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

Acyl-CoA dehydrogenase long chain acts as a tumor-suppressive factor in lung adenocarcinoma progression

This study investigated the role of long-chain acyl-CoA dehydrogenase (ACADL) in lung adenocarcinoma (LUAD). ACADL was significantly downregulated in human LUAD tissues compared to normal lung tissues. In vitro, ectopic expression of ACADL in murine LLC cells decreased cell viability, migration, and invasion, while ACADL knockdown exhibited the opposite effect. In vivo, ACADL overexpression impeded tumor growth and metastasis. Mechanistically, ACADL hindered tumor progression by inducing cell cycle arrest, promoting apoptosis, and suppressing the epithelial-mesenchymal transition (EMT) process. These findings suggest ACADL acts as a tumor suppressor in LUAD progression.

Integrated analysis of single‑cell and bulk RNA sequencing data to construct a risk assessment model based on plasma cell immune‑related genes for predicting patient prognosis and therapeutic response in lung adenocarcinoma

Plasma cells serve a crucial role in the human immune system and are important in tumor progression. However, the specific role of plasma cell immune-related genes (PCIGs) in tumor progression remains unclear. Therefore, the present study aimed to establish a risk assessment model for patients with lung adenocarcinoma (LUAD) based on PCIGs. The data used in the present study were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. After identifying nine PCIGs, a risk assessment model was constructed and a nomogram was developed for predicting patient prognosis. To explore the molecular mechanism and clinical significance, gene set enrichment analysis (GSEA), tumor mutational burden (TMB) analysis, tumor microenvironment (TME) analysis and drug sensitivity prediction were performed. Furthermore, the accuracy of the model was validated using reverse transcription-quantitative PCR (RT-qPCR). The present study constructed a risk assessment model consisting of nine PCIGs. Kaplan-Meier survival curves indicated a worse prognosis in the high-risk subgroup (risk score ≥0.982) compared with that in the low-risk subgroup. The nomogram exhibited predictive value for survival prediction (area under the curve=0.727). GSEA enrichment analysis revealed enrichment of the focal adhesion and extracellular matrix-receptor interaction pathways in the high-risk group. Moreover, the high-risk group exhibited a higher TMB, as demonstrated by the TME analysis showing lower ESTIMATE scores. Drug sensitivity prediction facilitated potential drug selection. Subsequently, differential gene expression was validated in multiple LUAD cell lines using RT-qPCR. In conclusion, the risk assessment model based on nine PCIGs may be used to predict the prognosis and drug selection in patients with LUAD.

Emerging clinical applications of single-cell RNA sequencing in oncology

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of complex tissues both in health and in disease. Over the past decade, scRNA-seq has been applied to tumour samples obtained from patients with cancer in hundreds of studies, thereby advancing the view that each tumour is a complex ecosystem and uncovering the diverse states of both cancer cells and the tumour microenvironment. Such studies have primarily investigated and provided insights into the basic biology of cancer, although considerable research interest exists in leveraging these findings towards clinical applications. In this Review, we summarize the available data from scRNA-seq studies investigating samples from patients with cancer with a particular focus on findings that are of potential clinical relevance. We highlight four main research objectives of scRNA-seq studies and describe some of the most relevant findings towards such goals. We also describe the limitations of scRNA-seq, as well as future approaches in this field that are anticipated to further advance clinical applicability.

NADH:ubiquinone oxidoreductase core subunit S8 expression and functional significance in non-small cell lung cancer

Hyperfunctional mitochondria provide a growth advantage by supporting the energy-intensive processes essential for non-small cell lung cancer (NSCLC). NADH:ubiquinone oxidoreductase core subunit S8 (NDUFS8) is a key subunit of mitochondrial complex I involved in oxidative phosphorylation (OXPHOS) and cellular energy production. Bioinformatics and local tissue examinations show that NDUFS8 expression is elevated in NSCLC compared to normal lung tissue. Both immortalized and primary human NSCLC cells exhibit higher NDUFS8 levels. Single-cell RNA sequencing confirmed NDUFS8 upregulation in cancerous cells of NSCLC tumor. Silencing NDUFS8 via shRNA or Cas9/sgRNA-mediated knockout (KO) disrupted mitochondrial functions, leading to decreased complex I activity, ATP depletion, mitochondrial depolarization, increased reactive oxygen species (ROS) production, and heightened lipid peroxidation. Furthermore, NDUFS8 silencing/KO triggered apoptosis and significantly reduced Akt-mTOR activation, cell viability, proliferation, and motility in various NSCLC cells. In contrast, ectopic overexpression of NDUFS8 boosted mitochondrial complex I activity and ATP levels, promoting Akt-mTOR activation, and enhancing NSCLC cell proliferation and motility. NDUFS8 also contributes to radioresistance in NSCLC; silencing or KO enhanced ionizing radiation (IR)-induced cytotoxicity, while overexpression mitigated it. Intratumoral injection of NDUFS8 shRNA-expressing adeno-associated virus significantly inhibited growth of primary NSCLC xenografts in nude mice, with observed NDUFS8 silencing, ATP reduction, oxidative damage, proliferation inhibition, Akt-mTOR inactivation and apoptosis in treated tissues. These findings highlight the pivotal pro-tumorigenic role of NDUFS8 in NSCLC.

Loss of SPHK1 fuels inflammation to drive KRAS-mutated lung adenocarcinoma

Inflammation is a widely recognized key contributor to KRAS-driven lung adenocarcinoma (LUAD). Tumor-associated macrophages (TAM) are an integral part of the tumor microenvironment and create a supportive niche that sustains inflammation-driven tumorigenesis. In the present study, we unravel a dual role of sphingosine kinase 1 (SPHK1) in KRAS-driven LUAD. While SPHK1 promotes tumorigenesis in in vitro experimental models, it paradoxically suppresses tumorigenesis in in vivo models of KRAS-mutated LUAD. Mechanistically, tumor-intrinsic loss of SPHK1 leads to disrupted lipid homeostasis, increased inflammation and infiltration by TAM, ultimately driving tumor progression. Thus, our study suggests that clinically targeting the SPHK1/S1P axis could potentially result in increased tumor progression, possibly by rewiring the tumor microenvironment toward a more inflammatory and pro-tumorigenic state.

Integrating bulk, single-cell, and spatial transcriptomics to identify and functionally validate novel targets to enhance immunotherapy in NSCLC

Programmed cell deaths (PCDs) are crucial for tumor progression. By analyzing 18 PCDs, we generated a robust multigene signature, Combined Cell Death Index (CCDI), comprising necroptosis and autophagy genes for non-small cell lung cancer (NSCLC). The CCDI accurately stratified patients by survival prognosis and predicted immunotherapy responses. We validated CCDI and prioritized CCDI genes using five single-cell RNA sequencing and two spatial transcriptomics datasets. CCDI positively correlates with tumor malignancy, invasiveness, and immunotherapy resistance. Four necroptosis genes (PTGES3, MYO6, CCT6A, and CTSH) may affect cancer cell evolution. In vitro, CTSH overexpression or PTGES3 knockdown inhibited NSCLC cell proliferation and migration while inducing necroptosis with necrosome formation. Moreover, we observed diminished CTSH, heightened PTGES3, and low necroptosis activity in 12 pairs of NSCLC tumors and normal tissues. CTSH overexpression or PTGES3 knockdown induced necroptosis and improved anti-PD1 therapy efficiency in syngeneic cancer mouse models. These findings indicate necroptosis genes as potential therapeutic targets in cancer treatments.

Joint analysis of chromatin accessibility and gene expression in the same single cells reveals cancer-specific regulatory programs

Biological analyses conducted at the single-cell scale have revealed profound impacts of heterogeneity and plasticity of chromatin states and gene expression on physiology and cancer. Here, we developed Parallel-seq, a technology for simultaneously measuring chromatin accessibility and gene expression in the same single cells. By combining combinatorial cell indexing and droplet overloading, Parallel-seq generates high-quality data in an ultra-high-throughput fashion and at a cost two orders of magnitude lower than alternative technologies (10× Multiome and ISSAAC-seq). We applied Parallel-seq to 40 lung tumor and tumor-adjacent clinical samples and obtained over 200,000 high-quality joint scATAC-and-scRNA profiles. Leveraging this large dataset, we characterized copy-number variations (CNVs) and extrachromosomal circular DNA (eccDNA) heterogeneity in tumor cells, predicted hundreds of thousands of cell-type-specific regulatory events, and identified enhancer mutations affecting tumor progression. Our analyses highlight Parallel-seq's power in investigating epigenetic and genetic factors driving cancer development at the cell-type-specific level and its utility for revealing vulnerable therapeutic targets.

