A Bioinformatic Approach to Enhance Undergraduate Student Understanding of the Cancer-Immunity Cycle

Recent advances in tumor immunology and cancer immunotherapy have generated significant interest in the field of immuno-oncology. With the promise of these advances comes an increasing need to train the next generation of scientists who will support ongoing basic and clinical research efforts in this field. At this time, however, there remains a documented underrepresentation of tumor immunology as a core content area in many undergraduate science curricula. This study introduces a novel pedagogical strategy that aimed to promote undergraduate student interest in tumor immunology in ways that support recent education guidelines published by the American Association of Immunologists, and it highlights the efficacy of this approach in enhancing student understanding of concepts relevant to the Cancer-Immunity Cycle. Using RNA-sequencing data obtained from clinical specimens catalogued in The Cancer Genome Atlas, students performed Kaplan-Meier survival analyses to identify Cancer-Immunity Cycle genes with prognostic significance. After correlating expression of such genes with tumor-infiltrating immune cell populations using a bioinformatic tool to deconvolute whole tumor-transcriptome data, students undertook an exercise that requires integration of course content and findings from the primary literature to generate hypotheses about the influence of genetic factors and immune cell types on the Cancer-Immunity Cycle and overall patient outcome. A pre-/post-project assessment instrument demonstrated the efficacy of this approach as a means of improving undergraduate student understanding of core cancer immunology concepts. This report describes these data and discusses potential ways in which the project can be adapted to extend its utility to broad and diverse student populations.