This item is under embargo and not available online per the author's request. For access information, please visit http://libanswers.wustl.edu/faq/5640.

ORCID

http://orcid.org/0000-0001-5598-0876

Date of Award

Spring 5-15-2021

Author's School

Graduate School of Arts and Sciences

Author's Department

Biology & Biomedical Sciences (Human & Statistical Genetics)

Degree Name

Doctor of Philosophy (PhD)

Degree Type

Dissertation

Abstract

Follicular lymphoma (FL) is the most common indolent non-Hodgkin’s lymphoma; however, it remains incurable with conventional therapies and is poorly responsive to checkpoint blockade. FL arises from B-lymphocytes and develops slowly (and often asymptomatically). A major research focus has been on how to avoid chemotherapy treatments, to limit the potential development of treatment-related side effects, and the risk of therapy-related second cancers. FL also carries an approximately 30% lifetime risk of transforming from an iNHL to more destructive lymphomas, which are associated with poorer prognosis. The most common transformation results in diffuse large B-cell lymphoma (DLBCL). However, many patients may not require treatment for decades and can be safely observed after their initial diagnosis. Therefore, understanding the underlying genetic landscape and molecular mechanisms of FL progression and identifying biomarkers of prognosis and novel drug targets is critically important. Unfortunately, characterizing the genetic landscape of cancer remains a challenge given cancer’s genetic diversity and clonal evolutionary properties. This complexity makes the job of understanding the mechanisms of cancer progression difficult to dissect. Unlike better profiled cancers, FL is characterized by clinical and pathological features, but lacks a comprehensively characterized genetic landscape. To address the need for a comprehensive genetic landscape of FL; we developed a custom capture panel, sequenced over 500 samples from over 370 patients, deconvoluted sample swaps, and conducted a genetic analysis as described in Chapter 2. We found that FL has a low to medium mutation burden which is consistent with current research. However, the question remained as to how many of these somatic mutations could actually represent high quality neoantigens. It should be noted that only a weak correlation exists between mutation burden and response to immune therapies while other studies have shown relatively few immunogenic neoantigens are necessary for a detectable response. Therefore, we hypothesized that immune based therapies, specifically personalized neoantigen cancer vaccines, have the potential to cure patients and avoid some of the consequences of conventional therapies. Personalized cancer vaccines aim to stimulate the immune system to selectively increase T-cell populations that react to tumor specific neoantigens to target and eliminate cancer cells. BioVaxID, a personalized idiotype cancer vaccine, showed early promise as a new active immunotherapy to treat FL. However, BioVaxID contained only a single cancer target and recently failed to meet their primary end points within a Phase III clinical trial. We set out to determine (in Chapter 3) whether whole exome sequencing and RNA sequencing could infer a patients’ MHC Class I alleles, and identify neoantigens and oligoclonal B-cells, to engineer personalized cancer vaccines. Additionally, we initiated a pilot trial using personalized neoantigen vaccine therapy combined with PD-1 blockade on patients with relapsed or refractory follicular lymphoma (NCT03121677). Initial results described in Chapter 3 demonstrates feasibility, safety, and potential immunologic and clinical responses. These chapters describe next-generation sequencing analysis methods that can be utilized to determine and streamline the feasibility of personalized cancer vaccines in FL and other cancers.

Language

English (en)

Chair and Committee

Obi L. Griffith Todd Fehniger

Committee Members

Malachi Griffith, Christopher A. Maher, Christopher A. Miller, Michael Province,

Available for download on Friday, February 18, 2022

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