Multi-Omics Biomarkers of Response to Immunotherapy in Combination with Epigenetic Modulators
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA871250 | ENA |
REPOSITORIES: ENA
Similar Datasets
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
| PRJNA593589 | ENA
Project description:Background: A pressing challenge in treating non-small cell lung cancer (NSCLC) lies in the significant variability of patient responses to immunotherapy. This issue underscores the critical need for innovative predictive models that can navigate beyond conventional biomarkers to enhance patient outcomes, particularly by addressing immunotherapy resistance or responsiveness. Hypothesis: We hypothesize that an integrated multi-omics approach will uncover interactions within the NSCLC tumor immune microenvironment (TIME) and identify novel biomarkers that are predictive of individual immunotherapy responses, thus aiding in the development of a robust personalized treatment planning model. Objective: To develop a predictive model for NSCLC immunotherapy response/resistance by identifying new biomarkers using co-detection by indexing (CODEX) and Digital Spatial Profiling of whole transcriptome atlas (DSP-WTA). Methods: We utilized a multi-omics approach, combining CODEX for spatial mapping of protein expression at the single-cell level and DSP-WTA for comprehensive transcriptomic insights from the cell types identified by CODEX. This methodology facilitated a detailed examination of the TIME in NSCLC samples from patients undergoing first-line immunotherapy. Results: Our analysis identified three cell types, proliferating tumor cells, granulocytes, and vessels, that are associated with resistance to immunotherapy. The high proportion of these cell types demonstrated a hazard ratio (HR) of 3.8 (p = 0.004) in the training cohort (N = 33) and 1.8 (p = 0.05) in the validation cohort (N = 35). In the response cell type model, higher levels of M1 macrophages, M2 macrophages, and CD4 T cells returned a HR of 0.4 (p = 0.019) in the training set and 0.49 (p = 0.036) in the validation set. Gene signatures related to these cell types also predicted outcomes with high accuracy. The resistant gene model, which included 8 genes associated with epithelial-mesenchymal transition (EMT) and cell migration showed a HR of 5.3 (p < 0.001) in the training set and 2.2 (p = 0.036) in the validation set. The response gene model, consisting of 8 genes associated with immunomodulation, had an HR of 0.22 (p = 0.005) in the training set and 0.38 (p = 0.034) in the validation set. Conclusion: This research highlights the potential of a multi-omics strategy 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
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.
2020-04-07 | GSE141479 | GEO
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)
| PRJNA668116 | ENA
Project description:An exploratory analysis of potential multi-omics biomarkers and patterns for response to vedolizumab in IBD
| PRJEB55125 | ENA
Project description:Immunotherapy study of phagocytosis checkpoint blockade in combination with radiotherapy
| PRJNA812010 | ENA
Project description:Tripartite target-agnostic combination immunotherapy cures established poorly immunogenic tumors
| PRJNA723833 | ENA
Project description:Immune checkpoint blockade (ICB) therapy intends to only benefit a fraction of cancer patients, and combination immunotherapy with a compound is a promising treatment to overcome this limitation. Here, a tumor immunological phenotype (TIP) gene signature and high throughput sequencing-based high throughput screening (HTS2) were combined to identify combination immunotherapy compounds. We firstly defined a TIP gene signature, which expression pattern distinguishes “cold” tumors from “hot” tumors, and predicts ICB response in cancer patients. Then, after screening thousands of compounds, we identified that aurora kinase inhibitors, including ENMD-2076 and TAK-901, could reprogram the expression pattern of TIP genes from “cold” tumor to “hot” tumor in triple negative breast cancer (TNBC) cells. The treatment of aurora kinase inhibitors on TNBC cells dramatically up-regulates expression of Th1 type chemokine genes CXCL10 and CXCL11, which promotes effective T cells infiltrating into tumor microenvironment and significantly improves anti-PD-1 efficacy in inhibiting the tumor growth of TNBC in preclinical models. Mechanistically, these aurora kinase inhibitors are mainly through inhibiting AURKA-STAT3 signaling pathway to stimulate the expression of CXCL10 and CXCL11. Our study established a high throughput strategy to discover candidate compounds for combination immunotherapy, and suggested the therapeutic potential of combining aurora kinase inhibitors with checkpoint blockade immunotherapy for the treatment of TNBC.
2020-12-31 | GSE160071 | GEO