Microenvironment In Non-small Cell Lung Cancer Research Articles

BZW2 is a potential regulator of non-small cell lung cancer progression

BackgroundPersonalized targeted therapy has become an important strategy for cancer treatment owing to its remarkable therapeutic efficacy and safety. However, drug resistance remains the primary cause of treatment failure. Basic leucine zipper and W2 domain 2 (BZW2), which is aberrantly expressed in cancer, has been implicated in tumor progression and may serve as a new therapeutic target. Therefore, the role of BZW2 in non-small cell lung cancer (NSCLC) requires further investigation. MethodsThe expression and genetic alterations of BZW2 in pan-cancers were explored using The Cancer Genome Atlas (TCGA) PanCancer databases. The mRNA and protein levels of BZW2 in patients with NSCLC were verified in our cohort. Functional experiments including CCK8, colony formation, and transwell assays were performed to evaluate the impact of BZW2 on the proliferative, migratory, and invasive capacities of SK-MES-1 cells. Gene Set Enrichment Analysis was used to identify underlying biological processes and pathways. Single-cell RNA (scRNA) sequencing data were employed to investigate the tumor microenvironment of NSCLC and the co-expression of BZW2 and stemness-related genes. ResultsDysregulated BZW2 expression was observed in various malignant tumors. BZW2 expression was found to be significantly elevated in NSCLC. BZW2 depletion inhibited the growth, mobility, and invasive abilities of lung squamous cell carcinoma SK-MES-1 cells. BZW2 may be related to signaling pathways such as nucleotide excision repair, ubiquitin-mediated proteolysis, and the P53 signaling pathway. Biological processes, including translational initiation, tRNA processing, and RNA methylation, were observed to be enriched in the high-BZW2 group. Furthermore, there was a positive correlation between BZW2 and the m6A- and m5C-related genes. scRNA analysis revealed a co-expression relationship between BZW2 and stemness-related genes such as CD44, SOX9, and CD133. ConclusionsElevated BZW2 expression is associated with the proliferation, migration, and invasion of NSCLC, and BZW2 may be a potential therapeutic target for NSCLC.

Construction of An Orthotopic Xenograft Model of Non-small Cell Lung Cancer Mimicking Disease Progression and Predicting Drug Activities.

Non-small cell lung cancer (NSCLC) is a highly lethal disease with a complex and heterogeneous tumor microenvironment. Currently, common animal models based on subcutaneous inoculation of cancer cell suspensions do not recapitulate the tumor microenvironment in NSCLC. Herein we describe a murine orthotopic lung cancer xenograft model that employs the intrapulmonary inoculation of three-dimensional multicellular spheroids (MCS). Specifically, fluorescent human NSCLC cells (A549-iRFP) were cultured in low-attachment 96-well microplates with collagen for 3 weeks to form MCS, which were then inoculated intercostally into the left lung of athymic nude mice to establish the orthotopic lung cancer model. Compared with the original A549 cell line, MCS of the A549-iRFP cell line responded similarly to anticancer drugs. The long-wavelength fluorescent signal of the A549-iRFP cells correlated strongly with common markers of cancer cell growth, including spheroid volume, cell viability, and cellular protein level, thus allowing dynamic monitoring of the cancer growth in vivo by fluorescent imaging. After inoculation into mice, the A549-iRFP MCS xenograft reliably progressed through phases closely resembling the clinical stages of NSCLC, including the expansion of the primary tumor, the emergence of neighboring secondary tumors, and the metastases of cancer cells to the contralateral right lung and remote organs. Moreover, the model responded to the benchmark antilung cancer drug, cisplatin with the anticipated toxicity and slower cancer progression. Therefore, this murine orthotopic xenograft model of NSCLC would serve as a platform to recapitulate the disease's progression and facilitate the development of potential anticancer drugs.

