The spatial transcriptomic landscape of non-small cell lung cancer brain metastasis

Neoantigens combined with in situ cancer vaccination induce personalized immunity and reshape the tumor microenvironment

Neoantigen (nAg) vaccines can induce anti-tumor specific immunity, and tumor killing promotes further antigen diffusion, which is expected to improve prognosis. However, the mutation of cancer cells under the selective pressure of vaccines and the immunosuppressive tumor microenvironment make the therapeutic effect unsatisfactory. Here, we develop a nanovaccine (nAg-MRDE/Mn) that can deliver nAg and induce in situ cancer vaccination to synergistically promote a personalized immune response, enhance antigen diffusion, and improve the microenvironment by modulating immunosuppressive cells and activating the innate immune response. Experiments show that nAgs are presented by dendritic cells and expressed by T cells, which cooperate with in situ vaccination to stimulate specific immunity. Cells involved in immunosuppression, such as M2 macrophages and regulatory T cells, are down-regulated, while M1 macrophages and natural killer cells are increased. In addition, the hydrogel loaded with chemokines and nAg-MRDE/Mn inhibits postoperative tumor recurrence, and the combination of nAg-MRDE/Mn and αPD-1 improves the therapeutic effect of αPD-1. This study validates the clinical potential of this strategy and provides ideas for improving neoantigen vaccines.

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.

Tumour and microenvironment crosstalk in NSCLC progression and response to therapy

The treatment landscape of non-small-cell lung cancer (NSCLC) is evolving rapidly, driven by advances in the development of targeted agents and immunotherapies. Despite this progress, some patients have suboptimal responses to treatment, highlighting the need for new therapeutic strategies. In the past decade, the important role of the tumour microenvironment (TME) in NSCLC progression, metastatic dissemination and response to treatment has become increasingly evident. Understanding the complexity of the TME and its interactions with NSCLC can propel efforts to improve current treatment modalities, overcome resistance and develop new treatments, which will ultimately improve the outcomes of patients. In this Review, we provide a comprehensive view of the NSCLC TME, examining its components and highlighting distinct archetypes characterized by spatial niches within and surrounding tumour nests, which form complex neighbourhoods. Next, we explore the interactions within these components, focusing on how inflammation and immunosuppression shape the dynamics of the NSCLC TME. We also address the emerging influences of patient-related factors, such as ageing, sex and health disparities, on the NSCLC-TME crosstalk. Finally, we discuss how various therapeutic strategies interact with and are influenced by the TME in NSCLC. Overall, we emphasize the interconnectedness of these elements and how they influence therapeutic outcomes and tumour progression.

Metabolic alterations and immune heterogeneity in gastric cancer metastasis

Cellular metabolic reprogramming supports tumor proliferation, invasion, and metastasis by enhancing resistance to stress and immune clearance. Understanding these metabolic changes within the tumor microenvironment is vital to developing effective therapies. We conducted single-cell RNA sequencing on 11 gastric cancer (GC) samples and eight metastatic lesions, analyzing 92,842 cells across eight cell types, including cancer cells, stromal cells, and immune cells. Our findings highlight that the mitochondrial ATP synthase subunit ATP5MC2 uniquely alters during early GC metastasis. Experiments and clinical data confirmed that ATP5MC2 upregulation facilitates cancer cell proliferation, invasion, and metastasis. Constructing a single-cell atlas revealed significant immune cell heterogeneity associated with GC metastasis and its molecular subtypes. This study underscores the role of ATP5MC2-driven metabolic changes and diverse immune landscapes in promoting GC metastasis, offering new avenues for anti-metastatic treatment development.

Immunotherapy in lung cancer brain metastases

Brain metastases (BM) occur frequently in lung cancer, particularly in non-small cell lung cancer (NSCLC) patients and remain a significant cause of morbidity and mortality. Standard therapies have limited efficacy due to poor crossing of the blood-brain barrier and the distinct features between BM and the primary tumor. This review explores the immune landscape of brain metastatic disease, emerging immunotherapeutic strategies, and promising biomarkers in NSCLC patients.

Identification of a Biomarker Panel in Extracellular Vesicles Derived From Non-Small Cell Lung Cancer (NSCLC) Through Proteomic Analysis and Machine Learning

Antigen fingerprint profiling of tumour-derived extracellular vesicles (TDEVs) in the body fluids is a promising strategy for identifying tumour biomarkers. In this study, proteomic and immunological assays reveal significantly higher CD155 levels in plasma extracellular vesicles (EVs) from patients with non-small cell lung cancer (NSCLC) than from healthy individuals. Utilizing CD155 as a bait protein on the EV membrane, CD155+ TDEVs are enriched from NSCLC patient plasma EVs. In the discovery cohort, 281 differentially expressed proteins are identified in TDEVs of the NSCLC group compared with the healthy control group. In the verification cohort, 49 candidate biomarkers are detected using targeted proteomic analysis. Of these, a biomarker panel of seven frequently and stably detected proteins-MVP, GYS1, SERPINA3, HECTD3, SERPING1, TPM4, and APOD-demonstrates good diagnostic performance, achieving an area under the curve (AUC) of 1.0 with 100% sensitivity and specificity in receiver operating characteristic (ROC) curve analysis, and 92.3% sensitivity and 88.9% specificity in confusion matrix analysis. Western blotting results confirm upregulation trends for MVP, GYS1, SERPINA3, HECTD3, SERPING1 and APOD, and TPM4 is downregulated in EVs of NSCLC patients compared with healthy individuals. These findings highlight the potential of this biomarker panel for the clinical diagnosis of NSCLC.

Spatial Transcriptomics: Biotechnologies, Computational Tools, and Neuroscience Applications

Spatial transcriptomics (ST) represents a revolutionary approach in molecular biology, providing unprecedented insights into the spatial organization of gene expression within tissues. This review aims to elucidate advancements in ST technologies, their computational tools, and their pivotal applications in neuroscience. It is begun with a historical overview, tracing the evolution from early image-based techniques to contemporary sequence-based methods. Subsequently, the computational methods essential for ST data analysis, including preprocessing, cell type annotation, spatial clustering, detection of spatially variable genes, cell-cell interaction analysis, and 3D multi-slices integration are discussed. The central focus of this review is the application of ST in neuroscience, where it has significantly contributed to understanding the brain's complexity. Through ST, researchers advance brain atlas projects, gain insights into brain development, and explore neuroimmune dysfunctions, particularly in brain tumors. Additionally, ST enhances understanding of neuronal vulnerability in neurodegenerative diseases like Alzheimer's and neuropsychiatric disorders such as schizophrenia. In conclusion, while ST has already profoundly impacted neuroscience, challenges remain issues such as enhancing sequencing technologies and developing robust computational tools. This review underscores the transformative potential of ST in neuroscience, paving the way for new therapeutic insights and advancements in brain research.

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.

A spatially resolved transcriptome landscape during thyroid cancer progression

Tumor microenvironment (TME) remodeling plays a pivotal role in thyroid cancer progression, yet its spatial dynamics remain unclear. In this study, we integrate spatial transcriptomics and single-cell RNA sequencing to map the TME architecture across para-tumor thyroid (PT) tissue, papillary thyroid cancer (PTC), locally advanced PTC (LPTC), and anaplastic thyroid carcinoma (ATC). Our integrative analysis reveals extensive molecular and cellular heterogeneity during thyroid cancer progression, enabling the identification of three distinct thyrocyte meta-clusters, including TG+IYG+ subpopulation in PT, HLA-DRB1+HLA-DRA+ subpopulation in early cancerous stages, and APOE+APOC1+ subpopulation in late-stage progression. We reveal stage-specific tumor leading edge remodeling and establish high-confidence cell-cell interactions, such as COL8A1-ITHB1 in PTC, LAMB2-ITGB4 in LPTC, and SERPINE1-PLAUR in ATC. Notably, both SERPINE1 expression level and SERPINE1+ fibroblast abundance correlate with malignant progression and prognosis. These findings provide a spatially resolved framework of TME remodeling, offering insights for thyroid cancer diagnosis and treatment.

