Defining Cancer Intervention Targets by Functional Genomics Analysis of Outbred F1 Mice

Our central hypothesis is that targeting regulatory genes of cancer progression will impede tumor growth and prevent disease recurrence. Our study goals are two-fold: to discover cancer regulatory genes with HER2/Neuexpressing Diversity Outbred (DO) F1 mice; and to test intervention strategies directed at actionable targets such as LILRB4, a myeloid antigen-presenting cell (APC) checkpoint molecule. The development of spontaneous mammary tumors in HER2/neu-transgenic mice captures the substantial interactions among tumor, stromal, and immune cells, and provides a translational research platform. We will use (BALBxDO)F1 NeuT mice and (FVBxDO)F1 d16HER2 mice to encompass the complete biological process of HER2/Neu-induced tumor in mice of individually distinct genetic background. The R-based R/QTL algorithm associates tumor growth phenotypes with each mouse’s unique haplotype to reveal Quantitative Trait Loci (QTL). Candidate genes in the QTL are identified with our bioinformatics pipeline and vetted with human clinical outcome data. Actionable targets are selected by integrating scRNA transcriptomic findings with functional pathway analysis. LILRB4 emerged from the panel of candidate molecules because it is expressed by myeloid antigen presenting cells (APC) and is correlated with poorer survival in human breast and lung cancer patients. Antigen presentation is the foundation of immune activation and an under-utilized opportunity for modifying anti-tumor immunity. LILRB4 activation blunts the antigen presentation machinery and signals the production of immune suppressive molecules. LILRB4 blockade may restore or enhances myeloid cell antigen presentation to prime and expand T cells, unlocking their anti-tumor activity. To test our hypothesis of targeting cancer regulatory genes for disease control, we will Aim 1 Identify candidate genes that regulate cancer progression 1A Identify and validate QTL that regulate tumor onset age and growth rate in (FVBxDO)F1 d16HER2 mice 1B Identify and vet candidate genes in each QTL using the established bioinformatics pipeline Aim 2 Define candidate gene expression profiles and select actionable targets 2A Deconvolute gene expression profiles in mouse mammary fat pads by scRNA-Seq 2B Identify actionable target genes by integrating gene expression profiles with functional pathway analysis 2C Associate enhancer accessibility with differential gene expression to capture additional candidate genes Aim 3 Test the hypothesis that LILRB4 blockade will expand tumor immunity to impede tumor growth 3A Evaluate the role of LILRB4 in tumor immunity by genetic knockout or antibody blockade 3B Test anti-tumor immune activation by combining LILRB4 blockade with a-HER2 mAb therapy or active vaccination