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

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

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

Significance

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

Cellular dynamics in tumour microenvironment along with lung cancer progression underscore spatial and evolutionary heterogeneity of neutrophil

BACKGROUND

The cellular dynamics in the tumour microenvironment (TME) along with non-small cell lung cancer (NSCLC) progression remain unclear.

METHODS

Multiplex immunofluorescence test detecting 10 immune-related markers on 553 primary tumour (PT) samples of NSCLC was conducted and spatial information in TME was assessed by the StarDist depth learning model. The single-cell transcriptomic atlas of PT (n = 4) and paired tumour-draining lymph nodes (TDLNs) (n = 5 for tumour-invaded, n = 3 for tumour-free) microenvironment was profiled. Various bioinformatics analyses based on Gene Expression Omnibus, TCGA and Array-Express databases were also used to validate the discoveries.

RESULTS

Spatial distances of CD4+ T cells-CD38+ T cells, CD4+ T cells-neutrophils and CD38+ T cells-neutrophils prolonged and they were replaced by CD163+ macrophages in PT along with tumour progression. Neutrophils showed unique stage and location-dependent prognostic effects. A high abundance of stromal neutrophils improved disease-free survival in the early-stage, whereas high intratumoural neutrophil infiltrates predicted poor prognosis in the mid-to-late-stage. Significant molecular and functional reprogramming in PT and TDLN microenvironments was observed. Diverse interaction networks mediated by neutrophils were found between positive and negative TDLNs. Five phenotypically and functionally heterogeneous subtypes of tumour-associated neutrophil (TAN) were further identified by pseudotime analysis, including TAN-0 with antigen-presenting function, TAN-1 with strong expression of interferon (IFN)-stimulated genes, the pro-tumour TAN-2 subcluster, the classical subset (TAN-3) and the pro-inflammatory subtype (TAN-4). Loss of IFN-stimulated signature and growing angiogenesis activity were discovered along the transitional trajectory. Eventually, a robust six neutrophil differentiation relevant genes-based model was established, showing that low-risk patients had longer overall survival time and may respond better to immunotherapy.

CONCLUSIONS

The cellular composition, spatial location, molecular and functional changes in PT and TDLN microenvironments along with NSCLC progression were deciphered, highlighting the immunoregulatory roles and evolutionary heterogeneity of TANs.

Regulation of PD-L1 expression in non-small cell lung cancer by interleukin-1β

Introduction

Programmed cell death-ligand 1 (PD-L1) is a biomarker for prediction of the clinical efficacy of immune checkpoint inhibitors in various cancer types. The role of cytokines in regulation of PD-L1 expression in tumor cells has not been fully characterized, however. Here we show that interleukin-1β (IL-1β) plays a key role in regulation of PD-L1 expression in non-small cell lung cancer (NSCLC).

Methods

We performed comprehensive screening of cytokine gene expression in NSCLC tissue using available single-cell RNA-Sequence data. Then we examined the role of IL-1β in vitro to elucidate its induction of PD-L1 on NSCLC cells.

Results

The IL-1β gene is highly expressed in the tumor microenvironment, particularly in macrophages. The combination of IL-1β and interferon-γ (IFN-γ) induced a synergistic increase in PD-L1 expression in NSCLC cell lines. IL-1β and IFN-γ also cooperatively activated mitogen-activated protein kinase (MAPK) signaling and promoted the binding of downstream transcription factors to the PD-L1 gene promoter. Furthermore, inhibitors of MAPK signaling blocked upregulation of PD-L1 by IL-1β and IFN-γ.

Discussion

Our study reports high levels of IL-1β in the tumor microenvironment may cooperate with IFN-γ to induce maximal PD-L1 expression in tumor cells via activation of MAPK signaling, with the IL-1β-MAPK axis being a promising therapeutic target for attenuation of PD-L1-mediated suppression of antitumor immunity.

m6A methylation reader IGF2BP2 activates endothelial cells to promote angiogenesis and metastasis of lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a common type of lung cancer with a high risk of metastasis, but the exact molecular mechanisms of metastasis are not yet understood.

METHODS

This study acquired single-cell transcriptomics profiling of 11 distal normal lung tissues, 11 primary LUAD tissues, and 4 metastatic LUAD tissues from the GSE131907 dataset. The lung multicellular ecosystems were characterized at a single-cell resolution, and the potential mechanisms underlying angiogenesis and metastasis of LUAD were explored.

RESULTS

We constructed a global single-cell landscape of 93,610 cells from primary and metastatic LUAD and found that IGF2BP2 was specifically expressed both in a LUAD cell subpopulation (termed as LUAD_IGF2BP2), and an endothelial cell subpopulation (termed as En_IGF2BP2). The LUAD_IGF2BP2 subpopulation progressively formed and dominated the ecology of metastatic LUAD during metastatic evolution. IGF2BP2 was preferentially secreted by exosomes in the LUAD_IGF2BP2 subpopulation, which was absorbed by the En_IGF2BP2 subpopulation in the tumor microenvironment. Subsequently, IGF2BP2 improved the RNA stability of FLT4 through m6A modification, thereby activating the PI3K-Akt signaling pathway, and eventually promoting angiogenesis and metastasis. Analysis of clinical data showed that IGF2BP2 was linked with poor overall survival and relapse-free survival for LUAD patients.

CONCLUSIONS

Overall, these findings provide a novel insight into the multicellular ecosystems of primary and metastatic LUAD, and demonstrate that a specific LUAD_IGF2BP2 subpopulation is a key orchestrator promoting angiogenesis and metastasis, with implications for the gene regulatory mechanisms of LUAD metastatic evolution, representing themselves as potential antiangiogenic targets.

Understanding tumour endothelial cell heterogeneity and function from single-cell omics

Anti-angiogenic therapies (AATs) are used to treat different types of cancers. However, their success is limited owing to insufficient efficacy and resistance. Recently, single-cell omics studies of tumour endothelial cells (TECs) have provided new mechanistic insight. Here, we overview the heterogeneity of human TECs of all tumour types studied to date, at the single-cell level. Notably, most human tumour types contain varying numbers but only a small population of angiogenic TECs, the presumed targets of AATs, possibly contributing to the limited efficacy of and resistance to AATs. In general, TECs are heterogeneous within and across all tumour types, but comparing TEC phenotypes across tumours is currently challenging, owing to the lack of a uniform nomenclature for endothelial cells and consistent single-cell analysis protocols, urgently raising the need for a more consistent approach. Nonetheless, across most tumour types, universal TEC markers (ACKR1, PLVAP and IGFBP3) can be identified. Besides angiogenesis, biological processes such as immunomodulation and extracellular matrix organization are among the most commonly predicted enriched signatures of TECs across different tumour types. Although angiogenesis and extracellular matrix targets have been considered for AAT (without the hoped success), the immunomodulatory properties of TECs have not been fully considered as a novel anticancer therapeutic approach. Therefore, we also discuss progress, limitations, solutions and novel targets for AAT development.

A transcriptional cell atlas identifies the decline in the AT2 niche in aged human lungs

Aging poses a global public health challenge, associated with molecular and physiological changes in the lungs. It increases susceptibility to acute and chronic lung diseases, yet the underlying molecular and cellular drivers in aged populations are not fully appreciated. To systematically profile the genetic changes associated with age, we present a single-cell transcriptional atlas comprising nearly half a million cells from the healthy lungs of human subjects spanning various ages, sexes, and smoking statuses. Most annotated cell lineages in aged lungs exhibit dysregulated genetic programs. Specifically, the aged alveolar epithelial cells, including both alveolar type II (AT2) and type I (AT1) cells, demonstrate loss of epithelial identities, heightened inflammaging characterized by increased expression of AP-1 transcription factor and chemokine genes, and significantly increased cellular senescence. Furthermore, the aged mesenchymal cells display a remarkable decrease in Collagen and Elastin transcription. The decline of the AT2 niche is further exacerbated by a weakened endothelial cell phenotype and a dysregulated genetic program in macrophages. These findings highlight the dysregulation observed in both AT2 stem cells and their supportive niche cells, potentially contributing to the increased susceptibility of aged populations to lung diseases.

