SIO: A Spatioimageomics Pipeline to Identify Prognostic Biomarkers Associated with the Ovarian Tumor Microenvironment.
SIO: A Spatioimageomics Pipeline to Identify Prognostic Biomarkers Associated with the Ovarian Tumor Microenvironment.
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DOI：
10.3390/cancers13081777
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发表时间：
2021-04-08
期刊：
影响因子：
5.2
通讯作者：
Wong STC
中科院分区：
文献类型：
--
作者：
Zhu Y;Ferri-Borgogno S;Sheng J;Yeung TL;Burks JK;Cappello P;Jazaeri AA;Kim JH;Han GH;Birrer MJ;Mok SC;Wong STC
在美国，高级别浆液性卵巢癌(HGSC)每年导致超过13,000人死亡。影响HGSC患者生存的一个重要因素是肿瘤微环境。然而，不同的细胞如何相互作用影响HGSC患者的生存在很大程度上仍不清楚。为了研究这一点，我们开发了一种结合成像质量细胞术(IMC)、位置特异性转录转录和深度学习的管道来识别各种间质、肿瘤和免疫细胞的分布以及它们的空间关系。我们的管道自动准确地分割细胞并提取显著的细胞特征，以识别生物标记物，以及不同细胞之间的多个最近邻相互作用，这些相互作用相互协调，影响HGSC患者的总体存活率。此外，我们将IMC数据与显微解剖的肿瘤和间质转录本相结合，以识别新的信号网络。这些结果可能导致发现HGSC患者新的存活率调节机制。肿瘤微环境中的基质细胞和免疫细胞(TME)直接影响高级别浆液性卵巢癌(HGSC)的恶性表型，然而，这些细胞如何相互作用影响HGSC患者的生存仍很不清楚。为了研究这种复杂的TME中的细胞-细胞通讯，我们开发了一种SpatioImageOmics(SpatioImageOmics)管道，该管道结合了成像质量细胞术(IMC)、位置特异性转录和深度学习来识别TME中各种间质、肿瘤和免疫细胞的分布及其空间关系。SIO管道自动准确地分割细胞并提取显著的细胞特征，以识别生物标记物，以及肿瘤、免疫和基质细胞之间的多个最近邻相互作用，这些相互作用相互协调，影响HGSC患者的总体存活率。此外，SIO将IMC数据与来自同一患者的显微解剖的肿瘤和间质转录本整合，以识别新的信号网络，这将导致发现HGSC患者新的存活率调节机制。
High-grade serous ovarian cancer (HGSC) caused more than 13,000 deaths annually in the United States. A critically important component that influences the HGSC patient survival is the tumor microenvironment. However, how different cells interact to influence HGSC patients’ survival remains largely unknown. To investigate this, we developed a pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of various stromal, tumor and immune cells as well as their spatial relationship. Our pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among different cells that coordinate to influence overall survival rates in HGSC patients. In addition, we integrated IMC data with microdissected tumor and stromal transcriptomes to identify novel signaling networks. These results may lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients. Stromal and immune cells in the tumor microenvironment (TME) have been shown to directly affect high-grade serous ovarian cancer (HGSC) malignant phenotypes, however, how these cells interact to influence HGSC patients’ survival remains largely unknown. To investigate the cell-cell communication in such a complex TME, we developed a SpatioImageOmics (SIO) pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of various stromal, tumor and immune cells as well as their spatial relationship in TME. The SIO pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among tumor, immune, and stromal cells that coordinate to influence overall survival rates in HGSC patients. In addition, SIO integrates IMC data with microdissected tumor and stromal transcriptomes from the same patients to identify novel signaling networks, which would lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients.
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影响因子：
254.7
作者：
Jemal, Ahmedin;Siegel, Rebecca;Thun, Michael J.
通讯作者：
Thun, Michael J.
DOI：
10.1084/jem.177.5.1231
发表时间：
1993-05-01
期刊：
The Journal of experimental medicine
影响因子：
--
作者：
Ikeyama S;Koyama M;Yamaoko M;Sasada R;Miyake M
通讯作者：
Miyake M
影响因子：
3.8
作者：
Chen J;Liu Z;Fang S;Fang R;Liu X;Zhao Y;Li X;Huang L;Zhang J
通讯作者：
Zhang J
影响因子：
4.6
作者：
Feng, Yangyang;Gao, Yue;Wang, Enhua
通讯作者：
Wang, Enhua
影响因子：
22.7
作者：
Ali, H. Raza;Jackson, Hartland W.;Bodenmiller, Bernd
通讯作者：
Bodenmiller, Bernd