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Dengue virus type 2 (DENV-2) contributes substantially to the dengue burden and dengue-related mortality in the tropics and sub-tropics. DENV-2 includes six genotypes, among which cosmopolitan genotype is the most widespread. The present study investigated the evolution, intra-genotype heterogeneity and dispersal of cosmopolitan genotype to understand unique genetic characteristics that have shaped the molecular epidemiology and distribution of cosmopolitan lineages. The spatial analysis demonstrated a wide geo-distribution of cosmopolitan genotype through an extensive inter-continental network, anchored in Southeast Asia and Indian sub-continent. Intra-genotype analyses using 3367 envelope gene sequences revealed six distinct lineages within the cosmopolitan genotype, namely the Indian sub-continent lineage and five other lineages. Indian sub-continent lineage was the most diverged among six lineages and has almost reached the nucleotide divergence threshold of 6% within E gene to qualify as a separate genotype. Genome wide amino acid signatures and selection pressure analyses further suggested differences in evolutionary characteristics between the Indian sub-continent lineage and other lineages. The present study narrates a comprehensive genomic analysis of cosmopolitan genotype and presents notable genetic characteristics that occurred during its evolution and global expansion. Whether those characteristics conferred a fitness advantage to cosmopolitan genotype in different geographies warrant further investigations.

Currently, molecular markers are not used when determining the prognosis and treatment strategy for patients with hepatocellular carcinoma (HCC). In the present study, we proposed that the identification of common pro‐oncogenic pathways in primary tumors (PT) and adjacent non‐malignant tissues (AT) typically used to predict HCC patient risks may result in HCC biomarker discovery. We examined the genome‐wide mRNA expression profiles of paired PT and AT samples from 321 HCC patients. The workflow integrated differentially expressed gene selection, gene ontology enrichment, computational classification, survival predictions, image analysis and experimental validation methods. We developed a 24‐ribosomal gene‐based HCC classifier (RGC), which is prognostically significant in both PT and AT. The RGC gene overexpression in PT was associated with a poor prognosis in the training (hazard ratio = 8.2, P = 9.4 × 10−6) and cross‐cohort validation (hazard ratio = 2.63, P = 0.004) datasets. The multivariate survival analysis demonstrated the significant and independent prognostic value of the RGC. The RGC displayed a significant prognostic value in AT of the training (hazard ratio = 5.0, P = 0.03) and cross‐validation (hazard ratio = 1.9, P = 0.03) HCC groups, confirming the accuracy and robustness of the RGC. Our experimental and bioinformatics analyses suggested a key role for c‐MYC in the pro‐oncogenic pattern of ribosomal biogenesis co‐regulation in PT and AT. Microarray, quantitative RT‐PCR and quantitative immunohistochemical studies of the PT showed that DKK1 in PT is the perspective biomarker for poor HCC outcomes. The common co‐transcriptional pattern of ribosome biogenesis genes in PT and AT from HCC patients suggests a new scalable prognostic system, as supported by the model of tumor‐like metabolic redirection/assimilation in non‐malignant AT. The RGC, comprising 24 ribosomal genes, is introduced as a robust and reproducible prognostic model for stratifying HCC patient risks. The adjacent non‐malignant liver tissue alone, or in combination with HCC tissue biopsy, could be an important target for developing predictive and monitoring strategies, as well as evidence‐based therapeutic interventions, that aim to reduce the risk of post‐surgery relapse in HCC patients.

Invasive ductal carcinoma (IDC) is a major histo-morphologic type of breast cancer. Histological grading (HG) of IDC is widely adopted by oncologists as a prognostic factor. However, HG evaluation is highly subjective with only 50%–85% inter-observer agreements. Specifically, the subjectivity in the assignment of the intermediate grade (histologic grade 2, HG2) breast cancers (comprising ~50% of IDC cases) results in uncertain disease outcome prediction and sub-optimal systemic therapy. Despite several attempts to identify the mechanisms underlying the HG classification, their molecular bases are poorly understood. We performed integrative bioinformatics analysis of TCGA and several other cohorts (total 1246 patients). We identified a 22-gene tumor aggressiveness grading classifier (22g-TAG) that reflects global bifurcation in the IDC transcriptomes and reclassified patients with HG2 tumors into two genetically and clinically distinct subclasses: histological grade 1-like (HG1-like) and histological grade 3-like (HG3-like). The expression profiles and clinical outcomes of these subclasses were similar to the HG1 and HG3 tumors, respectively. We further reclassified IDC into low genetic grade (LGG = HG1+HG1-like) and high genetic grade (HGG = HG3-like+HG3) subclasses. For the HG1-like and HG3-like IDCs we found subclass-specific DNA alterations, somatic mutations, oncogenic pathways, cell cycle/mitosis and stem cell-like expression signatures that discriminate between these tumors. We found similar molecular patterns in the LGG and HGG tumor classes respectively. Our results suggest the existence of two genetically-predefined IDC classes, LGG and HGG, driven by distinct oncogenic pathways. They provide novel prognostic and therapeutic biomarkers and could open unique opportunities for personalized systemic therapies of IDC patients. ASTAR (Agency for Sci., Tech. and Research, S’pore) Published version

