The pharmacoepigenomic landscape of cancer cell lines reveals the epigenetic component of drug sensitivity

Abstract

Aberrant DNA methylation accompanies genetic alterations during oncogenesis and tumour homeostasis and contributes to the transcriptional deregulation of key signalling pathways in cancer. Despite increasing efforts in DNA methylation profiling of cancer patients, there is still a lack of epigenetic biomarkers to predict treatment efficacy. To address this, we analyse 721 cancer cell lines across 22 cancer types treated with 453 anti-cancer compounds. We systematically detect the predictive component of DNA methylation in the context of transcriptional and mutational patterns, i.e., in total 19 DNA methylation biomarkers across 17 drugs and five cancer types. DNA methylation constitutes drug sensitivity biomarkers by mediating the expression of proximal genes, thereby enhancing biological signals across multi-omics data modalities. Our method reproduces anticipated associations, and in addition, we find that the NEK9 promoter hypermethylation may confer sensitivity to the NEDD8-activating enzyme (NAE) inhibitor pevonedistat in melanoma through downregulation of NEK9. In summary, we envision that epigenomics will refine existing patient stratification, thus empowering the next generation of precision oncology.

BioXNet: a biologically inspired neural network for deciphering anti-cancer drug response in precision medicine

“…These together underscore the limitations of cell lines used in cancer therapy studies [15][16][17][18] , as their absence of comprehensive biological systems such as the immune system may constrain their capacity to accurately represent the efficacy of specific drugs in the human body. On the clinical side, epigenetic information (methylation) was identified more important for drug response prediction and the mechanism of drug response, which was also found to be highly related to drug sensitivity 46 and resistance 30,31 in cancer therapies. This highlights the need of incorporating methylation information for anti-cancer drug discovery and therapy design.…”

“…We have collected the copy number variations (CNV), and the mutation of 14,812 genes. For the methylation data, to be consistent with that of TCGA, we collected methylation measured by 450K array from 46 . Note that the original values in CNV are the normalized log2 ratios and we further converted these values to categorical copy number statuses following the same procedure 48 in TCGA.…”

Current Applications and Challenges of Next-Generation Sequencing in Plasma Circulating Tumour DNA of Ovarian Cancer

“…FZD10 expression, a WNT pathway receptor, is also correlated with survival outcomes, and its downregulation increases cisplatin sensitivity, as the authors demonstrated in functional studies using SKOV3 and OVCAR3 cell lines [73]. Despite the crucial role of cell lines in identifying potentially relevant methylation profiles and their molecular impact on tumour development and treatment response, results in these models may not translate to the solid tumour or the clinical phenotype [74][75][76].…”