RNA Sequencing of the Tumor Microenvironment in Precision Cancer Immunotherapy

Cancer immunotherapy is a promising therapeutic area with the potential to impact a larger population of patients. Clinical adoption of RNA sequencing has potential to reveal novel prognostic or predictive biomarkers and expand the number of patients who can benefit from cancer immunotherapy. Algorithmic developments in RNA data allow for unprecedented profiling of the composition, specificity, and phenotype of the tumor immune response in a single assay. RNA sequencing (RNA-seq) provides an efficient high-throughput technique to robustly characterize the tumor immune microenvironment (TME). The increasing use of RNA-seq in clinical and basic science settings provides a powerful opportunity to access novel therapeutic biomarkers in the TME. Advanced computational methods are making it possible to resolve the composition of the tumor immune infiltrate, infer the immunological phenotypes of those cells, and assess the immune receptor repertoire in RNA-seq data. These immunological characterizations have increasingly important implications for guiding immunotherapy use. Here, we highlight recent studies that demonstrate the potential utility of RNA-seq in clinical settings, review key computational methods used for characterizing the TME for precision cancer immunotherapy, and discuss important considerations in data interpretation and current technological limitations. RNA sequencing (RNA-seq) provides an efficient high-throughput technique to robustly characterize the tumor immune microenvironment (TME). The increasing use of RNA-seq in clinical and basic science settings provides a powerful opportunity to access novel therapeutic biomarkers in the TME. Advanced computational methods are making it possible to resolve the composition of the tumor immune infiltrate, infer the immunological phenotypes of those cells, and assess the immune receptor repertoire in RNA-seq data. These immunological characterizations have increasingly important implications for guiding immunotherapy use. Here, we highlight recent studies that demonstrate the potential utility of RNA-seq in clinical settings, review key computational methods used for characterizing the TME for precision cancer immunotherapy, and discuss important considerations in data interpretation and current technological limitations. class of cancer drugs that use the immune system to target and eliminate cancer cells, such as checkpoint inhibitors, adoptive cell transfers, and vaccines. the proliferation of T and B cells into expanded clones following contact with an antigenic stimulant. algorithmic method to resolve data from a mixture into its constituent elements. method for reconstructing sequences for which there is no reference genome from next generation sequencing reads. algorithmic method to determine whether a particular gene pathway or signature is over or under-expressed from the RNA-sequencing data for a sample. the diversity of unique T and B cell receptors that allow for the recognition of antigenic stimulants and appropriate immunological responses. the cellular surroundings and inflammatory milieu of a tumor that includes stroma, blood vessels, and various cell types such as infiltrating immune cells and associated tissue cells. quantitative measure of the number of protein altering mutations in a tumor specimen. TMB has been correlated with patient response to checkpoint blockade.