Novel molecular tumor cell markers in regional lymph nodes and blood samples from patients undergoing surgery for non-small cell lung cancer

A Novel Prognostic Signature Revealed the Interaction of Immune Cells in Tumor Microenvironment Based on Single-Cell RNA Sequencing for Lung Adenocarcinoma

Background

The tumor immune microenvironment (TIME) played an important role in immunotherapy prognosis and treatment response. Immune cells constitute a large part of the tumor microenvironment and regulate tumor progression. Our research is dedicated to studying the infiltrating immune cell in lung adenocarcinoma (LUAD) and seeking potential targets.

Methods

The scRNA-seq data were collected from our FDZSH and two public datasets. The code for cell-type mapping algorithms was downloaded from the CIBERSORTx portal. The bioinformatics data of LUAD patients could be approached from The Cancer Genome Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analyses were performed to construct a risk model. TIMER2 and TIDE helped with the immune infiltration estimation, while PROGENy helped the cancer-related pathways' enrichment analysis. GSE31210 dataset and IMVigor ICB therapy cohort validated our findings as the external validation datasets.

Results

We clustered the scRNA-seq dataset (integrating our FDZSH datasets and other public datasets) into 23 subpopulations. After curated cell annotation, we implemented Cibersort and WGCNA analysis to anchor the brown module and natural killer cell cluster1 due to the most relationship with tumor trait. The overlap of the brown module gene, natural killer cell signature, and DEGs of tumor and adjacent normal samples was screened by LASSO Cox regression. The obtained 5-gene risk model showed an excellent prognostic performance in the validation dataset. Furthermore, there was a correlation between risk score and tumor-infiltrating immune cells and tumor genomics abnormity. Patients with higher risk scores had a significantly lower immunotherapy response rate.

Conclusion

Our observations implied that immune cells played a pivotal role in TIME and established a 5-gene signature (including IDH2, ADRB2, SFTPC, CCDC69, and CCND2) on the basement of nature killer markers targeted by WGCNA analysis. The significance of clinical outcome and immunotherapy response prediction was validated robustly.

Traditional chinese medicine syndromes classification associates with tumor cell and microenvironment heterogeneity in colorectal cancer: a single cell RNA sequencing analysis

BACKGROUND

Colorectal cancer (CRC) is one of the common gastrointestinal malignancies, tumor heterogeneity is the main cause of refractory CRC. Syndrome differentiation is the premise of individualized treatment of traditional Chinese medicine (TCM), but TCM syndrome lacks objective identification in CRC. This study is to investigate the correlation and significance of tumor heterogeneity and TCM syndromes classification in CRC.

METHODS

In this study, we using scRNA-seq technology, investigate the significance of tumor heterogeneity in TCM syndromes classification on CRC.

RESULTS

The results showed that 662 cells isolated from 11 primary CRC tumors are divided into 14 different cell clusters, and each cell subtype and its genes have different functions and signal transduction pathways, indicating significant heterogeneity. CRC tumor cell clusters have different proportions in Excess, Deficiency and Deficiency-Excess syndromes, and have their own characteristic genes, gene co-expression networks, gene functional interpretations as well as monocle functional evolution. Moreover, there were significant differences between the high expressions of MUC2, REG4, COL1A2, POSTN, SDPR, GPX1, ELF3, KRT8, KRT18, KRT19, FN1, SERPINE1, TCF4 and ZEB1 genes in Excess and Deficiency syndrome classification in CRC (P < 0.01).

CONCLUSIONS

The Excess and Deficiency syndromes classification may be related to tumor heterogeneity and its microenvironment in CRC.

Clinical-pathological and molecular characterization of long-term survivors with advanced non-small cell lung cancer

Objective: Long-term survivors (LS) of non-small cell lung cancer (NSCLC) without driver alterations, displaying an overall survival (OS) of more than 3 years, comprise around 10% of cases in several series treated with chemotherapy. There are classical prognosis factors for these cases [stage, Eastern Cooperative Oncology Group (ECOG), etc.], but more data are required in the literature. In this multi-center study, we focused on LS of advanced NSCLC with OS above 36 months to perform a clinical-pathological and molecular characterization.

Methods: In the first step, we conducted a clinical-pathological characterization of the patients. Afterwards, we carried out a genetic analysis by comparing LS to a sample of short-term survivors (SS) (with an OS less than 9 months). We initially used whole-genome RNA-seq to identify differentiating profiles of LS and SS, and later confirmed these with reverse transcription-polymerase chain reaction (RT-PCR) for the rest of the samples.

