Mutation-centric Network Construction using Long-Range Interactions

Somatic mutations can alter normal cells and lead to cancer development. Yet distinguishing functional driver mutations from neutral passenger mutations remains a significant challenge. Traditional genomic tools often prioritize linear overlap searches, failing to capture the complex, three-dimensional regulatory environment of the genome. We present a graph-based framework, MutationNetwork, for constructing mutation-centric networks by integrating long-range intrachromosomal interactions with local genomic overlaps. Our method utilizes a unique positive and negative indexing scheme to represent interacting genomic intervals as nodes. By encoding both interactions and overlaps as edges, we enable constant-time retrieval of complex relationship data. By iteratively expanding the graph from a seed mutation, we can quantify a mutations influence on the genomic landscape and assess its proximity to genes. We applied this framework to a dataset of 560 breast cancer whole-genome sequences, focusing on Triple-Negative Breast Cancer (TNBC) and Luminal A subtypes. Our results demonstrate that the generated mutation embeddings successfully cluster samples according to their biological subtypes, with the highest classification performance achieved at specific ranges. This approach provides a comprehensive view of mutation impact, offering a scalable solution for cancer patient stratification and the prioritization of potential non-coding driver mutations by assessing their network-level impact. Availability and implementationThe source code is available at https://github.com/Ramalh/MutationNetwork