Computational strategies for investigating cancer drug resistance using single-cell data
Abstract/Contents
- Abstract
- Treatment of metastatic cancer utilizes chemotherapies or newer targeted therapies. However, treatment is thwarted by drug resistance where some cancer cells can survive and lead to relapse. Recent studies suggest that cancer cells can evade cell death after treatment through non-genetic means such as distinct signaling or differentiation states. To better understand these resistance mechanisms, we must study the dynamics of the anti-cancer drug response across time. We use high-dimensional single-cell platforms like mass cytometry and novel algorithms to visualize and trace the trajectories of subpopulations with different fates upon drug exposure. We present the development of a free, open-source R package known as FLOWMAPR that can represent complex timecourse datasets as a single, interpretable 2D graph. We apply this analytical approach to two novel studies of drug resistance: treatment of multiple myeloma using bortezomib and dexamethasone and response to BRAF inhibition in melanoma. Through visualization and computational modeling, we identify specific signaling or metabolic features that distinguish drug-resistant cells in models of both cancer subtypes. Moreover, through combination treatments with inhibitors directed against these features, we demonstrate the ability to reduce the fraction of drug-resistant cells present in both cell lines and in patients. Taken together, these studies demonstrate the potential for computational analysis of high-dimensional single-cell data to reveal mechanistic insights into cancer drug resistance, paving the way for the design of new treatments or more effective combination regimens.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ko, Melissa E |
|---|---|
| Degree supervisor | Nolan, Garry P |
| Thesis advisor | Nolan, Garry P |
| Thesis advisor | Artandi, Steven E |
| Thesis advisor | Khavari, Paul A |
| Thesis advisor | Plevritis, Sylvia |
| Thesis advisor | Wernig, Marius |
| Degree committee member | Artandi, Steven E |
| Degree committee member | Khavari, Paul A |
| Degree committee member | Plevritis, Sylvia |
| Degree committee member | Wernig, Marius |
| Associated with | Stanford University, Cancer Biology Program. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Melissa E. Ko. |
|---|---|
| Note | Submitted to the Cancer Biology Program. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Melissa Ellen Ko
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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