Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma

PD-L1 promotes oncolytic virus infection via a metabolic shift that inhibits the type I IFN pathway

While conventional wisdom initially postulated that PD-L1 serves as the inert ligand for PD-1, an emerging body of literature suggests that PD-L1 has cell-intrinsic functions in immune and cancer cells. In line with these studies, here we show that engagement of PD-L1 via cellular ligands or agonistic antibodies, including those used in the clinic, potently inhibits the type I interferon pathway in cancer cells. Hampered type I interferon responses in PD-L1-expressing cancer cells resulted in enhanced efficacy of oncolytic viruses in vitro and in vivo. Consistently, PD-L1 expression marked tumor explants from cancer patients that were best infected by oncolytic viruses. Mechanistically, PD-L1 promoted a metabolic shift characterized by enhanced glycolysis rate that resulted in increased lactate production. In turn, lactate inhibited type I IFN responses. In addition to adding mechanistic insight into PD-L1 intrinsic function, our results will also help guide the numerous ongoing efforts to combine PD-L1 antibodies with oncolytic virotherapy in clinical trials.

Decoupling the correlation between cytotoxic and exhausted T lymphocyte states enhances melanoma immunotherapy response prediction

Cytotoxic T lymphocyte (CTL) and terminal exhausted T lymphocyte (ETL) activities crucially influence immune checkpoint inhibitor (ICI) response. Despite this, the efficacy of ETL and CTL transcriptomic signatures for response prediction remains limited. Investigating this across the TCGA and publicly available single-cell cohorts, we find a strong positive correlation between ETL and CTL expression signatures in most cancers. We hence posited that their limited predictability arises due to their mutually canceling effects on ICI response. Thus, we developed DETACH, a computational method to identify a gene set whose expression pinpoints to a subset of melanoma patients where the CTL and ETL correlation is low. DETACH enhances CTL's prediction accuracy, outperforming existing signatures. DETACH signature genes activity also demonstrates a positive correlation with lymphocyte infiltration and the prevalence of reactive T cells in the tumor microenvironment (TME), advancing our understanding of the CTL cell state within the TME.

TMPRSS2 is a tumor suppressor and its downregulation promotes antitumor immunity and immunotherapy response in lung adenocarcinoma

BACKGROUND

TMPRSS2, a key molecule for SARS-CoV-2 invading human host cells, has an association with cancer. However, its association with lung cancer remains insufficiently unexplored.

METHODS

In five bulk transcriptomics datasets, one single-cell RNA sequencing (scRNA-seq) dataset and one proteomics dataset for lung adenocarcinoma (LUAD), we explored associations between TMPRSS2 expression and immune signatures, tumor progression phenotypes, genomic features, and clinical prognosis in LUAD by the bioinformatics approach. Furthermore, we performed experimental validation of the bioinformatics findings.

RESULTS

TMPRSS2 expression levels correlated negatively with the enrichment levels of both immune-stimulatory and immune-inhibitory signatures, while they correlated positively with the ratios of immune-stimulatory/immune-inhibitory signatures. It indicated that TMPRSS2 levels had a stronger negative correlation with immune-inhibitory than with immune-stimulatory signatures. TMPRSS2 downregulation correlated with increased proliferation, stemness, genomic instability, tumor progression, and worse survival in LUAD. We further validated that TMPRSS2 was downregulated with tumor progression in the LUAD cohort we collected from Jiangsu Cancer Hospital, China. In vitro and in vivo experiments verified the association of TMPRSS2 deficiency with increased tumor cell proliferation and invasion and antitumor immunity in LUAD. Moreover, in vivo experiments demonstrated that TMPRSS2-knockdown tumors were more sensitive to BMS-1, an inhibitor of PD-1/PD-L1.

CONCLUSIONS

TMPRSS2 is a tumor suppressor, while its downregulation is a positive biomarker of immunotherapy in LUAD. Our data provide a potential link between lung cancer and pneumonia caused by SARS-CoV-2 infection.

The temporal progression of lung immune remodeling during breast cancer metastasis

Tumor metastasis requires systemic remodeling of distant organ microenvironments that impacts immune cell phenotypes, population structure, and intercellular communication. However, our understanding of immune phenotypic dynamics in the metastatic niche remains incomplete. Here, we longitudinally assayed lung immune transcriptional profiles in the polyomavirus middle T antigen (PyMT) and 4T1 metastatic breast cancer models from primary tumorigenesis, through pre-metastatic niche formation, to the final stages of metastatic outgrowth at single-cell resolution. Computational analyses of these data revealed a TLR-NFκB inflammatory program enacted by both peripherally derived and tissue-resident myeloid cells that correlated with pre-metastatic niche formation and mirrored CD14+ "activated" myeloid cells in the primary tumor. Moreover, we observed that primary tumor and metastatic niche natural killer (NK) cells are differentially regulated in mice and human patient samples, with the metastatic niche featuring elevated cytotoxic NK cell proportions. Finally, we identified cell-type-specific dynamic regulation of IGF1 and CCL6 signaling during metastatic progression that represents anti-metastatic immunotherapy candidate pathways.

A developmental constraint model of cancer cell states and tumor heterogeneity

Cancer is a disease that stems from a fundamental liability inherent to multicellular life forms in which an individual cell is capable of reneging on the interests of the collective organism. Although cancer is commonly described as an evolutionary process, a less appreciated aspect of tumorigenesis may be the constraints imposed by the organism's developmental programs. Recent work from single-cell transcriptomic analyses across a range of cancer types has revealed the recurrence, plasticity, and co-option of distinct cellular states among cancer cell populations. Here, we note that across diverse cancer types, the observed cell states are proximate within the developmental hierarchy of the cell of origin. We thus posit a model by which cancer cell states are directly constrained by the organism's "developmental map." According to this model, a population of cancer cells traverses the developmental map, thereby generating a heterogeneous set of states whose interactions underpin emergent tumor behavior.

SIRPG expression positively associates with an inflamed tumor microenvironment and response to PD-1 blockade

BACKGROUND

This study aimed to investigate the relationship between signal regulatory protein gamma (SIRPG) and tumor immune microenvironment phenotypes or T cell mediated-adaptive antitumor immunity, and its predictive value for response to PD-1 blockade in cancers.

METHODS

Pan-cancer analysis of SIRPG expression and immune deconvolution was performed using transcriptomic data across 33 tumor types. Transcriptomic and clinical data from 157 patients with non-small-cell lung cancer (NSCLC) and melanoma received PD-1 blockade were analyzed. Expression characteristics of SIRPG were investigated using single-cell RNA sequencing (scRNA-seq) data of 103,599 cells. The effect of SIRPG expression was evaluated via SIRPG knockdown or overexpression in Jurkat T cells.

RESULTS

The results showed that most cancers with high SIRPG expression had significantly higher abundance of T cells, B cells, NK cells, M1 macrophages and cytotoxic lymphocytes and increased expression level of immunomodulatory factors regulating immune cell recruitment, antigen presentation, T cell activation and cytotoxicity, but markedly lower abundance of neutrophils, M2 macrophages, and myeloid-derived suppressor cells. High SIRPG expression was associated with favorable response to PD-1 blockade in both NSCLC and melanoma. scRNA-seq data suggested SIRPG was mainly expressed in CD8+ exhausted T and CD4+ regulatory T cells, and positively associated with immune checkpoint expression including PDCD1 and CTLA4. In vitro test showed SIRPG expression in T cells could facilitate expression of PDCD1 and CTLA4.

