Single-cell RNA sequencing reveals distinct tumor microenvironment of ground glass nodules and solid nodules in lung adenocarcinoma
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PROVIDER: S-EPMC10513029 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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| S-EPMC11585206 | biostudies-literature
Project description:BackgroundThe concept of multi-step progression from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (ADC) has been proposed, and ground-glass nodules (GGNs) may play a critical role during the early lung tumorigenesis. We present the first comprehensive description of the genomic architecture of GGNs to unravel the genetic basis of GGN.MethodsWe investigated 30 GGN-like lungs ADC by performing >1,000× whole-exome sequencing (WES) and characterized the genomic variations and evaluate the relationship between the clinicopathologic and molecular characteristics in this disease.ResultsDespite the low somatic mutation burden, GGNs exhibited high intratumor heterogeneity (ITH) characterized by the proportion of subclonal mutations. Different mutagenesis shaped the genomes of GGN during cancer evolution and were mostly featured by molecular clock-like signatures that occur in clonal mutations and defective DNA mismatch signatures that occur in subclonal mutations. Moreover, 10.7-67.1% clonal mutations occurred after whole-genome doubling (WGD), indicating that WGD could be a frequent truncal event in GGNs. Samples with WGD showed higher genomic instability but lower ITH. These GGNs were characterized by recurrent focal copy-number changes that are highly associated with tumorigenesis, with only two genes (EGFR and RBM10) that were recurrently mutated. Additionally, GGNs with different pathological subtypes or computed tomography (CT) features exhibited distinct genetic characteristics. Lepidic predominant or pure GGNs in CT images carried a lower mutation burden and had a relatively stable genome than nonlepidic or mixed GGNs. GGNs with RBM10 mutations tended to accompany a pathologically lepidic pattern, indicating RBM10 may drive the distinct subtype of lung cancer with better prognosis.ConclusionsThese findings facilitated interpreting the genomic characteristics of GGNs, provided insight into the early stages of lung cancer evolution, and possessed potential clinical significance.
| S-EPMC8044491 | biostudies-literature
Project description:Lung adenocarcinoma featured as mixed ground-glass opacity (mGGO) doubled its volume half of the time in comparison with that featured as pure ground-glass opacity (pGGO). The mechanisms underlying the heterogeneous appearance of mGGO remain elusive. In this study, we macro-dissected the solid (S) components and ground-glass (GG) components of mGGO and performed single-cell sequencing analyses of six paired components from three mGGO patients. A total of 19,391 single-cell profiles were taken into analysis, and the data of each patient were analyzed independently to obtain a common alteration. Cancer cells and macrophages were the dominant cell types in the S and GG components, respectively. Cancer cells in the S components, which showed relatively malignant phenotypes, were likely to originate from both the GG and S components and monitor the surrounding tumor microenvironment (TME) through an intricate cell interaction network. SPP1 hi macrophages were enriched in the S components and showed increased activity of chemoattraction, while macrophages in the GG components displayed an active antimicrobial process with a higher stress-induced state. In addition, the CD47-SIRPA axis was demonstrated to be critical in the maintenance of the GG components. Taken together, our study unraveled the alterations of cell components and transcriptomic features between different components in mGGOs.
| S-EPMC9299373 | biostudies-literature
Project description:BackgroundThis study was designed to unravel the genomic landscape and evolution of early-stage subsolid lung adenocarcinomas (SSN-LUADs) manifesting as pure ground-glass nodules (pGGNs), heterogeneous ground-glass nodules (HGGNs) and part-solid nodules (PSNs).MethodsSamples subjected to either broad-panel next-generation sequencing (NGS) or whole-exome sequencing (WES) were included. Clinicopathologic and genomic features were compared among pGGN, HGGN and PSN, while tumour evolutionary trajectories and mutational signatures were evaluated in the entire cohort.ResultsIn total, 247 SSN-LUAD samples subjected to broad-panel NGS and 125 to WES were identified. Compared with PSNs, HGGNs had significantly lower tumour mutation count (P < 0.001), genomic alteration count (P < 0.001), and intra-tumour heterogeneity (P = 0.005). Statistically significant upward trends were observed in alterations involving driver mutations and oncogenic pathways from pGGNs to HGGNs to PSNs. EGFR mutation was proved to be a key early event in the progression of SSN-LUADs, with subsequently two evolutionary trajectories involving either RBM10 or TP53 mutation in the cancer-evolution models.ConclusionsThis study provided evidence for unravelling the previously unknown genomic underpinnings associated with SSN-LUAD evolution from pGGN to HGGN to PSN, proving that HGGN was an intermediate SSN form between pGGN and PSN genetically.
