Molecular classification of nonsmall cell lung cancer using a 4-protein quantitative assay

Improved graph neural network-based green anaconda optimization for segmenting and classifying the lung cancer

Normal lung cells incur genetic damage over time, which causes unchecked cell growth and ultimately leads to lung cancer. Nearly 85% of lung cancer cases are caused by smoking, but there exists factual evidence that beta-carotene supplements and arsenic in water may raise the risk of developing the illness. Asbestos, polycyclic aromatic hydrocarbons, arsenic, radon gas, nickel, chromium and hereditary factors represent various lung cancer-causing agents. Therefore, deep learning approaches are employed to quicken the crucial procedure of diagnosing lung cancer. The effectiveness of these methods has increased when used to examine cancer histopathology slides. Initially, the data is gathered from the standard benchmark dataset. Further, the pre-processing of the collected images is accomplished using the Gabor filter method. The segmentation of these pre-processed images is done through the modified expectation maximization (MEM) algorithm method. Next, using the histogram of oriented gradient (HOG) scheme, the features are extracted from these segmented images. Finally, the classification of lung cancer is performed by the improved graph neural network (IGNN), where the parameter optimization of graph neural network (GNN) is done by the green anaconda optimization (GAO) algorithm in order to derive the accuracy maximization as the major objective function. This IGNN classifies lung cancer into normal, adeno carcinoma and squamous cell carcinoma as the final output. On comparison with existing methods with respect to distinct performance measures, the simulation findings reveal the betterment of the introduced method.

A targeted multiplex mass spectrometry method for quantitation of abundant matrix and cellular proteins in formalin-fixed paraffin embedded arterial tissue

Assessment of proteins in formalin-fixed paraffin-embedded (FFPE) tissue traditionally hinges on immunohistochemistry and immunoblotting. These methods are far from optimal for quantitative studies and not suitable for large-scale testing of multiple protein panels. In this study, we developed and optimised a novel targeted isotope dilution mass spectrometry (MS)-based method for FFPE samples, designed to quantitate 17 matrix and cytosolic proteins abundantly present in arterial tissue. Our new method was developed on FFPE human tissue samples of the internal thoracic artery obtained from coronary artery bypass graft (CABG) operations. The workflow has a limit of 60 samples per day. Assay precision improved by normalisation to both beta-actin and smooth muscle actin with inter-assay coefficients of variation (CV) ranging from 5.3% to 31.9%. To demonstrate clinical utility of the assay we analysed 40 FFPE artery specimens from two groups of patients with or without type 2 diabetes. We observed increased levels of collagen type IV α1 and α2 in patients with diabetes. The assay is scalable for larger cohorts and advantageous for pathophysiological studies in diabetes and the method is easily convertible to analysis of other proteins in FFPE artery samples. SIGNIFICANCE: This article presents a novel robust and precise targeted mass spectrometry assay for relative quantitation of a panel of abundant matrix and cellular arterial proteins in archived formalin-fixed paraffin-embedded arterial samples. We demonstrate its utility in pathophysiological studies of cardiovascular disease in diabetes.

Chromogenic immunohistochemical quadruplex provides accurate diagnostic differentiation of non-small cell lung cancer

