Immunotherapies, like immune checkpoint modulators, are poised to transform the therapeutic landscape for cancer. While these therapies elicit remarkable responses in a subset of patients, a key hurdle for broadening the clinical benefit is identifying collateral targets that relieve tumor-induced changes in the tissue microenvironment that block endogenous anti-tumor signals. To identify these collateral targets, we have developed a phenotypic screen to identify mechanisms that malignant cells use to block immune cell response to cytokines that promote anti-tumor immunity, such as Interleukin-12. The objective of this Bioengineering Research Grant proposal is to identify collateral targets in breast and lung carcinomas by screening a set of transplantable models for these cancers and to validate these targets using pre-clinical mouse models and human data. Given that oncogenesis is an evolutionary process involving repeated mutation and selection, our central hypothesis is that malignant cells evolve to secrete proteins that collectively cross-regulate the response of CD4+ and CD8+ T cells to Interleukin-12 (IL12), a potent adjuvant of anti-tumor immunity. To test our central hypothesis, we will first establish conditions where a T cell response to IL12 can be predicted using a mechanistic math model. Next, we will recreate tumor-induced immunosuppression in vitro, where the mathematical model will be used to define how the T cell response to IL12 is altered upon co-culture with the different transplantable models for lung and breast cancer. Proteins secreted by tumor cells that recreate the observed phenotype will be identified using LC-MS/MS-based proteomics. Immunosuppressive mechanisms identified in vitro will be validated using pre-clinical mouse models and a retrospective cohort study using data obtained from large cancer studies that relate `omics with clinical outcomes. This multidisciplinary approach is projected to yield the following expected outcomes: 1) enable stratifying patients based on mechanistic pathology, 2) guide developing drugs against these collateral targets, and 3) identify patient cohorts that are likely to benefit from specific combination immunotherapies.
The proposed studies are an important step towards identifying collateral targets within the tumor microenvironment that can broaden the clinical bene?t of immunotherapies. The proposed research is expected to have an important positive impact on public health, at it will enable engineering improved immunotherapies for cancer and identifying patient cohorts that are likely to bene?t from these improved therapies. These studie will also provide practical experience for post-graduate, graduate and undergraduate students to work in multidisciplinary research teams by combining aspects from tumor immunology, bioanalytical chemistry, cellular signal transduction, and mechanistic math modeling.
