Prevalent mutations in prostate cancer

Quantitative and structural genetic alterations cause the development and progression of prostate cancer. A number of genes have been implicated in prostate cancer by genetic alterations and functional consequences of the genetic alterations. These include the ELAC2 (HPC2), MSR1, and RNASEL (HPC1) genes that have germline mutations in familial prostate cancer; AR, ATBF1, EPHB2 (ERK), KLF6, mitochondria DNA, p53, PTEN, and RAS that have somatic mutations in sporadic prostate cancer; AR, BRCA1, BRCA2, CHEK2 (RAD53), CYP17, CYP1B1, CYP3A4, GSTM1, GSTP1, GSTT1, PON1, SRD5A2, and VDR that have germline genetic variants associated with either hereditary and/or sporadic prostate cancer; and ANXA7 (ANX7), KLF5, NKX3-1 (NKX3.1), CDKN1B (p27), and MYC that have genomic copy number changes affecting gene function. More genes relevant to prostate cancer remain to be identified in each of these gene groups. For the genes that have been identified, most need additional genetic, functional, and/or biochemical examination. Identification and characterization of these genes will be a key step for improving the detection and treatment of prostate cancer.     

Cancer type‐dependent genetic interactions between cancer driver alterations indicate plasticity of epistasis across cell types

Cancers, like many diseases, are normally caused by combinations of genetic alterations rather than by changes affecting single genes. It is well established that the genetic alterations that drive cancer often interact epistatically, having greater or weaker consequences in combination than expected from their individual effects. In a stringent statistical analysis of data from > 3,000 tumors, we find that the co‐occurrence and mutual exclusivity relationships between cancer driver alterations change quite extensively in different types of cancer. This cannot be accounted for by variation in tumor heterogeneity or unrecognized cancer subtypes. Rather, it suggests that how genomic alterations interact cooperatively or partially redundantly to driver cancer changes in different types of cancers. This re‐wiring of epistasis across cell types is likely to be a basic feature of genetic architecture, with important implications for understanding the evolution of multicellularity and human genetic diseases. In addition, if this plasticity of epistasis across cell types is also true for synthetic lethal interactions, a synthetic lethal strategy to kill cancer cells may frequently work in one type of cancer but prove ineffective in another.     

Association of mutation and hypermethylation of p21 gene with susceptibility to breast cancer: a study from north India

p21 gene located at chromosome 6p21.2 is a possible tumour suppressor gene involved in the pathogenesis of breast cancer. Both genetic and epigenetic alterations in p21 have been implicated in breast carcinoma. In the present study, our main aim was to study the impact of these two kinds of alterations of p21 gene in Indian female breast cancer patients. A total of 150 female breast cancer patients of north India were screened by PCR-SSCP followed by direct sequencing and methylation specific PCR. Mutational screening of p21 gene revealed significant amount of mutations [32.66 % (49/150)] in exon 2, whereas p21 promoter was found hypermethylated in 42 of 150 (28 %) breast cancer patients in our population. The intriguing feature of the study was the G>T transition (GAG>TAG) at codon 107 and the A>C transition (AGC>CGC) at codon 146 possibly rendering p21 completely ineffective in its anti- proliferative activity. Our results suggest a significant association between the mutational and hypermethylation profile of p21 gene. Therefore, we show for the first time that the significant association of p21 mutation and hypermethylation leads to the complete inactivation of p21 gene in Indian female breast cancer patients. Complete silencing of the p21 gene seems to be the result not only of genetic alterations but also of epigenetic modification.     

An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes

Background

Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play “driver” roles in tumorigenesis, whereas others are only “passengers”.

Results

Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene expression data to identify driver methylation aberrations of cancer. Applying this approach to breast cancer data, we identified many novel cancer driver genes and found that some of the identified driver genes were subtype-specific for basal-like, luminal-A and HER2+ subtypes of breast cancer.

Conclusion

The proposed framework proved effective in identifying cancer driver genes from genome-wide gene methylation and expression data of cancer. These results may provide new molecular targets for potential targeted and selective epigenetic therapy.     

Genomewide landscape of gene–metabolome associations in Escherichia coli

Metabolism is one of the best‐understood cellular processes whose network topology of enzymatic reactions is determined by an organism's genome. The influence of genes on metabolite levels, however, remains largely unknown, particularly for the many genes encoding non‐enzymatic proteins. Serendipitously, genomewide association studies explore the relationship between genetic variants and metabolite levels, but a comprehensive interaction network has remained elusive even for the simplest single‐celled organisms. Here, we systematically mapped the association between > 3,800 single‐gene deletions in the bacterium Escherichia coli and relative concentrations of > 7,000 intracellular metabolite ions. Beyond expected metabolic changes in the proximity to abolished enzyme activities, the association map reveals a largely unknown landscape of gene–metabolite interactions that are not represented in metabolic models. Therefore, the map provides a unique resource for assessing the genetic basis of metabolic changes and conversely hypothesizing metabolic consequences of genetic alterations. We illustrate this by predicting metabolism‐related functions of 72 so far not annotated genes and by identifying key genes mediating the cellular response to environmental perturbations.     