Ubiquitin Ligases Siah1a/2 Control Alveolar Macrophage Functions to Limit Carcinogen-Induced Lung Adenocarcinoma

Cellular components of the tumor microenvironment, including myeloid cells, play important roles in the progression of lung adenocarcinoma (LUAD) and its response to therapy. Here, we characterize the function of the ubiquitin ligases Siah1a/2 in regulating the differentiation and activity of alveolar macrophages (AM) and assess the implication of Siah1a/2 control of AMs for carcinogen-induced LUAD. Macrophage-specific genetic ablation of Siah1a/2 promoted accumulation of AMs with an immature phenotype and increased expression of protumorigenic and pro-inflammatory Stat3 and β-catenin gene signatures. Administration of urethane to wild-type mice promoted enrichment of immature-like AMs and lung tumor development, which was enhanced by macrophage-specific Siah1a/2 ablation. The profibrotic gene signature seen in Siah1a/2-ablated immature-like macrophages was associated with increased tumor infiltration of CD14+ myeloid cells and poorer survival of patients with LUAD. Single-cell RNA-seq confirmed the presence of a cluster of immature-like AMs expressing a profibrotic signature in lungs of patients with LUAD, a signature enhanced in smokers. These findings identify Siah1a/2 in AMs as gatekeepers of lung cancer development.

SIGNIFICANCE

The ubiquitin ligases Siah1a/2 control proinflammatory signaling, differentiation, and profibrotic phenotypes of alveolar macrophages to suppress lung carcinogenesis.

The Brain Pre-Metastatic Niche: Biological and Technical Advancements

Metastasis, particularly brain metastasis, continues to puzzle researchers to this day, and exploring its molecular basis promises to break ground in developing new strategies for combatting this deadly cancer. In recent years, the research focus has shifted toward the earliest steps in the formation of metastasis. In this regard, significant progress has been achieved in understanding how the primary tumor affects distant organ sites before the arrival of tumor cells. The term pre-metastatic niche was introduced for this concept and encompasses all influences on sites of future metastases, ranging from immunological modulation and ECM remodeling to the softening of the blood-brain barrier. The mechanisms governing the spread of metastasis to the brain remain elusive. However, we begin to understand these processes by looking at the earliest steps in the formation of metastasis. This review aims to present recent findings on the brain pre-metastatic niche and to discuss existing and emerging methods to further explore the field. We begin by giving an overview of the pre-metastatic and metastatic niches in general before focusing on their manifestations in the brain. To conclude, we reflect on the methods usually employed in this field of research and discuss novel approaches in imaging and sequencing.

Tumor-associated fibrosis impairs immune surveillance and response to immune checkpoint blockade in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related deaths. Immune checkpoint blockade has improved survival for many patients with NSCLC, but most fail to obtain long-term benefit. Understanding the factors leading to reduced immune surveillance in NSCLC is critical in improving patient outcomes. Here, we show that human NSCLC harbors large amounts of fibrosis that correlates with reduced T cell infiltration. In murine NSCLC models, the induction of fibrosis led to increased lung cancer progression, impaired T cell immune surveillance, and failure of immune checkpoint blockade efficacy. Associated with these changes, we observed that fibrosis leads to numerically and functionally impaired dendritic cells and altered macrophage phenotypes that likely contribute to immunosuppression. Within cancer-associated fibroblasts, distinct changes within the Col13a1-expressing population suggest that these cells produce chemokines to recruit macrophages and regulatory T cells while limiting recruitment of dendritic cells and T cells. Targeting fibrosis through transforming growth factor-β receptor signaling overcame the effects of fibrosis to enhance T cell responses and improved the efficacy of immune checkpoint blockade but only in the context of chemotherapy. Together, these data suggest that fibrosis in NSCLC leads to reduced immune surveillance and poor responsiveness to checkpoint blockade and highlight antifibrotic therapies as a candidate strategy to overcome immunotherapeutic resistance.

Distinct immune microenvironment of lung adenocarcinoma in never-smokers from smokers

Lung cancer in never-smokers (LCINS) presents clinicopathological and molecular features distinct from that in smokers. Tumor microenvironment (TME) plays important roles in cancer progression and therapeutic response. To decipher the difference in TME between never-smoker and smoker lung cancers, we conduct single-cell RNA sequencing on 165,753 cells from 22 treatment-naive lung adenocarcinoma (LUAD) patients. We find that the dysfunction of alveolar cells induced by cigarette smoking contributes more to the aggressiveness of smoker LUADs, while the immunosuppressive microenvironment exerts more effects on never-smoker LUADs' aggressiveness. Moreover, the SPP1^hi pro macrophage is identified to be another independent source of monocyte-derived macrophage. Importantly, higher expression of immune checkpoint CD47 and lower expression of major histocompatibility complex (MHC)-I in cancer cells of never-smoker LUADs imply that CD47 may be a better immunotherapy target for LCINS. Therefore, this study reveals the difference of tumorigenesis between never-smoker and smoker LUADs and provides a potential immunotherapy strategy for LCINS.

Stromal circuits involving tumor-associated macrophages and cancer-associated fibroblasts

The tumor associated macrophages (TAM) represent one of most abundant subpopulations across several solid cancers and their number/frequency is associated with a poor clinical outcome. It has been clearly demonstrated that stromal cells, such as the cancer associated fibroblasts (CAFs), may orchestrate TAM recruitment, survival and reprogramming. Today, single cell-RNA sequencing (sc-RNA seq) technologies allowed a more granular knowledge about TAMs and CAFs phenotypical and functional programs. In this mini-review we discuss the recent discoveries in the sc-RNA seq field focusing on TAM and CAF identity and their crosstalk in the tumor microenvironment (TME) of solid cancers.

Pan-cancer whole-genome comparison of primary and metastatic solid tumours

Metastatic cancer remains an almost inevitably lethal disease1-3. A better understanding of disease progression and response to therapies therefore remains of utmost importance. Here we characterize the genomic differences between early-stage untreated primary tumours and late-stage treated metastatic tumours using a harmonized pan-cancer analysis (or reanalysis) of two unpaired primary4 and metastatic5 cohorts of 7,108 whole-genome-sequenced tumours. Metastatic tumours in general have a lower intratumour heterogeneity and a conserved karyotype, displaying only a modest increase in mutations, although frequencies of structural variants are elevated overall. Furthermore, highly variable tumour-specific contributions of mutational footprints of endogenous (for example, SBS1 and APOBEC) and exogenous mutational processes (for example, platinum treatment) are present. The majority of cancer types had either moderate genomic differences (for example, lung adenocarcinoma) or highly consistent genomic portraits (for example, ovarian serous carcinoma) when comparing early-stage and late-stage disease. Breast, prostate, thyroid and kidney renal clear cell carcinomas and pancreatic neuroendocrine tumours are clear exceptions to the rule, displaying an extensive transformation of their genomic landscape in advanced stages. Exposure to treatment further scars the tumour genome and introduces an evolutionary bottleneck that selects for known therapy-resistant drivers in approximately half of treated patients. Our data showcase the potential of pan-cancer whole-genome analysis to identify distinctive features of late-stage tumours and provide a valuable resource to further investigate the biological basis of cancer and resistance to therapies.

Transfer learning enables predictions in network biology

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding1,2 and computer vision3 by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets.

Tobacco exposure primes the secretion of CCL21 positively associated with tertiary lymphoid structure and response to immunotherapy

BACKGROUND

It has been reported that smoking history as a predictor of immunotherapy efficacy in patients with advanced lung cancer, however, the underlying mechanisms of this phenomenon remain largely unknown.

METHODS

The patients with lung adenocarcinoma's (LUAD) cohort and the orthotopical transplanted mouse model were used to explore the correlation between smoking status and tertiary lymphoid structure (TLS) and chemokine CCL21, respectively. Cell adhesion and co-immunoprecipitation assays were performed to explore the interaction between CD4+T cells and CD20+B cells under tobacco exposure. Chromatin immunoprecipitation-PCR was used to dissect the mechanism of upregulated CCL21 secretion in tobacco treatment. Serum CCL21 level was recorded in patients with LUAD treated with immunotherapy.

RESULTS

Here we observed that individuals with a smoking history exhibit an increased quantity and maturation level of TLS compared with non-smokers, along with higher levels of CCL21 secretion. Tobacco exposure promoted CCL21 expression in an epithelial cell-intrinsic manner, of which BaP, the main component of tobacco, facilitated the nuclear retention of the aryl hydrocarbon receptor that occupied the promoter of CCL21. Additionally, the activated CCL21/CCR7 axis increased the CD11a expression of CD4+T cells, boosting the interaction with CD20+B cells dependent on ICAM1, which potentially induced the TLSs formation. Patients with elevated serum levels of CCL21 benefited more from immunotherapy.

