Multi-Omics Biomarkers of Response to Immunotherapy in Combination with Epigenetic Modulators
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PROVIDER: PRJNA871250 | ENA |
REPOSITORIES: ENA
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Project description:Epigenetic immunomodulation by association of guadecitabine with ipilimumab (NIBIT-M4 Phase Ib trial, NCT02608437) has been shown to be safe and tolerable in advanced melanoma and to have significant immunomodulatory and antitumor activity [Di Giacomo et al. Clin Cancer Res. 2019 Dec 15;25(24):7351-7362]. Here we carried out a multi-omics assessment of pre-therapy (week 0, w0) and on-treatment (week 4, w4 and week 12, w12) tumor biopsies from enrolled patients looking at the specific parameters that could explain clinical benefit vs treatment failure. Available whole exome sequencing, reduced-representation bisulfite sequencing and RNA sequencing data, together with focused analysis of immune-related signatures by custom NanoString panels, were integrated with existing information on tumor immune contexture data by immunohistochemistry. Results indicated that clinical benefit vs progressive disease could be explained, even at the single patient level, by applying a multifactorial code integrating information from a variety of parameters investigated on sequential biopsies. Crucial parameters included: tumor genomic/transcriptomic landscape, immune structure of the tumor microenvironment (inferred by deconvolution of gene expression data or assessed by immunohistochemistry), profile of genes and molecules relevant for antigen processing and presentation (immunoproteasome subunits, HLA class I molecules on tumor cells), tumor classification by the immunological constant of rejection (Bertucci et al. British Journal of Cancer 2018; 119:1383–1391), genetic immunoediting score (the ratio between the observed versus the expected number of neoantigens), enrichment for differentiation-, metabolism-, proliferation- and immunity-related pathways as well as analysis for signatures related to B cell content and differentiation, TLS formation, follicular T helper cells, TEX subsets, tumor-associated endothelial cells, ICB response and guadecitabine-specific gene upregulation.
2023-03-01 | GSE211645 | GEO
Project description:Combination of host immune metabolic biomarkers for the PD-1 blockade cancer immunotherapy
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Project description:Background. Current clinical biomarkers for the PD-1 blockade therapy are insufficient because they rely only on the tumor properties such as PD-L1 expression frequency and the amount of tumor mutation burden. Identifying reliable responsive biomarkers based on the host immunity is necessary to improve the predictive values. Methods. We investigated the levels of plasma metabolites and T cell properties including energy metabolism markers in the blood of patients with non-small cell lung cancer before and after treatment with nivolumab (N=53). Predictive value of combination markers statistically selected were evaluated by cross validation and linear discriminant analysis on discovery and validation cohorts, respectively. Correlation between plasma metabolites and T cell markers were investigated. Results. The four metabolites derived from microbiome (hippuric acid), fatty acid oxidation (butyrylcarnitine) and redox (cystine and glutathione disulfide) provided high response probability (AUC=0.91). Similarly, a combination of four T cell markers, those related to mitochondrial activation (PGC-1 expression and reactive oxygen species), and the frequencies of CD8+ PD-1high and CD4+ T cells demonstrated even higher prediction value (AUC=0.96). Among the pool of all selected markers, the four T cell markers were exclusively selected as the highest predictive combination probably due to their linkage to the above mentioned metabolite markers. In a prospective validation set (N=24) these four cellular markers showed a high accuracy rate for the clinical responses of the patients (AUC= 0.92). Conclusion. Combination of biomarkers reflecting host immune activity is quite valuable for the responder prediction.
