Medical Treatment of Lung Cancer: Can Immune Cells Predict the Response? A Systematic Review

Assessing the causal relationship between 731 immunophenotypes and the risk of lung cancer: a bidirectional mendelian randomization study

BACKGROUND

Previous studies have observed a link between immunophenotypes and lung cancer, both of which are closely associated with genetic factors. However, the causal relationship between them remains unclear.

METHODS

Bidirectional Mendelian randomization (MR) was performed on publicly available genome-wide association study (GWAS) summary statistics to analyze the causal relationships between 731 immunophenotypes and lung cancer. Sensitivity analyses were conducted to verify the robustness, heterogeneity, and potential horizontal pleiotropy of our findings.

RESULTS

Following Bonferroni adjustment, CD14- CD16+ monocyte (OR = 0.930, 95%CI 0.900-0.960, P = 8.648 × 10- 6, PBonferroni = 0.006) and CD27 on CD24+ CD27+ B cells (OR = 1.036, 95%CI 1.020-1.053, P = 1.595 × 10 - 5, PBonferroni = 0.012) were identified as having a causal role in lung cancer via the inverse variance weighted (IVW) method. At a more relaxed threshold, CD27 on IgD+ CD24+ B cell (OR = 1.035, 95%CI 1.017-1.053, P = 8.666 × 10- 5, PBonferroni = 0.063) and CD27 on switched memory B cell (OR = 1.037, 95%CI 1.018-1.056, P = 1.154 × 10- 4, PBonferroni = 0.084) were further identified. No statistically significant effects of lung cancer on immunophenotypes were found.

CONCLUSIONS

The elevated level of CD14- CD16+ monocytes was a protective factor against lung cancer. Conversely, CD27 on CD24+ CD27+ B cell was a risk factor. CD27 on class-switched memory B cells and IgD+ CD24+ B cells were potential risk factors for lung cancer. This research enhanced our comprehension of the interplay between immune responses and lung cancer risk. Additionally, these findings offer valuable perspectives for the development of immunologically oriented therapeutic strategies.

Nano-mediated strategy for targeting and treatment of non-small cell lung cancer (NSCLC)

Lung cancer is the most common type of cancer, with over 2.1 million cases diagnosed annually worldwide. It has a high incidence and mortality rate, leading to extensive research into various treatment options, including the use of nanomaterial-based carriers for drug delivery. With regard to cancer treatment, the distinct biological and physico-chemical features of nano-structures have acquired considerable impetus as drug delivery system (DDS) for delivering medication combinations or combining diagnostics and targeted therapy. This review focuses on the use of nanomedicine-based drug delivery systems in the treatment of lung cancer, including the use of lipid, polymer, and carbon-based nanomaterials for traditional therapies such as chemotherapy, radiotherapy, and phototherapy. The review also discusses the potential of stimuli-responsive nanomaterials for drug delivery in lung cancer, and the limitations and opportunities for improving the design of nano-based materials for the treatment of non-small cell lung cancer (NSCLC).

A novel predictive model based on inflammatory response-related genes for predicting endometrial cancer prognosis and its experimental validation

Inflammatory response is an important feature of most tumors. Local inflammation promotes tumor cell immune evasion and chemotherapeutic drug resistance. We aimed to build a prognostic model for endometrial cancer patients based on inflammatory response-related genes (IRGs). RNA sequencing and clinical data for uterine corpus endometrial cancer were obtained from TCGA datasets. LASSO-penalized Cox regression was used to obtain the risk formula of the model: the score = e^{sum(corresponding coefficient × each gene's expression)}. The "ESTIMATE" and "pRRophetic" packages in R were used to evaluate the tumor microenvironment and the sensitivity of patients to chemotherapy drugs. Data sets from IMvigor210 were used to evaluate the efficacy of immunotherapy in cancer patients. For experimental verification, 37 endometrial cancer and 43 normal endometrial tissues samples were collected. The mRNA expression of the IRGs was measured using qRT-PCR. The effects of IRGs on the malignant biological behaviors of endometrial cancer were detected using CCK-8, colony formation, Transwell invasion, and apoptosis assays. We developed a novel prognostic signature comprising 13 IRGs, which is an independent prognostic marker for endometrial cancer. A nomogram was developed to predict patient survival accurately. Three key IRGs (LAMP3, MEP1A, and ROS1) were identified in this model. Furthermore, we verified the expression of the three key IRGs using qRT-PCR. Functional experiments also confirmed the influence of the three key IRGs on the malignant biological behavior of endometrial cancer. Thus, a characteristic model constructed using IRGs can predict the survival, chemotherapeutic drug sensitivity, and immunotherapy response in patients with endometrial cancer.

Mass Cytometry Reveals Classical Monocytes, NK Cells, and ICOS+ CD4+ T Cells Associated with Pembrolizumab Efficacy in Patients with Lung Cancer

PURPOSE

Immune checkpoint inhibitors (ICI) have revolutionized the treatment of non-small cell lung cancer (NSCLC), but predictive biomarkers of their efficacy are imperfect. The primary objective is to evaluate circulating immune predictors of pembrolizumab efficacy in patients with advanced NSCLC.