Distinct CD8+ T cell dynamics associate with response to neoadjuvant cancer immunotherapies

We leverage a clinical trial (NCT04080804) that compared neoadjuvant anti-PD-1, anti-PD-1+CTLA-4, and anti-PD-1+LAG-3 therapies in head and neck squamous cell carcinoma patients. Combination therapies promote higher pathologic response rates versus monotherapy, and major pathologic response is associated with better survival. To address whether successful immune checkpoint inhibitor (ICI) regimens act through similar or distinct pathways, we robustly and longitudinally characterize transcriptional and proteomic dynamics of CD8+ tumor-infiltrating lymphocytes (TILs) in a clonal manner. Anti-PD-1+LAG-3 reprograms CD8+ TIL with type-I interferon response and exhaustion gene programs into effector memory and resident memory (TEM/TRM). In contrast, anti-PD-1+CTLA-4 activates and expands pre-existing TEM/TRM CD8+ TIL, but does not rejuvenate exhausted phenotypes into T effector cells. Anti-PD-1+LAG-3, but not anti-PD-1+CTLA-4, induces widespread TCR sharing among the different transcriptional states, as well as increased TCR diversity in responding patients. Our data suggest doublet regimen-specific transcriptional and clonal dynamics of tumor-reactive CD8+ T cells.

Mechanistic insight for T-cell exclusion by cancer-associated fibroblasts in human lung cancer

The tumor stroma consists mainly of extracellular matrix, fibroblasts, immune cells, and vasculature. Its structure and functions are altered during malignancy: tumor cells transform fibroblasts into cancer-associated fibroblasts, which exhibit immunosuppressive activities on which growth and metastasis depend. These include exclusion of immune cells from the tumor nest, cancer progression, and inhibition of T-cell-based immunotherapy. To understand these complex interactions, we measure the density of different cell types in the stroma using immunohistochemistry techniques on tumor samples from lung cancer patients. We incorporate these data into a minimal dynamical system, explore the variety of outcomes, and finally establish a spatio-temporal model that explains the cell distribution. We reproduce that cancer-associated fibroblasts act as a barrier to tumor expansion, but also reduce the efficiency of the immune response. Our conclusion is that the final outcome depends on the parameter values for each patient and leads to either tumor invasion, persistence, or eradication as a result of the interplay between cancer cell growth, T-cell cytotoxicity, and fibroblast activity. However, despite the existence of a wide range of scenarios, distinct trajectories, and patterns allow quantitative predictions that may help in the selection of new therapies and personalized protocols.

Single-cell profiling uncovers the intricate pathological niche diversity in brain, lymph node, bone, and adrenal metastases of lung cancer

PURPOSE

The aim of this study is to explore the pathological niche of cancer metastasis and the site-specific interactions between tumor cells and the microenvironment, understand the mechanisms driving metastasis progression and identify potential therapeutic targets.

METHODS

Data from four lung cancer metastasis datasets (GSE123902, GSE131907, GSE148071, and GSE186344) were downloaded and subjected to stringent quality control and filtering. Cell types were identified using canonical markers, and pseudotime trajectory analysis was performed to evaluate cell differentiation. Functional and pathway enrichment analyses, including ssGSEA and GO/KEGG, were conducted. CellphoneDB was used to analyze intercellular communication, ranking receptor-ligand interactions based on communication strength.

RESULTS

Eleven cell types were identified after quality control, revealing significant heterogeneity and site-specific functionality in lung cancer metastases. CTLs showed notable activity in antigen presentation and T-cell differentiation pathways, with DNAJB1⁺ CTLs playing a dominant role in cytotoxicity and immune regulation. B cells, myeloid cells, and CAFs were involved in immune modulation, defense, and matrix remodeling through specific signaling pathways. Tumor cell subclusters drove proliferation, migration, and immune evasion via immune-regulatory, Hippo, and TGF-beta pathways. No overlapping pathways were observed across metastatic sites. Cell communication analysis identified PPIA-BSG and APP-CD74 as key axes in brain and lymph node metastases, while FN1-Integrin and CTLA4-CD86 dominated in bone and adrenal metastases, respectively.

CONCLUSIONS

In summary, this study highlights the functional heterogeneity and site-specific interactions of cells in lung cancer metastases, providing insights into the mechanisms shaping metastatic niches and potential therapeutic strategies.

Combining spatial transcriptomics and ECM imaging in 3D for mapping cellular interactions in the tumor microenvironment

Tumors are complex ecosystems composed of malignant and non-malignant cells embedded in a dynamic extracellular matrix (ECM). In the tumor microenvironment, molecular phenotypes are controlled by cell-cell and ECM interactions in 3D cellular neighborhoods (CNs). While their inhibition can impede tumor progression, routine molecular tumor profiling fails to capture cellular interactions. Single-cell spatial transcriptomics (ST) maps receptor-ligand interactions but usually remains limited to 2D tissue sections and lacks ECM readouts. Here, we integrate 3D ST with ECM imaging in serial sections from one clinical lung carcinoma to systematically quantify molecular states, cell-cell interactions, and ECM remodeling in CN. Our integrative analysis pinpointed known immune escape and tumor invasion mechanisms, revealing several druggable drivers of tumor progression in the patient under study. This proof-of-principle study highlights the potential of in-depth CN profiling in routine clinical samples to inform microenvironment-directed therapies. A record of this paper's transparent peer review process is included in the supplemental information.

Dissection of single-cell landscapes for the development of chimeric antigen receptor T cells in Hodgkin lymphoma

ABSTRACT

The success of targeted therapies for hematological malignancies has heralded their potential as both salvage treatment and early treatment lines, reducing the need for high-dose, intensive, and often toxic chemotherapeutic regimens. For young patients with classic Hodgkin lymphoma (cHL), immunotherapies provide the possibility to lessen long-term, treatment-related toxicities. However, suitable therapeutic targets are lacking. By integrating single-cell dissection of the tumor landscape and an in-depth, single-cell-based off-tumor antigen prediction, we identify CD86 as a promising therapeutic target in cHL. CD86 is highly expressed on Hodgkin and Reed-Sternberg cancer cells and cHL-specific tumor-associated macrophages. We reveal CD86-CTLA-4 as a key suppressive pathway in cHL, driving T-cell exhaustion. Cellular therapies targeting CD86 had extraordinary efficacy in vitro and in vivo and were safe in immunocompetent mouse models without compromising bacterial host defense in sepsis models. Our results prove the potential value of anti-CD86 immunotherapies for treating cHL.

High Glucose Contribution to the TCA Cycle Is a Feature of Aggressive Non-Small Cell Lung Cancer in Patients

SIGNIFICANCE

Intraoperative 13C-glucose infusions in patients with NSCLC show that tumors with high labeling of TCA cycle intermediates progress rapidly, resulting in metastasis and early death. Blocking this pathway suppresses metastasis of human NSCLC cells in mice.

Advances in cancer genomics and precision oncology

BACKGROUND

Next-generation sequencing has revolutionized genome science over the last two decades. Indeed, the wealth of sequence information on our genome has deepened our understanding on cancer. Cancer is a genetic disease caused by genetic or epigenetic alternations that affect the expression of genes that control cell functions, particularly cell growth and division. Utilization of next-generation sequencing in cancer gene panels has enabled the identification of actionable gene alterations in cancer patients to guide personalized precision medicine.

OBJECTIVE

The aim is to provide information that can identify actionable gene alterations, enabling personalized precision medicine for cancer patients.

RESULTS & DISCUSSION

Equipped with next-generation sequencing techniques, international collaboration programs on cancer genomics have identified numerous mutations, gene fusions, microsatellite variations, copy number variations, and epigenetics changes that promote the transformation of normal cells into tumors. Cancer classification has traditionally been based on cell type or tissue-of-origin and the morphological characteristics of the cancer. However, interactive genomic analyses have currently reclassified cancers based on systemic molecular-based taxonomy. Although all cancer-causing genes and mechanisms have yet to be completely understood or identified, personalized or precision medicine is now currently possible for some forms of cancer. Unlike the "one-size-fits-all" approach of traditional medicine, precision medicine allows for customized or personalized treatment based on genomic information.

CONCLUSION

Despite the availability of numerous cancer gene panels, technological innovation in genomics and expansion of knowledge on the cancer genome will allow precision oncology to manage even more types of cancers.

Mycoplasma ovipnuemoniae impairs the immune response of sheep and suppresses neutrophil function by inhibiting S100A9

Mycoplasma pneumonia is a chronic respiratory disease that seriously affects the health of sheep. To date, little information is available about the damage caused by Mycoplasma ovipneumoniae (MO) pneumonia to host lungs. Here, after sheep were infected with MO for 28 days, severe inflammatory reactions and pathological damage occurred. By using single-cell RNA sequencing (scRNA-seq), all the transcriptome changes in 11 cell types in sheep lung tissue were systematically analyzed, and the key biological processes regulating inflammation and immunity were identified. Moreover, we constructed both intercellular communication models and differential expression maps of key regulatory genes for each cell subgroup. We also specifically focused on the response of T cell subpopulations and neutrophils to MO infection. Long-term infection may affect an organism's immune response, inhibit intercellular communication, and highlight the important role of the cyclophilin A (CypA) and macrophage migration inhibitory factor (MIF) pathways in intercellular communication. Notably, MO infection decreased the toxicity of CD8 effector T cells and depleted regulatory T cells, thus inhibiting normal cell function. Subsequently, emphasis was placed on the important role of the neutrophil marker gene S100A9 in promoting neutrophil clearance of MO through activation of the ERK signaling pathway and reactive oxygen species (ROS) burst in vitro. These results contribute to understanding the progression of MO infection in the lungs and provide a rich database on the molecular basis of the response to different cell types in MO infection.