CONCLUSION

High SIRPG expression is associated with an inflamed immune phenotype in cancers and favorable response to PD-1 blockade, suggesting it would be a promising predictive biomarker for PD-1 blockade and novel immunotherapeutic target.

Biomaterials-mediated radiation-induced diseases treatment and radiation protection

In recent years, the intersection of the academic and medical domains has increasingly spotlighted the utilization of biomaterials in radioactive disease treatment and radiation protection. Biomaterials, distinguished from conventional molecular pharmaceuticals, offer a suite of advantages in addressing radiological conditions. These include their superior biological activity, chemical stability, exceptional histocompatibility, and targeted delivery capabilities. This review comprehensively delineates the therapeutic mechanisms employed by various biomaterials in treating radiological afflictions impacting the skin, lungs, gastrointestinal tract, and hematopoietic systems. Significantly, these nanomaterials function not only as efficient drug delivery vehicles but also as protective agents against radiation, mitigating its detrimental effects on the human body. Notably, the strategic amalgamation of specific biomaterials with particular pharmacological agents can lead to a synergistic therapeutic outcome, opening new avenues in the treatment of radiation- induced diseases. However, despite their broad potential applications, the biosafety and clinical efficacy of these biomaterials still require in-depth research and investigation. Ultimately, this review aims to not only bridge the current knowledge gaps in the application of biomaterials for radiation-induced diseases but also to inspire future innovations and research directions in this rapidly evolving field.

Contribution of crosstalk of mesothelial and tumoral epithelial cells in pleural metastasis of lung cancer

Background

Tumor metastasis commonly affects pleura in advanced lung cancer and results in malignant pleural effusion (MPE). MPE is related to poor prognosis, but without systematic investigation on different cell types and their crosstalk at single cell resolution.

Methods

We conducted single-cell RNA-sequencing (scRNA-seq) of lung cancer patients with pleural effusion. Next, our data were integrated with 5 datasets derived from individuals under normal, non-malignant disease and lung carcinomatous conditions. Mesothelial cells were re-clustered and their interactions with epithelial cells were comprehensively analyzed. Taking advantage of inferred ligand-receptor pairs, a prediction model of prognosis was constructed. The co-culture of mesothelial cells and malignant epithelial cells in vitro and RNA-seq was performed. Epidermal growth factor receptor (EGFR) antagonist cetuximab was utilized to prevent the lung cancer cells' invasiveness. Spatial distribution of cells in lung adenocarcinoma patients' samples were also analyzed to validate our findings.

Results

The most distinctive transcriptome profiles between tumor and control were revealed in mesothelial cells, which is the predominate cell type of pleura. Five subtypes were divided, including one predominately identified in MPE which was characterized by enriched cancer-related pathways (e.g., cell migration) along evolutionary trajectory from normal mesothelial cells. Cancer-associated mesothelial cells (CAMCs) exhibited varied interactions with different subtypes of malignant epithelial cells, and multiple ligands/receptors exhibited significant correlation with poor prognosis. Experimentally, mesothelial cells can increase the migration ability of lung cancer cells through co-culturing. EGFR was the only affected gene in cancer cells that exhibited interaction with mesothelial cells and was associated with poor prognosis. Using EGFR antagonist cetuximab prevented the lung cancer cells' increased invasiveness caused by mesothelial cells. Moreover, epithelial mitogen (EPGN)-EGFR interaction was supported through spatial distribution analysis, revealing the significant proximity between EPGN+ mesothelial cells and EGFR+ epithelial cells.

Conclusions

Our findings highlighted the important role of mesothelial cells and their interactions with cancer cells in pleural metastasis of lung cancer, providing potential targets for treatment.

PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data

Single-cell RNA sequencing has revealed cellular heterogeneity in complex tissues, notably benefiting research on diseases such as cancer. However, the integration of single-cell data from small samples with extensive clinical features in bulk data remains underexplored. In this study, we introduce PIPET, an algorithmic method for predicting relevant subpopulations in single-cell data based on multivariate phenotypic information from bulk data. PIPET generates feature vectors for each phenotype from differentially expressed genes in bulk data and then identifies relevant cellular subpopulations by assessing the similarity between single-cell data and these vectors. Subsequently, phenotype-related cell states can be analyzed based on these subpopulations. In simulated datasets, PIPET showed robust performance in predicting multiclassification cellular subpopulations. Application of PIPET to lung adenocarcinoma single-cell RNA sequencing data revealed cellular subpopulations with poor survival and associations with TP53 mutations. Similarly, in breast cancer single-cell data, PIPET identified cellular subpopulations associated with the PAM50 clinical subtypes and triple-negative breast cancer subtypes. Overall, PIPET effectively identified relevant cellular subpopulations in single-cell data, guided by phenotypic information from bulk data. This approach comprehensively delineates the molecular characteristics of each cellular subpopulation, offering insights into disease-related subpopulations and guiding personalized treatment strategies.

Single-cell and spatial transcriptomics analysis of non-small cell lung cancer

Lung cancer is the second most frequently diagnosed cancer and the leading cause of cancer-related mortality worldwide. Tumour ecosystems feature diverse immune cell types. Myeloid cells, in particular, are prevalent and have a well-established role in promoting the disease. In our study, we profile approximately 900,000 cells from 25 treatment-naive patients with adenocarcinoma and squamous-cell carcinoma by single-cell and spatial transcriptomics. We note an inverse relationship between anti-inflammatory macrophages and NK cells/T cells, and with reduced NK cell cytotoxicity within the tumour. While we observe a similar cell type composition in both adenocarcinoma and squamous-cell carcinoma, we detect significant differences in the co-expression of various immune checkpoint inhibitors. Moreover, we reveal evidence of a transcriptional "reprogramming" of macrophages in tumours, shifting them towards cholesterol export and adopting a foetal-like transcriptional signature which promotes iron efflux. Our multi-omic resource offers a high-resolution molecular map of tumour-associated macrophages, enhancing our understanding of their role within the tumour microenvironment.

HIF2A mediates lineage transition to aggressive phenotype of cancer-associated fibroblasts in lung cancer brain metastasis

Brain metastasis is the most devasting form of lung cancer. Recent studies highlight significant differences in the tumor microenvironment (TME) between lung cancer brain metastasis (LCBM) and primary lung cancer, which contribute significantly to tumor progression and drug resistance. Cancer-associated fibroblasts (CAFs) are the major component of pro-tumor TME with high plasticity. However, the lineage composition and function of CAFs in LCBM remain elusive. By reanalyzing single-cell RNA sequencing (scRNA-seq) data (GSE131907) from lung cancer patients with different stages of metastasis comprising primary lesions and brain metastasis, we found that CAFs undergo distinctive lineage transition during LCBM under a hypoxic situation, which is directly driven by hypoxia-induced HIF-2α activation. Transited CAFs enhance angiogenesis through VEGF pathways, trigger metabolic reprogramming, and promote the growth of tumor cells. Bulk RNA sequencing data was utilized as validation cohorts. Multiplex immunohistochemistry (mIHC) assay was performed on four paired samples of brain metastasis and their primary lung cancer counterparts to validate the findings. Our study revealed a novel mechanism of lung cancer brain metastasis featuring HIF-2α-induced lineage transition and functional alteration of CAFs, which offers potential therapeutic targets.

