Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body.
Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body.
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DOI：
10.1016/j.cell.2019.11.013
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发表时间：
2019-12-12
期刊：
影响因子：
64.5
通讯作者：
Ertürk A
中科院分区：
文献类型：
--
作者：
Pan C;Schoppe O;Parra-Damas A;Cai R;Todorov MI;Gondi G;von Neubeck B;Böğürcü-Seidel N;Seidel S;Sleiman K;Veltkamp C;Förstera B;Mai H;Rong Z;Trompak O;Ghasemigharagoz A;Reimer MA;Cuesta AM;Coronel J;Jeremias I;Saur D;Acker-Palmer A;Acker T;Garvalov BK;Menze B;Zeidler R;Ertürk A
长期以来，为了更好地了解和治疗癌症转移，一直需要可靠地检测播散的肿瘤细胞和肿瘤靶向治疗性抗体在全身的生物分布。在这里，我们开发了一个集成的管道，用于癌症转移的自动量化和治疗性抗体靶向，名为DeepMACT。首先，我们通过应用vDISCO方法对透明小鼠的转移进行成像，将癌细胞的荧光信号增强了100倍以上。其次，我们开发了深度学习算法，用于自动量化转移瘤，其准确性与人类专家的手动注释相匹配。在包括乳腺癌、肺癌和胰腺癌在内的5种具有不同器官趋向性的不同转移癌症模型中，基于深度学习的量化使我们能够系统地分析特征，如大小、形状、空间分布以及治疗性单抗在整个小鼠中靶向转移的程度。因此，DeepMACT可以在临床前阶段极大地改进基于抗体的有效疗法的发现。
Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the pre-clinical stage.
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影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者：
Thrun S
影响因子：
25
作者：
Cai, Ruiyao;Pan, Chenchen;Ertuerk, Ali
通讯作者：
Ertuerk, Ali
影响因子：
50.3
作者：
Hingorani, SR;Petricoin, EF;Tuveson, DA
通讯作者：
Tuveson, DA
用于研究人类乳腺癌转移的新小鼠模型。
影响因子：
3.7
作者：
Iorns E;Drews-Elger K;Ward TM;Dean S;Clarke J;Berry D;El Ashry D;Lippman M
通讯作者：
Lippman M
影响因子：
64.5
作者：
Lambert AW;Pattabiraman DR;Weinberg RA
通讯作者：
Weinberg RA