ArticleCell reports methods2022
Dual data and motif clustering improves the modeling and interpretation of phosphoproteomic data.
Article in Cell reports methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- AXL phosphosite mapping links FAK1 and YAP1 signaling to erlotinib resistance in EGFR-mutant lung cancer.Cell reports · 2026Article
- Dissecting signaling regulators driving AXL-mediated bypass resistance and associated phenotypes by phosphosite perturbations.bioRxiv : the preprint server for biology · 2023Article
- Phosphoproteomics data-driven signalling network inference: Does it work?Computational and structural biotechnology journal · 2023Article
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2 authors.
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Abstract
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.
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