Inflammation-based lung adenocarcinoma molecular subtype identification and construction of an inflammation-related signature with bulk and single-cell RNA-seq data

The role of inflammation is increasingly understood to have a central influence on therapeutic outcomes and prognosis in lung adenocarcinoma (LUAD). However, the detailed molecular divisions involved in inflammatory responses are yet to be fully elucidated. Our study identified two main inflammation-oriented LUAD grades: the inflammation-low (INF-low) and the inflammation-high (INF-high) subtypes. Both presented with unique clinicopathological features, implications for prognosis, and distinctive tumor microenvironment profiles. Broadly, the INF-low grade, marked by its dominant immunosuppressive tumor microenvironment, was accompanied by less favorable prognostic outcomes and a heightened prevalence of oncogenic mutations. In contrast, the INF-high grade exhibited more optimistic clinical trajectories, underscored by its immune-active environment. In addition, our efforts led to the conceptualization and empirical validation of an inflammation-centric predictive model with considerable predictive potency. Our study paves the way for a refined inflammation-centric LUAD classification and fosters a deeper understanding of tumor microenvironment intricacies.

Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery

Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.

SFXN1-mediated immune cell infiltration and tumorigenesis in lung adenocarcinoma: A potential therapeutic target

BACKGROUND

Sideroflexin 1 (SFXN1), a mitochondrial serine transporter implicated in one-carbon metabolism, is a prognostic biomarker in lung adenocarcinoma (LUAD). However, its role in LUAD progression remains elusive. This study aimed to investigate the functional significance of SFXN1 in LUAD and evaluate its potential as a therapeutic target.

METHODS

We analyzed SFXN1 expression and its diagnostic and prognostic value in LUAD using the Pan-cancer TCGA dataset. In vitro assays (CCK-8, cell cycle, EDU, wound-healing, and transwell) were employed to assess the role of SFXN1, complemented by in vivo experiments. RNA sequencing elucidated SFXN1-mediated cellular functions and potential mechanisms. Bulk RNA-seq and scRNA-seq data from TCGA and GEO were used to investigate the correlation between SFXN1 and the tumor immune microenvironment. RT-qPCR, Western blot, and IHC assays validated SFXN1 expression and its impact on the immune microenvironment in LUAD.

RESULTS

SFXN1 was upregulated in LUAD tissues and associated with poor prognosis. RNA-seq and scRNA-seq analyses revealed increased SFXN1 expression in tumor cells, accompanied by decreased infiltration of NK and cytotoxic T cells. SFXN1 knockdown significantly reduced cell proliferation and migration, and the inhibition of ERK phosphorylation and CCL20 expression may be the molecular mechanism involved. In vivo, targeting SFXN1 decreased Tregs infiltration and inhibited tumor growth.

CONCLUSIONS

Our findings suggest that SFXN1 may be a potential therapeutic target for LUAD treatment.

BiSpec Pairwise AI: guiding the selection of bispecific antibody target combinations with pairwise learning and GPT augmentation

PURPOSE

Bispecific antibodies (BsAbs), capable of targeting two antigens simultaneously, represent a significant advancement by employing dual mechanisms of action for tumor suppression. However, how to pair targets to develop effective and safe bispecific drugs is a major challenge for pharmaceutical companies.

METHODS

Using machine learning models, we refined the biological characteristics of currently approved or in clinical development BsAbs and analyzed hundreds of membrane proteins as bispecific targets to predict the likelihood of successful drug development for various target combinations. Moreover, to enhance the interpretability of prediction results in bispecific target combination, we combined machine learning models with Large Language Models (LLMs). Through a Retrieval-Augmented Generation (RAG) approach, we supplement each pair of bispecific targets' machine learning prediction with important features and rationales, generating interpretable analytical reports.

RESULTS

In this study, the XGBoost model with pairwise learning was employed to predict the druggability of BsAbs. By analyzing extensive data on BsAbs and designing features from perspectives such as target activity, safety, cell type specificity, pathway mechanism, and gene embedding representation, our model is able to predict target combinations of BsAbs with high market potential. Specifically, we integrated XGBoost with the GPT model to discuss the efficacy of each bispecific target pair, thereby aiding the decision-making for drug developers.

CONCLUSION

The novelty of this study lies in the integration of machine learning and GPT techniques to provide a novel framework for the design of BsAbs drugs. This holistic approach not only improves prediction accuracy, but also enhances the interpretability and innovativeness of drug design.

A blueprint for tumor-infiltrating B cells across human cancers

B lymphocytes are essential mediators of humoral immunity and play multiple roles in human cancer. To decode the functions of tumor-infiltrating B cells, we generated a B cell blueprint encompassing single-cell transcriptome, B cell-receptor repertoire, and chromatin accessibility data across 20 different cancer types (477 samples, 269 patients). B cells harbored extraordinary heterogeneity and comprised 15 subsets, which could be grouped into two independent developmental paths (extrafollicular versus germinal center). Tumor types grouped into the extrafollicular pathway were linked with worse clinical outcomes and resistance to immunotherapy. The dysfunctional extrafollicular program was associated with glutamine-derived metabolites through epigenetic-metabolic cross-talk, which promoted a T cell-driven immunosuppressive program. These data suggest an intratumor B cell balance between extrafollicular and germinal-center responses and suggest that humoral immunity could possibly be harnessed for B cell-targeting immunotherapy.

Single-Cell Analysis Identifies NOTCH3-Mediated Interactions between Stromal Cells That Promote Microenvironment Remodeling and Invasion in Lung Adenocarcinoma

Cancer immunotherapy has revolutionized the treatment of lung adenocarcinoma (LUAD); however, a significant proportion of patients do not respond. Recent transcriptomic studies to understand determinants of immunotherapy response have pinpointed stromal-mediated resistance mechanisms. To gain a better understanding of stromal biology at the cellular and molecular level in LUAD, we performed single-cell RNA sequencing of 256,379 cells, including 13,857 mesenchymal cells, from 9 treatment-naïve patients. Among the mesenchymal cell subsets, FAP+PDPN+ cancer-associated fibroblasts (CAF) and ACTA2+MCAM+ pericytes were enriched in tumors and differentiated from lung-resident fibroblasts. Imaging mass cytometry revealed that both subsets were topographically adjacent to the perivascular niche and had close spatial interactions with endothelial cells (EC). Modeling of ligand and receptor interactomes between mesenchymal and ECs identified that NOTCH signaling drives these cell-to-cell interactions in tumors, with pericytes and CAFs as the signal receivers and arterial and PLVAPhigh immature neovascular ECs as the signal senders. Either pharmacologically blocking NOTCH signaling or genetically depleting NOTCH3 levels in mesenchymal cells significantly reduced collagen production and suppressed cell invasion. Bulk RNA sequencing data demonstrated that NOTCH3 expression correlated with poor survival in stroma-rich patients and that a T cell-inflamed gene signature only predicted survival in patients with low NOTCH3. Collectively, this study provides valuable insights into the role of NOTCH3 in regulating tumor stroma biology, warranting further studies to elucidate the clinical implications of targeting NOTCH3 signaling.

