Driver event discovery is a crucial demand for breast cancer diagnosis and
therapy. Especially, discovering subtype-specificity of drivers can prompt the
personalized biomarker discovery and precision treatment of cancer patients.
still, most of the existing computational driver discovery studies mainly
exploit the information from DNA aberrations and gene interactions. Notably,
cancer driver events would occur due to not only DNA aberrations but also RNA
alternations, but integrating multi-type aberrations from both DNA and RNA is
still a challenging task for breast cancer drivers. On the one hand, the data
formats of different aberration types also differ from each other, known as
data format incompatibility. One the other hand, different types of aberrations
demonstrate distinct patterns across samples, known as aberration type
heterogeneity. To promote the integrated analysis of subtype-specific breast
cancer drivers, we design a "splicing-and-fusing" framework to address the
issues of data format incompatibility and aberration type heterogeneity
respectively. To overcome the data format incompatibility, the "splicing-step"
employs a knowledge graph structure to connect multi-type aberrations from the
DNA and RNA data into a unified formation. To tackle the aberration type
heterogeneity, the "fusing-step" adopts a dynamic mapping gene space
integration approach to represent the multi-type information by vectorized
profiles. The experiments also demonstrate the advantages of our approach in
both the integration of multi-type aberrations from DNA and RNA and the
discovery of subtype-specific breast cancer drivers. In summary, our
"splicing-and-fusing" framework with knowledge graph connection and dynamic
mapping gene space fusion of multi-type aberrations data from DNA and RNA can
successfully discover potential breast cancer drivers with subtype-specificity
indication.Comment: 14 pages, 5 figures, 1 tabl