Epigenetics in diagnosis, prognostic assessment and treatment of cancer:An update

Cancer cells contain multiple genetic and epigenetic changes. The relative specificity of many epigenetic changes for neoplastic cells has allowed the identification of diagnostic, prognostic and predictive biomarkers for a number of solid tumors and hematological malignancies. Moreover, epigenetically-acting drugs are already in routine use for cancer and numerous additional agents are in clinical trials. Here, we review recent progress in the development and application of epigenetic strategies for the diagnosis, risk stratification and treatment of cancer

Loss of the candidate tumor suppressor ZEB1 (TCF8, ZFHX1A) in Sézary syndrome

Cutaneous T-cell lymphoma is a group of incurable extranodal non-Hodgkin lymphomas that develop from the skin-homing CD4+ T cell. Mycosis fungoides and Sézary syndrome are the most common histological subtypes. Although next-generation sequencing data provided significant advances in the comprehension of the genetic basis of this lymphoma, there is not uniform consensus on the identity and prevalence of putative driver genes for this heterogeneous group of tumors. Additional studies may increase the knowledge about the complex genetic etiology characterizing this lymphoma. We used SNP6 arrays and GISTIC algorithm to prioritize a list of focal somatic copy-number alterations in a dataset of multiple sequential samples from 21 Sézary syndrome patients. Our results confirmed a prevalence of significant focal deletions over amplifications: single well-known tumor suppressors, such as TP53, PTEN, and RB1, are targeted by these aberrations. In our cohort, ZEB1 (TCF8, ZFHX1A) spans a deletion having the highest level of significance. In a larger group of 43 patients, we found that ZEB1 is affected by deletions and somatic inactivating mutations in 46.5% of cases; also, we found potentially relevant ZEB1 germline variants. The survival analysis shows a worse clinical course for patients with ZEB1 biallelic inactivation. Multiple abnormal expression signatures were found associated with ZEB1 depletion in Sézary patients we verified that ZEB1 exerts a role in oxidative response of Sézary cells. Our data confirm the importance of deletions in the pathogenesis of cutaneous T-cell lymphoma. The characterization of ZEB1 abnormalities in Sézary syndrome fulfils the criteria of a canonical tumor suppressor gene. Although additional confirmations are needed, our findings suggest, for the first time, that ZEB1 germline variants might contribute to the risk of developing this disease. Also, we provide evidence that ZEB1 activity in Sézary cells, influencing the reactive oxygen species production, affects cell viability and apoptosis

Combined burden and functional impact tests for cancer driver discovery using DriverPower

The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower's background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery

The Nefarious Nexus of Noncoding RNAs in Cancer

The past decade has witnessed enormous progress, which has seen the noncoding RNAs (ncRNAs) turn from the so called dark matter RNA to critical functional molecules, influencing most physiological processes in development and disease contexts. Many ncRNAs interact with each other and are part of networks that influence the cell transcriptome and proteome and consequently the outcome of biological processes. The regulatory circuits controlled by ncRNAs have become increasingly more relevant in cancer. Further understanding of these complex network interactions and how ncRNAs are regulated, is paving the way for the identification of better therapeutic strategies in cancer

EVALUATING THE THERAPEUTIC EFFICACY OF RESTORING WILD-TYPE P53 ACTIVITY IN P53-MUTANT TUMORS

The p53 transcription factor is the most frequently altered in human cancers usually via missense mutations that undermine its transcriptional activity. Clinically, TP53 mutations have been shown to be remarkably predictive of refractoriness to treatment, resulting in poor outcome. Consequently, the development of p53 pathway activating agents is rapidly evolving and gaining more attention in cancer therapeutics research, with several small molecule compounds currently in preclinical and clinical trials. However, it remains largely unknown what types or proportions of p53-mutant tumors will respond to p53 restoration-based therapies. Using a mouse model of Li Fraumeni syndrome, we genetically restored wild-type p53 in mice carrying a germline p53R172H(corresponding to the TP53R175H hotspot in humans) missense mutation and observed heterogeneous responses. We found that approximately 50% of tumors responded by regressing in volume whereas 50% of tumors failed to regress after p53 reinstatement. To gain insight into the molecular events underlying therapeutic response to p53 restoration, we sequenced the transcriptome of twelve p53-mutant thymic lymphomas that were sensitive (n=8) or resistant (n=4) to p53 restoration. Differential gene expression analyses suggested a critical role for the TNF pathway and RARγ, an effector in the TNF pathway, in promoting response as they were up-regulated in tumors sensitive to p53 restoration. Furthermore, we demonstrate that pharmacological activation of RARγ with the synthetic retinoid, CD437, sensitizes resistant tumors to p53 restoration while additively improving outcome and survival in tumors inherently sensitive to p53 restoration

Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

Annotation Enrichment Analysis: An Alternative Method for Evaluating the Functional Properties of Gene Sets

Gene annotation databases (compendiums maintained by the scientific community that describe the biological functions performed by individual genes) are commonly used to evaluate the functional properties of experimentally derived gene sets. Overlap statistics, such as Fisher's Exact Test (FET), are often employed to assess these associations, but don't account for non-uniformity in the number of genes annotated to individual functions or the number of functions associated with individual genes. We find FET is strongly biased toward over-estimating overlap significance if a gene set has an unusually high number of annotations. To correct for these biases, we develop Annotation Enrichment Analysis (AEA), which properly accounts for the non-uniformity of annotations. We show that AEA is able to identify biologically meaningful functional enrichments that are obscured by numerous false-positive enrichment scores in FET, and we therefore suggest it be used to more accurately assess the biological properties of gene sets