Single-cell and spatial genomic landscape of non-small cell lung cancer brain metastases

Brain metastases frequently develop in patients with non-small cell lung cancer (NSCLC) and are a common cause of cancer-related deaths, yet our understanding of the underlying human biology is limited. Here we performed multimodal single-nucleus RNA and T cell receptor, single-cell spatial and whole-genome sequencing of brain metastases and primary tumors of patients with treatment-naive NSCLC. Chromosomal instability (CIN) is a distinguishing genomic feature of brain metastases compared with primary tumors, which we validated through integrated analysis of molecular profiling and clinical data in 4,869 independent patients, and a new cohort of 12,275 patients with NSCLC. Unbiased analyses revealed transcriptional neural-like programs that strongly enriched in cancer cells from brain metastases, including a recurring, CINhigh cell subpopulation that preexists in primary tumors but strongly enriched in brain metastases, which was also recovered in matched single-cell spatial transcriptomics. Using multiplexed immunofluorescence in an independent cohort of treatment-naive pairs of primary tumors and brain metastases from the same patients with NSCLC, we validated genomic and tumor-microenvironmental findings and identified a cancer cell population characterized by neural features strongly enriched in brain metastases. This comprehensive analysis provides insights into human NSCLC brain metastasis biology and serves as an important resource for additional discovery.

STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data

Unraveling the spatial configuration of the tumor microenvironment (TME) is crucial for elucidating tumor-immune interactions based on immuno-oncology. We present STopover, a novel approach utilizing spatially resolved transcriptomics (SRT) data and topological analysis to investigate the TME. By gradually lowering the feature threshold, connected components (CCs) are extracted based on spatial distance and persistence, with Jaccard indices quantifying their spatial overlap, and transcriptomic profiles are permutated to assess statistical significance. Applied to lung and breast cancer SRT, STopover revealed immune and stromal cell infiltration patterns, predicted key cell-cell communication, and identified relevant regions, shedding light on cancer pathophysiology (URL: https://github.com/bsungwoo/STopover ).

Integrative analysis of genetic variability and functional traits in lung adenocarcinoma epithelial cells via single-cell RNA sequencing, GWAS, bayesian deconvolution, and machine learning

BACKGROUND

Lung adenocarcinoma remains a leading cause of cancer-related mortality worldwide, characterized by high genetic and cellular heterogeneity, especially within the tumor microenvironment.

OBJECTIVE

This study integrates single-cell RNA sequencing (scRNA-seq) with genome-wide association studies (GWAS) using Bayesian deconvolution and machine learning techniques to unravel the genetic and functional complexity of lung adenocarcinoma epithelial cells.

METHODS

We performed scRNA-seq and GWAS analysis to identify critical cell populations affected by genetic variations. Bayesian deconvolution and machine learning techniques were applied to investigate tumor progression, prognosis, and immune-epithelial cell interactions, particularly focusing on immune checkpoint markers such as PD-L1 and CTLA-4.

RESULTS

Our analysis highlights the importance of genes like SLC2A1, which regulates glucose metabolism and correlates with tumor invasiveness and poor prognosis. Immune-epithelial interactions suggest a suppressive tumor microenvironment, potentially hindering immune responses. Additionally, machine learning models identify core prognostic genes such as F12, GOLM1, and S100P, which are significantly associated with patient survival.

CONCLUSIONS

This comprehensive approach provides novel insights into lung adenocarcinoma biology, emphasizing the role of genetic and immune factors in tumor progression. The findings support the development of personalized therapeutic strategies targeting both tumor cells and the immune microenvironment.

Dendritic cell maturation in cancer

Dendritic cells (DCs) are specialized antigen-presenting cells that are present at low abundance in the circulation and tissues; they serve as crucial immune sentinels by continually sampling their environment, migrating to secondary lymphoid organs and shaping adaptive immune responses through antigen presentation. Owing to their ability to orchestrate tolerogenic or immunogenic responses to a specific antigen, DCs have a pivotal role in antitumour immunity and the response to immune checkpoint blockade and other immunotherapeutic approaches. The multifaceted functions of DCs are acquired through a complex, multistage process called maturation. Although the role of inflammatory triggers in driving DC maturation was established decades ago, less is known about DC maturation in non-inflammatory contexts, such as during homeostasis and in cancer. The advent of single-cell technologies has enabled an unbiased, high-dimensional characterization of various DC states, including mature DCs. This approach has clarified the molecular programmes associated with DC maturation and also revealed how cancers exploit these pathways to subvert immune surveillance. In this Review, we discuss the mechanisms by which cancer disrupts DC maturation and highlight emerging therapeutic opportunities to modulate DC states. These insights could inform the development of DC-centric immunotherapies, expanding the arsenal of strategies to enhance antitumour immunity.

Transitional CXCL14+ cancer-associated fibroblasts enhance tumour metastasis and confer resistance to EGFR-TKIs, revealing therapeutic vulnerability to filgotinib in lung adenocarcinoma

BACKGROUND

The heterogeneity of cancer-associated fibroblasts (CAFs) has become a crucial focus in understanding cancer biology and treatment response, revealing distinct subpopulations with specific roles in tumor pathobiology. CAFs have also been shown to promote resistance in lung cancer cells to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). However, the specific CAF subsets responsible for driving tumor advancement and resistance to EGFR-TKIs in lung adenocarcinoma (LUAD) remain poorly understood.

METHODS

We integrate multiple scRNA-seq datasets to identify cell subclusters most relevant to tumor stage, patient survival, and EGFR-TKIs response. Additionally, in vitro and in vivo experiments, clinical tissue sample immunohistochemistry and patient plasma ELISA experiments are performed to validate key findings in independent LUAD cohorts.

RESULTS

By analyzing multiple scRNA-seq and spatial transcriptomic datasets, we identified a unique subset of CXCL14+ myofibroblastic CAFs (myCAFs), emerging during the early differentiation phase of pan-cancer invasiveness-associated THBS2⁺ POSTN⁺ COL11A1⁺ myCAFs. Notably, plasma levels of CXCL14 in LUAD patients correlate significantly with tumor stage. Mechanistically, this subset enhances tumor aggressiveness through epithelial-to-mesenchymal transition and angiogenesis. Among standard treatment regimens, transitional CXCL14+ myCAFs specifically confer resistance to EGFR-TKIs, while showing no significant impact on chemotherapy or immunotherapy outcomes. Through a pharmacological screen of FDA-approved drugs, we identified Filgotinib as an effective agent to counteract the EGFR-TKIs resistance conferred by this CAF subset.

CONCLUSIONS

In summary, our study highlights the role of the differentiated stage from transitional CXCL14+ myCAFs to invasiveness-associated myCAFs in driving tumor progression and therapy resistance in LUAD, positioning Filgotinib as a promising targeted therapy for this process. These insights may enhance patient stratification and inform precision treatment strategies in LUAD.

KEY POINTS

Single-cell analysis identifies transitional CXCL14+ myofibroblastic cancer-associated fibroblasts (myCAFs) predominantly exist in the advanced-stage lung adenocarcinoma (LUAD). Transitional CXCL14+ myCAFs fuel metastasis by promoting epithelial-mesenchymal transition (EMT) and angiogenesis on the spatial level. CXCL14 is a potential diagnostic marker for LUAD patients and predict the occurrence of metastasis. Transitional CXCL14+ myCAFs induce the resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) and JAK1 inhibitor, filgotinib could reverse the effect.

Effect of respiratory phase on three-dimensional quantitative parameters of pulmonary subsolid nodules in low-dose computed tomography screening for lung cancer

Background

In the screening of pulmonary subsolid nodules (SSNs), it is crucial to compare the quantitative parameters under consistent computed tomography (CT) acquisition conditions, including the same degree of lung inflation. When non-end-inspiratory chest CT scan is performed due to poor breath holding, there is a risk of inaccurate measurement of quantitative parameters and erroneous assessment of pulmonary nodule growth. This study aims to investigate the effect of respiratory phase on three-dimensional (3D) quantitative parameters of SSNs, and to further explore the impact of respiratory phase change on the judgment of SSNs growth during the follow-up of low-dose CT (LDCT) screening.

