Classification Of Lung Adenocarcinoma Research Articles

Ultrasensitive PD-L1-Expressing Exosome Immunosensors Based on a Chemiluminescent Nickel–Cobalt Hydroxide Nanoflower for Diagnosis and Classification of Lung Adenocarcinoma

Ultrasensitive PD-L1-Expressing Exosome Immunosensors Based on a Chemiluminescent Nickel–Cobalt Hydroxide Nanoflower for Diagnosis and Classification of Lung Adenocarcinoma

Experience Sharing in Pathological Diagnosis of Early Adenocarcinoma of the Lung.

Background: In daily work, there are still many pathologists who have difficulty handling the diagnosis of atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, and lepidic adenocarcinoma, and the boundaries are not clear enough. Sometimes, the diagnosis is difficult, and there is sometimes poor reproducibility between different pathologists. Accurate diagnosis and differential diagnosis require a certain amount of experience. Methods: During the COVID-19 pandemic, we collected a large number (n = 381) of specimens of early lung adenocarcinoma, most of which (n = 356) were solitary lesions and 25 were multifocal lesions. There were 78 nodules in multifocal lesions, total 434 nodules. We summarized very careful microscopic observation and comparative analysis on all frozen and paraffin sections collected from many early lung adenocarcinoma specimens, continuously summarizing our experience. Results: Based on the World Health Organization's 2021 classification and diagnostic criteria for lung adenocarcinoma, new perspectives have been proposed on how to distinguish between atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, and lepidic adenocarcinoma. In particular, new perspectives have been proposed on how to identify invasive aspects, and there are also some new perspectives on early lung mucinous lesions. Conclusion: Atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, and lepidic adenocarcinoma all have corresponding morphological diagnostic criteria, but the morphological boundaries are sometimes not easy to determine and require some experience accumulation. The intraoperative frozen pathological diagnosis of early adenocarcinoma of the lung needs to be closely combined with imaging examination, and has very rich morphological experience.

Classification of Lung Adenocarcinoma Using Convolutional Neural Networks: A Bioinformatics Approach

Classification of Lung Adenocarcinoma Using Convolutional Neural Networks: A Bioinformatics Approach

Spread Through Air Spaces in Residual Tumor Classification for Clinical IA Lung Adenocarcinoma

BackgroundWe aimed to validate the prognostic implication of uncertain resection, R(un), proposed by International Association for the Study of Lung Cancer (IASLC) and evaluate the prognostic value of spread through air spaces (STAS) in reclassifying the R classification among patients with lung adenocarcinoma after segmentectomy. MethodsWe enrolled 1007 patients who underwent segmentectomy for c-stage IA lung adenocarcinoma between 2014 and 2017. Recurrence-free survival (RFS) and overall survival (OS) were compared to evaluate the prognostic value of IASLC-R(un) and STAS. Whether STAS would skip into complementary lobectomy was evaluated in a prospective cohort. ResultsThe current IASLC-R(un) failed to significantly stratify the RFS (P = .078) in segmentectomy, and STAS was a stronger risk factor of poor prognosis for both RFS and OS (P < .001). Moreover, the presence of STAS was associated with increased locoregional recurrence in patients undergoing segmentectomy (P < .001) but not in those treated with lobectomy (P = .187), indicating that only STAS-positive segmentectomy was consistent with the concept of R(un) in relapse pattern. After reclassifying STAS-positive segmentectomy into the R(un) category, the proposed R(un) showed an improvement in prognosis stratification. In addition, 2 of 30 patients (6.2%) in the prospective cohort who underwent initial segmentectomy and complementary lobectomy had STAS clusters in the complementary lobectomy specimens. ConclusionsUnfavorable prognosis, relapse patterns consistent with R(un), and pathologic verification that saltatory spread of STAS observed in complementary lobectomy specimens supported reclassifying STAS-positive segmentectomy as R(un). STAS is a critical concern for the surgical completeness evaluation after segmentectomy.

Transformaer-based model for lung adenocarcinoma subtypes.

