The genomes of cancer cells are frequently altered and structurally rearranged, which can enhance the function of cancer-related genes. The functional consequences of genomic rearrangements in cancer are elusive except for canonical in-frame fusions. Our goal is to identify non-canonical types of rearrangements that enhance the oncogenic function of genes and have clinical significance.

One of the main challenges in cancer treatment is intrinsic or acquired resistance to anti-cancer drugs. Anti-cancer drug resistance is usually driven by genetic and/or epigenetic changes and is also known to be associated with the tumor microenvironment. We aim to reveal drug resistance mechanisms using multi-omics data generated from preclinical and clinical samples.

Integrative analysis of genomic and proteomic data offers a unique opportunity to bridge the gap between genotype and phenotype. Recent advances in proteogenomic research have revealed new biological insights into cancer that cannot be obtained through genomic or proteomic approaches alone. We aim to develop novel analytical tools to integrate genomic, transcriptomic, and proteomic data to uncover the novel networks between genomic alterations and dysregulation of oncogenic signaling pathways.