对肿瘤微环境中癌细胞状态进行适当的表征是准确识别匹配的实验模型和精准治疗发展的关键。为了从批量RNA-seq数据中重建这些信息，我们开发了XDec Simplex Mapping (XDec-SM) 基于参考的去卷积方法，将肿瘤及其组成细胞的状态映射到生物可解释的低维空间中。当存在相关的单细胞编码数据时，该方法通过识别信息丰富的基因集来进行解卷积。应用于The Cancer Genome Atlas (TCGA) 中的乳腺肿瘤时，XDec-SM 推断出组成细胞类型及其比例的身份信息。XDec-SM 还推断出与DNA甲基化模式、驱动性体细胞突变、通路激活和基质细胞与乳腺癌细胞之间的代谢耦合相关的个体肿瘤内的癌细胞状态。通过将肿瘤、癌细胞系和PDX模型映射到同一图上，我们鉴定出具有匹配癌细胞状态的体外和体内模型。地图位置还能预测治疗反应，从而为通过与体内癌细胞状态相匹配的模型系统中的实验指导精准治疗打开前景。版权：©2023年Murillo等人。本文为开放获取文章，根据创作共用许可证，任何媒介下的自由使用、分发和复制均可，只要保留原始作者和来源的信息。

Proper characterization of cancer cell states within the tumor microenvironment is a key to accurately identifying matching experimental models and the development of precision therapies. To reconstruct this information from bulk RNA-seq profiles, we developed the XDec Simplex Mapping (XDec-SM) reference-optional deconvolution method that maps tumors and the states of constituent cells onto a biologically interpretable low-dimensional space. The method identifies gene sets informative for deconvolution from relevant single-cell profiling data when such profiles are available. When applied to breast tumors in The Cancer Genome Atlas (TCGA), XDec-SM infers the identity of constituent cell types and their proportions. XDec-SM also infers cancer cells states within individual tumors that associate with DNA methylation patterns, driver somatic mutations, pathway activation and metabolic coupling between stromal and breast cancer cells. By projecting tumors, cancer cell lines, and PDX models onto the same map, we identify in vitro and in vivo models with matching cancer cell states. Map position is also predictive of therapy response, thus opening the prospects for precision therapy informed by experiments in model systems matched to tumors in vivo by cancer cell state.Copyright: © 2023 Murillo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.