Vinay Varadan

Computational Analysis Of The Synergy Among Multiple Interacting Factors

Diseases such as cancer are often related to collaborative effects involving interactions of multiple genes within complex pathways, or to combinations of multiple SNPs. To understand the structure of such mechanisms, it is helpful to analyze genes in terms of the purely cooperative, as opposed to independent, nature of their contributions towards a phenotype. Here, we present an information‐theoretic analysis that provides a quantitative measure of the multivariate synergy and decomposes sets of genes into submodules each of which contains synergistically interacting genes. When the resulting computational tools are used for the analysis of gene expression or SNP data, this systems‐based methodology provides insight into the biological mechanisms responsible for disease.

DNA methylation patterns in luminal breast cancers differ from non-luminal subtypes and can identify relapse risk independent of other clinical variables

The diversity of breast cancers reflects variations in underlying biology and affects the clinical implications for patients. Gene expression studies have identified five major subtypes– Luminal A, Luminal B, basal‐like, ErbB2+ and Normal‐Like. We set out to determine the role of DNA methylation in subtypes by performing genome‐wide scans of CpG methylation in breast cancer samples with known expression‐based subtypes. Unsupervised hierarchical clustering using a set of most varying loci clustered the tumors into a Luminal A majority (82%) cluster, Basal‐like/ErbB2+ majority (86%) cluster and a non‐specific cluster with samples that were also inconclusive in their expression‐based subtype correlations. Contributing methylation loci were both gene associated loci (30%) and non‐gene associated (70%), suggesting subtype dependant genome‐wide alterations in the methylation landscape. The methylation patterns of significant differentially methylated genes in luminal A tumors are similar to those identified in CD24 + luminal epithelial cells and the patterns in basal‐like tumors similar to CD44 + breast progenitor cells. CpG islands in the HOXA cluster and other homeobox (IRX2, DLX2, NKX2‐2) genes were significantly more methylated in Luminal A tumors. A significant number of genes (2853, p < 0.05) exhibited expression–methylation correlation, implying possible functional effects of methylation on gene expression. Furthermore, analysis of these tumors by using follow‐up survival data identified differential methylation of islands proximal to genes involved in Cell Cycle and Proliferation (Ki‐67, UBE2C, KIF2C, HDAC4), angiogenesis (VEGF, BTG1, KLF5), cell fate commitment (SPRY1, OLIG2, LHX2 and LHX5) as having prognostic value independent of subtypes and other clinical factors.

Overcoming Therapeutic Resistance in HER2-Positive Breast Cancers with CDK4/6 Inhibitors

Using transgenic mouse models, cell line-based functional studies, and clinical specimens, we show that cyclin D1/CDK4 mediate resistance to targeted therapy for HER2-positive breast cancer. This is overcome using CDK4/6 inhibitors. Inhibition of CDK4/6 not only suppresses Rb phosphorylation, but also reduces TSC2 phosphorylation and thus partially attenuates mTORC1 activity. This relieves feedback inhibition of upstream EGFR family kinases, resensitizing tumors to EGFR/HER2 blockade. Consequently, dual inhibition of EGFR/HER2 and CDK4/6 invokes a more potent suppression of TSC2 phosphorylation and hence mTORC1/S6K/S6RP activity. The suppression of both Rb and S6RP enhances G1 arrest and a phenotype resembling cellular senescence. In vivo, CDK4/6 inhibitors sensitize patient-derived xenograft tumors to HER2-targeted therapies and delay tumor recurrence in a transgenic model of HER2-positive breast cancer.

Identification of Tumor Suppressors and Oncogenes from Genomic and Epigenetic Features in Ovarian Cancer

