Peeling off the hidden genetic heterogeneities of cancers based on disease-relevant functional modules

Discovering molecular heterogeneities in phenotypically defined disease is of critical importance both for understanding pathogenic mechanisms of complex diseases and for finding efficient treatments. Recently, it has been recognized that cellular phenotypes are determined by the concerted actions of many functionally related genes in modular fashions. The underlying modular mechanisms should help the understanding of hidden genetic heterogeneities of complex diseases. We defined a putative disease module to be the functional gene groups in terms of both biological process and cellular localization, which are significantly enriched with genes highly variably expressed across the disease samples. As a validation, we used two large cancer datasets to evaluate the ability of the modules for correctly partitioning samples. Then, we sought the subtypes of complex diffuse large B-cell lymphoma (DLBCL) using a public dataset. Finally, the clinical significance of the identified subtypes was verified by survival analysis. In two validation datasets, we achieved highly accurate partitions that best fit the clinical cancer phenotypes. Then, for the notoriously heterogeneous DLBCL, we demonstrated that two partitioned subtypes using an identified module ("cellular response to stress") had very different 5-year overall rates (65% vs. 14%) and were highly significantly (P < 0.007) correlated with the clinical survival rate. Finally, we built a multivariate Cox proportional-hazard prediction model that included 4 genes as risk predictors for survival over DLBCL. The proposed modular approach is a promising computational strategy for peeling off genetic heterogeneities and understanding the modular mechanisms of human diseases such as cancers.     

Unraveling the Hidden Heterogeneities of Breast Cancer Based on Functional miRNA Cluster

It has become increasingly clear that the current taxonomy of clinical phenotypes is mixed with molecular heterogeneity, which potentially affects the treatment effect for involved patients. Defining the hidden molecular-distinct diseases using modern large-scale genomic approaches is therefore useful for refining clinical practice and improving intervention strategies. Given that microRNA expression profiling has provided a powerful way to dissect hidden genetic heterogeneity for complex diseases, the aim of the study was to develop a bioinformatics approach that identifies microRNA features leading to the hidden subtyping of complex clinical phenotypes. The basic strategy of the proposed method was to identify optimal miRNA clusters by iteratively partitioning the sample and feature space using the two-ways super-paramagnetic clustering technique. We evaluated the obtained optimal miRNA cluster by determining the consistency of co-expression and the chromosome location among the within-cluster microRNAs, and concluded that the optimal miRNA cluster could lead to a natural partition of disease samples. We applied the proposed method to a publicly available microarray dataset of breast cancer patients that have notoriously heterogeneous phenotypes. We obtained a feature subset of 13 microRNAs that could classify the 71 breast cancer patients into five subtypes with significantly different five-year overall survival rates (45%, 82.4%, 70.6%, 100% and 60% respectively; p = 0.008). By building a multivariate Cox proportional-hazards prediction model for the feature subset, we identified has-miR-146b as one of the most significant predictor (p = 0.045; hazard ratios = 0.39). The proposed algorithm is a promising computational strategy for dissecting hidden genetic heterogeneity for complex diseases, and will be of value for improving cancer diagnosis and treatment.     

Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes

Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.     

基于耦合双向聚类技术的DLBCL异质性分析

基因芯片技术为疾病异质性研究提供了有力的工具。当前基于传统聚类分析的方法一般利用芯片上大量基因作为特征来发现疾病的亚型, 因此它们没有考虑到特征中包含的大量无关基因会掩盖有意义的疾病样本的分割。为了避免这个缺点, 提出了基于耦合双向聚类的异质性分析方法(Heterogeneous Analysis Based on Coupled Two-Way Clustering, HCTWC)来搜索有意义的基因簇以便发现样本的内在分割。该方法被应用于弥漫性大B细胞淋巴瘤(diffuse large B-cell lymphoma DLBCL)芯片数据集, 通过识别的基因簇作为特征对DLBCL样本聚类发现生存期分别为55%和25%的两类DLBCL亚型(P<0.05), 因此, HCTWC方法在解决疾病异质性是有效的。     

