Citation

  • Authors: Wang, L., Wrobel, J. A., Xie, L., Li, D., Zurlo, G., Shen, H., Yang, P., Wang, Z., Peng, Y., Gunawardena, H. P., Zhang, Q., Chen, X.
  • Year: 2018
  • Journal: Cell Chem Biol 25 619-633 e5
  • Applications: in vitro / DNA / jetPRIME
  • Cell type: MCF7
    Description: Human breast adenocarcinoma cells
    Known as: MCF-7, MCF 7

Abstract

To discriminate the patient subpopulations with different clinical outcomes within each breast cancer (BC) subtype, we introduce a robust, clinical-practical, activity-based proteogenomic method that identifies, in their oncogenically active states, candidate biomarker genes bearing patient-specific transcriptomic/genomic alterations of prognostic value. First, we used the intronic splicing enhancer (ISE) probes to sort ISE-interacting trans-acting protein factors (trans-interactome) directly from a tumor tissue for subsequent mass spectrometry characterization. In the retrospective, proteogenomic analysis of patient datasets, we identified those ISE trans-factor-encoding genes showing interaction-correlated expression patterns (iCEPs) as new BC-subtypic genes. Further, patient-specific co-alterations in mRNA expression of select iCEP genes distinguished high-risk patient subsets/subpopulations from other patients within a single BC subtype. Function analysis further validated a tumor-phenotypic trans-interactome contained the drivers of oncogenic splicing switches, representing the predominant tumor cells in a tissue, from which novel personalized biomarkers were clinically characterized/validated for precise prognostic prediction and subsequent individualized alignment of optimal therapy.

Pubmed