  Determining the primary site of origin for metastatic tumors is one of the
open problems in cancer care because the efficacy of treatment often depends on
the cancer tissue of origin. Classification methods that can leverage tumor
genomic data and predict the site of origin are therefore of great value.
Because tumor DNA point mutation data is very sparse, only limited accuracy
(64.5% for 12 tumor classes) was previously demonstrated by methods that rely
on point mutations as features (1). Tumor classification accuracy can be
greatly improved (to over 90% for 33 classes) by relying on gene expression
data (2). However, this additional data is often not readily available in
clinical setting, because point mutations are better profiled and targeted by
clinical mutational profiling.
  Here we sought to develop an accurate deep transfer learning and fine-tuning
method for tumor sub-type classification, where predicted class is indicative
of the primary site of origin. Our method significantly outperforms the
state-of-the-art for tumor classification using DNA point mutations, reducing
the error by more than 30% at the same time discriminating over many more
classes on The Cancer Genome Atlas (TCGA) dataset. Using our method, we achieve
state-of-the-art tumor type classification accuracy of 78.3% for 29 tumor
classes relying on DNA point mutations in the tumor only.
