Panel 1 | Cancer genomes harbor a functionally diverse spectrum of somatic mutations, even within whole-exome sequencing data.
Panel 2 | We have (a) developed robust methods to evaluate the density of recurrent mutations in cancer genomes and (b) shown their utility in identifying cancer-drivers.
Panel 3 | We have developed and protected technologies that leverage mutation density scores to identify significantly mutated regions (SMRs) which reveal a rich landscape of cancer drivers in protein-coding and non-coding elements (a), that are variably-sized (b) and statistically robust (c).
Panel 4 | For example, our methods can reveal precise non-coding alterations that affect critical regulatory elements in as many as 15% of patients in specific cancer types.
Panel 5 | Structural mapping and analyses permits the identification of precise molecular alterations to proteins, which can be cancer-specific (a), spatially coherent (c-d), and affect DNA-protein and protein-protein interactions (b, e-f).
Panel 6 | Our performance improves with increased sequencing data (a) and will continue to improve (b) as thousands of genomes are sequenced.