Regulated Phosphosignaling Associated with Breast Cancer Subtypes and Druggability

Kuan-Lin Huang1, Yige Wu2, Tina Primeau3

  • 1‡Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029; §Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029; ¶Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029.

Insights

Aberrant cancer cell signaling was mapped by analyzing thousands of phosphorylation sites. This study reveals key kinase regulators and potential therapeutic targets in breast cancer subtypes.

Area of Science:

  • Oncology
  • Molecular Biology
  • Biochemistry

Background:

  • Aberrant protein phosphorylation is a critical driver of cancer development and progression.
  • Understanding kinase-substrate interactions is essential for deciphering cancer signaling networks.

Purpose of the Study:

  • To comprehensively map kinase-substrate regulation across diverse breast cancer subtypes.
  • To identify master regulators of phosphorylation and potential therapeutic targets.

Main Methods:

  • Quantitative phosphoproteomics analysis of 77 breast tumors and 24 xenografts.
  • Identification and correlation of kinase-phosphosite pairs.
  • Bioinformatic analysis to predict kinase activity and identify druggable targets.

Main Results:

  • Discovered 2134 kinase-phosphosite pairs, extending existing knowledge.
  • Identified subtype-specific kinase signaling (e.g., EGFR, ERBB2, PRKG1, WNK1).
  • Revealed CDKs, MAPKs, and ataxia-telangiectasia proteins as dominant regulators.
  • Unveiled 113 kinase-substrate pairs and downstream cascades as potential druggable targets.
  • Linked kinase-substrate pairs to clinical and immune signatures.
  • Experimentally validated key activated phosphosites (ERBB2, EIF4EBP1, EGFR).

Conclusions:

  • Kinase-substrate regulation provides a deeper understanding of cancer signaling than genomic data alone.
  • This extensive phosphoproteomic map connects driver events to signaling effects and identifies therapeutic opportunities.

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