RNA sequencing identifies lung cancer lineage and facilitates drug repositioning

Abstract

AbstractBreakthrough therapies recently improve survival in lung adenocarcinoma (LUAD), yet we still lack a paradigm to support prospective confirmation. To classify three clusters including bronchioid, neuroendocrine, and squamoid, the non-negative matrix factorization (NMF) algorithm was first performed at The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), Gene Expression Omnibus (GEO) and preclinical models. In terms of prognostic value, the squamoid cluster is of poor prognostic factor. The neuroendocrine cluster is characterized by STK11 mutations and 14q13.3 amplifications. From an immunological perspective, the bronchioid cluster is considered an immune activation because of the highest immune-related genetic perturbation. Further analysis is the estimation of the relative cell abundance of the tumor microenvironment (TME), specific cell types can be reflected among three clusters. Meanwhile, the higher portion of immune cell infiltration belonged to bronchioid and squamous, not neuroendocrine cluster. Taken together, the neuroendocrine cluster show resistance to PD-L1 blockade. While pemetrexed or platinum-based therapies are suitable for bronchioid and squamoid clusters, respectively. Our emphasis was on phenotype-based action to explore compounds. Large-scale drug sensitivity databases including ConnectivityMap (CMAP), Cancer Cell Line Encyclopedia (CCLE), and Genomics of Drug Sensitivity in Cancer (GDSC) were analyzed. MEK inhibitors exhibited resistance in the bronchioid, while sensitive to the squamous cluster. Dinaciclib and alvocidib showed similar activity and sensitivity in the neuroendocrine cluster. A lineage factor named KLF5 recognized by two networks could be suppressed by verteporfin. This work adds to the knowledge of the lung cancer lineage and facilitates drug repositioning.