Statistical Approaches to Identifying Therapeutic Vulnerabilities from Cancer Genomics Data
| dc.contributor.advisor | Guerra, Rudy | en_US |
| dc.contributor.advisor | Korkut, Anil | en_US |
| dc.creator | Kong, Elisabeth K | en_US |
| dc.date.accessioned | 2023-08-09T16:37:02Z | en_US |
| dc.date.created | 2023-05 | en_US |
| dc.date.issued | 2023-04-17 | en_US |
| dc.date.submitted | May 2023 | en_US |
| dc.date.updated | 2023-08-09T16:37:02Z | en_US |
| dc.description | EMBARGO NOTE: This item is embargoed until 2025-05-01 | en_US |
| dc.description.abstract | An increased understanding of molecular mechanisms that regulate cellular processes has resulted in the growing prevalence of genomically targeted cancer therapies. However, durable responses to these therapies are rarely achieved in the majority of cancers due to cancer cells’ resistance to therapy. There is a significant challenge in identifying which patients will benefit from targeted therapies and matching the correct therapy to each patient. This demonstrates the need for a comprehensive statistical analysis of genomic alterations across patient cohorts to define driver events that can guide the selection of targeted therapies. Here, we address therapeutic vulnerability identification and precision therapy selection through two data collections. First, we present a dataset of cancers featuring EGFR and HER2 aberrations from The Cancer Genome Atlas. We assess the landscape of aberrations in these genes across disease types and observe differences in their pathway scores and most common variants. Second, we present a dataset from the ARTEMIS longitudinal trial composed of triple-negative breast cancer samples. We detail the batch effect correction process performed to enable integrated analysis from multiple sequencing batches. Through differential gene expression analysis, we observe increases in genes encoding for potentially actionable proteins that can be matched to targeted therapies for patients who were resistant to neoadjuvant therapy. Additionally, we observe a decrease in immune infiltration in patients who are resistant to neoadjuvant therapy compared to those who were sensitive to therapy. We utilize these two datasets to demonstrate two algorithms. First, the machine learning tool, REFLECT, maps the landscape of recurrent oncogenic co-alterations in cancers to propose targeted combination therapies. We evaluate REFLECT in the EGFR- and HER2-aberrant and ARTEMIS datasets to match specific co-alteration signatures to targeted cancer drugs. Then, we consider ImogiMap, a statistical analysis tool that identifies interactions between oncogenic processes and immune checkpoint receptors based on their impact on immune phenotypes. We evaluate the algorithm through EGFR-aberrant lung adenocarcinomas and HER2-aberrant breast cancers, as well as compare the cohorts that were sensitive and resistant to neoadjuvant therapy from the ARTEMIS trial. We expect this analysis will guide future precision medicine applications. | en_US |
| dc.embargo.lift | 2025-05-01 | en_US |
| dc.embargo.terms | 2025-05-01 | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Kong, Elisabeth K. "Statistical Approaches to Identifying Therapeutic Vulnerabilities from Cancer Genomics Data." (2023) Diss., Rice University. <a href="https://hdl.handle.net/1911/115103">https://hdl.handle.net/1911/115103</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/115103 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | statistics | en_US |
| dc.subject | genomics | en_US |
| dc.subject | targeted | en_US |
| dc.subject | cancer | en_US |
| dc.title | Statistical Approaches to Identifying Therapeutic Vulnerabilities from Cancer Genomics Data | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Statistics | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |
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