Clinical and Biological Relevance of Genomic Heterogeneity in Chronic Lymphocytic Leukemia

Background

Chronic lymphocytic leukemia (CLL) is typically regarded as an indolent B-cell malignancy. However, there is wide variability with regards to need for therapy, time to progressive disease, and treatment response. This clinical variability is due, in part, to biological heterogeneity between individual patients’ leukemias. While much has been learned about this biological variation using genomic approaches, it is unclear whether such efforts have sufficiently evaluated biological and clinical heterogeneity in CLL.

Methods

To study the extent of genomic variability in CLL and the biological and clinical attributes of genomic classification in CLL, we evaluated 893 unique CLL samples from fifteen publicly available gene expression profiling datasets. We used unsupervised approaches to divide the data into subgroups, evaluated the biological pathways and genetic aberrations that were associated with the subgroups, and compared prognostic and clinical outcome data between the subgroups.

Results

Using an unsupervised approach, we determined that approximately 600 CLL samples are needed to define the spectrum of diversity in CLL genomic expression. We identified seven genomically-defined CLL subgroups that have distinct biological properties, are associated with specific chromosomal deletions and amplifications, and have marked differences in molecular prognostic markers and clinical outcomes.

Conclusions

Our results indicate that investigations focusing on small numbers of patient samples likely provide a biased outlook on CLL biology. These findings may have important implications in identifying patients who should be treated with specific targeted therapies, which could have efficacy against CLL cells that rely on specific biological pathways.     

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.     

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.     

Identification of a prognostic metabolic gene signature in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a clinically diverse disease. Given the numerous genetic mutations and variations associated with it, a prognostic gene signature that can be related to the overall survival (OS) is a clinical implication. We used the mRNA expression profiles and clinicopathological data of patients with DLBCL from the Gene Expression Omnibus (GEO) database to identify a metabolism-related gene signature. Using LASSO regression analysis, a novel 13-metabolic gene signature was identified to evaluate prognosis. The information gathered was used to construct the nomogram model to improve risk stratification and quantify risk factors for individual patients. We performed gene set enrichment analysis to identify the enriched signalling axes to further understand the underlying biological pathways. The receiver operating characteristic (ROC) curve revealed a satisfactory performance in the training cohorts. The model also showed clinical benefit when compared to the standard prognostic factors (P < .05) in validation cohorts. This study aimed to combine metabolic dysregulation with clinical features of patients with DLBCL to generate a prognostic model that might not only indicate the value of the metabolic microenvironment for prognostic stratification but also improve the decision-making during individual therapy.     

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.     

The AP2M1 gene expression is a promising biomarker for predicting survival of patients with hepatocellular carcinoma

There is a growing need for the discovery of new prognostic factors for cases where the scoring and staging system of hepatocellular carcinoma (HCC) does not result in a clear definition. We analyzed whether AP-2 complex subunit mu (AP2M1) expression could be a new prognostic marker for HCC based on the roles of AP2M1 in influencing hepatocyte growth factor (HGF) promoter regulation and hepatitis C virus (HCV) assembly. Patient data were extracted from cohorts of the Gene Expression Omnibus (GSE10186), International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Differential expression value between matched cancer and normal liver was identified using ICGC cohort. Subsequently, we compared AP2M1 expression as a prognostic gene with other well-known prognostic genes for HCC, using the time-dependent area under the curve (AUC) of the Uno's C-index, the AUC value of the receiver operating characteristics at 5 years, Kaplan-Meier survival curve, and multivariate analysis. Particularly, TCGA and GSE10186 patients were divided into subgroups based on alcohol intake, hepatitis B, and C viral infections, and analyzed in the same methods. The AP2M1 expression values in patients with cancer were much higher than matched normal liver. The AP2M1 level showed excellent prognosis predictions in comparison with existing markers in the three independent cohorts (n = 647). In particular, it was more predictive of prognosis than other markers in alcohol intake and HCV infections. In conclusion, we were confident that AP2M1 provides sufficient value as a new prognostic marker for HCC especially patients with HCV infection and/or alcohol intake.     

