CLCA2 as a novel immunohistochemical marker for differential diagnosis of squamous cell carcinoma from adenocarcinoma of the lung

Multiomics2Targets identifies targets from cancer cohorts profiled with transcriptomics, proteomics, and phosphoproteomics

The availability of data from profiling of cancer patients with multiomics is rapidly increasing. However, integrative analysis of such data for personalized target identification is not trivial. Multiomics2Targets is a platform that enables users to upload transcriptomics, proteomics, and phosphoproteomics data matrices collected from the same cohort of cancer patients. After uploading the data, Multiomics2Targets produces a report that resembles a research publication. The uploaded matrices are processed, analyzed, and visualized using the tools Enrichr, KEA3, ChEA3, Expression2Kinases, and TargetRanger to identify and prioritize proteins, genes, and transcripts as potential targets. Figures and tables, as well as descriptions of the methods and results, are automatically generated. Reports include an abstract, introduction, methods, results, discussion, conclusions, and references and are exportable as citable PDFs and Jupyter Notebooks. Multiomics2Targets is applied to analyze version 3 of the Clinical Proteomic Tumor Analysis Consortium (CPTAC3) pan-cancer cohort, identifying potential targets for each CPTAC3 cancer subtype. Multiomics2Targets is available from https://multiomics2targets.maayanlab.cloud/.

Expression of Survivin, CK7, ASH1, HMGB3, L587S, and CLCA2 in Peripheral Blood of Lung Cancer Patients by Real-Time Polymerase Chain Reaction

Introduction The objective of the present study was to identify gene expression in peripheral blood by a real-time polymerase chain reaction (PCR) technique in patients who have lung carcinoma. Material and methods Peripheral blood samples of patients with non-small cell and small cell lung cancer were collected. Target genes included survivin, CK7, ASH1, HMGB3, L587S, and CLCA2. β-Actin was the reference gene. If the mean CT (threshold cycle) value for a target gene is ≥40, the gene expression is considered undetectable. Results Fifty patients with lung carcinoma were included and 30 healthy controls. Out of the six genes, survivin showed 26.8 times fold change as compared to controls; ASH1 and L587S were 0.54 and 0.06, respectively; and HMGB3, CLCA2, and CK7 had non-significant fold change in comparison to controls. The overall detection rate of the six target genes examined in lung cancer was 84%, with 42 out of 50 patients testing positive. Higher stages and ASH1 (p = 0.031), CK7 (p = <0.001), and HMGB3, p = 0.011 were associated significantly. CLCA2 had higher expression in patients without adrenal metastases (p = 0.044). Conclusions Lifestyle and geographical variation might be a probable cause of variable gene expression as compared to other studies. However, further research is needed to determine the clinical implication of these markers, especially in larger groups of early-stage patients.

A Monoclonal Human Alveolar Epithelial Cell Line ("Arlo") with Pronounced Barrier Function for Studying Drug Permeability and Viral Infections

In the development of orally inhaled drug products preclinical animal models regularly fail to predict pharmacological as well as toxicological responses in humans. Models based on human cells and tissues are potential alternatives to animal experimentation allowing for the isolation of essential processes of human biology and making them accessible in vitro. Here, the generation of a novel monoclonal cell line "Arlo," derived from the polyclonal human alveolar epithelium lentivirus immortalized cell line hAELVi via single-cell printing, and its characterization as a model for the human alveolar epithelium as well as a building block for future complex in vitro models is described. "Arlo" is systematically compared in vitro to primary human alveolar epithelial cells (hAEpCs) as well as to the polyclonal hAELVi cell line. "Arlo" cells show enhanced barrier properties with high transepithelial electrical resistance (TEER) of ≈3000 Ω cm2 and a potential difference (PD) of ≈30 mV under air-liquid interface (ALI) conditions, that can be modulated. The cells grow in a polarized monolayer and express genes relevant to barrier integrity as well as homeostasis as is observed in hAEpCs. Successful productive infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a proof-of-principle study offers an additional, attractive application of "Arlo" beyond biopharmaceutical experimentation.