SIGNIFICANCE

NOTCH3 signaling activates tumor-associated mesenchymal cells, increases collagen production, and augments cell invasion in lung adenocarcinoma, suggesting its critical role in remodeling tumor stroma.

Brain Metastasis from EGFR-Mutated Non-Small Cell Lung Cancer: Secretion of IL11 from Astrocytes Up-Regulates PDL1 and Promotes Immune Escape

Patients who have non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutations are more prone to brain metastasis (BM) and poor prognosis. Previous studies showed that the tumor microenvironment of BM in these patients is immunosuppressed, as indicated by reduced T-cell abundance and activity, although the mechanism of this immunosuppression requires further study. This study shows that reactive astrocytes play a critical role in promoting the immune escape of BM from EGFR-mutated NSCLC by increasing the apoptosis of CD8+ T lymphocytes. The increased secretion of interleukin 11(IL11) by astrocytes promotes the expression of PDL1 in BM, and this is responsible for the increased apoptosis of T lymphocytes. IL11 functions as a ligand of EGFR, and this binding activates EGFR and downstream signaling to increase the expression of PDL1, culminating in the immune escape of tumor cells. IL11 also promotes immune escape by binding to its intrinsic receptor (IL11Rα/glycoprotein 130 [gp130]). Additional in vivo studies show that the targeted inhibition of gp130 and EGFR suppresses the growth of BM and prolongs the survival time of mice. These results suggest a novel therapeutic strategy for treatment of NSCLC patients with EGFR mutations.

Patient-derived tumor-like cell clusters for personalized chemo- and immunotherapies in non-small cell lung cancer

Many patient-derived tumor models have emerged recently. However, their potential to guide personalized drug selection remains unclear. Here, we report patient-derived tumor-like cell clusters (PTCs) for non-small cell lung cancer (NSCLC), capable of conducting 100-5,000 drug tests within 10 days. We have established 283 PTC models with an 81% success rate. PTCs contain primary tumor epithelium self-assembled with endogenous stromal and immune cells and show a high degree of similarity to the original tumors in phenotypic and genotypic features. Utilizing standardized culture and drug-response assessment protocols, PTC drug-testing assays reveal 89% overall consistency in prospectively predicting clinical outcomes, with 98.1% accuracy distinguishing complete/partial response from progressive disease. Notably, PTCs enable accurate prediction of clinical outcomes for patients undergoing anti-PD1 therapy by combining cell viability and IFN-γ value assessments. These findings suggest that PTCs could serve as a valuable preclinical model for personalized medicine and basic research in NSCLC.

ScRNA-seq of gastric cancer tissues reveals differences in the immune microenvironment of primary tumors and metastases

Gastric carcinoma (GC) is regarded as one of the deadliest cancer characterized by diversity and haste metastasis and suffers limited understanding of the spatial variation between primary and metastatic GC tumors. In this project, transcriptome analysis on 46 primary tumorous, adjacent non-tumorous, and metastatic GC tissues was performed. The results demonstrated that metastatic tumorous tissues had diminished CD8+ T cells compared to primary tumors, which is mechanistically attributed to being due to innate immunity differences represented by marked differences in macrophages between metastatic and primary tumors, particularly those expressing ApoE, where their abundance is linked to unfavorable prognoses. Examining variations in gene expression and interactions indicated possible strategies of immune evasion hindering the growth of CD8+ T cells in metastatic tumor tissues. More insights could be gained into the immune evasion mechanisms by portraying information about the GC ecosystem.

Insights and Opportunity Costs in Applying Spatial Biology to Study the Tumor Microenvironment

SUMMARY

The recent development of high-dimensional spatial omics tools has revealed the functional importance of the tumor microenvironment in driving tumor progression. Here, we discuss practical factors to consider when designing a spatial biology cohort and offer perspectives on the future of spatial biology research.

Single-cell RNA sequencing reveals myeloid and T cell co-stimulation mediated by IL-7 anti-cancer immunotherapy

BACKGROUND

Immune checkpoint inhibitors unleash inhibitory signals on T cells conferred by tumors and surrounding stromal cells. Despite the clinical efficacy of checkpoint inhibitors, the lack of target expression and persistence of immunosuppressive cells limit the pervasive effectiveness of the therapy. These limitations may be overcome by alternative approaches that co-stimulate T cells and the immune microenvironment.

METHODS

We analyzed single-cell RNA sequencing data from multiple human cancers and a mouse tumor transplant model to discover the pleiotropic expression of the Interleukin 7 (IL-7) receptor on T cells, macrophages, and dendritic cells.

RESULTS

Our experiment on the mouse model demonstrated that recombinant IL-7 therapy induces tumor regression, expansion of effector CD8 T cells, and pro-inflammatory activation of macrophages. Moreover, spatial transcriptomic data support immunostimulatory interactions between macrophages and T cells.

CONCLUSION

These results indicate that IL-7 therapy induces anti-tumor immunity by activating T cells and pro-inflammatory myeloid cells, which may have diverse therapeutic applicability.

Single-cell and single-nucleus RNA-sequencing from paired normal-adenocarcinoma lung samples provide both common and discordant biological insights

Whether single-cell RNA-sequencing (scRNA-seq) captures the same biological information as single-nucleus RNA-sequencing (snRNA-seq) remains uncertain and likely to be context-dependent. Herein, a head-to-head comparison was performed in matched normal-adenocarcinoma human lung samples to assess biological insights derived from scRNA-seq versus snRNA-seq and better understand the cellular transition that occurs from normal to tumoral tissue. Here, the transcriptome of 160,621 cells/nuclei was obtained. In non-tumor lung, cell type proportions varied widely between scRNA-seq and snRNA-seq with a predominance of immune cells in the former (81.5%) and epithelial cells (69.9%) in the later. Similar results were observed in adenocarcinomas, in addition to an overall increase in cell type heterogeneity and a greater prevalence of copy number variants in cells of epithelial origin, which suggests malignant assignment. The cell type transition that occurs from normal lung tissue to adenocarcinoma was not always concordant whether cells or nuclei were examined. As expected, large differential expression of the whole-cell and nuclear transcriptome was observed, but cell-type specific changes of paired normal and tumor lung samples revealed a set of common genes in the cells and nuclei involved in cancer-related pathways. In addition, we showed that the ligand-receptor interactome landscape of lung adenocarcinoma was largely different whether cells or nuclei were evaluated. Immune cell depletion in fresh specimens partly mitigated the difference in cell type composition observed between cells and nuclei. However, the extra manipulations affected cell viability and amplified the transcriptional signatures associated with stress responses. In conclusion, research applications focussing on mapping the immune landscape of lung adenocarcinoma benefit from scRNA-seq in fresh samples, whereas snRNA-seq of frozen samples provide a low-cost alternative to profile more epithelial and cancer cells, and yield cell type proportions that more closely match tissue content.

Immune cell-related prognostic risk model and tumor immune environment modulation in esophageal carcinoma based on single-cell and bulk RNA sequencing

BACKGROUND

Immune cells play a pivotal role in the tumor microenvironment, exerting significant influence on tumor progression and patient outcomes, but the current biomarkers are insufficient to fully capture the complex and diverse tumor immune microenvironment and the impact of immunotherapy.