| S-EPMC9381762 | biostudies-literature
Project description:As an early type of lung adenocarcinoma, ground glass nodule (GGN) has been detected increasingly and now accounts for most lung cancer outpatients. GGN has a satisfactory prognosis and its characteristics are quite different from solid adenocarcinoma (SADC). We compared the GGN adenocarcinoma (GGN-ADC) with SADC using the single-cell RNA sequencing (scRNA-seq) to fully understand GGNs. The tumor samples of five patients with lung GGN-ADCs and five with SADCs underwent surgery were digested to a single-cell suspension and analyzed using 10× Genomic scRNA-seq techniques. We obtained 60,459 cells and then classified them as eight cell types, including cancer cells, endothelial cells, fibroblasts, T cells, B cells, Nature killer cells, mast cells, and myeloid cells. We provided a comprehensive description of the cancer cells and stromal cells. We found that the signaling pathways related to cell proliferation were downregulated in GGN-ADC cancer cells, and stromal cells had different effects in GGN-ADC and SADC based on the analyses of scRNA-seq results. In GGN-ADC, the signaling pathways of angiogenesis were downregulated, fibroblasts expressed low levels of some collagens, and immune cells were more activated. Furthermore, we used flow cytometry to isolate the cancer cells and T cells in 12 GGN-ADC samples and in an equal number of SADC samples, including CD4+ T and CD8+ T cells, and validated the expression of key molecules by quantitative real-time polymerase chain reaction analyses. Through comprehensive analyses of cell phenotypes in GGNs, we provide deep insights into lung carcinogenesis that will be beneficial in lung cancer prevention and therapy.
| S-EPMC7536439 | biostudies-literature
Project description:ObjectivesTo investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).MethodsThis study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic-radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed.ResultsFive radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic-radiomics model (AUC 0.77; 95% CI, 0.69-0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62-0.81) and Rad-score (AUC 0.72; 95% CI, 0.63-0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model.ConclusionsThe proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs.Key points• CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic-radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up.
| S-EPMC7305264 | biostudies-literature
Project description:Single cell RNA seqencing data of six paired samples from three chinese females with early stage lung adenicarcinomas featured as mixed ground glass opacity (mGGO). Each paired samples contain one sample from solid component and one from ground-glass like component within each mGGO. The result reveiled the intratumoral heterogeneity of mGGO.
2022-05-23 | GSE203360 | GEO
Project description:BackgroundGround glass opacity (GGO) is associated with favorable survival in lung cancer. However, the relevant evidence of the difference in prognostic factors between GGO and pure-solid nodules for pathological stage I invasive adenocarcinoma (IAC) is limited. We aimed to identify the impact of GGO on survival and find prognostic factor for part-GGO and pure-solid patients.MethodsBetween December 2007 and August 2018, patients with pathological stage I IAC were retrospectively reviewed and categorized into the pure-GGO, part-GGO, and pure-solid groups. Survival curves were analyzed by the Kaplan-Meier method and compared by log-rank tests. Least absolute shrinkage and selection operator and Cox regression models were used to obtained prognostic factors for disease-free survival (DFS) and overall survival (OS).ResultsThe number of patients with pure-GGO, part-GGO, and pure-solid was 134, 540, and 396, respectively. Part-GGO patients with consolidation-tumor-ratio (CTR) > 0.75 had similar outcome to those with pure-solid nodules. In part-GGO patients, CTR was negatively associated with OS (P = 0.007) and solid tumor size (STS) was negatively associated with DFS (P < 0.001). Visceral pleural invasion (VPI) was negatively associated with OS (P = 0.040) and DFS (P = 0.002). Sublobectomy was negatively associated with OS (P = 0.008) and DFS (P = 0.005), while extended N1 stations examination was associated with improved DFS (P = 0.005) in pure-solid patients.ConclusionsThough GGO component is a positively prognostic factors of patients with pathological stage I IAC, a small proportion of GGO components is not associated with favorable survival. VPI, STS and CTR are the significant predictors for part-GGO patients. Sublobectomy, especially wedge resection should be used cautiously in pure-solid patients.