Pan‐cancer RNA‐seq data stratifies tumours by some hallmarks of cancer

Numerous genetic and epigenetic alterations cause functional changes in cell biology underlying cancer. These hallmark functional changes constitute potentially tissue‐independent anticancer therapeutic targets. We hypothesized that RNA‐Seq identifies gene expression changes that underly those hallmarks, and thereby defines relevant therapeutic targets. To test this hypothesis, we analysed the publicly available TCGA‐TARGET‐GTEx gene expression data set from the University of California Santa CruzToil recompute project using WGCNA to delineate co‐correlated ‘modules’ from tumour gene expression profiles and functional enrichment of these modules to hierarchically cluster tumours. This stratified tumours according to T cell activation, NK‐cell activation, complement cascade, ATM, Rb, angiogenic, MAPK, ECM receptor and histone modification signalling. These correspond to the cancer hallmarks of avoiding immune destruction, tumour‐promoting inflammation, evading growth suppressors, inducing angiogenesis, sustained proliferative signalling, activating invasion and metastasis, and genome instability and mutation. This approach did not detect pathways corresponding to the cancer enabling replicative immortality, resisting cell death or deregulating cellular energetics hallmarks. We conclude that RNA‐Seq stratifies tumours along some, but not all, hallmarks of cancer and, therefore, could be used in conjunction with other analyses collectively to inform precision therapy.     

Mutational analysis of <Emphasis Type="Italic">CDKN2A</Emphasis> gene in a group of 390 larynx cancer patients

CDKN2A gene belongs to the genes involved in cell cycle regulation. When is absent or inactivated by mutation or promoter hypermethylation a cell may undertake an uncontrolled proliferation. Inactivation of CDKN2A gene is observed in many human malignancies, including larynx cancer. In this study we investigated mutations in exon 1 and exon 2 of CDKN2A gene in a large group of 390 laryngeal cancers. We found 40 different alterations (17%) and nearly half of them was not described previously. Out of these alterations two transversions in codon 108: c.322G>C (Asp108His) and c.322G>T (Asp108Tyr) as well as a G>A transition in codon 110 (Trp110X) were found more frequently (altogether: 7 cases in codon 108 and 10 cases in codon 110). This result, concerning the location of these codons in the ankyrin repeat structures, may suggest that these two codons may be critical hot-spots in larynx carcinogenesis.     

Drug Repositioning through Systematic Mining of Gene Coexpression Networks in Cancer

Gene coexpression network analysis is a powerful “data-driven” approach essential for understanding cancer biology and mechanisms of tumor development. Yet, despite the completion of thousands of studies on cancer gene expression, there have been few attempts to normalize and integrate co-expression data from scattered sources in a concise “meta-analysis” framework. We generated such a resource by exploring gene coexpression networks in 82 microarray datasets from 9 major human cancer types. The analysis was conducted using an elaborate weighted gene coexpression network (WGCNA) methodology and identified over 3,000 robust gene coexpression modules. The modules covered a range of known tumor features, such as proliferation, extracellular matrix remodeling, hypoxia, inflammation, angiogenesis, tumor differentiation programs, specific signaling pathways, genomic alterations, and biomarkers of individual tumor subtypes. To prioritize genes with respect to those tumor features, we ranked genes within each module by connectivity, leading to identification of module-specific functionally prominent hub genes. To showcase the utility of this network information, we positioned known cancer drug targets within the coexpression networks and predicted that Anakinra, an anti-rheumatoid therapeutic agent, may be promising for development in colorectal cancer. We offer a comprehensive, normalized and well documented collection of >3000 gene coexpression modules in a variety of cancers as a rich data resource to facilitate further progress in cancer research.     

Identification of cancer hallmark‐associated gene and lncRNA cooperative regulation pairs and dictate lncRNA roles in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is the most common malignant tumour in the oral and maxillofacial region. Numerous cancers share ten common traits (“hallmarks”) that govern the transformation of normal cells into cancer cells. Long non‐coding RNAs (lncRNAs) are important factors that contribute to tumorigenesis. However, very little is known about the cooperative relationships between lncRNAs and cancer hallmark‐associated genes in OSCC. Through integrative analysis of cancer hallmarks, somatic mutations, copy number variants (CNVs) and expression, some OSCC‐specific cancer hallmark‐associated genes and lncRNAs are identified. A computational framework to identify gene and lncRNA cooperative regulation pairs (GLCRPs) associated with different cancer hallmarks is developed based on the co‐expression and co‐occurrence of mutations. The distinct and common features of ten cancer hallmarks based on GLCRPs are characterized in OSCC. Cancer hallmark insensitivity to antigrowth signals and self‐sufficiency in growth signals are shared by most GLCRPs in OSCC. Some key GLCRPs participate in many cancer hallmarks in OSCC. Cancer hallmark‐associated GLCRP networks have complex patterns and specific functions in OSCC. Specially, some key GLCRPs are associated with the prognosis of OSCC patients. In summary, we generate a comprehensive landscape of cancer hallmark‐associated GLCRPs that can act as a starting point for future functional explorations, the identification of biomarkers and lncRNA‐based targeted therapy in OSCC.     