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Project description:Single-agent immunotherapy, such as immune checkpoint inhibition, has shown remarkable efficacy in selected cancer entities and individual patients. However, most patients fail to respond. This is likely due to diverse immunosuppressive mechanisms acting in a concerted way to suppress the host anti-tumor immune response reducing the efficacy of single-agent immunotherapy. Combination immunotherapy approaches that are effective in such poorly immunogenic tumors mostly rely on precise knowledge of immunological targets on tumor cells by vaccinations or antibodies engineered to directly target those. Thus, creating a target-agnostic combination immunotherapy that is effective in poorly immunogenic tumors for which an immunological target is not known is a major challenge. We show that combined adoptive cellular therapy (ACT) with lymphokine-activated killer cells (LAKs), cytokine-induced killer cells (CIKs), Vγ9Vδ2-T-cells (γδ-T-cells) and adaptive, tumor-specific T-cells (CTLs) display synergistic anti-tumor treatment effects, which is further enhanced by co-treatment with anti-PD1 antibodies. Most strikingly, combination of this ACT with anti-PD1 antibodies, local immunotherapy of agonists against Toll-like receptor 3, 7 and 9 and pre-ACT lymphodepletion, a protocol we named TRI-IT, eradicates established, poorly immunogenic tumors and induces durable anti-tumor immunity in a variety of poorly immunogenic syngeneic, autochthonous, as well as autologous humanized patient-derived models. Mechanistically, we show that TRI-IT co-activates adaptive cellular and humoral, as well as innate anti-tumor immune responses to mediate its anti-tumor effect without inducing off-target toxicity. Our data demonstrate the efficacy of a target-agnostic combination immunotherapy that eradicates and potentially cures established poorly immunogenic tumors.
2022-10-28 | GSE173107 | GEO
Project description:Immunotherapy Study of Evofosfamide in Combination With Ipilimumab (NCT03098160)
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Project description:Non–small cell lung cancer (NSCLC) has an increasing number of targeted and systemic therapies allowing subsets of patients to achieve long–term durable benefit. Fundamental to understanding responses to a given therapy is comprehensive molecular characterization of the underlying tumor immune microenvironment (TIME). The TIME may inform models to predict immunotherapy outcomes and features to delineate therapeutic responses and clinical endpoints. We hypothesize that an integrated multi–omics approach will uncover interactions within the NSCLC TIME and identify novel biomarkers that are predictive of immunotherapy responses, thus aiding precision oncology. To develop a spatially resolved TIME model for NSCLC immunotherapy, we utilized a multi–omics approach, combining spatial mapping of protein expression at the single–cell resolution by Phenocycler Fusion (PCF) and multi–cellular readout whole transcriptome profiling at cellular compartment resolution by Digital Spatial Profiling (DSP–GeoMx–WTA). This approach facilitated a detailed examination of the TIME in NSCLC samples from patients undergoing first–line immunotherapy. We studied two independent cohorts of advanced NSCLC tissue samples, treated with PD–1–based immunotherapies. We derived gene signatures from cell type signatures to predict treatment outcomes using a multistage Least Absolute Shrinkage and Selection Operator (LASSO) approach. Our spatial proteomic analysis identified three distinct cell types, proliferating tumor cells, granulocytes, and vessels, associated with resistance to immunotherapy. A high proportion of these cell types demonstrated a hazard ratio (HR) of 3.8 (p = 0.004) in the Yale training cohort and 1.8 (p = 0.05) in the UQ validation cohort. In the response cell–type model, higher levels of M1 macrophages, M2 macrophages, and CD4 T cells had an HR of 0.4 (p = 0.019) in the training cohort and 0.49 (p = 0.036) in the validation. In the transcriptomic analysis, gene signatures derived from these cell types predicted outcomes with high accuracy. The resistance gene model, which included 8 genes associated with epithelial–mesenchymal transition (EMT) and cell migration, showed an HR of 5.3 (p < 0.001) in the Yale training cohort, 2.2 (p = 0.036) in the UQ validation cohort, and 1.7 (p = 0.042) in the Greece validation cohort. Conversely, the response gene model, consisting of 8 genes associated with immunomodulation, had an HR of 0.22 (p = 0.005) in the Yale training cohort, 0.38 (p = 0.034) in the UQ validation cohort, and 0.56 (p = 0.041) in the Greece validation cohort. Multivariable analysis, adjusting for age, sex, disease stage, prior chemotherapy, type of immunotherapy, line of immunotherapy, histology, and smoking status, confirmed that the resistance signature remained significantly associated with worse outcomes, while the response signature remained associated with improved outcomes in both the UQ and Greece cohorts. This research highlights the potential of a multi–omics cell typing and gene expression profiling approach in advancing NSCLC treatment toward precision oncology. By offering insights into the TIME and unveiling novel biomarkers, our model seeks to define resistance and to improve prediction of response to treatment.
2024-08-22 | GSE271689 | GEO
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