Single-cell transcriptome analysis demonstrates inter-patient and intra-tumor heterogeneity in primary and metastatic lung adenocarcinoma

Basal and AT2 cells promote IPF-lung cancer co-occurrence via EMT: Single-cell analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, fibrotic interstitial lung disease. With IPF, the probability of complication with lung cancer (LCA) increases considerably, and the prognosis is worse than that of simple IPF. To understand the pathological mechanisms and molecular pathways shared by these two diseases, we used the single-cell analysis from the Gene Expression Omnibus (GEO) database, and find that basal cells (BCs) and alveolar type 2 cells (AT2 cells) are important components of lung epithelial cells. Changes in molecular pathways in BCs and AT2 cells may be involved in the common pathogenesis of IPF and LCA. KRT17 and S100A14 in BCs may promote the IPF co-occurrence with LCA by mediating the EMT. WFDC2 and KRT19 may be the elements in AT2 cells that activate the EMT process to promote IPF co-occurrence with LCA. In both IPF and LCA, FN1-WNT axis may be involved in the interaction between BCs and AT2 cells. Importantly, the results of immunofluorescence colocalization experiments on tissue samples from patients with IPF and LCA were consistent with these conclusions. Basal-macrophage interactions may have also induced the IPF co-occurrence with LCA via the CYBA-ERK1/2 axis. The regulation of M2 macrophage polarization by JUN/SOD2-glycolysis axis may therefore be involved in the co-morbidity mechanism of IPF and LCA. Therefore, our results suggest that molecular changes in BCs, AT2 cells and macrophages may play important roles in the pathogenesis of IPF co-occurrence with LCA, and the cellular interactions between these cells may be critical for the progression of both diseases.

The cancer stem cells characteristics analysis of LGR5 + cells that influence lung cancer risk by using single cell RNA-seq analysis

Lung adenocarcinoma (LUAD) is the most popular lung cancer type with highly mortality. We performed a single cell RNA-seq analysis to explore characteristic of cancer stem cells in LUAD. We downloaded the single cell RNA-seq data (GSE149655) from the GEO database, the scRNA-seq analysis was performed by using the "Seurat" and "harmony" R package. The FindMarkers function and "ClusterProlifer" package was used for differentially expressed genes (DEGs) and function enrichment analysis. The protein-protein interaction and transcriptional regulatory network were performed by STRING and ChIPBase database. Immunohistochemistry tests to be used to observe differences in the expression of specific genes in LUAD and paracancerous tissue samples. BEAS-2B and A549 cells was used for vitro assay and the qRT-PCR, western blotting, wound healing, trans-well assays, EdU tests, and flow cytometry were performed. A total of 9 cell clusters were obtained after scRNA-seq analysis, in which the cancer stem cells had higher proportion in LUAD samples. Subsequently, function enrichment analysis revealed that the amino sugar and nucleotide sugar metabolism and DNA replication pathways were activated in cancer stem cells (CSCs), which were further sub-divided into 3 subtypes, the LGR5 + stem cell is a major contributor to cancer progression, its hub genes, such as HLA-DPB1, CD74, CTSH and HLA-DRB5 mediated the unique transcriptional state. In addition, the marker genes of three CSCs were also overexpressed in LUAD cells and the CXCL3 played an important role in mediating cell proliferation, apoptosis, migration and invasion of tumor. We performed a scRNA-seq analysis and identified the LGR5 + stem cell as a major contributor in LUAD progression, our findings are expected to provide new insights into the pathogenesis of LUAD.

ctDNA can detect minimal residual disease in curative treated non-small cell lung cancer patients using a tumor agnostic approach

BACKGROUND

Circulating tumor DNA (ctDNA) has the potential to become a reliable biomarker for identifying minimal residual disease (MRD) and predicting recurrence in patients with non-small cell lung cancer (NSCLC) following curative treatment. However, there is a lack of studies that investigate the clinical validity of ctDNA using a tumor-agnostic approach, which can provide significant clinical benefits.