Lung cancer has the highest morbidity and mortality rate among all types of cancer. Histological subtypes serve as crucial markers for the development of lung cancer and possess significant clinical values for cancer diagnosis, prognosis, and prediction of treatment responses. However, existing studies only dichotomize normal and cancerous tissues, failing to capture the unique characteristics of tissue sectionsand cancertypes. Therefore, we have pioneered the classification of lung adenocarcinoma (LAD) cancer tissues into five subtypes (acinar, lepidic, micropapillary, papillary, and solid) based on sectiondata in whole-slide image sections. In addition, a novel model called HybridNet was designed to improve the classificationperformance. HybridNet primarily consists of two interactive streams: a Transformer and a convolutional neural network (CNN). The Transformer stream captures rich global representations using a self-attention mechanism, while the CNN stream extracts local semantic features to optimize image details. Specifically, during the dual-stream parallelism, the feature maps of the Transformer stream as weights are weighted and summed with those of the CNN stream backbone; at the end of the parallelism, the respective final features are concatenated to obtain more discriminative semanticinformation. Experimental results on a private dataset of LAD showed that HybridNet achieved 95.12% classification accuracy, and the accuracy of five histological subtypes (acinar, lepidic, micropapillary, papillary, and solid) reached 94.5%, 97.1%, 94%, 91%, and 99% respectively; the experimental results on the public BreakHis dataset show that HybridNet achieves the best results in three evaluation metrics: accuracy, recall and F1-score, with 92.40%, 90.63%, and 91.43%,respectively. The process of classifying LAD into five subtypes assists pathologists in selecting appropriate treatments and enables them to predict tumor mutation burden (TMB) and analyze the spatial distribution of immune checkpoint proteins based on this and other clinical data. In addition, the proposed HybridNet fuses CNN and Transformer information several times and is able to improve the accuracy of subtype classification, and also shows satisfactory performance on public datasets with some generalizationability.

The value of CT shape quantification in predicting pathological classification of lung adenocarcinoma

ObjectiveTo evaluate whether quantification of lung GGN shape is useful in predicting pathological categorization of lung adenocarcinoma and guiding the clinic.Methods98 patients with primary lung adenocarcinoma were pathologically confirmed and CT was performed preoperatively, and all lesions were pathologically ≤ 30 mm in size. On CT images, we measured the maximum area of the lesion’s cross-section (MA). The longest diameter of the tumor (LD) was marked with points A and B, and the perpendicular diameter (PD) was marked with points C and D, which was the longest diameter perpendicular to AB. and D, which was the longest diameter perpendicular to AB. We took angles A and B as big angle A (BiA) and small angle A (SmA). We measured the MA, LD, and PD, and for analysis we derived the LD/PD ratio and the BiA/SmA ratio. The data were analysed using the chi-square test, t-test, ROC analysis, and binary logistic regression analysis.ResultsPrecursor glandular lesions (PGL) and microinvasive adenocarcinoma (MIA) were distinguished from invasive adenocarcinoma (IAC) by the BiA/SmA ratio and LD, two independent factors (p = 0.007, p = 0.018). Lung adenocarcinoma pathological categorization was indicated by the BiA/SmA ratio of 1.35 and the LD of 11.56 mm with sensitivity of 81.36% and 71.79%, respectively; specificity of 71.79% and 74.36%, respectively; and AUC of 0.8357 (95% CI: 0.7558–0.9157, p < 0.001), 0.8666 (95% CI: 0.7866–0.9465, p < 0.001), respectively. In predicting the pathological categorization of lung adenocarcinoma, the area under the ROC curve of the BiA/SmA ratio combined with LD was 0.9231 (95% CI: 0.8700-0.9762, p < 0.001), with a sensitivity of 81.36% and a specificity of 89.74%.ConclusionsQuantification of lung GGN morphology by the BiA/SmA ratio combined with LD could be helpful in predicting pathological classification of lung adenocarcinoma.

Classification and immunotherapy assessment of lung adenocarcinoma based on coagulation-related genes.