Methods

There were 255 pulmonary SSNs retrospectively found in 230 subjects who received low-dose paired inspiratory and expiratory chest CT screening. Quantitative parameters of lung and SSNs on paired inspiratory and expiratory CT were obtained. The change ratio of expiratory to inspiratory parameters was calculated and labeled as parameter(E-I)/I. Quantitative parameters were compared between inspiratory and expiratory CT. The difference of the change ratio of different quantitative parameters was also compared. The change ratio of quantitative parameters of SSNs was compared between different density types, sizes and locations. The 255 nodules were divided into two groups (the changed and unchanged group) according to the growth criteria. The quantitative parameters and the change ratio of quantitative parameters were compared between the two groups. The significant factors were included in the multivariate logistic regression analysis.

Results

There were statistical differences in all quantitative parameters of lung nodules between the inspiratory CT and the expiratory CT (all P<0.05). The change ratio of long axis diameter of nodules (7.14%) was the smallest, and the change ratio of volume of nodules (20.21%) was the largest. Significant differences were found in the change ratio of most quantitative parameters between part-solid nodules (PSNs) and pure ground-glass nodules (pGGNs). There was no statistical difference in the change ratio of all nodules' parameters between the ≤10 mm group and the >10 mm group (all P>0.05). Nodule density(E-I)/I in lower lobes was greater than that in upper lobes (P<0.001). Significant differences were found in the change ratio of lung volume, the change ratio of long axis diameter and density of nodules, and all quantitative parameters of nodules on inspiratory CT between the changed group and the unchanged group (all P<0.05). Multivariate logistic regression analysis showed that the lung density, long axis diameter, short axis diameter, surface area and density of nodules on inspiratory CT were independent indicators for predicting whether SSNs change with respiratory phase.

Conclusions

Respiratory phase had the greatest effect on the volume of pulmonary SSNs and the least effect on the long axis diameter. During follow-up, LDCT scan in different respiratory phases may interfere with the judgment of the growth of pulmonary SSNs.

ANKRD11 as a potential biomarker for brain metastasis from lung adenocarcinoma via cerebrospinal fluid liquid biopsy

Background

Brain metastasis (BM) explains the majority of lung cancer-related mortality, especially lung adenocarcinoma (LUAD). The extensive clinical adoption of liquid biopsy presents a minimally invasive approach for the early detection and treatment management of brain metastasis from lung adenocarcinoma (BM-LUAD). Nonetheless, biomarkers for assessing BM-LUAD remain insufficient. This study aims to reveal novel biomarkers for BM-LUAD through the liquid biopsy of cerebrospinal fluid (CSF).

Methods

Circulating tumor DNA (ctDNA)-based panel sequencing was conducted on CSF samples from seven patients with BM-LUAD. Additionally, single-cell RNA sequencing (scRNA-seq) data from normal lung tissue, primary tumors, and BMs were analyzed using publicly available datasets. Functional assays were performed on cell lines, complemented by in vivo studies in nude mice.

Results

CSF liquid biopsy identified high-frequency mutations in EGFR, BRCA2, and ANKRD11 among patients with BM-LUAD. Further analysis highlighted ANKRD11 as a potential biomarker, showing reduced expression in tumor tissues and significant prognostic implications. ScRNA-seq revealed a progressive decrease in ANKRD11 expression along tumor progression, while in vitro and in vivo assays confirmed its role in suppressing tumor invasion and metastasis.

Conclusions

Our study highlights the potential of ctDNA-based CSF liquid biopsy in identifying BM-LUAD, and the efficacy in revealing novel biomarkers for BM-LUAD. Data analyses and functional assays indicated ANKRD11 as a predictive biomarker for BM-LUAD. This result addresses, to some extent, the existing gap in biomarkers for BM-LUAD.

The role of B2M in cancer immunotherapy resistance: function, resistance mechanism, and reversal strategies

Immunotherapy has emerged as a preeminent force in the domain of cancer therapeutics and achieved remarkable breakthroughs. Nevertheless, the high resistance has become the most substantial impediment restricting its clinical efficacy. Beta-2 microglobulin (B2M), the light chain of major histocompatibility complex (MHC) class I, plays an indispensable part by presenting tumor antigens to cytotoxic T lymphocytes (CTLs) for exerting anti-tumor effects. Accumulating evidence indicates that B2M mutation/defect is one of the key mechanisms underlying tumor immunotherapy resistance. Therefore, elucidating the role played by B2M and devising effective strategies to battle against resistance are pressing issues. This review will systematically expound upon them, aiming to provide insight into the potential of B2M as a promising target in anticancer immune response.

VISTA Uncovers Missing Gene Expression and Spatial-induced Information for Spatial Transcriptomic Data Analysis

Characterizing cell activities within a spatially resolved context is essential to enhance our understanding of spatially-induced cellular states and features. While single-cell RNA-seq (scRNA-seq) offers comprehensive profiling of cells within a tissue, it fails to capture spatial context. Conversely, subcellular spatial transcriptomics (SST) technologies provide high-resolution spatial profiles of gene expression, yet their utility is constrained by the limited number of genes they can simultaneously profile. To address this limitation, we introduce VISTA, a novel approach designed to predict the expression levels of unobserved genes specifically tailored for SST data. VISTA jointly models scRNA-seq data and SST data based on variational inference and geometric deep learning, and incorporates uncertainty quantification. Using four SST datasets, we demonstrate VISTA's superior performance in imputation and in analyzing large-scale SST datasets with satisfactory time efficiency and memory consumption. The imputation of VISTA enables a multitude of downstream applications, including the detection of new spatially variable genes, the discovery of novel ligand-receptor interactions, the inference of spatial RNA velocity, the generation for spatial transcriptomics with in-silico perturbation, and an improved decomposition of spatial and intrinsic variations.

The fibroinflammatory response in cancer

Fibroinflammation refers to the highly integrated fibrogenic and inflammatory responses mediated by the concerted function of fibroblasts and innate immune cells in response to tissue perturbation. This process underlies the desmoplastic remodelling of the tumour microenvironment and thus plays an important role in tumour initiation, growth and metastasis. More specifically, fibroinflammation alters the biochemical and biomechanical signalling in malignant cells to promote their proliferation and survival and further supports an immunosuppressive microenvironment by polarizing the immune status of tumours. Additionally, the presence of fibroinflammation is often associated with therapeutic resistance. As such, there is increasing interest in targeting this process to normalize the tumour microenvironment and thus enhance the treatment of solid tumours. Herein, we review advances made in unravelling the complexity of cancer-associated fibroinflammation that can inform the rational design of therapies targeting this.

The C5a/C5aR1 Axis Promotes Migration of Tolerogenic Dendritic Cells to Lymph Nodes, Impairing the Anticancer Immune Response

The precise mechanisms by which the complement system contributes to the establishment of an immunosuppressive tumor microenvironment and promotes tumor progression remain unclear. In this study, we investigated the expression and function of complement C5a receptor 1 (C5aR1) in human and mouse cancer-associated dendritic cells (DC). First, we observed an overexpression of C5aR1 in tumor-infiltrating DCs, compared with DCs from the blood or spleen. C5aR1 expression was restricted to type 2 conventional DCs and monocyte-derived DCs, which displayed a tolerogenic phenotype capable of inhibiting T-cell activation and promoting tumor growth. C5aR1 engagement in DCs drove their migration from tumors to tumor-draining lymph nodes, where C5a levels were higher. We used this knowledge to optimize an anticancer therapy aimed at enhancing DC activity. In three syngeneic tumor models, C5aR1 inhibition significantly enhanced the efficacy of poly I:C, a Toll-like receptor 3 agonist, in combination with PD-1/PD-L1 blockade. The contribution of C5aR1 inhibition to the antitumor activity of the combination treatment relied on type 1 conventional DCs and antigen-specific CD8+ T cells, required lymphocyte egress from secondary lymphoid organs, and was associated with an increase in IFNγ signaling. In conclusion, our study highlights the importance of the C5a/C5aR1 axis in the biology of cancer-associated DCs and provides compelling evidence for the therapeutic potential of modulating the complement system to enhance DC-mediated immune responses against tumors.

Single cell transcriptomic analysis reveals tumor immune infiltration by macrophage cells gene signature in lung adenocarcinoma

BACKGROUND

Tumor-associated macrophages (TAMs) play pivotal roles in innate immunity and contribute to the advancement of lung cancer. We aimed to identify novel TAM-related biomarkers and significance of macrophage infiltration in lung adenocarcinoma (LUAD) through an integrative analysis of single-cell RNA-sequencing (scRNA-seq) data. To describe the cell atlas and construct a novel prognostic signature in LUAD.

METHODS

The gene signature linked to TAMs was identified utilizing Scanpy from the scRNA-seq dataset GSE131907. Subsequent analysis involved evaluating the expression levels of these genes, their potential molecular mechanisms, and prognostic significance in LUAD using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We also constructed a risk score models through LASSO Cox regression for these genes. The underlying mechanism was further elucidated through the application of GSEA, ESTIMATE, TIDE, and other bioinformatic algorithms.

