Biomedical Knowledge Engineering Lab., Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
Comput Biol Med. 2022 Dec;151(Pt A):106192. doi: 10.1016/j.compbiomed.2022.106192. Epub 2022 Oct 17.
Tumor heterogeneity, including genetic and transcriptomic characteristics, can reduce the efficacy of anticancer pharmacological therapy, resulting in clinical variability in patient response to therapeutic medications. Multi-omics integration can allow in silico models to provide an additional perspective on a biological system.
In this study, we propose a gene-centric multi-channel (GCMC) architecture to integrate multi-omics for predicting cancer drug response. GCMC transformed multi-omics profiles into a three-dimensional tensor with an additional dimension for omics types. GCMC's convolutional encoders captures multi-omics profiles for each gene and yields gene-centric features to predict drug responses.
We evaluated GCMC on various datasets, including The Cancer Genome Atlas (TCGA) patients, patient-derived xenografts (PDX) mice models, and the Genomics of Drug Sensitivity in Cancer (GDSC) cell line datasets. GCMC achieved better performance than baseline models, including single-omics models, in more than 75% of 265 drugs from GDSC cell line datasets. Furthermore, as for the clinical applicability of GCMC, it achieved the best performance on TCGA and PDX datasets in terms of both AUPR and AUC. We also analyzed models' capability of integrating multi-omics profiles by measuring the contribution ratio of omics types. GCMC can incorporate multi-omics profiles in various manners to enhance performance for each drug type. These results suggested that GCMC can improve performance and feature extraction capability by integrating multi-omics profiles in a gene-centric manner.
肿瘤异质性，包括遗传和转录组特征，会降低抗癌药物治疗的疗效，导致患者对治疗药物的反应存在临床差异。多组学整合可以使基于计算机的模型为生物系统提供额外的视角。
在本研究中，我们提出了一种基于基因的多通道（GCMC）架构，用于整合多组学数据以预测癌症药物反应。GCMC 将多组学图谱转化为具有附加组学类型维度的三维张量。GCMC 的卷积编码器对每个基因的多组学图谱进行捕获，并生成以基因为中心的特征来预测药物反应。
我们在包括癌症基因组图谱（TCGA）患者、患者来源的异种移植（PDX）小鼠模型和癌症药物敏感性基因组学（GDSC）细胞系数据集在内的各种数据集上评估了 GCMC。与包括单组学模型在内的基线模型相比，GCMC 在 GDSC 细胞系数据集的 265 多种药物中的 75%以上取得了更好的性能。此外，就 GCMC 的临床适用性而言，它在 TCGA 和 PDX 数据集上的 AUPR 和 AUC 方面都取得了最佳性能。我们还通过测量组学类型的贡献比来分析模型整合多组学图谱的能力。GCMC 可以以各种方式整合多组学图谱，以提高每种药物类型的性能。这些结果表明，GCMC 可以通过以基因为中心的方式整合多组学图谱来提高性能和特征提取能力。
