SPARCoC: A New Framework for Molecular Pattern Discovery and Cancer Gene Identification

It is challenging to cluster cancer patients of a certain histopathological type into molecular subtypes of clinical importance and identify gene signatures directly relevant to the subtypes. Current clustering approaches have inherent limitations, which prevent them from gauging the subtle heterogeneity of the molecular subtypes. In this paper we present a new framework: SPARCoC (Sparse-CoClust), which is based on a novel Common-background and Sparse-foreground Decomposition (CSD) model and the Maximum Block Improvement (MBI) co-clustering technique. SPARCoC has clear advantages compared with widely-used alternative approaches: hierarchical clustering (Hclust) and nonnegative matrix factorization (NMF). We apply SPARCoC to the study of lung adenocarcinoma (ADCA), an extremely heterogeneous histological type, and a significant challenge for molecular subtyping. For testing and verification, we use high quality gene expression profiling data of lung ADCA patients, and identify prognostic gene signatures which could cluster patients into subgroups that are significantly different in their overall survival (with p-values < 0.05). Our results are only based on gene expression profiling data analysis, without incorporating any other feature selection or clinical information; we are able to replicate our findings with completely independent datasets. SPARCoC is broadly applicable to large-scale genomic data to empower pattern discovery and cancer gene identification.     

Genetics of preneoplasia: lessons from lung cancer

From biological, histopathologic, and clinical perspectives, lung cancer is a highly complex neoplasm probably having multiple preneoplastic pathways. The sequence of histopathologic changes in the bronchial mucosa that precedes the development of squamous carcinomas of the lung has been identified. For the other major forms of lung cancer, however, such sequences have been poorly documented. This review summarizes the current knowledge regarding the molecular and histopathologic pathogenesis of lung cancer and discusses the complexity of identifying novel molecular mechanisms involved in the development of the lung premalignant disease, and their relevance to the development of new strategies for early detection and chemoprevention. Although our current knowledge of the molecular pathogenesis of lung cancer is still meager, work over the last decade has taught several important lessons about the molecular pathogenesis of this tumor, including the following: a) Better characterization of the high-risk population is needed. b) There are several histopathologic and molecular pathways associated with the development of the major types of non-small cell lung cancer. c) Although there is a field effect phenomenon for lung preneoplastic lesions, recent data suggest that there are at least two distinct lung airway compartments (central and peripheral) for lung cancer pathogenesis. d) Inflammation may play an important role in lung cancer development and could be an important component of the field effect phenomenon. e) For lung adenocarcinoma, at least two pathways (smoking-related and nonsmoking-related) have been identified. f) Finally, the identification of deregulated molecular signaling pathways in lung cancer preneoplasias may provide a rationale for designing novel strategies for early detection and targeted chemoprevention of lung cancer.     

A Predictive Phosphorylation Signature of Lung Cancer

Background

Aberrant activation of signaling pathways drives many of the fundamental biological processes that accompany tumor initiation and progression. Inappropriate phosphorylation of intermediates in these signaling pathways are a frequently observed molecular lesion that accompanies the undesirable activation or repression of pro- and anti-oncogenic pathways. Therefore, methods which directly query signaling pathway activation via phosphorylation assays in individual cancer biopsies are expected to provide important insights into the molecular “logic” that distinguishes cancer and normal tissue on one hand, and enables personalized intervention strategies on the other.

Results

We first document the largest available set of tyrosine phosphorylation sites that are, individually, differentially phosphorylated in lung cancer, thus providing an immediate set of drug targets. Next, we develop a novel computational methodology to identify pathways whose phosphorylation activity is strongly correlated with the lung cancer phenotype. Finally, we demonstrate the feasibility of classifying lung cancers based on multi-variate phosphorylation signatures.

Conclusions

Highly predictive and biologically transparent phosphorylation signatures of lung cancer provide evidence for the existence of a robust set of phosphorylation mechanisms (captured by the signatures) present in the majority of lung cancers, and that reliably distinguish each lung cancer from normal. This approach should improve our understanding of cancer and help guide its treatment, since the phosphorylation signatures highlight proteins and pathways whose phosphorylation should be inhibited in order to prevent unregulated proliferation.     

Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study

Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.     

