Identifying Lymph Node Metastasis-related Factors in Breast Cancer using Differential Modular and Mutational Structural Analysis

Abstract

AbstractComplex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Network theory provides useful tools to study the underlying laws governing complex diseases. Within this framework, comparisons of network topologies, including the node, edge, and community, between different disease states can highlight key factors within these dynamic processes. Here, we propose a differential modular analysis approach that integrates protein-protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the “core network module” that quantifies the significant phenotypic variation. Then, based on this core network module, key factors including functional protein-protein interactions, pathways, and drive mutations are predicted by the topological-functional connection score and structural modeling. We applied the approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis.Author summaryMetastasis is the hallmark of cancer that is responsible for the greatest number of cancer-related deaths. However, it remains poorly understood. PPI networks not only provide a static picture of cellular function and biological processes, but also have emerged as new paradigms in the study of the dynamic process of disease progression, including cancer metastasis. Herein, a network-based strategy was proposed based on the integration of expression profiles with protein interactions, by filtering with “date hubs” and “inter-modular edges”, demonstrating that different network modules may provide robust predictors to represent the dynamic mechanisms involved in metastasis formation. Furthermore, the mapping of protein structure and mutation data on the network module level provides insight into signaling mechanisms; helps understand the mechanism of disease-related mutations; and helps in drug discovery. The application of our method to study the LNM in breast cancer highlights network modules defining protein communities that respond to therapeutics, and the implications of detailed structural and mechanistic insight into oncogenic activation and how it can advance allosteric precision oncology.