Introduction: This study on lung adenocarcinoma (LUAD), a common lung cancer subtype with high mortality. Aims: This study focuses on how tumor cell interactions affect immunotherapy responsiveness. Methods: Using public databases, we used non-negative matrix factorization clustering method, ssGSEA, CIBERSORT algorithm, immunophenotype score, survival analysis, protein-protein interaction network method to analyze gene expression data and coagulation-related genes. Results: We dividedLUAD patients into three coagulation-related subgroups with varying immune characteristics and survival rates. A cluster ofthreepatients, having the highest immune infiltration and survival rate, also showed the most potential for immunotherapy. We identified fivekey genes influencing patient survival using a protein-protein interaction network. Conclusion: This research offers valuable insights for forecasting prognosis and immunotherapy responsiveness in LUAD patients, helping to inform clinical treatment strategies.

Solitary lung adenocarcinoma: follow-up CT, pathological-molecular characteristics, and surgical prognosis for different morphological classifications

ObjectiveTo investigate the dynamic changes during follow-up computed tomography (CT), histological subtypes, gene mutation status, and surgical prognosis for different morphological presentations of solitary lung adenocarcinomas (SLADC).Materials and methodsThis retrospective study compared dynamic tumor changes and volume doubling time (VDT) in 228 patients with SLADC (morphological types I–IV) who had intermittent growth during follow-ups. The correlation between the morphological classification and histological subtypes, gene mutation status, and surgical prognosis was evaluated.ResultsAmong the 228 patients, 66 (28.9%) were classified as type I, 123 (53.9%) as type II, 16 (7%) as type III, and 23 (10.1%) as type IV. Type I had the shortest VDT (254 days), followed by types IV (381 days) and III (501 days), and then type II (993 days) (p < 0.05 each). Type I had a greater proportion of solid/micropapillary-predominant pattern than type II, and the lepidic-predominant pattern was more common in type II and III than in type I (p < 0.05 each). Furthermore, type II and IV SLADCs were correlated with positive epidermal growth factor receptor mutation (p < 0.05 each). Lastly, the Kaplan–Meier curves showed that the disease-free survival was longest for patients with type II tumors, followed by those with type III and IV tumors, and then those with type I tumors (p < 0.001 each).ConclusionA good understanding of the natural progression and pathological-molecular characteristics of different morphological SLADC types can help make accurate diagnoses, develop individual treatment strategies, and predict patient outcomes.Critical relevance statementA good understanding of the natural progression and pathological-molecular characteristics of different morphological solitary lung adenocarcinoma types can help make accurate diagnoses, develop individual treatment strategies, and predict patient outcomes.Key points• Type I–IV solitary lung adenocarcinomas exhibit varying natural progression on serial CT scans.• Morphological classification of solitary lung adenocarcinomas predicts histological subtype, gene status, and surgical prognosis.• This classification of solitary lung adenocarcinomas may help improve diagnostic, therapeutic, and prognosticating abilities.Graphical

Updates on lung adenocarcinoma: invasive size, grading andSTAS.

Advancements in the classification of lung adenocarcinoma have resulted in significant changes in pathological reporting. The eighth edition of the tumour-node-metastasis (TNM) staging guidelines calls for the use of invasive size in staging in place of total tumour size. This shift improves prognostic stratification and requires a more nuanced approach to tumour measurements in challenging situations. Similarly, the adoption of new grading criteria based on the predominant and highest-grade pattern proposed by the International Association for the Study of Lung Cancer (IASLC) shows improved prognostication, and therefore clinical utility, relative to previous grading systems. Spread through airspaces (STAS) is a form of tumour invasion involving tumour cells spreading through the airspaces, which has been highly researched in recent years. This review discusses updates in pathological T staging, adenocarcinoma grading and STAS and illustrates the utility and limitations of current concepts in lung adenocarcinoma.

Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas

Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease with five predominant histologic subtypes. Fully supervised convolutional neural networks can improve the accuracy and reduce the subjectivity of LUAD histologic subtyping using hematoxylin and eosin (H&E)-stained whole slide images (WSIs). However, developing supervised models with good prediction accuracy usually requires extensive manual data annotation, which is time-consuming and labor-intensive. This work proposes three self-supervised learning (SSL) pretext tasks to reduce labeling effort. These tasks not only leverage the multi-resolution nature of the H&E WSIs but also explicitly consider the relevance to the downstream task of classifying the LUAD histologic subtypes. Two tasks involve predicting the spatial relationship between tiles cropped from lower and higher magnification WSIs. We hypothesize that these tasks induce the model to learn to distinguish different tissue structures presented in the images, thus benefiting the downstream classification. The third task involves predicting the eosin stain from the hematoxylin stain, inducing the model to learn cytoplasmic features relevant to LUAD subtypes. The effectiveness of the three proposed SSL tasks and their ensemble was demonstrated by comparison with other state-of-the-art pretraining and SSL methods using three publicly available datasets. Our work can be extended to any other cancer type where tissue architectural information is important. The model could be used to expedite and complement the process of routine pathology diagnosis tasks. The code is available at https://github.com/rina-ding/ssl_luad_classification.

Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study.

Preoperative prediction models for histologic subtype and grade of stage IA lung adenocarcinoma (LUAD) according to the update of the WHO Classification of Tumors of the Lung in 2021 and the 2020 new grade system are yet to be explored. We aim to develop the noninvasive pathology and grade evaluation approach for patients with stage IA LUAD via CT-based radiomics approach and evaluate their performance in clinical practice. Chest CT scans were retrospectively collected from patients who were diagnosed with stage IA LUAD and underwent complete resection at two hospitals. A deep learning segmentation algorithm was first applied to assist lesion delineation. Expansion strategies such as bounding-box annotations were further applied. Radiomics features were then extracted and selected followed by radiomics modeling based on four classic machine learning algorithms for histologic subtype classification and grade stratification. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. The study included 294 and 145 patients with stage IA LUAD from two hospitals for radiomics analysis, respectively. For classification of four histological subtypes, multilayer perceptron (MLP) algorithm presented no annotation strategy preference and achieved the average AUC of 0.855, 0.922, and 0.720 on internal, independent, and external test sets with 1-pixel expansion annotation. Bounding-box annotation strategy also enabled MLP an acceptable and stable accuracy among test sets. Meanwhile, logistic regression was selected for grade stratification and achieved the average AUC of 0.928, 0.837, and 0.748 on internal, independent, and external test sets with optimal annotation strategies. DL-enhanced radiomics models had great potential to predict the fine histological subtypes and grades of early-stage LUADs based on CT images, which might serve as a promising noninvasive approach for the diagnosis and management of early LUADs.

Number of involved nodal stations: a better lymph node classification for clinical stage IA lung adenocarcinoma

BackgroundWith the popularization of lung cancer screening, more early-stage lung cancers are being detected. This study aims to compare three types of N classifications, including location-based N classification (pathologic nodal classification [pN]), the number of lymph node stations (nS)-based N classification (nS classification), and the combined approach proposed by the International Association for the Study of Lung Cancer (IASLC) which incorporates both pN and nS classification to determine if the nS classification is more appropriate for early-stage lung cancer. MethodsWe retrospectively reviewed the clinical data of lung cancer patients treated at the Cancer Hospital, Chinese Academy of Medical Sciences between 2005 and 2018. Inclusion criteria was clinical stage IA lung adenocarcinoma patients who underwent resection during this period. Sub-analyses were performed for the three types of N classifications. The optimal cutoff values for nS classification were determined with X-tile software. Kaplan‒Meier and multivariate Cox analyses were performed to assess the prognostic significance of the different N classifications. The prediction performance among the three types of N classifications was compared using the concordance index (C-index) and decision curve analysis (DCA). ResultsOf the 669 patients evaluated, 534 had pathological stage N0 disease (79.8%), 82 had N1 disease (12.3%) and 53 had N2 disease (7.9%). Multivariate Cox analysis indicated that all three types of N classifications were independent prognostic factors for prognosis (all P < 0.001). However, the prognosis overlaps between pN (N1 and N2, P = 0.052) and IASLC-proposed N classification (N1b and N2a1 [P = 0.407], N2a1 and N2a2 [P = 0.364], and N2a2 and N2b [P = 0.779]), except for nS classification subgroups (nS0 and nS1 [P < 0.001] and nS1 and nS >1 [P = 0.006]). There was no significant difference in the C-index values between the three N classifications (P = 0.370). The DCA results demonstrated that the nS classification provided greater clinical utility. ConclusionThe nS classification might be a better choice for nodal classification in clinical stage IA lung adenocarcinoma.