Ferredoxin reductase affects p53-dependent, 5-fluorouracil-induced apoptosis in colorectal cancer cells

Loss of p53 gene function, which occurs in most colon cancer cells, has been shown to abolish the apoptotic response to 5-fluorouracil (5-FU). To identify genes downstream of p53 that might mediate these effects, we assessed global patterns of gene expression following 5-FU treatment of isogenic cells differing only in their p53 status. The gene encoding mitochondrial ferredoxin reductase (protein, FR; gene, FDXR) was one of the few genes significantly induced by p53 after 5-FU treatment. The FR protein was localized to mitochondria and suppressed the growth of colon cancer cells when over-expressed. Targeted disruption of the FDXR gene in human colon cancer cells showed that it was essential for viability, and partial disruption of the gene resulted in decreased sensitivity to 5-FU-induced apoptosis. These data, coupled with the effects of pharmacologic inhibitors of reactive oxygen species, indicate that FR contributes to p53-mediated apoptosis through the generation of oxidative stress in mitochondria.     

Not all cancers are created equal: Tissue specificity in cancer genes and pathways

Tumors arise through waves of genetic alterations and clonal expansion that allow tumor cells to acquire cancer hallmarks, such as genome instability and immune evasion. Recent genomic analyses showed that the vast majority of cancer driver genes are mutated in a tissue-dependent manner, that is, are altered in some cancers but not others. Often the tumor type also affects the likelihood of therapy response. What is the origin of tissue specificity in cancer? Recent studies suggest that both cell-intrinsic and cell-extrinsic factors play a role. On one hand, cell type–specific wiring of the cell signaling network determines the outcome of cancer driver gene mutations. On the other hand, the tumor cells’ exposure to tissue-specific microenvironments (e.g. immune cells) also contributes to shape the tissue specificity of driver genes and of therapy response. In the future, a more complete understanding of tissue specificity in cancer may inform methods to better predict and improve therapeutic outcomes.     

UBC and YWHAZ as suitable reference genes for accurate normalisation of gene expression using MCF7, HCT116 and HepG2 cell lines

Relative quantification of in vitro gene expression using real-time PCR requires stably expressed reference gene for normalisation. In this study, total RNA from MCF7, HCT116 and HepG2 cells were extracted and converted to cDNA using commercially available kit, and real-time PCR was then performed to analyse the expression levels of twelve reference genes to select the most ideal reference gene for accurate normalisation in gene expression study. geNorm and NormFinder software were used to analyse the stabilities of the reference genes, which showed a wide range of C_t values. The geNorm analysis showed the following ranking for stability of genes: UBC, YWHAZ > RPLP > TBP > ACTB > HPRT1 > PPIA > GAPDH > GUSB > B2M > TUBB > RRN18S. A similar ranking of reference genes was obtained by NormFinder, and the four most stable reference genes were identical using both approaches. UBC and YWHAZ were proposed to be the two most suitable reference genes based on the above analyses. To further assess the stabilities of the UBC and YWHAZ in a formal experiment, MCF7, HCT116 and HepG2 cell lines were subjected to treatments with 5-aza-dC and TSA. Both UBC and YWHAZ exhibited stable expression levels across control and treatment groups. Therefore, we propose that UBC and YWHAZ are the two most suitable reference genes for our gene expression studies using MCF7, HCT116 and HepG2 cell lines.     

Vitrification leads to transcriptomic modifications of mice ovaries that do not affect folliculogenesis progression

Ovarian tissue cryopreservation is emerging as a promising alternative for fertility preservation of cancer survivors. To date, more than a hundred couples have successfully had babies using this procedure, although it is still considered experimental and demands further investigation. In this work, we evaluated the effects of vitrification, warming and autotransplantation procedures on the morphology and gene expression of murine ovaries. Ovaries were removed from adult female C57BL6 mice (n = 15), vitrified, warmed and autotransplanted (vitrified group), additionally, ovaries were autotransplanted without vitrification (control group, n = 15). After twenty days, grafted ovaries were harvested and used for histological and ultrastructural analysis, germinal vesicle (GV) oocyte collection, RNA sequencing, and Transmission Electron Microscopy (TEM). All classes of follicles and GV were observed in both control and vitrified/warmed transplanted ovaries, and the numbers of primordial, antral and atretic follicles were not different (p > 0.05). Using RNA-seq, we detected 16,602 vs 13,527 expressed genes in vitrified and control ovaries, respectively; and 623 significantly dysregulated genes (fold change >1.5; 332 up-regulated and 291 down-regulated). Cellular membranes, cytoskeletons, and extracellular matrices were found as the main functions of the differentially expressed genes. Moreover, vitrified samples also presented ultrastructural alterations in the cytoskeleton, cell junctions, and endoplasmic reticulum. Taken together, this work showed for the first time that ovarian cells might trigger a compensatory gene regulation mechanism to maintain cellular structure and folliculogenesis progression after vitrification and autotransplantation.