RESULTS

Single-cell atlas was described by analyze 29 scRNA-seq samples from 19 LUAD patients. The TAMs-related gene signature (TGS) was identified as an independent prognostic factor by LASSO Cox regression analysis using differential expression genes (DEGs) derived from pro- and anti-inflammatory macrophage cells. Risk score model including nine TAMs-related genes (FOSL1, ZNF697, ADM, UBE2S, TICAM1, S100P, BIRC3, TLE1, and DEFB1) were obtained for prognosis construction. Moreover, the risk model underwent additional validation in four external GEO cohorts: GSE31210, GSE72094, GSE26939, and GSE30219. Interestingly, TGS-high tumors revealed enrichments in TGF-β signaling and hypoxia pathways, which shown low immune infiltration and immunosuppression by ESTIMATE and TIDE algorithm. The TGS-high risk group exhibited lower richness and diversity in the T-cell receptor (TCR) repertoire.

CONCLUSION

This study introduces a novel TGS score developed through LASSO Cox regression analysis, utilizing DEGs in pro- and anti-inflammatory macrophage cells. High TGS tumors exhibited enrichment in TGF-β signaling and hypoxia pathways, suggesting their potential utility in predicting prognosis and immune responses in patients with LUAD. These results offer promising implications for the development of therapeutic strategies for LUAD.

Identification of blood-derived exosomal tumor RNA signatures as noninvasive diagnostic biomarkers for multi-cancer: a multi-phase, multi-center study

BACKGROUND

Cancer remains a leading global cause of mortality, making early detection crucial for improving survival outcomes. The study aims to develop a machine learning-enabled blood-derived exosomal RNA profiling platform for multi-cancer detection and localization.

METHODS

In this multi-phase, multi-center study, we analyzed RNA from exosomes derived from peripheral blood plasma in 818 participants across eight cancer types during the discovery phase. Machine learning techniques were applied to identify potential pan-cancer biomarkers. During the screening and model validation phases, the sample size was progressively expanded to 1,385 participants in two steps, while the candidate biomarkers were refined into a set of 12 exosomal tumor RNA signatures (ETR.sig). In the subsequent model construction phase, diagnostic models were developed using the expanded cohort and ETR.sig. Statistical analyses included the calculation of receiver operating characteristic (ROC) curves and AUC values to assess the models' ability to distinguish cancer cases from controls and determine tumor origins. To further validate and explore the biological relevance of the identified biomarkers, we integrated tissue RNA-seq, single-cell data, and clinical information.

RESULTS

Machine learning analysis initially identified 33 candidate biomarkers, which were narrowed down to 20 ETR.sig in the screening phase and 12 ETR.sig in the validation phase. In the model construction phase, a diagnostic model based on ETR.sig, built using the Random Forest (RF) algorithm, showed excellent performance with an AUC of 0.915 for distinguishing pan-cancer from controls. The multi-class classification model also demonstrated strong classification power, with macro-average and micro-average AUCs of 0.983 and 0.985, respectively, for differentiating between eight cancer types. Additionally, tumor origin classification using the RF-based diagnostic models achieved high AUC values: BRCA 0.976, COAD 0.98, KIRC 0.947, LIHC 0.967, LUAD 0.853, OV 0.972, PAAD 0.977, and PRAD 0.898. Integration of tissue RNA-seq, single-cell data, and clinical information revealed key associations between ETR.sig-related genes and tumor development.

CONCLUSIONS

The study demonstrates the robust potential of exosomal RNA as a minimally invasive biomarker resource for cancer detection. The developed ETR.sig platform offers a promising tool for precision oncology and broad-spectrum cancer screening, integrating advanced computational models with nanoscale vesicle biology for accurate and rapid diagnosis.

RNA aptamer-mediated RNA nanotechnology for potential treatment of cardiopulmonary diseases

Ribonucleic acid (RNA) aptamers are single-stranded RNAs that bind to target proteins or other molecules with high specificity and affinity, modulating biological functions through distinct mechanisms. These aptamers can act n as antagonists to block pathological interactions, agonists to activate signaling pathways, or delivery vehicles for therapeutic cargos such as siRNAs and miRNAs. The advances in RNA nanotechnology further enhances the versatility of RNA aptamers, offering scalable platforms for engineering. In this review, we have summarized recent developments in RNA aptamer-mediated RNA nanotechnology and provide an overview of its potential in treating cardiovascular and respiratory disorders, including atherosclerosis, acute coronary syndromes, heart failure, lung cancer, pulmonary hypertension, asthma, chronic obstructive pulmonary disease (COPD), acute lung injury, viral respiratory infections, and pulmonary fibrosis. By integrating aptamer technologies with innovative delivery systems, RNA aptamers hold the potential to revolutionize the treatment landscape for cardiopulmonary diseases.

Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data

High-dimensional multiplexed imaging can reveal the spatial organization of tumour tissues at the molecular level. However, owing to the scale and information complexity of the imaging data, it is challenging to discover and thoroughly characterize the heterogeneity of tumour microenvironments. Here we show that self-supervised representation learning on data from imaging mass cytometry can be leveraged to distinguish morphological differences in tumour microenvironments and to precisely characterize distinct microenvironment signatures. We used self-supervised masked image modelling to train a vision transformer that directly takes high-dimensional multiplexed mass-cytometry images. In contrast with traditional spatial analyses relying on cellular segmentation, the vision transformer is segmentation-free, uses pixel-level information, and retains information on the local morphology and biomarker distribution. By applying the vision transformer to a lung-tumour dataset, we identified and validated a monocytic signature that is associated with poor prognosis.

Intratumoral Collagen Deposition Supports Angiogenesis Suggesting Anti-angiogenic Therapy in Armored and Cold Tumors

A previous study classifies solid tumors based on collagen deposition and immune infiltration abundance, identifying a refractory subtype termed armored & cold tumors, characterized by elevated collagen deposition and diminished immune infiltration. Beyond its impact on immune infiltration, collagen deposition also influences tumor angiogenesis. This study systematically analyzes the association between immuno-collagenic subtypes and angiogenesis across diverse cancer types. As a result, armored & cold tumors exhibit the highest angiogenic activity in lung adenocarcinoma (LUAD). Single-cell and spatial transcriptomics reveal close interactions and spatial co-localization of fibroblasts and endothelial cells. In vitro experiments demonstrate that collagen stimulates tumor cells to express vascular endothelial growth factor A (VEGFA) and directly enhances vessel formation and endothelial cell proliferation through sex determining region Y box 18 (SOX18) upregulation. Collagen inhibition via multiple approaches effectively suppresses tumor angiogenesis in vivo. In addition, armored & cold tumors display superior responsiveness to anti-angiogenic therapy in advanced LUAD cohorts. Post-immunotherapy resistance, the transformation into armored & cold tumors emerges as a potential biomarker for selecting anti-angiogenic therapy. In summary, collagen deposition is shown to drive angiogenesis across various cancers, providing a novel and actionable framework to refine therapeutic strategies combining chemotherapy with anti-angiogenic treatments.

Deciphering key roles of B cells in prognostication and tailored therapeutic strategies for lung adenocarcinoma: a multi-omics and machine learning approach towards predictive, preventive, and personalized treatment strategies

Background

Lung adenocarcinoma (LUAD) remains a significant global health challenge, with an urgent need for innovative predictive, preventive, and personalized medicine (PPPM) strategies to improve patient outcomes. This study leveraged multi-omics and machine learning approaches to uncover the prognostic roles of B cells in LUAD, thereby reinforcing the PPPM approach.

Methods

We integrated multi-omics data, including bulk RNA, ATAC-seq, single-cell RNA, and spatial transcriptomics sequencing, to characterize the B cell landscape in LUAD within the PPPM framework. Subsequently, we developed an integrative machine learning program that generated the Scissor+ related B cell score (SRBS). This score was validated in the training and validation sets, and its prognostic value was assessed along with clinical features to develop predictive nomograms. This study further assessed the role of SRBS and SRBS genes in response to immunotherapy and identified personalized drug targets for distinct risk subgroups, with gene expression verified experimentally to ensure tailored medical interventions.

Results

Our analysis identified 79 Scissor+ B cell genes linked to LUAD prognosis, supporting the predictive aspect of PPPM. The SRBS model, which utilizes multiple machine learning algorithms, performed excellently in predicting prognosis and clinical transformation, embodying the preventive and personalized aspects of PPPM. Multifactorial analysis confirmed that SRBS was an independent prognostic factor. We observed varying biological functions and immune cell infiltration in the tumor immune microenvironment (TIME) between the high- and low-SRBS groups, underscoring personalized treatment approaches. Notably, patients with elevated SRBS may exhibit resistance to immunotherapy but show increased sensitivity to chemotherapy and targeted therapies. Additionally, we found that LDHA, as an SRBS gene with significant clinical implications, may regulate the sensitivity of LUAD cells to cisplatin.

Conclusion

This study presents a B cell-associated gene signature that serves as a prognostic marker to facilitate personalized treatment for patients with LUAD, adhering to the principles of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00390-4.