Development of a novel blood-based RNA gene expression platform for early-stage lung cancer diagnosis.

8557 Background: Blood-based methods using circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA) are under development for early and less invasive detection of lung cancer, although detection of the earliest stage cancers (stages 0-II) using these modalities is suboptimal. We hypothesized that a machine learning approach using RNA gene expression may offer important information on the biology of the patient, allowing for gene expression profiles to be used as a surrogate measurement of cancer disease phenotype and as a promising direction for early detection of lung cancer. In a previous study, 23 miRNA biomarkers were successfully discovered and validated for the non-invasive diagnostic classification of lung adenocarcinoma, achieving 97.7% sensitivity, 98.7% specificity in blood obtained from 383 clinical subjects. The aim of this study was to train a machine learning algorithm, from the 23 miRNA features, to test the signature for early lung cancer detection. Methods: A large and diverse clinical cohort was obtained from the NIH Gene Expression Omnibus database, GEO Accession Number GSE137140 ( n=3,744), comprised of miRNA extracted from serum samples consisting of subjects with pre-operative lung cancer ( n=1,566) and non-cancer controls ( n=2,178). Our analytic plan leveraged machine learning methods derived from XGBoost classification, a popular supervised-learning algorithm that uses sequentially built shallow decision trees to provide accurate results and avoidance of overfitting. The algorithm was trained using XGBoost 1.4.1.1 R library programmed with R v3.6.3. Results: The lung cancer cohort was heavily weighted towards early-stage lung cancer (87.7% stage I/II), including representation across prevalent histologic types (adenocarcinoma 77.8%, non-adenocarcinoma 22.2%) and those who self-reported as never smokers (37.9%). The 23-miRNA signature achieved 98% sensitivity, 89% specificity in the held-out test set (Table). When incorporating age and gender, the 23-miRNA signature achieved 95.5% sensitivity, 90.3% specificity. Conclusions: A machine learning approach using RNA gene expression in patient serum achieved high sensitivity and specificity in a large, predominantly early-stage, lung cancer cohort. A multi-analyte, multimodal approach that leverages machine learning algorithms with RNA gene expression profiles and available demographics and clinical risk-factors, represents the possibility to accurately detect lung cancer in the earliest stages. This approach has successfully been translated from microarray to PCR instrumentation, with further validation of this machine learning method and approach currently underway. [Table: see text]

Thin-slice computed tomography enables to classify pulmonary subsolid nodules into pre-invasive lesion/minimally invasive adenocarcinoma and invasive adenocarcinoma: a retrospective study

