Shengqing (Stan) Gu
Assistant Professor
The University of Texas MD Anderson Cancer Center
Department of Hematopoietic Biology and Malignancy
Our lab is interested in enhancing the efficacy of cancer immunotherapy by modulating cell-cell interactions. To achieve this goal, we integrate various cutting-edge experimental and computational approaches in the areas of functional genomics, single-cell/spatial multi-omics, data mining, machine learning, and mathematical modeling, among others. Here are several research directions in the lab:
Context-dependent gene regulation
We aim to perturb gene expression in the tumor to enhance immunotherapy with desired immunomodulatory effects. Our previous research revealed that gene expression regulation depends on the cellular genetic/epigenetic context. For example, cancer cells with different baseline NF-κB expression can show different responses to SMAC mimetics in upregulating MHC-I. Therefore, to achieve the desired immunomodulatory effects, we need rational, personalized strategies to perturb gene expression. To this end, we will integrate functional genomics, single-cell/spatial technologies, and machine learning approaches to elucidate the context-dependent gene regulation mechanisms.
Novel mechanisms of cell-cell interaction
The essence of the anti-cancer immune response lies in cell-cell interactions. Our previous research investigated multiple aspects of such interaction at different levels. We applied machine learning models to public bulk tumor gene expression data and identified SERPINB9 and MAN2A1 as regulators of cancer cells’ response to cytotoxic T cells. Using in vivo CRISPR screens, we identified a ubiquitin E3 ligase, COP1, whose inhibition can alter macrophage infiltration and sensitize tumors to immunotherapy. We also identified multiple regulators of the MHC-I/II antigen presentation machinery in cancer cells that can modulate cancer-T-cell interaction. We are interested in other cell-cell interaction mechanisms in the tumor microenvironment.
Dynamics of anti-cancer immune response
With the expanding options for cancer therapy and vast possibilities for drug scheduling, we need quantitative models of the response dynamics to rationally design combination strategies. Our previous research integrated a branching model and ODE models to simulate the dynamics of ovarian cancer clinical course and predict optimal combinations of surgery and chemotherapy for this disease. We also used clonal tracing of cancer cells to characterize the dynamics and heterogeneity of response to immune checkpoint blockade therapy. We aim to further integrate experimental/clinical data and mathematical modeling to quantify the dynamics of the anti-cancer immune response, which can help us gain mechanistic insights into this biological process and optimize combination therapy strategies.
Education & Training
PhD - University of Toronto - 2016