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Computational Approaches for Cancer Precision Medicine

Stetson, Lindsay C
http://rave.ohiolink.edu/etdc/view?acc_num=case1428050439

Abstract Details

2015, Doctor of Philosophy, Case Western Reserve University, Systems Biology and Bioinformatics.
Many types of cancer have no proven means of prevention or effective therapies. Precision medicine is an emerging approach for disease treatment that takes into account the biology of the patient in an effort to improve therapeutic outcome. While significant advances have been made in precision medicine when it comes to select cancers such as breast and lung, precision medicine is still not used by clinicians when initiating treatment for most cancer patients. Advances in DNA sequencing and large-scale studies such as The Cancer Genome Atlas (TCGA) have led to a better understanding of the molecular initiators and drivers of cancer, but challenges remain in bringing precision medicine from the bench to the bedside. A major impediment to achieving personalized therapy is the small number of drugs developed to target the proteins encoded by potential driver genes. Additionally, the large numbers of genetic aberrations in cancer discovered through next-generation sequencing have not always translated into actionable drug targets. In this dissertation these two challenges are addressed. First, we demonstrate that data from large-scale pharmacogenomic studies can be computationally mined to create omic signatures of drug response. The benefit of this study is the ability to rapidly and cost-effectively identify drugs and research compounds that can be repositioned or repurposed for use in different cancer types. Additionally, we demonstrate that this approach can successfully identify the precise subgroup of cancer patients that will benefit from a drug treatment based on their unique tumor biology. We then show that proteomics studies completed as part of TCGA can be computationally mined to create protein models predictive of patient survival. The resulting protein signatures can be used by clinicians to identify those patients that are high-risk and should be treated more aggressively or referred to clinical trial. Additionally, proteins that are correlated to patient survival are potential actionable drug targets. Both drug development and clinical trials are expensive; computational approaches such as those described in this dissertation are critical to the cost-effective and timely development of precision medicines.
Jill Barnholtz-Sloan, PhD (Advisor)
Jean-Eudes Dazard, PhD (Committee Member)
Thomas LaFramboise, PhD (Committee Member)
Andrew Sloan, MD (Committee Member)
Bioinformatics

Recommended Citations

Citations

  • Stetson, L. C. (2015). Computational Approaches for Cancer Precision Medicine [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1428050439

    APA Style (7th edition)

  • Stetson, Lindsay. Computational Approaches for Cancer Precision Medicine. 2015. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1428050439.

    MLA Style (8th edition)

  • Stetson, Lindsay. "Computational Approaches for Cancer Precision Medicine." Doctoral dissertation, Case Western Reserve University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1428050439

    Chicago Manual of Style (17th edition)

case1428050439
411
© 2015, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.