Results: A total of 94 patients were included, who were mainly men, former smokers, having adenocarcinoma (AC)-type NSCLC with an ECOG of 0–1. We obtained an initial differential transcriptome expression, displaying 5 over- and 33 under-expressed genes involved in different pathways: namely, the secretin receptor, surfactant protein, trefoil factor 1 (TFF1), serpin, Ca-channels, and Toll-like receptor (TLRs) families. Finally, RT-PCR analysis of 40 (20 LS/20 SS) samples confirmed that four genes (surfactant proteins and SFTP) were significantly down-regulated in SS compared to LS by using an analysis of covariance (ANCOVA) model: SFTPA1 (P = 0.023), SFTPA2 (P = 0.027), SFTPB (P = 0.02), and SFTPC (P = 0.047).

Conclusions: We present a sequential genetic analysis of a sample of NSCLC LS with no driver alterations, obtaining a differential RNA-seq/RT-PCR profile showing an abnormal expression of SF genes.

Prognostic significance of KRT19 in Lung Squamous Cancer

Backgroud: Keratin 19 (KRT19) is the intermediate filament that constitutes the cytoskeleton and regulates cell-cycle and cell death. Objective: We aimed to assess whether KRT19 was involved in lung cancer development. Methods: The expression of KRT19 in lung cancer was evaluated from mRNA expression on open databse and protein abundance on tumor tissue array. Results: Using open microarray gene expression datasets and differential expression analysis, we found that KRT19 was upregulated in lung cancer compared with normal tissue. Further analysis suggested that KRT19 mRNA expression was correlated with tumor progression and overall survival in lung cancer patients. As KRT19 was overexpressed in adenocarcinoma (AC) and squamous cell carcinoma (SCC), we examined the prognostic value of KRT19 protein abundance by tissue microarray (TMA). The results suggested that protein expression of KRT19 was significantly associated with overall survival of SCC. Conclusions: Giving the prognostic role of KRT19 in lung cancer, KRT19 could be considered as an potential molecular marker in lung cancer, especially in SCC.

Expression profiling and intracellular localization studies of the novel Proline-, Histidine-, and Glycine-rich protein 1 suggest an essential role in gastro-intestinal epithelium and a potential clinical application in colorectal cancer diagnostics

Background

The primary function of the intestines is the absorption of water and nutrients. Although our knowledge about these processes on the cellular level is extensive, a number of important intracellular elements remain unknown. Here, we characterize the novel proline-, histidine-, glycine-rich 1 (PHGR1) mRNA and protein on the molecular level and propose a functional role of the PHGR1 protein in the intestinal and gastric epithelium.

Methods

PHGR1 mRNA and protein expression in human tissues and cell lines were characterized by quantitative RT-PCR, in situ hybridization, Northern blotting, Western blotting, and immunohistochemistry. Glycosylation was assessed by a chemical deglycosylation assay, whereas intracellular localization was studied by immunofluorescent staining of cell line cells. PHGR1 mRNA levels in HT29 cells was reduced by RNA interference and the resulting global changes in gene expression assessed by microarray hybridization.

Results

PHGR1 mRNA and protein were found to be expressed specifically in epithelial cells of intestinal mucosa, with the highest expression in the most mature and differentiated cells. PHGR1 protein was found to be glycosylated and to localize to both the cytoplasm and nucleus. Transcript profiling and gene ontology analysis of HT29 cells subjected to PHGR1 knockdown suggested a functional relationship with transport and metabolic processes. Examination of PHGR1 mRNA and protein levels in lymph nodes with known colorectal cancer metastases indicated that they may serve as biomarkers for detection of such metastases.

Conclusions

Functional analyses of the novel PHGR1 mRNA and protein suggest an essential role in gastrointestinal epithelium and a clinical application in detection of colorectal cancer lymph node metastases.

Electronic supplementary material

The online version of this article (10.1186/s12876-018-0752-8) contains supplementary material, which is available to authorized users.

Detection of disseminated tumor cells in lymph nodes from patients with early stage non-small cell lung cancer

BACKGROUND

The regional lymph node involvement is a major prognostic factor in patients with non-small cell lung cancer (NSCLC) undergoing surgical resection. Disease relapse is common, suggesting that early disseminated disease is already present in the regional lymph nodes at the time of surgery, and that the current nodal staging classification might be suboptimal. Early detection of disseminated tumor cells (DTCs) in lymph nodes could potentially enable identification of subcategories of patients with high risk of disease relapse.