Abstract 1534: The tumor immune microenvironment at different stages of non-small cell lung cancer

Abstract Introduction: To better understand the immune microenvironment in non-small cell lung cancer (NSCLC), we evaluated immune cell populations within the tumor parenchyma and their interaction at the periphery of the tumors at different stages of disease. Methods: 27 whole tumor slides were collected from NSCLC patients of all stages after surgical resection. Tissue slides were stained with multiplex Opal immunofluorescence for CD8, CD68, CK, PD-1, PD-L1, FoxP3, and DAPI using a Leica Bond automated stainer. Slides were scanned at 20x magnification by Akoya Polaris whole slide scanner and spectrally unmixed. Spatial analyses were performed on the HALO software. Results: Density of CD8 T cells, T regulatory (Treg) cells, and macrophages were significantly higher (p=0.001) in the interface region compared to the tumor and stroma. There is abundant CD8 T cell infiltration in the tumor at early stage I and this decreased significantly (p=0.05) at higher stages. PD-1− CD8 T cells significantly dropped with higher stage, while PD-1+ CD8 T cells did not. On the other hand, macrophage infiltration increased as stage increased, with most of the increase driven by PD-L1− macrophages. No statistically significant trends were observed in Treg cells across stages, but they appeared to be higher in stage II. No differences in immune infiltration were observed between the NSCLC subtypes adenocarcinoma and squamous cell carcinoma. No correlation was found between proximity of immune cell subsets to tumor cells and stage. Conclusion: The tumor microenvironment in NSCLC is rich in CD8+ cells at early stage and their density decreases as the stage of the disease increases, while macrophage infiltration increases as the disease stage progresses. Citation Format: Mackenzie Hansen, Qin Zhou, Yoko S. DeRose, Magdalena Kovacsovics, Benjamin Brintz, Benjamin L. Witt, Kim L. O'Neill, Siwen Hu-Lieskovan. The tumor immune microenvironment at different stages of non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1534.

Abstract 1516: Large scale, transcriptome-based analysis of TCR clonality reveals functional immunity in non-small cell lung cancer

Abstract Tumor-related T-cell activation and subsequent clonal expansion are essential for an effective anti-cancer immune response. Thus, quantifying the clonal distribution of T-cells in cancer tissue from patients might provide prognostic and predictive information, particularly in high immunogenic tumors like non-small cell lung cancer (NSCLC). We evaluated TCR clonality in the tumor microenvironment of NSCLC, its relation to the in situ immune phenotype, and its clinical impact in a cohort of 182 resected NSCLC patients. The α and β TCR clones were determined based on RNA sequencing data, and the Gini index was used to weight individual clonal distribution. We used matched FFPE cancer tissue for multiplex immunofluorescence cell analysis and in-situ sequencing of T-cell clones. The analysis revealed a broad spectrum of TCR clonality patterns, including tissue with high TCR diversity and high evenness (low Gini coefficient) and tissue with clonal dominance with low evenness (high Gini coefficient). Highly expended clones were detected in 15/182 patients, whereas 43/182 revealed no clonal enrichment. A low TCR clone evenness (high Gini coefficient) was significantly more frequent in normal lung areas than in the corresponding tumor areas (n=20, p=0.02). In the cancer tissue, a high Gini index further correlated with high tumor mutational burden, indicating neoantigen-induced T cell clone expansions. Correspondingly, high TCR clonality was associated with an inflamed tumor phenotype associated with PD-1+ immune cells, CD3+, CD8A+, CD163+, and CD138+ cells, as well as higher expression of genes connected to immune activation (PRF1, GZMB, GZMH) and T cell exhaustion (PD-L1, LAG3, TIGIT). Cancer tissue with dominant T-cell clones also showed higher numbers of mature tertiary lymphoid structures with germinal centers. In situ sequencing suggested a close spatial relation between specific T cell clones and tumor cells. Finally, NSCLC patients with dominant T-cell clones showed strong responses and longer survival when treated with immune checkpoint inhibitors. This suggests that TCR clonality analysis is a strong candidate for precision diagnostics. Plausibly, the dominant clones found in NSCLC are responsible for a specific but malfunctioning anti-tumor response that can be unleashed by immune checkpoint inhibition. Citation Format: Hui Yu, Anastasia Magoulopoulou, Masafumi Hori, Amanda Lindberg, Max Backman, Hans Brunström, Millaray Marincevic Zuniga, Johanna Mattsson, Akira Saito, Karin Leandersson, Mats Nilsson, Rose-Marie Amini, Patrick Micke, Carina Strell. Large scale, transcriptome-based analysis of TCR clonality reveals functional immunity in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1516.

