BACKGROUND Compared to the many uses of DNA-level testing in clinical oncology, development of RNA-based diagnostics has been more limited. An exception to this trend is the growing use of mRNA-based methods in early-stage breast cancer. Although DNA and mRNA are used together in breast cancer research, the distinct contribution of mRNA beyond that of DNA in clinical challenges has not yet been directly assessed. We hypothesize that mRNA harbors prognostically useful information independently of genomic variation. To validate this, we use both genomic mutations and gene expression to predict five-year breast cancer recurrence in an integrated test model. This is accomplished first by comparing the feature importance of DNA and mRNA features in a model trained on both, and second, by evaluating the difference in performance of models trained on DNA and mRNA data separately. RESULTS We find that models trained on DNA and mRNA data give more weight to mRNA features than to DNA features, and models trained only on mRNA outperform models trained on DNA alone. CONCLUSIONS The evaluation process presented here may serve as a framework for the interpretation of the relative contribution of individual molecular markers. It also suggests that mRNA has a distinct contribution in a diagnostic setting, beyond and independently of DNA mutation data.

中文翻译：

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直接比较表明，基于mRNA的诊断方法包含无法从基因组突变中直接获悉的信息。

背景技术与DNA水平检测在临床肿瘤学中的许多用途相比，基于RNA的诊断方法的开发受到了更大的限制。这种趋势的一个例外是在早期乳腺癌中越来越多地使用基于mRNA的方法。尽管在乳腺癌研究中同时使用了DNA和mRNA，但在临床挑战中，mRNA超越DNA的独特贡献尚未得到直接评估。我们假设mRNA具有独立于基因组变异的预后有用信息。为了验证这一点，我们在整合的测试模型中使用基因组突变和基因表达来预测五年乳腺癌复发。首先，可以通过在经过训练的模型中比较DNA和mRNA特征的特征重要性，然后再通过 通过评估分别针对DNA和mRNA数据训练的模型的性能差异。结果我们发现，在DNA和mRNA数据上训练的模型对mRNA特征的权重比对DNA特征的权重更大，仅在mRNA上训练的模型胜过仅在DNA上训练的模型。结论本文介绍的评估过程可作为解释单个分子标记物相对贡献的框架。这也表明，mRNA在诊断环境中具有独特的贡献，而不仅限于DNA突变数据。结论本文介绍的评估过程可作为解释单个分子标记物相对贡献的框架。这也表明，mRNA在诊断环境中具有独特的作用，而不仅限于DNA突变数据。结论本文介绍的评估过程可作为解释单个分子标记物相对贡献的框架。这也表明，mRNA在诊断环境中具有独特的作用，而不仅限于DNA突变数据。