Browsing by Author "Chen, Can"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Enhancing Leukemia Treatment: The Role of Combined Therapies Based on Amino Acid Starvation
    (MDPI, 2024-03-16) Chen, Can; Zhang, Ji; Pediatrics, School of Medicine
    Cancer cells demand amino acids beyond their usage as "building blocks" for protein synthesis. As a result, targeting amino acid acquisition and utilization has emerged as a pivotal strategy in cancer treatment. In the setting of leukemia therapy, compelling examples of targeting amino acid metabolism exist at both pre-clinical and clinical stages. This review focuses on summarizing novel insights into the metabolism of glutamine, asparagine, arginine, and tryptophan in leukemias, and providing a comprehensive discussion of perturbing their metabolism to improve the therapeutic outcomes. Certain amino acids, such as glutamine, play a vital role in the energy metabolism of cancer cells and the maintenance of redox balance, while others, such as arginine and tryptophan, contribute significantly to the immune microenvironment. Therefore, assessing the efficacy of targeting amino acid metabolism requires comprehensive strategies. Combining traditional chemotherapeutics with novel strategies to perturb amino acid metabolism is another way to improve the outcome in leukemia patients via overcoming chemo-resistance or promoting immunotherapy. In this review, we also discuss several ongoing or complete clinical trials, in which targeting amino acid metabolism is combined with other chemotherapeutics in treating leukemia.
  • Thumbnail Image
    Item
    Variational Autoencoder-based Model Improves Polygenic Prediction in Blood Cell Traits
    (bioRxiv, 2025-01-18) Li, Xiaoqi; Pang, Minxing; Wen, Jia; Zhou, Laura Y.; Raffield, Laura M.; Zhou, Haibo; Yao, Huaxiu; Chen, Can; Sun, Quan; Li, Yun; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Genetic prediction of complex traits, enabled by large-scale genomic studies, has created new measures to understand individual genetic predisposition. Polygenic Risk Scores (PRS) offer a way to aggregate information across the genome, enabling personalized risk prediction for complex traits and diseases. However, conventional PRS calculation methods that rely on linear models are limited in their ability to capture complex patterns and interaction effects in high-dimensional genomic data. In this study, we seek to improve the predictive power of PRS through applying advanced deep learning techniques. We show that the Variational AutoEncoder-based model for PRS construction (VAE-PRS) outperforms currently state-of-the-art methods for biobank-level data in 14 out of 16 blood cell traits, while being computationally efficient. Through comprehensive experiments, we found that the VAE-PRS model offers the ability to capture interaction effects in high-dimensional data and shows robust performance across different pre-screened variant sets. Furthermore, VAE-PRS is easily interpretable via assessing the contribution of each individual marker to the final prediction score through the SHapley Additive exPlanations (SHAP) method, providing potential new insights in identifying trait-associated genetic variants. In summary, VAE-PRS presents a novel measure to genetic risk prediction by harnessing the power of deep learning methods, which could further facilitate the development of personalized medicine and genetic research.