  It is increasingly common clinically for cancer specimens to be examined
using techniques that identify somatic mutations. In principle these mutational
profiles can be used to diagnose the tissue of origin, a critical task for the
3-5% of tumors that have an unknown primary site. Diagnosis of primary site is
also critical for screening tests that employ circulating DNA. However, most
mutations observed in any new tumor are very rarely occurring mutations, and
indeed the preponderance of these may never have been observed in any previous
recorded tumor. To create a viable diagnostic tool we need to harness the
information content in this "hidden genome" of variants for which no direct
information is available. To accomplish this we propose a multi-level
meta-feature regression to extract the critical information from rare variants
in the training data in a way that permits us to also extract diagnostic
information from any previously unobserved variants in the new tumor sample. A
scalable implementation of the model is obtained by combining a
high-dimensional feature screening approach with a group-lasso penalized
maximum likelihood approach based on an equivalent mixed-effect representation
of the multilevel model. We apply the method to the Cancer Genome Atlas
whole-exome sequencing data set including 3702 tumor samples across 7 common
cancer sites. Results show that our multi-level approach can harness
substantial diagnostic information from the hidden genome.
