A pipeline for malignancy and therapy agnostic assessment of cancer drug response using cell mass measurements.
A pipeline for malignancy and therapy agnostic assessment of cancer drug response using cell mass measurements.
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DOI：
10.1038/s42003-022-04270-3
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发表时间：
2022-11-26
影响因子：
5.9
通讯作者：
中科院分区：
文献类型：
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作者：
功能精确医学通过直接在患者的肿瘤细胞上测试药物疗效，为基于基因组学的癌症治疗指导提供了一个有希望的补充。在这里，我们描述了一种工作流程，它利用单细胞质量测量与在线Brightfield成像和基于机器学习的图像分类来扩大这种癌症功能测试的临床应用。使用这些图像管理的质量测量，我们表征了60种不同药物的质量响应信号，这些药物具有12种不同的细胞类型，具有不同的作用机制，表明与标准的细胞活力分析相比，检测几种慢效药物的反应的能力有所提高。此外，我们使用这个工作流程来评估各种原发肿瘤标本格式的药物反应，包括血液、骨髓、细针抽吸物(FNA)和恶性液体，所有这些都是在两天内产生的报告，结果与患者的临床反应一致。这种工作流程结合了高分辨率测量、广泛的药物和恶性肿瘤适用性以及快速返回的结果，表明它非常适合于对癌症药物反应进行临床相关的功能评估。提出了一种从样本采集到数据分析的细胞质量药敏检测流程。
Functional precision medicine offers a promising complement to genomics-based cancer therapy guidance by testing drug efficacy directly on a patient’s tumor cells. Here, we describe a workflow that utilizes single-cell mass measurements with inline brightfield imaging and machine-learning based image classification to broaden the clinical utility of such functional testing for cancer. Using these image-curated mass measurements, we characterize mass response signals for 60 different drugs with various mechanisms of action across twelve different cell types, demonstrating an improved ability to detect response for several slow acting drugs as compared with standard cell viability assays. Furthermore, we use this workflow to assess drug responses for various primary tumor specimen formats including blood, bone marrow, fine needle aspirates (FNA), and malignant fluids, all with reports generated within two days and with results consistent with patient clinical responses. The combination of high-resolution measurement, broad drug and malignancy applicability, and rapid return of results offered by this workflow suggests that it is well-suited to performing clinically relevant functional assessment of cancer drug response. A pipeline for drug sensitivity testing using cell mass from sample collection to data analysis is presented.
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影响因子：
48
作者：
Godin, Michel;Delgado, Francisco Feijo;Son, Sungmin;Grover, William H.;Bryan, Andrea K.;Tzur, Amit;Jorgensen, Paul;Payer, Kris;Grossman, Alan D.;Kirschner, Marc W.;Manalis, Scott R.
通讯作者：
Manalis, Scott R.
影响因子：
28.2
作者：
通讯作者：
--
DOI：
10.1158/1078-0432.ccr-20-0073
发表时间：
2020-07-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Narasimhan V;Wright JA;Churchill M;Wang T;Rosati R;Lannagan TRM;Vrbanac L;Richardson AB;Kobayashi H;Price T;Tye GXY;Marker J;Hewett PJ;Flood MP;Pereira S;Whitney GA;Michael M;Tie J;Mukherjee S;Grandori C;Heriot AG;Worthley DL;Ramsay RG;Woods SL
通讯作者：
Woods SL
影响因子：
28.4
作者：
Marquart, John;Chen, Emerson Y.;Prasad, Vinay
通讯作者：
Prasad, Vinay
影响因子：
82.9
作者：
Letai, Anthony
通讯作者：
Letai, Anthony