Lineage-specific proteome remodeling of diverse lung cancer cells by targeted epigenetic inhibitors.
Chuwei LinCatherine SniezekRoss M GiglioRashmi KarkiChristopher McGannBenjamin A GarciaJose L McFaline-FigeroaDevin K SchweppePublished in: bioRxiv : the preprint server for biology (2024)
Epigenetic inhibitors exhibit powerful antiproliferative and anticancer activities. However, cellular responses to small-molecule epigenetic inhibition are heterogenous and dependent on factors such as the genetic background, metabolic state, and on-/off-target engagement of individual small-molecule drugs. To determine the mechanisms that drive these heterogeneous cellular responses, we quantified chromatin, proteome, and transcriptome remodeling due to histone deacetylase inhibitor (HDACi) -treated cells derived from diverse genetic backgrounds. We utilized high-throughput sample multiplexed proteomics and integrated intelligent data acquisition methods to map proteomes of cancer cell lines in response to HDACi. We determined cell type-specific and ubiquitous cellular responses based on the quantification of 10,621 total proteins. We then established how coordinated remodeling of the proteome, transcriptome and chromatin state of HDACi treated cancer cells revealed convergent (JUN, MAP2K3, CDKN1A) and divergent (CCND3, ASF1B, BRD7) molecular phenotypes. HDACi- regulated proteins differ greatly across cell lines owing to heterogeneous molecular states of these cell lines. Finally, we demonstrated that HDACi treatment drove a highly cell-type specific response that may in part be explained by cell line-specific off-target drug engagement.
Keyphrases
- small molecule
- gene expression
- genome wide
- single cell
- dna methylation
- high throughput
- histone deacetylase
- transcription factor
- rna seq
- dna damage
- social media
- emergency department
- electronic health record
- mass spectrometry
- big data
- copy number
- papillary thyroid
- young adults
- protein protein
- cancer therapy
- artificial intelligence
- single molecule
- squamous cell
- machine learning
- high density
- signaling pathway
- adverse drug
- pi k akt