Proteomic analysis of chronic lymphocytic leukemia subtypes with mutated or unmutated Ig V(H) genes

Chronic lymphocytic leukemia (CLL) is a common hematopoietic malignant disease with variable outcome. CLL has been divided into distinct groups based on whether somatic hypermutation has occurred in the variable region of the immunoglobulin heavy-chain locus or alternatively if the cells express higher levels of the CD38 protein. We have analyzed the proteome of 12 cases of CLL (six mutated (M-CLL) and six unmutated (UM-CLL) immunoglobulin heavy-chain loci; seven CD38-negative and five CD38-positive) using two-dimensional electrophoresis and mass spectrometry. Statistical evaluation using principal component analysis indicated significant differences in patterns of protein expression between the cases with and without somatic mutation. Specific proteins indicated by principal component analysis as varying between the prognostic groups were characterized using mass spectrometry. The levels of F-actin-capping protein beta subunit, 14-3-3 beta protein, and laminin-binding protein precursor were significantly increased in M-CLL relative to UM-CLL. In addition, primary sequence data from tandem mass spectrometry showed that nucleophosmin was present as several protein spots in M-CLL but was not detected in UM-CLL samples, suggesting that several post-translationally modified forms of nucleophosmin vary between these two sample groups. No specific differences were found between CD38-positive and -negative patient samples using the same approach. The results presented show that proteomic analysis can complement other approaches in identifying proteins that may have potential value in the biological and diagnostic distinction between important clinical subtypes of CLL.     

A Systems Biology-Based Gene Expression Classifier of Glioblastoma Predicts Survival with Solid Tumors

Accurate prediction of survival of cancer patients is still a key open problem in clinical research. Recently, many large-scale gene expression clusterings have identified sets of genes reportedly predictive of prognosis; however, those gene sets shared few genes in common and were poorly validated using independent data. We have developed a systems biology-based approach by using either combined gene sets and the protein interaction network (Method A) or the protein network alone (Method B) to identify common prognostic genes based on microarray gene expression data of glioblastoma multiforme and compared with differential gene expression clustering (Method C). Validations of prediction performance show that the 23-prognostic gene classifier identified by Method A outperforms other gene classifiers identified by Methods B and C or previously reported for gliomas on 17 of 20 independent sample cohorts across five tumor types. We also find that among the 23 genes are 21 related to cellular proliferation and two related to response to stress/immune response. We further find that the increased expression of the 21 genes and the decreased expression of the other two genes are associated with poorer survival, which is supportive with the notion that cellular proliferation and immune response contribute to a significant portion of predictive power of prognostic classifiers. Our results demonstrate that the systems biology-based approach enables to identify common survival-associated genes.     

CD markers variations in chronic lymphocytic leukemia: New insights into prognosis

Chronic lymphocytic leukemia (CLL) is one of the most commonly occurring adult leukemias that is associated with clonal accumulation of mature apoptosis-resistant B-cells in bone marrow, peripheral blood, and specific tissues. Different pathogenesis factors can contribute to the aggression of the clinical course in this disease. Cytogenetic abnormalities and surface biomarkers of neoplastic CLL cells can be effective in the outcome of CLL, and the examination of changing CD markers expressions in the progression of CLL can be related to the prognosis of this disease. Changing expression levels of CD markers on lymphocytes and other cells in CLL patients can play a role in the aggressive clinical outcomes such as organomegaly, immunodeficiency, and advanced disease stages through their interaction with CLL microenvironment. Given the involvement of CD markers in the pathogenesis of CLL, it can be stated that recognizing the expression changes of CD markers in the cells involved in CLL can be a proper approach to evaluate prognosis among these patients.     

Analysis of CD38 and ZAP70 mRNA expression among cytogenetic subgroups of Iranian chronic-lymphocytic-leukemia patients

Chromosomal abnormalities and ZAP70 expression profile are two major independent prognostic markers in B-cell chronic lymphocytic leukemia. We investigated a possible correlation between these two markers. ZAP70 expression using real-time RT-PCR was examined in 20 B-cell chronic lymphocytic leukemia patients with del13q14, 13 patients with del11q22, 15 patients with trisomy 12, and 16 patients with no detected chromosomal abnormalities. Molecular analysis revealed that ZAP70 expression in the del13q subgroup was the same as in the control group, while it increased 2.78-fold in the del11q subgroup and 2.95-fold in the trisomy 12 subgroup, compared to the 15 cases in the control group. Comparison of the mean and standard deviation of the ZAP70 expression profile within the subgroups showed it to be highly variable among the individuals of the del11q and trisomy 12 subgroups, versus tight clustering for the del13q subgroup. Therefore, there is a correlation between del13q aberration, which has good prognosis with normal levels of ZAP70 expression. Due to a high degree of variation, no conformity is seen for del11q and trisomy 12 subgroups, making this grouping poor for prognostic discrimination. As a result, neither of these markers can serve as sole discriminators to determine the course of the disease; the use of both markers improves prognostic assessment.     

