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Comprehensive Benchmarking and Integration of Tumour Microenvironment Cell Estimation Methods

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Jimenez-Sanchez, Alejandro 
Cast, Oliver 


Various computational approaches have been developed for estimating the relative abundance of different cell types in the tumour microenvironment (TME) using bulk tumour RNA data. However, a comprehensive comparison across diverse data sets that objectively evaluates the performance of these approaches has not been conducted. Here we benchmarked seven widely used tools and gene sets and introduce ConsensusTME, a method that integrates gene sets from all the other methods for relative TME cell estimation of 18 cell types. We collected a comprehensive benchmark dataset consisting of pan-cancer data (DNA-derived purity, leukocyte methylation, and H&E-derived lymphocyte counts) and cell-specific benchmark data sets (peripheral blood cells and tumour tissues). Although none of the methods outperformed others in every benchmark, ConsensusTME ranked top three in all cancer-related benchmarks and was the best performing tool overall. We provide a web resource to interactively explore the benchmark results and an objective evaluation to help researchers select the most robust and accurate method to further investigate the role of the TME in cancer (



Algorithms, Computational Biology, Datasets as Topic, Gene Expression Profiling, Humans, Models, Genetic, Neoplasms, Transcriptome, Tumor Microenvironment

Journal Title

Cancer Research

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American Association for Cancer Research


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Cancer Research UK (C14303/A17197)
Fund for Innovation in Cancer Informatics (ICI) (via Memorial Sloan Kettering Cancer Center) (BD523775)
Target Ovarian Cancer (Cambridge-MM18)
A. Jiménez-Sánchez was supported by a doctoral fellowship from the Cancer Research UK Cambridge Institute and the Mexican National Council of Science and Technology (CONACyT). O. Cast and M.L. Miller were supported by the Brown Performance Group, Innovation in Cancer Informatics Discovery Grant (BD523775). M.L. Miller was supported by Cancer Research UK core grant (C14303/A17197) and the Target Ovarian Cancer Translational Project Grant (Cambridge1320 MM18).