Integration of 3D bioprinting and multi-algorithm machine learning identified glioma susceptibilities and microenvironment characteristics

IF 13 1区 生物学 Q1 CELL BIOLOGY
Min Tang, Shan Jiang, Xiaoming Huang, Chunxia Ji, Yexin Gu, Ying Qi, Yi Xiang, Emmie Yao, Nancy Zhang, Emma Berman, Di Yu, Yunjia Qu, Longwei Liu, David Berry, Yu Yao
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Abstract

Glioma, with its heterogeneous microenvironments and genetic subtypes, presents substantial challenges for treatment prediction and development. We integrated 3D bioprinting and multi-algorithm machine learning as a novel approach to enhance the assessment and understanding of glioma treatment responses and microenvironment characteristics. The bioprinted patient-derived glioma tissues successfully recapitulated molecular properties and drug responses of native tumors. We then developed GlioML, a machine learning workflow incorporating nine distinct algorithms and a weighted ensemble model that generated robust gene expression-based predictors, each reflecting the diverse action mechanisms of various compounds and drugs. The ensemble model superseded the performance of all individual algorithms across diverse in vitro systems, including sphere cultures, complex 3D bioprinted multicellular models, and 3D patient-derived tissues. By integrating bioprinting, the evaluative scope of the treatment expanded to T cell-related therapy and anti-angiogenesis targeted therapy. We identified promising compounds and drugs for glioma treatment and revealed distinct immunosuppressive or angiogenic myeloid-infiltrated tumor microenvironments. These insights pave the way for enhanced therapeutic development for glioma and potentially for other cancers, highlighting the broad application potential of this integrative and translational approach.

Abstract Image

三维生物打印与多算法机器学习的整合识别了胶质瘤的易感性和微环境特征
胶质瘤具有异质性微环境和遗传亚型,给治疗预测和开发带来了巨大挑战。我们将三维生物打印与多算法机器学习相结合,作为一种新方法来加强对胶质瘤治疗反应和微环境特征的评估和理解。生物打印的患者来源胶质瘤组织成功再现了原生肿瘤的分子特性和药物反应。然后,我们开发了GlioML,这是一种机器学习工作流程,包含九种不同的算法和一个加权集合模型,可生成基于基因表达的稳健预测因子,每种预测因子都反映了各种化合物和药物的不同作用机制。在各种体外系统(包括球体培养、复杂的三维生物打印多细胞模型和三维患者衍生组织)中,该集合模型的性能超越了所有单个算法。通过整合生物打印技术,治疗的评估范围扩大到了T细胞相关治疗和抗血管生成靶向治疗。我们发现了有希望用于胶质瘤治疗的化合物和药物,并揭示了不同的免疫抑制性或血管生成性骨髓浸润肿瘤微环境。这些见解为加强胶质瘤以及其他潜在癌症的治疗开发铺平了道路,凸显了这种综合转化方法的广泛应用潜力。
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来源期刊
Cell Discovery
Cell Discovery Biochemistry, Genetics and Molecular Biology-Molecular Biology
CiteScore
24.20
自引率
0.60%
发文量
120
审稿时长
20 weeks
期刊介绍: Cell Discovery is a cutting-edge, open access journal published by Springer Nature in collaboration with the Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences (CAS). Our aim is to provide a dynamic and accessible platform for scientists to showcase their exceptional original research. Cell Discovery covers a wide range of topics within the fields of molecular and cell biology. We eagerly publish results of great significance and that are of broad interest to the scientific community. With an international authorship and a focus on basic life sciences, our journal is a valued member of Springer Nature's prestigious Molecular Cell Biology journals. In summary, Cell Discovery offers a fresh approach to scholarly publishing, enabling scientists from around the world to share their exceptional findings in molecular and cell biology.
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