High-content microscopy reveals a morphological signature of bortezomib resistance

• Published in: eLife Vol. 12
• Main Authors: Kelley, Megan E, Berman, Adi Y, Stirling, David R, Cimini, Beth A, Han, Yu, Singh, Shantanu, Carpenter, Anne E, Kapoor, Tarun M, Way, Gregory P
• Format: Journal Article
• Language: English
• Published: England eLife Science Publications, Ltd 27.09.2023; eLife Sciences Publications Ltd; eLife Sciences Publications, Ltd
• Subjects: Antineoplastic Agents - pharmacology; Antitumor agents; Apoptosis; Boronic Acids - pharmacology; Boronic Acids - therapeutic use; Bortezomib; Bortezomib - pharmacology; Cancer; Cancer Biology; cancer resistance; Cancer therapies; Cell Line, Tumor; Cell morphology; cell painting; Cells; Cloning; Computational and Systems Biology; Cytology; Drug delivery; Drug resistance; Drug Resistance, Neoplasm; Experiments; high content imaging; Inhibitor drugs; linear modeling; Machine learning; Microscope and microscopy; Microscopy; Mismatch repair; Morphology; Multiple myeloma; Mutation; Proteasome Endopeptidase Complex - metabolism; Proteasome inhibitors; Proteasome Inhibitors - pharmacology; Proteasomes; Pyrazines - pharmacology; Targeted cancer therapy; Tumor cell lines; Ubiquitin; Taiwan; computational biology; systems biology; cancer resistance; cancer biology; cell painting; microscopy; high content imaging; linear modeling; human
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.91362

Drug resistance is a challenge in anticancer therapy. In many cases, cancers can be resistant to the drug prior to exposure, that is, 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, indicating specificity for mechanisms of resistance to bortezomib. 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.