Title: Learning a Generative Model of Cancer Metastasis,
Abstract: We introduce a Unified Disentanglement Network (UFDN) trained on The Cancer
Genome Atlas (TCGA). We demonstrate that the UFDN learns a biologically
relevant latent space of gene expression data by applying our network to two
classification tasks of cancer status and cancer type. Our UFDN specific
algorithms perform comparably to random forest methods. The UFDN allows for
continuous, partial interpolation between distinct cancer types. Furthermore,
we perform an analysis of differentially expressed genes between skin cutaneous
melanoma(SKCM) samples and the same samples interpolated into glioblastoma
(GBM). We demonstrate that our interpolations learn relevant metagenes that
recapitulate known glioblastoma mechanisms and suggest possible starting points
for investigations into the metastasis of SKCM into GBM.