Abstract 1536: Deciphering the crosstalk within the tumor microenvironment of NSCLC by a virtual microdissection approach

Abstract Introduction: Non-small cell lung cancer (NSCLC) is a highly heterogenous disease with the largest number of cancer-related mortality worldwide, one of the reasons for this is the complex and diverse tumor microenvironment (TME) comprising of numerous cell types. Several studies have already highlighted the importance of TME in dictating progression steps and response to therapies; however, a transcriptome-based molecular subtyping of patients in lung adenocarcinomas (LUADs) and lung squamous cell carcinomas (LUSCs) can further determine the distinct tumor immune microenvironment (TiME), which can eventually provide a systematic overview to improve the diagnosis and prognosis of patients. Material and method: To elucidate such nature of interactions between tumor cells and cells comprising the TME, we exploited the transcriptome of 300 early stages (Ib-IIIa) NSCLC recruited in the prospective observational clinical trial PROMOLE. With the help of a clustering approach, initially we performed a molecular-based virtual stratification/dissection on the NSCLC patients. Next, to elucidate the relative cell-type abundance, a deconvolution approach was applied to identify the possibility of tumor infiltrating immune cells within these subgroups. Immunohistochemistry (IHC) was then used to substantiate these predictions on tumor cells. Results and discussion: The resulting subgroups of LUADs and LUSCs are biologically well-characterized by mutational and gene expression profiles. Cell-type abundance approach identified samples which are enriched with tumor infiltrating immune cells like Neutrophils, Tcells, macrophages, etc. These findings were positively confirmed by IHC with multiple cell markers such as MPO, CD4, CD8, CD68, etc. Integrating these two results highlighted the proportion of TiME in the two different sub-populations along with shedding some light on the crosstalk happening between different cancer-/immune- cell lines. Conclusion: The in-silico predictions on bulk RNA data by virtual micro-dissection, distinguished the two distinct NSCLC subtypes, each associated with clinical and molecular features. Furthermore, the immune cells infiltration suggests a possible role of infiltrating tumor immune cells with the prognosis of patients. Our analysis successfully performed an intra-sample and inter-sample comparison, which can unveil new prognostic markers that can provide relevant information for cancer immunotherapy. Citation Format: Sushant Parab, Francesca Napoli, Davide Corà, Gabriella Doronzo, Valentina Communanza, Luisella Righi, Luca Primo, Valentina Monica, Lorenzo Manganaro, Bianco Selene, Paolo Bironzo, Giorgio Scagliotti, Federico Bussolino. Deciphering the crosstalk within the tumor microenvironment of NSCLC by a virtual microdissection approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1536.

Abstract 7323: Tissue resident memory T cell abundances impact non-small-cell lung cancer immune microenvironment and patient prognosis

Abstract Tissue resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, whether TRM exerts any immunosurveillance role in the tumor immune microenvironment (TIME) and progression of non-small-cell lung cancer (NSCLC), which accounts for 85% of all lung cancers, remains unclear. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we could infer the abundance of TRM in NSCLC. We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in TIME and significantly impacts the prognosis of NSCLC patients. In addition, TRM signatures are strongly associated with immune checkpoint genes and the prognosis of NSCLC patients, suggesting that TRM signatures are promising prognostic markers for immunotherapy in NSCLC. We then built a machine learning model to predict patient survival based on the TRM signatures and immune related genes. The accuracy of the model was validated by Kaplan-Meyer survival analysis, receiver operating characteristic curves, principal component analysis, and t-distributed random neighbor embedding. We developed a 4-gene risk score that effectively stratified patients into low-risk and high-risk categories. The patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell activation, macrophage regulation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. Altogether, this study provides valuable insights into the complex interactions between NSCLC TRM and TIME and their impact on patient prognosis, highlighting the importance of TRM in shaping the NSCLC microenvironment. The development of a simplified 4-gene risk score provides a practical prognostic marker for risk stratification. Keywords: Tissue resident memory T cell, non-small-cell lung cancer, prognosis, tumor immune microenvironment, machine learning Citation Format: Aidan Shen, Aliesha Garrett, Junhua Mai, Yangzhi Zhu, Chongming Jiang. Tissue resident memory T cell abundances impact non-small-cell lung cancer immune microenvironment and patient prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7323.