Molecular biology of CLL

Recently, major advances have occurred in our understanding of the biology of chronic lymphocytic leukemia (CLL). CD5-positive CLL cells were once assumed to originate from immature, immunologically incompetent B lymphocytes, and to passively accumulate due to increased life time. In 1999, two research groups demonstrated that CLL, which is a morphologically uniform but clinically heterogenous disease, can be classified into two major subgroups on the basis of the mutational status of the immunoglobulin heavy chain (IgH) genes. It was also suggested that these two groups both originate from mature cells that have passed the antigen selection process. This hypothesis was confirmed by gene expression studies indicating a uniform pattern characteristic to memory cells, as well as specific B-cell receptor (BCR) structures supporting the existence of a functional antigen selection. The differences in the BCR signal transduction mechanisms may underlie the different clinical behavior in which zeta-associated tyrosine kinase (ZAP-70) may play a pivotal role, since elevated ZAP-70 expression is likely to be an unfavorable prognostic factor in CLL. The diagnostic testing for ZAP-70 expression plays an important role in the therapeutic decisions.     

Comprehensive analysis of prognostic immune-related genes in the tumor microenvironment of cutaneous melanoma

Cutaneous malignant melanoma (hereafter called melanoma) is one of the most aggressive cancers with increasing incidence and mortality rates worldwide. In this study, we performed a systematic investigation of the tumor microenvironmental and genetic factors associated with melanoma to identify prognostic biomarkers for melanoma. We calculated the immune and stromal scores of melanoma patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and found that they were closely associated with patients’ prognosis. Then the differentially expressed genes were obtained based on the immune and stromal scores, and prognostic immune-related genes further identified. Functional analysis and the protein–protein interaction network further revealed that these genes enriched in many immune-related biological processes. In addition, the abundance of six infiltrating immune cells was analyzed using prognostic immune-related genes by TIMER algorithm. The unsupervised clustering analysis using immune-cell proportions revealed eight clusters with distinct survival patterns, suggesting that dendritic cells were most abundant in the microenvironment and CD8⁺ T cells and neutrophils were significantly related to patients’ prognosis. Finally, we validated these genes in three independent cohorts from the Gene Expression Omnibus database. In conclusion, this study comprehensively analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for melanoma.     

Methylation markers identify high risk patients in IGHV mutated chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) exhibits a very variable clinical course. Altered DNA methylation of genes has shown promise as a source of novel prognostic makers in a number of cancers. Here we have studied the potential utility of a panel of methylation markers (CD38, HOXA4 and BTG4) in 118 CLL patients. Each of the three loci assessed exhibited frequent methylation, as determined by COBRA analysis, and individually correlated with either good (CD38, BTG4 methylation) or poor (HOXA4 methylation) prognosis. Using a combined approach to produce an overall methylation score, we found that methylation score was significantly associated with time to first treatment in CLL patients. Multivariate Cox regression analysis revealed that methylation score was the strongest predictor of time to first treatment, and was independent of IGHV gene mutational status and CD38 expression. This study provides proof of principle that a panel of methylation markers can be used for additional risk stratification of CLL patients.     