The aim was to investigate the ability of thin-slice computed tomography (TSCT) to differentiate invasive pulmonary adenocarcinomas (IACs) from pre-invasive/minimally invasive adenocarcinoma (AAH-MIAs), manifesting as subsolid nodules (SSNs) of diameter less than 30 mm. The CT findings of 810 patients with single subsolid nodules diagnosed by pathology of resection specimens were analyzed (atypical adenomatous hyperplasia, n = 13; adenocarcinoma in situ, n = 175; minimally invasive adenocarcinoma, n = 285; and invasive adenocarcinoma, n = 337). According to the classification of lung adenocarcinoma published by WHO classification of thoracic tumors in 2015, TSCT features of 368 pure ground-glass nodules (pGGN) and 442 part-solid nodules (PSNs) were compared AAH-MIAs with IACs. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed. In pGGNs, multivariate analysis of factors found to be significant by univariate analysis revealed that higher mean-CT values (p = 0.006, OR 1.006, 95% CI 1.002–1.010), larger tumor size (p < 0.001, OR 1.483, 95% CI 1.304–1.688) with air bronchogram and non-smooth margins were significantly associated with IACs. The optimal cut-off tumor diameter for AAH-MIAs lesions was less than 10.75 mm (sensitivity, 82.8%; specificity, 80.6%) and optimal cut-off mean-CT value − 629HU (sensitivity, 78.1%; specificity, 50.7%). In PSNs, multivariate analysis of factors found to be significant by univariate analysis revealed that smaller tumor diameter (p < 0.001, OR 0.647, 95% CI 0.481–0.871), smaller size of solid component (p = 0.001, OR 83.175, 95% CI 16.748–413.079),and lower mean-CT value of solid component (p < 0.001, OR 1.009, 95% CI 1.004–1.014) were significantly associated with AAH-MIAs (p < 0.05). The optimal cut-off tumor diameter, size of solid component, and mean-CT value of solid component for AAH-MIAs lesions were less than 14.595 mm (sensitivity, 71.1%; specificity, 83.4%), 4.995 mm (sensitivity, 97.8%; specificity, 92.3%) and − 227HU (sensitivity, 65.6%; specificity, 76.3%), respectively. In subsolid nodules, whether pGGN or PSNs, the characteristics of TSCT can help in distinguishing IACs from AAH-MIAs.

The Swin-Transformer network based on focal loss is used to identify images of pathological subtypes of lung adenocarcinoma with high similarity and class imbalance.

The classification of primary lung adenocarcinoma is complex and varied. Different subtypes of lung adenocarcinoma have different treatment methods and different prognosis. In this study, we collected 11 datasets comprising subtypes of lung cancer and proposed FL-STNet model to provide the assistance for improving clinical problems of pathologic classification in primary adenocarcinoma of lung. Samples were collected from 360 patients diagnosed with lung adenocarcinoma and other subtypes of lung diseases. In addition, an auxiliary diagnosis algorithm based on Swin-Transformer, which used Focal Loss for function in training, was developed. Meanwhile, the diagnostic accuracy of the Swin-Transformer was compared to pathologists. The Swin-Transformer captures not only information in the overall tissue structure but also the local tissue details in the images of lung cancer pathology. Furthermore, training FL-STNet with the Focal Loss function can further balance the difference in the amount of data between different subtypes, improving recognition accuracy. The average classification accuracy, F1, and AUC of the proposed FL-STNet reached 85.71%, 86.57%, and 0.9903. The average accuracy of the FL-STNet was higher by 17% and 34%, respectively, than in the senior pathologist and junior pathologist group. The first deep learning based on an 11-category classifier was developed for classifying lung adenocarcinoma subtypes based on WSI histopathology. Aiming at the deficiencies of the current CNN and Vit, FL-STNet model is proposed in this study by introducing Focal Loss and combining the advantages of Swin-Transformer model.

Bioinformatics analysis based on DNA methylation data identified in lung adenocarcinoma subgroups with different immune characteristics and clinical outcomes.

DNA methylation can be used to predict clinical outcomes and improve the classification of tumors. The present study aimed to develop a new lung adenocarcinoma (LUAD) classification system according to the immune cell gene-related methylation sites and to reveal the survival outcomes, clinical characteristics, immune cell infiltration, stem cell characteristics, and genomic variations of each molecular subgroup. The DNA methylation sites of LUAD samples collected from The Cancer Genome Atlas (TCGA) database were analyzed, and the prognosis-related differential methylation sites (DMS) were screened. Consistent clustering of the samples was conducted using ConsensusClusterPlus, and the classification results were verified by principal component analysis (PCA). The survival and clinical results, immune cell infiltration, stemness, DNA mutation, and copy number variation (CNV) of each molecular subgroup were analyzed. A total of 40 DMS were obtained by difference and univariate COX analyses, and the TCGA LUAD samples were divided into three subgroups: cluster 1 (C1), cluster 2 (C2), and cluster 3 (C3). Among these subgroups, the overall survival (OS) of C3 was significantly higher than that of C1 and C2. Compared with C1 and C3, C2 had the lowest innate immune cell and adaptive immune cell infiltration scores; the lowest stromal score, immune score, and iconic immune checkpoint expression; and the highest expression of messenger RNA (mRNA) expression-based stemness indices (mRNAsi), DNA methylation-based stemness index (mDNAsi), and tumor mutational burden (TMB). In this study, we proposed a LUAD typing system based on DMS, which was closely related to the survival, clinical features, immune characteristics, and genomic variations of LUAD, and may contribute to the development of personalized therapy for new specific subtypes.

