Sitharthan Kamalakaran

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.

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.

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.

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.

Effective normalization for copy number variation detection from whole genome sequencing

BackgroundWhole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations.MethodsWe apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions.ResultsThe GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls.ConclusionsChoice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.

Loss of DOK2 induces carboplatin resistance in ovarian cancer via suppression of apoptosis

OBJECTIVE Ovarian cancers are highly heterogeneous and while chemotherapy is the preferred treatment many patients are intrinsically resistant or quickly develop resistance. Furthermore, all tumors that recur ultimately become resistant. Recent evidence suggests that epigenetic deregulation may be a key factor in the onset and maintenance of chemoresistance. We set out to identify epigenetically silenced genes that affect chemoresistance. METHODS The epigenomes of a total of 45 ovarian samples were analyzed to identify epigenetically altered genes that segregate with platinum response, and further filtered with expression data to identify genes that were suppressed. A tissue culture carboplatin resistance screen was utilized to functionally validate this set of candidate platinum resistance genes. RESULTS Our screen correctly identified 19 genes that when suppressed altered the chemoresistance of the cells in culture. Of the genes identified in the screen we further characterized one gene, docking protein 2 (DOK2), an adapter protein downstream of tyrosine kinase, to determine if we could elucidate the mechanism by which it increased resistance. The loss of DOK2 decreased the level of apoptosis in response to carboplatin. Furthermore, in cells with reduced DOK2, the level of anoikis was decreased. CONCLUSIONS We have developed a screening methodology that analyzes the epigenome and informatically identifies candidate genes followed by in vitro culture screening of the candidate genes. To validate our screening methodology we further characterized one candidate gene, DOK2, and showed that loss of DOK2 induces chemotherapy resistance by decreasing the level of apoptosis in response to treatment.

Methylation detection oligonucleotide microarray analysis: a high-resolution method for detection of CpG island methylation

Methylation of CpG islands associated with genes can affect the expression of the proximal gene, and methylation of non-associated CpG islands correlates to genomic instability. This epigenetic modification has been shown to be important in many pathologies, from development and disease to cancer. We report the development of a novel high-resolution microarray that detects the methylation status of over 25 000 CpG islands in the human genome. Experiments were performed to demonstrate low system noise in the methodology and that the array probes have a high signal to noise ratio. Methylation measurements between different cell lines were validated demonstrating the accuracy of measurement. We then identified alterations in CpG islands, both those associated with gene promoters, as well as non-promoter-associated islands in a set of breast and ovarian tumors. We demonstrate that this methodology accurately identifies methylation profiles in cancer and in principle it can differentiate any CpG methylation alterations and can be adapted to analyze other species.

PAPAyA: a platform for breast cancer biomarker signature discovery, evaluation and assessment

BackgroundThe decision environment for cancer care is becoming increasingly complex due to the discovery and development of novel genomic tests that offer information regarding therapy response, prognosis and monitoring, in addition to traditional histopathology. There is, therefore, a need for translational clinical tools based on molecular bioinformatics, particularly in current cancer care, that can acquire, analyze the data, and interpret and present information from multiple diagnostic modalities to help the clinician make effective decisions.ResultsWe present a platform for molecular signature discovery and clinical decision support that relies on genomic and epigenomic measurement modalities as well as clinical parameters such as histopathological results and survival information. Our P hysician A ccessible P reclinical A naly tics A pplication (PAPAyA) integrates a powerful set of statistical and machine learning tools that leverage the connections among the different modalities. It is easily extendable and reconfigurable to support integration of existing research methods and tools into powerful data analysis and interpretation pipelines. A current configuration of PAPAyA with examples of its performance on breast cancer molecular profiles is used to present the platform in action.ConclusionPAPAyA enables analysis of data from (pre)clinical studies, formulation of new clinical hypotheses, and facilitates clinical decision support by abstracting molecular profiles for clinicians.

Identification of a Novel Clone, ST736, among Enterococcus faecium Clinical Isolates and Its Association with Daptomycin Nonsusceptibility

ABSTRACT Resistance to daptomycin in enterococcal clinical isolates remains rare but is being increasingly reported in the United States and worldwide. There are limited data on the genetic relatedness and microbiological and clinical characteristics of daptomycin-nonsusceptible enterococcal clinical isolates. In this study, we assessed the population genetics of daptomycin-nonsusceptible Enterococcus faecium (DNSE) clinical isolates by multilocus sequence typing (MLST) and whole-genome sequencing analysis. Forty-two nonduplicate DNSE isolates and 43 randomly selected daptomycin-susceptible E. faecium isolates were included in the analysis. All E. faecium isolates were recovered from patients at a tertiary care medical center in suburban New York City from May 2009 through December 2013. The daptomycin MICs of the DNSE isolates ranged from 6 to >256 μg/ml. Three major clones of E. faecium (ST18, ST412, and ST736) were identified among these clinical isolates by MLST and whole-genome sequence-based analysis. A newly recognized clone, ST736, was seen in 32 of 42 (76.2%) DNSE isolates and in only 14 of 43 (32.6%) daptomycin-susceptible E. faecium isolates (P < 0.0001). This report provides evidence of the association between E. faecium clone ST736 and daptomycin nonsusceptibility. The identification and potential spread of this novel E. faecium clone and its association with daptomycin nonsusceptibility constitute a challenge for patient management and infection control at our medical center.

Brief‐exposure to preoperative bevacizumab reveals a TGF‐β signature predictive of response in HER2‐negative breast cancers

To best define biomarkers of response, and to shed insight on mechanism of action of certain clinically important agents for early breast cancer, we used a brief‐exposure paradigm in the preoperative setting to study transcriptional changes in patient tumors that occur with one dose of therapy prior to combination chemotherapy. Tumor biopsies from breast cancer patients enrolled in two preoperative clinical trials were obtained at baseline and after one dose of bevacizumab (HER2‐negative), trastuzumab (HER2‐positive) or nab‐paclitaxel, followed by treatment with combination chemo‐biologic therapy. RNA‐Sequencing based PAM50 subtyping at baseline of 46 HER2‐negative patients revealed a strong association between the basal‐like subtype and pathologic complete response (pCR) to chemotherapy plus bevacizumab (p ≤ 0.0027), but did not provide sufficient specificity to predict response. However, a single dose of bevacizumab resulted in down‐regulation of a well‐characterized TGF‐β activity signature in every single breast tumor that achieved pCR (p ≤ 0.004). The TGF‐β signature was confirmed to be a tumor‐specific read‐out of the canonical TGF‐β pathway using pSMAD2 (p ≤ 0.04), with predictive power unique to brief‐exposure to bevacizumab (p ≤ 0.016), but not trastuzumab or nab‐paclitaxel. Down‐regulation of TGF‐β activity was associated with reduction in tumor hypoxia by transcription and protein levels, suggesting therapy‐induced disruption of an autocrine‐loop between tumor stroma and malignant cells. Modulation of the TGF‐β pathway upon brief‐exposure to bevacizumab may provide an early functional readout of pCR to preoperative anti‐angiogenic therapy in HER2‐negative breast cancer, thus providing additional avenues for exploration in both preclinical and clinical settings with these agents.