Metastatic site influences driver gene function in pancreatic cancer.

Driver gene mutations can increase the metastatic potential of the primary tumor1-3, but their role in sustaining tumor growth at metastatic sites is poorly understood. A paradigm of such mutations is inactivation of SMAD4 - a transcriptional effector of TGFß signaling - which is a hallmark of multiple gastrointestinal malignancies4,5. SMAD4 inactivation mediates TGFß's remarkable anti- to pro-tumorigenic switch during cancer progression and can thus influence both tumor initiation and metastasis6-14. To determine whether metastatic tumors remain dependent on SMAD4 inactivation, we developed a mouse model of pancreatic ductal adenocarcinoma (PDAC) that enables Smad4 depletion in the pre-malignant pancreas and subsequent Smad4 reactivation in established metastases. As expected, Smad4 inactivation facilitated the formation of primary tumors that eventually colonized the liver and lungs. By contrast, Smad4 reactivation in metastatic disease had strikingly opposite effects depending on the tumor's organ of residence: suppression of liver metastases and promotion of lung metastases. Integrative multiomic analysis revealed organ-specific differences in the tumor cells' epigenomic state, whereby the liver and lungs harbored chromatin programs respectively dominated by the KLF and RUNX developmental transcription factors, with Klf4 depletion being sufficient to reverse Smad4's tumor-suppressive activity in liver metastases. Our results show how epigenetic states favored by the organ of residence can influence the function of driver genes in metastatic tumors. This organ-specific gene-chromatin interplay invites consideration of anatomical site in the interpretation of tumor genetics, with implications for the therapeutic targeting of metastatic disease.

Profiling salivary miRNA expression levels in Fanconi anemia patients - a pilot study.

The overarching goal of this study is to predict the risk of developing oral squamous cell carcinoma (OSCC) in Fanconi anemia (FA) patients. We have compared the microRNA (miRNA, miR) expression levels in saliva samples from FA patients (n = 50) who are at a low-moderate and/or high risk of developing OSCC to saliva samples from healthy controls (n = 16). The miRNA expression levels in saliva samples were quantified using qPCR. We observed that miR-744, miR-150-5P, and miR-146B-5P had the best discriminatory capacity between FA patients and controls, with an area under the curve (AUC) of 94.0%, 92.9% and 85.3%, respectively. Our data suggest that miR-1, miR-146B-5P, miR-150-5P, miR-155-5P, and miR-744 could be used as panel to predict the risk of developing OSCC in FA patients, with a 89.3% sensitivity and a 68.2% specificity (AUC = 81.5%). Our preliminary data support the notion that the expression levels of salivary miRNAs have the potential to predict the risk of developing OSCC in FA patients and in the future may reduce deaths associated with OSCC.

The MHC Class II Antigen-Processing and Presentation Pathway Is Dysregulated in Type 1 Diabetes.

Peptide loading of MHC class II (MHCII) molecules is facilitated by HLA-DM (DM), which catalyzes CLIP release, stabilizes empty MHCII, and edits the MHCII-bound peptide repertoire. HLA-DO (DO) binds to DM and modulates its activity, resulting in an altered set of peptides presented at the cell surface. MHCII-peptide presentation in individuals with type 1 diabetes (T1D) is abnormal, leading to a breakdown in tolerance; however, no direct measurement of the MHCII pathway activity in T1D patients has been performed. In this study, we measured MHCII Ag-processing pathway activity in humans by determining MHCII, MHCII-CLIP, DM, and DO levels by flow cytometry for peripheral blood B cells, dendritic cells, and monocytes from 99 T1D patients and 97 controls. Results showed that MHCII levels were similar for all three APC subsets. In contrast, MHCII-CLIP levels, independent of sex, age at blood draw, disease duration, and diagnosis age, were significantly increased for all three APCs, with B cells showing the largest increase (3.4-fold). DM and DO levels, which usually directly correlate with MHCII-CLIP levels, were unexpectedly identical in T1D patients and controls. Gene expression profiling on PBMC RNA showed that DMB mRNA was significantly elevated in T1D patients with residual C-peptide. This resulted in higher levels of DM protein in B cells and dendritic cells. DO levels were also increased, suggesting that the MHCII pathway maybe differentially regulated in individuals with residual C-peptide. Collectively, these studies show a dysregulation of the MHCII Ag-processing pathway in patients with T1D.

