13: Cancer Heterogeneity via Network-based Block-wise Regulation Analysis

Music City Center 

Molecular heterogeneity is a hallmark of cancer. Network-based analysis can be more informative than that based on simpler statistics. Downstream molecular measurements, such as protein expression, are highly regulated by gene expression and also show heterogeneous patterns across populations in regulation. Incorporating gene expression and gene regulation can better delineate the "source" of molecular heterogeneity. Gene expression networks typically exhibit a block structure, where correlations within blocks are stronger than those between blocks. This block-wise organization extends to regulatory patterns as well. In this work, we propose a novel heterogeneity analysis framework based on gene expression network and gene regulation accounting for block structures among genes. This approach can simultaneously identify sample subgroups, gene block structures, and subgroup-specific networks and regulatory mechanisms. An effective computational algorithm and theoretical properties are provided. In the analysis of cancer datasets, the proposed approach identifies heterogeneity and molecular characteristics different from the alternatives and with sound biological implication.

Heterogeneity analysis

Gene expression network

Regulation

Block selection 

International Chinese Statistical Association