Multi-omic landscape of human gliomas from diagnosis to treatment and recurrence

Gliomas are among the most lethal cancers, with limited treatment options. To uncover hallmarks of therapeutic escape and tumor microenvironment (TME) evolution, we applied spatial proteomics, transcriptomics, and glycomics to 670 lesions from 310 adult and pediatric patients. Single-cell analysis shows high B7H3+ tumor cell prevalence in glioblastoma (GBM) and pleomorphic xanthoastrocytoma (PXA), while most gliomas, including pediatric cases, express targetable tumor antigens in less than 50% of tumor cells, potentially explaining trial failures. Longitudinal samples of isocitrate dehydrogenase (IDH)-mutant gliomas reveal recurrence driven by tumor-immune spatial reorganization, shifting from T-cell and vasculature-associated myeloid cell-enriched niches to microglia and CD206+ macrophage-dominated tumors. Multi-omic integration identified N-glycosylation as the best classifier of grade, while the immune transcriptome best predicted GBM survival. Provided as a community resource, this study opens new avenues for glioma targeting, classification, outcome prediction, and a baseline of TME composition across all stages.

Comparative investigation of lung adenocarcinoma and squamous cell carcinoma transcriptome to reveal potential candidate biomarkers: An explainable AI approach

Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnosis. NSCLC includes subtypes distinguished by histological characteristics, specifically lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). This study aims to compare and identify abnormal gene expression patterns in LUAD and LUSC samples relative to adjacent healthy tissues using an explainable artificial intelligence (XAI) framework. The LASSO algorithm was employed to identify the top gene features in the LUAD and LUSC datasets. An ensemble-based extreme gradient boosting (XGBoost) machine learning (ML) algorithm was trained and interpreted using SHapley Additive exPlanations (SHAP), with top features undergoing biological annotation through survival and functional enrichment analyses. The XAI-based SHAP module addresses the opaque nature of ML models. Notably, 35 and 33 genes were identified for LUAD and LUSC, respectively, using the LASSO algorithm. Performance metrics such as average accuracy and Matthew's correlation coefficient were evaluated. The XGBoost model demonstrated an average accuracy of 99.1 % for LUAD and 98.6 % for LUSC. The SFTPC gene emerged as the most significant feature across both NSCLC subtypes. For LUAD, genes such as STX11, CLEC3B, EMP2, and LYVE1 significantly influenced the XAI-SHAP framework. Conversely, GKN2, OGN, SLC39A8, and MMRN1 were identified for LUSC. Survival analysis and functional validation of these genes highlighted the physiological functions observed to be dysregulated in the NSCLC subtypes. These identified genes have the potential to enhance current medical diagnostics and therapeutics.

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.

Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments

The tumor microenvironment (TME) plays a crucial role in orchestrating tumor cell behavior and cancer progression. Recent advances in spatial profiling technologies have uncovered novel spatial signatures, including univariate distribution patterns, bivariate spatial relationships, and higher-order structures. These signatures have the potential to revolutionize tumor mechanism and treatment. In this review, we summarize the current state of spatial signature research, highlighting computational methods to uncover spatially relevant biological significance. We discuss the impact of these advances on fundamental cancer biology and translational research, address current challenges and future research directions.

Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas

Objective

Primary lung carcinomas (LCs) often metastasize to the brain, resulting in a grim prognosis for affected individuals. This population-based study aimed to investigate their survival period and immune status, while also establishing a predictive model.

Methods

The records of 86,763 primary LCs from the Surveillance, Epidemiology, and End Results (SEER) database were extracted, including 15,180 cases with brain metastasis (BM) and 71,583 without BM. Univariate and multivariate Cox regression were employed to construct a prediction model. Multiple machine learning methods were applied to validate the model. Flow cytometry and ELISA were used to explore the immune status in a real-world cohort.

Results

The research findings revealed a 17.49% prevalence of BM from LCs, with a median survival of 8 months, compared with 16 months for their counterparts (p <0.001). A nomogram was developed to predict survival at 1, 3, and 5 years on the basis of these variables, with the time-dependent area under the curve (AUC) of 0.857, 0.814, and 0.786, respectively. Moreover, several machine learning approaches have further verified the reliability of this model's performance. Flow cytometry and ELISA analysis suggested the prediction model was related the immune status.

Conclusions

BM from LCs have an inferior prognosis. Considering the substantial impact of these factors, the nomogram model is a valuable tool for guiding clinical decision-making in managing patients with this condition.

Tumor Metastasis: Mechanistic Insights and Therapeutic Intervention

Metastasis remains a leading cause of cancer‐related deaths, defined by a complex, multi‐step process in which tumor cells spread and form secondary growths in distant tissues. Despite substantial progress in understanding metastasis, the molecular mechanisms driving this process and the development of effective therapies remain incompletely understood. Elucidating the molecular pathways governing metastasis is essential for the discovery of innovative therapeutic targets. The rapid advancements in sequencing technologies and the expansion of biological databases have significantly deepened our understanding of the molecular drivers of metastasis and associated drug resistance. This review focuses on the molecular drivers of metastasis, particularly the roles of genetic mutations, epigenetic changes, and post‐translational modifications in metastasis progression. We also examine how the tumor microenvironment influences metastatic behavior and explore emerging therapeutic strategies, including targeted therapies and immunotherapies. Finally, we discuss future research directions, stressing the importance of novel treatment approaches and personalized strategies to overcome metastasis and improve patient outcomes. By integrating contemporary insights into the molecular basis of metastasis and therapeutic innovation, this review provides a comprehensive framework to guide future research and clinical advancements in metastatic 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.

Molecular principles underlying aggressive cancers

Aggressive tumors pose ultra-challenges to drug resistance. Anti-cancer treatments are often unsuccessful, and single-cell technologies to rein drug resistance mechanisms are still fruitless. The National Cancer Institute defines aggressive cancers at the tissue level, describing them as those that spread rapidly, despite severe treatment. At the molecular, foundational level, the quantitative biophysics discipline defines aggressive cancers as harboring a large number of (overexpressed, or mutated) crucial signaling proteins in major proliferation pathways populating their active conformations, primed for their signal transduction roles. This comprehensive review explores highly aggressive cancers on the foundational and cell signaling levels, focusing on the differences between highly aggressive cancers and the more treatable ones. It showcases aggressive tumors as harboring massive, cancer-promoting, catalysis-primed oncogenic proteins, especially through certain overexpression scenarios, as predisposed aggressive tumor candidates. Our examples narrate strong activation of ERK1/2, and other oncogenic proteins, through malfunctioning chromatin and crosslinked signaling, and how they activate multiple proliferation pathways. They show the increased cancer heterogeneity, plasticity, and drug resistance. Our review formulates the principles underlying cancer aggressiveness on the molecular level, discusses scenarios, and describes drug regimen (single drugs and drug combinations) for PDAC, NSCLC, CRC, HCC, breast and prostate cancers, glioblastoma, neuroblastoma, and leukemia as examples. All show overexpression scenarios of master transcription factors, transcription factors with gene fusions, copy number alterations, dysregulation of the epigenetic codes and epithelial-to-mesenchymal transitions in aggressive tumors, as well as high mutation loads of vital upstream signaling regulators, such as EGFR, c-MET, and K-Ras, befitting these principles.