Genetic and phenotypic determinants of morphologies in 3D cultures and xenografts of lung tumor cell lines.

We previously proposed the classification of lung adenocarcinoma into two groups: the bronchial epithelial phenotype (BE phenotype) with high-level expressions of bronchial epithelial markers and actionable genetic abnormalities of tyrosine kinase receptors and the non-BE phenotype with low-level expressions of bronchial Bronchial epithelial (BE) epithelial markers and no actionable genetic abnormalities of tyrosine kinase receptors. Here, we performed a comprehensive analysis of tumor morphologies in 3D cultures and xenografts across a panel of lung cancer cell lines. First, we demonstrated that 40 lung cancer cell lines (23 BE and 17 non-BE) can be classified into three groups based on morphologies in 3D cultures on Matrigel: round (n=31), stellate (n=5), and grape-like (n=4). The latter two morphologies were significantly frequent in the non-BE phenotype (1/23 BE, 8/17 non-BE, p=0.0014), and the stellate morphology was only found in the non-BE phenotype. SMARCA4 mutations were significantly frequent in stellate-shaped cells (4/4 stellate, 4/34 non-stellate, p=0.0001). Next, from the 40 cell lines, we successfully established 28 xenograft tumors (18 BE and 10 non-BE) in NOD/SCID mice and classified histological patterns of the xenograft tumors into three groups: solid (n=20), small nests in desmoplasia (n=4), and acinar/papillary (n=4). The latter two patterns were characteristically found in the BE phenotype. The non-BE phenotype exhibited a solid pattern with significantly less content of alpha-SMA-positive fibroblasts (p=0.0004) and collagen (p=0.0006) than the BE phenotype. Thus, the morphology of the tumors in 3D cultures and xenografts, including stroma genesis, reflects the intrinsic properties of the cancer cell lines. Furthermore, this study serves as an excellent resource for lung adenocarcinoma cell lines, with clinically relevant information on molecular and morphological characteristics and drug sensitivity.

A signature-based classification of lung adenocarcinoma that stratifies tumor immunity.

Immune-related subgroup classification in immune checkpoint blockade (ICB) therapy is largely inconclusive in lung adenocarcinoma (LUAD). First, the single-sample Gene Set Enrichment Analysis (ssGSEA) and K-means algorithms were used to identify immune-based subtypes for the LUAD cohort based on the immunogenomic profiling of 29 immune signatures from The Cancer Genome Atlas (TCGA) database (n = 504). Second, we examined the prognostic and predictive value of immune-based subtypes using bioinformatics analysis. Survival analysis and additional COX proportional hazards regression analysis were conducted for LUAD. Then, the immune score, tumor-infiltrating immune cells (TIICs), and immune checkpoint expression of the three subtypes were analyzed. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) of the differentially expressed genes (DEGs) between three immune-based subtypes were subsequently analyzed for functional enrichment pathways. A total of three immune-based subtypes with distinct immune signatures have been identified for LUAD and designated as cluster 1 (C1), cluster 2 (C2), and cluster 3 (C3). Patients in C3 had higher stromal, immune, and ESTIMATE scores, whereas those in C1 had the opposite. Patients in C1 had an enrichment of macrophages M0 and activation of dendritic cells, whereas tumors in C3 had an enrichment of CD8+ T cells, activation of CD4+ memory T cells, and macrophages M1. C3 had a higher immune cell infiltration and a better survival prognosis than other subtypes. Furthermore, patients in C3 had higher expression levels of immune checkpoint proteins such as PD-L1, PD1, CTLA4, LAG3, IDO1, and HAVCR2. No significant differences were found in cluster TMB scores. We also found that immune-related pathways were enriched in C3. LUAD subtypes based on immune signatures may aid in the development of novel treatment strategies for LUAD.