Emerging therapeutic agents for lung cancer

Lung cancer continues to be the most common cause of cancer-related mortality worldwide. Recent advances in molecular diagnostics and immunotherapeutics have propelled the rapid development of novel treatment agents across all cancer subtypes, including lung cancer. Additionally, more pharmaceutical therapies for lung cancer have been approved by the US Food and Drug Administration in the last 5 years than in previous two decades. These drugs have ushered in a new era of lung cancer managements that have promising efficacy and safety and also provide treatment opportunities to patients who otherwise would have no conventional chemotherapy available. In this review, we summarize recent advances in lung cancer therapeutics with a specific focus on first in-human or early-phase I/II clinical trials. These drugs either offer better alternatives to drugs in their class or are a completely new class of drugs with novel mechanisms of action. We have divided our discussion into targeted agents, immunotherapies, and antibody drug conjugates for small cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). We briefly review the emerging agents and ongoing clinical studies. We have attempted to provide the most current review on emerging therapeutic agents on horizon for lung cancer.     

Molecular imaging of autoimmune diseases and inflammation

Molecular imaging methods allow the noninvasive detection and localization of specific molecules. Agents that report on molecular disease biomarkers can be used to diagnose and monitor disease. Many inflammatory diseases have molecular signatures within altered tissues. Although tissue biopsy is still the gold standard for detecting these signatures, several molecular imaging markers have been developed. Pharmacologic agents that block specific immune molecules have recently entered the clinic, and these drugs have already transformed the way we care for patients with immune-mediated diseases. The use of immunomodulatory drugs is usually guided by clinical assessment of the patient's response. Unfortunately, clinical assessment may miss the signs of inflammation, and many of the serologic markers of immune-mediated diseases correlate poorly with the underlying inflammatory activity within target tissues. Molecular imaging methods have the potential to improve our ability to detect and characterize tissue inflammation. We discuss some of the molecular signatures of immune activation and review molecular imaging methods that have been developed to detect active tissue inflammation.     

Breast tumor metastasis: analysis via proteomic profiling

The ability to predict the metastatic behavior of a patient's cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and, to date, they predict individual outcomes poorly. The ongoing development of high-throughput proteomic profiling technologies is opening new avenues for the investigation of cancer and, through application in tissue-based studies and animal models, will facilitate the identification of molecular signatures that are associated with breast tumor cell phenotype. The appropriate use of these approaches has the potential to provide efficient biomarkers, and to improve our knowledge of tumor biology. This, in turn, will enable the development of targeted therapeutics aimed at ameliorating the lethal dissemination of breast cancer. In this review, we focus on the accumulating proteomic signatures of breast tumor progression, particularly those that correlate with the occurrence of distant metastases, and discuss some of the expected future developments in the field.     

Development and validation of a prognostic gene-expression signature for lung adenocarcinoma

Although several prognostic signatures have been developed in lung cancer, their application in clinical practice has been limited because they have not been validated in multiple independent data sets. Moreover, the lack of common genes between the signatures makes it difficult to know what biological process may be reflected or measured by the signature. By using classical data exploration approach with gene expression data from patients with lung adenocarcinoma (n = 186), we uncovered two distinct subgroups of lung adenocarcinoma and identified prognostic 193-gene gene expression signature associated with two subgroups. The signature was validated in 4 independent lung adenocarcinoma cohorts, including 556 patients. In multivariate analysis, the signature was an independent predictor of overall survival (hazard ratio, 2.4; 95% confidence interval, 1.2 to 4.8; p = 0.01). An integrated analysis of the signature revealed that E2F1 plays key roles in regulating genes in the signature. Subset analysis demonstrated that the gene signature could identify high-risk patients in early stage (stage I disease), and patients who would have benefit of adjuvant chemotherapy. Thus, our study provided evidence for molecular basis of clinically relevant two distinct two subtypes of lung adenocarcinoma.     

Breast tumor metastasis: analysis via proteomic profiling

The ability to predict the metastatic behavior of a patient’s cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and, to date, they predict individual outcomes poorly. The ongoing development of high-throughput proteomic profiling technologies is opening new avenues for the investigation of cancer and, through application in tissue-based studies and animal models, will facilitate the identification of molecular signatures that are associated with breast tumor cell phenotype. The appropriate use of these approaches has the potential to provide efficient biomarkers, and to improve our knowledge of tumor biology. This, in turn, will enable the development of targeted therapeutics aimed at ameliorating the lethal dissemination of breast cancer. In this review, we focus on the accumulating proteomic signatures of breast tumor progression, particularly those that correlate with the occurrence of distant metastases, and discuss some of the expected future developments in the field.     

