  Deep Learning can significantly benefit cancer proteomics and genomics. In
this study, we attempt to determine a set of critical proteins that are
associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia
patients. A Deep Learning network consisting of autoencoders forming a
hierarchical model from which high-level features are extracted without labeled
training data. Dimensional reduction reduced the number of critical proteins
from 231 to 20. Deep Learning found an excellent correlation between FLT3-ITD
mutation with the levels of these 20 critical proteins (accuracy 97%,
sensitivity 90%, specificity 100%). Our Deep Learning network could hone in on
20 proteins with the strongest association with FLT3-ITD. The results of this
study allow a novel approach to determine critical protein pathways in the
FLT3-ITD mutation, and provide proof-of-concept for an accurate approach to
model big data in cancer proteomics and genomics.