A novel NGS‐based diagnostic algorithm for classifying multifocal lung adenocarcinomas in pN0M0 patients

The classification of multifocal lung adenocarcinomas (MLAs), including multiple primary lung adenocarcinomas (MPLAs) and intrapulmonary metastases (IPMs), has great clinical significance in staging and treatment determination. However, the application of molecular approaches in pN0M0 MLA diagnosis has not been well investigated. Here, we performed next-generation sequencing (NGS) analysis in 45 pN0M0 MLA patients (101 lesion pairs) who were initially diagnosed as having MPLA by comprehensive histologic assessment (CHA). Five additional patients with intrathoracic metastases were used as positive controls, while 197 patients with unifocal lung adenocarcinomas (425 random lesion pairs) were used as negative controls. By utilizing a predefined NGS criterion, all IPMs in the positive control group could be accurately classified, whereas 13 lesion pairs (3.1%) in the negative control cohort were misdiagnosed as IPMs. Additionally, 14 IPM lesion pairs were diagnosed in the study group, with at least 7 misdiagnoses. We thus developed a refined algorithm, incorporating both NGS and histologic results, that could correctly diagnose all the known MPLAs and IPMs. In particular, all IPMs identified by the refined algorithm were diagnosed to be IPMs or suspected IPMs by CHA reassessment. The refined algorithm-diagnosed MPLAs patients also had significantly better progression-free survival than the refined algorithm-diagnosed IPMs (p < 0.0001), which is superior to conventional NGS or CHA diagnoses. Overall, we developed an NGS-based algorithm that could accurately distinguish IPMs from MPLAs in MLA patients. Our results demonstrate a promising clinical utility of NGS to complement traditional CHA-based MLA diagnosis and help determine patient staging and treatment.

Prognosis of the second predominant subtype in lung adenocarcinoma: a retrospective single-center cohort study.

The histologic classification of lung adenocarcinoma (LUAD) was mainly divided into three pathological subtype groups: the low-grade predominant subtype group (lepidic), the intermediate-grade predominant subtype group (papillary and acinar), and the high-grade predominant subtype group (micropapillary and solid). Previous studies have focused on the prognostic impact of predominant subtypes of lung adenocarcinoma. In this investigation, we investigated the effect of the second predominant subtype on prognosis. The data of LUAD postoperative patients were retrospectively collected. Exclusion criteria included cases in which the pathologic results revealed a single characteristic, the presence ofinvasive mucinous LUAD, or if the first predominant and the second predominant groups could not be distinguished. Categorical variables were compared with the two-tailed Pearson χ2 test and continuous variables with the Student's t-test. Follow-up was conducted by telephone and other methods. Independent prognostic factors of the second major subtype were determined by the Kaplan-Meier method and log-rank test. The Cox proportional risk regression model was used to analyze the possible prognostic factors. Among 293 patients, the meanage was 61.9 years and 47.1% were male. The results revealed that when the predominant group was the low-grade group, the second predominant groups had no significant influence on overall survival (OS) (P=0.15) but significantly influenced disease free survival (DFS) (P=0.037). Subsequently, when the predominant group was the intermediate-grade group, the second predominant groups significantly influenced OS (P=0.024) but had no significant influence on DFS (P=0.3). Moreover, when the predominant group was the high-grade group, the second predominant groups significantly influenced OS (P=0.033) but had no significant influence on DFS (P=0.31). The independent prognostic effect of the second predominant group was not identified for OS and DFS of lung adenocarcinoma. The effects of the second predominant subtype groups on OS and DFS were not evenly distributed among different predominant subtype groups, and the low-grade second predominant subtype exhibited some protective effects on the middle-grade predominant subtypes.