HLA-DR cancer cells expression correlates with T cell infiltration and is enriched in lung adenocarcinoma with indolent behavior

Differential GPR56 Expression in T Cell Subpopulations for Early-Stage Lung Adenocarcinoma Patient Identification

OBJECTIVE

This study aimed to investigate the expression of GPR56 in the T cells of early-stage lung adenocarcinoma (LUAD) patients and clarify its diagnostic significance.

METHODS

Blood samples were collected from 32 patients with stage IA LUAD and 31 healthy controls. GPR56 and perforin were analysed in circulating T-cell subsets by flow cytometry. In addition, a correlation between perforin and GPR56 expression was detected. Changes in GPR56+ cells in early LUAD patients were analysed, and the diagnostic significance of GPR56+ T cells for early LUAD was studied by receiver operating characteristic (ROC) curve analysis.

RESULTS

The expression of GPR56 in CD8+ T cells from early-stage LUAD patients was significantly greater than that in CD4+ T cells. The percentage of perforin-positive GPR56+ cells in early-stage LUAD patients was high. GPR56 levels in the T cells of LUAD patients were significantly lower than those in healthy controls. ROC analysis revealed that the area under the curve for the percentage of GPR56-positive CD8+ TEMRA cells to distinguish early-stage LUAD patients from healthy individuals- reached 0.7978.

CONCLUSION

The decreased expression of GPR56 in the peripheral blood of early-stage LUAD patients correlated with perforin levels, reflecting compromised antitumor immunity and aiding early-stage LUAD screening.

Upregulation of HLA-II related to LAG-3+CD4+ T cell infiltration is associated with patient outcome in human glioblastoma

Glioblastoma (GBM) is the most common malignant diffuse glioma of the brain. Although immunotherapy with immune checkpoint inhibitors (ICIs), such as programmed cell death protein (PD)-1/PD ligand-1 inhibitors, has revolutionized the treatment of several cancers, the clinical benefit in GBM patients has been limited. Lymphocyte-activation gene 3 (LAG-3) binding to human leukocyte antigen-II (HLA-II) plays an essential role in triggering CD4+ T cell exhaustion and could interfere with the efficiency of anti-PD-1 treatment; however, the value of LAG-3-HLA-II interactions in ICI immunotherapy for GBM patients has not yet been analyzed. Therefore, we aimed to investigate the expression and regulation of HLA-II in human GBM samples and the correlation with LAG-3+CD4+ T cell infiltration. Human leukocyte antigen-II was highly expressed in GBM and correlated with increased LAG-3+CD4+ T cell infiltration in the stroma. Additionally, HLA-IIHighLAG-3High was associated with worse patient survival. Increased interleukin-10 (IL-10) expression was observed in GBM, which was correlated with high levels of HLA-II and LAG-3+ T cell infiltration in stroma. HLA-IIHighIL-10High GBM associated with LAG-3+ T cells infiltration synergistically showed shorter overall survival in patients. Combined anti-LAG-3 and anti-IL-10 treatment inhibited tumor growth in a mouse brain GL261 tumor model. In vitro, CD68+ macrophages upregulated HLA-II expression in GBM cells through tumor necrosis factor-α (TNF-α). Blocking TNF-α-dependent inflammation inhibited tumor growth in a mouse GBM model. In summary, T cell-tumor cell interactions, such as LAG-3-HLA-II, could confer an immunosuppressive environment in human GBM, leading to poor prognosis in patients. Therefore, targeting the LAG-3-HLA-II interaction could be beneficial in ICI immunotherapy to improve the clinical outcome of GBM patients.

Class II HLA-DRB4 is a predictive biomarker for survival following immunotherapy in metastatic non-small cell lung cancer

