A Computational Platform for Analyzing Cancer Immunologic Data
Cancer immunotherapies have shown striking clinical success in a fraction of cancer patients. However, it remains unclear why some patients respond well to immunotherapy, whereas others do not.
Somatic mutations resulting in tumor-specific epitopes (or “classical” neo-antigens) may be an important biomarker of clinical response. However, “non-classical” neo-antigens resulting from fusions and other structural alterations have received less attention. Using an integrative computational-experimental approach, we seek to comprehensively characterize the full antigenic landscape of tumors. Other investigators include R. Grossman, S. Wang, and T. Gajewski.
Understanding how the immune system can reliably mount effective anti-tumor responses remains one of the long-standing goals for the field of cancer immunology. Informatics has a critical role in resolving this goal as advances in computing and genomic sequencing have brought powerful new tools for characterizing the tumor-immune microenvironment. In the past few years, we examined the complexity of predicting tumor-specific epitopes (or neo-antigens). Notably, in collaboration with the Genomic Data Commons at the University of Chicago, we successfully facilitated the implementation of HLA typing and neo-antigen prediction calling as a common informatics resource for the University community.
However, in studying the antigenic landscape of tumors, our efforts have raised more questions than answers. How many neo-antigens are truly immunogenic? How many TCRs can recognize neo-antigens in a tumor? How does the general immune infiltration in a tumor influence anti-tumor responses? In the course of our research, we have made strides in understanding the broader tumor-immune microenvironment. Dr. Khan has identified new challenges and opportunities in leveraging RNA data in identifying TCR repertoire and infiltration in tumors (Lau et al., Trends in Cancer, 2019). At the same time, Dr. Savage has made pioneering contributions in connecting epitopes with specific T cells (Leonard et al., Immunity, 2017).