CONCLUSIONS

Patients with a smoking history exhibited higher levels of TLS via the CCL21-dependent mechanism, serum CCL21 was identified as a reliable biomarker for predicting the efficacy of immunotherapy.

SOX9 drives KRAS-induced lung adenocarcinoma progression and suppresses anti-tumor immunity

The SOX9 transcription factor ensures proper tissue development and homeostasis and has been implicated in promoting tumor progression. However, the role of SOX9 as a driver of lung adenocarcinoma (LUAD), or any cancer, remains unclear. Using CRISPR/Cas9 and Cre-LoxP gene knockout approaches in the Kras^G12D-driven mouse LUAD model, we found that loss of Sox9 significantly reduces lung tumor development, burden and progression, contributing to significantly longer overall survival. SOX9 consistently drove organoid growth in vitro, but SOX9-promoted tumor growth was significantly attenuated in immunocompromised mice compared to syngeneic mice. We demonstrate that SOX9 suppresses immune cell infiltration and functionally suppresses tumor associated CD8⁺ T, natural killer and dendritic cells. These data were validated by flow cytometry, gene expression, RT-qPCR, and immunohistochemistry analyses in Kras^G12D-driven murine LUAD, then confirmed by interrogating bulk and single-cell gene expression repertoires and immunohistochemistry in human LUAD. Notably, SOX9 significantly elevates collagen-related gene expression and substantially increases collagen fibers. We propose that SOX9 increases tumor stiffness and inhibits tumor-infiltrating dendritic cells, thereby suppressing CD8⁺ T cell and NK cell infiltration and activity. Thus, SOX9 drives Kras^G12D-driven lung tumor progression and inhibits anti-tumor immunity at least partly by modulating the tumor microenvironment.

Identification and validation of a novel cuproptosis-related stemness signature to predict prognosis and immune landscape in lung adenocarcinoma by integrating single-cell and bulk RNA-sequencing

Background

Cancer stem cells (CSCs) play vital roles in lung adenocarcinoma (LUAD) recurrence, metastasis, and drug resistance. Cuproptosis has provided a novel insight into the treatment of lung CSCs. However, there is a lack of knowledge regarding the cuproptosis-related genes combined with the stemness signature and their roles in the prognosis and immune landscape of LUAD.

Methods

Cuproptosis-related stemness genes (CRSGs) were identified by integrating single-cell and bulk RNA-sequencing data in LUAD patients. Subsequently, cuproptosis-related stemness subtypes were classified using consensus clustering analysis, and a prognostic signature was constructed by univariate and least absolute shrinkage operator (LASSO) Cox regression. The association between signature with immune infiltration, immunotherapy, and stemness features was also investigated. Finally, the expression of CRSGs and the functional roles of target gene were validated in vitro.

Results

We identified six CRSGs that were mainly expressed in epithelial and myeloid cells. Three distinct cuproptosis-related stemness subtypes were identified and associated with the immune infiltration and immunotherapy response. Furthermore, a prognostic signature was constructed to predict the overall survival (OS) of LUAD patients based on eight differently expressed genes (DEGs) with cuproptosis-related stemness signature (KLF4, SCGB3A1, COL1A1, SPP1, C4BPA, TSPAN7, CAV2, and CTHRC1) and confirmed in validation cohorts. We also developed an accurate nomogram to improve clinical applicability. Patients in the high-risk group showed worse OS with lower levels of immune cell infiltration and higher stemness features. Ultimately, further cellular experiments were performed to verify the expression of CRSGs and prognostic DEGs and demonstrate that SPP1 could affect the proliferation, migration, and stemness of LUAD cells.

Conclusion

This study developed a novel cuproptosis-related stemness signature that can be used to predict the prognosis and immune landscape of LUAD patients, and provided potential therapeutic targets for lung CSCs in the future.

Tumor-derived semaphorin 4A improves PD-1-blocking antibody efficacy by enhancing CD8+ T cell cytotoxicity and proliferation

Immune checkpoint inhibitors (ICIs) have caused revolutionary changes in cancer treatment, but low response rates remain a challenge. Semaphorin 4A (Sema4A) modulates the immune system through multiple mechanisms in mice, although the role of human Sema4A in the tumor microenvironment remains unclear. This study demonstrates that histologically Sema4A-positive non-small cell lung cancer (NSCLC) responded significantly better to anti-programmed cell death 1 (PD-1) antibody than Sema4A-negative NSCLC. Intriguingly, SEMA4A expression in human NSCLC was mainly derived from tumor cells and was associated with T cell activation. Sema4A promoted cytotoxicity and proliferation of tumor-specific CD8⁺ T cells without terminal exhaustion by enhancing mammalian target of rapamycin complex 1 and polyamine synthesis, which led to improved efficacy of PD-1 inhibitors in murine models. Improved T cell activation by recombinant Sema4A was also confirmed using isolated tumor-infiltrating T cells from patients with cancer. Thus, Sema4A might be a promising therapeutic target and biomarker for predicting and promoting ICI efficacy.

m6 A-Dependent Modulation via IGF2BP3/MCM5/Notch Axis Promotes Partial EMT and LUAD Metastasis

The importance of mRNA N6-methyladenosine (m⁶ A) modification during tumor metastasis is controversial as it plays distinct roles in different biological contexts. Moreover, how cancer cell plasticity is shaped by m⁶ A modification is interesting but remains uncharacterized. Here, this work shows that m⁶ A reader insulin like growth factor 2 mRNA binding protein 3 (IGF2BP3) is remarkably upregulated in metastatic lung adenocarcinoma (LUAD) and indicates worse prognosis of patients. Interestingly, IGF2BP3 induces partial epithelial-mesenchymal-transition (EMT) and confers LUAD cells plasticity to metastasize through m⁶ A-dependent overactivation of Notch signaling. Mechanistically, IGF2BP3 recognized m⁶ A-modified minichromosome maintenance complex component (MCM5) mRNAs to prolong stability of them, subsequently upregulating MCM5 protein, which competitively inhibits SIRT1-mediated deacetylation of Notch1 intracellular domain (NICD1), stabilizes NICD1 protein and contributes to m⁶ A-dependent IGF2BP3-mediated cellular plasticity. Notably, a tight correlation of the IGF2BP3/MCM5/Notch axis is evidenced in clinical LUAD specimens. Therefore, this study elucidates a critical role of m⁶ A modification on LUAD cell plasticity in fostering tumor metastasis via the above axis, providing potential targets for metastatic LUAD.

SKP2 knockout in Rb1/p53 deficient mouse models of osteosarcoma induces immune infiltration and drives a transcriptional program with a favorable prognosis

Purpose

Osteosarcoma (OS) is an aggressive bone malignancy with a poor prognosis. One putative proto-oncogene in OS is SKP2, encoding a substrate recognition factor of the SCF E3 ubiquitin ligase. We previously demonstrated that SKP2 knockout in murine OS improved survival and delayed tumorigenesis. Here we aim to define the SKP2 drives transcriptional program and its clinical implication in OS.

Experimental Design

We performed RNA-sequencing (RNA-seq) on tumors from a transgenic OS mouse model with conditional Trp53 and Rb1 knockouts in the osteoblast lineage ("DKO": Osx1-Cre;Rb1lox/lox;p53lox/lox) and a triple-knockout model with additional Skp2 germline knockout ("TKO": Osx1-Cre;Rb1lox/lox;p53lox/lox;SKP2-/-). We validated our RNA-seq findings using qPCR and immunohistochemistry. To investigate the clinical implications of our results, we analyzed a human OS patient cohort ("NCI-TARGET OS") with RNA-seq and clinical data.

Results

We found large differences in gene expression after SKP2 knockout. Strikingly, we observed increased expression of genes related to immune microenvironment infiltration in TKO tumors. We observed significant increases in signature genes for macrophages and to a lesser extent, T cells, B cells and vascular cells. We also uncovered a set of relevant transcription factors that may mediate the changes. In OS patient cohorts, high expression of genes upregulated in TKO was correlated with favorable overall survival, which was largely explained by the macrophage gene signatures. This relationship was further supported by our finding that SKP2 expression was negatively correlated with macrophage infiltration in the NCI-TARGET OS and the TCGA Sarcoma cohort.

