Figure 1

The interpretation bottleneck of personalized medicine. A typical cancer genomics workflow, from sequence to report, is illustrated. The upstream, relatively automated steps (shown by their light color here) involve (1) the production of millions of short sequence reads from a tumor sample; (2) alignment to the reference genome and application of event detection algorithms; (3) filtering, manual review and validation to identify high-quality events; and (4) annotation of events and application of functional prediction algorithms. These steps culminate in (5) the production of dozens to thousands of potential tumor-driving events that must be interpreted by a skilled analyst and synthesized in a report. Each event must be researched in the context of current literature (PubMed), drug-gene interaction databases (DGIdb), relevant clinical trials (ClinTrials) and known clinical actionability from sources such as My Cancer Genome (MCG). In our opinion, this attempt to infer clinical actionability represents the most severe bottleneck of the process. The analyst must find their way through the dark by extensive manual curation before handing off (6) a report for clinical evaluation and application by medical professionals.