Sage Bionetworks, Seattle, WA, USA.
The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
Nat Commun. 2024 Aug 27;15(1):7362. doi: 10.1038/s41467-024-50618-0.
We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.
我们通过一个社区范围内的 DREAM 挑战赛来评估去卷积方法，这些方法从肿瘤样本的批量表达中推断免疫浸润水平。我们使用混合癌症和健康免疫细胞的体外和计算机上的转录谱来评估六个已发表的和 22 个社区贡献的方法。尽管一些已发表的方法能够很好地预测大多数细胞类型，但它们要么没有经过训练来评估所有功能性 CD8+T 细胞状态，要么准确性较低。一些社区贡献的方法解决了这一差距，包括一种基于深度学习的方法，其出色的性能确立了这种范式在去卷积中的适用性。尽管这些方法主要是使用来自健康组织的免疫细胞开发的，但它们能够很好地预测肿瘤衍生免疫细胞的水平。我们的混合和纯化转录谱将是开发去卷积方法的宝贵资源，包括应对我们在方法中观察到的常见挑战，例如对功能性 CD4+T 细胞状态的敏感识别。