The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes critical to the development and progression of cancer. We seek to identify those genetic and epigenetic aberrations that have the most impact on gene function within the tumor. First, we perform a bioinformatic analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We separately examined CNV and DNA methylation for 42 primary serous ovarian cancer samples using MOMA-ROMA assays and 379 tumor samples analyzed by The Cancer Genome Atlas. We have identified 346 genes with significant deletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with altered copy number and correlated changes in expression. Among these genes CCNE1, POP4, UQCRB, PHF20L1 and C19orf2 were identified within both data sets. We were specifically interested in copy number variation as our base genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We therefore identify changes in DNA methylation and expression for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic features for these modalities and perform a correlation analysis with expression. We predicted 611 potential oncogenes and tumor suppressors candidates by integrating these data types. Genes with a strong correlation for methylation dependent expression changes exhibited at varying copy number aberrations include CDCA8, ATAD2, CDKN2A, RAB25, AURKA, BOP1 and EIF2C3. We provide copy number variation and DNA methylation analysis for over 11,500 individual genes covering the genetic landscape of ovarian cancer tumors. We show the extent of genomic and epigenetic alterations for known tumor suppressors and oncogenes and also use these defined features to identify potential ovarian cancer gene candidates.

Computational inference of the molecular logic for synaptic connectivity in C. elegans

MOTIVATION The nematode C. elegans is an ideal model organism in which to investigate the biomolecular mechanisms underlying the connectivity of neurons, because synaptic connections are described in a comprehensive wiring diagram and methods for defining gene expression profiles of individual neurons are now available. RESULTS Here we present computational techniques linking these two types of information. A systems-based approach (EMBP: Entropy Minimization and Boolean Parsimony) identifies sets of synergistically interacting genes whose joint expression predicts neural connectivity. We introduce an information theoretic measure of the multivariate synergy, a fundamental concept in systems biology, connecting the members of these gene sets. We present and validate our preliminary results based on publicly available information, and demonstrate that their synergy is exceptionally high indicating joint involvement in pathways. Our strategy provides a robust methodology that will yield increasingly more accurate results as more neuron-specific gene expression data emerge. Ultimately, we expect our approach to provide important clues for universal mechanisms of neural interconnectivity.

Secure communication using a chaos based signal encryption scheme

The large-scale proliferation of wireless communications both inside and outside the home-office environment has led to an increased demand for effective and cheap encryption schemes. Now a new chaos based signal encryption scheme is proposed to transmit digital information signals by using the conventional synchronization of chaos and digital encryption approaches. In this scheme, either a chaotic or hyperchaotic system is used to generate a digital key after thresholding a chaotic signal. This signal along with the information digital signal is used to generate the encrypted signal. Then the encrypted signal is masked by one of the chaotic signals of the transmitter and is transmitted through the channel to the receiver as well as used to drive the transmitter chaotic system using the concept of self-modulation. At the receiver end, a suitable feedback loop is constructed for unmasking and then the decryption rule is used to recover the information signal. By suitable combinations of the chaotic signals, the effect of additional nonlinear-keys has also been considered. The effect of typical perturbing factors, like channel noise and parameter mismatch, are included and their corresponding performance analysis is discussed. By considering an appropriate circuit configuration, simulation results are presented.

Novel recurrently mutated genes in African American colon cancers

Significance Colorectal cancer is a leading cause of cancer-related deaths world-wide. African Americans exhibit the highest colon cancer incidence and mortality among all ethnic groups in the United States. Despite this finding, there is a dearth of knowledge on the genetic mechanisms underlying colon carcinogenesis in African Americans. We thus initiated this study to characterize the mutational landscapes of African American colon cancers. We identified new genes that are significantly mutated in colon cancer and that are highly preferentially targeted for mutations in colon cancers arising in African Americans as compared with Caucasians. These findings suggest differences in routes of colon carcinogenesis between the different ethnic groups and also may have implications for the ethnicity associated differences in tumor incidence and outcome. We used whole-exome and targeted sequencing to characterize somatic mutations in 103 colorectal cancers (CRC) from African Americans, identifying 20 new genes as significantly mutated in CRC. Resequencing 129 Caucasian derived CRCs confirmed a 15-gene set as a preferential target for mutations in African American CRCs. Two predominant genes, ephrin type A receptor 6 (EPHA6) and folliculin (FLCN), with mutations exclusive to African American CRCs, are by genetic and biological criteria highly likely African American CRC driver genes. These previously unsuspected differences in the mutational landscapes of CRCs arising among individuals of different ethnicities have potential to impact on broader disparities in cancer behaviors.

Translating next generation sequencing to practice: opportunities and necessary steps.