EXPERIMENTAL DESIGN

We used high-dimensional mass cytometry (CyTOF) in baseline blood samples of patients with advanced NSCLC treated with pembrolizumab. CyTOF data were analyzed by machine-learning algorithms (Citrus, tSNE) and confirmed by manual gating followed by principal component analysis (between-group analysis).

RESULTS

We analyzed 27 patients from the seminal KEYNOTE-001 study (median follow-up of 60.6 months). We demonstrate that blood baseline frequencies of classical monocytes, natural killer (NK) cells, and ICOS+ CD4+ T cells are significantly associated with improved objective response rates, progression-free survival, and overall survival (OS). In addition, we report that a baseline immune peripheral score combining these three populations strongly predicts pembrolizumab efficacy (OS: HR = 0.25; 95% confidence interval = 0.12-0.51; P < 0.0001).

CONCLUSIONS

As this immune monitoring is easy in routine practice, we anticipate our findings may improve prediction of ICI benefit in patients with advanced NSCLC.

A Prognostic Risk Score Based on Hypoxia-, Immunity-, and Epithelialto-Mesenchymal Transition-Related Genes for the Prognosis and Immunotherapy Response of Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer (NSCLC), is associated with poor prognosis. However, current stage-based clinical methods are insufficient for survival prediction and decision-making. This study aimed to establish a novel model for evaluating the risk of LUAD based on hypoxia, immunity, and epithelial-mesenchymal transition (EMT) gene signatures.

Methods: In this study, we used data from TCGA-LUAD for the training cohort and GSE68465 and GSE72094 for the validation cohorts. Immunotherapy datasets GSE135222, GSE126044, and IMvigor210 were obtained from a previous study. Using bioinformatic and machine algorithms, we established a risk model based on hypoxia, immune, and EMT gene signatures, which was then used to divide patients into the high and low risk groups. We analyzed differences in enriched pathways between the two groups, following which we investigated whether the risk score was correlated with stemness scores, genes related to m6A, m5C, m1A and m7G modification, the immune microenvironment, immunotherapy response, and multiple anti-cancer drug sensitivity.

Results: Overall survival differed significantly between the high-risk and low-risk groups (HR = 4.26). The AUCs for predicting 1-, 3-, and 5-year survival were 0.763, 0.766, and 0.728, respectively. In the GSE68465 dataset, the HR was 2.03, while the AUCs for predicting 1-, 3-, and 5-year survival were 0.69, 0.651, and 0.618, respectively. The corresponding values in the GSE72094 dataset were an HR of 2.36 and AUCs of 0.653, 0.662, and 0.749, respectively. The risk score model could independently predict OS in patients with LUAD, and highly correlated with stemness scores and numerous m6A, m5C, m1A and m7G modification-related genes. Furthermore, the risk model was significantly correlated with multiple immune microenvironment characteristics. In the GSE135222 dataset, the HR was 4.26 and the AUC was 0.702. Evaluation of the GSE126044 and IMvigor210 cohorts indicated that PD-1/PD-LI inhibitor treatment may be indicated in patients with low risk scores, while anti-cancer therapy with various drugs may be indicated in patients with high risk scores.

Conclusion: Our novel risk model developed based on hypoxia, immune, and EMT gene signatures can aid in predicting clinical prognosis and guiding treatment in patients with LUAD.

Detection of PD-L1 Expression in Temozolomide-Resistant Glioblastoma by Using PD-L1 Antibodies Conjugated with Lipid‑Coated Superparamagnetic Iron Oxide

Purpose

Targeted superparamagnetic iron oxide (SPIO) nanoparticles are a promising tool for molecular magnetic resonance imaging (MRI) diagnosis. Lipid-coated SPIO nanoparticles have a nonfouling property that can reduce nonspecific binding to off-target cells and prevent agglomeration, making them suitable contrast agents for molecular MRI diagnosis. PD-L1 is a poor prognostic factor for patients with glioblastoma. Most recurrent glioblastomas are temozolomide resistant. Diagnostic probes targeting PD-L1 could facilitate early diagnosis and be used to predict responses to targeted PD-L1 immunotherapy in patients with primary or recurrent glioblastoma. We conjugated lipid-coated SPIO nanoparticles with PD-L1 antibodies to identify PD-L1 expression in glioblastoma or temozolomide-resistant glioblastoma by using MRI.

Methods

The synthesized PD-L1 antibody-conjugated SPIO (PDL1-SPIO) nanoparticles were characterized using dynamic light scattering, zeta potential assays, transmission electron microscopy images, Prussian blue assay, in vitro cell affinity assay, and animal MRI analysis.

Results

PDL1-SPIO exhibited a specific binding capacity to PD-L1 of the mouse glioblastoma cell line (GL261). The presence and quantity of PDL1-SPIO in temozolomide-resistant glioblastoma cells and tumor tissue were confirmed through Prussian blue staining and in vivo T2* map MRI, respectively.