Lung cancer is the most common cancer worldwide and has the highest mortality rate. Carcinomas comprise 95% of all lung malignancies, the vast majority of which are non-small cell lung carcinomas (NSCLC). Increasingly, the diagnosis of lung cancer is established by examination of small tissue specimens obtained by minimally invasive techniques. It is critical to employ these tissues at maximum efficiency in order to render an accurate pathologic diagnosis and to perform theranostic studies, either genomic or by immunohistochemistry, to demonstrate genetic mutations that make patients eligible for molecularly targeted agents. Currently Thyroid Transcription Factor-1 (TTF-1) and Napsin A are the most commonly used immunohistochemical (IHC) stains to identify primary lung adenocarcinoma, and p40 and cytokeratin 5/6 (CK5/6) are used for squamous cell carcinoma. IHC stains for these markers, are performed either individually (IHC brown staining) or in combination as dual immunostains (i.e. TTF-1 + Napsin A and p40 + CK5/6, utilizing brown and red chromogens). Here we present a novel, truly multiplex immunohistochemical approach that combines staining with the above four antibodies on a single tissue section utilizing four different chromogens to accurately diagnose primary lung adenocarcinomas, squamous cell carcinomas, and combined adenosquamous carcinomas of the lung. Each marker is represented by a distinct color that can be read by a pathologist, using standard, bright field microscopy. We evaluated the ability of pathologists to differentiate NSCLCs using the multiplexed assay as compared to standard, single marker per slide diaminobenzidine (DAB)-based IHC. All cases in a cohort of 264 NSCLCs showed concordance of information (including positivity of stain, intensity of stain and coverage) between single IHC stains and the multiplex assay. This new multiplex IHC offers the capability to accurately diagnose and sub-classify primary lung NSCLCs, while conserving precious tissue for additional testing.

Opportunities and Challenges in Implementation of Multiparameter Single Cell Analysis Platforms for Clinical Translation

The high-content interrogation of single cells with platforms optimized for the multiparameter characterization of cells in liquid and solid biopsy samples can enable characterization of heterogeneous populations of cells ex vivo. Doing so will advance the diagnosis, prognosis, and treatment of cancer and other diseases. However, it is important to understand the unique issues in resolving heterogeneity and variability at the single cell level before navigating the validation and regulatory requirements in order for these technologies to impact patient care. Since 2013, leading experts representing industry, academia, and government have been brought together as part of the Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium to foster the potential of high-content data integration for clinical translation.

Predicting survival time of lung cancer patients using radiomic analysis

OBJECTIVES

This study investigates the prediction of Non-small cell lung cancer (NSCLC) patient survival outcomes based on radiomic texture and shape features automatically extracted from tumor image data.

MATERIALS AND METHODS

Retrospective analysis involves CT scans of 315 NSCLC patients from The Cancer Imaging Archive (TCIA). A total of 24 image features are computed from labeled tumor volumes of patients within groups defined using NSCLC subtype and TNM staging information. Spearman's rank correlation, Kaplan-Meier estimation and log-rank tests were used to identify features related to long/short NSCLC patient survival groups. Automatic random forest classification was used to predict patient survival group from multivariate feature data. Significance is assessed at P < 0.05 following Holm-Bonferroni correction for multiple comparisons.

RESULTS

Significant correlations between radiomic features and survival were observed for four clinical groups: (group, [absolute correlation range]): (large cell carcinoma (LCC) [0.35, 0.43]), (tumor size T2, [0.31, 0.39]), (non lymph node metastasis N0, [0.3, 0.33]), (TNM stage I, [0.39, 0.48]). Significant log-rank relationships between features and survival time were observed for three clinical groups: (group, hazard ratio): (LCC, 3.0), (LCC, 3.9), (T2, 2.5) and (stage I, 2.9). Automatic survival prediction performance (i.e. below/above median) is superior for combined radiomic features with age-TNM in comparison to standard TNM clinical staging information (clinical group, mean area-under-the-ROC-curve (AUC)): (LCC, 75.73%), (N0, 70.33%), (T2, 70.28%) and (TNM-I, 76.17%).

CONCLUSION

Quantitative lung CT imaging features can be used as indicators of survival, in particular for patients with large-cell-carcinoma (LCC), primary-tumor-sizes (T2) and no lymph-node-metastasis (N0).