Next‐generation sequencing (NGS) approaches for measuring RNA and DNA benefit from greatly increased sensitivity, dynamic range and detection of novel transcripts. These technologies are rapidly becoming the standard for molecular assays and represent huge potential value to the practice of oncology. However, many challenges exist in the transition of these technologies from research application to clinical practice. This review discusses the value of NGS in detecting mutations, copy number changes and RNA quantification and their applications in oncology, the challenges for adoption and the relevant steps that are needed for translating this potential to routine practice.

A microRNA panel to discriminate carcinomas from high-grade intraepithelial neoplasms in colonoscopy biopsy tissue

Objective It is a challenge to differentiate invasive carcinomas from high-grade intraepithelial neoplasms in colonoscopy biopsy tissues. In this study, microRNA profiles were evaluated in the transformation of colorectal carcinogenesis to discover new molecular markers for identifying a carcinoma in colonoscopy biopsy tissues where the presence of stromal invasion cells is not detectable by microscopic analysis. Methods The expression of 723 human microRNAs was measured in laser capture microdissected epithelial tumours from 133 snap-frozen surgical colorectal specimens. Three well-known classification algorithms were used to derive candidate biomarkers for discriminating carcinomas from adenomas. Quantitative reverse-transcriptase PCR was then used to validate the candidates in an independent cohort of macrodissected formalin-fixed paraffin-embedded colorectal tissue samples from 91 surgical resections. The biomarkers were applied to differentiate carcinomas from high-grade intraepithelial neoplasms in 58 colonoscopy biopsy tissue samples with stromal invasion cells undetectable by microscopy. Results One classifier of 14 microRNAs was identified with a prediction accuracy of 94.1% for discriminating carcinomas from adenomas. In formalin-fixed paraffin-embedded surgical tissue samples, a combination of miR-375, miR-424 and miR-92a yielded an accuracy of 94% (AUC=0.968) in discriminating carcinomas from adenomas. This combination has been applied to differentiate carcinomas from high-grade intraepithelial neoplasms in colonoscopy biopsy tissues with an accuracy of 89% (AUC=0.918). Conclusions This study has found a microRNA panel that accurately discriminates carcinomas from high-grade intraepithelial neoplasms in colonoscopy biopsy tissues. This microRNA panel has considerable clinical value in the early diagnosis and optimal surgical decision-making of colorectal cancer.

Major chromosomal breakpoint intervals in breast cancer co-localize with differentially methylated regions.

Solid tumors exhibit chromosomal rearrangements resulting in gain or loss of multiple chromosomal loci (copy number variation, or CNV), and translocations that occasionally result in the creation of novel chimeric genes. In the case of breast cancer, although most individual tumors each have unique CNV landscape, the breakpoints, as measured over large datasets, appear to be non-randomly distributed in the genome. Breakpoints show a significant regional concentration at genomic loci spanning perhaps several megabases. The proximal cause of these breakpoint concentrations is a subject of speculation, but is, as yet, largely unknown. To shed light on this issue, we have performed a bio-statistical analysis on our previously published data for a set of 119 breast tumors and normal controls (Wiedswang et al., 2003), where each sample has both high-resolution CNV and methylation data. The method examined the distribution of closeness of breakpoint regions with differentially methylated regions (DMR), coupled with additional genomic parameters, such as repeat elements and designated “fragile sites” in the reference genome. Through this analysis, we have identified a set of 93 regional loci called breakpoint enriched DMR (BEDMRs) characterized by altered DNA methylation in cancer compared to normal cells that are associated with frequent breakpoint concentrations within a distance of 1 Mb. BEDMR loci are further associated with local hypomethylation (66%), concentrations of the Alu SINE repeats within 3 Mb (35% of the cases), and tend to occur near a number of cancer related genes such as the protocadherins, AKT1, DUB3, GAB2. Furthermore, BEDMRs seem to deregulate members of the histone gene family and chromatin remodeling factors, e.g., JMJD1B, which might affect the chromatin structure and disrupt coordinate signaling and repair. From this analysis we propose that preference for chromosomal breakpoints is related to genome structure coupled with alterations in DNA methylation and hence, chromatin structure, associated with tumorigenesis.