Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data
Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data
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DOI：
10.1073/pnas.1510227113
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发表时间：
2016-10-25
影响因子：
11.1
通讯作者：
Lelieveldt, Boudewijn P. F.
中科院分区：
文献类型：
--
作者：
Abdelmoula, Walid M.;Balluff, Benjamin;Lelieveldt, Boudewijn P. F.
识别对患者预后有不利影响的肿瘤亚群对于更有针对性地研究肿瘤如何发展有害表型以及个性化治疗至关重要。质谱学成像已经证明了发现肿瘤内分子异质性的能力。挑战一直是对结果数据进行客观分析，以确定那些影响患者预后的肿瘤亚群。在这里，我们介绍了空间映射t分布随机邻居嵌入(t-SNE)，这是一种数据的非线性可视化，能够更好地解决生物分子肿瘤内的异质性。以一种无偏见的方式，t-SNE可以发现在统计上与胃癌患者生存和乳腺癌原发肿瘤转移状态相关的肿瘤亚群。
The identification of tumor subpopulations that adversely affect patient outcomes is essential for a more targeted investigation into how tumors develop detrimental phenotypes, as well as for personalized therapy. Mass spectrometry imaging has demonstrated the ability to uncover molecular intratumor heterogeneity. The challenge has been to conduct an objective analysis of the resulting data to identify those tumor subpopulations that affect patient outcome. Here we introduce spatially mapped t-distributed stochastic neighbor embedding (t-SNE), a nonlinear visualization of the data that is able to better resolve the biomolecular intratumor heterogeneity. In an unbiased manner, t-SNE can uncover tumor subpopulations that are statistically linked to patient survival in gastric cancer and metastasis status in primary tumors of breast cancer.