METHODS

We analyzed samples from 45 NSCLC patients recruited in a prospective national multicenter study, all of whom had undergone curative treatment. A total of 38 pre-treatment plasma samples and 76 post-treatment plasma samples were examined using a commercially available cancer personalized profiling by deep sequencing (CAPP-seq) strategy, and a tumor-agnostic approach. Post-treatment samples were collected at two distinct landmark time points: Follow-up 1 (0.5-4.5 months post-treatment) and Follow-up 2 (4.5-7.5 months post-treatment).

RESULTS

Detectable ctDNA post-treatment was significantly associated with increased risk of tumor recurrence and shorter recurrence-free survival (RFS). Using only a single blood sample taken from Follow-up 2, we correctly identified MRD in 50% of the patients who later experienced recurrence. However, subgroup analysis further revealed that in patients treated with radiotherapy or chemoradiotherapy (CRT), ctDNA detection was significantly linked to shorter RFS in the MRD analysis from Follow-up 2, but not in the MRD analysis from Follow-up 1.

CONCLUSION

These findings suggest that post-treatment ctDNA, detected using a tumor-agnostic approach, is a reliable biomarker for predicting recurrence in NSCLC patients following curative treatment. However, the optimal timing for blood sampling to detect MRD appears to depend on the type of curative treatment received.

Integrated analyses of multi-omic data derived from paired primary lung cancer and brain metastasis reveal the metabolic vulnerability as a novel therapeutic target

BACKGROUND

Lung cancer brain metastases (LC-BrMs) are frequently associated with dismal mortality rates in patients with lung cancer; however, standard of care therapies for LC-BrMs are still limited in their efficacy. A deep understanding of molecular mechanisms and tumor microenvironment of LC-BrMs will provide us with new insights into developing novel therapeutics for treating patients with LC-BrMs.

METHODS

Here, we performed integrated analyses of genomic, transcriptomic, proteomic, metabolomic, and single-cell RNA sequencing data which were derived from a total number of 154 patients with paired and unpaired primary lung cancer and LC-BrM, spanning four published and two newly generated patient cohorts on both bulk and single cell levels.

RESULTS

We uncovered that LC-BrMs exhibited a significantly greater intra-tumor heterogeneity. We also observed that mutations in a subset of genes were almost always shared by both primary lung cancers and LC-BrM lesions, including TTN, TP53, MUC16, LRP1B, RYR2, and EGFR. In addition, the genome-wide landscape of somatic copy number alterations was similar between primary lung cancers and LC-BrM lesions. Nevertheless, several regions of focal amplification were significantly enriched in LC-BrMs, including 5p15.33 and 20q13.33. Intriguingly, integrated analyses of transcriptomic, proteomic, and metabolomic data revealed mitochondrial-specific metabolism was activated but tumor immune microenvironment was suppressed in LC-BrMs. Subsequently, we validated our results by conducting real-time quantitative reverse transcription PCR experiments, immunohistochemistry, and multiplexed immunofluorescence staining of patients' paired tumor specimens. Therapeutically, targeting oxidative phosphorylation with gamitrinib in patient-derived organoids of LC-BrMs induced apoptosis and inhibited cell proliferation. The combination of gamitrinib plus anti-PD-1 immunotherapy significantly improved survival of mice bearing LC-BrMs. Patients with a higher expression of mitochondrial metabolism genes but a lower expression of immune genes in their LC-BrM lesions tended to have a worse survival outcome.

CONCLUSIONS

In conclusion, our findings not only provide comprehensive and integrated perspectives of molecular underpinnings of LC-BrMs but also contribute to the development of a potential, rationale-based combinatorial therapeutic strategy with the goal of translating it into clinical trials for patients with LC-BrMs.

Differential cellular communication in tumor immune microenvironment during early and advanced stages of lung adenocarcinoma