CASPAR: a hierarchical bayesian approach to predict survival times in cancer from gene expression data

MOTIVATION: DNA microarrays allow the simultaneous measurement of thousands of gene expression levels in any given patient sample. Gene expression data have been shown to correlate with survival in several cancers, however, analysis of the data is difficult, since typically at most a few hundred patients are available, resulting in severely underdetermined regression or classification models. Several approaches exist to classify patients in different risk classes, however, relatively little has been done with respect to the prediction of actual survival times. We introduce CASPAR, a novel method to predict true survival times for the individual patient based on microarray measurements. CASPAR is based on a multivariate Cox regression model that is embedded in a Bayesian framework. A hierarchical prior distribution on the regression parameters is specifically designed to deal with high dimensionality (large number of genes) and low sample size settings, that are typical for microarray measurements. This enables CASPAR to automatically select small, most informative subsets of genes for prediction. RESULTS: Validity of the method is demonstrated on two publicly available datasets on diffuse large B-cell lymphoma (DLBCL) and on adenocarcinoma of the lung. The method successfully identifies long and short survivors, with high sensitivity and specificity. We compare our method with two alternative methods from the literature, demonstrating superior results of our approach. In addition, we show that CASPAR can further refine predictions made using clinical scoring systems such as the International Prognostic Index (IPI) for DLBCL and clinical staging for lung cancer, thus providing an additional tool for the clinician. An analysis of the genes identified confirms previously published results, and furthermore, new candidate genes correlated with survival are identified.     

CUL4B regulates autophagy via JNK signaling in diffuse large B-cell lymphoma

Aberrant expression of CUL4B was identified in various types of solid cancers. Cumulative evidences support the oncogenic role of CUL4B in cancers, including regulation of cell proliferation and signal transduction. However, its clinical value and potential pathogenic mechanism in diffuse large B-cell lymphoma (DLBCL) have not been described previously. Therefore, we hypothesize that overexpressed CUL4B may contribute to the pathogenesis of DLBCL. The aim of this study is to assess the expression and the biological function of CUL4B in DLBCL progression. In our study, CUL4B overexpression was observed in DLBCL tissues, and its upregulation was closely associated with poor prognosis in patients. Furthermore, the functional roles of CUL4B was detected both in vitro and in vivo. We demonstrated that silencing CUL4B could not only induce cell proliferation inhibition, cell cycle arrest, and motility attenuation of DLBCL cells in vitro, but also decrease tumor growth in DLBCL xenografts mice. In addition, we identi?ed that CUL4B may act as a potent inductor of JNK phosphorylation in regulation of autophagy. Our findings demonstrated a significant role of CUL4B in the development and progression of DLBCL. CUL4B may act as a useful biomarker and a novel therapeutic target in DLBCL.     

Challenges to developing proteomic-based breast cancer diagnostics

Over the past decade, multiple genetic and histological approaches have accelerated development of new breast cancer diagnostics and treatment paradigms. Multiple distinct genetic subtypes of breast cancers have been defined, and this has progressively led toward more personalized medicine in regard to treatment options. There still remains a deficiency in the development of molecular diagnostic assays that can be used for breast cancer detection and pretherapy clinical decisions. In particular, the type of cancer-specific biomarker typified by a serum or tissue-derived protein. Progress in this regard has been minimal, especially in comparison to the rapid advancements in genetic and histological assays for breast cancers. In this review, some potential reasons for this large gap in developing protein biomarkers will be discussed, as well as new strategies for improving these approaches. Improvements in the study design of protein biomarker discovery strategies in relation to the genetic subtypes and histology of breast cancers is also emphasized. The current successes in use of genetic and histological assays for breast cancer diagnostics are summarized, and in that context, the current limitations of the types of breast cancer-related clinical samples available for protein biomarker assay development are discussed. Based on these limitations, research strategies emphasizing identification of glycoprotein biomarkers in blood and MALDI mass spectrometry imaging of tissues are described.     

