Dataset Information

Profiling Tumor Immune Microenvironment of Non-Small Cell Lung Cancer Using Multiplex Immunofluorescence.

ABSTRACT: This study attempted to profile the tumor immune microenvironment (TIME) of non-small cell lung cancer (NSCLC) by multiplex immunofluorescence of 681 NSCLC cases. The number, density, and proportion of 26 types of immune cells in tumor nest and tumor stroma were evaluated, revealing some close interactions particularly between intrastromal neutrophils and intratumoral regulatory T cells (Treg) (r ² = 0.439, P < 0.001), intrastromal CD4+CD38+ T cells and CD20-positive B cells (r ² = 0.539, P < 0.001), and intratumoral CD8-positive T cells and M2 macrophages expressing PD-L1 (r ² = 0.339, P < 0.001). Three immune subtypes correlated with distinct immune characteristics were identified using the unsupervised consensus clustering approach. The immune-activated subtype had the longest disease-free survival (DFS) and demonstrated the highest infiltration of CD4-positive T cells, CD8-positive T cells, and CD20-positive B cells. The immune-defected subtype was rich in cancer stem cells and macrophages, and these patients had the worst prognosis. The immune-exempted subtype had the highest levels of neutrophils and Tregs. Intratumoral CD68-positive macrophages, M1 macrophages, and intrastromal CD4+ cells, CD4+FOXP3- cells, CD8+ cells, and PD-L1+ cells were further found to be the most robust prognostic biomarkers for DFS, which were used to construct and validate the immune-related risk score for risk stratification (high vs. median vs. low) and the prediction of 5-year DFS rates (23.2% vs. 37.9% vs. 43.1%, P < 0.001). In conclusion, the intricate and intrinsic structure of TIME in NSCLC was demonstrated, showing potency in subtyping and prognostication.

SUBMITTER: Peng H

PROVIDER: S-EPMC8600321 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundConventional immunohistochemistry technologies were limited by the inability to simultaneously detect multiple markers and the lack of identifying spatial relationships among cells, hindering understanding of the biological processes in cancer immunology.MethodsTissue slices of primary tumours from 553 IA∼IIIB non-small cell lung cancer (NSCLC) cases were stained by multiplex immunofluorescence (mIF) assay for 10 markers, including CD4, CD38, CD20, FOXP3, CD66b, CD8, CD68, PD-L1, CD133 and CD163, evaluating the amounts of 26 phenotypes of cells in tumour nest and tumour stroma. StarDist depth learning model was utilised to determine the spatial location of cells based on mIF graphs. Single-cell RNA sequencing (scRNA-seq) on four primary NSCLC cases was conducted to investigate the putative cell interaction networks.ResultsSpatial proximity among CD20+ B cells, CD4+ T cells and CD38+ T cells (r2 = 0.41) was observed, whereas the distribution of regulatory T cells was associated with decreased infiltration levels of CD20+ B cells and CD38+ T cells (r2 = -0.45). Univariate Cox analyses identified closer proximity between CD8+ T cells predicted longer disease-free survival (DFS). In contrast, closer proximity between CD133+ cancer stem cells (CSCs), longer distances between CD4+ T cells and CD20+ B cells, CD4+ T cells and neutrophils, and CD20+ B cells and neutrophils were correlated with dismal DFS. Data from scRNA-seq further showed that spatially adjacent N1-like neutrophils could boost the proliferation and activation of T and B lymphocytes, whereas spatially neighbouring M2-like macrophages showed negative effects. An immune-related risk score (IRRS) system aggregating robust quantitative and spatial prognosticators showed that high-IRRS patients had significantly worse DFS than low-IRRS ones (HR 2.72, 95% CI 1.87-3.94, p < .001).ConclusionsWe developed a framework to analyse the cell interaction networks in tumour microenvironment, revealing the spatial architecture and intricate interplays between immune and tumour cells.