Heterogeneous behavior of each cell type and their cross-talks in tumor immune microenvironment (TIME) refers to tumor immunological heterogeneity that emerges during tumor progression and represents formidable challenges for effective anti-tumor immune response and promotes drug resistance. To comprehensively elucidate the heterogeneous behavior of individual cell types and their interactions across different stages of tumor development at system level, a computational framework was devised that integrates cell specific data from single-cell RNASeq into networks illustrating interactions among signaling and metabolic response genes within and between cells in TIME. This study identified stage specific novel markers which remodel the cross-talks, thereby facilitating immune stimulation. Particularly, multicellular knockout of metabolic gene APOE (Apolipoprotein E in mast cell, myeloid cell and fibroblast) combined with signaling gene CAV1 (Caveolin1 in endothelial and epithelial cells) resulted in the activation of T-cell mediated signaling pathways. Additionally, this knockout also initiated intervention of cytotoxic gene regulations during tumor immune cell interactions at the early stage of Lung Adenocarcinoma (LUAD). Furthermore, a unique interaction motif from multiple cells emerged significant in regulating the overall immune response at the advanced stage of LUAD. Most significantly, FCER1G (Fc Fragment of IgE Receptor Ig) was identified as the common regulator in activating the anti-tumor immune response at both stages. Predicted markers exhibited significant association with patient overall survival in patient specific dataset. This study uncovers the significance of signaling and metabolic interplay within TIME and discovers important targets to enhance anti-tumor immune response at each stage of tumor development.

CYP24A1 affected macrophage polarization through degradation of vitamin D as a candidate biomarker for ovarian cancer prognosis

Ovarian cancer (OC) is a fatal gynecological malignancy with a poor prognosis in which mitochondria-related genes are involved deeply. In this study, we aim to screen mitochondria-related genes that play a role in OC prognosis and investigate its effects. Through single-cell sequencing technology and bioinformatics analysis, including TCGA ovarian cancer data analysis, gene expression signature analysis (GES), immune infiltration analysis, Gene Ontology (GO) enrichment analysis, Gene Set Enrichment Analysis (GSEA), and Principal Component Analysis (PCA), our findings revealed that CYP24A1 regulated macrophage polarization through vitamin D (VD) degradation and served as a target gene for the second malignant subtype of OC through bioinformatics analyses. For further validation, the expression and function of CYP24A1 in OC cells was investigated. And the expression of CYP24A1 was much higher in carcinoma than in paracancerous tissue, whereas the VD content decreased in the OC cell lines with CYP24A1 overexpression. Moreover, macrophages were polarized towards M1 after the intervention of VD-treated OC cell lines and inhibited the malignant phenotypes of OC. However, the effect could be reversed by overexpressing CYP24A1, resulting in the polarization of M2 macrophages, thereby promoting tumor progression, as verified by constructing xenograft models in vitro. In conclusion, our findings suggested that CYP24A1 induced M2 macrophage polarization through interaction with VD, thus promoting the malignant progression of OC.

A A, Suchita J, Gacche RN. Cancer metastases: Tailoring the targets

Metastasis is an intricate and formidable pathophysiological process encompassing the dissemination of cancer cells from the primary tumour body to distant organs. It stands as a profound and devastating phenomenon that constitutes the primary driver of cancer-related mortality. Despite great strides of advancements in cancer research and treatment, tailored anti-metastasis therapies are either lacking or have shown limited success, necessitating a deeper understanding of the intrinsic elements driving cancer invasiveness. This comprehensive review presents a contemporary elucidation of pivotal facets within the realm of cancer metastasis, commencing with the intricate processes of homing and invasion. The process of angiogenesis, which supports tumour growth and metastasis, is addressed, along with the pre-metastatic niche, wherein the primary tumour prepares for a favorable microenvironment at distant sites for subsequent metastatic colonization. The landscape of metastasis-related genetic and epigenetic mechanisms, involvement of metastasis genes and metastasis suppressor genes, and microRNAs (miRNA) are also discussed. Furthermore, immune modulators' impact on metastasis and their potential as therapeutic targets are addressed. The interplay between cancer cells and the immune system, including immune evasion mechanisms employed by metastatic cells, is discussed, highlighting the importance of targeting immune modulation in arresting metastatic progression. Finally, this review presents promising treatment opportunities derived from the insights gained into the mechanisms of metastasis. Identifying novel therapeutic targets and developing innovative strategies to disrupt the metastatic cascade holds excellent potential for improving patient outcomes and ultimately reducing cancer-related mortality.