ArrayVigil: a methodology for statistical comparison of gene signatures using segregated-one-tailed (SOT) Wilcoxon's signed-rank test

Due to versatile diagnostic and prognostic fidelity molecular signatures or fingerprints are anticipated as the most powerful tools for cancer management in the near future. Notwithstanding the experimental advancements in microarray technology, methods for analyzing either whole arrays or gene signatures have not been firmly established. Recently, an algorithm, ArraySolver has been reported by Khan for two-group comparison of microarray gene expression data using two-tailed Wilcoxon signed-rank test. Most of the molecular signatures are composed of two sets of genes (hybrid signatures) wherein up-regulation of one set and down-regulation of the other set collectively define the purpose of a gene signature. Since the direction of a selected gene's expression (positive or negative) with respect to a particular disease condition is known, application of one-tailed statistics could be a more relevant choice. A novel method, ArrayVigil, is described for comparing hybrid signatures using segregated-one-tailed (SOT) Wilcoxon signed-rank test and the results compared with integrated-two-tailed (ITT) procedures (SPSS and ArraySolver). ArrayVigil resulted in lower P values than those obtained from ITT statistics while comparing real data from four signatures.     

Prognostic classification of breast cancer and gene expression profiling

Clinical and pathological heterogeneity of breast cancer, partly responsible of therapeutic failures, reflects complex and combinatory molecular alterations until now poorly documented by classical investigation tools. Thorough molecular typing is crucial. The advent of DNA microarray-based gene expression profiling allowed consistent progresses in this direction. A novel molecular taxonomy of breast cancer has been defined, signatures that predict clinical outcome or therapeutic response have been identified, some of them being tested in ongoing prospective clinical trials. In this review, we present the main results and their potential clinical applications. We also discuss their current limits and future hopes in the therapeutic management of patients.     

Vascular-homing peptides for targeted drug delivery and molecular imaging: Meeting the clinical challenges

The vasculature of each organ expresses distinct molecular signatures critically influenced by the pathological status. The heterogeneous profile of the vascular beds has been successfully unveiled by the in vivo phage display, a high-throughput tool for mapping normal, diseased, and tumor vasculature. Specific challenges of this growing field are targeted therapies against cancer and cardiovascular diseases, as well as novel bioimaging diagnostic tools. Tumor vasculature-homing peptides have been extensively evaluated in several preclinical and clinical studies both as targeted-therapy and diagnosis. To date, results from several Phase I and II trials have been reported and many other trials are currently ongoing or recruiting patients. In this review, advances in the identification of novel peptide ligands and their corresponding receptors on tumor endothelium through the in vivo phage display technology are discussed. Emphasis is given to recent findings in the clinical setting of vascular-homing peptides selected by in vivo phage display for the treatment of advanced malignancies and their altered vascular beds.     

Genomic signatures of breast cancer metastasis

Despite significant advances in the treatment of primary cancer, the ability to predict the metastatic behavior of a patient's cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and they predict individual outcomes poorly. However, the recent introduction of high-throughput microarray technology has opened new avenues in genomic investigation of cancer, and through application in tissue-based studies and appropriate animal models, has facilitated the identification of gene expression signatures that are associated with the lethal progression of breast cancer. The use of these approaches has the potential to greatly impact our knowledge of tumor biology, to provide efficient biomarkers, and enable development towards customized prognostication and therapies for the individual.     

