Generalize Drug Response Prediction by Latent Independent Projection for Asymmetric Constrained Domain Generalization

• URL: http://arxiv.org/abs/2502.04034v1
• Date: Thu, 06 Feb 2025 12:53:45 GMT
• Title: Generalize Drug Response Prediction by Latent Independent Projection for Asymmetric Constrained Domain Generalization
• Authors: Ran Song, Yinpu Bai, Hui Liu,
• Abstract summary: We propose a novel domain generalization framework, termed panCancerDR, to address this challenge. We conceptualize each cancer type as a distinct source domain, with its cell lines serving as domain-specific samples. Our empirical experiments demonstrate that panCancerDR effectively learns task-relevant features from diverse source domains.
• Score: 11.649397977546435
• License:
• Abstract: The accurate prediction of drug responses remains a formidable challenge, particularly at the single-cell level and in clinical treatment contexts. Some studies employ transfer learning techniques to predict drug responses in individual cells and patients, but they require access to target-domain data during training, which is often unavailable or only obtainable in future. In this study, we propose a novel domain generalization framework, termed panCancerDR, to address this challenge. We conceptualize each cancer type as a distinct source domain, with its cell lines serving as domain-specific samples. Our primary objective is to extract domain-invariant features from the expression profiles of cell lines across diverse cancer types, thereby generalize the predictive capacity to out-of-distribution samples. To enhance robustness, we introduce a latent independence projection (LIP) module that encourages the encoder to extract informative yet non-redundant features. Also, we propose an asymmetric adaptive clustering constraint, which clusters drug-sensitive samples into a compact group while drives resistant samples dispersed across separate clusters in the latent space. Our empirical experiments demonstrate that panCancerDR effectively learns task-relevant features from diverse source domains, and achieves accurate predictions of drug response for unseen cancer type during training. Furthermore, when evaluated on single-cell and patient-level prediction tasks, our model-trained solely on in vitro cell line data without access to target-domain information-consistently outperforms and matched current state-of-the-art methods. These findings highlights the potential of our method for real-world clinical applications.

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