RNA sequencing identifies lung cancer lineage and facilitates drug repositioning

Abstract

Breakthrough 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.

High-resolution transcriptomics analysis of CXCL13+ EPSTI1+ CDK1+ cells with a specific focus on lung adenocarcinoma

“…Immune checkpoint levels are higher in LUSC than in LUAD, which exhibits activated MHC-II ( 56 ). Our previous findings also indicated that different TME profiles are associated with distinct subtypes, such as the squamous subtype having an increased CXCL13 , EPSTI1 , and CDK1 positive subpopulation compared to the bronchioid subtype ( 9 ). Apart from this, populations previously negative for NKX2-1 and with solid pathology exhibited higher proportions of PD-L1.…”

“…We used the random effects model to validate that high scores were a factor in unfavorable prognosis, after detecting no significant heterogeneity across the datasets (HR =1.21; 95% CI: 1.05–1.4; Figure 4F ). Immune-related S3 and squamous clusters with a poorer prognosis were found to have a higher score ( Figure 4G ) ( 9 , 35 ). Combined with our results, a subset of the population with a poorer prognosis may be suitable for ICIs.…”

“…In addition, four datasets, namely GSE154826, GSE179994, GSE111907, and GSE184398, which contained FACS information, were utilized for further analysis ( 14 - 17 ). Compound perturbation datasets including Cancer Cell Line Encyclopedia (CCLE), Genomics of Drug Sensitivity in Cancer (GDSC), ConnectivityMap (CMAP), and our data regarding the dinaciclib/alvocidib-treated NCI-H1944 cell line, were described in detail in our previous study ( 9 ). For the comprehensive analysis of bulk RNA sequencing, we included the stage IB–IIIA naive cohort The Cancer Genome Atlas (TCGA)-LUAD (n=320) and the ICIs-treated cohort called Stand Up to Cancer-Mark Foundation (SU2C-MARK) LUAD (n=47) ( 9 , 18 ).…”

“…Further studies are needed to obtain a more immunologically characterized subpopulation. Based on previous data and the immunotherapy-related website ICBatlas ( 9 , 10 ), EPSTI1 has been identified as a favorable indicator of ICI response, and may have the potential to describe complex subpopulations. Furthermore, it is more practical to use streamlined signatures in clinical settings.…”