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  1. Artikel ; Online: Comprehensive peripheral blood immunoprofiling reveals five immunotypes with immunotherapy response characteristics in patients with cancer.

    Dyikanov, Daniiar / Zaitsev, Aleksandr / Vasileva, Tatiana / Wang, Iris / Sokolov, Arseniy A / Bolshakov, Evgenii S / Frank, Alena / Turova, Polina / Golubeva, Olga / Gantseva, Anna / Kamysheva, Anna / Shpudeiko, Polina / Krauz, Ilya / Abdou, Mary / Chasse, Madison / Conroy, Tori / Merriam, Nicholas R / Alesse, Julia E / English, Noel /
    Shpak, Boris / Shchetsova, Anna / Tikhonov, Evgenii / Filatov, Ivan / Radko, Anastasia / Bolshakova, Anastasiia / Kachalova, Anastasia / Lugovykh, Nika / Bulahov, Andrey / Kilina, Anastasiia / Asanbekov, Syimyk / Zheleznyak, Irina / Skoptsov, Pavel / Alekseeva, Evgenia / Johnson, Jennifer M / Curry, Joseph M / Linnenbach, Alban J / South, Andrew P / Yang, EnJun / Morozov, Kirill / Terenteva, Anastasiya / Nigmatullina, Lira / Fastovetz, Dmitry / Bobe, Anatoly / Balabanian, Linda / Nomie, Krystle / Yong, Sheila T / Davitt, Christopher J H / Ryabykh, Alexander / Kudryashova, Olga / Tazearslan, Cagdas / Bagaev, Alexander / Fowler, Nathan / Luginbuhl, Adam J / Ataullakhanov, Ravshan I / Goldberg, Michael F

    Cancer cell

    2024  Band 42, Heft 5, Seite(n) 759–779.e12

    Abstract: 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 ... ...

    Abstract 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 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. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.
    Mesh-Begriff(e) Humans ; Neoplasms/immunology ; Neoplasms/therapy ; Neoplasms/blood ; Immunotherapy/methods ; Flow Cytometry/methods ; Transcriptome ; Prognosis ; Gene Expression Profiling/methods ; Female ; Biomarkers, Tumor/blood ; Biomarkers, Tumor/genetics ; Biomarkers, Tumor/immunology
    Sprache Englisch
    Erscheinungsdatum 2024-05-10
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2078448-X
    ISSN 1878-3686 ; 1535-6108
    ISSN (online) 1878-3686
    ISSN 1535-6108
    DOI 10.1016/j.ccell.2024.04.008
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes.

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

    Cancer cell

    2022  Band 40, Heft 8, Seite(n) 879–894.e16

    Abstract: 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 ... ...

    Abstract 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
    Mesh-Begriff(e) Algorithms ; CD8-Positive T-Lymphocytes ; Humans ; Machine Learning ; Neoplasms/genetics ; RNA-Seq ; Sequence Analysis, RNA ; Transcriptome ; Tumor Microenvironment/genetics
    Sprache Englisch
    Erscheinungsdatum 2022-07-06
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2078448-X
    ISSN 1878-3686 ; 1535-6108
    ISSN (online) 1878-3686
    ISSN 1535-6108
    DOI 10.1016/j.ccell.2022.07.006
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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