Multi-omics analyses reveal biological and clinical insights in recurrent stage I non-small cell lung cancer

Post-operative recurrence rates of stage I non-small cell lung cancer (NSCLC) range from 20% to 40%. Nonetheless, the molecular mechanisms underlying recurrence hitherto remain largely elusive. Here, we generate genomic, epigenomic and transcriptomic profiles of paired tumors and adjacent tissues from 122 stage I NSCLC patients, among which 57 patients develop recurrence after surgery during follow-up. Integrated analyses illustrate that the presence of predominantly solid or micropapillary histological subtypes, increased genomic instability, and APOBEC-related signature are associated with recurrence. Furthermore, TP53 missense mutation in DNA-binding domain could contribute to shorter time to recurrence. DNA hypomethylation is pronounced in recurrent NSCLC, and PRAME is the significantly hypomethylated and overexpressed gene in recurrent lung adenocarcinoma (LUAD). Mechanistically, hypomethylation at TEAD1 binding site facilitates the transcriptional activation of PRAME. Inhibition of PRAME restrains the tumor metastasis via downregulation of epithelial-mesenchymal transition-related genes. We also identify that enrichment of AT2 cells with higher copy number variation burden, exhausted CD8 + T cells and Macro_SPP1, along with the reduced interaction between AT2 and immune cells, is essential for the formation of ecosystem in recurrent LUAD. Finally, multi-omics clustering could stratify the NSCLC patients into 4 subclusters with varying recurrence risk and subcluster-specific therapeutic vulnerabilities. Collectively, this study constitutes a promising resource enabling insights into the biological mechanisms and clinical management for post-operative recurrence of stage I NSCLC.

Spatial transcriptome analysis of lung squamous cell carcinoma arising from interstitial pneumonia provides insights into tumor heterogeneity

Interstitial pneumonia (IP) is a refractory disease that causes severe inflammation and fibrosis in the interstitium of the lungs, often resulting in the development of lung cancer (LC) during treatment. Previous studies have demonstrated that the prognosis of LC complicated by IP is inferior to that of LC without IP. It is therefore of the utmost importance to gain a deeper understanding of the heterogeneity of such tumors. In the present study, we conducted spatial transcriptome analysis of squamous cell carcinoma arising from IP. The results suggested involvement of the glucocorticoid receptor pathway in treatment resistance. Immunostaining of squamous cell carcinoma specimens from patients with IP demonstrated that the tumors expressed NR3C1 to varying degrees. Furthermore, higher NR3C1 expression levels were associated with a significantly increased risk of recurrence. Our results point to a novel subtype of lung squamous cell carcinoma. Further analysis of the molecular mechanisms associated with this subtype may facilitate the development of novel diagnostic criteria and therapeutic approaches.

Clinical characteristics and outcomes of surgical resection for brain metastases from lung adenocarcinoma

Objective

The purpose of this study was to explore the clinical characteristics, survival time and prognostic factors of patients undergoing craniotomy for brain metastases (BM) from lung adenocarcinoma (LUAD).

Methods

A total of 208 patients with BM from LUAD who underwent craniotomy at the Zhengzhou University People's Hospital, Henan province, China from March 2005 to October 2022 were included in this retrospective study. All patients were confirmed as BM of LUAD by histopathology. The clinical data included patient gender, age, occupation, family history of tumor, smoking history, alcohol drinking history, neurological symptoms, history of lung cancer treatment, tumor location, tumor number, tumor size, gene status, expression of S-100, CEA, Ki67, and PD-L1 by immunohistochemistry, KPS after craniotomy, whether tumor therapy was continued after craniotomy, and survival time. Univariate and multivariate Cox regression was used to analyze the prognostic factors of patients undergoing craniotomy for LUAD BM.

Results

A total of 208 patients met the inclusion and exclusion criteria, including 110 males (52.9%) and 98 females (47.1%), with an average age of 61.4 years. 203 patients (97.6%) had neurological symptoms. 84 patients (40.4%) had smoking history, 89 patients (42.8%) had alcohol drinking history, and 31 patients (14.9%) had the family history of tumor. Only 5 patients (2.4%) had received lung cancer treatment before craniotomy. The intracranial location of BM was mostly in the frontal lobe (54, 26.0%) and the metastatic sites were mostly single (117, 56.3%); the metastatic tumor size was mostly between 2-5 cm (141, 67.8%). Genetically, 43.3% patients (90 cases) had EGFR mutations, and immunohistochemical analysis showed that most patients were PD-L1 positive (160, 76.9%) and Ki67 > 30% (137, 65.9%). Most patients (145, 69.7%) had KPS score under 80 after craniotomy. Only 72 patients (34.7%) received continued tumor therapy after craniotomy. 190 patients (91.3%) were successfully followed up. The median survival time was 11.5 months, and the 3-year survival rate was 15.7%. Multivariate analysis revealed that smoking history, Ki67 percentage, KPS after craniotomy, and molecular targeted therapy after craniotomy were independent factors affecting the survival time of patients.

Conclusions

Although survival remains poor, patients who had no-smoking history, Ki67 percentage ≤30%, KPS≥80 after craniotomy, and molecular targeted therapy after craniotomy can improve the prognosis and prolong the survival time.

Spatial Profiling Reveals Equivalence-Derived Molecular Signatures of Brain Mimicry and Adaptation in Breast Cancer Brain Metastases

Brain metastases (BrMets), common for advanced-stage breast cancer patients, are associated with poor median survival and accompanied by severe neurologic decline. Halting the progression of breast cancer brain metastases (BCBMs) may require modulation of the tumor microenvironment (TME), yet little is known about the impact of the primary breast TME on brain tropism, or how, once there, metastatic breast cancer cells coexist with brain-resident cells (e.g., neurons and glia). Traditionally, studies in this space have focused on differential expression analysis, overlooking potential insights gained from investigating genes with equivalent expression between groups. This is particularly crucial in distant metastasis, where tumor cells may co-opt the transcriptional programs of the host organ (e.g., brain) to facilitate successful seeding and outgrowth. Prior to our work, no computational framework existed to determine biologically-relevant equivalent gene expression. To resolve molecular mechanisms of BCBM enabled by metastatic cancer cells and/or resident brain cells, we leveraged Nanostring GeoMx to perform spatially-resolved transcriptomic profiling on 235 patient-derived tissue cores from BCBM (including adjacent normal brain), primary invasive breast cancers, and normal (non-cancer) brain; analyzing 18,677 RNAs in 450 areas of interest (AOIs). We introduce the "Equivalent Expression Index" a highly specific and accurate algorithm that identifies statistically significant "Equivalently-Expressed Genes". This method facilitated the identification of molecular remodeling and mimicry genes within tissue-specific TMEs. By integrating differential expression analysis with the Equivalent Expression Index, we discovered multiple novel gene signatures associated with BCBM and primary tumor brain-metastatic potential. We demonstrate that the Equivalent Expression Index is a powerful tool to uncover shared gene expression programs representing the adaptation of metastatic cells and brain-resident cells to the BCBM microenvironment.