NTSR1 promotes epithelial-mesenchymal transition and metastasis in lung adenocarcinoma through the Wnt/β-catenin pathway

BACKGROUND

Lung adenocarcinoma (LUAD) patients are implicated in poor prognoses and increased mortality rates. Metastasis, as a leading cause of LUAD-related deaths, requires further investigation. Highly metastatic cancer cells often exhibit extensive characteristics of epithelial-mesenchymal transition (EMT). This study attempted to identify novel targets associated with LUAD metastasis and validate their specific molecular mechanisms.

METHODS

Bioinformatics was conducted to determine NTSR1 expression in LUAD and the enriched pathways. Immunohistochemical analysis was used to assess NTSR1 expression in LUAD tissue. qRT-PCR examined expressions of NTSR1 and Wnt/β-Catenin pathway-related genes in LUAD cells. Transwell assayed cell migration and invasion. Cell adhesion experiments were conducted to evaluate cell adhesion capacity. Western blot analysis was employed to examine expression of EMT, Wnt/β-Catenin pathway, and cell adhesion markers.

RESULTS

NTSR1 was upregulated in LUAD tissues and cells, and enriched in EMT pathway. Knockdown of NTSR1 reduced migration, invasion, and adhesion abilities in LUAD cells, and inhibited EMT progression and Wnt/β-Catenin pathway. Rescue experiments demonstrated that β-Catenin activator SKL2001 reversed repressive influence of NTSR1 knockdown on LUAD cell malignant phenotypes and EMT progression.

CONCLUSION

The data obtained in this study suggested that NTSR1 stimulated EMT and metastasis in LUAD via Wnt/β-Catenin pathway. This finding may provide options for overcoming LUAD metastasis.

Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol

Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.

Magnetic resonance-based imaging biopsy with signatures including topological Betti number features for prediction of primary brain metastatic sites

This study incorporated topology Betti number (BN) features into the prediction of primary sites of brain metastases and the construction of magnetic resonance-based imaging biopsy (MRB) models. The significant features of the MRB model were selected from those obtained from gray-scale and three-dimensional wavelet-filtered images, BN and inverted BN (iBN) maps, and clinical variables (age and gender). The primary sites were predicted as either lung cancer or other cancers using MRB models, which were built using seven machine learning methods with significant features chosen by three feature selection methods followed by a combination strategy. Our study dealt with a dataset with relatively smaller brain metastases, which included effective diameters greater than 2 mm, with metastases ranging from 2 to 9 mm accounting for 17% of the dataset. The MRB models were trained by T1-weighted contrast-enhanced images of 494 metastases chosen from 247 patients and applied to 115 metastases from 62 test patients. The most feasible model attained an area under the receiver operating characteristic curve (AUC) of 0.763 for the test patients when using a signature including features of BN and iBN maps, gray-scale and wavelet-filtered images, and clinical variables. The AUCs of the model were 0.744 for non-small cell lung cancer and 0.861 for small cell lung cancer. The results suggest that the BN signature boosted the performance of MRB for the identification of primary sites of brain metastases including small tumors.

FDG PET/CT Tumor Dissemination Characteristic Predicts the Outcome of First-Line Systemic Therapy in Non-small Cell Lung Cancer

RATIONALE AND OBJECTIVES

To explore the correlation between the tumor dissemination characteristic at 18F-fluoro-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images and the outcome of first-line systemic therapy for stage IV non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

The current retrospective study included 101 NSCLC patients receiving first-line systemic therapy with baseline 18F-FDG PET/CT images available. The distance between the two lesions that were the farthest apart was defined as Dmax to calculate the tumor dissemination. The tumor metabolic volume (MTV) of the primary tumor and the MTV of the whole-body tumor lesions (MTVwb) were calculated using 18F-FDG PET/CT imaging. The Kaplan-Meier survival analyses and Cox predictive model were performed to assess the relationship between the parameters and survival.

RESULTS

Dmax and MTVwb were independent prognostic factors for overall survival (OS) (p = 0.019 and p = 0.011, respectively) and progression-free survival (PFS) (p = 0.043 and p = 0.009, respectively). Poor PFS and OS were associated with high MTVwb (>54.0 cm3) and high Dmax (>48.5 cm) (p = 0.006 and p = 0.008, respectively). When MTVwb and Dmax were combined, three risk groups were stratified with no (score 0), one (score 1), or two (score 2) factors (p < 0.001 for PFS, p < 0.001 for OS). The group with a score of 0 had a considerably longer PFS and OS than those who received a score of 1 or 2 (PFS: 61.1%, 43.5%, and 21.1%, respectively, OS: 77.8%, 54.3%, and 36.8%, respectively).

