Characterizing treatment responses for common lung cancer (LC) subtypes in Latinos and Asians

SPECIFIC AIMS The purpose of UCaTS Project 2 is to target clinically important genomic markers of early resistance to EGFR tyrosine kinase inhibitors (EGFR-TKIs) in EGFR-mutant non-small cell lung cancer (NSCLC), to develop quantitative models of Receptor Tyrosine Kinase (RTK)-driven signaling pathways under inhibition, and to

explore the role race, ethnicity and genetic ancestry has in predicting baseline EGFR signaling and EGFR-TKI treatment response. EGFR-mutant NSCLC comprises a substantial subset of lung adenocarcinoma (15% in Western/White populations) and occurs with a higher frequency in Asian Americans, Native Hawaiians, Pacific

Islanders (AANHPI ~50%), and Latinos (~38%). EGFR mutations show strong associations with smoking status, gender, race/ethnicity, and genetic ancestry. Women of AANHPI and Latino race/ethnicity have, compared to non-Latino Whites (NLW), lower smoking rates and higher frequency of EGFR mutant tumors. Interestingly, a

recent study showed that high Indigenous American Ancestry (which is closely associated with Asian ancestry) confers a higher risk of developing EGFR-mutant lung cancer in Latinos. This suggests that somatic EGFR mutations have close associations of biological and non-biological aspects associated with race/ethnicity or

ancestry. Through this research, we will develop novel therapeutic strategies with combinations of FDA approved and NCI-CTEP drugs to translate into NCI-sponsored clinical trials overcoming EGFR-TKI resistance mechanisms associated in EGFR-mutant NSCLC. We will also assess whether race/ethnicity and genetic

ancestry impacts EGFR signaling and influences responses to EGFR inhibition. Patient-derived xenografts (PDXs) have been broadly used in lung cancer research and drug development. We have extensive experience in establishing PDXs and conducting PDX-based research. Through the Jackson Laboratory (JAX), UCSF and UT Southwestern collaborations, we established and characterized over 200 lung

cancer PDXs in which 25 have EGFR-activating mutations and we have identified an additional 10 EGFR-mutant lung cancer models in PDXnet. We anticipate that UCaTS will generate at least 25 additional EGFR-mutant PDXs. We have performed detailed histopathological and genomic characterization on many of these PDXs

focused on oncogene driven NSCLC where we identified multiple putative resistance mechanisms that mediate early resistance to current EGFR-targeted therapeutic approaches in these models. We have also shown that EGFR-mutant PDXs can potentially be used to optimize treatment combinations to overcome EGFR-TKI

resistance and to identify the most efficacious drugs or drug combinations including FDA approved and NCI- CTEP agents. We will use a high-content live-cell imaging platform to analyze intracellular EGFR signaling, which provides a high-resolution assessment of cellular adaptation to inhibition. The work for this project arises out of

our current research, which will be used to address the following specific aims.: Aim 1: Develop targeted treatment combinations with FDA approved drugs and/or NCI-CTEP agents to overcome mechanisms of resistance to EGFR-TKIs that herald early tumor progression. We plan to a) determine the most effective dual MET/EGFR targeting strategy in EGFR-mutant PDX models harboring MET-

mediated bypass tract mechanisms of EGFR-TKI resistance; b) determine the most effective dual EGFR- blockade approach in EGFR-mutant PDXs harboring uncommon EGFR-mutations; and c) overcome the limited apoptotic response in RBM10 deficient EGFR-mutant NSCLC with the BCL-2/BCL-xL inhibitor and NCI-CTEP

agent pelcitoclax. Aim 2: Modeling EGFR inhibitor effects using signaling dynamics in live cells. The premise is that targeted inhibitor therapy can be optimized by characterizing quantitative variation in RTK-driven intracellular signaling across different EGFR mutants, genetic backgrounds, and inhibitor classes. We will use kinase activity

biosensors to track AKT and ERK activity patterns with single-cell resolution in the set of PDX models used in Aim 1. This high-content analysis, in combination with the diverse set of EGFR mutations and genetic backgrounds represented by the PDX panel, will provide the data for a quantitative model with an unprecedented

scope. Leveraging machine learning methods, our model will allow us to identify the primary signaling parameters determining response to multiple classes (and combinations) of EGFR inhibitors, MET inhibitors, and Bcl-2/Bcl-xL family inhibitors. Importantly, our model will help to answer a long-standing question of whether

EGFR inhibitor resistance involves a shared set of signaling determinants across individuals and backgrounds, or instead depends on patient or genetic background-specific factors. Furthermore, when our results are integrated with Aim 1, we will have the unique opportunity to validate our cellular model against in vivo data.

Aim 3: Examining the role of race/ethnicity and genetic ancestry in EGFR signaling and responses to EGFR therapies. As our study will test EGFR therapies in large numbers of EGFR-mutant models from AANHPI, Latinos, and NLW, we will evaluate whether baseline signaling and responses to the third generation EGFR-TKI

osimertinib are influenced by race/ethnicity. In Latinos, we will also estimate individual levels of Indigenous American ancestry and will evaluate its associations with baseline signaling and treatment response. IMPACT: This project will advance EGFR therapy and our understanding of EGFR signaling in lung cancer with

a focus on minorities, including information that will lead to a more equitable translation into clinical trials.