The immune landscape in brain metastasis

The prognosis for patients with brain metastasis remains dismal despite intensive therapy including surgical resection, radiotherapy, chemo-, targeted, and immunotherapy. Thus, there is a high medical need for new therapeutic options. Recent advances employing high-throughput and spatially resolved single-cell analyses have provided unprecedented insights into the composition and phenotypes of the diverse immune cells in the metastatic brain, revealing a unique immune landscape starkly different from that of primary brain tumors or other metastatic sites. This review summarizes the current evidence on the composition and phenotypes of the most prominent immune cells in the brain metastatic niche, along with their dynamic interactions with metastatic tumor cells and each other. As the most abundant immune cell types in this niche, we explore in detail the phenotypic heterogeneity and functional plasticity of tumor-associated macrophages, including both resident microglia and monocyte-derived macrophages, as well as the T-cell compartment. We also review preclinical and clinical trials evaluating the therapeutic potential of targeting the immune microenvironment in brain metastasis. Given the substantial evidence highlighting a significant role of the immune microenvironmental niche in brain metastasis pathogenesis, a comprehensive understanding of the key molecular and cellular factors within this niche holds great promise for developing novel therapeutic approaches as well as innovative combinatory treatment strategies for brain metastasis.

ITGB4/GNB5 axis promotes M2 macrophage reprogramming in NSCLC metastasis

OBJECTIVE

Metastasis of non-small cell lung cancer (NSCLC) is a leading cause of high mortality. In recent years, the role of M2 macrophages in promoting tumor metastasis within the tumor microenvironment has garnered increasing attention. This study aims to investigate the role and potential mechanisms of the ITGB4/GNB5 axis in regulating M2 macrophage reprogramming and influencing NSCLC metastasis.

METHODS

This study first used single-cell sequencing technology to reveal the diverse subpopulation structure of NSCLC tumor tissues. Data analysis then identified the correlation between M2 macrophages and the malignant phenotype of NSCLC. Flow cytometry and immunohistochemistry were used to detect changes in M2 macrophages in NSCLC tissues. The impact of the ITGB4/GNB5 axis on M2 macrophage function was assessed through RNA sequencing and proteomic analysis. Finally, in vitro cell experiments and in vivo mouse models were used to validate the function and regulatory mechanisms of this axis.

RESULTS

Our study found diverse cellular subpopulations in NSCLC tumor tissues, with M2 macrophages closely associated with the malignant phenotype of NSCLC. We identified ITGB4 as a characteristic gene of NSCLC and predicted GNB5 as an interacting gene through database analysis. Activation of the ITGB4/GNB5 axis was shown to enhance M2 macrophage polarization, promoting their accumulation in the tumor microenvironment. This change further facilitated NSCLC invasion and metastasis by modulating related cytokines and signaling pathways. Animal experiments demonstrated that inhibition of the ITGB4/GNB5 axis significantly reduced tumor growth and metastasis.

CONCLUSION

The ITGB4/GNB5 axis reshapes the TME by promoting M2 macrophage polarization and functional enhancement, thereby facilitating tumor invasion and metastasis in NSCLC. This research provides new insights into the molecular mechanisms of NSCLC and offers potential molecular targets for future targeted therapies.

Metastatic brain tumors: from development to cutting-edge treatment

Metastatic brain tumors, also called brain metastasis (BM), represent a challenging complication of advanced tumors. Tumors that commonly metastasize to the brain include lung cancer and breast cancer. In recent years, the prognosis for BM patients has improved, and significant advancements have been made in both clinical and preclinical research. This review focuses on BM originating from lung cancer and breast cancer. We briefly overview the history and epidemiology of BM, as well as the current diagnostic and treatment paradigms. Additionally, we summarize multiomics evidence on the mechanisms of tumor occurrence and development in the era of artificial intelligence and discuss the role of the tumor microenvironment. Preclinically, we introduce the establishment of BM models, detailed molecular mechanisms, and cutting-edge treatment methods. BM is primarily treated with a comprehensive approach, including local treatments such as surgery and radiotherapy. For lung cancer, targeted therapy and immunotherapy have shown efficacy, while in breast cancer, monoclonal antibodies, tyrosine kinase inhibitors, and antibody-drug conjugates are effective in BM. Multiomics approaches assist in clinical diagnosis and treatment, revealing the complex mechanisms of BM. Moreover, preclinical agents often need to cross the blood-brain barrier to achieve high intracranial concentrations, including small-molecule inhibitors, nanoparticles, and peptide drugs. Addressing BM is imperative.

Tumor-Activated Neutrophils Promote Lung Cancer Progression through the IL-8/PD-L1 Pathway

BACKGROUND

Lung cancer remains a major global health threat due to its complex microenvironment, particularly the role of neutrophils, which are crucial for tumor development and immune evasion mechanisms. This study aimed to delve into the impact of lung cancer cell-conditioned media on neutrophil functions and their potential implications for lung cancer progression.

METHODS

Employing in vitro experimental models, this study has analyzed the effects of lung cancer cell-conditioned media on neutrophil IL-8 and IFN-γ secretion, apoptosis, PD-L1 expression, and T-cell proliferation by using techniques, such as ELISA, flow cytometry, immunofluorescence, and CFSE proliferation assay. The roles of IL-8/PD-L1 in regulating neutrophil functions were further explored using inhibitors for IL-8 and PD-L1.

RESULTS

Lung cancer cell lines were found to secrete higher levels of IL-8 compared to normal lung epithelial cells. The conditioned media from lung cancer cells significantly reduced apoptosis in neutrophils, increased PD-L1 expression, and suppressed T-cell proliferation and IFN-γ secretion. These effects were partially reversed in the presence of IL-8 inhibitors in Tumor Tissue Culture Supernatants (TTCS), while being further enhanced by IL-8. Both apoptosis and PD-L1 expression in neutrophils demonstrated dose-dependency to TTCS. Additionally, CFSE proliferation assay results further confirmed the inhibitory effect of lung cancer cell-conditioned media on T-- cell proliferation.

CONCLUSION

This study has revealed lung cancer cell-conditioned media to modulate neutrophil functions through regulating factors, such as IL-8, thereby affecting immune regulation and tumor progression in the lung cancer microenvironment.

Development of brain metastases in non-small-cell lung cancer: high-risk features

Aim: Brain metastases (BM) are a common site of disease progression and treatment failure in non-small-cell lung cancer (NSCLC) and can be identified in up to 30-50% of patients. Although they are common, there is no standardized screening protocol for development of BM in NSCLC. Multiple clinical variables predict increased BM occurrence, and, when present, should be used to initiate screening MRI.Materials & methods: We performed a single center retrospective review of NSCLC patients, examining BM development and overall survival. Available clinical, radiographic and molecular data were reviewed for association with BM and overall survival. A predictive model for BM development was created for multivariate analysis.Results: Risk factors for new BM development in NSCLC included younger age, larger primary lung tumor, Karnofsky performance score (KPS) <70, pre-existing liver or bone metastases, large cell histology and family history of cancer. Factors associated with decreased OS were larger primary lung tumor, extracranial metastases at time of diagnosis, large cell histology and poorly-differentiated carcinoma histology.Conclusion: There are multiple high risk features for developing BM in NSCLC. Each of these factors should routinely be investigated, and presence should prompt brain MRI to allow earlier diagnosis and treatment of BM.

[TIM3+CD8+ T Cell Expression and Clinical Significance in the Central and Non-central Tumor Microenvironment of Non-small Cell Lung Cancer]

BACKGROUND

One of the most important treatment modalities for non-small cell lung cancer (NSCLC) is immune checkpoint inhibitor. Nevertheless, a small percentage of patients do not respond well to these therapies, highlighting the significance of identifying important CD8+ T cell subsets for immunotherapy and creating trustworthy biomarkers. The purpose of this study is to assess the potential utility of TIM3+CD8+ T cells as new biomarkers by examining their expressions in various areas of the NSCLC tumor microenvironment.