Project description:BACKGROUND:The clinical efficacy observed with inhibitors of programed cell death 1/programed cell death ligand 1 (PD-L1/PD-1) in cancer therapy has prompted studies to characterize the immune response in several tumor types, including lung cancer. However, the immunological profile of non-small cell lung carcinoma (NSCLC) treated with neoadjuvant chemotherapy (NCT) is not yet fully characterized, and it may be therapeutically important. The aim of this retrospective study was to characterize and quantify PD-L1/PD-1 expression and tumor-associated immune cells (TAICs) in surgically resected NSCLCs from patients who received NCT or did not receive NCT (non-NCT). METHODS:We analyzed immune markers in formalin-fixed, paraffin-embedded tumor tissues resected from 112 patients with stage II/III NSCLC, including 61 non-NCT (adenocarcinoma [ADC] = 33; squamous cell carcinoma [SCC] = 28) and 51 NCT (ADC = 31; SCC = 20). We used multiplex immunofluorescence to identify and quantify immune markers grouped into two 6-antibody panels: panel 1 included AE1/AE3, PD-L1, CD3, CD4, CD8, and CD68; panel 2 included AE1/AE3, PD1, granzyme B, FOXP3, CD45RO, and CD57. RESULTS:PD-L1 expression was higher (> overall median) in NCT cases (median, 19.53%) than in non-NCT cases (median, 1.55%; P = 0.022). Overall, density of TAICs was higher in NCT-NSCLCs than in non-NCT-NSCLCs. Densities of CD3+ cells in the tumor epithelial compartment were higher in NCT-ADCs and NCT-SCCs than in non-NCT-ADCs and non-NCT-SCCs (P = 0.043). Compared with non-NCT-SCCs, NCT-SCCs showed significantly higher densities of CD3 + CD4+ (P = 0.019) and PD-1+ (P < 0.001) cells in the tumor epithelial compartment. Density of CD68+ tumor-associated macrophages (TAMs) was higher in NCT-NSCLCs than in non-NCT-NSCLCs and was significantly higher in NCT-SCCs than in non-NCT-SCCs. In NCT-NSCLCs, higher levels of epithelial T lymphocytes (CD3 + CD4+) and epithelial and stromal TAMs (CD68+) were associated with better outcome in univariate and multivariate analyses. CONCLUSIONS:NCT-NSCLCs exhibited higher levels of PD-L1 expression and T-cell subset regulation than non-NCT-NSCLCs, suggesting that NCT activates specific immune response mechanisms in lung cancer. These results suggest the need for clinical trials and translational studies of combined chemotherapy and immunotherapy prior to surgical resection of locally advanced NSCLC.

Project description:Every day, more evidence is revealed regarding the importance of the relationship between the response to cancer immunotherapy and the cancer immune microenvironment. It is well established that a profound characterization of the immune microenvironment is needed to identify prognostic and predictive immune biomarkers. To this end, we find phenotyping cells by multiplex immunofluorescence (mIF) a powerful and useful tool to identify cell types in biopsy specimens. Here, we describe the use of mIF tyramide signal amplification for labeling up to eight markers on a single slide of formalin-fixed, paraffin-embedded tumor tissue to phenotype immune cells in tumor tissues. Different panels show different markers, and the different panels can be used to characterize immune cells and relevant checkpoint proteins. The panel design depends on the research hypothesis, the cell population of interest, or the treatment under investigation. To phenotype the cells, image analysis software is used to identify individual marker expression or specific co-expression markers, which can differentiate already selected phenotypes. The individual-markers approach identifies a broad number of cell phenotypes, including rare cells, which may be helpful in a tumor microenvironment study. To accurately interpret results, it is important to recognize which receptors are expressed on different cell types and their typical location (i.e., nuclear, membrane, and/or cytoplasm). Furthermore, the amplification system of mIF may allow us to see weak marker signals, such as programmed cell death ligand 1, more easily than they are seen with single-marker immunohistochemistry (IHC) labeling. Finally, mIF technologies are promising resources for discovery of novel cancer immunotherapies and related biomarkers. In contrast with conventional IHC, which permits only the labeling of one single marker per tissue sample, mIF can detect multiple markers from a single tissue sample, and at the same time, deliver extensive information about the cell phenotypes composition and their spatial localization. In this matter, the phenotyping process is critical and must be done accurately by a highly trained personal with knowledge of immune cell protein expression and tumor pathology.

Dataset Information

Profiling Tumor Immune Microenvironment of Non-Small Cell Lung Cancer Using Multiplex Immunofluorescence.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets