MMCL-CDR: Enhancing Cancer Drug Response Prediction with Multi-Omics and Morphology Images Contrastive Representation Learning

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-12-09 DOI:10.1093/bioinformatics/btad734
Yang Li, Zihou Guo, Xin Gao, Guohua Wang
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Abstract

Motivation Cancer is a complex disease that results in a significant number of global fatalities. Treatment strategies can vary among patients, even if they have the same type of cancer. The application of precision medicine in cancer shows promise for treating different types of cancer, reducing healthcare expenses, and improving recovery rates. To achieve personalized cancer treatment, machine learning models have been developed to predict drug responses based on tumor and drug characteristics. However, current studies either focus on constructing homogeneous networks from single data source or heterogeneous networks from multi-omics data. While multi-omics data have shown potential in predicting drug responses in cancer cell lines, there is still a lack of research that effectively utilizes insights from different modalities. Furthermore, effectively utilizing the multi-modal knowledge of cancer cell lines poses a challenge due to the heterogeneity inherent in these modalities. Results To address these challenges, we introduce MMCL-CDR, a multi-modal approach for cancer drug response prediction that integrates copy number variation, gene expression, morphology images of cell lines and chemical structure of drugs. The objective of MMCL-CDR is to align cancer cell lines across different data modalities by learning cell line representations from omic and image data, and combined with structural drug representations to enhance the prediction of Cancer Drug Responses (CDR). We have carried out comprehensive experiments and show that our model significantly outperforms other state-of-the-art methods in CDR prediction. The experimental results also prove that the model can learn more accurate cell line representation by integrating multi-omics and morphological data from cell lines, thereby improving the accuracy of CDR prediction. In addition, the ablation study and qualitative analysis also confirm the effectiveness of each part of our proposed model. Last but not least, MMCL-CDR opens up a new dimension for cancer drug response prediction through multimodal contrastive learning, pioneering a novel approach that integrates multi-omics and multi-modal drug and cell line modeling. Availability and Implementation MMCL-CDR is available at https://github.com/catly/MMCL-CDR
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MMCL-CDR:利用多图像和形态图像对比表征学习加强癌症药物反应预测
动机 癌症是一种复杂的疾病,在全球造成大量死亡。即使是同一种癌症,不同患者的治疗策略也会有所不同。精准医疗在癌症中的应用为治疗不同类型的癌症、降低医疗费用和提高康复率带来了希望。为了实现个性化癌症治疗,人们开发了机器学习模型,根据肿瘤和药物特征预测药物反应。然而,目前的研究要么侧重于从单一数据源构建同构网络,要么侧重于从多组学数据构建异构网络。虽然多组学数据在预测癌症细胞系的药物反应方面已显示出潜力,但仍然缺乏有效利用不同模式的洞察力的研究。此外,有效利用癌症细胞系的多模态知识也是一项挑战,因为这些模态存在固有的异质性。结果 为了应对这些挑战,我们引入了 MMCL-CDR,这是一种用于癌症药物反应预测的多模态方法,它整合了拷贝数变异、基因表达、细胞系形态图像和药物化学结构。MMCL-CDR 的目标是通过从 omic 和图像数据中学习细胞系表征,并结合药物结构表征来对不同数据模式的癌细胞系进行比对,从而增强对癌症药物反应(CDR)的预测。我们进行了全面的实验,结果表明我们的模型在 CDR 预测方面明显优于其他最先进的方法。实验结果还证明,该模型可以通过整合细胞系的多组学和形态学数据,学习到更准确的细胞系表征,从而提高 CDR 预测的准确性。此外,消融研究和定性分析也证实了我们提出的模型各部分的有效性。最后但并非最不重要的一点是,MMCL-CDR 通过多模态对比学习为癌症药物反应预测开辟了一个新的维度,开创了一种将多组学和多模态药物及细胞系建模相结合的新方法。可用性与实施 MMCL-CDR 可从 https://github.com/catly/MMCL-CDR 网站获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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