Metabolic reprogramming of neutrophils in the tumor microenvironment: Emerging therapeutic targets

Neutrophils are pivotal in the immune system and have been recognized as significant contributors to cancer development and progression. These cells undergo metabolic reprogramming in response to various stimulus, including infections, diseases, and the tumor microenvironment (TME). Under normal conditions, neutrophils primarily rely on aerobic glucose metabolism for energy production. However, within the TME featured by hypoxic and nutrient-deprived conditions, they shift to altered anaerobic glycolysis, lipid metabolism, mitochondrial metabolism and amino acid metabolism to perform their immunosuppressive functions and facilitate tumor progression. Targeting neutrophils within the TME is a promising therapeutic approach. Yet, focusing on their metabolic pathways presents a novel strategy to enhance cancer immunotherapy. This review synthesizes the current understanding of neutrophil metabolic reprogramming in the TME, with an emphasis on the underlying molecular mechanisms and signaling pathways. Studying neutrophil metabolism in the TME poses challenges, such as their short lifespan and the metabolic complexity of the environment, necessitating the development of advanced research methodologies. This review also discusses emerging solutions to these challenges. In conclusion, given their integral role in the TME, targeting the metabolic pathways of neutrophils could offer a promising avenue for cancer therapy.

Automated Laser-Assisted Single-Cell Sorting for Cell Functional and RNA Sequencing

Accurate and efficient sorting of single target cells is crucial for downstream single-cell analysis, such as RNA sequencing, to uncover cellular heterogeneity and functional characteristics. However, conventional single-cell sorting techniques, such as manual micromanipulation or fluorescence-activated cell sorting, do not match current demands and are limited by low throughput, low sorting efficiency and precision, or limited cell viability. Here, we report an automated, highly efficient single-cell sorter, integrating laser-induced forward transfer (LIFT) with a high-throughput picoliter micropore array. The micropore array was surface-functionalized to manipulate liquid surface tension, facilitating the formation of single-cell picoliter droplets in the micropores to realize automated and highly efficient (>80%) single-cell isolation. Using an in-house built microscopic system, rare target cells were identified and automatically retrieved by LIFT with precise sorting efficiency (about 100%) for downstream single-cell analysis while maintaining high cell viability (about 80%). As a case demonstration, we demonstrated the accurate sorting of rare transfected PC-9 cells and post-transfection cell culture, minimizing cell loss and the risk of contamination. Furthermore, we performed single-cell RNA sequencing and showed that high-quality single-cell transcriptome information was efficiently and reliably obtained during cell sorting, preventing additional costs due to low sorting accuracy. The single-cell sorter will become invaluable for single-cell analysis, laying the foundation for multiomics analysis and precision medicine research.

Cancer-associated fibroblasts promote EGFR-TKI resistance via the CTHRC1/glycolysis/H3K18la positive feedback loop

Acquired resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) remains a major challenge in the treatment of lung cancer. Cancer associated fibroblasts (CAFs) play a key role in promoting resistance to anti-cancer therapies. This study identified a subpopulation of CAFs characterized by the overexpression of collagen triple helix repeat-containing 1 (CTHRC1) through single-cell RNA sequencing of lung cancer patients undergoing EGFR-TKI treatment. These CTHRC1+ CAFs were enriched in drug-resistant tumors. Mechanistically, CTHRC1+ CAFs enhance the glycolytic activity of cancer cells by activating the TGF-β/Smad3 signaling pathway. Excess lactate produced in the process of glycolysis further upregulates CTHRC1 expression in CAFs through histone lactylation, creating a positive feedback loop that sustains EGFR-TKI resistance. The study also demonstrated that Gambogenic Acid, a natural compound, can disrupt this feedback loop, thereby improving the efficacy of EGFR-TKI therapy. Additionally, the presence of CTHRC1+ CAFs in tumor tissues could serve as a biomarker for predicting the response to EGFR-TKI therapy and patient prognosis. Overall, this study highlights the significant role of CAFs in EGFR-TKI resistance and suggests that targeting CTHRC1+ CAFs could be a promising strategy to overcome drug resistance in lung cancer.

Cancer brain metastasis: molecular mechanisms and therapeutic strategies

Brain metastases (BMs) are the most common intracranial tumors in adults and the major cause of cancer-related morbidity and mortality. The occurrence of BMs varies according to the type of primary tumors with most frequence in lung cancer, melanoma and breast cancer. Among of them, lung cancer has been reported to have a higher risk of BMs than other types of cancers with 40 ~ 50% of such patients will develop BMs during the course of disease. BMs lead to many neurological complications and result in a poor quality of life and short life span. Although the treatment strategies were improved for brain tumors in the past decades, the prognosis of BMs patients is grim. Poorly understanding of the molecular and cellular characteristics of BMs and the complicated interaction with brain microenvironment are the major reasons for the dismal prognosis of BM patients. Recent studies have enhanced understanding of the mechanisms of BMs. The newly identified potential therapeutic targets and the advanced therapeutic strategies have brought light for a better cure of BMs. In this review, we summarized the mechanisms of BMs during the metastatic course, the molecular and cellular landscapes of BMs, and the advances of novel drug delivery systems for overcoming the obstruction of blood-brain barrier (BBB). We further discussed the challenges of the emerging therapeutic strategies, such as synergistic approach of combining targeted therapy with immunotherapy, which will provide vital clues for realizing the precise and personalized medicine for BM patients in the future.

Integration of single-cell and bulk RNA sequencing to identify a distinct tumor stem cells and construct a novel prognostic signature for evaluating prognosis and immunotherapy in LUAD

BACKGROUND

Cancer stem cells (CSCs) are crucial for lung adenocarcinoma (LUAD). This study investigates tumor stem cell gene signatures in LUAD using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq), aiming to develop a prognostic tumor stem cell marker signature (TSCMS) model.

METHODS

LUAD scRNA-seq and RNA-seq data were analyzed. CytoTRACE software quantified the stemness score of tumor-derived epithelial cell clusters. Gene Set Variation Analysis (GSVA) identified potential biological functions in different clusters. The TSCMS model was constructed using Lasso-Cox regression, and its prognostic value was assessed through Kaplan-Meier, Cox regression, and receiver-operating characteristic (ROC) curve analyses. Immune infiltration was evaluated using the Cibersortx algorithm, and drug response prediction was performed using the pRRophetic package. TAF10 functional investigations in LUAD cells involved bioinformatics analysis, qRT-PCR, Western blot, immunohistochemistry, and assays for cell proliferation.

RESULTS

Seven distinct cell clusters were identified by CytoTRACE, with epithelial cell cluster 1 (Epi_C1) showing the highest stemness potential. The TSCMS model included 49 tumor stemness-related genes; high-risk patients exhibited lower immune and ESTIMATE scores and increased tumor purity. Significant differences in immune landscapes and chemotherapy sensitivity were observed between risk groups. TAF10 positively correlated with RNA expression-based stemness scores in various tumors, including LUAD. It was over-expressed in LUAD cell lines and clinical tumor tissues, with high expression linked to poor prognosis. Silencing TAF10 inhibited LUAD cell proliferation and tumor sphere formation.

CONCLUSIONS

This study demonstrates the TSCMS model's prognostic value in LUAD, reveals insights into immune infiltration and therapeutic response, and identifies TAF10 as a potential therapeutic target.

Single-cell transcriptomics reveals the multidimensional dynamic heterogeneity from primary to metastatic gastric cancer

Reprogramming of the tumor microenvironment (TME) plays a critical role in gastric cancer (GC) progression and metastasis. However, the multidimensional features between primary tumors and organ-specific metastases remain poorly understood. In this study, we characterized the dynamic heterogeneity of GC from primary to metastatic stages. We identified seven major cell types and 27 immune and stromal subsets. Immune cells decreased, while immunosuppressive cells increased in ovarian and peritoneal metastases. A 30-gene signature for ovarian metastasis was validated in GC cohorts. Additionally, critical ligand-receptor interactions, including LGALS9-MET in liver metastasis and PVR-TIGIT in lymph node metastasis, were identified as potential therapeutic targets. Furthermore, CLOCK, a transcription factor, was associated with poor prognosis and influenced immune cell interactions and migration. Collectively, this study provides valuable insights into TME dynamics in GC and highlights potential avenues for targeted therapies.