| S-EPMC9635791 | biostudies-literature
Project description:BackgroundWe invest computed tomography (CT) image differences between non-invasive adenocarcinomas (NIAs) and invasive adenocarcinomas (IAs) presenting as pure ground glass nodules (GGNs).MethodsFrom 2013 to 2019, 48 pure GGNs were surgically resected in 45 patients. Of these, 40 were pathologically diagnosed as non-small cell lung cancers (NSCLCs). We assessed them using the Synapse Vincent (Fujifilm Co., Ltd., Tokyo, Japan) three-dimensional (3D) analysis system; we drew histograms of the CT densities. We calculated the maximum, minimum, means, and standard deviations of the densities. The proportions of GGNs of high CT density were compared between the two groups. The diagnostic performance was investigated via receiver operating curve (ROC) analysis.ResultsOf the 40 pure GGNs, 20 were NIAs (4 adenocarcinomas in situ and 16 minimally IAs) and 20 IAs. Significant correlations were evident between histological invasiveness and the maximum and mean CT densities and the standard deviation. Neither the nodule volume nor the minimum CT density significantly predicted invasiveness. A CT volume density proportion >-300 Hounsfield units optimally predicted the invasiveness of pure GGNs; the cutoff was 5.41% with a sensitivity of 85% and a specificity of 95%.ConclusionsCT density reflected the invasiveness of pure GGNs. A CT volume proportion density >-300 Hounsfield units may significantly predict histological invasiveness.
| S-EPMC10174765 | biostudies-literature
Project description:BackgroundPatients with consistent lung pure ground-glass nodules (pGGNs) have a high incidence of lung adenocarcinoma that can be classified as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Regular follow-up is recommended for AIS and MIA, while surgical resection should be considered for IAC. This study sought to develop a multi-parameter prediction model to increase the diagnostic accuracy in discriminating between IAC and AIS or MIA.MethodsThe training data set comprised consecutive patients with lung pGGNs who underwent resection from January to December 2017 at the Zhongshan Hospital. Of the 370 resected pGGNs, 344 were pathologically confirmed to be AIS, MIA, or IAC and were included in the study. The 26 benign pGGNs were excluded. We compared differences in the clinical features (e.g., age and gender), the content of serum tumor biomarkers, the computed tomography (CT) parameters (e.g., nodule size and the maximal CT value), and the morphologic characteristics of nodules (e.g., lobulation, spiculation, pleura indentation, vacuole sign, and normal vessel penetration or abnormal vessel) between the pathological subtypes of AIS, MIA, and IAC. An abnormal vessel was defined as "vessel curve" or "vessel enlargement". Statistical analyses were performed using the chi-square test, analysis of variance (ANOVA), and rank test. The IAC prediction model was constructed via a multivariate logistical regression. Our prediction model for lung pGGNs was further validated in a data set comprising consecutive patients from multiple medical centers in China from July to December 2018. In total, 345 resected pGGNs were pathologically diagnosed as lung adenocarcinoma in the validation data set.ResultsIn the training data set, patients with pGGNs ≥10 mm in size had a high incidence (74.5%) of IAC. The maximal CT value of IAC [-416.1±121.2 Hounsfield unit (HU)] was much higher than that of MIA (-507.7±138.0 HU) and AIS (-602.6±93.3 HU) (P<0.001). IAC was more common in pGGNs that displayed any of the following CT manifestations: lobulation, spiculation, pleura indentation, vacuole sign, and vessel abnormality. The IAC prediction model was constructed using the parameters that were assessed as risk factors (i.e., the nodule size, maximal CT value, and CT signs). The receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) of this model for diagnosing IAC was 0.910, which was higher than that of the AUC for nodule size alone (0.891) or the AUC for the maximal CT value alone (0.807) (P<0.05, respectively). A multicenter validation data set was used to validate the performance of our prediction model in diagnosing IAC, and our model was found to have an AUC of 0.883, which was higher than that of the AUC of 0.827 for the module size alone model or the AUC of 0.791 for the maximal CT value alone model (P<0.05, respectively).ConclusionsOur multi-parameter prediction model was more accurate at diagnosing IAC than models that used only nodule size or the maximal CT value alone. Thus, it is an efficient tool for identifying the IAC of malignant pGGNs and deciding if surgery is needed.
| S-EPMC8482342 | biostudies-literature