Efficient next generation RNA sequencing platforms have allowed for whole genome expression profiling of individual populations of immune cells as a novel means of searching for patterns of gene expression to aid in the identification of meaningful signals unique to various disease states. Single cell sequencing techniques have provided additional information useful in deconvolution strategies applied to model development on purified populations of immune cells. Recent interest in peripheral leukocyte subset gene expression profiles suggests that diagnostic information for many disorders may be contained therein. Mononuclear phagocytic cells including the various CD14+ subsets have been studied extensively in various disease states including some solid tumors. Previous large clinical studies at Immunis.AI have determined that transcriptomic profiles of CD14+ cell populations subtraction normalized from CD2+ cell populations were associated with aggressive disease phenotypes such as prostate cancer. Tumor heterogeneity, multifocality, and oligoclonality have been a significant barrier to development of meaningful tissue based multigene signatures for predicting cancer biologic behavior. The findings at Immunis.AI strongly suggest that analysis of RNA expression data from the body's immune surveillance cells has the potential to summarize the entire heterogeneous tumor. The investigators believe that the CD14/CD2 log ratio can be understood as a subtraction normalization of gene expression which yields superior signal of early-stage cancer.
Differential immunogenomic signatures from peripheral blood CD14 (phagocytic) and CD2 (non-phagocytic) cells have been associated with multiple cancers and disease states. In particular several large clinical studies at Immunis.AI have demonstrated robust immunogenomic signatures in early-stage prostate cancer. Immunis.AI therefore hypothesizes that a peripheral blood immunogenomic signature will identify patients with various stages of breast cancer from healthy negative controls.