A cuproptosis score model and prognostic score model can evaluate clinical characteristics and immune microenvironment in NSCLC

BackgroundCuproptosis-related genes (CRGs) are associated with lung adenocarcinoma. However, the links between CRGs and non-small-cell lung cancer (NSCLC) are not clear. In this study, we aimed to develop two cuproptosis models and investigate their correlation with NSCLC in terms of clinical features and tumor microenvironment.MethodsCRG expression profiles and clinical data from NSCLC and normal tissues was obtained from GEO (GSE42127) and TCGA datasets. Molecular clusters were classified into three patterns based on CRGs and cuproptosis cluster-related specific differentially expressed genes (CRDEGs). Then, two clinical models were established. First, a prognostic score model based on CRDEGs was established using univariate/multivariate Cox analysis. Then, through principal component analysis, a cuproptosis score model was established based on prognosis-related genes acquired via univariate analysis of CRDEGs. NSCLC patients were divided into high/low risk groups.ResultsEighteen CRGs were acquired, all upregulated in tumor tissues, 15 of which significantly (P < 0.05). Among the three CRG clusters, cluster B had the best prognosis. In the CRDEG clusters, cluster C had the best survival. In the prognostic score model, the high-risk group had worse prognosis, higher tumor mutation load, and lower immune infiltration while in the cuproptosis score model, a high score represented better survival, lower tumor mutation load, and high-level immune infiltration.ConclusionsThe cuproptosis score model and prognostic score model may be associated with NSCLC prognosis and immune microenvironment. These novel findings on the progression and immune landscape of NSCLC may facilitate the provision of more personalized immunotherapy interventions for NSCLC patients.

Targeting the MET gene: unveiling therapeutic opportunities in immunotherapy within the tumor immune microenvironment of non-small cell lung cancer

Non-small cell lung cancer (NSCLC) represents the most prevalent histological subtype of lung cancer. Within this disease, the MET gene emerges as a critical therapeutic target, exhibiting various forms of dysregulation. Although MET tyrosine kinase inhibitors, HGF/c-MET targeting antibodies, and antibody-drug conjugates constitute the primary treatment modalities for patients with MET-altered NSCLC, numerous questions remain regarding their optimal application. The advent of immunotherapy holds promise for enhancing therapeutic outcomes in patients with MET-altered NSCLC. MET mutations can reshape the tumor immune microenvironment of NSCLC by reducing tumor immunogenicity, inducing exhaustion in immune-activated cells, and promoting immune evasion, which are crucial for modulating treatment responses. Furthermore, we emphasize the promising synergy of immunotherapy with emerging treatments and the challenges and opportunities in refining these approaches to improve patient outcomes.

Targeting CENP-E augments immunotherapy in non-small cell lung cancer via stabilizing PD-L1

Centromere-associated protein E (CENP-E) plays a critical role in mitosis and chromosome misalignment, which may represent a potential therapeutic target in tumors. CENP-E is frequently overexpressed in lung cancer and act as a driver gene. However, it remains unclear whether CENP-E regulates the immune microenvironment in non-small cell lung cancer (NSCLC). Our study revealed that CENP-E is highly expressed and predicts a worse survival in NSCLC patients; inhibition of CENP-E leads to an upregulation of PD-L1 expression, consequently impacting the immune microenvironment of NSCLC by modulating the balance between CD8+ T cells and regulatory T cells (Tregs). Mechanistically, we demonstrated that downregulation of CENP-E could stabilize PD-L1 mRNA through the targeting of its 3′UTR by TTP. The genetic knockdown or pharmacological inhibition of CENP-E, in combination with PD-L1 antibody, could enhance the antitumor effect in NSCLC. Thus, our findings have revealed a role of CENP-E in immunotherapy and suggest that combination of CENP-E inhibitor with PD-L1 antibody could be an effective treatment option for NSCLC.

Salidroside regulates tumor microenvironment of non-small cell lung cancer via Hsp70/Stub1/Foxp3 pathway in Tregs

