The Guo Lab
Research Areas
Immunosuppression's Influence on the Alternative Splicing Landscape
Non-canonical alternative splicing events have been demonstrated to occur in head and neck squamous cell carcinoma, and result in functionally active aberrant isoforms that contribute to carcinogenesis. Our preliminary data also demonstrates that high splicing burden is associated with higher recurrence rates in oral cancer as well as relative immunosuppression. These splicing events are hypothesized to generate immunogenic neoantigens given nonself sequences that are generated from these novel isoforms. However, the role the immune system has in regulating the proliferation of aberrant splicing is not well understood. Therefore, our lab seeks to evaluate splicing alterations in the setting of immunosuppression.
Identification of Tumor Specific Alternative Splicing Events Using Novel Bioinformatic Methods
Determining the impact of splicing on cancer requires not only identifying each variant, but further characterizing whether this event is tumor-specific as splice events can cause mutation-independent oncogenic pathway activation in the case of head and neck cancer. The ability of aberrant splicing to change protein function specific to tumors also results in the generation of neoantigens that can represent novel targets for enhancing response to immunotherapy. While the study of somatic mutations alone has led to the discovery of neoantigens in cancers, these alterations may be limited in tumors with low mutational burden. This is particularly evident in head and neck cancer, whose tumors display variable mutational changes, but have a large degree of splicing events. In this context, knowledge of tumor-specific alternative splicing events is critical to our understanding of head and neck cancer onset and for the creation of drugs for immunotherapy treatments. Therefore, we have developed the R package OutSplice which is capable of detecting splicing based upon outlier expression of splice junctions in tumors relative to normal samples.