This study aimed to clarify genetic and epigenetic alterations that occur during lung carcinogenesis and to design perspective sets of newly identified biomarkers. The original method includes chromosome 3 specific NotI-microarrays containing 180 NotI clones associated with genes for hybridization with 40 paired normal/tumor DNA samples of primary lung tumors: 28 squamous cell carcinomas (SCC) and 12 adenocarcinomas (ADC). The NotI-microarray data were confirmed by qPCR and bisulfite sequencing analyses. Forty-four genes showed methylation and/or deletions in more than 15% of non-small cell lung cancer (NSCLC) samples. In general, SCC samples were more frequently methylated/deleted than ADC. Moreover, the SCC alterations were observed already at stage I of tumor development, whereas in ADC many genes showed tumor progression specific methylation/deletions. Among genes frequently methylated/deleted in NSCLC, only a few were already known tumor suppressor genes: RBSP3 (CTDSPL), VHL and THRB. The RPL32, LOC285205, FGD5 and other genes were previously not shown to be involved in lung carcinogenesis. Ten methylated genes, i.e., IQSEC1, RBSP3, ITGA 9, FOXP1, LRRN1, GNAI2, VHL, FGD5, ALDH1L1 and BCL6 were tested for expression by qPCR and were found downregulated in the majority of cases. Three genes (RBSP3, FBLN2 and ITGA9) demonstrated strong cell growth inhibition activity. A comprehensive statistical analysis suggested the set of 19 gene markers, ANKRD28, BHLHE40, CGGBP1, RBSP3, EPHB1, FGD5, FOXP1, GORASP1/TTC21, IQSEC1, ITGA9, LOC285375, LRRC3B, LRRN1, MITF, NKIRAS1/RPL15, TRH, UBE2E2, VHL, WNT7A, to allow early detection, tumor progression, metastases and to discriminate between SCC and ADC with sensitivity and specificity of 80-100%.

Background Lung cancer is the leading cause of cancer deaths in the world. The most common type of lung cancer is lung adenocarcinoma (AC). The genetic mechanisms of the early stages and lung AC progression steps are poorly understood. There is currently no clinically applicable gene test for the early diagnosis and AC aggressiveness. Among the major reasons for the lack of reliable diagnostic biomarkers are the extraordinary heterogeneity of the cancer cells, complex and poorly understudied interactions of the AC cells with adjacent tissue and immune system, gene variation across patient cohorts, measurement variability, small sample sizes and sub-optimal analytical methods. We suggest that gene expression profiling of the primary tumours and adjacent tissues (PT-AT) handled with a rational statistical and bioinformatics strategy of biomarker prediction and validation could provide significant progress in the identification of clinical biomarkers of AC. To minimise sample-to-sample variability, repeated multivariate measurements in the same object (organ or tissue, e.g. PT-AT in lung) across patients should be designed, but prediction and validation on the genome scale with small sample size is a great methodical challenge. Results To analyse PT-AT relationships efficiently in the statistical modelling, we propose an Extreme Class Discrimination (ECD) feature selection method that identifies a sub-set of the most discriminative variables (e.g. expressed genes). Our method consists of a paired Cross-normalization (CN) step followed by a modified sign Wilcoxon test with multivariate adjustment carried out for each variable. Using an Affymetrix U133A microarray paired dataset of 27 AC patients, we reviewed the global reprogramming of the transcriptome in human lung AC tissue versus normal lung tissue, which is associated with about 2,300 genes discriminating the tissues with 100% accuracy. Cluster analysis applied to these genes resulted in four distinct gene groups which we classified as associated with (i) up-regulated genes in the mitotic cell cycle lung AC, (ii) silenced/suppressed gene specific for normal lung tissue, (iii) cell communication and cell motility and (iv) the immune system features. The genes related to mutagenesis, specific lung cancers, early stage of AC development, tumour aggressiveness and metabolic pathway alterations and adaptations of cancer cells are strongly enriched in the AC PT-AT discriminative gene set. Two AC diagnostic biomarkers SPP1 and CENPA were successfully validated on RT-RCR tissue array. ECD method was systematically compared to several alternative methods and proved to be of better performance and as well as it was validated by comparison of the predicted gene set with literature meta-signature. Conclusions We developed a method that identifies and selects highly discriminative variables from high dimensional data spaces of potential biomarkers based on a statistical analysis of paired samples when the number of samples is small. This method provides superior selection in comparison to conventional methods and can be widely used in different applications. Our method revealed at least 23 hundreds patho-biologically essential genes associated with the global transcriptional reprogramming of human lung epithelium cells and lung AC aggressiveness. This gene set includes many previously published AC biomarkers reflecting inherent disease complexity and specifies the mechanisms of carcinogenesis in the lung AC. SPP1, CENPA and many other PT-AT discriminative genes could be considered as the prospective diagnostic and prognostic biomarkers of lung AC.