Messina: A Novel Analysis Tool to Identify Biologically Relevant Molecules in Disease

Background

Morphologically similar cancers display heterogeneous patterns of molecular aberrations and follow substantially different clinical courses. This diversity has become the basis for the definition of molecular phenotypes, with significant implications for therapy. Microarray or proteomic expression profiling is conventionally employed to identify disease-associated genes, however, traditional approaches for the analysis of profiling experiments may miss molecular aberrations which define biologically relevant subtypes.

Methodology/Principal Findings

Here we present Messina, a method that can identify those genes that only sometimes show aberrant expression in cancer. We demonstrate with simulated data that Messina is highly sensitive and specific when used to identify genes which are aberrantly expressed in only a proportion of cancers, and compare Messina to contemporary analysis techniques. We illustrate Messina by using it to detect the aberrant expression of a gene that may play an important role in pancreatic cancer.

Conclusions/Significance

Messina allows the detection of genes with profiles typical of markers of molecular subtype, and complements existing methods to assist the identification of such markers. Messina is applicable to any global expression profiling data, and to allow its easy application has been packaged into a freely-available stand-alone software package.     

Molecular Diagnostic and Prognostic Subtyping of Gliomas in Tunisian Population

It has become increasingly evident that morphologically similar gliomas may have distinct clinical phenotypes arising from diverse genetic signatures. To date, glial tumours from the Tunisian population have not been investigated. To address this, we correlated the clinico-pathology with molecular data of 110 gliomas by a combination of HM450K array, MLPA and TMA-IHC. PTEN loss and EGFR amplification were distributed in different glioma histological groups. However, 1p19q co-deletion and KIAA1549:BRAF fusion were, respectively, restricted to Oligodendroglioma and Pilocytic Astrocytoma. CDKN2A loss and EGFR overexpression were more common within high-grade gliomas. Furthermore, survival statistical correlations led us to identify Glioblastoma (GB) prognosis subtypes. In fact, significant lower overall survival (OS) was detected within GB that overexpressed EGFR and Cox2. In addition, IDH1R132H mutation seemed to provide a markedly survival advantage. Interestingly, the association of IDHR132H mutation and EGFR normal status, as well as the association of differentiation markers, defined GB subtypes with good prognosis. By contrast, poor survival GB subtypes were defined by the combination of PTEN loss with PDGFRa expression and/or EGFR amplification. Additionally, GB presenting p53-negative staining associated with CDKN2A loss or p21 positivity represented a subtype with short survival. Thus, distinct molecular subtypes with individualised prognosis were identified. Interestingly, we found a unique histone mutation in a poor survival young adult GB case. This tumour exceptionally associated the H3F3A G34R mutation and MYCN amplification as well as 1p36 loss and 10q loss. Furthermore, by exhibiting a remarkable methylation profile, it emphasised the oncogenic power of G34R mutation connecting gliomagenesis and chromatin regulation.     

Moving towards a new era of genomics in the neuronal ceroid lipofuscinoses

The neuronal ceroid lipofuscinoses (NCL) are a group of disorders defined by shared clinical and pathological features, including seizures and progressive decline in vision, neurocognition, and motor functioning, as well as accumulation of autofluorescent lysosomal storage material, or ‘ceroid lipofuscin’. Research has revealed thirteen distinct genetic subtypes. Precisely how the gene mutations lead to the clinical phenotype is still incompletely understood, but recent research progress is starting to shed light on disease mechanisms, in both gene-specific and shared pathways. As the application of new sequencing technologies to genetic disease diagnosis has grown, so too has the spectrum of clinical phenotypes caused by mutations in the NCL genes. Most genes causing NCL have probably been identified, underscoring the need for a shift towards applying genomics approaches to achieve a deeper understanding of the molecular basis of the NCLs and related disorders. Here, we summarize the current understanding of the thirteen identified NCL genes and the proteins they encode, touching upon the spectrum of clinical manifestations linked to each of the genes, and we highlight recent progress leading to a broader understanding of key pathways involved in NCL disease pathogenesis and commonalities with other neurodegenerative diseases.     

