Artificial intelligence-guided discovery of gastric cancer continuum.
Artificial intelligence-guided discovery of gastric cancer continuum.
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DOI：
10.1007/s10120-022-01360-3
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发表时间：
2023-03
期刊：
影响因子：
7.4
通讯作者：
Sahoo, Debashis
中科院分区：
文献类型：
--
作者：
Vo, Daniella;Ghosh, Pradipta;Sahoo, Debashis
详细了解胃癌的前、早期和晚期肿瘤状态有助于开发更好的胃癌进展风险模型和阻止这种进展的药物治疗。我们建立了一个胃癌的布尔蕴涵网络，并部署了机器学习算法来开发已知肿瘤前状态的预测模型，例如，萎缩性胃炎、肠上皮化生（IM）和低度至高度肠肿瘤（L/HGIN）以及GC。我们的方法利用了不对称布尔蕴涵关系的存在，这些关系在几乎所有的胃癌数据集上都是不变的。不变的非对称布尔蕴涵关系可以破译生物数据背后的基本时间序列。基于这种方法，我们开发了一个健康粘膜→ GC连续体模型。我们的模型在区分健康样本和GC样本方面比公开可用的模型表现得更好。虽然没有在IM和L/HGIN数据集上训练，但该模型可以通过患者样本中的化生→异型增生→瘤形成级联来识别进展为GC的风险。该模型可以对所有公开可用的小鼠模型进行排名，因为它们能够最好地概括人GC起始和进展期间的基因表达模式。一个布尔蕴涵网络，使迄今未定义的连续状态的GC启动过程中的识别。开发的模型现在可以作为合理化候选治疗靶点的起点，以拦截GC进展。在线版本包含补充材料，可通过10.1007/s10120-022-01360-3获得。
Detailed understanding of pre-, early and late neoplastic states in gastric cancer helps develop better models of risk of progression to gastric cancers (GCs) and medical treatment to intercept such progression. We built a Boolean implication network of gastric cancer and deployed machine learning algorithms to develop predictive models of known pre-neoplastic states, e.g., atrophic gastritis, intestinal metaplasia (IM) and low- to high-grade intestinal neoplasia (L/HGIN), and GC. Our approach exploits the presence of asymmetric Boolean implication relationships that are likely to be invariant across almost all gastric cancer datasets. Invariant asymmetric Boolean implication relationships can decipher fundamental time-series underlying the biological data. Pursuing this method, we developed a healthy mucosa → GC continuum model based on this approach. Our model performed better against publicly available models for distinguishing healthy versus GC samples. Although not trained on IM and L/HGIN datasets, the model could identify the risk of progression to GC via the metaplasia → dysplasia → neoplasia cascade in patient samples. The model could rank all publicly available mouse models for their ability to best recapitulate the gene expression patterns during human GC initiation and progression. A Boolean implication network enabled the identification of hitherto undefined continuum states during GC initiation. The developed model could now serve as a starting point for rationalizing candidate therapeutic targets to intercept GC progression. The online version contains supplementary material available at 10.1007/s10120-022-01360-3.
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