CONCLUSION

The combination of tumor dissemination characteristic (Dmax) and tumor burden (MTVwb) can further improve the prognosis stratification of NSCLC.

The expression and prognostic value of disulfidptosis progress in lung adenocarcinoma

Disulfidptosis is a new cell death model caused by accumulating intracellular disulfides bonding to actin cytoskeleton proteins. This study aimed to investigate the expression and prognostic value of disulfidptosis-related genes (DRGs) in lung adenocarcinoma (LUAD). The data of expression profiles and scRNA-seq were collected from TCGA and GEO databases. The different expressions of DRGs between normal and LUAD tissues were compared. The LASSO analysis and multivariate Cox regression analysis were utilized to develop a DRGs model for the prognosis evaluation in LUAD. The model's predictive accuracy was evaluated with the area under the receiver operating characteristic curve (AUC) and C-index. Survival analysis, univariate and multivariate Cox regression analysis were used to assessing the predictive value of the DRGs model. ScRNA-seq data were analyzed with "Seurat" and "Monocle 2" packages. There were significant differences in 22 DRGs between normal and tumor tissues. A model with five DRGs (ACTB, FLNB, NCKAP1, SLC3A2, SLC7A11) was constructed. The AUC and C-index of the model were significantly higher than that based on clinical parameters. Survival analysis, univariate and multivariate Cox regression analysis demonstrated risk score was an independent prognostic predictor. In the scRNA-seq study, we identified 14 clusters and 11 cell types. Clusters 2, 8, and 13 were annotated into Epithelial cells. SLC7A11 and SLC3A2, NCKAP1 and FLNB, ACTB expressed most abundantly in Epithelial cells, Endothelial cells, Naive CD4 T, respectively. We explored the expression of DRGs in LUAD and constructed a predictive DRGs model, which was stable and reliable for predicting LUAD prognosis.

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

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

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

Background

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

Methods

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

Results

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

Conclusions

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

Gemcitabine Versus Docetaxel Plus Cisplatin as Induction Chemotherapy in Nasopharyngeal Carcinoma

OBJECTIVES

To compare tumor volume reduction after induction chemotherapy (IC) with gemcitabine plus cisplatin (GP) and docetaxel plus cisplatin (DP) and to evaluate the influence on subsequent radiotherapy in locoregionally advanced nasopharyngeal carcinoma (NPC).

STUDY DESIGN

Retrospective clinical study.

METHODS

Patients who received GP or DP IC followed by concurrent chemoradiotherapy (CCRT) were retrospectively enrolled. Propensity score matching (PSM) was adopted to control the balance between the GP and DP groups.

RESULTS

A total of 41 patients treated with GP and 53 patients treated with DP were enrolled. After matching, 33 sub-pairs of 66 patients were generated in the post-PSM cohort. As compared with DP, GP was superior in its gross tumor volume of the nasopharynx (GTVnx) reduction (28.88% vs. 18.73%; P = .014) but equivalent in its gross tumor volume of the lymph nodes (GTVnd) reduction (37.58% vs. 29.79%; P = .229). Univariate and multivariate analyses confirmed that the chemotherapy regimen was an independent factor associated with the reduction in GTVnx (P = .011). The GP group exhibited advantages in the dosimetric parameters of the planning target volume of high-risk volume and low-risk volume (PTV1 and PTV2), lenses, temporal lobes, and parotid glands. Univariate and multivariate analyses confirmed that chemotherapy regimen was an independent factor associated with the dosimetric parameters of PTV1, PTV2, lenses, temporal lobes, and parotid glands.

CONCLUSION

GP regimen achieves a greater GTVnx reduction than DP regimen and has an advantage in the dosimetry of subsequent CCRT.