An explainable AI-driven biomarker discovery framework for Non-Small Cell Lung Cancer classification

Non-Small Cell Lung Cancer (NSCLC) exhibits intrinsic heterogeneity at the molecular level that aids in distinguishing between its two prominent subtypes - Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). This paper proposes a novel explainable AI (XAI)-based deep learning framework to discover a small set of NSCLC biomarkers. The proposed framework comprises three modules - an autoencoder to shrink the input feature space, a feed-forward neural network to classify NSCLC instances into LUAD and LUSC, and a biomarker discovery module that leverages the combined network comprising the autoencoder and the feed-forward neural network. In the biomarker discovery module, XAI methods uncovered a set of 52 relevant biomarkers for NSCLC subtype classification. To evaluate the classification performance of the discovered biomarkers, multiple machine-learning models are constructed using these biomarkers. Using 10-Fold cross-validation, Multilayer Perceptron achieved an accuracy of 95.74% (±1.27) at 95% confidence interval. Further, using Drug-Gene Interaction Database, we observe that 14 of the discovered biomarkers are druggable. In addition, 28 biomarkers aid the prediction of the survivability of the patients. Out of 52 discovered biomarkers, we find that 45 biomarkers have been reported in previous studies on distinguishing between the two NSCLC subtypes. To the best of our knowledge, the remaining seven biomarkers have not yet been reported for NSCLC subtyping and could be further explored for their contribution to targeted therapy of lung cancer.

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks

In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells.

Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies distinct proteotypes associated with patient outcomes

Non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths worldwide. Only a fraction of NSCLC harbor actionable driver mutations and there is an urgent need for patient-derived model systems that will enable the development of new targeted therapies. NSCLC and other cancers display profound proteome remodeling compared to normal tissue that is not predicted by DNA or RNA analyses. Here, we generate 137 NSCLC patient-derived xenografts (PDXs) that recapitulate the histology and molecular features of primary NSCLC. Proteome analysis of the PDX models reveals 3 adenocarcinoma and 2 squamous cell carcinoma proteotypes that are associated with different patient outcomes, protein-phosphotyrosine profiles, signatures of activated pathways and candidate targets, and in adenocarcinoma, stromal immune features. These findings portend proteome-based NSCLC classification and treatment and support the PDX resource as a viable model for the development of new targeted therapies.

With non-small cell lung cancer (NSCLC) being the leading cause of cancer deaths worldwide, the development of targeted therapies remains crucial. Here, the generation and multi-omics characterization of 137 NSCLC patient-derived xenografts provides a resource for potential classifications and targets.

Polymodal Control of TMEM16x Channels and Scramblases

The TMEM16A/anoctamin-1 calcium-activated chloride channel (CaCC) contributes to a range of vital functions, such as the control of vascular tone and epithelial ion transport. The channel is a founding member of a family of 10 proteins (TMEM16x) with varied functions; some members (i.e., TMEM16A and TMEM16B) serve as CaCCs, while others are lipid scramblases, combine channel and scramblase function, or perform additional cellular roles. TMEM16x proteins are typically activated by agonist-induced Ca2+ release evoked by Gq-protein-coupled receptor (GqPCR) activation; thus, TMEM16x proteins link Ca2+-signalling with cell electrical activity and/or lipid transport. Recent studies demonstrate that a range of other cellular factors—including plasmalemmal lipids, pH, hypoxia, ATP and auxiliary proteins—also control the activity of the TMEM16A channel and its paralogues, suggesting that the TMEM16x proteins are effectively polymodal sensors of cellular homeostasis. Here, we review the molecular pathophysiology, structural biology, and mechanisms of regulation of TMEM16x proteins by multiple cellular factors.

