Assessing the Mechanism of Drug Resistance in Lung Cancer

Non small cell lung cancer (NSCLC) is the most common histologic category of lung cancer, but even within the category this disease is highly heterogeneous. In the first line setting, ~20% of patients present with activating mutations that confer drug sensitivity to targeted therapies, about half of patients have a high mutation burden or PD-L1 expression and are candidates for immunotherapy and the remainder are treated with standard cytotoxic chemotherapy. While some patients respond to initial therapy, approximately 20-30% do not respond and are considered to have primary resistance to therapy. Regardless of initial therapy, patients with NSCLC almost uniformly develop secondary resistance, disease progression and ultimately succumb to their disease. The overall goal of this application is to assess secondary mechanisms of resistance. There are currently two models, stochastic and hierarchical, of tumor cell heterogeneity, which in turn explain response or resistance to therapy. While considered mutually exclusive within a tumor, either model may predominate in an individual tumor and potentially in the same tumor after an individual treatment. In the stochastic model, all tumor cells are considered biologically equivalent in terms of self-renewal and formation of new tumor cells and treatment resistance develops due to genomically mediated differential sensitivity to treatment and subsequent survival and proliferation of the remaining treatment resistant cells. In the hierarchical model, only cancer stem cells, with inherent properties for self-renewal and differentiation, are able to initiate tumor growth. In this model, resistance arises from the primary treatment resistance of cancer stem cells, which survive therapy and continue proliferating. In this application, we propose evaluating both the stochastic and hierarchical models secondary (treatment induced) resistance to immunotherapy, cytotoxic chemotherapy and targeted therapies by evaluating the somatic mutation spectrum (stochastic) as well as cancer stem cells (hierarchial). Our overall hypothesis is that the mechanism of secondary resistance can be predicted by a patients’ mutation spectrum and treatment regimen.