Skip to Main Content
 

Global Search Box

 
 
 

ETD Abstract Container

Abstract Header

Bioinformatics approaches to cancer biomarker discovery and characterization

Liao, Peter Lee Ming, Liao
http://orcid.org/0000-0002-8853-6656
http://rave.ohiolink.edu/etdc/view?acc_num=case1525694252170957

Abstract Details

2018, Doctor of Philosophy, Case Western Reserve University, Systems Biology and Bioinformatics.
Cancers are a heterogeneous set of diseases that are defined by uncontrolled cellular growth with the potential to invade or spread to adjacent and distant tissues. While sharing certain biological capabilities that define the development and behavior of all human malignancies, cancers are governed by complex molecular changes that are often tumor-specific. As a result, even tumors arising from the same cell-type can exhibit highly divergent prognoses and treatment responses depending upon the underlying molecular mechanisms that are dysregulated and that drive its abnormal growth and cellular processes. New data collection methods grant researchers unprecedented capability to investigate and characterize cancers on a systems level. Rather than being restricted in measurement to a specific target molecule or set of molecules, “-omics” approaches allow experiments to identify and measure thousands of molecules at a time. These “-omics” approaches can therefore characterize significant proportions of the genetic, transcript, protein, and post-translational modification landscapes that underlie and drive human malignancies. Because cancers represent such a diverse set of diseases, clinicians and researchers rely on biomarkers for a variety of uses in cancer, ranging from diagnosis to prognosis and prediction of treatment response. A good cancer biomarker is a molecular signal that is capable of distinguishing, for example, disease from normal, high-risk from low risk disease, or disease cases that may be particularly susceptible to targeted treatments. In this dissertation, I demonstrate the use of multiple bioinformatics tools for cancer biomarker discovery and characterization. Models of epigenetic age, termed epigenetic clocks, are investigated in gliomas and are shown to be associated with previously defined prognostic molecular subtypes and are independently predictive of survival. I introduce a novel method for phosphoproteomics analysis, termed pKSEA, which uses in silico kinase-substrate predictions to infer changes in kinase activity. pKSEA is described, benchmarked against previously published data, and compared to existing methods. Three examples are provided of pKSEA analysis in cancer-related data, identifying kinase activity signals that may be useful as biomarkers in identifying and targeting high risk glioblastomas, as well as identifying treatment-related phosphorylation signaling changes in response to kinase inhibition and phosphatase activation in cancer cells.
Jill Barnholtz-Sloan, PhD (Advisor)
143 p.
Bioinformatics; Oncology
cancer; biomarker; proteomics; epigenetics; phosphorylation; bioinformatics

Recommended Citations

Citations

  • Liao, Liao, P. L. M. (2018). Bioinformatics approaches to cancer biomarker discovery and characterization [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1525694252170957

    APA Style (7th edition)

  • Liao, Liao, Peter. Bioinformatics approaches to cancer biomarker discovery and characterization. 2018. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1525694252170957.

    MLA Style (8th edition)

  • Liao, Liao, Peter. "Bioinformatics approaches to cancer biomarker discovery and characterization." Doctoral dissertation, Case Western Reserve University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1525694252170957

    Chicago Manual of Style (17th edition)

case1525694252170957
286
© 2018, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.