Optimized Protocol for Quantitative Multiple Reaction Monitoring-Based Proteomic Analysis of Formalin-Fixed, Paraffin-Embedded Tissues

Despite a clinical, economic, and regulatory imperative to develop companion diagnostics, precious few new biomarkers have been successfully translated into clinical use, due in part to inadequate protein assay technologies to support large-scale testing of hundreds of candidate biomarkers in formalin-fixed paraffin-embedded (FFPE) tissues. Although the feasibility of using targeted, multiple reaction monitoring mass spectrometry (MRM-MS) for quantitative analyses of FFPE tissues has been demonstrated, protocols have not been systematically optimized for robust quantification across a large number of analytes, nor has the performance of peptide immuno-MRM been evaluated. To address this gap, we used a test battery approach coupled to MRM-MS with the addition of stable isotope-labeled standard peptides (targeting 512 analytes) to quantitatively evaluate the performance of three extraction protocols in combination with three trypsin digestion protocols (i.e., nine processes). A process based on RapiGest buffer extraction and urea-based digestion was identified to enable similar quantitation results from FFPE and frozen tissues. Using the optimized protocols for MRM-based analysis of FFPE tissues, median precision was 11.4% (across 249 analytes). There was excellent correlation between measurements made on matched FFPE and frozen tissues, both for direct MRM analysis (R(2) = 0.94) and immuno-MRM (R(2) = 0.89). The optimized process enables highly reproducible, multiplex, standardizable, quantitative MRM in archival tissue specimens.

Novel image markers for non-small cell lung cancer classification and survival prediction

Background

Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients.

Results

In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated.

Conclusions

The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers.

Prognostic significance of FRA expression in epithelial cancers using AQUA(®) technology

AIM

Although agents that target FRA have advanced through clinical trials, comprehensive analyses of FRA expression in epithelial cancers compared with clinical variables and prognosis are limited.

MATERIALS & METHODS

FRA expression was examined in non-small-cell lung cancer (NSCLC), ovarian cancer and endometrial cancer cohorts using AQUA(®) technology.

RESULTS

For the NSCLC cohort, FRA expression was significantly higher in adenocarcinoma samples (p < 0.001) than other histologies, and in females (p = 0.003) versus males. High FRA expression was significantly associated with better survival in NSCLC cases (p = 0.01) while significantly and independently associated with worse prognosis in endometrial (p < 0.001) and ovarian cancers (p < 0.001).

CONCLUSION

These studies confirm the prognostic value of FRA in multiple indications. The opposing prognostic effects observed may suggest differential biology.

Sarcomatoid lung carcinomas show high levels of programmed death ligand-1 (PD-L1)

Programmed death-1 (PD-1) is a coinhibitory inducible receptor present on T-cells and macrophages. Tumor cells with increased programmed death ligand-1 (PD-L1) are believed to escape immunity through activation of PD-1/PD-L1 pathway and suppression of effector-immune responses. Recent strategies targeting the PD-1/PD-L1 axis have shown promising results in patients with several tumors types, including lung carcinomas. Preliminary data suggest that PD-L1 protein expression might have predictive response to such therapies. Sarcomatoid carcinomas (SCs) of the lung include rare subtypes of poorly differentiated non-small-cell lung carcinomas of high grade and aggressive behavior. The biology of these neoplasms is poorly understood and they frequently show increased local inflammatory and lymphocytic infiltration. Here, we report the expression of PD-L1 in 13 SCs from two large retrospective lung cancer cohorts. Using automated quantitative immunofluoresence and a mouse monoclonal antibody directed against the extracellular domain of PD-L1, we show that 9 of 13 patients (69.2%) with SCs are positive for PD-L1 and their levels are higher than in conventional non-small-cell lung carcinoma. These results provide rationale for the potential use of targeted immunotherapy in lung SCs.

Genomic analyses across six cancer types identify basal-like breast cancer as a unique molecular entity

To improve our understanding of the biological relationships among different types of cancer, we have characterized variation in gene expression patterns in a set of 1,707 samples representing 6 human cancer types (breast, ovarian, brain, colorectal, lung adenocarcinoma and squamous cell lung cancer). In the unified dataset, breast tumors of the Basal-like subtype were found to represent a unique molecular entity as any other cancer type, including the rest of breast tumors, while showing striking similarities with squamous cell lung cancers. Moreover, gene signatures tracking various cancer- and stromal-related biological processes such as proliferation, hypoxia and immune activation were found expressed similarly in different proportions of tumors across the various cancer types. These data suggest that clinical trials focusing on tumors with common profiles and/or biomarker expression rather than their tissue of origin are warranted with a special focus on Basal-like breast cancer and squamous cell lung carcinoma.