LEVEL OF EVIDENCE

3 Laryngoscope, 132:2379-2387, 2022.

The dynamic dysregulated network identifies stage-specific markers during lung adenocarcinoma malignant progression and metastasis

Brain metastasis occurs in approximately 30% of patients with lung adenocarcinoma (LUAD) and is closely associated with poor prognosis, recurrence, and death. However, dynamic gene regulation and molecular mechanism driving LUAD progression remain poorly understood. In this study, we performed a comprehensive single-cell transcriptome analysis using data from normal, early stage, advanced stage, and brain metastasis LUAD. Our single-cell-level analysis reveals the cellular composition heterogeneity at different stages during LUAD progression. We identified stage-specific risk genes that could contribute to LUAD progression and metastasis by reprogramming immune-related and metabolic-related functions. We constructed an early advanced metastatic dysregulated network and revealed the dynamic changes in gene regulations during LUAD progression. We identified 6 early advanced (HLA-DRB1, HLA-DQB1, SFTPB, SFTPC, PLA2G1B, and FOLR1), 8 advanced metastasis (RPS15, RPS11, RPL13A, RPS24, HLA-DRB5, LYPLA1, KCNJ15, and PSMA3), and 2 common risk genes in different stages (SFTPD and HLA-DRA) as prognostic markers in LUAD. Particularly, decreased expression of HLA-DRA, HLA-DRB1, HLA-DQB1, and HLA-DRB5 refer poor prognosis in LUAD by controlling antigen processing and presentation and T cell activation. Increased expression of PSMA3 and LYPLA1 refer poor prognosis by reprogramming fatty acid metabolism and RNA catabolic process. Our findings will help further understanding the pathobiology of brain metastases in LUAD.

Differential Infiltration of Immune Cells Driven by Tumor Heterogeneity Reveals Two Immune Subtypes in Lung Adenocarcinoma

Intra-tumoral heterogeneity (ITH) is a critical factor leading to aggressive progression and response to immunotherapy in lung adenocarcinoma (LUAD). However, the relationship between ITH and immune cells in the tumor microenvironment (TME) has not been systematically elucidated. In the present study, we evaluated the ITH status of LUAD samples based on the mutational data obtained from The Cancer Genome Atlas database. First, we identified five key immune pathways with a significantly continuous downtrend among normal, low-heterogeneous, and high-heterogeneous samples and further excavated nine key immune cells related to the key immune pathways and tumor heterogeneity. Then, two immune subtypes were defined by a consensus clustering algorithm based on the infiltration of these immune cells. Differences between these two immune subtypes were remarkable, including alterations of tumor mutation burden and DNA copy number variation at the genomic level, various metabolic pathways, and the different clinical outcome, which was also validated in two independent Gene Expression Omnibus datasets. The results revealed that ITH was significantly associated with prognosis and infiltrating immune cells in the TME. Our study provides novel insights in understanding the relationship between ITH and immune cells and contributes to the immunotherapy of LUAD patients.

Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis

Single-cell sequencing (SCS) is an emerging high-throughput technology that can be used to study the genomics, transcriptomics, and epigenetics at a single cell level. SCS is widely used in the diagnosis and treatment of various diseases, including cancer. Over the years, SCS has gradually become an effective clinical tool for the exploration of tumor metastasis mechanisms and the development of treatment strategies. Currently, SCS can be used not only to analyze metastasis-related malignant biological characteristics, such as tumor heterogeneity, drug resistance, and microenvironment, but also to construct metastasis-related cell maps for predicting and monitoring the dynamics of metastasis. SCS is also used to identify therapeutic targets related to metastasis as it provides insights into the distribution of tumor cell subsets and gene expression differences between primary and metastatic tumors. Additionally, SCS techniques in combination with artificial intelligence (AI) are used in liquid biopsy to identify circulating tumor cells (CTCs), thereby providing a novel strategy for treating tumor metastasis. In this review, we summarize the potential applications of SCS in the field of tumor metastasis and discuss the prospects and limitations of SCS to provide a theoretical basis for finding therapeutic targets and mechanisms of metastasis.