METHODS

The advent of single-cell sequencing allows us to explore the tumor microenvironment at an unprecedented resolution, enabling the identification and characterization of distinct subsets of immune cells, thereby paving the way for the development of prognostic models using immune cells. Leveraging single-cell data, our study deeply investigated the intricacies of immune microenvironment heterogeneity in esophageal carcinoma.

RESULTS

We elucidated the composition, functionality, evolution, and intercellular communication patterns of immune cells, culminating in the construction of an independent prognostic model at the single-cell level. Furthermore, we conducted a comprehensive analysis of disparities in immune infiltration and immune checkpoint expression between patients categorized into high- and low-risk groups, which may impact patient prognosis.

CONCLUSION

In summary, our study harnessed multiomics data to delineate the immune profile of esophageal carcinoma patients, provide a method for leveraging molecular signatures of immune cells to identify potential biomarkers, while concurrently providing evidence for the potential benefits of immunotherapy.

PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.

Identification of a set of genes potentially responsible for resistance to ferroptosis in lung adenocarcinoma cancer stem cells

Scientific literature supports the evidence that cancer stem cells (CSCs) retain inside low reactive oxygen species (ROS) levels and are, therefore, less susceptible to cell death, including ferroptosis, a type of cell death dependent on iron-driven lipid peroxidation. A collection of lung adenocarcinoma (LUAD) primary cell lines derived from malignant pleural effusions (MPEs) of patients was used to obtain 3D spheroids enriched for stem-like properties. We observed that the ferroptosis inducer RSL3 triggered lipid peroxidation and cell death in LUAD cells when grown in 2D conditions; however, when grown in 3D conditions, all cell lines underwent a phenotypic switch, exhibiting substantial resistance to RSL3 and, therefore, protection against ferroptotic cell death. Interestingly, this phenomenon was reversed by disrupting 3D cells and growing them back in adherence, supporting the idea of CSCs plasticity, which holds that cancer cells have the dynamic ability to transition between a CSC state and a non-CSC state. Molecular analyses showed that ferroptosis resistance in 3D spheroids correlated with an increased expression of antioxidant genes and high levels of proteins involved in iron storage and export, indicating protection against oxidative stress and low availability of iron for the initiation of ferroptosis. Moreover, transcriptomic analyses highlighted a novel subset of genes commonly modulated in 3D spheroids and potentially capable of driving ferroptosis protection in LUAD-CSCs, thus allowing to better understand the mechanisms of CSC-mediated drug resistance in tumors.

A Pathologically Friendly Strategy for Determining the Organ-specific Spatial Tumor Microenvironment Topology in Lung Adenocarcinoma Through the Integration of snRandom-seq and Imaging Mass Cytometry

Heterogeneous organ-specific responses to immunotherapy exist in lung cancer. Dissecting tumor microenvironment (TME) can provide new insights into the mechanisms of divergent responses, the process of which remains poor, partly due to the challenges associated with single-cell profiling using formalin-fixed paraffin-embedded (FFPE) materials. In this study, single-cell nuclei RNA sequencing and imaging mass cytometry (IMC) are used to dissect organ-specific cellular and spatial TME based on FFPE samples from paired primary lung adenocarcinoma (LUAD) and metastases. Single-cell analyses of 84 294 cells from sequencing and 250 600 cells from IMC reveal divergent organ-specific immune niches. For sites of LUAD responding well to immunotherapy, including primary LUAD and adrenal gland metastases, a significant enrichment of B, plasma, and T cells is detected. Spatially resolved maps reveal cellular neighborhoods recapitulating functional units of the tumor ecosystem and the spatial proximity of B and CD4+ T cells at immunogenic sites. Various organ-specific densities of tertiary lymphoid structures are observed. Immunosuppressive sites, including brain and liver metastases, are deposited with collagen I, and T cells at these sites highly express TIM-3. This study originally deciphers the single-cell landscape of the organ-specific TME at both cellular and spatial levels for LUAD, indicating the necessity for organ-specific treatment approaches.

Shaoxia: a web-based interactive analysis platform for single cell RNA sequencing data

BACKGROUND

In recent years, Single-cell RNA sequencing (scRNA-seq) is increasingly accessible to researchers of many fields. However, interpreting its data demands proficiency in multiple programming languages and bioinformatic skills, which limited researchers, without such expertise, exploring information from scRNA-seq data. Therefore, there is a tremendous need to develop easy-to-use software, covering all the aspects of scRNA-seq data analysis.

RESULTS

We proposed a clear analysis framework for scRNA-seq data, which emphasized the fundamental and crucial roles of cell identity annotation, abstracting the analysis process into three stages: upstream analysis, cell annotation and downstream analysis. The framework can equip researchers with a comprehensive understanding of the analysis procedure and facilitate effective data interpretation. Leveraging the developed framework, we engineered Shaoxia, an analysis platform designed to democratize scRNA-seq analysis by accelerating processing through high-performance computing capabilities and offering a user-friendly interface accessible even to wet-lab researchers without programming expertise.

CONCLUSION

Shaoxia stands as a powerful and user-friendly open-source software for automated scRNA-seq analysis, offering comprehensive functionality for streamlined functional genomics studies. Shaoxia is freely accessible at http://www.shaoxia.cloud , and its source code is publicly available at https://github.com/WiedenWei/shaoxia .

Spatial genomics: mapping human steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.

Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns.

Unravelling immune microenvironment features underlying tumor progression in the single-cell era

The relationship between the immune cell and tumor occurrence and progression remains unclear. Profiling alterations in the tumor immune microenvironment (TIME) at high resolution is crucial to identify factors influencing cancer progression and enhance the effectiveness of immunotherapy. However, traditional sequencing methods, including bulk RNA sequencing, exhibit varying degrees of masking the cellular heterogeneity and immunophenotypic changes observed in early and late-stage tumors. Single-cell RNA sequencing (scRNA-seq) has provided significant and precise TIME landscapes. Consequently, this review has highlighted TIME cellular and molecular changes in tumorigenesis and progression elucidated through recent scRNA-seq studies. Specifically, we have summarized the cellular heterogeneity of TIME at different stages, including early, late, and metastatic stages. Moreover, we have outlined the related variations that may promote tumor occurrence and metastasis in the single-cell era. The widespread applications of scRNA-seq in TIME will comprehensively redefine the understanding of tumor biology and furnish more effective immunotherapy strategies.

Identification of Lung Adenocarcinoma Subtypes Based on MHC-II Gene Expression Profile and Immunological Analysis

INTRODUCTION

Major histocompatibility complex class II molecule (MHC-II) is pivotal in anti-tumor immunity, and targeting MHC-II in tumors may help improve patient survival. But function of MHC-II in the immunotherapy and prognosis of lung adenocarcinoma (LUAD) patients has not been thoroughly studied and reported.

METHODS

We selected LUAD-related MHC-II genes from public databases based on previous literature reports. We identified different subtypes according to expression differences of these genes in different LUAD samples through cluster analysis. We used R package to conduct a series of analyses on different subtypes, exploring their survival differences, gene expression differences, pathway enrichment differences, and differences in immune characteristics and immune therapy. Finally, we screened potential drugs from the cMAP database.