BackgroundThe treatment of non-small cell lung cancer (NSCLC) is challenging due to immune tolerance and evasion. Salidroside (SAL) is an extract in traditional Chinese medicine and has a potential antitumor effect. However, the mechanism of SAL in regulating the immunological microenvironment of NSCLC is yet to be clarified.MethodsThe mouse model with Lewis lung cancer cell line (3LL) in C57BL/6 mice was established. And then, the percentage of tumor-infiltrating T cell subsets including Treg was detected in tumor-bearing mice with or without SAL treatment. In vitro, the effect of SAL on the expression of IL-10, Foxp3 and Stub1 and the function of Treg were detected by flow cytometry. Network pharmacology prediction and molecular docking software were used to predict the target of SAL and intermolecular interaction. Furthermore, the effect of SAL on the expression of Hsp70 and the co-localization of Stub1-Foxp3 in Treg was confirmed by flow cytometry and confocal laser microscopy. Finally, Hsp70 inhibitor was used to verify the above molecular expression.ResultsWe discovered that SAL treatment inhibits the growth of tumor cells by decreasing the percentage of tumor-infiltrated CD4+Foxp3+T cells. SAL treatment downregulates the expression of Foxp3 in Tregs, but increases the expression of Stub1, an E3 ubiquitination ligase upstream of Foxp3, and the expression of Hsp70. Inhibiting the expression of Hsp70 reverses the inhibition of SAL on Foxp3 and disrupts the colocalization of Stub1 and Foxp3 in the nucleus of Tregs.ConclusionsSAL inhibits tumor growth by regulating the Hsp70/stub1/Foxp3 pathway in Treg to suppress the function of Treg. It is a new mechanism of SAL for antitumor therapy.

Location of CD39+ T cell subpopulations within tumors predict differential outcomes in non-small cell lung cancer

PurposeAn improved mechanistic understanding of immunosuppressive pathways in non-small cell lung cancer (NSCLC) is important to develop novel diagnostic and therapeutic approaches. Here, we investigate the prognostic significance of the...

Two microRNAs of plasma-derived small extracellular vesicles as biomarkers for metastatic non-small cell lung cancer

BackgroundMicroRNAs (miRNAs) of plasma-derived small extracellular vesicles (sEVs) have been proven to be associated with metastasis in several types of cancer. This study aimed to detect miRNAs of plasma-derived sEVs as potential biomarkers for metastatic non-small cell lung cancer (NSCLC).MethodsWe assessed the miRNA profiles of plasma-derived sEVs from healthy individuals as the control group (CT group), NSCLC patients without distant organ metastasis as the NM-NSCLC group and patients with distant organ metastasis as the M-NSCLC group. Next-generation sequencing (NGS) was performed on samples, and differentially expressed miRNAs (DEMs) of the three groups were screened. Kyoto Encyclopedia of Genes and Genomes (KEGG) and ClueGO were used to predict potential pathways of DEMs. MiRNA enrichment analysis and annotation tool (miEAA) was used to understand changes in the tumour microenvironment in NSCLC. Quantitative reverse transcription polymerase chain reaction (qRT‒PCR) analysis was used to validate target miRNAs.ResultNGS was performed on 38 samples of miRNAs of plasma-derived sEVs, and DEMs were screened out between the above three groups. Regarding the distribution of DEMs in the NM-NSCLC and M-NSCLC groups, KEGG pathway analysis showed enrichment in focal adhesion and gap junctions and ClueGO in the Rap1 and Hippo signaling pathways; miEAA found that fibroblasts were over-represented. From our screening, miRNA-200c-3p and miRNA-4429 were found to be predictive DEMs among the CT, NM-NSCLC and M-NSCLC groups, and qRT‒PCR was applied to verify the results. Finally, it was revealed that expression levels of miR-200c-3p and miR-4429 were significantly upregulated in M-NSCLC patients.ConclusionThis study identified miRNA-200c-3p and miRNA-4429 as potential biomarkers for NSCLC metastasis.

Identification of WD-Repeat Protein 72 as a Novel Prognostic Biomarker in Non-Small-Cell Lung Cancer.

WD-repeat protein 72(WDR72; OMIM∗613214), a scaffolding protein lacking intrinsic enzymatic activity, produces numerous β-propeller blade formations, serves as a binding platform to assemble protein complexes and is critical for cell growth, differentiation, adhesion, and migration. Despite evidence supporting a basic role of WDR72 in the tumorigenesis of particular cancers, the value of WDR72 in non-small-cell lung cancer (NSCLC), the tumor with the highest mortality rate globally, is undocumented. We investigated the prognostic value of WDR72 in NSCLC and studied its potential immune function and its correlation with ferroptosis. According to The Cancer Genome Atlas, Cancer Cell Line Encyclopedia, Genotype-Tissue Expression, and Gene Set Cancer Analysis, we used multiple bioinformatic strategies to investigate the possible oncogenic role of WDR72, analyze WDR72 and prognosis, and immune cell infiltration in different tumors correlation. WDR72 exhibited a high expression in NSCLC and a positive association with prognosis. WDR72 expression was related to immune cell infiltration and tumor immune microenvironment in NSCLC. Finally, we validated WDR72 in human NSCLC; it has a predictive value in NSCLC related to its function in tumor progression and immunity. The significance of our study is that WDR72 can be used as a potential indicator of lung cancer prognosis. Helping physicians more accurately predict patient survival and risk of disease progression.

