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From conversations to copy numbers: Bioinformatic approaches to analyzing cancer patient data

Tallman, David
http://orcid.org/0000-0003-0089-6786
http://rave.ohiolink.edu/etdc/view?acc_num=osu1681431648372299

Abstract Details

2023, Doctor of Philosophy, Ohio State University, Molecular, Cellular and Developmental Biology.
The number of cancer diagnoses worldwide is on the rise as populations continues to grow older. In the US, the amount of money allocated to cancer research by the National Cancer Institute increases yearly. With increasing focus towards cancer research, it is important researchers maintain perspective and to ensure that these resources are utilized efficiently. The research mission of the Stover Lab is to improve the outcomes of patients with cancer. We keep the patients in mind during the entire research process, from project conception to publication. In this dissertation, three distinct research projects undertaken during my PhD are summarized. In Chapter 2, we investigated the survivorship needs of patients with gynecological cancers. By extracting posts made on the American Cancer Society’s Cancer Survivorship forums, we discovered some of the needs of cancer patients by looking at their posted conversations and concerns. We developed an analysis methodology to allow post extraction that pertain to custom themes. We showed its utility by extracting and qualitatively analyzing posts that pertain to the psychosocial aspects of survivorship. In Chapter 3, a novel image analysis-based algorithms were developed to investigate the patterns of expression of HER2 in breast cancer patients. Current treatment strategy for breast cancer is reliant on determining whether a patient is HER2 positive using a clinical immunohistochemistry stain for HER2. The criteria used by pathologists for this test is simplistic, in that it only looks at a proportion of intensely stained cells and uses a single cutoff to define a patient as HER2 positive or negative. We believe there is an opportunity to gather more information from these IHC stains and use this information to further delineate breast cancer patients based on their HER2 expression, better predicting patient outcomes. We showed a new method that quantifies the heterogeneity of HER2 expression and significantly predicted recurrence free survival in a cohort of HER2 positive breast cancer. We hope that this may be researched further and implemented into clinical practice, improving on a clinical test that is already being used worldwide. Lastly, in Chapter 4, we discussed the development of a bioinformatics tool that discovers and quantifies the existence of patterns in copy number profiles of cancer samples. Here, we highlight the importance of bioinformatic tool development to help facilitate a tools path to being used in the clinic. Copy number-based signatures have been shown to have potential in the discovery of new biomarkers, and here, we give an overview of our analysis package while comparing it to two other analysis pipelines recently published. We used this comparison to stress the importance of factors such as reliability, broad applicability, and ease-of-use when developing new bioinformatics tools. Overall, we focus on the importance of keeping cancer patients in mind during all stages of research, from molecular mechanisms to patient metal health.
Daniel Stover (Advisor)
Ramesh Ganju (Committee Member)
Raghu Machiraju (Committee Member)
Anne Strohecker (Committee Member)
126 p.
Molecular Biology
bioinformatics, cancer, breast cancer, copy number, HER2, immunofluorescence, survivorship, heterogeneity

Recommended Citations

Citations

  • Tallman, D. (2023). From conversations to copy numbers: Bioinformatic approaches to analyzing cancer patient data [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1681431648372299

    APA Style (7th edition)

  • Tallman, David. From conversations to copy numbers: Bioinformatic approaches to analyzing cancer patient data. 2023. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1681431648372299.

    MLA Style (8th edition)

  • Tallman, David. "From conversations to copy numbers: Bioinformatic approaches to analyzing cancer patient data." Doctoral dissertation, Ohio State University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=osu1681431648372299

    Chicago Manual of Style (17th edition)

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This open access ETD is published by The Ohio State University and OhioLINK.