  We present a novel method for extracting cancer signatures by applying
statistical risk models (http://ssrn.com/abstract=2732453) from quantitative
finance to cancer genome data. Using 1389 whole genome sequenced samples from
14 cancers, we identify an "overall" mode of somatic mutational noise. We give
a prescription for factoring out this noise and source code for fixing the
number of signatures. We apply nonnegative matrix factorization (NMF) to genome
data aggregated by cancer subtype and filtered using our method. The resultant
signatures have substantially lower variability than those from unfiltered
data. Also, the computational cost of signature extraction is cut by about a
factor of 10. We find 3 novel cancer signatures, including a liver cancer
dominant signature (96% contribution) and a renal cell carcinoma signature (70%
contribution). Our method accelerates finding new cancer signatures and
improves their overall stability. Reciprocally, the methods for extracting
cancer signatures could have interesting applications in quantitative finance.