Chromosome 3-specific NotI microarray (NMA) containing 180 clones with 188 genes was used in the study to analyze 18 high grade serous ovarian cancer (HGSOC) samples and 7 benign ovarian tumors. We aimed to find novel methylation-dependent biomarkers for early detection and prognosis of HGSOC. Thirty five NotI markers showed frequency of methylation/deletion more or equal to 17%. To check the results of NMA hybridizations several samples for four genes (LRRC3B, THRB, ITGA9 and RBSP3 (CTDSPL)) were bisulfite sequenced and confirmed the results of NMA hybridization. A set of eight biomarkers: NKIRAS1/RPL15, THRB, RBPS3 (CTDSPL), IQSEC1, NBEAL2, ZIC4, LOC285205 and FOXP1, was identified as the most prominent set capable to detect both early and late stages of ovarian cancer. Sensitivity of this set is equal to (72 +/- 11)% and specificity (94 +/- 5)%. Early stages represented the most complicated cases for detection. To distinguish between Stages I + II and Stages III + IV of ovarian cancer the most perspective set of biomarkers would include LOC285205, CGGBP1, EPHB1 and NKIRAS1/RPL15. The sensitivity of the set is equal to (80 +/- 13)% and the specificity is (88 +/- 12)%. Using this technique we plan to validate this panel with new epithelial ovarian cancer samples and add markers from other chromosomes. Funding Agencies|Swedish Cancer Society||Swedish Institute||Swedish Research Council||Karolinska Institute||Russian Ministry of Education and Science|02.740.11.522716.552.11.7034|Russian Foundation for Basic Research|10-04-01213-a11-04-00269

The ecotropic virus integration site 1(Evi1) transcription factor, encoded in the MECOM complex locus, is implicated in several cancers, including ovarian cancer(OC). High-grade serous OC(HG-SOC) represents its most common and aggressive form. The pathobiological and clinical roles of MECOM and its products in HG-SOC are poorly understood. Here, across all HG-SOC stages and grades, our meta-analysis demonstrated MECOM amplification in about 80% and EVI1 overexpression in 100% HG-SOC samples, relative to normal controls. We observed that Evi1 directly promotes double-strand DNA breaks repair via binding to DNA recombination/repair protein complexes, including a novel Rad50 isoform. To elucidate the molecular basis and clinical significance of these findings, we studied genome-wide genome-wide Evi1-DNA binding, DNA copy number alterations, gene expression and proteome-scale Evi1 interactions, combining cell line and clinical meta-data and network/pathway analyses. Our experiments revealed major, previously unknown Evi1-binding DNA motifs specifying expression of 309 target genes, whose protein products were classified into eight functional and prognosis-significant modules implicated in embryogenesis, EMT, RNA metabolism, cancer cell survival, retinoic acid, anti-viral and therapeutic responses. The Evi1 pathway modules can collectively stratify the HG-SOC patients into four high-confidence post-surgery survival subgroups(P Starting from early stages of HG-SOC, MECOM/Evi1 amplifies genomic instability and activates the Evi1 pathway that promotes most cancer hallmarks. Thus, MECOM/Evi1 and the Evi1 pathway provide mechanistic means for HG-SOC re-classification, introduces high-confidence early diagnostic and prognostic biomarkers and perspective therapeutic targets. Citation Format: Arsen O. Batagov, Surya Pavan Yenamandra, Piroon Jenjaroenpun, Anna V. Ivshina, Vladimir A. Kuznetsov. The key role of MECOM complex locus and its products in high-grade ovarian carcinoma pathogenesis and clinical outcomes. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1990. doi:10.1158/1538-7445.AM2015-1990

Background Many different genetic alterations are observed in cancer cells. Individual cancer genes display point mutations such as base changes, insertions and deletions that initiate and promote cancer growth and spread. Somatic hypermutation is a powerful mechanism for generation of different mutations. It was shown previously that somatic hypermutability of proto-oncogenes can induce development of lymphomas. Methodology/Principal Findings We found an exceptionally high incidence of single-base mutations in the tumor suppressor genes RASSF1 and RBSP3 (CTDSPL) both located in 3p21.3 regions, LUCA and AP20 respectively. These regions contain clusters of tumor suppressor genes involved in multiple cancer types such as lung, kidney, breast, cervical, head and neck, nasopharyngeal, prostate and other carcinomas. Altogether in 144 sequenced RASSF1A clones (exons 1–2), 129 mutations were detected (mutation frequency, MF = 0.23 per 100 bp) and in 98 clones of exons 3–5 we found 146 mutations (MF = 0.29). In 85 sequenced RBSP3 clones, 89 mutations were found (MF = 0.10). The mutations were not cytidine-specific, as would be expected from alterations generated by AID/APOBEC family enzymes, and appeared de novo during cell proliferation. They diminished the ability of corresponding transgenes to suppress cell and tumor growth implying a loss of function. These high levels of somatic mutations were found both in cancer biopsies and cancer cell lines. Conclusions/Significance This is the first report of high frequencies of somatic mutations in RASSF1 and RBSP3 in different cancers suggesting it may underlay the mutator phenotype of cancer. Somatic hypermutations in tumor suppressor genes involved in major human malignancies offer a novel insight in cancer development, progression and spread.