Novel hypoxia-related gene signature for predicting prognoses that correlate with the tumor immune microenvironment in NSCLC.

Background: Intratumoral hypoxia is widely associated with the development of malignancy, treatment resistance, and worse prognoses. The global influence of hypoxia-related genes (HRGs) on prognostic significance, tumor microenvironment characteristics, and therapeutic response is unclear in patients with non-small cell lung cancer (NSCLC). Method: RNA-seq and clinical data for NSCLC patients were derived from The Cancer Genome Atlas (TCGA) database, and a group of HRGs was obtained from the MSigDB. The differentially expressed HRGs were determined using the limma package; prognostic HRGs were identified via univariate Cox regression. Using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression, an optimized prognostic model consisting of nine HRGs was constructed. The prognostic model's capacity was evaluated by Kaplan‒Meier survival curve analysis and receiver operating characteristic (ROC) curve analysis in the TCGA (training set) and GEO (validation set) cohorts. Moreover, a potential biological pathway and immune infiltration differences were explained. Results: A prognostic model containing nine HRGs (STC2, ALDOA, MIF, LDHA, EXT1, PGM2, ENO3, INHA, and RORA) was developed. NSCLC patients were separated into two risk categories according to the risk score generated by the hypoxia model. The model-based risk score had better predictive power than the clinicopathological method. Patients in the high-risk category had poor recurrence-free survival in the TCGA (HR: 1.426; 95% CI: 0.997-2.042; p = 0.046) and GEO (HR: 2.4; 95% CI: 1.7-3.2; p < 0.0001) cohorts. The overall survival of the high-risk category was also inferior to that of the low-risk category in the TCGA (HR: 1.8; 95% CI: 1.5-2.2; p < 0.0001) and GEO (HR: 1.8; 95% CI: 1.4-2.3; p < 0.0001) cohorts. Additionally, we discovered a notable distinction in the enrichment of immune-related pathways, immune cell abundance, and immune checkpoint gene expression between the two subcategories. Conclusion: The proposed 9-HRG signature is a promising indicator for predicting NSCLC patient prognosis and may be potentially applicable in checkpoint therapy efficiency prediction.

Abstract 6768: The potential predictive role of spatial phenotyping in non-small cell lung cancer

Abstract Background Lung cancers are the leading cause of cancer-related deaths with a 5-year-survival of only ~20%. Whilst immunotherapies have led to durable and prolonged survival, only a subset of patients remain responsive. Additional biomarkers are thus needed to better predict if patients will respond or develop resistance against immune checkpoint inhibitor (ICI) therapies. Spatial phenotyping of the tumor microenvironment (TME) is now recognized as a proxy for ICI therapy outcomes. Our study employs an end-to-end spatial biology strategy to survey non-small-cell lung cancer (NSCLC) tissues for new biomarkers that could guide immunotherapy treatments. Methods We phenotyped pre-treatment biopsies from non-small-cell lung cancer (NSCLC) patients treated with single-agent Nivolumab. We first performed 57-plex whole-slide Single Cell Spatial Phenotyping on the PhenoCycler®-Fusion platform. Next, we developed the spatial analysis of a larger NSCLC cohort using customizable PhenoCode Signature Panels (PSP) for high-throughput immune profile (CD3/CD8/CD20/CD68/PanCK + CD4 add-in) and immuno-contexture (CD8/CD68/PD-L1/FoxP3/PanCK + PD-1 add-in) imaging. The PSP panel content combines the barcode-based antibody chemistry from the PhenoCycler platform with the signal amplification of Opal chemistry from the PhenoImager platform. PSP panels were profiled across n=27 NSCLC biopsies and provided a wider breadth of tissue profiling. Results Our whole-slide single-cell spatial phenotyping analyses revealed high phenotypic diversity in the TME of patients responsive and resistant to ICI therapy. However, higher-throughput analyses of the same tissues using targeted PSP panels revealed no significant differences in quantities of immune cell lineages, including T-cells and macrophages. On the contrary, we discovered multiple quantifiable and statistically significant spatial signatures that appear to be predictive of treatment benefit, which confirms the potential biomarker value of spatial associations in patient tissues. Conclusions This study amounts to a uniquely comprehensive Single Cell Spatial Phenotyping analysis of pre-treatment NSCLC biopsies from a single-agent Nivolumab study. Our data catalogue the diversity in the immune microenvironment of NSCLC but highlighted that immune cell quantification is insufficient to stratify patient cohorts. Single-cell spatial phenotyping, on the other hand, promises to reveal new biomarkers that may aid in better stratification of patients in pre-treatment evaluations. Citation Format: Ning Ma, Aditya Pratapa, James Monkman, Ken O’Byrne, Oliver Braubach, Arutha Kulasinghe. The potential predictive role of spatial phenotyping in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6768.