Emerging Systemic Treatment Perspectives on Brain Metastases: Moving Toward a Better Outlook for Patients

The diagnosis of brain metastases has historically been a dreaded, end-stage complication of systemic disease. Additionally, with the increasing effectiveness of systemic therapies that prolong life expectancy and improved imaging tools, the incidence of intracranial progression is becoming more common. Within this context, there has been increasing attention directed at understanding the molecular underpinnings of intracranial progression. Exploring the unique features of brain metastases compared with their extracranial counterparts to identify aberrant signaling pathways, which can be targeted pharmacologically, may help lead to new treatments for this patient population. Additionally, critical discoveries outside the sphere of the central nervous system are increasingly being applied to brain metastases with the emergence of immune checkpoint inhibition, becoming a prevalent treatment option for patients with brain metastases across multiple histologies. As novel treatment strategies are considered, they require thoughtful incorporation of agents that can cross the blood-brain barrier and can synergize with pre-existing agents through rational combinations. Lastly, as clinicians and scientists continue to understand key molecular features of these tumors, they will continue to influence the treatment algorithms that are developing for the management of these patients. Due to the complexity of treatment decisions for patients with brain metastases, an emerging tool is the utilization of multidisciplinary brain metastasis tumor boards to ensure optimal treatment decisions are made and that patients are provided access to applicable clinical trials. Looking to the future, the collective effort to understand the various tumor-intrinsic and tumor-extrinsic factors that promote central nervous system seeding and propagation will have the potential to change the clinical trajectory for these patients.

Identification of a ZC3H12D-regulated competing endogenous RNA network for prognosis of lung adenocarcinoma at single-cell level

Background

To identify hub genes from the competing endogenous RNA (ceRNA) network of lung adenocarcinoma (LUAD) and to explore their potential functions on prognosis of patients from a single-cell perspective.

Methods

We performed RNA-sequencing of LUAD to construct ceRNA regulatory network, integrating with public databases to identify the vital pathways related to patients’ prognosis and to reveal the expression level of hub genes under different conditions, the functional enrichment of co-expressed genes and their potential immune-related mechanisms.

Results

ZC3H12D-hsa-miR-4443-ENST00000630242 axis was found to be related with LUAD. Lower ZC3H12D expression was significantly associated with shorter overall survival (OS) of patients (HR = 2.007, P < 0.05), and its expression was higher in early-stage patients, including T1 (P < 0.05) and N0 (P < 0.05). Additionally, ZC3H12D expression was higher in immune cells displayed by single-cell RNA-sequencing data, especially in Treg cells of lung cancer and CD8 T cells, B cells and CD4 T cells of LUAD. The functional enrichment analysis showed that the co-expressed genes mainly played a role in lymphocyte activation and cytokine-cytokine receptor interaction. In addition, ZC3H12D was associated with multiple immune cells and immune molecules, including immune checkpoints CTLA4, CD96 and TIGIT.

Conclusion

ZC3H12D-hsa-miR-4443-ENST00000630242 ceRNA network was identified in LUAD. ZC3H12D could affect prognosis of patients by regulating mRNA, miRNA, lncRNA, immune cells and immune molecules. Therefore, it may serve as a vital predictive marker and could be regarded as a potential therapeutic target for LUAD in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08992-1.

Single-Cell RNA Sequencing in Lung Cancer: Revealing Phenotype Shaping of Stromal Cells in the Microenvironment

The lung tumor microenvironment, which is composed of heterogeneous cell populations, plays an important role in the progression of lung cancer and is closely related to therapeutic efficacy. Increasing evidence has shown that stromal components play a key role in regulating tumor invasion, metastasis and drug resistance. Therefore, a better understanding of stromal components in the tumor microenvironment is helpful for the diagnosis and treatment of lung cancer. Rapid advances in technology have brought our understanding of disease into the genetic era, and single-cell RNA sequencing has enabled us to describe gene expression profiles with unprecedented resolution, enabling quantitative analysis of gene expression at the single-cell level to reveal the correlations among heterogeneity, signaling pathways, drug resistance and microenvironment molding in lung cancer, which is important for the treatment of this disease. In this paper, several common single-cell RNA sequencing methods and their advantages and disadvantages are briefly introduced to provide a reference for selection of suitable methods. Furthermore, we review the latest progress of single-cell RNA sequencing in the study of stromal cells in the lung tumor microenvironment.