Conclusion

Our findings indicate that SKP2 may mediate immune exclusion from the OS tumor microenvironment, suggesting that SKP2 modulation in OS may induce anti-tumor immune activation.

The combination therapy of isomucronulatol 7-O-beta-glucoside (IMG) and CEP-9722 targeting ferroptosis-related biomarkers in non-small cell lung cancer (NSCLC)

BACKGROUND

NSCLC is a malignant tumor with a high incidence. Ferroptosis presents an essential function in regulating carcinogenesis and tumor progression. However, the ferroptosis-associated prognostic model based on single-cell sequencing of NSCLC remains unexplored. Our study aims to establish a potential predictive model for NSCLC patients and provide available targeted drugs for clinical treatment.

METHODS

The data on NSCLC patients were collected from TCGA and GEO databases to analyze their gene expression profiles. ConsensusCluster was adopted to divide the patients into different groups based on ferroptosis-related genes. Then, the univariable Cox and LASSO analyses were applied to data analysis and model establishment. Single-cell analysis was used to explore the risk score genes in different cell populations and states. The protein levels of these genes were also investigated through the HPA database. Drug sensitivity was evaluated in CellMiner database. CCK8 and colony formation assays were performed to validate potential drugs' effects on lung cancer cell lines.

RESULTS

A ferroptosis-related prognostic model involving 14 genes in NSCLC patients was established. The risk score model was developed in training set GSE31210 and validated in the test set TCGA. The low-risk score group showed a better prognosis than the high-risk score group. The single-cell analysis revealed that the risk score genes were mainly derived from lung tumor cells. Most risk score genes were more highly expressed in tumor tissue than in normal tissue, according to the HPA database. Besides, these genes were associated with 106 drugs in CellMiner database. Finally, the drug effects on NSCLC cell growth were evaluated by cck8 and colony formation.

CONCLUSIONS

We identified an effective ferroptosis-related prognostic model based on single-cell sequencing. The potential prediction model is devoted to exploring clinical therapeutic targets for NSCLC.

The effectiveness of blood-activating and stasis-transforming traditional Chinese medicines (BAST) in lung cancer progression-a comprehensive review

ETHNOPHARMACOLOGICAL RELEVANCE

Blood-activating and stasis-transforming traditional Chinese medicines (BAST) are a class of herbs that have the effect of dilating blood vessels and dispersing stagnation. Modern pharmaceutical research has demonstrated that they are capable of improving hemodynamics and micro-flow, resist thrombosis and promote blood flow. BAST contain numerous active ingredients, which can theoretically regulate multiple targets at the same time and have a wide range of pharmacological effects in the treatment of diseases including human cancers. Clinically, BAST have minimal side effects and can be used in combination with Western medicine to improve patients' quality of life, lessen adverse effects and minimize the risk of recurrence and metastasis of cancers.

AIM OF THE REVIEW

We aimed to summarize the research progression of BAST on lung cancer in the past five years and present a prospect for the future. Particularly, this review further analyzes the effects and molecular mechanisms that BAST inhibit the invasion and metastasis of lung cancer.

MATERIALS AND METHODS

Relevant studies about BSAT were collected from PubMed and Web of science.

RESULTS

Lung cancer is one of the malignant tumors with the highest mortality rate. Most patients with lung cancer are diagnosed at an advanced stage and are highly susceptible to metastasis. Recent studies have shown that BAST, a class of traditional Chinese medicine (TCM) with the function of opening veins and dispersing blood stasis, significantly improve hemodynamics and microcirculation, prevent thrombosis and promote blood flow, and thereby inhibiting the invasion and metastasis of lung cancer. In the current review, we analyzed 51 active ingredients extracted from BAST. It was found that BAST and their active ingredients contribute to the prevention of invasion and metastasis of lung cancer through multiple mechanisms, such as regulation of EMT process, specific signaling pathway and metastasis-related genes, tumor blood vessel formation, immune microenvironment and inflammatory response of tumors.

CONCLUSIONS

BSAT and its active ingredients have showed promising anticancer activity and significantly inhibit the invasion and metastasis of lung cancer. A growing number of studies have realized their potential clinical significance in the therapy of lung cancer, which will provide substantial evidences for the development of new TCM for lung cancer therapy.

Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR

The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium™ platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different datasets, including new Visium liver metastasis ST data, with experimental validation of protein colocalization. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.

Function of CD8+, conventional CD4+, and regulatory CD4+ T cell identification in lung cancer

Lung cancer is the malignant tumor with the highest mortality rate in the world. There is obvious heterogeneity within the tumor. Single cell sequencing technology enables scholars to obtain information about the cell type, status, subpopulation distribution and communication behavior between cells in the tumor microenvironment from the cellular level. However, due to the problem of sequencing depth, some genes with low expression cannot be detected, which results in that most of the specific genes of immune cells cannot be recognized, and lead to defects in the functional identification of immune cells. In this paper, we used single cell sequencing data of 12346 T cells in 14 treatment-naïve non-small-cell lung cancer patients to identify immune cell-specific genes and infer the function of three types of T cells. The method, named GRAPH-LC, implemented this function by gene interaction network and graph learning methods. Graph learning methods are used to extract genes feature and dense neural network is used to identify immune cell-specific genes. The experiments on 10-cross validation shows that the AUROC and AUPR reached at least 0.802, 0.815 on identifying cell-specific genes of three types of T cells. And we did functional enrichment analysis on the top 15 expressed genes. By functional enrichment analysis, we got 95 GO terms and 39 KEGG pathways that related to three types of T cells. The use of this technology will help to deeply understand the mechanism of the occurrence and development of lung cancer, find new diagnostic markers and therapeutic targets, and provide a theoretical reference for the precise treatment of lung cancer patients in the future.

EGFR + lung adenocarcinomas coopt alveolar macrophage metabolism and function to support EGFR signaling and growth

The limited efficacy of currently approved immunotherapies in EGFR-mutant lung adenocarcinoma (LUAD) underscores the need to better understand mechanisms governing local immunosuppression. Elevated surfactant and GM-CSF secretion from the transformed epithelium induces tumor-associated alveolar macrophages (TA-AM) to proliferate and support tumor growth by rewiring inflammatory functions and lipid metabolism. TA-AM properties are driven by increased GM-CSF-PPARγ signaling and inhibition of airway GM-CSF or PPARγ in TA-AMs suppresses cholesterol efflux to tumor cells, which impairs EGFR phosphorylation and restrains LUAD progression. In the absence of TA-AM metabolic support, LUAD cells compensate by increasing cholesterol synthesis, and blocking PPARγ in TA-AMs simultaneous with statin therapy further suppresses tumor progression and increases T cell effector functions. These results reveal new therapeutic combinations for immunotherapy resistant EGFR-mutant LUADs and demonstrate how such cancer cells can metabolically co-opt TA-AMs through GM-CSF-PPARγ signaling to provide nutrients that promote oncogenic signaling and growth.

Molecular, metabolic and functional CD4 T cell paralysis impedes tumor control

CD4 T cells are important effectors of anti-tumor immunity, yet the regulation of CD4 tumor-specific T (T TS ) cells during cancer development is still unclear. We demonstrate that CD4 T TS cells are initially primed in the tumor draining lymph node and begin to divide following tumor initiation. Distinct from CD8 T TS cells and previously defined exhaustion programs, CD4 T TS cell proliferation is rapidly frozen in place and differentiation stunted by a functional interplay of T regulatory cells and both intrinsic and extrinsic CTLA4 signaling. Together these mechanisms paralyze CD4 T TS cell differentiation, redirecting metabolic and cytokine production circuits, and reducing CD4 T TS cell accumulation in the tumor. Paralysis is actively maintained throughout cancer progression and CD4 T TS cells rapidly resume proliferation and functional differentiation when both suppressive reactions are alleviated. Strikingly, Treg depletion alone reciprocally induced CD4 T TS cells to themselves become tumor-specific Tregs, whereas CTLA4 blockade alone failed to promote T helper differentiation. Overcoming their paralysis established long-term tumor control, demonstrating a novel immune evasion mechanism that specifically cripples CD4 T TS cells to favor tumor progression.