LINC00628 is differentially expressed between lung adenocarcinoma and squamous cell carcinoma and is associated with the prognosis of NSCLC

Non-small cell lung cancer (NSCLC) remains the most frequent malignancy worldwide, and lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) represent two major subtypes. LINC00628 has been demonstrated to promote LUAD progression; however, its clinical role in NSCLC remains elusive. The aim of the present study was to analyze the expression of long intergenic non-protein coding RNA 628 (LINC00628) in NSCLC, including in the LUAD and LUSC subtypes. In addition, its roles in NSCLC development and prognosis were also examined. Data from The Cancer Genome Atlas (TCGA) database were first used to assess the expression and prognostic potential in both LUAD and LUSC, then LINC00628 expression in 128 NSCLC tissues was measured using reverse transcription-quantitative PCR. A receiver operating characteristic curve was used to assess the ability of LINC00628 to discriminate between patients with LUAD and LUSC. Kaplan-Meier curves were used to analyze the relationship between LINC00628 expression and the overall survival of patients. Cox regression analysis was used to explore the potential prognostic factors that might be independently associated with NSCLC overall survival. Both in silico and tissue analysis data indicated that the expression of LINC00628 was significantly upregulated in NSCLC tissue compared with matched normal controls (P<0.001). LINC00628 expression levels were also significantly higher in LUAD cases than in patients with LUSC (P<0.001). In addition, LINC00628 could discriminate LUAD from LUSC cases. The expression of LINC00628 was significantly associated with tumor size (P=0.013), histological type (P=0.009), lymph node metastasis (P=0.021) and TNM stage (P=0.008). Survival analysis based on data from both TCGA and patients included in the present study identified an association between LINC00628 and overall survival in LUAD, but this relationship was not observed in LUSC for TCGA data. Cox regression analysis demonstrated that high LINC00628 expression was associated with poor overall survival in patients with LUAD (P=0.001), but not in patients with LUSC (P=0.088). In conclusion, LINC00628 expression was upregulated in NSCLC and associated with patient prognosis. Patients with LUAD had higher LINC00628 expression levels than those with LUSC, and increased LINC00628 served as an independent prognostic factor in LUAD, but not LUSC.

Decreased expression of CLCA2 and the correlating with immune infiltrates in patients with cervical squamous cell carcinoma: A bioinformatics analysis

OBJECTIVE

Calcium-activated chloride channel 2 (CLCA2) is closely related to the invasion, metastasis, and prognosis of some common malignant tumors. The present study aimed to evaluate the role of CLCA2 in cervical squamous cell carcinoma (CESC) using bioinformatics analysis.

MATERIALS AND METHODS

The mRNA sequencing data and the corresponding clinical data were obtained from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database respectively. Then univariate analysis of variance was used to analyze the differential mRNA expression of CLCA2 between normal, cervical Intraepithelial neoplasia (CIN), and CESC tissues and clinicopathological characteristics. The Gene Expression Profiling Interactive Analysis (GEPIA) was used to assess the association between CLCA2 and Disease-Free Survival (DFS), overall survival (OS). The Gene Set Enrichment Analysis (GSEA) was used to explore the associated signaling pathways. The Tumor Immune Estimation Resource (TIMER) was used to predict the potential biological roles of CLCA2 in tumor-immune of CESC.

RESULTS

CLCA2 expression was significantly decreased in CESC tissues compared with normal and CIN tissues (P < 0.05). Meanwhile, obese patients had lower levels of CLCA2 expression than normal-weight CESC patients (P < 0.05). However, there was no significant difference in the expression level of CLCA2 in patients with different T stage, lymph node status, metastasis, and FIGO stage in CC(P > 0.05). The survival analysis indicated that for DFS, CESC with high CLCA2 expression was associated with better prognoses compared with those with low expression levels (P < 0.05). But for the OS, there was no difference. GSEA revealed that 4 pathways exhibited significant differential enrichment in the CLCA2 high-expression phenotype, including the P53 signaling pathway, the ERBB signaling pathway, the NOTCH signaling pathway, and the ubiquitin-mediated proteolysis. The TIMER reveals the expression of CLCA2 showed a significant inverse association with the number of B cells, Macrophage cells, and Dendritic Cell infiltration.

CONCLUSION

The present study indicates that CLCA2 expression may be a potential prognostic marker for patients with CESC.

