High-content microscopy reveals a morphological signature of bortezomib resistance
- Rockefeller University, United States
- Broad Institute, United States
- University of Colorado Anschutz Medical Campus, United States
Abstract
Drug resistance is a challenge in anticancer therapy. In many cases, cancers can be resistant to the drug prior to exposure, i.e., possess intrinsic drug resistance. However, we lack target-independent methods to anticipate resistance in cancer cell lines or characterize intrinsic drug resistance without a priori knowledge of its cause. We hypothesized that cell morphology could provide an unbiased readout of drug resistance. To test this hypothesis, we used HCT116 cells, a mismatch repair-deficient cancer cell line, to isolate clones that were resistant or sensitive to bortezomib, a well-characterized proteasome inhibitor and anticancer drug to which many cancer cells possess intrinsic resistance. We then expanded these clones and measured high-dimensional single-cell morphology profiles using Cell Painting, a high-content microscopy assay. Our imaging- and computation-based profiling pipeline identified morphological features that differed between resistant and sensitive cells. We used these features to generate a morphological signature of bortezomib resistance. We then employed this morphological signature to analyze a set of HCT116 clones (five resistant and five sensitive) that had not been included in the signature training dataset, and correctly predicted sensitivity to bortezomib in seven cases, in the absence of drug treatment. This signature predicted bortezomib resistance better than resistance to other drugs targeting the ubiquitin-proteasome system. Our results establish a proof-of-concept framework for the unbiased analysis of drug resistance using high-content microscopy of cancer cells, in the absence of drug treatment.
Data availability
All data generated during this study are provided in the dataset cpg0028-kelley-resistance, available in the Cell Painting Gallery on the Registry of Open Data on AWS (https://registry.opendata.aws/cellpainting-gallery/). Processed data, source data files, and code to reproduce this analysis are available at https://github.com/broadinstitute/profiling-resistance-mechanisms (Way et al., 2023).
Article and author information
Author details
Funding
Starr Cancer Consortium (112-0039)
- Anne E Carpenter
- Tarun M Kapoor
National Institutes of Health (R35 GM122547)
- Anne E Carpenter
National Institutes of Health (R35 GM130234)
- Tarun M Kapoor
National Institutes of Health (T32 GM066699)
- Megan E Kelley
National Institutes of Health (T32 GM115327)
- Adi Y Berman
National Science Foundation (NSF GRFP 2019272977)
- Adi Y Berman
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Kelley et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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High-content microscopy reveals a morphological signature of bortezomib resistance
eLife 12:e91362.
https://doi.org/10.7554/eLife.91362
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