Single-Cell RNA Sequencing Analysis of Gene Regulatory Network Changes in the Development of Lung Adenocarcinoma

Lung cancer is a highly heterogeneous disease. Cancer cells and other cells within the tumor microenvironment interact to determine disease progression, as well as response to or escape from treatment. Understanding the regulatory relationship between cancer cells and their tumor microenvironment in lung adenocarcinoma is of great significance for exploring the heterogeneity of the tumor microenvironment and its role in the genesis and development of lung adenocarcinoma. This work uses public single-cell transcriptome data (distant normal, nLung; early LUAD, tLung; advanced LUAD, tL/B), to draft a cell map of lung adenocarcinoma from onset to progression, and provide a cell-cell communication view of lung adenocarcinoma in the different disease stages. Based on the analysis of cell populations, it was found that the proportion of macrophages was significantly reduced in the development of lung adenocarcinoma, and patients with lower proportions of macrophages exhibited poor prognosis. We therefore constructed a process to screen an intercellular gene regulatory network that reduces any error generated by single cell communication analysis and increases the credibility of selected cell communication signals. Based on the key regulatory signals in the macrophage-tumor cell regulatory network, we performed a pseudotime analysis of the macrophages and found that signal molecules (TIMP1, VEGFA, SPP1) are highly expressed in immunosuppression-associated macrophages. These molecules were also validated using an independent dataset and were significantly associated with poor prognosis. Our study provides an effective method for screening the key regulatory signals in the tumor microenvironment and the selected signal molecules may serve as a reference to guide the development of diagnostic biomarkers for risk stratification and therapeutic targets for lung adenocarcinoma.

Lineage tracing reveals clonal progenitors and long-term persistence of tumor-specific T cells during immune checkpoint blockade

Paired single-cell RNA and T cell receptor sequencing (scRNA/TCR-seq) has allowed for enhanced resolution of clonal T cell dynamics in cancer. Here, we report a scRNA/TCR-seq analysis of 187,650 T cells from 31 tissue regions, including tumor, adjacent normal tissues, and lymph nodes (LN), from three patients with non-small cell lung cancer after immune checkpoint blockade (ICB). Regions with viable cancer cells are enriched for exhausted CD8+ T cells, regulatory CD4+ T cells (Treg), and follicular helper CD4+ T cells (TFH). Tracking T cell clonotypes across tissues, combined with neoantigen specificity assays, reveals that TFH and tumor-specific exhausted CD8+ T cells are clonally linked to TCF7+SELL+ progenitors in tumor draining LNs, and progressive exhaustion trajectories of CD8+ T, Treg, and TFH cells with proximity to the tumor microenvironment. Finally, longitudinal tracking of tumor-specific CD8+ and CD4+ T cell clones reveals persistence in the peripheral blood for years after ICB therapy.

Spatial analysis of human lung cancer reveals organized immune hubs enriched for stem-like CD8 T cells and associated with immunotherapy response

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 that they were associated with beneficial responses to PD-1-blockade. Immunity hubs were enriched for many interferon-stimulated genes, T cells in multiple differentiation states, and CXCL9/10/11 + macrophages that preferentially interact with CD8 T cells. Critically, we discovered the stem-immunity hub, a subtype of immunity hub strongly associated with favorable PD-1-blockade outcomes, distinct from mature tertiary lymphoid structures, and enriched for stem-like TCF7+PD-1+ CD8 T cells and activated CCR7 + LAMP3 + dendritic cells, as well as chemokines that organize these cells. These results elucidate the spatial organization of the human intratumoral immune response and its relevance to patient immunotherapy outcomes.

An integrated bioinformatic analysis of bulk and single-cell sequencing clarifies immune microenvironment and metabolic profiles of lung adenocarcinoma to predict immunotherapy efficacy

Targeting the tumor microenvironment is increasingly recognized as an effective treatment of advanced lung adenocarcinoma (LUAD). However, few studies have addressed the efficacy of immunotherapy for LUAD. Here, a novel method for predicting immunotherapy efficacy has been proposed, which combines single-cell and bulk sequencing to characterize the immune microenvironment and metabolic profile of LUAD. TCGA bulk dataset was used to cluster two immune subtypes: C1 with “cold” tumor characteristics and C2 with “hot” tumor characteristics, with different prognosis. The Scissor algorithm, which is based on these two immune subtypes, identified GSE131907 single cell dataset into two groups of epithelial cells, labeled as Scissor_C1 and Scissor_C2. The enrichment revealed that Scissor_C1 was characterized by hypoxia, and a hypoxic microenvironment is a potential inducing factor for tumor invasion, metastasis, and immune therapy non-response. Furthermore, single cell analysis was performed to investigate the molecular mechanism of hypoxic microenvironment-induced invasion, metastasis, and immune therapy non-response in LUAD. Notably, Scissor_C1 cells significantly interacted with T cells and cancer-associated fibroblasts (CAF), and exhibited epithelial–mesenchymal transition and immunosuppressive features. CellChat analysis revealed that a hypoxic microenvironment in Scissor_C1elevated TGFβ signaling and induced ANGPTL4 and SEMA3C secretion. Interaction with endothelial cells with ANGPTL4, which increases vascular permeability and achieves distant metastasis across the vascular endothelium. Additionally, interaction of tumor-associated macrophages (TAM) and Scissor_C1 via the EREG/EFGR pathway induces tyrosine kinase inhibitor drug-resistance in patients with LAUD. Thereafter, a subgroup of CAF cells that exhibited same features as those of Scissor_C1 that exert immunosuppressive functions in the tumor microenvironment were identified. Moreover, the key genes (EPHB2 and COL1A1) in the Scissor_C1 gene network were explored and their expressions were verified using immunohistochemistry. Finally, the metabolism dysfunction in cells crosstalk was determined, which is characterized by glutamine secretion by TAM and uptake by Scissor_C1 via SLC38A2 transporter, which may induce glutamine addiction in LUAD cells. Overall, single-cell sequencing clarifies how the tumor microenvironment affects immunotherapy efficacy via molecular mechanisms and biological processes, whereas bulk sequencing explains immunotherapy efficacy based on clinical information.

Intratumoral tertiary lymphoid structure (TLS) maturation is influenced by draining lymph nodes of lung cancer

BACKGROUND

Tertiary lymphoid structure (TLS) is an organized infiltration of immune cells, showing features of germinal center (GC) commonly seen in secondary lymphoid organs. However, its relationship with tumor-draining lymph nodes (TDLNs) has not been studied and we hypothesized that TDLN may influence maturation of intratumoral TLS in non-small cell lung cancer (NSCLC).

METHODS

Tissue slides of 616 patients that had undergone surgeries were examined. Cox proportional hazard regression model was used to assess risk factors of patients' survival, and logistic regression model was used for their relationship with TLS. Single-cell RNA-sequencing (scRNA-seq) was employed to explore transcriptomic features of TDLNs. Immunohistochemistry, multiplex immunofluorescence and flow cytometry were performed to analyze cellular composition. Cellular components of NSCLC samples from The Cancer Genome Atlas database were inferred with Microenvironment Cell Populations-counter (MCP-counter) method. Murine NSCLC models were used to dissect underlying mechanisms for relationship between TDLN and TLS maturation.

RESULTS

While GC⁺ TLS was associated with better prognosis, GC^- TLS was not. TDLN metastasis reduced the prognostic relevance of TLS, and was associated with less GC formation. Primary tumor sites showed reduced B cell infiltration in TDLN-positive patients, and scRNA-seq revealed diminished memory B cell formation in tumor-invaded TDLNs, together with an emphasis on weakened interferon (IFN)-γ response. Murine NSCLC models revealed that IFN-γ signaling is involved in memory B cell differentiation in TDLNs and GC formation in primary tumors.

CONCLUSIONS

Our research emphasizes the influence of TDLN on intratumoral TLS maturation and suggests a role of memory B cells and IFN-γ signaling in this communication.

Cellular states are coupled to genomic and viral heterogeneity in HPV-related oropharyngeal carcinoma

Head and neck squamous cell carcinoma (HNSCC) includes a subset of cancers driven by human papillomavirus (HPV). Here we use single-cell RNA-seq to profile both HPV-positive and HPV-negative oropharyngeal tumors, uncovering a high level of cellular diversity within and between tumors. First, we detect diverse chromosomal aberrations within individual tumors, suggesting genomic instability and enabling the identification of malignant cells even at pathologically negative margins. Second, we uncover diversity with respect to HNSCC subtypes and other cellular states such as the cell cycle, senescence and epithelial-mesenchymal transitions. Third, we find heterogeneity in viral gene expression within HPV-positive tumors. HPV expression is lost or repressed in a subset of cells, which are associated with a decrease in HPV-associated cell cycle phenotypes, decreased response to treatment, increased invasion and poor prognosis. These findings suggest that HPV expression diversity must be considered during diagnosis and treatment of HPV-positive tumors, with important prognostic ramifications.