The calcium-activated chloride channel-associated protein rCLCA2 is expressed throughout rat epidermis, facilitates apoptosis and is downmodulated by UVB

The rodent chloride channel regulatory proteins mCLCA2 and its porcine and human homologues pCLCA2 and hCLCA2 are expressed in keratinocytes but their localization and significance in the epidermis have remained elusive. hCLCA2 regulates cancer cell migration, invasion and apoptosis, and its loss predicts poor prognosis in many tumors. Here, we studied the influences of epidermal maturation and UV-irradiation (UVR) on rCLCA2 (previous rCLCA5) expression in cultured rat epidermal keratinocytes (REK) and correlated the results with mCLCA2 expression in mouse skin in vivo. Furthermore, we explored the influence of rCLCA2 silencing on UVR-induced apoptosis. rClca2 mRNA was strongly expressed in REK cells, and its level in organotypic cultures remained unchanged during the epidermal maturation process from a single cell layer to fully differentiated, stratified cultures. Immunostaining confirmed its uniform localization throughout the epidermal layers in REK cultures and in rat skin. A single dose of UVR modestly downregulated rClca2 expression in organotypic REK cultures. The immunohistochemical staining showed that CLCA2 localized in basal and spinous layers also in mouse skin, and repeated UVR induced its partial loss. Interestingly, silencing of rCLCA2 reduced the number of apoptotic cells induced by UVR, suggesting that by facilitating apoptosis, CLCA2 may protect keratinocytes against the risk of malignancy posed by UVB-induced corrupt DNA.

The Diagnostic and Prognostic Roles of Combined Expression of Novel Biomarkers in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma: An Immunohistochemical Study

Background & Objective:

Diagnosis and discrimination of lung adenocarcinoma (LUAD) from lung squamous cell carcinoma (LUSC) is critical to select the appropriate treatment regimen as recently targeted therapies require accurate subtyping of nonsmall-cell lung carcinoma (NSCLCs). There are currently several biomarkers that could be used for differentiation between LUAD and LUSC, but they have less sensitivity, specificity, and clinical applicability. The aim of this study was to assess the diagnostic and prognostic values of CLCA2, SPATS2, ST6GALNAC1, and Adipophilin tissue expression in the tissues retrieved from LUAD and LUSC patients using immunohistochemistry.

Methods:

The current study was performed on the samples retrieved from sixty primary lung masses that were diagnosed as LUAD and LUSC. Immunohistochemistry was performed by using a panel of CLCA2, SPATS2, and ST6GALNAC1. We assessed the diagnostic roles of the studied markers in the discrimination between LUAD and LUSC and their prognostic values.

Results:

SPATS2 and CLCA2 were expressed higher in LUSC than LUAD. ST6GALNAC1 and Adipophilin showed higher expression in LUAD than LUSC (P<0.001). The sensitivity and specificity of CLCA2, SPATS2, ST6GALNAC1 and Adipophilin in adequate subtyping and reaching the accurate diagnosis was 100%. We found only significant difference in survival rate between the patients with negative and positive CLCA2 expression (P=0.038 and P=0.019, respectively).

Conclusion:

The combination of biomarkers of CLCA2, SPATS2, ST6GALNAC1, and Adipophilin may lead to an appropriate subtyping of lung cancer and reaching accurate diagnosis with the highest sensitivity and specificity.

Metal Nanoparticles for Electrochemical Sensing: Progress and Challenges in the Clinical Transition of Point-of-Care Testing

With the rise in public health awareness, research on point-of-care testing (POCT) has significantly advanced. Electrochemical biosensors (ECBs) are one of the most promising candidates for the future of POCT due to their quick and accurate response, ease of operation, and cost effectiveness. This review focuses on the use of metal nanoparticles (MNPs) for fabricating ECBs that has a potential to be used for POCT. The field has expanded remarkably from its initial enzymatic and immunosensor-based setups. This review provides a concise categorization of the ECBs to allow for a better understanding of the development process. The influence of structural aspects of MNPs in biocompatibility and effective sensor design has been explored. The advances in MNP-based ECBs for the detection of some of the most prominent cancer biomarkers (carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), Herceptin-2 (HER2), etc.) and small biomolecules (glucose, dopamine, hydrogen peroxide, etc.) have been discussed in detail. Additionally, the novel coronavirus (2019-nCoV) ECBs have been briefly discussed. Beyond that, the limitations and challenges that ECBs face in clinical applications are examined and possible pathways for overcoming these limitations are discussed.

Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms

Lung cancer is one of the most common cancer types worldwide and causes more than one million deaths annually. Lung adenocarcinoma (AC) and lung squamous cell cancer (SCC) are two major lung cancer subtypes and have different characteristics in several aspects. Identifying their differentially expressed genes and different gene expression patterns can deepen our understanding of these two subtypes at the transcriptomic level. In this work, we used several machine learning algorithms to investigate the gene expression profiles of lung AC and lung SCC samples retrieved from Gene Expression Omnibus. First, the profiles were analyzed by using a powerful feature selection method, namely, Monte Carlo feature selection. A feature list, ranking all features according to their importance, and some informative features were obtained. Then, the feature list was used in the incremental feature selection method to extract optimal features, which can allow the support vector machine (SVM) to yield the best performance for classifying lung AC and lung SCC samples. Some top genes (CSTA, TP63, SERPINB13, CLCA2, BICD2, PERP, FAT2, BNC1, ATP11B, FAM83B, KRT5, PARD6G, PKP1) were extensively analyzed to prove that they can be differentially expressed genes between lung AC and lung SCC. Meanwhile, a rule learning procedure was applied on informative features to construct the classification rules. These rules provide a clear procedure of classification and show some different gene expression patterns between lung AC and lung SCC.

Molecular and Morphological Profiling of Lung Cancer: A Foundation for "Next-Generation" Pathologists and Oncologists

The pathological diagnosis of lung cancer has largely been based on the morphological features observed microscopically. Recent innovations in molecular and genetic technology enable us to compare conventional histological classifications, protein expression status, and gene abnormalities. The introduction of The Cancer Genome Atlas (TCGA) project along with the widespread use of the next-generation sequencer (NGS) have facilitated access to enormous data regarding the molecular profiles of lung cancer. The World Health Organization classification of lung cancer, which was revised in 2015, is based on this progress in molecular pathology; moreover, immunohistochemistry has come to play a larger role in diagnosis. In this article, we focused on genetic and epigenetic abnormalities in non-small cell carcinoma (adenocarcinoma and squamous cell carcinoma), neuroendocrine tumor (including carcinoids, small cell carcinoma, and large cell neuroendocrine carcinoma), and carcinoma with rare histological subtypes. In addition, we summarize the therapeutic targeted reagents that are currently available and undergoing clinical trials. A good understanding of the morphological and molecular profiles will be necessary in routine practice when the NGS platform is widely used.

Identification of beta-arrestin-1 as a diagnostic biomarker in lung cancer

Background

Distinguishing lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC) has a tremendous therapeutic implication. Sometimes, the commonly used immunohistochemistry (IHC) markers fail to discriminate between them, urging for the identification of new diagnostic biomarkers.

Methods

We performed IHC on tissue microarrays from two cohorts of lung cancer patients to analyse the expression of beta-arrestin-1, beta-arrestin-2 and clinically used diagnostic markers in ADC and SCC samples. Logistic regression models were applied for tumour subtype prediction. Parallel reaction monitoring (PRM)-based mass spectrometry was used to quantify beta-arrestin-1 in plasma from cancer patients and healthy donors.

Results

Beta-arrestin-1 expression was significantly higher in ADC versus SCC samples. Beta-arrestin-1 displayed high sensitivity, specificity and negative predictive value. Its usefulness in an IHC panel was also shown. Plasma beta-arrestin-1 levels were considerably higher in lung cancer patients than in healthy donors and were higher in patients who later experienced a progressive disease than in patients showing complete/partial response following EGFR inhibitor therapy.

Conclusions

Our data identify beta-arrestin-1 as a diagnostic marker to differentiate ADC from SCC and indicate its potential as a plasma biomarker for non-invasive diagnosis of lung cancer. Its utility to predict response to EGFR inhibitors is yet to be confirmed.

