High-content microscopy reveals a morphological signature of bortezomib resistance.
High-content microscopy reveals a morphological signature of bortezomib resistance.
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高内涵显微镜揭示了硼替佐米耐药性的形态学特征。
DOI：
10.1101/2023.05.02.539137
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Way,GP
中科院分区：
文献类型：
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作者：
Kelley,ME;Berman,AY;Stirling,DR;Cimini,BA;Han,Y;Singh,S;Carpenter,AE;Kapoor,TM;Way,GP
耐药性是抗癌治疗中的一个挑战。在许多情况下，癌症在接触药物之前就可能对药物产生耐药性，即具有内在的耐药性。然而，我们缺乏独立于靶点的方法来预测癌细胞系的耐药性或在事先不了解其原因的情况下表征内在耐药性。我们假设细胞形态可以提供耐药性的无偏读数。为了检验这一假设，我们使用 HCT116 细胞（一种错配修复缺陷的癌细胞系）来分离对硼替佐米耐药或敏感的克隆，硼替佐米是一种充分表征的蛋白酶体抑制剂和抗癌药物，许多癌细胞对硼替佐米具有内在耐药性。然后，我们扩展了这些克隆，并使用细胞绘画（一种高内涵显微镜检测）测量了高维单细胞形态特征。我们基于成像和计算的分析流程确定了耐药细胞和敏感细胞之间不同的形态特征。我们利用这些特征来生成硼替佐米耐药性的形态学特征。然后，我们利用这种形态学特征来分析一组未包含在特征训练数据集中的 HCT116 克隆（5 个耐药性和 5 个敏感），并在没有药物治疗的情况下正确预测了 7 个病例对硼替佐米的敏感性。该特征预测硼替佐米的耐药性优于针对泛素-蛋白酶体系统的其他药物的耐药性，表明硼替佐米耐药机制的特异性。我们的结果建立了一个概念验证框架，在没有药物治疗的情况下，使用癌细胞的高内涵显微镜对耐药性进行公正的分析。
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.