An integrative genomic approach to uncover molecular mechanisms of prokaryotic traits

With mounting availability of genomic and phenotypic databases, data integration and mining become increasingly challenging. While efforts have been put forward to analyze prokaryotic phenotypes, current computational technologies either lack high throughput capacity for genomic scale analysis, or are limited in their capability to integrate and mine data across different scales of biology. Consequently, simultaneous analysis of associations among genomes, phenotypes, and gene functions is prohibited. Here, we developed a high throughput computational approach, and demonstrated for the first time the feasibility of integrating large quantities of prokaryotic phenotypes along with genomic datasets for mining across multiple scales of biology (protein domains, pathways, molecular functions, and cellular processes). Applying this method over 59 fully sequenced prokaryotic species, we identified genetic basis and molecular mechanisms underlying the phenotypes in bacteria. We identified 3,711 significant correlations between 1,499 distinct Pfam and 63 phenotypes, with 2,650 correlations and 1,061 anti-correlations. Manual evaluation of a random sample of these significant correlations showed a minimal precision of 30% (95% confidence interval: 20%-42%; n = 50). We stratified the most significant 478 predictions and subjected 100 to manual evaluation, of which 60 were corroborated in the literature. We furthermore unveiled 10 significant correlations between phenotypes and KEGG pathways, eight of which were corroborated in the evaluation, and 309 significant correlations between phenotypes and 166 GO concepts evaluated using a random sample (minimal precision = 72%; 95% confidence interval: 60%-80%; n = 50). Additionally, we conducted a novel large-scale phenomic visualization analysis to provide insight into the modular nature of common molecular mechanisms spanning multiple biological scales and reused by related phenotypes (metaphenotypes). We propose that this method elucidates which classes of molecular mechanisms are associated with phenotypes or metaphenotypes and holds promise in facilitating a computable systems biology approach to genomic and biomedical research.     

Age-related copy number variations and expression levels of F-box protein FBXL20 predict ovarian cancer prognosis

About 70% of ovarian cancer (OvCa) cases are diagnosed at advanced stages (stage III/IV) with only 20–40% of them survive over 5 years after diagnosis. A reliably screening marker could enable a paradigm shift in OvCa early diagnosis and risk stratification. Age is one of the most significant risk factors for OvCa. Older women have much higher rates of OvCa diagnosis and poorer clinical outcomes. In this article, we studied the correlation between aging and genetic alterations in The Cancer Genome Atlas Ovarian Cancer dataset. We demonstrated that copy number variations (CNVs) and expression levels of the F-Box and Leucine-Rich Repeat Protein 20 (FBXL20), a substrate recognizing protein in the SKP1-Cullin1-F-box-protein E3 ligase, can predict OvCa overall survival, disease-free survival and progression-free survival. More importantly, FBXL20 copy number loss predicts the diagnosis of OvCa at a younger age, with over 60% of patients in that subgroup have OvCa diagnosed at age less than 60 years. Clinicopathological studies further demonstrated malignant histological and radiographical features associated with elevated FBXL20 expression levels. This study has thus identified a potential biomarker for OvCa prognosis.     

Angiogenesis in gynecological cancers and the options for anti-angiogenesis therapy

Angiogenesis is required in cancer, including gynecological cancers, for the growth of primary tumors and secondary metastases. Development of anti-angiogenesis therapy in gynecological cancers and improvement of its efficacy have been a major focus of fundamental and clinical research. However, survival benefits of current anti-angiogenic agents, such as bevacizumab, in patients with gynecological cancer, are modest. Therefore, a better understanding of angiogenesis and the tumor microenvironment in gynecological cancers is urgently needed to develop more effective anti-angiogenic therapies, either or not in combination with other therapeutic approaches. We describe the molecular aspects of (tumor) blood vessel formation and the tumor microenvironment and provide an extensive clinical overview of current anti-angiogenic therapies for gynecological cancers. We discuss the different phenotypes of angiogenic endothelial cells as potential therapeutic targets, strategies aimed at intervention in their metabolism, and approaches targeting their (inflammatory) tumor microenvironment.