CLCA2 is a positive regulator of store-operated calcium entry and TMEM16A

The Chloride Channel Accessory (CLCA) protein family was first characterized as regulators of calcium-activated chloride channel (CaCC) currents (ICaCC), but the mechanism has not been fully established. We hypothesized that CLCAs might regulate ICaCC by modulating intracellular calcium levels. In cells stably expressing human CLCA2 or vector, we found by calcium imaging that CLCA2 moderately enhanced intracellular-store release but dramatically increased store-operated entry of calcium upon cytosolic depletion. Moreover, another family member, CLCA1, produced similar effects on intracellular calcium mobilization. Co-immunoprecipitation revealed that CLCA2 interacted with the plasma membrane store-operated calcium channel ORAI-1 and the ER calcium sensor STIM-1. The effect of CLCA2 on ICaCC was tested in HEK293 stably expressing calcium-activated chloride channel TMEM16A. Co-expression of CLCA2 nearly doubled ICaCC in response to a calcium ionophore. These results unveil a new mechanism by which CLCA family members activate ICaCC and suggest a broader role in calcium-dependent processes.

ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis

For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures-weighted rank-based Jaccard and Cosine measures-and then propose a novel computational framework to detect condensed gene co-expression modules ( C o n G E M s) through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm-RANWAR-was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data.

CLCA2 Interactor EVA1 Is Required for Mammary Epithelial Cell Differentiation

CLCA2 is a p53-, p63-inducible transmembrane protein that is frequently downregulated in breast cancer. It is induced during differentiation of human mammary epithelial cells, and its knockdown causes epithelial-to-mesenchymal transition (EMT). To determine how CLCA2 promotes epithelial differentiation, we searched for interactors using membrane dihybrid screening. We discovered a strong interaction with the cell junctional protein EVA1 (Epithelial V-like Antigen 1) and confirmed it by co-immunoprecipitation. Like CLCA2, EVA1 is a type I transmembrane protein that is regulated by p53 and p63. It is thought to mediate homophilic cell-cell adhesion in diverse epithelial tissues. We found that EVA1 is frequently downregulated in breast tumors and breast cancer cell lines, especially those of mesenchymal phenotype. Moreover, knockdown of EVA1 in immortalized human mammary epithelial cells (HMEC) caused EMT, implying that EVA1 is essential for epithelial differentiation. Both EVA1 and CLCA2 co-localized with E-cadherin at cell-cell junctions. The interacting domains were delimited by deletion analysis, revealing the site of interaction to be the transmembrane segment (TMS). The primary sequence of the CLCA2 TMS was found to be conserved in CLCA2 orthologs throughout mammals, suggesting that its interaction with EVA1 co-evolved with the mammary gland. A screen for other junctional interactors revealed that CLCA2 was involved in two different complexes, one with EVA1 and ZO-1, the other with beta catenin. Overexpression of CLCA2 caused downregulation of beta catenin and beta catenin-activated genes. Thus, CLCA2 links a junctional adhesion molecule to cytosolic signaling proteins that modulate proliferation and differentiation. These results may explain how attenuation of CLCA2 causes EMT and why CLCA2 and EVA1 are frequently downregulated in metastatic breast cancer cell lines.

The impact of the Cancer Genome Atlas on lung cancer

The Cancer Genome Atlas (TCGA) has profiled more than 10,000 samples derived from 33 types of cancer to date, with the goal of improving our understanding of the molecular basis of cancer and advancing our ability to diagnose, treat, and prevent cancer. This review focuses on lung cancer as it is the leading cause of cancer-related mortality worldwide in both men and women. Particularly, non-small cell lung cancers (including lung adenocarcinoma and lung squamous cell carcinoma) were evaluated. Our goal was to demonstrate the impact of TCGA on lung cancer research under 4 themes: diagnostic markers, disease progression markers, novel therapeutic targets, and novel tools. Examples are given related to DNA mutation, copy number variation, messenger RNA, and microRNA expression along with methylation profiling.