Conclusion

This is the first study to demonstrate that PDL1-SPIO can specifically target temozolomide-resistant glioblastoma with PD-L1 expression in the brain and can be quantified through MRI analysis, thus making it suitable for the diagnosis of PD-L1 expression in temozolomide-resistant glioblastoma in vivo.

Advanced Nanoparticle-Based Drug Delivery Systems and Their Cellular Evaluation for Non-Small Cell Lung Cancer Treatment

Lung cancers, the number one cancer killer, can be broadly divided into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC being the most commonly diagnosed type. Anticancer agents for NSCLC suffer from various limitations that can be partly overcome by the application of nanomedicines. Nanoparticles is a branch within nanomedicine that can improve the delivery of anticancer drugs, whilst ensuring the stability and sufficient bioavailability following administration. There are many publications available in the literature exploring different types of nanoparticles from different materials. The effectiveness of a treatment option needs to be validated in suitable in vitro and/or in vivo models. This includes the developed nanoparticles, to prove their safety and efficacy. Many researchers have turned towards in vitro models that use normal cells or specific cells from diseased tissues. However, in cellular works, the physiological dynamics that is available in the body could not be mimicked entirely, and hence, there is still possible development of false positive or false negative results from the in vitro models. This article provides an overview of NSCLC, the different nanoparticles available to date, and in vitro evaluation of the nanoparticles. Different types of cells suitable for in vitro study and the important precautions to limit the development of false results are also extensively discussed.

Chimeric Antigen Receptor T-Cell Therapy in Glioblastoma: Current and Future

Glioblastoma (GBM) is a highly aggressive glioma with an extremely poor prognosis after conventional treatment. Recent advances in immunotherapy offer hope for these patients with incurable GBM. Our present review aimed to provide an overview of immunotherapy for GBM, especially chimeric antigen receptor T-cell (CAR T) therapy. CAR T-cell immunotherapy, which involves the engineering of T cells to kill tumors by targeting cell surface-specific antigens, has been successful in eliminating B-cell leukemia by targeting CD19. IL-13Rα2, EGFRvIII, and HER2-targeted CAR T cells have shown significant clinical efficacy and safety in phase 1 or 2 clinical trials conducted in patients with GBM; these findings support the need for further studies to examine if this therapy can ultimately benefit this patient group. However, local physical barriers, high tumor heterogeneity, and antigen escape make the use of CAR T therapy, as a treatment for GBM, challenging. The potential directions for improving the efficacy of CAR T in GBM are to combine the existing traditional therapies and the construction of multi-target CAR T cells.

Radiomic Immunophenotyping of GSEA-Assessed Immunophenotypes of Glioblastoma and Its Implications for Prognosis: A Feasibility Study

Simple Summary

Characterization of immunophenotypes in GBM is important for therapeutic stratification and helps predict treatment response and prognosis. However, identifying immunophenotypes of patients with GBM requires multiple laboratory experiments and is time consuming. We developed a non-invasive method to evaluate enrichment levels of CTL, aDC, Treg, and MDSC immune cells to classify immunophenotypes of GBM tumor microenvironment with radiomic features of MR imaging. Five immunophenotypes (G1–G5) of GBM can be classified with specific gene set enrichment analysis. G2 had the worst prognosis and comprised highly enriched MDSCs and lowly enriched CTLs. G3 had the best prognosis and comprised lowly enriched MDSCs and Tregs and highly enriched CTLs. Moreover, the developed radiomics models can successfully identified these two groups by immune cell subsets enriched levels prediction. Therefore, it is possible to characterize immunophenotypes of GBM and predict patient prognosis with radiomics methods.

Abstract

Characterization of immunophenotypes in glioblastoma (GBM) is important for therapeutic stratification and helps predict treatment response and prognosis. Radiomics can be used to predict molecular subtypes and gene expression levels. However, whether radiomics aids immunophenotyping prediction is still unknown. In this study, to classify immunophenotypes in patients with GBM, we developed machine learning-based magnetic resonance (MR) radiomic models to evaluate the enrichment levels of four immune subsets: Cytotoxic T lymphocytes (CTLs), activated dendritic cells, regulatory T cells (Tregs), and myeloid-derived suppressor cells (MDSCs). Independent testing data and the leave-one-out cross-validation method were used to evaluate model effectiveness and model performance, respectively. We identified five immunophenotypes (G1 to G5) based on the enrichment level for the four immune subsets. G2 had the worst prognosis and comprised highly enriched MDSCs and lowly enriched CTLs. G3 had the best prognosis and comprised lowly enriched MDSCs and Tregs and highly enriched CTLs. The average accuracy of T1-weighted contrasted MR radiomics models of the enrichment level for the four immune subsets reached 79% and predicted G2, G3, and the “immune-cold” phenotype (G1) according to our radiomics models. Our radiomic immunophenotyping models feasibly characterize the immunophenotypes of GBM and can predict patient prognosis.