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Comment
. 2022 Aug 8;40(8):809-811.
doi: 10.1016/j.ccell.2022.07.008.

Decoding tumor microenvironments through artificial tumor transcriptomes

Liqing Tian  1 , Jinghui Zhang  2
Affiliations
Comment

Decoding tumor microenvironments through artificial tumor transcriptomes

Liqing Tian et al. Cancer Cell. .

Abstract

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.

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Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Figures

Figure 1.
Figure 1.. Reconstructing tumor and blood microenvironment using Kassandra
Zaitsev et al. curated and harmonized RNA-seq data from diverse sorted cell populations representing 51 unique cell types. By admixing different cell populations in silico, millions of artificial transcriptomes were created to match the profile of real patient tumor or blood samples. The data were used to train Kassandra, a stepwise machine learning algorithm designed to accurately estimate cellular composition of TME including hierarchical populations. A clinical application of Kassandra shown the potential of predicting PD-L1 immunohistochemistry (IHC) level and immunotherapy response in bladder cancer patients by deconvoluting PD1+ CD8+ T cells from bulk RNA-seq data.
See this image and copyright information in PMC

Comment on

  • Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes.
    Zaitsev A, Chelushkin M, Dyikanov D, Cheremushkin I, Shpak B, Nomie K, Zyrin V, Nuzhdina E, Lozinsky Y, Zotova A, Degryse S, Kotlov N, Baisangurov A, Shatsky V, Afenteva D, Kuznetsov A, Paul SR, Davies DL, Reeves PM, Lanuti M, Goldberg MF, Tazearslan C, Chasse M, Wang I, Abdou M, Aslanian SM, Andrewes S, Hsieh JJ, Ramachandran A, Lyu Y, Galkin I, Svekolkin V, Cerchietti L, Poznansky MC, Ataullakhanov R, Fowler N, Bagaev A. Zaitsev A, et al. Cancer Cell. 2022 Aug 8;40(8):879-894.e16. doi: 10.1016/j.ccell.2022.07.006. Cancer Cell. 2022. PMID: 35944503

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