Uncovering the DNA methylome in chronic lymphocytic leukemia

Over the past two decades, aberrant DNA methylation has emerged as a key player in the pathogenesis of chronic lymphocytic leukemia (CLL), and knowledge regarding its biological and clinical consequences in this disease has evolved rapidly. Since the initial studies relating DNA hypomethylation to genomic instability in CLL, a plethora of reports have followed showing the impact of DNA hypermethylation in silencing vital single gene promoters and the reversible nature of DNA methylation through inhibitor drugs. With the recognition that DNA hypermethylation events could potentially act as novel prognostic and treatment targets in CLL, the search for aberrantly methylated genes, gene families and pathways has ensued. Subsequently, the advent of microarray and next-generation sequencing technologies has supported the hunt for such targets, allowing exploration of the methylation landscape in CLL at an unprecedented scale. In light of these analyses, we now understand that different CLL prognostic subgroups are characterized by differential methylation profiles; we recognize DNA methylation of a number of signaling pathways genes to be altered in CLL, and acknowledge the role of DNA methylation outside of traditional CpG island promoters as fundamental players in the regulation of gene expression. Today, the significance and timing of altered DNA methylation within the complex epigenetic network of concomitant epigenetic messengers such as histones and miRNAs is an intensive area of research. In CLL, it appears that DNA methylation is a rather stable epigenetic mark occurring rather early in the disease pathogenesis. However, other consequences, such as how and why aberrant methylation marks occur, are less explored. In this review, we will not only provide a comprehensive summary of the current literature within the epigenetics field of CLL, but also highlight some of the novel findings relating to when, where, why and how altered DNA methylation materializes in CLL.     

Ferroptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of glioma patients

Background: Glioma is a malignant intracranial tumor and the most fatal cancer. The role of ferroptosis in the clinical progression of gliomas is unclear.Method: Univariate and least absolute shrinkage and selection operator (Lasso) Cox regression methods were used to develop a ferroptosis-related signature (FRSig) using a cohort of glioma patients from the Chinese Glioma Genome Atlas (CGGA), and was validated using an independent cohort of glioma patients from The Cancer Genome Atlas (TCGA). A single-sample gene set enrichment analysis (ssGSEA) was used to calculate levels of the immune infiltration. Multivariate Cox regression was used to determine the independent prognostic role of clinicopathological factors and to establish a nomogram model for clinical application.Results: We analyzed the correlations between the clinicopathological features and ferroptosis-related gene (FRG) expression and established an FRSig to calculate the risk score for individual glioma patients. Patients were stratified into two subgroups with distinct clinical outcomes. Immune cell infiltration in the glioma microenvironment and immune-related indexes were identified that significantly correlated with the FRSig, the tumor mutation burden (TMB), copy number alteration (CNA), and immune checkpoint expression was also significantly positively correlated with the FRSig score. Ultimately, an FRSig-based nomogram model was constructed using the independent prognostic factors age, World Health Organization (WHO) grade, and FRSig score.Conclusion: We established the FRSig to assess the prognosis of glioma patients. The FRSig also represented the glioma microenvironment status. Our FRSig will contribute to improve patient management and individualized therapy by offering a molecular biomarker signature for precise treatment.     

Identification of 4-lncRNA prognostic signature in head and neck squamous cell carcinoma

Deregulated long noncoding RNAs (lncRNA) have been critically implicated in tumorigenesis and serve as novel diagnostic and prognostic biomarkers. Here we sought to develop a prognostic lncRNA signature in patients with head and neck squamous cell carcinoma (HNSCC). Original RNA-seq data of 499 HNSCC samples were retrieved from The Cancer Genome Atlas database, which was randomly divided into training and testing set. Univariate Cox regression survival analysis, robust likelihood-based survival model and random sampling iterations were applied to identify prognostic lncRNA candidates in the training cohort. A prognostic risk score was developed based on the Cox coefficient of four individual lncRNA imputed as follows: (0.14546 × expression level of RP11-366H4.1) + (0.27106 × expression level of LINC01123) + (0.54316 × expression level of RP11-110I1.14) + (−0.48794 × expression level of CTD-2506J14.1). Kaplan-Meier analysis revealed that patients with high-risk score had significantly reduced overall survival as compared with those with low-risk score when patients in training, testing, and validation cohorts were stratified into high- or low-risk subgroups. Multivariate survival analysis further revealed that this 4-lncRNA signature was a novel and important prognostic factor independent of multiple clinicopathological parameters. Importantly, ROC analyses indicated that predictive accuracy and sensitivity of this 4-lncRNA signature outperformed those previously well-established prognostic factors. Noticeably, prognostic score based on quantification of these 4-lncRNA via qRT-PCR in another independent HNSCC cohort robustly stratified patients into subgroups with high or low survival. Taken together, we developed a robust 4-lncRNA prognostic signature for HNSCC that might provide a novel powerful prognostic biomarker for precision oncology.