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Novel RNA-Affinity Proteogenomics Dissects Tumor Heterogeneity for Revealing Personalized Markers in Precision Prognosis of Cancer

Overview of attention for article published in Cell Chemical Biology, May 2018
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Title
Novel RNA-Affinity Proteogenomics Dissects Tumor Heterogeneity for Revealing Personalized Markers in Precision Prognosis of Cancer
Published in
Cell Chemical Biology, May 2018
DOI 10.1016/j.chembiol.2018.01.016
Pubmed ID
Authors

Li Wang, John A. Wrobel, Ling Xie, DongXu Li, Giada Zurlo, Huali Shen, Pengyuan Yang, Zefeng Wang, Yibing Peng, Harsha P. Gunawardena, Qing Zhang, Xian Chen

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.

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Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 24%
Other 4 19%
Researcher 3 14%
Student > Master 3 14%
Student > Bachelor 2 10%
Other 2 10%
Unknown 2 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 38%
Agricultural and Biological Sciences 4 19%
Medicine and Dentistry 2 10%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Social Sciences 1 5%
Other 2 10%
Unknown 3 14%