Single-Cell Landscape Highlights Heterogenous Microenvironment, Novel Immune Reaction Patterns, Potential Biomarkers and Unique Therapeutic Strategies of Cervical Squamous Carcinoma, Human Papillomavirus-Associated (HPVA) and Non-HPVA Adenocarcinoma

Cervical adenocarcinomas (ADCs), including human papillomavirus (HPV)-associated (HPVA) and non-HPVA (NHPVA), though exhibiting a more malignant phenotype and poorer prognosis, are treated identically to squamous cell carcinoma (SCC). This clinical dilemma requires a deeper investigation into their differences. Herein a transcriptomic atlas of SCC, HPVA, and NHPVA-ADC using single-cell RNA (scRNA) and T-cell receptor sequencing (TCR-seq) is presented. Regarding structural cells, the malignancy origin of epithelial cells, angiogenic tip cells and two subtypes of fibroblasts is revealed. The promalignant properties of the structural cells using organoids are further confirmed. Regarding immune cells, myeloid cells with multiple functions other than antigen presentation and exhausted T lymphocytes contribute to immunosuppression. From the perspective of HPV infection, not only is HPV-dependent and independent cervical cancer oncogenesis proposed but also three immune reaction patterns mediated by T cells (coordinated/inactive/imbalanced) are identified. Strikingly, diagnostic biomarkers to distinguish ADC from SCC are discovered and prognostic biomarkers with marker genes for malignant epithelial cells, tip cells, and SPP1/C1QC macrophages are generated. Importantly, the efficacy of anti-CD96 and anti-TIGIT, not inferior to anti-PD1, in animal experiments is confirmed and targeted therapies specifically for HPV-positive SCC, HPVA and NHPVA-ADC, providing essential clues for further clinical trials, are proposed.

TGF-β in the microenvironment induces a physiologically occurring immune-suppressive senescent state

TGF-β induces senescence in embryonic tissues. Whether TGF-β in the hypoxic tumor microenvironment (TME) induces senescence in cancer and how the ensuing senescence-associated secretory phenotype (SASP) remodels the cellular TME to influence immune checkpoint inhibitor (ICI) responses are unknown. We show that TGF-β induces a deeper senescent state under hypoxia than under normoxia; deep senescence correlates with the degree of E2F suppression and is marked by multinucleation, reduced reentry into proliferation, and a distinct 14-gene SASP. Suppressing TGF-β signaling in tumors in an immunocompetent mouse lung cancer model abrogates endogenous senescent cells and suppresses the 14-gene SASP and immune infiltration. Untreated human lung cancers with a high 14-gene SASP display immunosuppressive immune infiltration. In a lung cancer clinical trial of ICIs, elevated 14-gene SASP is associated with increased senescence, TGF-β and hypoxia signaling, and poor progression-free survival. Thus, TME-induced senescence may represent a naturally occurring state in cancer, contributing to an immune-suppressive phenotype associated with immune therapy resistance.

An integrated single-cell transcriptomic dataset for non-small cell lung cancer

As single-cell RNA sequencing (scRNA-seq) has emerged as a great tool for studying cellular heterogeneity within the past decade, the number of available scRNA-seq datasets also rapidly increased. However, reuse of such data is often problematic due to a small cohort size, limited cell types, and insufficient information on cell type classification. Here, we present a large integrated scRNA-seq dataset containing 224,611 cells from human primary non-small cell lung cancer (NSCLC) tumors. Using publicly available resources, we pre-processed and integrated seven independent scRNA-seq datasets using an anchor-based approach, with five datasets utilized as reference and the remaining two, as validation. We created two levels of annotation based on cell type-specific markers conserved across the datasets. To demonstrate usability of the integrated dataset, we created annotation predictions for the two validation datasets using our integrated reference. Additionally, we conducted a trajectory analysis on subsets of T cells and lung cancer cells. This integrated data may serve as a resource for studying NSCLC transcriptome at the single cell level.

Multi-modal quantification of pathway activity with MAYA

Signaling pathways can be activated through various cascades of genes depending on cell identity and biological context. Single-cell atlases now provide the opportunity to inspect such complexity in health and disease. Yet, existing reference tools for pathway scoring resume activity of each pathway to one unique common metric across cell types. Here, we present MAYA, a computational method that enables the automatic detection and scoring of the diverse modes of activation of biological pathways across cell populations. MAYA improves the granularity of pathway analysis by detecting subgroups of genes within reference pathways, each characteristic of a cell population and how it activates a pathway. Using multiple single-cell datasets, we demonstrate the biological relevance of identified modes of activation, the robustness of MAYA to noisy pathway lists and batch effect. MAYA can also predict cell types starting from lists of reference markers in a cluster-free manner. Finally, we show that MAYA reveals common modes of pathway activation in tumor cells across patients, opening the perspective to discover shared therapeutic vulnerabilities.

Benchmarking integration of single-cell differential expression

Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. However, strategies to integrate differential expression analysis of single-cell data remain underinvestigated. Here, we benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches. We show that batch effects, sequencing depth and data sparsity substantially impact their performances. Notably, we find that the use of batch-corrected data rarely improves the analysis for sparse data, whereas batch covariate modeling improves the analysis for substantial batch effects. We show that for low depth data, single-cell techniques based on zero-inflation model deteriorate the performance, whereas the analysis of uncorrected data using limmatrend, Wilcoxon test and fixed effects model performs well. We suggest several high-performance methods under different conditions based on various simulation and real data analyses. Additionally, we demonstrate that differential expression analysis for a specific cell type outperforms that of large-scale bulk sample data in prioritizing disease-related genes.

Single-cell transcriptomic analysis of tumor heterogeneity and intercellular networks in human urothelial carcinoma

BACKGROUND

Heterogeneity of tumor cells and the tumor microenvironment (TME) is significantly associated with clinical outcomes and treatment responses in patients with urothelial carcinoma (UC). Comprehensive profiling of the cellular diversity and interactions between malignant cells and TME may clarify the mechanisms underlying UC progression and guide the development of novel therapies. This study aimed to extend our understanding of intra-tumoral heterogeneity and the immunosuppressive TME in UC and provide basic support for the development of novel UC therapies.

METHODS

Seven patients with UC were included who underwent curative surgery at our hospital between July 2020 and October 2020. We performed single-cell RNA sequencing (scRNA-seq) analysis in seven tumors with six matched adjacent normal tissues and integrated the results with two public scRNA-seq datasets. The functional properties and intercellular interactions between single cells were characterized, and the results were validated using multiplex immunofluorescence staining, flow cytometry, and bulk transcriptomic datasets. All statistical analyses were performed using the R package with two-sided tests. Wilcoxon-rank test, log-rank test, one-way analysis of variance test, and Pearson correlation analysis were used properly.

RESULTS

Unsupervised t-distributed stochastic neighbor embedding clustering analysis identified ten main cellular subclusters in urothelial tissues. Of them, seven urothelial subtypes were noted, and malignant urothelial cells were characterized with enhanced cellular proliferation and reduced immunogenicity. CD8 + T cell subclusters exhibited enhanced cellular cytotoxicity activities along with increased exhaustion signature in UC tissues, and the recruitment of CD4 + T regulatory cells was also increased in tumor tissues. Regarding myeloid cells, coordinated reprogramming of infiltrated neutrophils, M2-type polarized macrophages, and LAMP3 + dendritic cells contribute to immunosuppressive TME in UC tissues. Tumor tissues demonstrated enhanced angiogenesis mediated by KDR + endothelial cells and RGS5 + /ACTA2 + pericytes. Through deconvolution analysis, we identified multiple cellular subtypes may influence the programmed death-ligand 1 (PD-L1) immunotherapy response in patients with UC.

CONCLUSION

Our scRNA-seq analysis clarified intra-tumoral heterogeneity and delineated the pro-tumoral and immunosuppressive microenvironment in UC tissues, which may provide novel therapeutic targets.