METHODS

Based on biopsy techniques, tumor tissue samples were obtained from patients with NSCLC and categorized into tumor central and non-central regions. Using flow cytometry, the infiltration of TIM3+CD8+ T cells and the surface expression of programmed cell death 1 (PD-1) on these cells were examined, and their correlations with the effectiveness of immunotherapy were assessed.

RESULTS

The non-central region of tumor tissues had considerably larger infiltration of TIM3+CD8+ T lymphocytes compared to the non-central region (P<0.0001). This pattern was found in both subgroups with tumor diameters ≥3 cm or <3 cm (P<0.01). In comparison to TIM3-CD8+ T cells, TIM3+CD8+ T cells showed higher levels of PD-1 (P<0.001), with more PD-1+TIM3+CD8+ T cells invading the non-central region (P<0.01). Clinical responders to immunotherapy had considerably lower infiltration levels of TIM3+CD8+ T cells in the tumor non-central region compared to non-responders, with lower levels correlated with better clinical outcomes (P<0.01), while no correlation was identified in the tumor central region (P>0.05). According to reciever operating characteristic (ROC) curve analysis, TIM3+CD8+ T cells in the tumor non-central region had an area under the curve (AUC) of 0.9375 for predicting the effectiveness of immunotherapy, which was considerably higher than that of TIM3+CD8+ T cells in the tumor central region and programmed cell death ligand 1 (PD-L1) [tumor proportion score (TPS)].

CONCLUSIONS

In the tumor microenvironment of NSCLC, TIM3+CD8+ T cells show regional distribution patterns. The expression of this cell population in the non-central region of the tumor microenvironment may be a biomarker for predicting the effectiveness of immunotherapy.

Spatial transcriptomics in breast cancer: providing insight into tumor heterogeneity and promoting individualized therapy

A comprehensive understanding of tumor heterogeneity, tumor microenvironment and the mechanisms of drug resistance is fundamental to advancing breast cancer research. While single-cell RNA sequencing has resolved the issue of "temporal dynamic expression" of genes at the single-cell level, the lack of spatial information still prevents us from gaining a comprehensive understanding of breast cancer. The introduction and application of spatial transcriptomics addresses this limitation. As the annual technical method of 2020, spatial transcriptomics preserves the spatial location of tissues and resolves RNA-seq data to help localize and differentiate the active expression of functional genes within a specific tissue region, enabling the study of spatial location attributes of gene locations and cellular tissue environments. In the context of breast cancer, spatial transcriptomics can assist in the identification of novel breast cancer subtypes and spatially discriminative features that show promise for individualized precise treatment. This article summarized the key technical approaches, recent advances in spatial transcriptomics and its applications in breast cancer, and discusses the limitations of current spatial transcriptomics methods and the prospects for future development, with a view to advancing the application of this technology in clinical practice.

Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images

Immune checkpoint inhibitors (ICI) have become integral to treatment of non-small cell lung cancer (NSCLC). However, reliable biomarkers predictive of immunotherapy efficacy are limited. Here, we introduce HistoTME, a novel weakly supervised deep learning approach to infer the tumor microenvironment (TME) composition directly from histopathology images of NSCLC patients. We show that HistoTME accurately predicts the expression of 30 distinct cell type-specific molecular signatures directly from whole slide images, achieving an average Pearson correlation of 0.5 with the ground truth on independent tumor cohorts. Furthermore, we find that HistoTME-predicted microenvironment signatures and their underlying interactions improve prognostication of lung cancer patients receiving immunotherapy, achieving an AUROC of 0.75 [95% CI: 0.61-0.88] for predicting treatment responses following first-line ICI treatment, utilizing an external clinical cohort of 652 patients. Collectively, HistoTME presents an effective approach for interrogating the TME and predicting ICI response, complementing PD-L1 expression, and bringing us closer to personalized immuno-oncology.

The ketogenic diet modulates tumor-associated neutrophil polarization via the AMOT-YAP/TAZ axis to inhibit colorectal cancer progression

Despite significant advances in the diagnosis and treatment of colorectal cancer (CRC), the prognosis for late-stage patients remains poor, highlighting the urgent need for new preventive and therapeutic strategies. Recent studies have focused on the ketogenic diet (KD) and its metabolite, β-hydroxybutyrate (BHB), for their tumor-suppressive effects and modulation of inflammatory responses. Using the azoxymethane (AOM) / dextran sulfate sodium (DSS)-induced mouse CRC model, we found that the ketogenic diet and BHB inhibit pro-tumor N2-type tumor-associated neutrophils (TANs) while promoting the polarization of TANs towards the anti-tumor N1 type. This shift in TANs polarization affects tumor growth and metastasis. The underlying mechanism involves BHB acting on the intracellular receptor histone deacetylases 3 (HDAC3), which modulates the activation of the AMOT-YAP/TAZ axis, leading to the inhibition of pro-carcinogenic factor transcription and release. Moreover, clinical cohort data corroborate these findings, showing that CRC patients with elevated BHB levels have significantly lower rates of lymph node involvement, which is associated with a higher infiltration ratio of anti-carcinogenic N1-type TANs in the tumor microenvironment (TME). These results suggest that BHB levels could serve as a prognostic biomarker for CRC. In conclusion, our findings indicate that BHB derived from KD regulates TANs polarization in CRC via the HDAC3-AMOT-YAP/TAZ axis, effectively inhibiting tumor growth and metastasis. These insights establish a novel theoretical basis for employing the KD in the treatment of CRC and for developing cancer adjuvant immunotherapy strategy based on the polarization of neutrophils.

Integrated proteomic and glycoproteomic analysis reveals heterogeneity and molecular signatures of brain metastases from lung adenocarcinomas

Brain metastasis is a major cause of poor prognosis and death in lung adenocarcinoma (LUAD); however, the understanding of therapeutic strategies and mechanisms for brain metastases from LUAD (BM-LUAD) remains notably limited, especially at the proteomics levels. To address this issue, we conducted integrated proteomic and glycoproteomic analyses on 49 BM-LUAD tumors, revealing two distinct subtypes of the disease: BM-S1 and BM-S2. Whole exome sequencing analysis revealed that somatic mutations in STK11 and KEAP1, as well as copy number deletions on chr19p13.3, such as STK11, UQCR11, and SLC25A23, were more frequently detected in BM-S2. In BM-S1 tumors, we observed significant infiltration of GFAP + astrocytes, as evidenced by elevated levels of GFAP, GABRA2, GABRG1 and GAP43 proteins and an enrichment of astrocytic signatures in both our proteomic data and external spatial transcriptomic data. Conversely, BM-S2 tumors demonstrated higher levels of PD-1 immune cell infiltration, supported by the upregulation of PD-1 and LAG-3 genes. These findings suggest distinct microenvironmental adaptations required by the different BM-LUAD subtypes. Additionally, we observed unique glycosylation patterns between the subtypes, with increased fucosylation in BM-S1 and enhanced sialylation in BM-S2, primarily affected by glycosylation enzymes such as FUT9, B4GALT1, and ST6GAL1. Specifically, in BM-S2, these sialylation modifications are predominantly localized to the lysosomes, underscoring the critical role of N-glycosylation in the tumor progression of BM-LUAD. Overall, our study not only provides a comprehensive multi-omic data resource but also offers valuable biological insights into BM-LUAD, highlighting potential mechanisms and therapeutic targets for further investigation.