Aberrant Mucin5B expression in lung adenocarcinomas detected by iTRAQ labeling quantitative proteomics and immunohistochemistry

Background

Lung cancer is the number one cause of cancer-related deaths in the United States and worldwide. The complex protein changes and/or signature of protein expression in lung cancer, particularly in non-small cell lung cancer (NSCLC) has not been well defined. Although several studies have investigated the protein profile in lung cancers, the knowledge is far from complete. Among early studies, mucin5B (MUC5B) has been suggested to play an important role in the tumor progression. MUC5B is the major gel-forming mucin in the airway. In this study, we investigated the overall protein profile and MUC5B expression in lung adenocarcinomas, the most common type of NSCLCs.

Methods

Lung adenocarcinoma tissue in formalin-fixed paraffin-embedded (FFPE) blocks was collected and microdissected. Peptides from 8 tumors and 8 tumor-matched normal lung tissue were extracted and labeled with 8-channel iTRAQ reagents. The labeled peptides were identified and quantified by LC-MS/MS using an LTQ Orbitrap Velos mass spectrometer. MUC5B expression identified by iTRAQ labeling was further validated using immunohistochemistry (IHC) on tumor tissue microarray (TMA).

Results

A total of 1288 peptides from 210 proteins were identified and quantified in tumor tissues. Twenty-two proteins showed a greater than 1.5-fold differences between tumor and tumor-matched normal lung tissues. Fifteen proteins, including MUC5B, showed significant changes in tumor tissues. The aberrant expression of MUC5B was further identified in 71.1% of lung adenocarcinomas in the TMA.

Discussions

A subset of tumor-associated proteins was differentially expressed in lung adenocarcinomas. The differential expression of MUC5B in lung adenocarcinomas suggests its role as a potential biomarker in the detection of adenocarcinomas.

Prediction of lung tumor types based on protein attributes by machine learning algorithms

Early diagnosis of lung cancers and distinction between the tumor types (Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are very important to increase the survival rate of patients. Herein, we propose a diagnostic system based on sequence-derived structural and physicochemical attributes of proteins that involved in both types of tumors via feature extraction, feature selection and prediction models. 1497 proteins attributes computed and important features selected by 12 attribute weighting models and finally machine learning models consist of seven SVM models, three ANN models and two NB models applied on original database and newly created ones from attribute weighting models; models accuracies calculated through 10-fold cross and wrapper validation (just for SVM algorithms). In line with our previous findings, dipeptide composition, autocorrelation and distribution descriptor were the most important protein features selected by bioinformatics tools. The algorithms performances in lung cancer tumor type prediction increased when they applied on datasets created by attribute weighting models rather than original dataset. Wrapper-Validation performed better than X-Validation; the best cancer type prediction resulted from SVM and SVM Linear models (82%). The best accuracy of ANN gained when Neural Net model applied on SVM dataset (88%). This is the first report suggesting that the combination of protein features and attribute weighting models with machine learning algorithms can be effectively used to predict the type of lung cancer tumors (SCLC and NSCLC).

Molecular classification of non-small-cell lung cancer: diagnosis, individualized treatment, and prognosis

Non-small-cell lung cancer (NSCLC) is the most common cause of premature death among the malignant diseases worldwide. The current staging criteria do not fully capture the complexity of this disease. Molecular biology techniques, particularly gene expression microarrays, proteomics, and next-generation sequencing, have recently been developed to facilitate effectively its molecular classification. The underlying etiology, pathogenesis, therapeutics, and prognosis of NSCLC based on an improved molecular classification scheme may promote individualized treatment and improve clinical outcomes. This review focuses on the molecular classification of NSCLC based on gene expression microarray technology reported during the past decade, as well as their applications for improving the diagnosis, staging and treatment of NSCLC, including the discovery of prognostic markers or potential therapeutic targets. We highlight some of the recent studies that may refine the identification of NSCLC subtypes using novel techniques such as epigenetics, proteomics, or deep sequencing.