Development of biosensors for cancer clinical testing

Biosensors are devices that combine a biochemical recognition/binding element (ligand) with a signal conversion unit (transducer). Biosensors are already used for several clinical applications, for example for electrochemical measurement of blood glucose concentrations. Application of biosensors in cancer clinical testing has several potential advantages over other clinical analysis methods including increased assay speed and flexibility, capability for multi-target analyses, automation, reduced costs of diagnostic testing and a potential to bring molecular diagnostic assays to community health care systems and to underserved populations. They have the potential for facilitating Point of Care Testing (POCT), where state-of-the-art molecular analysis is carried out without requiring a state-of-the-art laboratory. However, not many biosensors have been developed for cancer-related testing. One major challenge in harnessing the potential of biosensors is that cancer is a very complex set of diseases. Tumors vary widely in etiology and pathogenesis. Oncologists rely heavily on histological characterization of tumors and a few biomarkers that have demonstrated clinical utility to aid in patient management decisions. New genomic and proteomic molecular tools are being used to profile tumors and produce "molecular signatures." These signatures include genetic and epigenetic signatures, changes in gene expression, protein profiles and post-translational modifications of proteins. These molecular signatures provide new opportunities for utilizing biosensors. Biosensors have enormous potential to deliver the promise of new molecular diagnostic strategies to patients. This article describes some of the basic elements of cancer biology and cancer biomarkers relevant for the development of biosensors for cancer clinical testing, along with the challenges in using this approach.     

Urinary proteomic profiling for diagnostic bladder cancer biomarkers

The ability to detect and monitor bladder cancer in noninvasively obtained urine samples is a major goal. While a number of protein biomarkers have been identified and commercially developed, none have greatly improved the accuracy of sample evaluation over invasive cystoscopy. The ongoing development of high-throughput proteomic profiling technologies will facilitate the identification of molecular signatures that are associated with bladder disease. The appropriate use of these approaches has the potential to provide efficient biomarkers for the early detection and monitoring of recurrent bladder cancer. Identification of disease-associated proteins will also advance our knowledge of tumor biology, which, in turn, will enable development of targeted therapeutics aimed at reducing morbidity from bladder cancer. In this article, we focus on the accumulating proteomic signatures of urine in health and disease, and discuss expected future developments in this field of research.     

NCBP1 promotes the development of lung adenocarcinoma through up‐regulation of CUL4B

Lung cancer is the most frequent cancer type and is the leading cause of tumour‐associated deaths worldwide. Nuclear cap‐binding protein 1 (NCBP1) is necessary for capped RNA processing and intracellular localization. It has been reported that silencing of NCBP1 resulted in cell growth reduction in HeLa cells. Nevertheless, its clinical significance and underlying molecular mechanisms in non–small‐cell lung cancer remain unclear. In this study, we found that NCBP1 was significantly overexpressed in lung cancer tissues and several lung cancer cell lines. Through knockdown and overexpression experiments, we showed that NCBP1 promoted lung cancer cell growth, wound healing ability, migration and epithelial‐mesenchymal transition. Mechanistically, we found that cullin 4B (CUL4B) was a downstream target gene of NCBP1 in NSCLC. NCBP1 up‐regulated CUL4B expression via interaction with nuclear cap‐binding protein 3 (NCBP3). CUL4B silencing significantly reversed NCBP1‐induced tumorigenesis in vitro. Based on these findings, we propose a model involving the NCBP1‐NCBP3‐CUL4B oncoprotein axis, providing novel insight into how CUL4B is activated and contributes to LUAD progression.     

Integrative analysis from multi‐centre studies identifies a function‐derived personalized multi‐gene signature of outcome in colorectal cancer

Colorectal cancer (CRC) is highly heterogeneous leading to variable prognosis and treatment responses. Therefore, it is necessary to explore novel personalized and reproducible prognostic signatures to aid clinical decision‐making. The present study combined large‐scale gene expression profiles and clinical data of 1828 patients with CRC from multi‐centre studies and identified a personalized gene prognostic signature consisting of 46 unique genes (called function‐derived personalized gene signature [FunPGS]) from an integrated statistics and function‐derived perspective. In the meta‐training and multiple independent validation cohorts, the FunPGS effectively discriminated patients with CRC with significantly different prognosis at the individual level and remained as an independent factor upon adjusting for clinical covariates in multivariate analysis. Furthermore, the FunPGS demonstrated superior performance for risk stratification with respect to other recently reported signatures and clinical factors. The complementary value of the molecular signature and clinical factors was further explored, and it was observed that the composite signature called IMCPS greatly improved the predictive performance of survival estimation relative to molecular signatures or clinical factors alone. With further prospective validation in clinical trials, the FunPGS may become a promising and powerful personalized prognostic tool for stratifying patients with CRC in order to achieve an optimal systemic therapy.