Recent advances in immunotherapy demonstrate the need to further understand the characteristics of an individual cancer patient’s immune system and how it influences responses to cancer treatment. Here, we developed an immunoprofiling platform to evaluate the features in the blood of cancer patients to test the hypothesis that peripheral immune cell heterogeneity could be used to stratify these patients into different categories or immunotypes to monitor disease progression and treatment response. To that end, we established a unique diagnostic immunoprofiling assay and analytical framework based on the analysis of leukocytes in the peripheral blood using multiparameter flow cytometry. Supervised manual gating of flow cytometry data from a cohort of 50 healthy donors identified 415 cell types and immune activation states that were used to train and later independently validate machine learning models to automatically identify immune cell subsets from raw cytometry data. By applying this tool to peripheral blood samples from a mixed cohort of 299 healthy donors and 323 cancer patients, we developed a machine-learning classification model that can differentiate between these two groups with 93% accuracy. This model was further refined using spectral clustering with bootstrapping, revealing 5 clusters, or immunotypes, characterized by specific physiological immune profiles: (1) Myeloid-derived suppressor/NK cell, (2) Terminally-differentiated CD8+ T cells, (3) Mixed CD4+ T helper cells, (4) CD4+ Th1 & CD8+ T cell memory, and (5) Naive T and B lymphocytes. Interestingly, very few healthy donors could be found in clusters 1 and 2 but were assigned most frequently to cluster 5. Matched RNA-seq was used to further validate these profiles using the cellular deconvolution algorithm, Kassandra, and differential gene expression analysis revealed immunotype-specific signatures that are consistent with immune response potential. Patients in the terminally-differentiated CD8+ T cell cluster had a narrower range of HLA-types than the other clusters, and TCR repertoire analysis indicated significantly increased clonality and reduced clonotype diversity. Within this cluster there was a high degree of overlap between TCR sequences in the peripheral blood and the tumor, indicating a relationship between peripheral blood immunotype and tumor infiltration. Altogether, the establishment of these immunotypes using peripheral blood immunoprofiling represents a promising signature that can be used to identify and stratify cancer patients that will benefit from immune-based therapies. Citation Format: Daniiar Dyikanov, Iris Wang, Tatiana Vasileva, Polina Shpudeiko, Polina Turova, Arseniy A. Sokolov, Olga Golubeva, Evgenii Tikhonov, Anna Kamysheva, Ilya Krauz, Mary Abdou, Madison Chasse, Tori Conroy, Nicholas R. Merriam, Boris Shpak, Anastasia Radko, Anastasiia Kilina, Lira Nigmatullina, Linda Balabanian, Christopher J. Davitt, Alexander A. Ryabykh, Olga Kudryashova, Cagdas Tazearslan, Ravshan Ataullakhanov, Alexander Bagaev, Aleksandr Zaitsev, Nathan Fowler, Michael F. Goldberg. Comprehensive immunoprofiling of peripheral blood reveals five conserved immunotypes with implications for immunotherapy in cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6664.
Background: Biomarker gene expression is becoming more commonly utilized for clinical decision-making in oncology clinical practice. However, complex tumor tissue comprises a population of cancer cells (CC) and the tumor microenvironment (TME), causing expression signals belonging to the CC and TME calculated from bulk RNA-seq of the tumor tissue to be indistinguishable. To circumvent this, Helenus, a gene expression deconvolution tool, was developed to estimate TME-specific gene expression, consequently, providing precise CC-specific gene expression. Methods: Helenus performs the “subtraction” of TME gene expression from the total expression calculated from bulk RNA-seq of the tumor tissue. To accurately reconstruct the CC expression profile, LightGBM gene models were trained on artificial transcriptomes created from > 1,000 different solid tumor cancer cell lines and > 3,000 samples of various TME cellular proportions. The LighGBM gene models included genes expressed predominantly in the TME (e.g., CD3E), both the TME and the CC (e.g., BCL6), or in the CC (e.g., HER2). The input features included: 1) RNA percentages of TME cell types predicted by the cell deconvolution tool Kassandra (Zaitsev et al., 2022); 2) evaluation of TME target gene expression via the estimation of its weighted average expression profile in TME cell populations; and 3) a set of TME- and CC-specific genes. The resulting predictions were adjusted based on the CC cell fraction. To evaluate Helenus’ performance, CC and TME RNA were mixed at different ratios using various cancer cell lines and peripheral blood-derived TME cell populations and suspensions of tumor cells prepared from cancer tissue across multiple tumor purity dilutions. Results: Helenus deconvolution resulted in an increased concordance correlation value from 0.73 to 0.98 between the real gene expression profile of pure CC and the reconstructed CC expression from bulk RNA-seq. Helenus showed high concordance between the gene expression profile of sorted cancer cell lines and the deconvolved gene expression across a wide range of CC RNA concentrations (20-90%) mixed with imitated TME RNA at varying concentrations. Helenus demonstrated high performance calculating gene expression of multiple clinically relevant biomarkers in the TME:cancer cell line mixes: CD274 (PD-L1) (mean absolute error [MAE] ~3.5-fold reduction); HLA-A (~2.8-fold MAE reduction); MKI67 (Ki-67, ~2.2-fold MAE reduction), ERBB2 (HER2, ~1.7-fold MAE reduction). Helenus deconvolved CC expression and found significant correlations with CC gene amplifications and deletions (e.g., BCL-2, VNN3) independent of tumor purity (p < 0.003). Conclusion: Helenus, the CC gene expression deconvolution tool, was developed with high accuracy to contribute to tumor diagnosis, disease monitoring, treatment decisions, and clinically relevant biomarker identification. Citation Format: Valentina Beliaeva, Ekaterina Ivleva, Boris Shpak, Daniil Litvinov, Anastasia Zotova, Krystle Nomie, Daniiar Dyikanov, Alexander Kuznetsov, Maria Savchenko, Aleksandr Zaitsev, Nathan Fowler, Alexander Bagaev. Computational cancer cell gene expression deconvolution from tumor bulk RNA-seq via the machine learning algorithm Helenus. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5401.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.