Development of Algorithms to Determine Genetic Markers for Immunotherapy Response in Melanoma
Abstract/Contents
- Abstract
- We hope to characterize response to immune checkpoint inhibitor therapy by distinguishing between genomic correlates of responders versus non responders to this therapy and validating this computational technique with data from robust CRISPR screens in in vivo models. Such characterization seeks to assist in the creation of therapeutic supplements to existing immunotherapies and thus improve response in patients. To that end, we aim to better understand the genetic underpinnings of why some melanoma patients respond to CTLA-4 blockade therapy and use results to guide development of future therapeutics which improve melanoma prognosis. Existing literature characterizes genetic correlates to blockade therapy, but none incorporate in vitro models for response as our methodology does. This work seeks to build an algorithm for prediction of such genetic markers of immune checkpoint inhibitor therapy response and to demonstrate that this methodology is applicable in in vivo contexts. Such an algorithm could then be further applied to a wide variety of diseases and therapies in order to more quickly and accurately characterize genetic markers of the response, and to more rapidly develop targeted therapeutics that improves clinical outcomes. Ultimately, we see that our proposed algorithm for such in silico analysis successfully predicts markers that perform well in in vivo experiments. We hope this leads to more rapid and informed development of potential therapeutics to improve response to immune checkpoint inhibitors, an active area of research in oncology and immunology today.
Description
| Type of resource | text |
|---|---|
| Publication date | September 1, 2023; May 8, 2023 |
Creators/Contributors
| Author | Limaye, Aditi |
|---|---|
| Thesis advisor | Satpathy, Ansuman |
| Thesis advisor | Lundberg, Emma |
Subjects
| Subject | Melanoma |
|---|---|
| Subject | Immunology |
| Subject | Computational biology |
| Genre | Text |
| Genre | Thesis |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International license (CC BY-NC).
Preferred citation
- Preferred citation
- Limaye, A. (2023). Development of Algorithms to Determine Genetic Markers for Immunotherapy Response in Melanoma. Stanford Digital Repository. Available at https://purl.stanford.edu/gt561sz6314. https://doi.org/10.25740/gt561sz6314.
Collection
Undergraduate Theses, School of Engineering
View other items in this collection in SearchWorksContact information
- Contact
- alimaye@alumni.stanford.edu
Also listed in
Loading usage metrics...