Contemporary analyses focused on a limited number of clinical and molecular biomarkers have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma. Here we describe a precision medicine platform known as the Molecular Twin consisting of advanced machine-learning models and use it to analyze a dataset of 6,363 clinical and multi-omic molecular features from patients with resected pancreatic ductal adenocarcinoma to accurately predict disease survival (DS). We show that a full multi-omic model predicts DS with the highest accuracy and that plasma protein is the top single-omic predictor of DS. A parsimonious model learning only 589 multi-omic features demonstrated similar predictive performance as the full multi-omic model. Our platform enables discovery of parsimonious biomarker panels and performance assessment of outcome prediction models learning from resource-intensive panels. This approach has considerable potential to impact clinical care and democratize precision cancer medicine worldwide.

当代分析集中于有限数量的临床和分子生物标志物，无法准确预测胰腺导管腺癌的临床结果。在这里，我们描述了一个名为“分子双胞胎”的精准医学平台，该平台由先进的机器学习模型组成，并使用它来分析来自胰腺导管腺癌切除患者的 6,363 个临床和多组学分子特征的数据集，以准确预测疾病生存 (DS) 。我们表明，完整的多组学模型能够以最高的准确度预测 DS，并且血浆蛋白是 DS 的顶级单组学预测因子。仅学习 589 个多组学特征的简约模型表现出与完整多组学模型相似的预测性能。我们的平台能够发现简约的生物标志物面板，并对从资源密集型面板中学习的结果预测模型进行性能评估。这种方法在影响临床护理和全球精准癌症医学民主化方面具有巨大潜力。