Dissertation
Modeling drug resistance to BRAF inhibition in cutaneous melanoma
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Spring 2023
DOI: 10.25820/etd.007247
Abstract
Over a decade’s worth of -omics analysis in cutaneous melanoma have revealed that cutaneous melanomas have disparate drug resistance mechanisms to targeted BRAF inhibition. Yet, clinical management of cutaneous melanoma with BRAF inhibitors remains the same for all patients. The lag in clinical adoption of this knowledge can be attributed to the heterogeneity of cutaneous melanoma, where diverse phenotypes make a one-size fits all approach unobtainable. Thus, a precision understanding of how different subtypes of melanoma evades BRAF inhibition will be essential to combat drug resistance. It is widely appreciated that melanocyte differentiation status, which can be determined by gene expression patterns, is a predictor of drug resistance. Here we use this well-described classification system to map the drug resistance mechanisms associated with each subtype. Through computational and genetic screening approaches we revealed revealing common and shared mechanisms of resistances to BRAF inhibition between undifferentiated and differentiated melanomas. Notably we demonstrate that Rac1-signaling as a critical mediator of drug resistance to BRAFi in an inherently drug resistant, de-differentiated, melanoma subtype, and this resistance mechanism is amenable to Src-blockade. To guide the translation of our findings, we also used a machine learning approach to develop a simple classifier to resolve Rac1-dependent melanomas based on a minimal set of genes upregulated in recurrent chromatin states. We demonstrate the prognostic value of this classification system on data from patients undergoing BRAF inhibition or immune checkpoint blockade—finding that subtype assignment tracks with increased resistance to both classes of therapies.
Details
- Title: Subtitle
- Modeling drug resistance to BRAF inhibition in cutaneous melanoma
- Creators
- Eliot Zhu
- Contributors
- Adam J Dupuy (Advisor)Christopher Stipp (Committee Member)Michael Henry (Committee Member)Rebecca Dodd (Committee Member)David Gordon (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Biomedical Science (Cancer Biology)
- Date degree season
- Spring 2023
- DOI
- 10.25820/etd.007247
- Publisher
- University of Iowa
- Number of pages
- vi, 89 pages
- Copyright
- Copyright 2022 Eliot Zhu
- Comment
This thesis has been optimized for improved web viewing. If you require the original version, contact the University Archives at the University of Iowa: https://www.lib.uiowa.edu/sc/contact/.
- Language
- English
- Date submitted
- 01/21/2023
- Date approved
- 02/19/2023
- Description illustrations
- illustrations (some color)
- Description bibliographic
- Includes bibliographical references (pages 80-89).
- Public Abstract (ETD)
Cutaneous melanoma is an aggressive form of skin cancer. In the US alone, approximately 90,000 new cases and 9,000 deaths due to melanoma were reported in 2017. Selective inhibitors of BRAFV600E/K work potently against melanomas driven by oncogenic BRAF. Oncogenic BRAF drives aberrant activation of the MAPK signaling pathway. Unfortunately, drug resistance is ubiquitous, and most patients will progress within two years of therapy. Large-scale genome- and transcriptome- profiling reveal that resistance to MAPK inhibition (MAPKi) is either MAPK- dependent or redundant. MAPK-dependent (resistance ~50%) resistance is characterized by the reactivation of MAPK, typically through mutations that augment MAPK signaling. This thesis aims to understand the diversity of drug resistance mechanisms and identify strategies to overcome them. To this end, we revealed a signaling axis that critically drives MAPK- redundant resistance, particularly in de-differentiated melanomas. We also show using functional genomic screens that drug resistance is non-uniform across melanomas, with MAPK-dependent resistance mechanisms largely conserved across melanomas while MAPK-redundant resistance mechanisms were not. Finally, we devised a computational approach to mine large-scale pharmacogenomic datasets to identify strategies to overcome drug resistance.
- Academic Unit
- Biomedical Science Program
- Record Identifier
- 9984428943302771
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