Immune checkpoint inhibitors (ICI) are important treatment options for metastatic non-small cell lung cancer (mNSCLC). However, not all patients benefit from ICIs and can experience immune-related adverse events (irAEs). Limited understanding exists for germline determinants of ICI efficacy and toxicity, but Human Leukocyte Antigen (HLA) genes have emerged as a potential predictive biomarker. We performed HLA typing on 85 patients with mNSCLC, on ICI therapy and analyzed the impact of HLA Class II genotype on progression free survival (PFS), overall survival (OS), and irAEs. Most patients received pembrolizumab (83.5%). HLA-DRB4 genotype was seen in 34/85 (40%) and its presence correlated with improved OS in both univariate (p = 0.022; 26.3 months vs 10.2 months) and multivariate analysis (p = 0.011, HR 0.49, 95% CI [0.29, 0.85]). PFS did not reach significance (univariate, p = 0.12, 8.2 months vs 5.1 months). Eleven patients developed endocrine irAEs. HLA-DRB4 was the predominant genotype among these patients (9/11, 81.8%). Cumulative incidence of endocrine irAEs was higher in patients with HLA-DRB4 (p = 0.0139). Our study is the first to suggest that patients with metastatic NSCLC patients on ICI therapy with HLA-DRB4 genotype experience improved survival outcomes. Patients with HLA-DRB4 had the longest median OS (26.3 months). Additionally, we found a correlation between HLA-DRB4 and the occurrence of endocrine irAEs.

Cancer-associated fibroblasts in early-stage lung adenocarcinoma correlate with tumor aggressiveness

Lung adenocarcinoma (LUAD) is the predominant type of lung cancer in the U.S. and exhibits a broad variety of behaviors ranging from indolent to aggressive. Identification of the biological determinants of LUAD behavior at early stages can improve existing diagnostic and treatment strategies. Extracellular matrix (ECM) remodeling and cancer-associated fibroblasts play a crucial role in the regulation of cancer aggressiveness and there is a growing need to investigate their role in the determination of LUAD behavior at early stages. We analyzed tissue samples isolated from patients with LUAD at early stages and used imaging-based biomarkers to predict LUAD behavior. Single-cell RNA sequencing and histological assessment showed that aggressive LUADs are characterized by a decreased number of ADH1B+ CAFs in comparison to indolent tumors. ADH1B+ CAF enrichment is associated with distinct ECM and immune cell signatures in early-stage LUADs. Also, we found a positive correlation between the gene expression of ADH1B+ CAF markers in early-stage LUADs and better survival. We performed TCGA dataset analysis to validate our findings. Identified associations can be used for the development of the predictive model of LUAD aggressiveness and novel therapeutic approaches.

Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization

The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.

Tumoral Densities of T-Cells and Mast Cells Are Associated With Recurrence in Early-Stage Lung Adenocarcinoma

Introduction

Lung cancer is the deadliest cancer in the United States and worldwide, and lung adenocarcinoma (LUAD) is the most prevalent histologic subtype in the United States. LUAD exhibits a wide range of aggressiveness and risk of recurrence, but the biological underpinnings of this behavior are poorly understood. Past studies have focused on the biological characteristics of the tumor itself, but the ability of the immune response to contain tumor growth represents an alternative or complementary hypothesis. Emerging technologies enable us to investigate the spatial distribution of specific cell types within the tumor nest and characterize this immune response. This study aimed to investigate the association between immune cell density within the primary tumor and recurrence-free survival (RFS) in stage I and II LUAD.

Methods

This study is a prospective collection with retrospective evaluation. A total of 100 patients with surgically resected LUAD and at least 5-year follow-ups, including 69 stage I and 31 stages II tumors, were enrolled. Multiplexed immunohistochemistry panels for immune markers were used for measurement.

Results

Cox regression models adjusted for sex and EGFR mutation status revealed that the risk of recurrence was reduced by 50% for the unit of one interquartile range (IQR) change in the tumoral T-cell (adjusted hazard ratio per IQR increase = 0.50, 95% confidence interval: 0.27-0.93) and decreased by 64% in mast cell density (adjusted hazard ratio per IQR increase = 0.36, confidence interval: 0.15-0.84). The analyses were reported without the type I error correction for the multiple types of immune cell testing.

Conclusions

Analysis of the density of immune cells within the tumor and surrounding stroma reveals an association between the density of T-cells and RFS and between mast cells and RFS in early-stage LUAD. This preliminary result is a limited study with a small sample size and a lack of an independent validation set.

