Dual data and motif clustering improves the modeling and interpretation of phosphoproteomic data.
Journal
Cell reports methods
ISSN: 2667-2375
Titre abrégé: Cell Rep Methods
Pays: United States
ID NLM: 9918227360606676
Informations de publication
Date de publication:
28 02 2022
28 02 2022
Historique:
entrez:
1
4
2022
pubmed:
2
4
2022
medline:
2
4
2022
Statut:
ppublish
Résumé
Cell signaling is orchestrated in part through a network of protein kinases and phosphatases. Dysregulation of kinase signaling is widespread in diseases such as cancer and is readily targetable through inhibitors. Mass spectrometry-based analysis can provide a global view of kinase regulation, but mining these data is complicated by its stochastic coverage of the proteome, measurement of substrates rather than kinases, and the scale of the data. Here, we implement a dual data and motif clustering (DDMC) strategy that simultaneously clusters peptides into similarly regulated groups based on their variation and their sequence profile. We show that this can help to identify putative upstream kinases and supply more robust clustering. We apply this clustering to clinical proteomic profiling of lung cancer and identify conserved proteomic signatures of tumorigenicity, genetic mutations, and immune infiltration. We propose that DDMC provides a general and flexible clustering strategy for the analysis of phosphoproteomic data.
Identifiants
pubmed: 35360705
doi: 10.1016/j.crmeth.2022.100167
pmc: PMC8967184
mid: NIHMS1784833
pii:
doi:
Substances chimiques
Phosphoproteins
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Subventions
Organisme : NCI NIH HHS
ID : P30 CA016042
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA215709
Pays : United States
Déclaration de conflit d'intérêts
DECLARATION OF INTERESTS The authors declare no competing interests.
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