Abstract 6763: Spatial profiling of cancer-associated fibroblasts in non-small cell lung cancer

Abstract Cancer-associated fibroblast (CAF) is an unique component of the tumor microenviroment (TME). CAFs heterogeneous have been identified by recent single-cell studies. However, their spatial distribution, orgnization patterns and neighbors remain incompletely understood. Deep profiling of CAFs in tissue context may provides new insights into improved understanding of their functions and identify targets for the development of CAF-targeted therapies.We obtained the open-source CosMx SMI dataset generated on 8 tissue sections from 5 non-small-cell lung cancer (NSCLC) tissues (He, S. et al. Nat Biotechnol. 2022). We extracted the cells’ spatial locations and transcriptome data for subsequent analyses. Quanlity filtering, data normalization, batch correction and clustering analysis was performed by Seurat package. Cell type was defined based on canonical immune markers and curated gene sets. In addition, we applied NMF clustering approach to define the spatial subtypes of CAFs based on the composition of neighboring cells. After that, Four spatial subtypes of CAFs were defined, exhibiting distinct spatial distribution patterns and compositions of neiboring cells, including CAFs that are clustered with tumor cells, CAFs localized within stromal, CAFs co-localized with B cells and T cells within the tertiary lymphoid structures, and CAFs localized within myeloid-enriched areas. Notably, we observed an intimate link between their spatial location and tarnscriptioanl states, i.e., these different spatial CAF subtypes showed clearly distinct expression profiles, may suggest their heterogeneous functions in the tumor microenviroment. For example, tumor-enriched CAFs showed high expression of contractile (e.g., ACTA2, TAGLN) and extracellular matrix (COL1A1) related genes, resembling myofibroblast (myCAFs), whereas the TLS-niches CAFs showed high expression of genes involved in antigen presentation such as major histocompatibility complex class II molecules and CD74, suggesting their antigen presentation function and were identified as apCAFs. Consistently, we observed high expression of inflammation related genes such as interleukin and PDGFRA in the myeloid-enriched CAFs suggesting an inflammatory phenotype (iCAFs). In addition, these CAFs in different spatial subtypes showed differential expression of molecular pathways regulating stress and inflammation response, likely reflecting their complex interactions with other components of the tunmor microenvironment. We also included single-cell RNA sequencing (scRNA-seq) and spatially resolved whole transcriptomics datasets for cross validation of our novel discoveries in additional cohorts.In summary, by applying a novel approach to classify CAFs in the spatial context, this study provided novel insights into an improved understanding of the phenotypic and functional heterogeneity of CAFs in the tunmor microenvironment of NSCLC. Citation Format: Yunhe Liu, Guangchun Han, Enyu Dai, Guangsheng Pei, Jiahui Jiang, Humam Kadara, Linghua Wang. Spatial profiling of cancer-associated fibroblasts in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6763.

Integrative bioinformatics approaches to establish potential prognostic immune-related genes signature and drugs in the non-small cell lung cancer microenvironment.

