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Comprehensive peripheral blood immunoprofiling reveals five immunotypes with immunotherapy response characteristics in cancer patients

The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient’s immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry and RNA-seq to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types, and gene expression profiles.

Type: RNASeq
Archiver: European Genome-Phenome Archive (EGA)

2 Publications

Publications	Citations
Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Bagaev A, Kotlov N, Nomie K, Svekolkin V, Gafurov A, Isaeva O, Osokin N, Kozlov I, Frenkel F, Gancharova O, Almog N, Tsiper M, Ataullakhanov R, Fowler N. Cancer Cell 39: 2021 845-865.e7	383
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. Cancer Cell 40: 2022 879-894.e16	31

Publications

Citations

Conserved pan-cancer microenvironment subtypes predict response to immunotherapy.
Bagaev A, Kotlov N, Nomie K, Svekolkin V, Gafurov A, Isaeva O, Osokin N, Kozlov I, Frenkel F, Gancharova O, Almog N, Tsiper M, Ataullakhanov R, Fowler N. Cancer Cell 39: 2021 845-865.e7

383

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. Cancer Cell 40: 2022 879-894.e16