.

Innovation in algorithms and techniques is outpacing what IT can support, thus requiring flexibility and agility in infrastructures.

Collaboration across international boundaries is an absolute necessity and that introduces challenges with security and access rights.

Finally, as genomics makes its way into clinics, clinical guidelines like HIPAA will kick in.

At the clinical level, you have barriers around the conservation and validity of the sample, validity and repeatability of laboratory results, novelty and interpretation of biomarkers, merging genomics data with clinical data, actionability and eventually changing the healthcare delivery paradigm.

There are too few clinical specialists and key healthcare professionals, like pharmacists, who are trained in clinical genomics. New clinical pathways and guidelines will have to be created. Systems will need to be put in place to increase transparency and accountability of different stakeholders of genomic data usage. Equality and justice need to be ensured and protection against discrimination needs to be put in place (GINA).

Reimbursement methods need to consider flexible pricing for tailored therapeutics responses along with standardization and harmonization (CPT codes).

Path Forward

Looking ahead, we need to develop a standardized genetic terminology (HL7, G4GH, eMERGE) and make sure EHRs support the ability to browse sequenced data. Current EHRs will need standards around communication, querying, storing, and compressing large volumes of data while interfacing with EHRs’ identifiable patient information.

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Intel is partnering with Intermountain Health to create a new set of Clinical Decision Support (CDS) applications by combining clinical, genomic, and family health history data. The goal is to promote widespread use of CDS that will help clinicians/counselors in assessing risk and assist genetic counselors in ordering genetic tests.

The solution will be agnostic to data collection tools, scale to different clinical domains and other healthcare institutions, be standards based where they exist, work across all EHRs, leverage state-of-the-art technologies, and be flexible to incorporate other data sources (e.g., imaging data, personal device data).

What questions do you have?

Ketan Paranjape is the general manager Life Sciences at Intel Corporation.