Mitochondrial carrier homolog 2 is important for mitochondrial functionality and non-small cell lung cancer cell growth

Discovering new molecular targets for non-small cell lung cancer (NSCLC) is critically important. Enhanced mitochondrial function can promote NSCLC progression by enabling metabolic reprogramming, resistance to apoptosis, and increased cell proliferation. Mitochondrial carrier homolog 2 (MTCH2), located in the outer mitochondrial membrane, is pivotal in regulating mitochondrial activities. This study examines MTCH2 expression and its functional role in NSCLC. Bioinformatic analysis showed that MTCH2 is overexpressed in NSCLC tissues, correlating with poor prognosis and other key clinical parameters of the patients. In addition, single-cell sequencing data revealed higher MTCH2 expression levels in cancer cells of NSCLC tumor mass. Moreover, MTCH2 is also upregulated in locally-treated NSCLC tissues and multiple primary/established human NSCLC cells. In various NSCLC cells, silencing MTCH2 via targeted shRNA or knockout (KO) using the CRISPR/Cas9 method significantly hindered cell proliferation, migration and invasion, while inducing apoptosis. MTCH2 knockdown or KO robustly impaired mitochondrial function, as indicated by reduced mitochondrial respiration, decreased complex I activity, lower ATP levels, lower mitochondrial membrane potential (mitochondrial depolarization), and increased reactive oxygen species (ROS) production. Conversely, ectopic overexpression of MTCH2 in primary NSCLC cells enhanced mitochondrial complex I activity and ATP production, promoting cell proliferation and migration. In vivo, the intratumoral injection of MTCH2 shRNA adeno-associated virus (aav) impeded the growth of subcutaneous xenografts of primary NSCLC cells in nude mice. In MTCH2 shRNA aav-injected NSCLC xenograft tissues, there was decreases in MTCH2 expression, mitochondrial complex I activity, ATP content, and the glutathione (GSH)/glutathione disulfide (GSSG) ratio, but increase in thiobarbituric acid reactive substances (TBAR) activity. Additionally, MTCH2 silencing led to reduced Ki-67 staining but increased apoptosis in NSCLC xenografts. Collectively, these findings demonstrate that overexpressed MTCH2 promotes NSCLC cell growth potentially through the maintenance of mitochondrial hyper-function, highlighting MTCH2 as a novel and promising therapeutic target for treating this disease.

A novel classification method for LUAD that guides personalized immunotherapy on the basis of the cross-talk of coagulation- and macrophage-related genes

Purpose

The coagulation process and infiltration of macrophages affect the progression and prognosis of lung adenocarcinoma (LUAD) patients. This study was designed to explore novel classification methods that better guide the precise treatment of LUAD patients on the basis of coagulation and macrophages.

Methods

Weighted gene coexpression network analysis (WGCNA) was applied to identify M2 macrophage-related genes, and TAM marker genes were acquired through the analysis of scRNA-seq data. The MSigDB and KEGG databases were used to obtain coagulation-associated genes. The intersecting genes were defined as coagulation and macrophage-related (COMAR) genes. Unsupervised clustering analysis was used to evaluate distinct COMAR patterns for LUAD patients on the basis of the COMAR genes. The R package "limma" was used to identify differentially expressed genes (DEGs) between COMAR patterns. A prognostic risk score model, which was validated through external data cohorts and clinical samples, was constructed on the basis of the COMAR DEGs.

Results

In total, 33 COMAR genes were obtained, and three COMAR LUAD subtypes were identified on the basis of the 33 COMAR genes. There were 341 DEGs identified between the three COMAR subtypes, and 60 prognostic genes were selected for constructing the COMAR risk score model. Finally, 15 prognosis-associated genes (CORO1A, EPHA4, FOXM1, HLF, IFIH1, KYNU, LY6D, MUC16, PPARG, S100A8, SPINK1, SPINK5, SPP1, VSIG4, and XIST) were included in the model, which was efficient and robust in predicting LUAD patient prognosis and clinical outcomes in patients receiving anti-PD-1/PD-L1 immunotherapy.

Conclusions

LUAD can be classified into three subtypes according to COMAR genes, which may provide guidance for precise treatment.

Transcriptomic profiles of single-cell autophagy-related genes (ATGs) in lung diseases

Autophagy related genes (ATGs) play essential roles in maintaining cellular functions, although biological and pathological alterations of ATG phenotypes remain poorly understood. To address this knowledge gap, we utilized the single-cell sequencing technology to elucidate the transcriptomic atlas of ATGs in lung diseases, with a focus on lung epithelium and lymphocytes. This study conducted a comprehensive investigation into RNA profiles of ATGs in the lung tissues obtained from healthy subjects and patients with different lung diseases through single-cell RNA sequencing (scRNA-seq), including COVID-19 related acute lung damage, idiopathic pulmonary fibrosis (IPF), chronic obstructive pulmonary disease (COPD), systemic sclerosis (SSC), and lung adenocarcinoma (LUAD). Our findings revealed significant variations of ATGs expression across lung epithelial cell subsets, e.g., over-expression of MAPK8 in basal cells, ATG10 in club cells, and BCL2 in a goblet cell subset. The changes of autophagy-related pathways varied between lung epithelial and lymphocyte subsets. We identified the disease-associated changes in ATG expression, including significant alterations in BCL2, BCL2L1, PRKCD, and PRKCQ in inflammatory lung diseases (COPD and IPF), and MAP2K7, MAPK3, and RHEB in lung cancer (LUAD), as compared to normal lung tissues. Key ligand-receptor pairs (e.g., CD6-ALCAM, CD99-CD99) and signaling pathways (e.g., APP, CD74) might serve as biomarkers for lung diseases. To evaluate ATGs responses to external challenges, we examined ATGs expression in different epithelial cell lines exposed to cigarette smoking extract (CSE), lysophosphatidylcholine (lysoPC), lipopolysaccharide (LPS), and cholesterol at various doses and durations. Notable changes were observed in CFLAR, EIF2S1, PPP2CA, and PPP2CB in A549 and H1299 against CSE and LPS. The heterogeneity of ATGs expression was dependent on cell subsets, pathologic conditions, and challenges, as well as varied among cellular phenotypes, functions, and behaviors, and the severity of lung diseases. In conclusion, our data might provide new insights into the roles of ATGs in epithelial biology and pulmonary disease pathogenesis, with implications for disease progression and prognosis.

Single-cell RNA sequencing reveals tumor heterogeneity in small cell neuroendocrine cervical carcinoma

Small cell neuroendocrine cervical carcinoma (SCNECC) is an aggressive gynecological malignancy with poor prognosis. The precision therapeutic strategies for SCNECC are severely limited by the complex tumor microenvironment. Here, we mapped the single-cell landscape of a total of six samples from matched SCNECC cancerous foci and normal adjacent cervical tissues. Through analysis of 68,455 high-quality cells, malignant epithelial cells were identified with increased neuroendocrine differentiation and reduced keratinization. Within four epithelial cell clusters, the key transcription factors ASCL1, NEUROD1, POU2F3, and YAP1 defined molecular subtypes. Transitional trajectory among subtypes characterized two distinct carcinogenesis pathways in SCNECC. The P-type SCNECC showed potentially enhanced immune infiltration over other subtypes. Intercellular communication analysis identified several immune checkpoints and differentially expressed signaling pathways among subtypes. Through western blotting, the TC-YIK cell line was identified as an N-type SCNECC cell with high expression of SLFN11 and mTOR. Based on immunohistochemical staining of malignant subtyping markers, a cohort of 66 SCNECC patients from our hospital were divided into five subtypes. We further combined YAP1 expression with other clinicopathological factors (Cox p < 0.05) to establish a prognostic nomogram. Overall, these findings provide clues for tumorigenesis, precision treatments and prognostic prediction in SCNECC.

Targeting HVEM-GPT2 axis: a novel approach to T cell activation and metabolic reprogramming in non-small cell lung cancer therapy

BACKGROUND

The modulation of tumor microenvironments through immune checkpoint pathways is pivotal for the development of effective cancer immunotherapies. This study aims to explore the role of HVEM in non-small cell lung cancer (NSCLC) microenvironment.

METHODS

The lung cancer datasets for this study were directly downloaded from The Cancer Genome Atlas (TCGA). Single-cell data were sourced from the Tumor Immune Single-cell Hub (TISCH). Multiplex immunohistochemistry (mIHC) was used to explore the cellular composition and spatial distribution of HVEM in lung cancer immune microenvironment. The immune microenvironment of HVEM KO mice bearing mouse lung cancer cell was also evaluated. Co-cultured system and phenotype assays facilitated the examination of Jurkat T cells' effect on A549 and H1299 lung cancer cells. Quantitative PCR and Western blotting determined gene and protein expression, respectively, cellular respiration was measured through oxygen consumption rate (OCR) assays. Lung cancer cells co-cultured with Jurkat T cells were xenografted into nude mice to evaluate tumor growth and metastatic potential. Next, RNA-seq, COIP, Dual-luciferase reporter experiment, and CHIP-seq were used to explore the potential underlying mechanism.

