Bernal Yanara A, Blanco Alejandro, Oróstica Karen, Delgado Iris, Armisén Ricardo
Universidad del Desarrollo.
Universidad de Talca.
Res Sq. 2024 Dec 17:rs.3.rs-5604105. doi: 10.21203/rs.3.rs-5604105/v1.
The integration of conventional omics data such as genomics and transcriptomics data into artificial intelligence models has advanced significantly in recent years; however, their low applicability in clinical contexts, due to the high complexity of models, has been limited in their direct use inpatients. We integrated classic omics, including DNA mutation and RNA gene expression, added a novel focus on promising omics methods based on A>I(G) RNA editing, and developed a drug response prediction model.
We analyzed 104 patients from the Breast Cancer Genome-Guided Therapy Study (NCT02022202). This study was used to train (70%) with 10-fold cross-validation and test (30%) the drug response classification models. We assess the performance of the random forest (RF), generalized linear model (GLM), and support vector machine (SVM) with the Caret package in classifying therapy response via various combinations of clinical data, tumoral and germline mutation data, gene expression data, and RNA editing data via the LASSO and PCA strategies.
First, we characterized the cohort on the basis of clinical data, mutation landscapes, differential gene expression, and RNAediting sites in 69 nonresponders and 35 responders to therapy. Second, regarding the prediction models, we demonstrated that RNA editing data improved or maintained the performance of the RF model for predicting drug response across all combinations. To select the final model, we compared the score between models with different data combinations, highlighting an score of 0.96 (95% CI: 0.957--0.961) and an AUC of 0.922, using LASSO for feature selection. Finally, we developed a nonresponse risk score on the basis of features that contributed to the selected model, focusing on three RNA-edited sites in the genes KDM4B, miRNA200/TTLL10-AS1, and BEST1. The score was created to facilitate the clinical translation of our findings, presenting a probability of therapy response according to RNA editing site patterns.
Our study highlights the potential of RNA editing as a valuable addition to predictive modeling for drug response in patients with breast cancer. The nonresponse risk score could represent a tool for clinical translation, offering a probability-based assessment of therapy response. These findings suggest that incorporating RNA editing into predictive models could enhance personalized treatment strategies and improve decision-making in oncology.
近年来，将基因组学和转录组学等传统组学数据整合到人工智能模型中取得了显著进展；然而，由于模型高度复杂，它们在临床环境中的适用性较低，在直接用于患者方面受到限制。我们整合了经典组学，包括DNA突变和RNA基因表达，新增了对基于A>I(G)RNA编辑的有前景的组学方法的关注，并开发了一种药物反应预测模型。
我们分析了来自乳腺癌基因组导向治疗研究（NCT02022202）的104名患者。本研究用于通过10倍交叉验证进行训练（70%），并测试（30%）药物反应分类模型。我们使用Caret软件包评估随机森林（RF）、广义线性模型（GLM）和支持向量机（SVM）在通过lasso和主成分分析（PCA）策略将临床数据、肿瘤和种系突变数据、基因表达数据以及RNA编辑数据进行各种组合来分类治疗反应方面的性能。
首先，我们根据69名无反应者和35名治疗反应者的临床数据、突变图谱、差异基因表达和RNA编辑位点对队列进行了特征描述。其次，关于预测模型，我们证明RNA编辑数据在所有组合中均改善或维持了RF模型预测药物反应的性能。为了选择最终模型，我们比较了不同数据组合模型之间的F1分数，使用lasso进行特征选择时，突出显示F1分数为0.96（95%CI：0.957 - 0.961），曲线下面积（AUC）为0.922。最后，我们基于对所选模型有贡献的特征开发了一个无反应风险评分，重点关注KDM4B、miRNA200/TTLL10 - AS1和BEST1基因中的三个RNA编辑位点。创建该评分是为了促进我们研究结果的临床转化，根据RNA编辑位点模式呈现治疗反应的概率。
我们的研究强调了RNA编辑作为乳腺癌患者药物反应预测模型中有价值补充的潜力。无反应风险评分可代表一种临床转化工具，提供基于概率的治疗反应评估。这些发现表明，将RNA编辑纳入预测模型可增强个性化治疗策略并改善肿瘤学决策。
