Targeting cell regulatory states to complement MEK/autophagy inhibition in pancreatic cancer

点击翻译按钮获取中文摘要

Abstract Pancreatic ductal adenocarcinoma (PDAC) is characterized by its broad, primary resistance to genotoxic chemotherapy, targeted therapy, and immunotherapy. The singular driving oncogene of PDAC is KRAS, which is mutated in 95% of cases. Unfortunately, inhibitors of the specific mutant KRAS alleles found in PDAC are not yet clinically available, and all attempts to target downstream effector pathways have been stymied by feedback loops and other resistance mechanisms. Work from our collaborators found that inhibition of the MEK pathway, a key KRAS effector, results in compensatory upregulation of autophagy, a survival pathway. Combination MEK and autophagy inhibition was shown to have synergistic effects in reducing viability of PDAC cell lines and led to tumor responses in model systems and individual case reports from human patients. However, only a subset of patients have responded to MEK/autophagy inhibitors in early clinical trials. Among those who respond, most tumors progress within a few months. Nevertheless, this is potentially the first example of an active regimen lacking genotoxic agents. Published mechanisms for the synergy between MEK and autophagy inhibitors in PDAC do not readily explain the rapid emergence of resistance, do not account for cellular heterogeneity within the malignant epithelial compartment, and do not address the role of the tumor stroma in the response to therapy. The overall goal of this proposal is take a holistic approach to understanding cellular responses to MEK/autophagy inhibition by studying the effects of treatment on every cell type in the tumor. We will do this by performing single cell RNA sequencing and analyzing the data using single cell regulatory network analysis – an advanced computational framework designed to systematically measure the activity of every transcription factor in each individual cell. This innovative new approach enables us both to identify specific cellular subtypes impacted by treatment, and to rapidly identify the mechanistic drivers of observed responses. We will deploy this approach on tumor samples from four different model systems: genetically engineered (KPC) mice, human patient derived xenografts (PDXs), patient-derived tumor “explant” models (freshly cultured intact tissue slices), and samples from an active clinical trial of MEK/autophagy inhibition in PDAC patients. Extensive preliminary data in human cell lines, human PDAC explants, and KPC mouse tumors demonstrate that MEK/autophagy inhibition primarily impacts specific subsets of three major cell types: epithelial cells, fibroblasts, and macrophage/monocytes. Our three aims will study the effects and mechanisms of MEK/autophagy inhibition in each cell type, with the goals of annotating phenotypes, learning whether effects are cell autonomous, determining the transcription factors driving treatment response, understanding impacts on the remaining cells, and identifying new therapeutic combinations to potentiate anticancer effects.