Integrative analysis reveals clinically relevant molecular fingerprints in pancreatic cancer

Abstract

Pancreatic cancer is a highly aggressive cancer with an exceedingly low rate of response to treatments, which calls for comprehensive molecular characterization of pancreatic cancer cell lines (PCCLs). We screened multi-layer molecular data of 36 PCCLs, including gene mutation, gene expression, microRNA (miRNA) expression, and protein profiles. Our comparative analysis of genomic mutations found that PCCLs recapitulated genomic alterations of the primary tumor and suggested potential therapeutic strategies for clinical interventions. The panel of 36 PCCLs was classified into 2 subgroups based on transcriptomic mRNA expression, wherein the C1 subgroup was characterized with differentiation, whereas C2 cell lines were featured with immunity, angiogenesis, epidermis, and proliferation. Transcriptomic classification was further recapitulated by miRNA and protein expression. Additionally, the differential proteins between C1 and C2 subgroups were prominently involved in epidermal growth factor receptor (EGFR) signaling, phosphatidylinositol 3-kinase (PI3K) signaling, and mitogen-activated protein kinase (MAPK) signaling pathways. Tumor samples from different subgroups exhibited distinct infiltration of CD4 naive cells and monocytes. Remarkably, patients in subgroups C1 showed longer survival, whereas those in C2 had worse clinical outcome. Further integrative analysis revealed that temozolomide and NVP-TAE684 showed higher sensitivity in the C1 subgroup, whereas the C2 cell lines were more sensitive to SR1001 and SRT-1720. Our results also showed that PCCLs with mutations in CDKN2A, TP53, and SMAD4 were more sensitive to certain anti-cancer drugs. Our integrative analysis identified molecular features of pancreatic cancer that were associated with clinical significance and drug sensitivity, providing potentially effective strategies for precision treatments of patients with pancreatic cancer.

Keywords

• pancreatic cancer
• cancer cell lines
• drug response
• multi-omics data
• integrative analysis
• pancreatic cancer subgroups