Journal
FRONTIERS IN IMMUNOLOGY
Volume 13, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fimmu.2022.926304
Keywords
biomarker; immunotherapy; precision medicine; metabolism; immune checkpoint-based therapy
Categories
Funding
- Spanish Ministry of Science and Innovation (MICINN) (EUC-ERDF) [SAF2017-84918-R0]
- Spanish Ministry of Science and Innovation (MICINN) - MCIN/AEI [PID2019-107139RB-C21, PID2020-115051RB-I00]
- Fundacio la Marato de TV3 [201330.10]
- Foundation Olga Torres [A-2019/2020]
- Fundacio ICREA-Premi Icrea Academia
- Catalan Agency for Management of University and Research Grants (AGAUR) [2017-SGR-1174, 2017-SGR-1033]
- Spanish Association Against Cancer (AECC) [PROYE19040POST_001]
- Instituto de Salud Carlos III [PI13/01728, PI19/0740]
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In this study, researchers developed an immune-metabolic signature suitable for personalized immune checkpoint inhibitor therapies. The signature classified tumors into three distinct immune-metabolic clusters, providing a new predictive tool for patient selection for specific immune-metabolic therapeutic approaches.
Existing immune signatures and tumor mutational burden have only modest predictive capacity for the efficacy of immune check point inhibitors. In this study, we developed an immune-metabolic signature suitable for personalized ICI therapies. A classifier using an immune-metabolic signature (IMMETCOLS) was developed on a training set of 77 metastatic colorectal cancer (mCRC) samples and validated on 4,200 tumors from the TCGA database belonging to 11 types. Here, we reveal that the IMMETCOLS signature classifies tumors into three distinct immune-metabolic clusters. Cluster 1 displays markers of enhanced glycolisis, hexosamine byosinthesis and epithelial-to-mesenchymal transition. On multivariate analysis, cluster 1 tumors were enriched in pro-immune signature but not in immunophenoscore and were associated with the poorest median survival. Its predicted tumor metabolic features suggest an acidic-lactate-rich tumor microenvironment (TME) geared to an immunosuppressive setting, enriched in fibroblasts. Cluster 2 displays features of gluconeogenesis ability, which is needed for glucose-independent survival and preferential use of alternative carbon sources, including glutamine and lipid uptake/beta-oxidation. Its metabolic features suggest a hypoxic and hypoglycemic TME, associated with poor tumor-associated antigen presentation. Finally, cluster 3 is highly glycolytic but also has a solid mitochondrial function, with concomitant upregulation of glutamine and essential amino acid transporters and the pentose phosphate pathway leading to glucose exhaustion in the TME and immunosuppression. Together, these findings suggest that the IMMETCOLS signature provides a classifier of tumors from diverse origins, yielding three clusters with distinct immune-metabolic profiles, representing a new predictive tool for patient selection for specific immune-metabolic therapeutic approaches.
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