An immune scoring system predicts prognosis and immune characteristics in lung adenocarcinoma brain metastases by RNA sequencing

BACKGROUND

Previous studies have reported that the tumor immune microenvironment (TIME) was associated with the prognosis of lung cancer patients and the efficacy of immunotherapy. However, given the significant challenges in obtaining specimens of brain metastases (BrMs), few studies explored the correlation between the TIME and the prognosis in patients with BrMs from lung adenocarcinoma (LUAD).

METHODS

Transcript profiling of archival formalin-fixed and paraffin-embedded specimens of BrMs from 70 LUAD patients with surgically resected BrMs was carried out using RNA sequencing. An immune scoring system, the green-yellow module score (GYMS), was developed to predict prognosis and immune characteristics in both BrMs and primary LUAD using Weighted Correlation Network analysis (WGCNA) and GSVA analysis. We comprehensively evaluated the immunological role of GYMS based on gene expression profile of LUAD BrMs by systematically correlating GYMS with immunological characteristics and immunotherapy responsiveness in the BrMs. Immunohistochemistry was applied for validation.

RESULTS

We found that the high-GYMS group had better clinical prognosis and inflamed immune landscape including high infiltrations of various immune cells, increased immunomodulatory expression, and enriched immune-related pathways by using RNA-seq and immunohistochemical analysis. Low-GYMS group presented a lacked immune infiltration characteristic. Besides, the high-GYMS group had lower TIDE score and higher T-cell inflamed score than low-GYMS group. The GYMS has been validated in independent BrMs cohorts and primary NSCLC cohort treated with anti-PD-1/PD-L1, showing strong reproducibility and stability in both primary LUAD and BrMs. In addition, we construct a GYMS-related risk signature for patients with LUAD BrMs to predict prognosis.

CONCLUSIONS

We identified two immune-related subtypes which used to estimate prognosis and immune characteristics and developed a reliable GYMS-related risk signature in LUAD BrMs. These results will enhance the understanding of the immune microenvironment in LUAD BrMs and lay the theoretical foundation for the development of personalized therapies for LUAD patients with BrMs.

AI-Assisted High-Throughput Tissue Microarray Workflow

Immunohistochemical (IHC) studies of formalin-fixed paraffin-embedded (FFPE) samples are a gold standard in oncology for tumor characterization, and the identification of prognostic and predictive markers. However, despite the abundance of archived FFPE samples, their research use is limited due to the labor-intensive nature of IHC on large cohorts. This study aimed to create a high-throughput workflow using modern technologies to facilitate IHC biomarker studies on large patient groups. Semiautomatic constructed tissue microarrays (TMAs) were created for two tumor patient cohorts and IHC stained for seven antibodies (ABs). AB expression in the tumor and surrounding stroma was quantified using the AI-supported image analysis software QuPath. The data were correlated with clinicopathological information using an R-script, all results were automatically compiled into formatted reports. By minimizing labor time to 7.7%-compared to whole-slide studies-the established workflow significantly reduced human and material resource consumption. It successfully correlated AB expression with overall patient survival and additional clinicopathological data, providing publication-ready figures and tables. The AI-assisted high-throughput TMA workflow, validated on two patient cohorts, streamlines modern histopathological research by offering cost and time efficiency compared to traditional whole-slide studies. It maintains research quality and preserves patient tissue while significantly reducing material and human resources, making it ideal for high-throughput research centers and collaborations.

Neuroanatomical location of lung cancer brain metastases in 234 patients with a focus on cancer subtyping and biomarkers

Brain metastases are frequent in neuropathology practices; however, the literature on their distribution is frequently derived from imaging studies. This work examined metastases of lung cancer to the brain through the lens of pathology specimens. All brain surgical pathology cases accessioned from 2011-2020 were retrieved from a regional laboratory. Specimens were classified by neuroanatomical location, diagnostic category, and diagnosis with a hierarchical free text string-matching algorithm. All reports classified as probable metastasis per algorithm were reviewed by a pathologist. Lung biomarkers and selected immunostains were retrieved with text parsing and reviewed. Among 4,625 cases of brain surgical resection specimens, 854 were classified as probable metastasis by the algorithm. On report review, 538/854 cases were confirmed as metastasis with a known primary site. The 538 cases were from 511 patients and 234/511 patients had lung primaries. Small cell lung cancer lesions were most frequently found in the cerebellum (17/30). Lesions from lung adenocarcinoma (59/164) and non-small cell carcinoma-not otherwise specified (NSCLC-NOS) (15/34) were most commonly found in the frontal lobe. Squamous cell carcinoma lesions were most commonly found in the frontal and occipital lobes (8/27). 72/234 cases were reported as NSCLC-NOS and could be further subclassified using immunostaining (41/72). Lung biomarker data were retrieved in ~38% of cases. PD-L1 positivity was dependent on neuroanatomical distribution (p = 0.04); other examined biomarkers were not. The distribution of lung tumours metastatic to the brain is dependent on the lung cancer subtype (p<0.001). The reporting of histologic subtype could be further optimized in the local environment.

Machine learning-based prognostic models and factors influencing the benefit of surgery on primary lesion for patients with lung cancer brain metastases

Brain metastasis is very common in lung cancer and it's a fatal disease with extremely poor prognosis. Until now, there has been a lack of accurate and efficient prognostic models for patients with lung cancer brain metastases (LCBM), and the factors influencing the effectiveness of the surgery on primary lesion for these patients remain unclear. We used 7 machine learning algorithms to create prognostic models to predict the overall survival (OS) of LCBM based on the data from the Surveillance Epidemiology and End Results. Then, a series of validation methods, including area under the curve values, receiver operating characteristic curve analysis, calibration curves, decision curve analysis and external data validation were used to confirm the high discrimination, accuracy, and clinical applicability of the XGBoost models. Propensity score matching adjusted analysis was conducted for further stratified analysis to find factors influencing the benefit of surgery on primary lesion for LCBM. Models using XGBoost algorithm performed best. Surgery on primary lesion was a favorable independent prognostic factor for LCBM. Age > 70 years old, blacks, grade IV, stage T4, N3, other distant organ metastases, squamous cell carcinoma, large cell carcinoma and no radiation were all unfavorable factors of primary lung tumor surgery for the prognosis of LCBM. Our study is the first one to create highly accurate AI models to predict the OS of LCBM. Our in-depth stratified analysis found some influence factors of surgery on primary lesion for the prognosis of LCBM.

Overcoming tyrosine kinase inhibitor resistance in lung cancer brain metastasis with CTLA4 blockade

Lung cancer brain metastasis (LCBM) poses a significant clinical challenge due to acquired resistance to tyrosine kinase inhibitor (TKI) treatment. To elucidate its underlying mechanisms, we employed single-cell RNA sequencing analysis on surgically obtained LCBM samples with diverse genetic backgrounds and TKI treatment histories. Our study uncovers that TKI treatment elevates the immune checkpoint CTLA4 expression in T cells, promoting an immune-suppressive microenvironment. This immunomodulation is initiated by tumor-derived HMGB1 in response to TKIs. In LCBM syngeneic murine models with TKI-sensitive or TKI-resistant EGFR mutations, combining CTLA4 blockade with TKIs demonstrates enhanced efficacy over TKI monotherapy or TKIs with PD1 blockade. These findings provide insights into the TKI resistance mechanisms and highlight the potential of CTLA4 blockade in effectively overcoming TKI resistance in LCBM.

