In this issue of Cancer Cell, Zaitsev et al. (2022) present a machine-learning-based approach, trained from millions of artificial transcriptomes with admixed cell populations, for reconstructing tumor microenvironments (TMEs). The high accuracy of this approach, demonstrated through extensive validation, enables systematic investigation of TMEs in both research and clinical settings. Understanding the tumor microenviron-ment (TME) is critical for exploring the therapeutical potential and rational design of immunotherapy (Wei et al., 2018). Many computational methods have been developed to deconvolute transcriptome sequencing (RNA-seq) data generated from bulk tumor samples, which has greatly advanced our understanding of TME cellular composition in recent years (Thorsson et al., 2018). Existing methods were primarily designed to model cell type-specific gene expression profiles (Newman et al., 2015) or to perform deep learning using single-cell RNA-seq (scRNA-seq) of the same tissue type (Newman et al., 2019; Menden et al., 2020). Therefore, they are not optimized for capturing rare or hierarchical TME subpopulations and have limited applications in samples that lack scRNA-seq or flow cytometry data.
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