Enhancing the efficacy of immunotherapy by optimal use of SMAC mimetics - PROJECT SUMMARY Shenqqing (Stan) Gu, Ph.D., is an experimental-computational biologist whose career goal is to develop personalized strategies for optimal combination immunotherapy to cure cancer. Titled “Enhancing the efficacy of immunotherapy by optimal use of SMAC mimetics”, the proposed research seeks to elucidate the context dependencies of SMAC mimetics’ effects and examine its optimal combination with existing immune checkpoint blockade therapy. Career development plan: Dr. Gu is a recipient of Sara Elizabeth O’Brien Trust Fellowship. His previous work has focused on the regulation of cancer-immune interaction, using various approaches including clonal tracing, data mining, and genome-wide CRISPR screening, which have prepared him to conduct the proposed research. Dr. Gu has outlined specific training activities to expand his skill set in four areas: 1) T cell biology, 2) machine learning, 3) single-cell technologies, 4) leadership and professional skills. This skill set is necessary to succeed in my independent research career. Collaborators/Environment: Dr. Gu’s collaboration team assembles world-leading experts in cancer immunology, computational biology, translational and clinical research, melanoma, and single-cell technology. Leveraging the extensive collaboration resources at DFCI, Harvard, and NCI, and access to a large amount of clinical samples, Dr. Gu is uniquely placed to identify the optimal usage of SMAC mimetics to enhance the efficacy of immunotherapy of cancer. Research: Our previous research identified that SMAC mimetics can upregulate MHC-I in some cancer cells and potentiate immunotherapy, but it is unclear which subset of cancer patients can benefit from SMAC mimetic treatment. To facilitate effective use of SMAC mimetics in the clinic, this proposal will interrogate SMAC mimetics regarding the optimal contexts of gene regulation and rational combination with immune checkpoint blockade. Aim 1 will integrate genetic/epigenetic feature selection and functional validation to identify the context dependencies of SMAC mimetics’ effects on cancer cells. Aim 2 will integrate multi-omic profiling at single-cell level to examine the effects of SMAC mimetic treatment on immune cells. Aim 3 will evaluate the efficacy of different scheduling of combination of SMAC mimetics and ICB using in vitro co-culture and in vivo transplantation models. Outcomes/Impact: This project will reveal the genetic/epigenetic context dependencies of SMAC mimetics’ effects, elucidate their effects on immune cells, and examine the optimal combination with immunotherapy. Data from this study can help improve the design of clinical trials testing SMAC mimetics in cancer patients. The development career transition award will enable Dr. Gu to become a leader in the new field of developing personalized combination immunotherapy strategies.