Deciphering endothelial heterogeneity in health and disease at single-cell resolution: progress and perspectives

Endothelial cells (ECs) constitute the inner lining of vascular beds in mammals and are crucial for homeostatic regulation of blood vessel physiology, but also play a key role in pathogenesis of many diseases, thereby representing realistic therapeutic targets. However, it has become evident that ECs are heterogeneous, encompassing several subtypes with distinct functions, which makes EC targeting and modulation in diseases challenging. The rise of the new single-cell era has led to an emergence of studies aimed at interrogating transcriptome diversity along the vascular tree, and has revolutionized our understanding of EC heterogeneity from both a physiological and pathophysiological context. Here, we discuss recent landmark studies aimed at teasing apart the heterogeneous nature of ECs. We cover driving (epi)genetic, transcriptomic, and metabolic forces underlying EC heterogeneity in health and disease, as well as current strategies used to combat disease-enriched EC phenotypes, and propose strategies to transcend largely descriptive heterogeneity towards prioritization and functional validation of therapeutically targetable drivers of EC diversity. Lastly, we provide an overview of the most recent advances and hurdles in single EC OMICs.

Neoadjuvant chemotherapy plus nivolumab with or without ipilimumab in operable non-small cell lung cancer: the phase 2 platform NEOSTAR trial

Neoadjuvant ipilimumab + nivolumab (Ipi+Nivo) and nivolumab + chemotherapy (Nivo+CT) induce greater pathologic response rates than CT alone in patients with operable non-small cell lung cancer (NSCLC). The impact of adding ipilimumab to neoadjuvant Nivo+CT is unknown. Here we report the results and correlates of two arms of the phase 2 platform NEOSTAR trial testing neoadjuvant Nivo+CT and Ipi+Nivo+CT with major pathologic response (MPR) as the primary endpoint. MPR rates were 32.1% (7/22, 80% confidence interval (CI) 18.7-43.1%) in the Nivo+CT arm and 50% (11/22, 80% CI 34.6-61.1%) in the Ipi+Nivo+CT arm; the primary endpoint was met in both arms. In patients without known tumor EGFR/ALK alterations, MPR rates were 41.2% (7/17) and 62.5% (10/16) in the Nivo+CT and Ipi+Nivo+CT groups, respectively. No new safety signals were observed in either arm. Single-cell sequencing and multi-platform immune profiling (exploratory endpoints) underscored immune cell populations and phenotypes, including effector memory CD8+ T, B and myeloid cells and markers of tertiary lymphoid structures, that were preferentially increased in the Ipi+Nivo+CT cohort. Baseline fecal microbiota in patients with MPR were enriched with beneficial taxa, such as Akkermansia, and displayed reduced abundance of pro-inflammatory and pathogenic microbes. Neoadjuvant Ipi+Nivo+CT enhances pathologic responses and warrants further study in operable NSCLC. (ClinicalTrials.gov registration: NCT03158129 .).

Single-cell RNA-seq Reveals Intratumoral Heterogeneity in Osteosarcoma Patients: A Review

While primary bone malignancies make up just 0.2% of all cancers, osteosarcoma (OS) is the third most common cancer in adolescents. Due to its highly complex and heterogeneous tumor microenvironment (TME), OS has proven difficult to treat. There has been little to no improvement in therapy for this disease over the last 40 years. Even the recent success of immunotherapies in other blood-borne and solid malignancies has not translated to OS. With frequent recurrence and lung metastases continuing to pose a challenge in the clinic, recent advancements in molecular profiling, such as single-cell RNA sequencing (scRNA-seq), have proven useful in identifying novel biomarkers of OS tumors while providing new insight into this TME that could potentially lead to new therapeutic options. This review combines the analyses of over 150,000 cells from 18 lesions ranging from primary, recurrent, and metastatic OS lesions, revealing distinct cellular populations and gene signatures that exist between them. Here, we detail these previous findings and ultimately convey the intratumoral heterogeneity that exists within OS tumor specimens.

A Novel Molecular Classification Method for Glioblastoma Based on Tumor Cell Differentiation Trajectories

The latest 2021 WHO classification redefines glioblastoma (GBM) as the hierarchical reporting standard by eliminating glioblastoma, IDH-mutant and only retaining the tumor entity of "glioblastoma, IDH-wild type." Knowing that subclassification of tumors based on molecular features is supposed to facilitate the therapeutic choice and increase the response rate in cancer patients, it is necessary to carry out molecular classification of the newly defined GBM. Although differentiation trajectory inference based on single-cell sequencing (scRNA-seq) data holds great promise for identifying cell heterogeneity, it has not been used in the study of GBM molecular classification. Single-cell transcriptome sequencing data from 10 GBM samples were used to identify molecular classification based on differentiation trajectories. The expressions of identified features were validated by public bulk RNA-sequencing data. Clinical feasibility of the classification system was examined in tissue samples by immunohistochemical (IHC) staining and immunofluorescence, and their clinical significance was investigated in public cohorts and clinical samples with complete clinical follow-up information. By analyzing scRNA-seq data of 10 GBM samples, four differentiation trajectories from the glioblastoma stem cell-like (GSCL) cluster were identified, based on which malignant cells were classified into five characteristic subclusters. Each cluster exhibited different potential drug sensitivities, pathways, functions, and transcriptional modules. The classification model was further examined in TCGA and CGGA datasets. According to the different abundance of five characteristic cell clusters, the patients were classified into five groups which we named Ac-G, Class-G, Neo-G, Opc-G, and Undiff-G groups. It was found that the Undiff-G group exhibited the worst overall survival (OS) in both TCGA and CGGA cohorts. In addition, the classification model was verified by IHC staining in 137 GBM samples to further clarify the difference in OS between the five groups. Furthermore, the novel biomarkers of glioblastoma stem cells (GSCs) were also described. In summary, we identified five classifications of GBM and found that they exhibited distinct drug sensitivities and different prognoses, suggesting that the new grouping system may be able to provide important prognostic information and have certain guiding significance for the treatment of GBM, and identified the GSCL cluster in GBM tissues and described its characteristic program, which may help develop new potential therapeutic targets for GSCs in GBM.

LncRNA PCAT6 promotes proliferation, migration, invasion, and epithelial-mesenchymal transition of lung adenocarcinoma cell by targeting miR-545-3p

BACKGROUND

Lung cancer is a high incidence cancer on a worldwide basis and has become a major public health problem. Lung adenocarcinoma (LUAD) makes up approximately half of all lung cancers and is a threat to human health. Long non-coding RNAs (lncRNAs) is an important regulator of the development and progression of lung adenocarcinoma. In this manuscript we examined the role and potential mechanism of lncRNA PCAT6 in the development of LUAD.

METHODS AND RESULTS

Differences in lncRNA PCAT6 levels between LUAD samples and normal samples were first explored in the GEPIA database. We found that lncRNA PCAT6 expression was elevated, which was also validated in lung adenocarcinoma tissues and cell lines. Using western blotting, CCK-8, EdU, wound healing and transwell assays, we found that knockdown of lncRNA PCAT6 inhibited EMT, proliferation, migration, and invasion of LUAD cells. We noted a predicted a binding site for lncRNA PCAT6 and miR-545-3p through conducting bioinformatic analyses, and their binding was subsequently verified by a dual-luciferase reporter assay. Rescue experiments confirmed that miR-545-3p inhibitor partially abolished the inhibition function of lncRNA PCAT6 knockdown on LUAD cells. In addition, we predicted the downstream target genes of miR-545-3p and verified them by RT-qPCR. We found that EGFR was reduced in the silence of lncRNA PCAT6 and upregulated after miR-545-3p inhibition.

CONCLUSION

This study demonstrates that lncRNA PCAT6 promotes a more aggressive LUAD phenotype by sponging miR-545-3p. This finding may provide new ideas for the treatment of lung cancer.

