Research

Tumor is a disease of the genome with profound genomic aberrations and complex cellular hierarchy. Despite being diagnosed with the same tumor, each individual is presented with distinct genetic backgrounds with unique molecular abnormalities. In such context, we aim to explore and investigate the major “driver” genetic alterations that are responsible for not only triggering tumor transformation but maintaining tumor cellular homeostasis. Through such an approach, we hope to identify and provide the most optimal therapeutic option for each cancer patient. 

Elucidation of underlying biological mechanisms that dictate therapeutic response within clinical practice is a difficult enterprise. To address such challenges, we employed various machine learning-based algorithms to dissect and integrative a wide array of different data types, including genomics, transcriptomics, and proteomics, to identify essential molecular correlates that could enhance the efficacy of current treatment modalities, including chemotherapy, targeted therapy, and immunotherapy. 

A substantial number of studies have highlighted the significant roles of tumor-associated stromal/immune cells in tumor malignancy. To dissect and delineate the complexity of tumor microenvironment composition, we have adopted differential analytical approaches, including single-cell sequencing and spatial transcriptomics to highlight the diverse cellular origination as well as differentiation hierarchies.