Reactive astrocytes promote tumor progression by up-regulating tumor protocadherin 1 expression in lung cancer brain metastasis

Brain metastasis (BM) is one of the main causes of death in patients with non-small cell lung carcinoma. The specific pathological processes of BM, which are inextricably linked to the brain tumor microenvironment, such as the abundance of astrocytes, lead to limited treatment options and poor prognosis. Reactive astrocytes are acquired in the BM; however, the underlying mechanisms remain unclear. This study aimed to explore the mechanisms by which astrocytes promote BM development. We determined the crucial role of reactive astrocytes in promoting the proliferation and migration of brain metastatic lung tumor cells by upregulating protocadherin 1 (PCDH1) expression in an in vitro co-culture model. The overexpression of PCDH1 was confirmed in clinical BM samples using immunohistochemical staining. Survival analysis indicated that high-PCDH1 expression was associated with poor survival in patients with lung adenocarcinoma. In vivo assays further showed that silence of PCDH1 effectively inhibited the tumor progression of brain metastases and prolonged the survival of animals. RNA sequencing has revealed that PCDH1 plays an important role in cell proliferation and adhesion. In conclusion, the present study revealed the promoting role of astrocytes in enhancing the aggressive phenotype of brain metastatic tumor cells by regulating the expression of PCDH1, which might be a biomarker for BM diagnosis and prognosis, suggesting the potential efficacy of targeting important astrocyte-tumor interactions in the treatment of patients with non-small cell lung carcinoma with BM.

Nicotine promotes M2 macrophage polarization through α5-nAChR/SOX2/CSF-1 axis in lung adenocarcinoma

α5-nicotinic acetylcholine receptor (α5-nAChR) plays a vital part in lung adenocarcinoma (LUAD). However, it is not comprehensively understood that how the α5-nAChR affects LUAD. Through diverse bioinformatics analyses and immunohistochemistry, the expressions of α5-nAChR and SOX2 as well as their relations were dissected. α5-nAChR regulated the differentiation of monocytes into M2 macrophages by targeting the STAT3/SOX2/CSF-1 signaling in the coculture system by western blotting and ChIP. α5-nAChR-mediated macrophage-mediated LUAD cell migration via SOX2/CSF-1 signaling in the cocultured medium. Correlations of α5-nAChR, SOX2 and M2 phenotype tumor-associated macrophages (TAMs) were validated in mouse LUAD models and clinical samples. α5-nAChR expression was connected to SOX2 expression, smoking and bad prognosis of LUAD among clinical samples. Nicotine-induced SOX2 expression was mediated by α5-nAChR via STAT3. Additionally, SOX2-mediated macrophage colony-stimulating factor (CSF-1) expression contributed to LUAD progression in vitro. Furthermore, α5-nAChR expression was strongly linked to pSTAT3, SOX2 and M2 macrophage marker CD206 expression and negatively correlated with M1 macrophage marker CD86 expression in vivo. It is indicated that M2 macrophages are mediated by the new α5-nAChR /SOX2/CSF-1 axis in nicotine-related LUAD, which is a potential therapeutic strategy for cancer.

Spatial Multiomics Reveals Intratumoral Immune Heterogeneity with Distinct Cytokine Networks in Lung Cancer Brain Metastases

The tumor microenvironment of brain metastases has become a focus in the development of immunotherapeutic drugs. However, countless patients with brain metastasis have not experienced clinical benefit. Thus, understanding the immune cell composition within brain metastases and how immune cells interact with each other and other microenvironmental cell types may be critical for optimizing immunotherapy. We applied spatial whole-transcriptomic profiling with extensive multiregional sampling (19-30 regions per sample) and multiplex IHC on formalin-fixed, paraffin-embedded lung cancer brain metastasis samples. We performed deconvolution of gene expression data to infer the abundances of immune cell populations and inferred spatial relationships from the multiplex IHC data. We also described cytokine networks between immune and tumor cells and used a protein language model to predict drug-target interactions. Finally, we performed deconvolution of bulk RNA data to assess the prognostic significance of immune-metastatic tumor cellular networks. We show that immune cell infiltration has a negative prognostic role in lung cancer brain metastases. Our in-depth multiomics analyses further reveal recurring intratumoral immune heterogeneity and the segregation of myeloid and lymphoid cells into distinct compartments that may be influenced by distinct cytokine networks. By using computational modeling, we identify drugs that may target genes expressed in both tumor core and regions bordering immune infiltrates. Finally, we illustrate the potential negative prognostic role of our immune-metastatic tumor cell networks. Our findings advocate for a paradigm shift from focusing on individual genes or cell types toward targeting networks of immune and tumor cells.

SIGNIFICANCE

Immune cell signatures are conserved across lung cancer brain metastases, and immune-metastatic tumor cell networks have a prognostic effect, implying that targeting cytokine networks between immune and metastatic tumor cells may generate more precise immunotherapeutic approaches.

Single-cell RNA sequencing and spatial transcriptome reveal potential molecular mechanisms of lung cancer brain metastasis

BACKGROUND

Lung cancer is a highly aggressive and prevalent disease worldwide. By the time it is first diagnosed, distant metastases have usually already occurred. Among them, the prognosis of patients with brain metastasis from lung cancer is very poor. Therefore, it is particularly important to identify the evolutionary status of tumor cells during lung cancer brain metastases and discover the underlying mechanisms of lung cancer brain metastases.

METHODS

In this study, we analysed three types of data: single-cell RNA sequencing, bulk RNA sequencing, and spatial transcriptome. Firstly, we identified early metastatic epithelial cell clusters (EMEC) using CNV and trajectory analysis in scRNA-seq data. Secondly, we integrated scRNA-seq and spatial transcriptome data with the help of MIA (Multimodal intersection analysis) to explore the biological characteristics of EMEC. Finally, we used bulk RNA-seq data to validate the molecular characteristics of EMEC.

RESULT

A total of 55,763 single cells were obtained and divided into 9 cell types. In brain metastasis, we found a significantly higher proportion of epithelial cells. In addition, we identified a specific subpopulation of epithelial cells, which was named as "early metastatic epithelial cell clusters (EMEC)". It is enriched in oxidative phosphorylation, coagulation, complement. Moreover, we also found that EMEC underwent cellular communication with other immune cells through ligand-receptor pairs such as MIF-(CD74 + CXCR4) and MIF-(CD74 + CD44). Next, we validated that EMEC were associated with poor clinical prognosis using three independent external datasets. Finally, spatial transcriptome analysis revealed specificity in the spatial distribution of EMEC, which shifted from the peripheral regions to the central regions of the tumour as the depth of tumor invasion progressed.

CONCLUSION

This study reveals the potential molecular mechanisms of lung cancer brain metastasis from both single-cell and spatial transcriptomic perspectives, providing biological insights and clinical reference value for detecting patients suffering from lung cancer brain metastasis.

De novo GTP synthesis is a metabolic vulnerability for the interception of brain metastases

Patients with brain metastases (BM) face a 90% mortality rate within one year of diagnosis and the current standard of care is palliative. Targeting BM-initiating cells (BMICs) is a feasible strategy to treat BM, but druggable targets are limited. Here, we apply Connectivity Map analysis to lung-, breast-, and melanoma-pre-metastatic BMIC gene expression signatures and identify inosine monophosphate dehydrogenase (IMPDH), the rate-limiting enzyme in the de novo GTP synthesis pathway, as a target for BM. We show that pharmacological and genetic perturbation of IMPDH attenuates BMIC proliferation in vitro and the formation of BM in vivo. Metabolomic analyses and CRISPR knockout studies confirm that de novo GTP synthesis is a potent metabolic vulnerability in BM. Overall, our work employs a phenotype-guided therapeutic strategy to uncover IMPDH as a relevant target for attenuating BM outgrowth, which may provide an alternative treatment strategy for patients who are otherwise limited to palliation.

