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A novel gene signature unveils three distinct immune-metabolic rewiring patterns conserved across diverse tumor types and associated with outcome

ABSTRACT: Existing immune markers and tumor mutational burden signatures have modest predictive accuracies of immune-check-point inhibitors (ICI) therapeutic efficacy. 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 75 metastatic colorectal cancer (mCRC) samples and validated on 4,200 tumors from the TCGA database belonging to 11 types. Here we reveal that IMMETCOLS signature classifies tumors into 3 distinct immune-metabolic clusters. Cluster 1 displays enhanced glycolysis-Warburg, hexosamine biosynthesis and epithelial-to-mesenchymal biomarkers. On multivariate analysis, Cluster 1 tumors were enriched in pro-immune signature but not Immunophenoscore, and were associated with the poorest median survival. It’s 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/β-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 up-regulation of glutamine and essential amino acids transporters and the pentose phosphate pathway leading to glucose exhaustion in the TME and immunosuppression. Together these findings suggest that 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

ORGANISM(S): Homo sapiens

PROVIDER: GSE206613 | GEO | 2022/06/26

REPOSITORIES: GEO

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Project description:Background:Acral melanoma (AM) has distinct characteristics as compared to cutaneous melanoma and exhibits poor response to immune checkpoint inhibitors (ICI). Tumor-intrinsic mechanisms of immune exclusion have been identified in many cancers but less studied in AM. Methods: We characterized clinically annotated tumors from patients diagnosed with AM at our institution in correlation with ICI response using whole transcriptome RNAseq, whole exome sequencing, CD8 immunohistochemistry, and multispectral immunofluorescence imaging. A defined interferon-γ-associated T cell-inflamed gene signature was used to categorize tumors into non-T cell-inflamed and T cell-inflamed phenotypes. In combination with AM tumors from two published studies, we systematically assessed the immune landscape of AM and detected differential gene expression and pathway activation in a non-T cell-inflamed tumor microenvironment (TME). Two single-cell(sc) RNAseq AM cohorts and 11 bulk RNAseq cohorts of various tumor types were used for independent validation on pathways associated with lack of ICI response. In total, 892 specimens were included in this study. Results: 72.5% of AM tumors showed low expression of the T cell-inflamed gene signature, with 23.9% of total tumors categorized as the non-T cell-inflamed phenotype. Patients of low CD3 + CD8 + PD1 + intratumoral T cell density showed poor prognosis. We identified 11 oncogenic pathways significantly upregulated in non-T cell-inflamed relative to T cell-inflamed TME shared across all three acral cohorts (MYC, HGF, MITF, VEGF, EGFR, SP1, ERBB2, TFEB, SREBF1, SOX2, and CCND1). scRNAseq analysis revealed that tumor cell-expressing pathway scores were significantly higher in low vs high T cell-infiltrated AM tumors. We further demonstrated that the 11 pathways were enriched in ICI non-responders compared to responders across cancers, including acral melanoma, cutaneous melanoma, triple-negative breast cancer, and non-small cell lung cancer. Pathway activation was associated with low expression of interferon stimulated genes, suggesting suppression of antigen presentation. Across the 11 pathways, fatty acid synthase and CXCL8 were unifying downstream target molecules suggesting potential nodes for therapeutic intervention. Conclusions: A unique set of pathways is associated with immune exclusion and ICI resistance in AM. These data may inform immunotherapy combinations for immediate clinical translation.

Project description:Background: Immune-checkpoint inhibition (ICI) only benefits a subgroup of patients with head and neck squamous cell carcinoma (HNSCC). Several molecular and cellular components of the tumor microenvironment (TME) have been hypothesized to drive either response or resistance. Here, spatially defined whole transcriptome data were analysed in search of associations of compartment-specific gene-signatures with HNSCC immunotherapy outcomes. Methods: Pre-treatment biopsy samples from 50 immunotherapy-treated recurrent or metastatic HNSCC patients as well as 12 matched post-treatment biopsies obtained after 4 weeks of treatment, constructed in tissue microarray format (YTMA496), were included in the study. The GeoMx Human Whole Transcriptome Atlas (NanoString Technologies) assay was performed on samples to allow RNA quantification of 18,677 protein encoding genes, using in situ hybridization, in three molecularly defined tissue compartments; tumor (CK), leukocyte (CD45), macrophage (CD68). Differentially expressed genes (DEGs) (P<0.05) between pre- and post-treatment biopsies in each of the tissue compartments were identified. Next, these DEGs were used as a “biological” filter for the initial 18,677 gene set and analysed using LASSO logistic regression models with the aim to obtain pre-treatment gene expression signatures for best overall response (RECIST 1.1.). The performance of each compartment signature was evaluated using the receiver operating characteristic (ROC) curve and AUCs were calculated for Best Overall Response (BOR) to ICI. Results: A six-gene signature (DDX4, COL17A1, HBA1, MMP1, GPNMB, TTN) in the CD45 compartment presented the highest AUC (0.83), followed by signatures in the CK and CD68 compartments (AUC: 0.72 and 0.68, respectively). Cross-testing of the CD45 signature in the other two compartments, as well as in a third, artificially generated “pseudo-bulk” compartment (all compartments combined), showed poor performance, indicating spatial specificity. Interestingly, the CD45 signature included three extracellular-matrix protein-encoding genes (MMP1, COL17A1, TTN), all associated with resistance (negative coefficients in the BOR model) to ICI. Fibroblast and dendritic cell populations, characterized using the CIBERSORTx gene matrix, were the immune phenotypes most closely associated with the CD45 signature. Conclusions: Our results indicate that CD45 molecular tissue compartment gene expression demonstrates increased association with ICI resistance in HNSCC. Extracellular matrix genes rather than immune-cell related genes dominated the CD45 compartment signature, highlighting the importance of non-immune stromal components within the TME and the importance of the use of spatial information in the understanding of ICI resistance.

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A novel gene signature unveils three distinct immune-metabolic rewiring patterns conserved across diverse tumor types and associated with outcome

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