METHOD

Lymph node samples were collected from 128 NSCLC patients at the time of surgery and the presence of DTCs determined by immunomagnetic selection (IMS) using the MOC31 antibody recognizing EpCAM. Results obtained with IMS were compared to the pathological staging obtained by histopathology. Associations between the presence of DTCs and clinicopathological variables and patient outcome were investigated.

RESULTS

DTCs were detected in 40 % of the lymph node samples by IMS. Their presence was significantly associated with pN status as assessed by histopathology, and samples from 83 % of the patients with lymph node metastases (pN1-2) had detectable DTCs. In the group of patients who were negative for lymph node metastases by standard histopathology (pN0) DTCs were detected in 32 %. The presence of DTCs was not associated with any other clinicopathological variables. Patients with IMS-positive samples showed decreased relapse free survival compared to patients with IMS-negative samples, but the difference was not statistically significant. The pN status was significantly associated with both relapse free and overall survival, but the presence of DTCs had no prognostic impact in the subcategory of patients with pN0 status.

CONCLUSION

Our findings do not support further development of lymph node DTC detection for clinical use in early stage NSCLC.

Evaluation of new classifications of N descriptor in non-small cell lung cancer (NSCLC) based on the number and the ratio of metastatic lymph nodes

Background

The aim of the study was to evaluate the prognostic power of new classifications of N descriptor created basing on the number (NLN) and the ratio of metastatic lymph nodes (RLN) in NSCLC compared to the current classification (CLN).

Methods

The data of 529 patients with NSCLC operated with the intention of radical resection, were analyzed. The new categories of N descriptor were created as follows: 1) NLN - median number of metastatic nodes was 3, thus in NLN0 the number of metastatic nodes =0, in NLN1 1-2, in NLN2 ≥ 3, 2) RLN - median ratio (number of metastatic lymph nodes to all nodes removed) was 12.4 %, thus in RLN0 the ratio was 0, in RLN1 < 13 %, in RLN2 > 13 %. The prognostic value of each classification was calculated on the basis of hazard ratios defined in multivariate Cox proportional hazard model.

Results

The new classifications of N descriptor turned out to be an independent strong prognostic factor (p <0.001) with a 5-year survival rate NLN0-62 %, NLN1-39 %, NLN2-26 % and RLN0-62 %, RLN1-37 % and RLN2-26 %. For 5-year survival rates in CLN0-62 %, CLN1-42 %, CLN2-24 % (p < 0.001), a higher prognostic value of new classifications was not demonstrated, the hazard ratio amounted to 2.22, 2.08, 2.49 for NLN2, RLN2 and CLN2 respectively.

Conclusion

Despite the significantly high prognostic power, the new classifications cannot be considered superior over CLN. There are some deficiencies in the current classification, therefore further studies on its improvement are needed.

Circulating tumor cells in lung cancer: detection methods and clinical applications

Circulating tumor cells (CTCs) are tumor cells that have disseminated from primary and metastatic sites, and circulate in the bloodstream. Advanced immunological and molecular-based methods can be used to detect and analyze the cells with the characteristics of tumor cells, and can be detected and analyzed in the blood of cancer patients. The most commonly used methods in lung cancer combine the processes of immunomagnetic enrichment and immunocytochemical detection, morphology-based enrichment coupled with reverse transcriptase polymerase chain reaction (RT-PCR), and RT-PCR alone. CTC analysis is considered a liquid biopsy approach for early diagnosis, risk stratification, evaluation of curative efficacy, and early detection of lung cancer relapse. In this review, we discuss the present techniques for analyzing CTCs, and the restrictions of using these methods in lung cancer. We also review the clinical studies in lung cancer and discuss the underlying associations between these studies and their future applications to this disease.

Autoimmunity and tumor immunology: two facets of a probabilistic immune system

Background

The immune system of vertebrates has evolved the ability to mount highly elaborate responses to a broad range of pathogen-driven threats. Accordingly, it is quite a challenge to understand how a primitive adaptive immune system that probably lacked much of its present complexity could provide its bearers with significant evolutionary advantage, and therefore, continue to be selected for.

Results

We have developed a very simple model of the immune system that captures the probabilistic communication between its innate and adaptive components. Probabilistic communication arises specifically from the fact that antigen presenting cells collect and present a range of antigens from which the adaptive immune system must (probabilistically) identify its target. Our results show that although some degree of self-reactivity in the immune repertoire is unavoidable, the system is generally able to correctly target pathogens rather than self-antigens. Particular circumstances that impair correct targeting and that may lead to infection-induced autoimmunity can be predicted within this framework. Notably, the probabilistic immune system exhibits the remarkable ability to detect sudden increases in the abundance of rare self-antigens, which represents a first step towards developing anti-tumoral responses.

