Abstract 1452: Big data analysis revealed signalling activity and key regulators in human prostate cancer cell lines

Abstract Background: Human prostate cancer (PCa) cell lines are the most used models for research. The commonly used PCa cell lines can be roughly categorized into 3 groups: androgen receptor (AR)+, neuroendocrine (NE)+ and double negative (AR- NE-) cell lines. However, although these cell lines have been extensively used in PCa research since their establishment decades ago, a global molecular characterization of these cell lines is still lacking, especially in the aspect of cell signaling activity and their clinical relevance, which requires a large scale of data mining. Methods: The RNA-seq, DNA-seq and proteomic datasets were gathered from public cohort and our lab’s collection. From these data, a PCa cell line multi-omics database (PCMD) was established. Single sample gene set enrichment analysis (ssGSEA) was used to reveal the signaling pathways enriched in each PCa cell line. Additionally, human PCa patient single cell RNAseq data-based deconvolution and human PCa patient bulk RNAseq data-based nearest-neighbor (NN) graph analysis were used to evaluate the clinical relevance of each PCa cell line. The DNA-seq, ATAC-seq, DNase HS-seq and Bisulfite-seq datasets were used to annotate the key genes. Results: The PCMD database was established by compiling more than 1000 RNASeq datasets derived from various PCa cell lines. The important signaling pathways and transcription factors were revealed for each PCa cell line. Further, the cell lines were annotated with relative clinical stages. Additionally, an interactive webApp was established for scientists to explore and visualize PCMD data. Conclusion: our research characterized PCa cell lines using unbiased strategies and large sample number. It is our hope that this study would aid researchers to develop hypothesis and choose appropriate cell line to use. Funding: This study is supported by: Feist-Weiller Cancer Center, LSU Health Shreveport. Citation Format: Siyuan Cheng, Lin Li, Xiuping Yu. Big data analysis revealed signalling activity and key regulators in human prostate cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1452.