RESULTS

We identified two MHC-II-related LUAD subtypes. Our analyses presented that patients with cluster2 subtype showed better prognosis, higher immune scores, higher levels of immune cell infiltration and immune function activation. In addition, patients with this subtype had higher immunophenoscore, lower TIDE scores, and DEPTH scores. We also identified 10 small molecule drugs, such as lenalidomide, VX-745, and tyrphostin-AG-1295.

CONCLUSION

Overall, MHC-II is not only a potential biomarker for accurately distinguishing LUAD subtypes but also a predictive factor for their survival. Our study offers novel insights into understanding of impact of MHC-II in LUAD and offers a new perspective for improving the accurate classification of LUAD patients and enhancing drug treatment.

An esophagus cell atlas reveals dynamic rewiring during active eosinophilic esophagitis and remission

Coordinated cell interactions within the esophagus maintain homeostasis, and disruption can lead to eosinophilic esophagitis (EoE), a chronic inflammatory disease with poorly understood pathogenesis. We profile 421,312 individual cells from the esophageal mucosa of 7 healthy and 15 EoE participants, revealing 60 cell subsets and functional alterations in cell states, compositions, and interactions that highlight previously unclear features of EoE. Active disease displays enrichment of ALOX15+ macrophages, PRDM16+ dendritic cells expressing the EoE risk gene ATP10A, and cycling mast cells, with concomitant reduction of TH17 cells. Ligand-receptor expression uncovers eosinophil recruitment programs, increased fibroblast interactions in disease, and IL-9+IL-4+IL-13+ TH2 and endothelial cells as potential mast cell interactors. Resolution of inflammation-associated signatures includes mast and CD4+ TRM cell contraction and cell type-specific downregulation of eosinophil chemoattractant, growth, and survival factors. These cellular alterations in EoE and remission advance our understanding of eosinophilic inflammation and opportunities for therapeutic intervention.

Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages

Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8+ T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8+ T cells, driven by specific stromal cells such as CTHCR1+ fibroblasts. This study also highlights EGFR(L858R) neoantigens in GGO samples, suggesting potential CD8+ T cell activation. Our findings offer valuable insights into lung adenocarcinoma heterogeneity, suggesting avenues for targeted therapies in early-stage disease.

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization

Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq

We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.

Unraveling the tumor immune microenvironment of lung adenocarcinoma using single-cell RNA sequencing

Tumor immune microenvironment (TIME) and its indications for lung cancer patient prognosis and therapeutic response have become new hotspots in cancer research in recent years. Tumor cells, immune cells, various regulatory factors, and their interactions in the TIME have been suggested to commonly influence lung cancer development and therapeutic outcome. The heterogeneity of TIME is composed of dynamic immune-related components, including various cancer cells, immune cells, cytokine/chemokine environments, cytotoxic activity, or immunosuppressive factors. The specific composition of cell subtypes may facilitate or hamper the response to immunotherapy and influence patient prognosis. Various markers have been found to stratify the patient prognosis or predict the therapeutic outcome. In this article, we systematically reviewed the recent advancement of TIME studies in lung adenocarcinoma (LUAD) using single-cell RNA sequencing (scRNA-seq) techniques, with specific focuses on the roles of TIME in LUAD development, TIME heterogeneity, indications of TIME in patient prognosis and therapeutic response during immunotherapy and drug resistance. The main findings in TIME heterogeneity and relevant markers or models for prognosis stratification and response prediction have been summarized. We hope that this review provides an overview of TIME status in LUAD and an inspiration for future development of strategies and biomarkers in LUAD treatment.

A specific enterotype derived from gut microbiome of older individuals enables favorable responses to immune checkpoint blockade therapy

Immunotherapy has revolutionized cancer treatment, but inconsistent responses persist. Our study delves into the intriguing phenomenon of enhanced immunotherapy sensitivity in older individuals with cancers. Through a meta-analysis encompassing 25 small-to-mid-sized trials of immune checkpoint blockade (ICB), we demonstrate that older individuals exhibit heightened responsiveness to ICB therapy. To understand the underlying mechanism, we reanalyze single-cell RNA sequencing (scRNA-seq) data from multiple studies and unveil distinct upregulation of exhausted and cytotoxic T cell markers within the tumor microenvironment (TME) of older patients. Recognizing the potential role of gut microbiota in modulating the efficacy of immunotherapy, we identify an aging-enriched enterotype linked to improved immunotherapy outcomes in older patients. Fecal microbiota transplantation experiments in mice confirm the therapeutic potential of the aging-enriched enterotype, enhancing treatment sensitivity and reshaping the TME. Our discoveries confront the prevailing paradox and provide encouraging paths for tailoring cancer immunotherapy strategies according to an individual's gut microbiome profile.

Anti-EGFR ScFv functionalized exosomes delivering LPCAT1 specific siRNAs for inhibition of lung cancer brain metastases

Brain metastasis (BM) is one of the leading causes of cancer-related deaths in patients with advanced non-small cell lung cancer (NSCLC). However, limited treatments are available due to the presence of the blood-brain barrier (BBB). Upregulation of lysophosphatidylcholine acyltransferase 1 (LPCAT1) in NSCLC has been found to promote BM. Conversely, downregulating LPCAT1 significantly suppresses the proliferation and metastasis of lung cancer cells. In this study, we firstly confirmed significant upregulation of LPCAT1 in BM sites compared to primary lung cancer by analyzing scRNA dataset. We then designed a delivery system based on a single-chain variable fragment (scFv) targeting the epidermal growth factor receptor (EGFR) and exosomes derived from HEK293T cells to enhance cell-targeting capabilities and increase permeability. Next, we loaded LPCAT1 siRNA (siLPCAT1) into these engineered exosomes (exoscFv). This novel scFv-mounted exosome successfully crossed the BBB in an animal model and delivered siLPCAT1 to the BM site. Silencing LPCAT1 efficiently arrested tumor growth and inhibited malignant progression of BM in vivo without detectable toxicity. Overall, we provided a potential platform based on exosomes for RNA interference (RNAi) therapy in lung cancer BM.

The Application of Nanoparticles Targeting Cancer-Associated Fibroblasts

Cancer-associated fibroblasts (CAF) are the most abundant stromal cells in the tumor microenvironment (TME), especially in solid tumors. It has been confirmed that it can not only interact with tumor cells to promote cancer progression and metastasis, but also affect the infiltration and function of immune cells to induce chemotherapy and immunotherapy resistance. So, targeting CAF has been considered an important method in cancer treatment. The rapid development of nanotechnology provides a good perspective to improve the efficiency of targeting CAF. At present, more and more researches have focused on the application of nanoparticles (NPs) in targeting CAF. These studies explored the effects of different types of NPs on CAF and the multifunctional nanomedicines that can eliminate CAF are able to enhance the EPR effect which facilitate the anti-tumor effect of themselves. There also exist amounts of studies focusing on using NPs to inhibit the activation and function of CAF to improve the therapeutic efficacy. The application of NPs targeting CAF needs to be based on an understanding of CAF biology. Therefore, in this review, we first summarized the latest progress of CAF biology, then discussed the types of CAF-targeting NPs and the main strategies in the current. The aim is to elucidate the application of NPs in targeting CAF and provide new insights for engineering nanomedicine to enhance immune response in cancer treatment.

Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy

The organization of immune cells in human tumors is not well understood. Immunogenic tumors harbor spatially localized multicellular 'immunity hubs' defined by expression of the T cell-attracting chemokines CXCL10/CXCL11 and abundant T cells. Here, we examined immunity hubs in human pre-immunotherapy lung cancer specimens and found an association with beneficial response to PD-1 blockade. Critically, we discovered the stem-immunity hub, a subtype of immunity hub strongly associated with favorable PD-1-blockade outcome. This hub is distinct from mature tertiary lymphoid structures and is enriched for stem-like TCF7+PD-1+CD8+ T cells, activated CCR7+LAMP3+ dendritic cells and CCL19+ fibroblasts as well as chemokines that organize these cells. Within the stem-immunity hub, we find preferential interactions between CXCL10+ macrophages and TCF7-CD8+ T cells as well as between mature regulatory dendritic cells and TCF7+CD4+ and regulatory T cells. These results provide a picture of the spatial organization of the human intratumoral immune response and its relevance to patient immunotherapy outcomes.

Lipidomics reveals new lipid-based lung adenocarcinoma early diagnosis model

Lung adenocarcinoma (LUAD) continues to pose a significant mortality risk with a lack of dependable biomarkers for early noninvasive cancer detection. Here, we find that aberrant lipid metabolism is significantly enriched in lung cancer cells. Further, we identified four signature lipids highly associated with LUAD and developed a lipid signature-based scoring model (LSRscore). Evaluation of LSRscore in a discovery cohort reveals a robust predictive capability for LUAD (AUC: 0.972), a result further validated in an independent cohort (AUC: 0.92). We highlight one lipid signature biomarker, PE(18:0/18:1), consistently exhibiting altered levels both in cancer tissue and in plasma of LUAD patients, demonstrating significant predictive power for early-stage LUAD. Transcriptome analysis reveals an association between increased PE(18:0/18:1) levels and dysregulated glycerophospholipid metabolism, which consistently displays strong prognostic value across two LUAD cohorts. The combined utility of LSRscore and PE(18:0/18:1) holds promise for early-stage diagnosis and prognosis of LUAD.

Dendritic cells as orchestrators of anticancer immunity and immunotherapy

Dendritic cells (DCs) are a heterogeneous group of antigen-presenting innate immune cells that regulate adaptive immunity, including against cancer. Therefore, understanding the precise activities of DCs in tumours and patients with cancer is important. The classification of DC subsets has historically been based on ontogeny; however, single-cell analyses are now additionally revealing a diversity of functional states of DCs in cancer. DCs can promote the activation of potent antitumour T cells and immune responses via numerous mechanisms, although they can also be hijacked by tumour-mediated factors to contribute to immune tolerance and cancer progression. Consequently, DC activities are often key determinants of the efficacy of immunotherapies, including immune-checkpoint inhibitors. Potentiating the antitumour functions of DCs or using them as tools to orchestrate short-term and long-term anticancer immunity has immense but as-yet underexploited therapeutic potential. In this Review, we outline the nature and emerging complexity of DC states as well as their functions in regulating adaptive immunity across different cancer types. We also describe how DCs are required for the success of current immunotherapies and explore the inherent potential of targeting DCs for cancer therapy. We focus on novel insights on DCs derived from patients with different cancers, single-cell studies of DCs and their relevance to therapeutic strategies.

Silencing of circ_0088036 inhibits growth and invasion of lung adenocarcinoma through miR-203/SP1 axis

Lung cancer is the leading cause of cancer-related deaths worldwide. Circular RNA (circRNA) circ_0088036 is a recently discovered circRNA known for its roles in rheumatoid arthritis. The study aimed to study the function of circ_0088036 in lung adenocarcinoma (LUAD). Circ_0088036 expressions were analyzed in the Gene Expression Omnibus (GEO) database. The relationship between circ_0088036 expressions and clinicopathological data of LUAD was assessed. The messenger RNA and protein levels were analyzed by quantitative real-time polymerase chain reaction and Western blot. Cell viability, apoptosis, and invasion were tested by Cell Counting Kit-8, flow cytometry, and transwell assay. The direct interaction between microRNA-203 (miR-203) and circ_0088036 or specificity protein 1 (SP1) was confirmed by dual-luciferase reporter assay, RNA pull-down, and RNA immunoprecipitation assays. Circ_0088036 was overexpressed in LUAD from the analysis of the GEO database. The poor prognosis was found in the patients with high expressions of circ_0088036. The level of Circ_0088036 was increased in LUAD tissues and cells. In terms of function, the deletion of circ_0088036 inhibited LUAD tumorigenesis in vitro by repressing cell growth, invasion, and epithelial-mesenchymal transition (EMT). In mechanism, circ_0088036 could competitively sponge miR-203, thereby affecting the expressions of the target gene SP1. In addition, lessening of miR-203 and enlarging of SP1 could eliminate the anticancer effect of short hairpin RNA-circ_0088036 on LUAD cells. Besides, the knockout of circ_0088036 hindered the growth of xenografted tumors in vivo. Circ_0088036 promoted the LUAD cell growth, invasion, and EMT via modulating the miR-203/SP1 axis in LUAD.

Prognostic correlation between specialized capillary endothelial cells and lung adenocarcinoma

Background

In-depth analysis of the functional changes occurring in endothelial cells (ECs) involved in capillary formation can help to elucidate the mechanism of tumour vascular growth.

Methods

Appropriate datasets were retrieved from the GEO database to obtain single-cell data on LUAD samples and adjacent normal tissue samples. ECs were selected by an automatic annotation program in R and further subdivided based on reported EC marker genes. Functional changes in different types of capillary ECs were then visualized, and the concrete expression was classified by genetic data in the TCGA. Finally, a prognostic model was constructed to predict immunoinfiltration status, survival and drug therapy effects.

Results

The LUAD data contained in the GSE183219 dataset were suitable for our analysis. After dimensionality reduction analysis and cell annotation, EC general capillary and EC aerocyte subsets as capillary specialized phenotypes showed a series of functional changes in tumour samples, with a total of 108 genes found to undergo functional changes. Use of CellPhoneDB revealed a close interaction of activity between ECs. After integration of TCGA, GSE68465 and GSE11969 datasets, the genes obtained were analysed by cluster analysis and risk model construction, identifying 8 genes. Drug sensitivity, immune cell and molecular differences can be accurately predicted.

Conclusions

EC general capillary and EC aerocyte subsets are recognized capillary ECs in the tumour microenvironment, and the functional changes between them are relevant to the prognosis and treatment of LUAD patients and have the potential to be used in target therapy.