Development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data

Metastasis is the primary cause of lung adenocarcinoma (LUAD)-related death. This study evaluated the metastasis-associated genes (MAGs) in single-cell RNA sequencing (scRNA-seq) data from LUAD tissues and developed a MAG signature to predict overall survival (OS) of LUAD patients. The LUAD scRNA-seq data was downloaded from the Gene Expression Omnibus (GEO) Database and MAGs were identified from LUAD scRNA-seq data. The LUAD transcriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA). Cox and LASSO regression analyses were performed to identify differentially expressed MAGs (DEMAGs) with prognostic value that were then used to construct a MAG signature and MAG-nomogram model. Finally, a functional enrichment analysis was performed via Gene Set Enrichment Analysis (GSEA). 414 MAGs and 22 prognostic DEMAGs were revealed in the study. Multivariate Cox proportional hazards regression analysis was utilized to construct a 7-MAG signature for predicting the OS of LUAD patients. Patients with high risk scores had a significantly worse OS than those with low risk scores in the training group (n = 236), and the 7-MAG signature was successfully confirmed in the testing group (n = 232) and the entire TCGA-LUAD cohort (n = 468). Furthermore, univariate and multivariate Cox regression suggested that the 7-MAG signature was an independent prognostic indicator. Additionally, based on the 7-MAG signature, a nomogram was established that could more intuitively help to predict the OS of LUAD patients. The GSEA revealed the underlying molecular mechanisms of the 7-MAG signature in LUAD metastasis. In conclusion, a 7-MAG signature was developed based on LUAD scRNA-seq data that could effectively predict LUAD patient prognosis and provide novel insights for therapeutic targets and the potential molecular mechanism of metastatic LUAD.

Applications of single-cell sequencing in cancer research: progress and perspectives

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics sequencing, is a powerful tool to decipher the cellular and molecular landscape at a single-cell resolution, unlike bulk sequencing, which provides averaged data. The use of single-cell sequencing in cancer research has revolutionized our understanding of the biological characteristics and dynamics within cancer lesions. In this review, we summarize emerging single-cell sequencing technologies and recent cancer research progress obtained by single-cell sequencing, including information related to the landscapes of malignant cells and immune cells, tumor heterogeneity, circulating tumor cells and the underlying mechanisms of tumor biological behaviors. Overall, the prospects of single-cell sequencing in facilitating diagnosis, targeted therapy and prognostic prediction among a spectrum of tumors are bright. In the near future, advances in single-cell sequencing will undoubtedly improve our understanding of the biological characteristics of tumors and highlight potential precise therapeutic targets for patients.

Molecular Profiles of Brain Metastases: A Focus on Heterogeneity

Simple Summary

Precision cancer medicine depends on the characterization of tumor samples, usually by a single-tumor biopsy, to administer an optimal therapeutic. However, primary tumors and their metastases are often heterogeneous. A metastatic lesion may harbor a completely different genetic makeup to that of its parent tumor, and a single tumor sampling may be ineffective in selecting the most efficient therapy. Brain metastases, due to their low availability and specific microenvironment, pose a particular challenge for precision medicine. In this review, we highlight the genetic landscape of brain metastases, with a particular focus on their heterogeneity. To illustrate this problem, we present phenotypic alterations in brain metastases originating from lung cancer, breast cancer, and melanoma. This article may help clinicians better understand alterations in brain metastases and the relevance of their heterogeneity.

Abstract

Brain metastasis is a common and devastating clinical entity. Intratumor heterogeneity in brain metastases poses a crucial challenge to precision medicine. However, advances in next-generation sequencing, new insight into the pathophysiology of driver mutations, and the creation of novel tumor models have allowed us to gain better insight into the genetic landscapes of brain metastases, their temporal evolution, and their response to various treatments. A plethora of genomic studies have identified the heterogeneous clonal landscape of tumors and, at the same time, introduced potential targets for precision medicine. As an example, we present phenotypic alterations in brain metastases originating from three malignancies with the highest brain metastasis frequency: lung cancer, breast cancer, and melanoma. We discuss the barriers to precision medicine, tumor heterogeneity, the significance of blood-based biomarkers in tracking clonal evolution, the phylogenetic relationship between primary and metastatic tumors, blood–brain barrier heterogeneity, and limitations to ongoing research.