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Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes

2022; Cell Press; Volume: 40; Issue: 8 Linguagem: Inglês

10.1016/j.ccell.2022.07.006

1878-3686

Aleksandr Zaitsev, Maksim Chelushkin, Daniiar Dyikanov, Ilya Cheremushkin, Boris Shpak, Krystle Nomie, Vladimir Zyrin, Ekaterina Nuzhdina, Yaroslav Lozinsky, Anastasia Zotova, Sandrine Degryse, Nikita Kotlov, Artur Baisangurov, Vladimir Shatsky, Daria Afenteva, A. A. Kuznetsov, Susan Raju Paul, Diane Davies, Patrick M. Reeves, Michael Lanuti, Michael F. Goldberg, Cagdas Tazearslan, Madison Chasse, Iris Wang, Mary Abdou, Sharon M Aslanian, Samuel W. Andrewes, James J. Hsieh, Akshaya Ramachandran, Yang Lyu, Ilia Galkin, Viktor Svekolkin, Leandro Cerchietti, Mark C. Poznansky, Ravshan Ataullakhanov, Nathan Fowler, Alexander Bagaev,

Cancer Cells and Metastasis

Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. We present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of >9,400 tissue and blood sorted cell RNA profiles incorporated into millions of artificial transcriptomes to accurately reconstruct the tumor microenvironment (TME). Bioinformatics correction for technical and biological variability, aberrant cancer cell expression inclusion, and accurate quantification and normalization of transcript expression increased Kassandra stability and robustness. Performance was validated on 4,000 H&E slides and 1,000 tissues by comparison with cytometric, immunohistochemical, or single-cell RNA-seq measurements. Kassandra accurately deconvolved TME elements, showing the role of these populations in tumor pathogenesis and other biological processes. Digital TME reconstruction revealed that the presence of PD-1-positive CD8+ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.