CRIF1 deficiency induces FOXP3LOW inflammatory non-suppressive regulatory T cells, thereby promoting antitumor immunity

Recently identified human FOXP3lowCD45RA- inflammatory non-suppressive (INS) cells produce proinflammatory cytokines, exhibit reduced suppressiveness, and promote antitumor immunity unlike conventional regulatory T cells (Tregs). In spite of their implication in tumors, the mechanism for generation of FOXP3lowCD45RA- INS cells in vivo is unclear. We showed that the FOXP3lowCD45RA- cells in human tumors demonstrate attenuated expression of CRIF1, a vital mitochondrial regulator. Mice with CRIF1 deficiency in Tregs bore Foxp3lowINS-Tregs with mitochondrial dysfunction and metabolic reprograming. The enhanced glutaminolysis activated α-ketoglutarate-mTORC1 axis, which promoted proinflammatory cytokine expression by inducing EOMES and SATB1 expression. Moreover, chromatin openness of the regulatory regions of the Ifng and Il4 genes was increased, which facilitated EOMES/SATB1 binding. The increased α-ketoglutarate-derived 2-hydroxyglutarate down-regulated Foxp3 expression by methylating the Foxp3 gene regulatory regions. Furthermore, CRIF1 deficiency-induced Foxp3lowINS-Tregs suppressed tumor growth in an IFN-γ-dependent manner. Thus, CRIF1 deficiency-mediated mitochondrial dysfunction results in the induction of Foxp3lowINS-Tregs including FOXP3lowCD45RA- cells that promote antitumor immunity.

Comprehensive Analysis of Lung Adenocarcinoma and Brain Metastasis through Integrated Single-Cell Transcriptomics

Lung adenocarcinoma (LUAD) is a highly prevalent and lethal form of lung cancer, comprising approximately half of all cases. It is often diagnosed at advanced stages with brain metastasis (BM), resulting in high mortality rates. Current BM management involves complex interventions and conventional therapies that offer limited survival benefits with neurotoxic side effects. The tumor microenvironment (TME) is a complex system where cancer cells interact with various elements, significantly influencing tumor behavior. Immunotherapies, particularly immune checkpoint inhibitors, target the TME for cancer treatment. Despite their effectiveness, it is crucial to understand metastatic lung cancer and the specific characteristics of the TME, including cell-cell communication mechanisms, to refine treatments. Herein, we investigated the tumor microenvironment of brain metastasis from lung adenocarcinoma (LUAD-BM) and primary tumors across various stages (I, II, III, and IV) using single-cell RNA sequencing (scRNA-seq) from publicly available datasets. Our analysis included exploring the immune and non-immune cell composition and the expression profiles and functions of cell type-specific genes, and investigating the interactions between different cells within the TME. Our results showed that T cells constitute the majority of immune cells present in primary tumors, whereas microglia represent the most dominant immune cell type in BM. Interestingly, microglia exhibit a significant increase in the COX pathway. Moreover, we have shown that microglia primarily interact with oligodendrocytes and endothelial cells. One significant interaction was identified between DLL4 and NOTCH4, which demonstrated a relevant association between endothelial cells and microglia and between microglia and oligodendrocytes. Finally, we observed that several genes within the HLA complex are suppressed in BM tissue. Our study reveals the complex molecular and cellular dynamics of BM-LUAD, providing a path for improved patient outcomes with personalized treatments and immunotherapies.

Integrative single-cell analysis of LUAD: elucidating immune cell dynamics and prognostic modeling based on exhausted CD8+ T cells

Background

The tumor microenvironment (TME) plays a pivotal role in the progression and metastasis of lung adenocarcinoma (LUAD). However, the detailed characteristics of LUAD and its associated microenvironment are yet to be extensively explored. This study aims to delineate a comprehensive profile of the immune cells within the LUAD microenvironment, including CD8+ T cells, CD4+ T cells, and myeloid cells. Subsequently, based on marker genes of exhausted CD8+ T cells, we aim to establish a prognostic model for LUAD.

Method

Utilizing the Seurat and Scanpy packages, we successfully constructed an immune microenvironment atlas for LUAD. The Monocle3 and PAGA algorithms were employed for pseudotime analysis, pySCENIC for transcription factor analysis, and CellChat for analyzing intercellular communication. Following this, a prognostic model for LUAD was developed, based on the marker genes of exhausted CD8+ T cells, enabling effective risk stratification in LUAD patients. Our study included a thorough analysis to identify differences in TME, mutation landscape, and enrichment across varying risk groups. Moreover, by integrating risk scores with clinical features, we developed a new nomogram. The expression of model genes was validated via RT-PCR, and a series of cellular experiments were conducted, elucidating the potential oncogenic mechanisms of GALNT2.

Results

Our study developed a single-cell atlas for LUAD from scRNA-seq data of 19 patients, examining crucial immune cells in LUAD's microenvironment. We underscored pDCs' role in antigen processing and established a Cox regression model based on CD8_Tex-LAYN genes for risk assessment. Additionally, we contrasted prognosis and tumor environments across risk groups, constructed a new nomogram integrating clinical features, validated the expression of model genes via RT-PCR, and confirmed GALNT2's function in LUAD through cellular experiments, thereby enhancing our understanding and approach to LUAD treatment.

Conclusion

The creation of a LUAD single-cell atlas in our study offered new insights into its tumor microenvironment and immune cell interactions, highlighting the importance of key genes associated with exhausted CD8+ T cells. These discoveries have enabled the development of an effective prognostic model for LUAD and identified GALNT2 as a potential therapeutic target, significantly contributing to the improvement of LUAD diagnosis and treatment strategies.

Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis

Here we demonstrate that the large language model GPT-4 can accurately annotate cell types using marker gene information in single-cell RNA sequencing analysis. When evaluated across hundreds of tissue and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations. This capability can considerably reduce the effort and expertise required for cell type annotation. Additionally, we have developed an R software package GPTCelltype for GPT-4's automated cell type annotation.

Single-cell RNA sequencing reveals recruitment of the M2-like CCL8high macrophages in Lewis lung carcinoma-bearing mice following hypofractionated radiotherapy

BACKGROUND

Tumor-associated macrophages (TAMs) play a pivotal role in reshaping the tumor microenvironment following radiotherapy. The mechanisms underlying this reprogramming process remain to be elucidated.

METHODS

Subcutaneous Lewis lung carcinoma (LLC) murine model was treated with hypofrationated radiotherapy (8 Gy × 3F). Single-cell RNA sequencing was utilized to identify subclusters and functions of TAMs. Multiplex assay and enzyme-linked immunosorbent assay (ELISA) were employed to measure serum chemokine levels. Bindarit was used to inhibit CCL8, CCL7, and CCL2. The infiltration of TAMs after combination treatment with hypofractionated radiotherapy and Bindarit was quantified with flow cytometry, while the influx of CD206 and CCL8 was assessed by immunostaining.

RESULTS

Transcriptome analysis identified a distinct subset of M2-like macrophages characterized by elevated Ccl8 expression level following hypofractionated radiotherapy in LLC-bearing mice. Remarkbly, hypofractionated radiotherapy not only promoted CCL8high macrophages infiltration but also reprogrammed them by upregulating immunosuppressive genes, thereby fostering an immunosuppressive tumor microenvironment. Additioinally, hypofractionated radiotherapy enhanced the CCL signaling pathway, augmenting the pro-tumorigenic functions of CCL8high macrophages and boosting TAMs recruitment. The adjunctive treatment combining hypofractionated radiotherapy with Bindarit effectively reduced M2 macrophages infiltration and prolonged the duration of local tumor control.

CONCLUSIONS

Hypofractionated radiotherapy enhances the infiltration of CCL8high macrophages and amplifies their roles in macrophage recruitment through the CCL signaling pathway, leading to an immunosuppressive tumor microenvironment. These findings highlight the potential of targeting TAMs and introduces a novel combination to improve the efficacy of hypofractionated radiotherapy.