Wu Bo-Run, Ormazabal Arriagada Sofia, Hsu Te-Cheng, Lin Tsung-Wei, Lin Che
Graduate Institute of Communication Engineering, National Taiwan University (NTU), Taipei, Taiwan.
Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan.
NPJ Precis Oncol. 2024 Oct 29;8(1):245. doi: 10.1038/s41698-024-00700-z.
Cancer prognosis requires precision to identify high-risk patients and improve survival outcomes. Conventional methods struggle with the complexity of genetic biomarkers and diverse medical data. Our study uses deep learning to distil high-dimensional medical data into low-dimensional feature vectors exploring shared patterns across cancer types. We developed a multi-task bimodal neural network integrating RNA Sequencing and clinical data from three The Cancer Genome Atlas project datasets: Breast Invasive Carcinoma, Lung Adenocarcinoma, and Colon Adenocarcinoma. Our approach significantly improved prognosis prediction, especially for Colon Adenocarcinoma, with up to 26% increase in concordance index and 41% in the area under the precision-recall curve. External validation with Small Cell Lung Cancer achieved comparable metrics, indicating that supplementing small datasets with data from other cancers can improve performance. This work represents initial strides in using multi-task learning for prognosis prediction across cancer types, potentially revealing shared mechanisms among cancers and contributing to future applications in precision medicine.
癌症预后需要精确性来识别高危患者并改善生存结果。传统方法在处理基因生物标志物的复杂性和多样的医学数据时面临困难。我们的研究使用深度学习将高维医学数据提炼为低维特征向量，探索不同癌症类型之间的共同模式。我们开发了一个多任务双峰神经网络，整合了来自三个癌症基因组图谱（The Cancer Genome Atlas）项目数据集的RNA测序和临床数据：乳腺浸润性癌、肺腺癌和结肠腺癌。我们的方法显著改善了预后预测，特别是对于结肠腺癌，一致性指数提高了26%，精确召回曲线下面积提高了41%。用小细胞肺癌进行的外部验证取得了类似的指标，表明用其他癌症的数据补充小数据集可以提高性能。这项工作代表了在使用多任务学习进行跨癌症类型预后预测方面迈出的初步步伐，可能揭示癌症之间的共同机制，并为精准医学的未来应用做出贡献。
