Huusari Riikka, Wang Tianduanyi, Szedmak Sandor, Dias Diogo, Aittokallio Tero, Rousu Juho
Department of Computer Science, Aalto University, Otakaari 1B, FI-00076 Espoo, Finland.
Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Tukholmankatu 8, FI-00270 Helsinki, Finland.
Brief Bioinform. 2025 Mar 4;26(2). doi: 10.1093/bib/bbaf099.
Drug combinations are required to treat advanced cancers and other complex diseases. Compared with monotherapy, combination treatments can enhance efficacy and reduce toxicity by lowering the doses of single drugs-and there especially synergistic combinations are of interest. Since drug combination screening experiments are costly and time-consuming, reliable machine learning models are needed for prioritizing potential combinations for further studies. Most of the current machine learning models are based on scalar-valued approaches, which predict individual response values or synergy scores for drug combinations. We take a functional output prediction approach, in which full, continuous dose-response combination surfaces are predicted for each drug combination on the cell lines. We investigate the predictive power of the recently proposed comboKR method, which is based on a powerful input-output kernel regression technique and functional modeling of the response surface. In this work, we develop a scaled-up formulation of the comboKR, which also implements improved modeling choices: we (1) incorporate new modeling choices for the output drug combination response surfaces to the comboKR framework, and (2) propose a projected gradient descent method to solve the challenging pre-image problem that is traditionally solved with simple candidate set approaches. We provide thorough experimental analysis of comboKR 2.0 with three real-word datasets within various challenging experimental settings, including cases where drugs or cell lines have not been encountered in the training data. Our comparison with synergy score prediction methods further highlights the relevance of dose-response prediction approaches, instead of relying on simple scoring methods.
治疗晚期癌症和其他复杂疾病需要药物联合使用。与单一疗法相比，联合治疗可以通过降低单一药物的剂量来提高疗效并降低毒性，其中特别令人感兴趣的是协同联合用药。由于药物联合筛选实验成本高且耗时，因此需要可靠的机器学习模型来对潜在联合用药进行优先级排序，以便进一步研究。当前大多数机器学习模型都基于标量值方法，该方法预测药物联合的个体反应值或协同分数。我们采用功能输出预测方法，即针对细胞系上的每种药物联合预测完整、连续的剂量反应联合表面。我们研究了最近提出的comboKR方法的预测能力，该方法基于强大的输入-输出核回归技术和反应表面的功能建模。在这项工作中，我们开发了comboKR的放大公式，该公式还实现了改进的建模选择：我们（1）将输出药物联合反应表面的新建模选择纳入comboKR框架，（2）提出一种投影梯度下降方法来解决传统上用简单候选集方法解决的具有挑战性的原像问题。我们在各种具有挑战性的实验设置下，使用三个真实世界数据集对comboKR 2.0进行了全面的实验分析，包括在训练数据中未遇到药物或细胞系的情况。我们与协同分数预测方法的比较进一步突出了剂量反应预测方法的相关性，而不是依赖于简单的评分方法。