Quantitative analysis of microRNAs in tissue microarrays by in situ hybridization

MicroRNAs (miRNAs) have emerged as key regulators in the pathogenesis of cancers where they can act as either oncogenes or tumor suppressors. Most miRNA measurement methods require total RNA extracts which lack critical spatial information and present challenges for standardization. We have developed and validated a method for the quantitative analysis of miRNA expression by in situ hybridization (ISH) allowing for the direct assessment of tumor epithelial expression of miRNAs. This co-localization based approach (called qISH) utilizes DAPI and cytokeratin immunofluorescence to establish subcellular compartments in the tumor epithelia, then multiplexed with the miRNA ISH, allows for quantitative measurement of miRNA expression within these compartments. We use this approach to assess miR-21, miR-92a, miR-34a, and miR-221 expression in 473 breast cancer specimens on tissue microarrays. We found that miR-221 levels are prognostic in breast cancer illustrating the high-throughput method and confirming that miRNAs can be valuable biomarkers in cancer. Furthermore, in applying this method we found that the inverse relationship between miRNAs and proposed target proteins is difficult to discern in large population cohorts. Our method demonstrates an approach for large cohort, tissue microarray-based assessment of miRNA expression.

Classification of lung cancer tumors based on structural and physicochemical properties of proteins by bioinformatics models

Rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important in diagnosis of this disease. Furthermore sequence-derived structural and physicochemical descriptors are very useful for machine learning prediction of protein structural and functional classes, classifying proteins and the prediction performance. Herein, in this study is the classification of lung tumors based on 1497 attributes derived from structural and physicochemical properties of protein sequences (based on genes defined by microarray analysis) investigated through a combination of attribute weighting, supervised and unsupervised clustering algorithms. Eighty percent of the weighting methods selected features such as autocorrelation, dipeptide composition and distribution of hydrophobicity as the most important protein attributes in classification of SCLC, NSCLC and COMMON classes of lung tumors. The same results were observed by most tree induction algorithms while descriptors of hydrophobicity distribution were high in protein sequences COMMON in both groups and distribution of charge in these proteins was very low; showing COMMON proteins were very hydrophobic. Furthermore, compositions of polar dipeptide in SCLC proteins were higher than NSCLC proteins. Some clustering models (alone or in combination with attribute weighting algorithms) were able to nearly classify SCLC and NSCLC proteins. Random Forest tree induction algorithm, calculated on leaves one-out and 10-fold cross validation) shows more than 86% accuracy in clustering and predicting three different lung cancer tumors. Here for the first time the application of data mining tools to effectively classify three classes of lung cancer tumors regarding the importance of dipeptide composition, autocorrelation and distribution descriptor has been reported.

The biology of personalized cancer medicine: facing individual complexities underlying hallmark capabilities

It is a time of great promise and expectation for the applications of knowledge about mechanisms of cancer toward more effective and enduring therapies for human disease. Conceptualizations such as the hallmarks of cancer are providing an organizing principle with which to distill and rationalize the abject complexities of cancer phenotypes and genotypes across the spectrum of the human disease. A countervailing reality, however, involves the variable and often transitory responses to most mechanism-based targeted therapies, returning full circle to the complexity, arguing that the unique biology and genetics of a patient's tumor will in the future necessarily need to be incorporated into the decisions about optimal treatment strategies, the frontier of personalized cancer medicine. This perspective highlights considerations, metrics, and methods that may prove instrumental in charting the landscape of evaluating individual tumors so to better inform diagnosis, prognosis, and therapy. Integral to the consideration is remarkable heterogeneity and variability, evidently embedded in cancer cells, but likely also in the cell types composing the supportive and interactive stroma of the tumor microenvironment (e.g., leukocytes and fibroblasts), whose diversity in form, regulation, function, and abundance may prove to rival that of the cancer cells themselves. By comprehensively interrogating both parenchyma and stroma of patients' cancers with a suite of parametric tools, the promise of mechanism-based therapy may truly be realized.