Introduction: Research has revealed that the tumor microenvironment (TME) is associated with the progression of malignancy. The combination of meaningful prognostic biomarkers related to the TME is expected to be a reliable direction for improving the diagnosis and treatment of non-small cell lung cancer (NSCLC). Method and Result: Therefore, to better understand the connection between the TME and survival outcomes of NSCLC, we used the "DESeq2" R package to mine the differentially expressed genes (DEGs) of two groups of NSCLC samples according to the optimal cutoff value of the immune score through the ESTIMATE algorithm. A total of 978 up-DEGs and 828 down-DEGs were eventually identified. A fifteen-gene prognostic signature was established via LASSO and Cox regression analysis and further divided the patients into two risk sets. The survival outcome of high-risk patients was significantly worse than that of low-risk patients in both the TCGA and two external validation sets (p-value < 0.05). The gene signature showed high predictive accuracy in TCGA (1-year area under the time-dependent ROC curve (AUC) = 0.722, 2-year AUC = 0.708, 3-year AUC = 0.686). The nomogram comprised of the risk score and related clinicopathological information was constructed, and calibration plots and ROC curves were applied, KEGG and GSEA analyses showed that the epithelial-mesenchymal transition (EMT) pathway, E2F target pathway and immune-associated pathway were mainly involved in the high-risk group. Further somatic mutation and immune analyses were conducted to compare the differences between the two groups. Drug sensitivity provides a potential treatment basis for clinical treatment. Finally, EREG and ADH1C were selected as the key prognostic genes of the two overlapping results from PPI and multiple Cox analyses. They were verified by comparing the mRNA expression in cell lines and protein expression in the HPA database, and clinical validation further confirmed the effectiveness of key genes. Conclusion: In conclusion, we obtained an immune-related fifteen-gene prognostic signature and potential mechanism and sensitive drugs underling the prognosis model, which may provide accurate prognosis prediction and available strategies for NSCLC.

Integrated analysis reveals the microenvironment of non-small cell lung cancer and a macrophage-related prognostic model.

In the treatment of non-small cell lung cancer (NSCLC), recent advances in immunotherapy have heralded a new era. Despite the success of immune therapy, a subset of patients persistently fails to respond. Therefore, to better improve the efficacy of immunotherapy and achieve the purpose of precision therapy, the research and exploration of tumor immunotherapy biomarkers have received much attention. Single-cell transcriptomic profiling was used to reveal tumor heterogeneity and the microenvironment in NSCLC. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was utilized to speculate the relative fractions of 22 infiltration immunocyte types in NSCLC. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used for the construction of risk prognostic models and predictive nomograms of NSCLC. Spearman's correlation analysis was employed to explore the relationship between risk score and tumor mutation burden (TMB) and immune checkpoint inhibitors (ICIs). Screening of chemotherapeutic agents in the high- and low-risk groups was performed with the "pRRophetic" package in R. Intercellular communication analysis was conducted using the "CellChat" package. We found that most tumor-infiltrating immune cells were T cells and monocytes. We also found that there was a significant difference in the tumor-infiltrating immune cells and ICIs across different molecular subtypes. Further analysis showed that M0 and M1 mononuclear macrophages were significantly different in different molecular subtypes. The risk prediction model was shown to have to ability to accurately predict the prognosis, immune cell infiltration, and chemotherapy efficacy of patients in the high and low-risk groups. Finally, we found that the carcinogenic effect of migration inhibitory factor (MIF) is mediated by binding to CD74, CXCR4, and CD44 receptors involved in MIF cell signaling. We have revealed the tumor microenvironment (TME) of NSCLC through single-cell data analysis and constructed a prognosis model of macrophage-related genes. These results could provide new therapeutic targets for NSCLC.

A GPX4-targeted photosensitizer to reverse hypoxia-induced inhibition of ferroptosis for non-small cell lung cancer therapy.

Ferroptosis therapy is gradually becoming a new strategy for the treatment of non-small cell lung cancer (NSCLC) because of its active iron metabolism. Because the hypoxic microenvironment in NSCLC inhibits ferroptosis heavily, the therapeutic effect of some ferroptosis inducers is severely limited. To address this issue, this work describes a promising photosensitizer ENBS-ML210 and its application against hypoxia of NSCLC treatment based on type I photodynamic therapy and glutathione peroxidase 4 (GPX4)-targeted ferroptosis. ENBS-ML210 can promote lipid peroxidation and reduce GPX4 expression by generating superoxide anion radicals under 660 nm light irradiation, which reverses the hypoxia-induced resistance of ferroptosis and effectively kills H1299 tumor cells. Finally, the excellent synergistic antitumor effects are confirmed both in vitro and in vivo. We firmly believe that this method will provide a new direction for the clinical treatment of NSCLC in the future.

Multiplex immunofluorescence and single-cell transcriptomic profiling reveal the spatial cell interaction networks in the non-small cell lung cancer microenvironment.

Conventional 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. Tissue 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. Spatial 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). We developed a framework to analyse the cell interaction networks in tumour microenvironment, revealing the spatial architecture and intricate interplays between immune and tumour cells.