Surya Pavan Yenamandra

A robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinoma

BackgroundLung 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.ResultsTo 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.ConclusionsWe 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.

QmRLFS-finder: a model, web server and stand-alone tool for prediction and analysis of R-loop forming sequences

The possible formation of three-stranded RNA and DNA hybrid structures (R-loops) in thousands of functionally important guanine-rich genic and inter-genic regions could suggest their involvement in transcriptional regulation and even development of diseases. Here, we introduce the first freely available R-loop prediction program called Quantitative Model of R-loop Forming Sequence (RLFS) finder (QmRLFS-finder), which predicts RLFSs in nucleic acid sequences based on experimentally supported structural models of RLFSs. QmRLFS-finder operates via a web server or a stand-alone command line tool. This tool identifies and visualizes RLFS coordinates from any natural or artificial DNA or RNA input sequences and creates standards-compliant output files for further annotation and analysis. QmRLFS-finder demonstrates highly accurate predictions of the detected RLFSs, proposing new perspective to further discoveries in R-loop biology, biotechnology and molecular therapy. QmRLFS-finder is freely available at http://rloop.bii.a-star.edu.sg/?pg=qmrlfs-finder.

Sense-antisense gene-pairs in breast cancer and associated pathological pathways

More than 30% of human protein-coding genes form hereditary complex genome architectures composed of sense-antisense (SA) gene pairs (SAGPs) transcribing their RNAs from both strands of a given locus. Such architectures represent important novel components of genome complexity contributing to gene expression deregulation in cancer cells. Therefore, the architectures might be involved in cancer pathways and, in turn, be used for novel drug targets discovery. However, the global roles of SAGPs in cancer pathways has not been studied. Here we investigated SAGPs associated with breast cancer (BC)-related pathways using systems biology, prognostic survival and experimental methods. Gene expression analysis identified 73 BC-relevant SAGPs that are highly correlated in BC. Survival modelling and metadata analysis of the 1161 BC patients allowed us to develop a novel patient prognostic grouping method selecting the 12 survival-significant SAGPs. The qRT-PCR-validated 12-SAGP prognostic signature reproducibly stratified BC patients into low- and high-risk prognostic subgroups. The 1381 SAGP-defined differentially expressed genes common across three studied cohorts were identified. The functional enrichment analysis of these genes revealed the GABPA gene network, including BC-relevant SAGPs, specific gene sets involved in cell cycle, spliceosomal and proteasomal pathways. The co-regulatory function of GABPA in BC cells was supported using siRNA knockdown studies. Thus, we demonstrated SAGPs as the synergistically functional genome architectures interconnected with cancer-related pathways and associated with BC patient clinical outcomes. Taken together, SAGPs represent an important component of genome complexity which can be used to identify novel aspects of coordinated pathological gene networks in cancers.

Tumor‐adjacent tissue co‐expression profile analysis reveals pro‐oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma

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.

Genome and transcriptome delineation of two major oncogenic pathways governing invasive ductal breast cancer development

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.

Toward predictive R-loop computational biology: genome-scale prediction of R-loops reveals their association with complex promoter structures, G-quadruplexes and transcriptionally active enhancers

Abstract R-loops are three-stranded RNA:DNA hybrid structures essential for many normal and pathobiological processes. Previously, we generated a quantitative R-loop forming sequence (RLFS) model, quantitative model of R-loop-forming sequences (QmRLFS) and predicted ∼660 000 RLFSs; most of them located in genes and gene-flanking regions, G-rich regions and disease-associated genomic loci in the human genome. Here, we conducted a comprehensive comparative analysis of these RLFSs using experimental data and demonstrated the high performance of QmRLFS predictions on the nucleotide and genome scales. The preferential co-localization of RLFS with promoters, U1 splice sites, gene ends, enhancers and non-B DNA structures, such as G-quadruplexes, provides evidence for the mechanical linkage between DNA tertiary structures, transcription initiation and R-loops in critical regulatory genome regions. We introduced and characterized an abundant class of reverse-forward RLFS clusters highly enriched in non-B DNA structures, which localized to promoters, gene ends and enhancers. The RLFS co-localization with promoters and transcriptionally active enhancers suggested new models for in cis and in trans regulation by RNA:DNA hybrids of transcription initiation and formation of 3D-chromatin loops. Overall, this study provides a rationale for the discovery and characterization of the non-B DNA regulatory structures involved in the formation of the RNA:DNA interactome as the basis for an emerging quantitative R-loop biology and pathobiology.

Abstract 1990: The key role of MECOM complex locus and its products in high-grade ovarian carcinoma pathogenesis and clinical outcomes

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