Comprehensive analysis of BUBs gene family in lung adenocarcinoma with immunological analysis

Lung adenocarcinoma (LUAD) is one of the most commonly malignant tumors, and major challenges remain in the treatment of LUAD. Budding uninhibited by benzimidazole 1/3 (BUB1/3) play significant roles in the process of spindle-assembly checkpoint (SAC) during mitosis. However, their roles in LUAD have not been established. Here, we performed an immunological analysis of BUB1/3 in LUAD using a comprehensive bioinformatics approach, quantitative real-time-PCR and Western blotting technique. Our results indicated that the expression levels of BUB1 and BUB3 in LUAD samples were higher than the expression levels in the control groups and were associated with some clinicopathologic parameters in patients with LUAD. BUB1/3 and their related genes were enriched in cell immune, and the immune infiltration analysis revealed that the BUB1/3 expression profile was significantly correlated with characteristics of immune cell infiltration. Survival analysis showed that the disease-free survival and overall survival of patients with LUAD decreased with an increase in the BUB1/3 expression levels. The mRNA and protein expression levels of BUB1 and BUB3 in each of the LUAD cell lines were upregulated to varying degrees. BUB1 and BUB3 are the potential immunological therapeutic intervention targets for patients with LUAD.

Single-cell RNA sequencing reveals the effects of chemotherapy on human pancreatic adenocarcinoma and its tumor microenvironment

The tumor microenvironment (TME) in pancreatic ductal adenocarcinoma (PDAC) is a complex ecosystem that drives tumor progression; however, in-depth single cell characterization of the PDAC TME and its role in response to therapy is lacking. Here, we perform single-cell RNA sequencing on freshly collected human PDAC samples either before or after chemotherapy. Overall, we find a heterogeneous mixture of basal and classical cancer cell subtypes, along with distinct cancer-associated fibroblast and macrophage subpopulations. Strikingly, classical and basal-like cancer cells exhibit similar transcriptional responses to chemotherapy and do not demonstrate a shift towards a basal-like transcriptional program among treated samples. We observe decreased ligand-receptor interactions in treated samples, particularly between TIGIT on CD8 + T cells and its receptor on cancer cells, and identify TIGIT as the major inhibitory checkpoint molecule of CD8 + T cells. Our results suggest that chemotherapy profoundly impacts the PDAC TME and may promote resistance to immunotherapy.

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.

M2 macrophage-derived exosomal miR-1911-5p promotes cell migration and invasion in lung adenocarcinoma by down-regulating CELF2 -activated ZBTB4 expression

Lung adenocarcinoma (LUAD) is one of the most aggressive, lethal cancers, comprising around 40% of lung cancer cases. Metastases are the primary cause of LUAD deaths. The mechanism underlying metastatic LUAD and tumor microenvironment remain largely unknown. To explore the effect of M2 macrophage-derived exosomes on LUAD progression. Quantitative-PCR (q-PCR) and western blot were used to measure the expression of RNAs and proteins separately. Co-culture experiments wound healing and Transwell invasion assays were performed to evaluate the effect of M2 macrophage-derived exosomes on LUAD cell migration and invasion. RNA pulldown and luciferase reporter, RNA-binding immunoprecipitation (RIP), and mRNA stability assays were conducted to explore the downstream mechanism of exosomal microRNA-1911-5p (miR-1911-5p). M2 macrophage-derived exosomes accelerated the migration and invasion of LUAD cells. M2 macrophages-secreted exosomal miR-1911-5p enhanced cell migration and invasion in LUAD. Mechanically, miR-1911-5p targeted CUGBP- and ETR-3-like family 2 (CELF2) to downregulate zinc finger and BTB domain containing 4 (ZBTB4) in LUAD. Additionally, miR-1911-5p promoted LUAD progression via ZBTB4. The present study demonstrated that M2 macrophage-derived exosomal miR-1911-5p facilitates the migration and invasion of LUAD cells by inhibiting CELF2-activated ZBTB4, which might offer insight into LUAD treatment.

Microfluidic Mixer for In Situ Ammonia Analysis of Single Cells in Mass Spectrometry

Mass spectrometry (MS) is a powerful tool for exploring single-cell heterogeneity. However, due to the ultralow absolute content of most substances in a single cell, existing methods can only analyze high-content substances; conventional methods are incompetent for quantitative analysis of important trace-amount small-molecule metabolites such as ammonia and sulfide. Herein, a method integrating single-cell extraction, online derivatization, and MS for multifunctional and more accurate MS analysis is reported. For application, ammonia content in a single cell was analyzed, where the cellular heterogeneity in ammonia metabolism was revealed. First, the extraction room of a microfluidic probe was covered on the target single cell, and the extraction fluid was allowed to flow through a single cell and dissolve cellular ammonia. Then, the ammonia was mixed and reacted with the pretreatment reagent input from another inlet to achieve the derivatization and signal amplification, enhancing the analysis sensitivity on MS. Finally, the sample was sent to MS, and the ammonia content was successfully quantitatively evaluated by analyzing its derivative urotropine, demonstrating the potential of this method to advance single-cell mass spectrometry analysis to higher sensitivity.

Single-cell analysis reveals prognostic fibroblast subpopulations linked to molecular and immunological subtypes of lung cancer

Fibroblasts are poorly characterised cells that variably impact tumour progression. Here, we use single cell RNA-sequencing, multiplexed immunohistochemistry and digital cytometry (CIBERSORTx) to identify and characterise three major fibroblast subpopulations in human non-small cell lung cancer: adventitial, alveolar and myofibroblasts. Alveolar and adventitial fibroblasts (enriched in control tissue samples) localise to discrete spatial niches in histologically normal lung tissue and indicate improved overall survival rates when present in lung adenocarcinomas (LUAD). Trajectory inference identifies three phases of control tissue fibroblast activation, leading to myofibroblast enrichment in tumour samples: initial upregulation of inflammatory cytokines, followed by stress-response signalling and ultimately increased expression of fibrillar collagens. Myofibroblasts correlate with poor overall survival rates in LUAD, associated with loss of epithelial differentiation, TP53 mutations, proximal molecular subtypes and myeloid cell recruitment. In squamous carcinomas myofibroblasts were not prognostic despite being transcriptomically equivalent. These findings have important implications for developing fibroblast-targeting strategies for cancer therapy.

m7G-related gene NUDT4 as a novel biomarker promoting cancer cell proliferation in lung adenocarcinoma

Background

Lung cancer is the leading cause of mortality in cancer patients. N7-methylguanosine (m7G) modification as a translational regulation pattern has been reported to participate in multiple types of cancer progression, but little is known in lung cancer. This study attempts to explore the role of m7G-related proteins in genetic and epigenetic variations in lung adenocarcinoma, and its relationship with clinical prognosis, immune infiltration, and immunotherapy.

Methods

Sequencing data were obtained from the Genomic Data Commons (GDC) Data Portal and Gene Expression Omnibus (GEO) databases. Consensus clustering was utilized to distinguish m7G clusters, and responses to immunotherapy were also evaluated. Moreover, univariate and multivariate Cox and Least absolute shrinkage and selection operator LASSO Cox regression analyses were used to screen independent prognostic factors and generated risk scores for constructing a survival prediction model. Multiple cell types such as epithelial cells and immune cells were identified to verify the bulk RNA results. Short hairpin RNA (shRNA) Tet-on plasmids, Clustered Regularly Interspaced Short Palindromic Repeats CRISPR/Cas9 for knockout plasmids, and nucleoside diphosphate linked to moiety X-type motif 4 (NUDT4) overexpression plasmids were constructed to inhibit or promote tumor cell NUDT4 expression, then RT-qPCR, Cell Counting Kit-8 CCK8 proliferation assay, and Transwell assay were used to observe tumor cell biological functions.

Results

Fifteen m7G-related genes were highly expressed in tumor samples, and 12 genes were associated with poor prognosis. m7G cluster-B had lower immune infiltration level, worse survival, and samples that predicted poor responses to immunotherapy. The multivariate Cox model showed that NUDT4 and WDR4 (WD repeat domain 4) were independent risk factors. Single-cell m7G gene set variation analysis (GSVA) scores also had a negative correlation tendency with immune infiltration level and T-cell Programmed Death-1 PD-1 expression, but the statistics were not significant. Knocking down and knocking out the NUDT4 expression significantly inhibited cell proliferation capability in A549 and H1299 cells. In contrast, overexpressing NUDT4 promoted tumor cell proliferation. However, there was no difference in migration capability in the knockdown, knockout, or overexpression groups.

Conclusions

Our study revealed that m7G modification-related proteins are closely related to the tumor microenvironment, immune cell infiltration, responses to immunotherapy, and patients' prognosis in lung adenocarcinoma and could be useful biomarkers for the identification of patients who could benefit from immunotherapy. The m7G modification protein NUDT4 may be a novel biomarker in promoting the progression of lung cancer.