Conclusion

A simple probabilistic model of the communication between the innate and adaptive immune system provides a robust immune response, including targeting tumors, but at the price of being at risk of developing autoimmunity.

The lung-specific proteome defined by integration of transcriptomics and antibody-based profiling

The combined action of multiple cell types is essential for the physiological function of the lung, and increased awareness of the molecular constituents characterizing each cell type is likely to advance the understanding of lung biology and disease. In the current study, we used genome-wide RNA sequencing of normal lung parenchyma and 26 additional tissue types, combined with antibody-based protein profiling, to localize the expression to specific cell types. Altogether, 221 genes were found to be elevated in the lung compared with their expression in other analyzed tissues. Among the gene products were several well-known markers, but also several proteins previously not described in the context of the lung. To link the lung-specific molecular repertoire to human disease, survival associations of pneumocyte-specific genes were assessed by using transcriptomics data from 7 non-small-cell lung cancer (NSCLC) cohorts. Transcript levels of 10 genes (SFTPB, SFTPC, SFTPD, SLC34A2, LAMP3, CACNA2D2, AGER, EMP2, NKX2-1, and NAPSA) were significantly associated with survival in the adenocarcinoma subgroup, thus qualifying as promising biomarker candidates. In summary, based on an integrated omics approach, we identified genes with elevated expression in lung and localized corresponding protein expression to different cell types. As biomarker candidates, these proteins may represent intriguing starting points for further exploration in health and disease.

Lung cancer transcriptomes refined with laser capture microdissection

We evaluated the importance of tumor cell selection for generating gene signatures in non-small cell lung cancer. Tumor and nontumor tissue from macroscopically dissected (Macro) surgical specimens (31 pairs from 32 subjects) was homogenized, extracted, amplified, and hybridized to microarrays. Adjacent scout sections were histologically mapped; sets of approximately 1000 tumor cells and nontumor cells (alveolar or bronchial) were procured by laser capture microdissection (LCM). Within histological strata, LCM and Macro specimens exhibited approximately 67% to 80% nonoverlap in differentially expressed (DE) genes. In a representative subset, LCM uniquely identified 300 DE genes in tumor versus nontumor specimens, largely attributable to cell selection; 382 DE genes were common to Macro, Macro with preamplification, and LCM platforms. RT-qPCR validation in a 33-gene subset was confirmatory (ρ = 0.789 to 0.964, P = 0.0013 to 0.0028). Pathway analysis of LCM data suggested alterations in known cancer pathways (cell growth, death, movement, cycle, and signaling components), among others (eg, immune, inflammatory). A unique nine-gene LCM signature had higher tumor-nontumor discriminatory accuracy (100%) than the corresponding Macro signature (87%). Comparison with Cancer Genome Atlas data sets (based on homogenized Macro tissue) revealed both substantial overlap and important differences from LCM specimen results. Thus, cell selection via LCM enhances expression profiling precision, and confirms both known and under-appreciated lung cancer genes and pathways.

CD24+/CD38- as new prognostic marker for non-small cell lung cancer

BACKGROUND

Lung cancer is the leading cause of death among cancers in the world. The annual death toll due to this disease exceeds the combined deaths caused by colon, breast, prostate, and pancreatic cancers. As a result, there has been a tremendous effort to identify new biomarkers for early detection and diagnosis of lung cancer.

METHODS

In this study we report the results of screening a panel of eight non-small cell lung cancer (NSCLC) cell lines originating from different subtypes of lung cancer in an attempt to identify potential biomarkers unique to this disease. We used real-time polymerase chain reaction and flow cytometry techniques to analyze the expression of ALDHA1, EpCAM, CD133, CD24, and CD38 in this panel.

RESULTS

We demonstrate for the first time that the majority of NSCLC cells do not express levels of CD38 that would qualify it as a new biomarker for the disease. In contrast, we found that CD24 is over-expressed in 6 out of 8 of the cell lines. The combined CD24+/CD38-/low phenotype was detected in 50% of the cell lines that are also positive for CD133 and EpCAM.

CONCLUSIONS

We report that CD24+/CD38-/low signature could potentially be used as a new biomarker for the early detection of NSCLC.