Department of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0703, Leichtag Building 132, La Jolla, CA, 92093-0703, USA.
Moores Cancer Center, University of California San Diego, La Jolla, USA.
Gastric Cancer. 2023 Mar;26(2):286-297. doi: 10.1007/s10120-022-01360-3. Epub 2023 Jan 24.
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.
详细了解胃癌的前期、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干预此类进展的医疗方法。
我们构建了胃癌的布尔蕴涵网络，并部署了机器学习算法，以开发已知前期肿瘤状态（如萎缩性胃炎、肠上皮化生（IM）和低至高级别肠上皮内瘤变（L/HGIN））和胃癌的预测模型。我们的方法利用了可能在几乎所有胃癌数据集都不变的不对称布尔蕴涵关系的存在。不变的不对称布尔蕴涵关系可以揭示生物数据背后的基本时间序列。基于这种方法，我们开发了一种健康黏膜→胃癌连续体模型。
我们的模型在区分健康与胃癌样本方面优于公开可用的模型。尽管该模型未在 IM 和 L/HGIN 数据集上进行训练，但它可以通过上皮化生→发育不良→肿瘤的级联反应在患者样本中识别出进展为胃癌的风险。该模型可以对所有公开可用的小鼠模型进行排名，以评估它们在重现人类胃癌起始和进展过程中的基因表达模式方面的能力。
布尔蕴涵网络能够识别胃癌起始过程中迄今尚未定义的连续体状态。现在，开发的模型可以作为合理化候选治疗靶点以干预胃癌进展的起点。