RESULTS

In our study, we investigated the role of HVEM in the microenvironment of NSCLC and its implications in immunotherapy. Crucially, HVEM, part of the tumor necrosis factor receptor superfamily, influences T cell activation, potentially impacting immunotherapeutic outcomes. Using the TIDE algorithm, our results showcased a link between HVEM levels and immune dysfunction in NSCLC patients. Delving deeper into the NSCLC microenvironment, we found HVEM predominantly expressed in T cell subpopulations. CD8 + HVEM + and CD4 + HVEM + indicated better prognosis in lung adenocarcinoma tissue microarray using multiplex immunohistochemistry. Activated T cells, particularly from the Jurkat cell line, significantly inhibited NSCLC progression, reducing both proliferation and invasion capabilities of A549 and H1299 lung cancer cell lines. In vivo models reinforced these observations. Manipulating HVEM expression revealed its essential role in T cell survival and activation. In addition, animal experiments revealed the importance of HVEM in maintaining activated peripheral immunity and inflamed local tumor microenvironment. Furthermore, our data suggest that HVEM is pivotal in T cell metabolic reprogramming, transitioning from oxidative phosphorylation to aerobic glycolysis. RNA sequencing illuminated a potential relationship between HVEM and GPT2, an enzyme tied to amino acid metabolism and cellular energetics. Subsequent experiments confirmed that HVEM's influence on T cell activation and metabolism is potentially mediated through its regulation of GPT2. In addition, GATA1 was validated to regulate HVEM expression in activated Jurkat T cells.

CONCLUSIONS

Our study establishes that HVEM significantly influences T cell functionality and NSCLC cell dynamics, pinpointing the HVEM-GPT2 axis as a promising target for NSCLC therapy.

Functional heterogeneity of fibroblasts in primary tumors and metastases

Cancer-associated fibroblasts (CAFs) are abundant components of the tumor microenvironment (TME) of most solid malignancies and have emerged as key regulators of cancer progression and therapy response. Although recent technological advances have uncovered substantial CAF molecular heterogeneity at the single-cell level, defining functional roles for most described CAF populations remains challenging. With the aim of bridging CAF molecular and functional heterogeneity, this review focuses on recently identified functional interactions of CAF subtypes with malignant cells, immune cells, and other stromal cells in primary tumors and metastases. Dissecting the heterogeneous functional crosstalk of specific CAF populations with other components is starting to uncover candidate combinatorial strategies for therapeutically targeting the TME and cancer progression.

A deep learning framework for in silico screening of anticancer drugs at the single-cell level

Tumor heterogeneity plays a pivotal role in tumor progression and resistance to clinical treatment. Single-cell RNA sequencing (scRNA-seq) enables us to explore heterogeneity within a cell population and identify rare cell types, thereby improving our design of targeted therapeutic strategies. Here, we use a pan-cancer and pan-tissue single-cell transcriptional landscape to reveal heterogeneous expression patterns within malignant cells, precancerous cells, as well as cancer-associated stromal and endothelial cells. We introduce a deep learning framework named Shennong for in silico screening of anticancer drugs for targeting each of the landscape cell clusters. Utilizing Shennong, we could predict individual cell responses to pharmacologic compounds, evaluate drug candidates' tissue damaging effects, and investigate their corresponding action mechanisms. Prioritized compounds in Shennong's prediction results include FDA-approved drugs currently undergoing clinical trials for new indications, as well as drug candidates reporting anti-tumor activity. Furthermore, the tissue damaging effect prediction aligns with documented injuries and terminated discovery events. This robust and explainable framework has the potential to accelerate the drug discovery process and enhance the accuracy and efficiency of drug screening.

Transcriptome size matters for single-cell RNA-seq normalization and bulk deconvolution

The variation of transcriptome size across cell types significantly impacts single-cell RNA sequencing (scRNA-seq) data normalization and bulk RNA-seq cellular deconvolution, yet this intrinsic feature is often overlooked. Here we introduce ReDeconv, a computational algorithm that incorporates transcriptome size into scRNA-seq normalization and bulk deconvolution. ReDeconv introduces a scRNA-seq normalization approach, Count based on Linearized Transcriptome Size (CLTS), which corrects differential expressed genes typically misidentified by standard count per 10 K normalization, as confirmed by orthogonal validations. By maintaining transcriptome size variation, CLTS-normalized scRNA-seq enhances the accuracy of bulk deconvolution. Additionally, ReDeconv mitigates gene length effects and models expression variances, thereby improving deconvolution outcomes, particularly for rare cell types. Evaluated with both synthetic and real datasets, ReDeconv surpasses existing methods in precision. ReDeconv alters the practice and provides a new standard for scRNA-seq analyses and bulk deconvolution. The software packages and a user-friendly web portal are available.

Canonical and noncanonical NOTCH signaling in the nongenetic resistance of cancer: distinct and concerted control

Therapeutic resistance in cancer is responsible for numerous cancer deaths in clinical practice. While target mutations are well recognized as the basis of genetic resistance to targeted therapy, nontarget mutation resistance (or nongenetic resistance) remains poorly characterized. Despite its complex and unintegrated mechanisms in the literature, nongenetic resistance is considered from our perspective to be a collective response of innate or acquired resistant subpopulations in heterogeneous tumors to therapy. These subpopulations, e.g., cancer stem-like cells, cancer cells with epithelial-to-mesenchymal transition, and drug-tolerant persisters, are protected by their resistance traits at cellular and molecular levels. This review summarizes recent advances in the research on resistant populations and their resistance traits. NOTCH signaling, as a central regulator of nongenetic resistance, is discussed with a special focus on its canonical maintenance of resistant cancer cells and noncanonical regulation of their resistance traits. This novel view of canonical and noncanonical NOTCH signaling pathways is translated into our proposal of reshaping therapeutic strategies targeting NOTCH signaling in resistant cancer cells. We hope that this review will lead researchers to study the canonical and noncanonical arms of NOTCH signaling as an integrated resistant mechanism, thus promoting the development of innovative therapeutic strategies.

Stromal architecture and fibroblast subpopulations with opposing effects on outcomes in hepatocellular carcinoma

Dissecting the spatial heterogeneity of cancer-associated fibroblasts (CAFs) is vital for understanding tumor biology and therapeutic design. By combining pathological image analysis with spatial proteomics, we revealed two stromal archetypes in hepatocellular carcinoma (HCC) with different biological functions and extracellular matrix compositions. Using paired single-cell RNA and epigenomic sequencing with Stereo-seq, we revealed two fibroblast subsets CAF-FAP and CAF-C7, whose spatial enrichment strongly correlated with the two stromal archetypes and opposing patient prognosis. We discovered two functional units, one is the intratumor inflammatory hub featured by CAF-FAP plus CD8_PDCD1 proximity and the other is the marginal wound-healing hub with CAF-C7 plus Macrophage_SPP1 co-localization. Inhibiting CAF-FAP combined with anti-PD-1 in orthotopic HCC models led to improved tumor regression than either monotherapy. Collectively, our findings suggest stroma-targeted strategies for HCC based on defined stromal archetypes, raising the concept that CAFs change their transcriptional program and intercellular crosstalk according to the spatial context.

Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics

An essential task in spatial transcriptomics is identifying spatially variable genes (SVGs). Here, we present Celina, a statistical method for systematically detecting cell type-specific SVGs (ct-SVGs)-a subset of SVGs exhibiting distinct spatial expression patterns within specific cell types. Celina utilizes a spatially varying coefficient model to accurately capture each gene's spatial expression pattern in relation to the distribution of cell types across tissue locations, ensuring effective type I error control and high power. Celina proves powerful compared to existing methods in single-cell resolution spatial transcriptomics and stands as the only effective solution for spot-resolution spatial transcriptomics. Applied to five real datasets, Celina uncovers ct-SVGs associated with tumor progression and patient survival in lung cancer, identifies metagenes with unique spatial patterns linked to cell proliferation and immune response in kidney cancer, and detects genes preferentially expressed near amyloid-β plaques in an Alzheimer's model.

Single-cell and spatial transcriptomics reveal a potential role of ATF3 in brain metastasis of lung adenocarcinoma

Background

Brain metastasis (BrM) has been a challenge for lung cancer treatment, but the mechanisms underlying lung cancer BrM remain elusive. This study aims to dissect cellular components and their spatial distribution in human BrM tumors of lung adenocarcinoma (LUAD) and identify potential therapeutic targets.

Methods

We performed single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) on three LUAD BrMs, and validated our findings using public scRNA-seq data of 10 LUAD BrMs. Western blotting, quantitative real-time polymerase chain reaction (qRT-PCR) and functional experiments were employed for experimental studies.

Results

By combining scRNA-seq and ST, our analysis revealed the inter- and intra-tumoral heterogeneity of cellular components and their spatial localization within LUAD BrMs. Through RNA velocity and transcription factor (TF) regulatory activity analyses, we identified ATF3 as a potential regulator of the mesenchymal-epithelial transition (MET) program, which plays crucial roles in the colonization of tumor cells at metastatic sites. Furthermore, we demonstrated that knockdown of ATF3 significantly inhibited cancer cell proliferation while promoting cancer cell migration. Mechanistically, ATF3 knockdown could reverse the MET program. Additionally, we revealed that LGALS3/ANXA2-mediated cell-cell interaction between macrophage and tumor cells may also promote the MET program.

Conclusions

Our study provides a single-cell atlas of the cellular composition in BrM of LUAD and identifies ATF3 as a potential therapeutic target for BrM treatment.