Spatial oncology: Translating contextual biology to the clinic

Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.

Radiotherapy Improves Survival in NSCLC After Oligoprogression on Immunotherapy: A Cohort Study

Introduction

The patterns of oligoprogression after first-line immune checkpoint inhibitors (ICIs) for metastatic NSCLC are yet to be well established. An increasing volume of data suggests that directed radiotherapy improves survival outcomes in patients with progression after ICIs.

Methods

A retrospective cohort study was performed on patients with metastatic NSCLC who had completed first-line programmed death-(ligand) 1 inhibitor therapy with or without chemotherapy at two high-volume cancer centers. We sought to characterize the frequency and location of oligoprogression and determine the overall survival (OS) after radiotherapy in this population.

Results

A total of 159 patients were included in the study. At first progression, 62 (39.0%) were classified as undergoing oligoprogression. Multivariate analysis confirmed the presence of brain metastases was associated with an increased likelihood of oligoprogression (OR = 2.44, p = 0.04) with most (63.2%) of these patients experiencing progression intracranially. The presence of liver metastases was associated with a decreased likelihood of oligoprogression (OR = 0.17, p < 0.01). For patients with oligoprogression, those who received radiotherapy had a longer median progression-free survival-2 (PFS2) (17 versus 11.5 mo, HR = 0.51, p = 0.02) and a longer median OS (23 versus 13 mo, HR = 0.40, p < 0.001) compared with those who did not receive radiotherapy. No difference in PFS2 or OS outcomes was observed between patients who received radiotherapy versus those who did not for systemic progression.

Conclusions

In patients with oligoprogressive metastatic NSCLC after treatment with first-line ICIs, radiotherapy significantly improves OS and PFS2 outcomes. Patients with baseline brain metastases are more likely to experience oligoprogression. Further prospective studies in directed, less heterogeneous populations of patients with metastatic NSCLC will be fundamental to optimize management.

Advances in spatial multi-omics in tumors

Single-cell techniques have convincingly demonstrated that tumor tissue usually contains multiple genetically defined cell subclones with different gene mutation sets as well as various transcriptional profiles, but the spatial heterogeneity of the microenvironment and the macrobiological characteristics of the tumor ecosystem have not been described. For the past few years, spatial multi-omics technologies have revealed the cellular interactions, microenvironment, and even systemic tumor-host interactions in the tumor ecosystem at the spatial level, which can not only improve classical therapies such as surgery, radiotherapy, and chemotherapy but also promote the development of emerging targeted therapies in immunotherapy. Here, we review some emerging spatial omics techniques in cancer research and therapeutic applications and propose prospects for their future development.

Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology

The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real-world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up-to-date progress made via ST-based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward-looking viewpoints endorsed by many high-resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.

Dissection of the cell communication interactions in lung adenocarcinoma identified a prognostic model with immunotherapy efficacy assessment and a potential therapeutic candidate gene ITGB1

Background

The tumor microenvironment (TME) in lung adenocarcinoma (LUAD) influences tumor progression and immunosuppressive phenotypes through cell communication. We aimed to decipher cellular communication and molecular patterns in LUAD.

Methods

We analyzed scRNA-seq data from LUAD patients in multiple cohorts, revealing complex cell communication networks within the TME. Using cell chat analysis and COSmap technology, we inferred LUAD's spatial organization. Employing the NMF algorithm and survival screening, we identified a cell communication interactions (CCIs) model and validated it across various datasets.

Results

We uncovered intricate cell communication interactions within the TME, identifying three LUAD patient subtypes with distinct prognosis, clinical characteristics, mutation status, expression patterns, and immune infiltration. Our CCI model exhibited robust performance in prognosis and immunotherapy response prediction. Several potential therapeutic targets and agents for high CCI score patients with immunosuppressive profiles were identified. Machine learning algorithms pinpointed the novel candidate gene ITGB1 and validated its role in LUAD tumor phenotype in vitro.

Conclusion

Our study elucidates molecular patterns and cell communication interactions in LUAD as effective biomarkers and predictors of immunotherapy response. Targeting cell communication interactions offers novel avenues for LUAD immunotherapy and prognostic evaluations, with ITGB1 emerging as a promising therapeutic target.

Effect of adding individualized health education for patients with brain metastasis of lung cancer undergoing radiotherapy, as measured by MRI and cognitive testing

OBJECTIVE

To explore the clinical efficacy of individualized health education (IHE) and care mode based on magnetic resonance imaging (MRI) combined with Mini-Mental State Examination (MMSE) for lung cancer patients with brain metastases undergoing radiotherapy.

METHODS

This retrospective study involved 50 lung cancer patients with brain metastases. Patients were divided into a control group (n=25, conventional care) and an intervention group (n=25, individualized health education (IHE) care) according to their nursing model. Both groups underwent enhanced brain MRI scans. The patients were assessed using the Mini Mental State Scale (MMSE) before and at 1 month after radiotherapy. At the same time, Montreal Cognitive Assessment (MoCA) was used to assess the degree of cognitive impairment in both groups before and after the intervention. Finally, the European Organization for Research and Treatment of Cancer (EORTC QLQ-C30) questionnaire was used to evaluate the overall health status and quality of life (QOL) (including physical function, emotional function, and social function) of the two groups of patients after radiotherapy. The patients' self-care ability in daily life was assessed using Alzheimer's Disease Collaborative Study Activities of Daily Living (ADCS-ADL).

RESULTS

Following intervention, there was no significant difference in MMSE total scores between the control and intervention groups (P > 0.05), or in physical function scores (P > 0.05). However, the intervention group had significantly higher overall QOL scores compared to the control group (P < 0.05), particularly in emotional and social function (P < 0.05). There was no significant difference in total MoCA scores between the two groups (P > 0.05), but the intervention group showed superior scores in visual-spatial, executive function, naming, and attention compared to the control group (all P < 0.05). Following intervention, the intervention group demonstrated better ADCS-ADL scores than the control group (P < 0.05).

CONCLUSION

The IHE mode effectively improved emotional and social functions and enhanced QOL in lung cancer patients with brain metastases undergoing radiotherapy.

Full-length target sequences of GeoMx digital spatial profiling probes reveal that gene-promiscuity predicts probe sensitivity to EDTA tissue decalcification

GeoMx Digital Spatial Profiling (Nanostring) is a commercial spatial transcriptomics method to selectively analyze regions of interest within intact tissue sections. We show that decalcification with ethylene-diamine-tetra-acetic (EDTA) variably attenuates probe counts, while probes that are more resistant to this effect consequently appear overexpressed after quantile normalization. By determining the undisclosed full-length target sequences of probes used in the human whole transcriptome panel, hereby updating target transcripts and genes, we find that the gene-promiscuity of probes is an important factor that determines sensitivity to EDTA incubation.

Roles of the NR2F Family in the Development, Disease, and Cancer of the Lung

The NR2F family, including NR2F1, NR2F2, and NR2F6, belongs to the nuclear receptor superfamily. NR2F family members function as transcription factors and play essential roles in the development of multiple organs or tissues in mammals, including the central nervous system, veins and arteries, kidneys, uterus, and vasculature. In the central nervous system, NR2F1/2 coordinate with each other to regulate the development of specific brain subregions or cell types. In addition, NR2F family members are associated with various cancers, such as prostate cancer, breast cancer, and esophageal cancer. Nonetheless, the roles of the NR2F family in the development and diseases of the lung have not been systematically summarized. In this review, we mainly focus on the lung, including recent findings regarding the roles of the NR2F family in development, physiological function, and cancer.