Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity.
Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity.
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DOI：
10.1038/s41467-022-31771-w
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发表时间：
2022-07-22
影响因子：
16.6
通讯作者：
中科院分区：
文献类型：
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作者：
生物标志物对于精准医学来说是不可或缺的。然而，利用人体组织进行聚焦的单生物标记物开发因样本空间异质性而变得复杂。为了应对这一挑战，我们测试了一种代表原发肿瘤的方法，该方法将来自多个采样区的细胞外基质的多个原位生物标记物协同集成到肿瘤内图形神经网络中。令人惊讶的是，这种计算模型与传统的非图形模型相比，其不同的预后价值接近于组合的常规预后生物标记物(肿瘤大小、结节状态、组织学分级、分子亚型等)。对995例乳腺癌患者进行回顾性研究。这种巨大的预测价值源于图形整合的原位生物标记物之间的隐含但可解释的区域相互作用，否则，如果它们单独发展成单一的常规(空间均质)生物标记物，就会失去这种预测价值。我们的研究展示了通过记录现有生物标记物之间的区域相互作用而不是开发新的生物标记物来预测癌症预后的另一种途径。由于所需的生物标记物均质化步骤，使用多区域采样的癌症预后是昂贵的，并且不完全可靠。在这里，作者开发了一个肿瘤内图形神经网络，用于预测多区域癌症样本的预后，该网络基于不需要均质化的原位生物标记物和基因表达。
Biomarkers are indispensable for precision medicine. However, focused single-biomarker development using human tissue has been complicated by sample spatial heterogeneity. To address this challenge, we tested a representation of primary tumor that synergistically integrated multiple in situ biomarkers of extracellular matrix from multiple sampling regions into an intratumor graph neural network. Surprisingly, the differential prognostic value of this computational model over its conventional non-graph counterpart approximated that of combined routine prognostic biomarkers (tumor size, nodal status, histologic grade, molecular subtype, etc.) for 995 breast cancer patients under a retrospective study. This large prognostic value, originated from implicit but interpretable regional interactions among the graphically integrated in situ biomarkers, would otherwise be lost if they were separately developed into single conventional (spatially homogenized) biomarkers. Our study demonstrates an alternative route to cancer prognosis by taping the regional interactions among existing biomarkers rather than developing novel biomarkers. Cancer prognosis using multiregion sampling is costly and not completely reliable due to the required biomarker homogenisation step. Here, the authors develop an intratumor graph neural network for prognosis in multiregion cancer samples based on in situ biomarkers and gene expression that does not need homogenisation.
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DOI：
10.3390/bioengineering8020017
发表时间：
2021-01-21
期刊：
Bioengineering (Basel, Switzerland)
影响因子：
--
作者：
Ouellette JN;Drifka CR;Pointer KB;Liu Y;Lieberthal TJ;Kao WJ;Kuo JS;Loeffler AG;Eliceiri KW
通讯作者：
Eliceiri KW
影响因子：
28.1
作者：
Lu MY;Williamson DFK;Chen TY;Chen RJ;Barbieri M;Mahmood F
通讯作者：
Mahmood F
影响因子：
8.8
作者：
Litchfield, Kevin;Stanislaw, Stacey;Turajlic, Samra
通讯作者：
Turajlic, Samra
影响因子：
82.9
作者：
Andor N;Graham TA;Jansen M;Xia LC;Aktipis CA;Petritsch C;Ji HP;Maley CC
通讯作者：
Maley CC
影响因子：
4.3
作者：
Cox TR;Erler JT
通讯作者：
Erler JT