Integrated Multi-omics Analysis of Early Lung Adenocarcinoma Links Tumor Biological Features with Predicted Indolence or Aggressiveness

Lung adenocarcinoma (LUAD) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate the biological determinants of early LUAD indolence or aggressiveness using radiomics as a surrogate of behavior. We present a set of 92 patients with LUAD with data collected across different methodologies. Patients were risk-stratified using the CT-based Score Indicative of Lung cancer Aggression (SILA) tool (0 = least aggressive, 1 = most aggressive). We grouped the patients as indolent (x ≤ 0.4, n = 14), intermediate (0.4 > x ≤ 0.6, n = 27), and aggressive (0.6 > x ≤ 1, n = 52). Using Cytometry by time of flight (CyTOF), we identified subpopulations with high HLA-DR expression that were associated with indolent behavior. In the RNA sequencing (RNA-seq) dataset, pathways related to immune response were associated with indolent behavior, while pathways associated with cell cycle and proliferation were associated with aggressive behavior. We extracted quantitative radiomics features from the CT scans of the patients. Integrating these datasets, we identified four feature signatures and four patient clusters that were associated with survival. Using single-cell RNA-seq, we found that indolent tumors had significantly more T cells and less B cells than aggressive tumors, and that the latter had a higher abundance of regulatory T cells and Th cells. In conclusion, we were able to uncover a correspondence between radiomics and tumor biology, which could improve the discrimination between indolent and aggressive LUAD tumors, enhance our knowledge in the biology of these tumors, and offer novel and personalized avenues for intervention.

Significance

This study provides a comprehensive profiling of LUAD indolence and aggressiveness at the biological bulk and single-cell levels, as well as at the clinical and radiomics levels. This hypothesis generating study uncovers several potential future research avenues. It also highlights the importance and power of data integration to improve our systemic understanding of LUAD and to help reduce the gap between basic science research and clinical practice.

Characteristics of circulating adaptive immune cells in patients with colorectal cancer

Adaptive immune cells prevent solid tumor progression by targeting and killing tumor cells. However, there are no comprehensive studies on peripheral circulating adaptive immune cell characterization in colorectal cancer (CRC) patients or the effect of tumor-node-metastasis (TNM) stages on these cells. In this study, the number, phenotype, and function of different subsets of circulating adaptive immune cells in peripheral blood of CRC patients were analyzed. We found remarkable differences in CRC patients compared with those in healthy controls, including reduced absolute counts of total T cells, helper T lymphocytes (Th), cytotoxic T lymphocytes (Tc), and double-negative T lymphocytes, a decreased proportion of INF-γ⁺ cells in total T cells and Th, and increased percentages of B cells, plasmablasts, and activated T cells. Compared with early-stage CRC patients, advanced-stage CRC patients showed more severe immunosenescence, which manifested as decreased proportions of CD8⁺ naive T cells with strong proliferative ability and CD8⁺ central memory T cells with immune surveillance function. Proportions and absolute counts of CD8⁺ and CD4⁺ terminally differentiated effector memory T cells were increased, indicating immunosenescence. The immune cell characteristics analyzed in this study serve as a starting point for further research to determine potential clinical implications.

Integrated Cells and Collagen Fibers Spatial Image Analysis

Modern technologies designed for tissue structure visualization like brightfield microscopy, fluorescent microscopy, mass cytometry imaging (MCI) and mass spectrometry imaging (MSI) provide large amounts of quantitative and spatial information about cells and tissue structures like vessels, bronchioles etc. Many published reports have demonstrated that the structural features of cells and extracellular matrix (ECM) and their interactions strongly predict disease development and progression. Computational image analysis methods in combination with spatial analysis and machine learning can reveal novel structural patterns in normal and diseased tissue. Here, we have developed a Python package designed for integrated analysis of cells and ECM in a spatially dependent manner. The package performs segmentation, labeling and feature analysis of ECM fibers, combines this information with pre-generated single-cell based datasets and realizes cell-cell and cell-fiber spatial analysis. To demonstrate performance and compatibility of our computational tool, we integrated it with a pipeline designed for cell segmentation, classification, and feature analysis in the KNIME analytical platform. For validation, we used a set of mouse mammary gland tumors and human lung adenocarcinoma tissue samples stained for multiple cellular markers and collagen as the main ECM protein. The developed package provides sufficient performance and precision to be used as a novel method to investigate cell-ECM relationships in the tissue, as well as detect structural patterns correlated with specific disease outcomes.