The treatment of advanced lung cancer has undergone a dramatic change over the past ten years. We have witnessed the development of newer treatments, often termed targeted therapies. These drugs are significantly different from traditional chemotherapies in that they aim to specifically disrupt the most critical processes needed for a cancer cells' growth and survival, therefore eliminating some of the general toxicities of chemotherapies. Lung cancers with defined genetic abnormalities, especially those with EGFR and ALK mutation, are often sensitive to these new targeted therapies that can specifically inhibit the function of EGFR or ALK. This has led to more routine prospective genetic testing of lung cancers to determine which patients should get personalized targeted therapy instead of chemotherapy. However, even in these modern treatment paradigms, problems have emerged. First, some cancers with the defined genetic mutations (which usually predict a response) are unexpectedly not sensitive to the targeted treatments. Second, even when cancers initially respond, they eventually develop resistance over time. This grant application aims to validate a biomarker that will predict which cancers among the genetically defined subset will fail to respond to treatment.

The biomarker under exploration is a protein called BIM. In our preliminary data, we have observed that EGFR mutant cancers with minimal expression of BIM were insensitive to the targeted therapy, whereas those cancers with the same genetic abnormality that have high expression of BIM undergo dramatic shrinkage in response to treatment. BIM is a protein that governs a cellular death process called apoptosis. This is the critical mode of cell death induced by targeted therapies. Cellular death is the first step toward achieving a clinical remission for patients. Thus, our data suggests that cancers with low expression of BIM failed to undergo cell death and thus failed to respond to treatments. Indeed, our initial assessments of patient samples support this hypothesis. In addition, we have observed that patients whose cancers lose sensitivity to therapy over time to targeted therapies also lose BIM expression. Importantly, we have identified other methods to induce cell death in the cancers lacking BIM. Thus, if the goals of this research are realized, we will be able to prospectively determine which patients will have the greatest benefit from targeted therapies, and which patients will require a different strategy for maximal benefit.