Detecting salivary host and microbiome RNA signature for aiding diagnosis of oral and throat cancer.

OBJECTIVE: Oral squamous cell carcinoma (OSCC) and oropharyngeal squamous cell carcinoma (OPSCC) can go undetected resulting in late detection and poor outcomes. We describe the development and validation of CancerDetect for Oral & Throat cancer™ (CDOT), to detect markers of OSCC and/or OPSCC within a high-risk population. MATERIAL AND METHODS: We collected saliva samples from 1,175 individuals who were 50 years or older, or adults with a tobacco use history. 945 of those were used to train a classifier using machine learning methods, resulting in a salivary microbial and human metatranscriptomic signature. The classifier was then independently validated on the 230 remaining samples prospectively collected and unseen by the classifier, consisting of 20 OSCC (all stages), 76 OPSCC (all stages), and 134 negatives (including 14 pre-malignant). RESULTS: On the validation cohort, the specificity of the CDOT test was 94 %, sensitivity was 90 % for participants with OSCC, and 84.2 % for participants with OPSCC. Similar classification results were observed among people in early stage (stages I & II) vs late stage (stages III & IV). CONCLUSIONS: CDOT is a non-invasive test that can be easily administered in dentist offices, primary care centres and specialised cancer clinics for early detection of OPSCC and OSCC. This test, having received FDA's breakthrough designation for accelerated review, has the potential to enable early diagnosis, saving lives and significantly reducing healthcare expenditure.

Ordered and deterministic cancer genome evolution after p53 loss.

Although p53 inactivation promotes genomic instability1 and presents a route to malignancy for more than half of all human cancers2,3, the patterns through which heterogenous TP53 (encoding human p53) mutant genomes emerge and influence tumorigenesis remain poorly understood. Here, in a mouse model of pancreatic ductal adenocarcinoma that reports sporadic p53 loss of heterozygosity before cancer onset, we find that malignant properties enabled by p53 inactivation are acquired through a predictable pattern of genome evolution. Single-cell sequencing and in situ genotyping of cells from the point of p53 inactivation through progression to frank cancer reveal that this deterministic behaviour involves four sequential phases-Trp53 (encoding mouse p53) loss of heterozygosity, accumulation of deletions, genome doubling, and the emergence of gains and amplifications-each associated with specific histological stages across the premalignant and malignant spectrum. Despite rampant heterogeneity, the deletion events that follow p53 inactivation target functionally relevant pathways that can shape genomic evolution and remain fixed as homogenous events in diverse malignant populations. Thus, loss of p53-the 'guardian of the genome'-is not merely a gateway to genetic chaos but, rather, can enable deterministic patterns of genome evolution that may point to new strategies for the treatment of TP53-mutant tumours.

The salivary metatranscriptome as an accurate diagnostic indicator of oral cancer.

Despite advances in cancer treatment, the 5-year mortality rate for oral cancers (OC) is 40%, mainly due to the lack of early diagnostics. To advance early diagnostics for high-risk and average-risk populations, we developed and evaluated machine-learning (ML) classifiers using metatranscriptomic data from saliva samples (n = 433) collected from oral premalignant disorders (OPMD), OC patients (n = 71) and normal controls (n = 171). Our diagnostic classifiers yielded a receiver operating characteristics (ROC) area under the curve (AUC) up to 0.9, sensitivity up to 83% (92.3% for stage 1 cancer) and specificity up to 97.9%. Our metatranscriptomic signature incorporates both taxonomic and functional microbiome features, and reveals a number of taxa and functional pathways associated with OC. We demonstrate the potential clinical utility of an AI/ML model for diagnosing OC early, opening a new era of non-invasive diagnostics, enabling early intervention and improved patient outcomes.

Novel insights into breast cancer copy number genetic heterogeneity revealed by single-cell genome sequencing.

Copy number alterations (CNAs) play an important role in molding the genomes of breast cancers and have been shown to be clinically useful for prognostic and therapeutic purposes. However, our knowledge of intra-tumoral genetic heterogeneity of this important class of somatic alterations is limited. Here, using single-cell sequencing, we comprehensively map out the facets of copy number alteration heterogeneity in a cohort of breast cancer tumors. Ou/var/www/html/elife/12-05-2020/backup/r analyses reveal: genetic heterogeneity of non-tumor cells (i.e. stroma) within the tumor mass; the extent to which copy number heterogeneity impacts breast cancer genomes and the importance of both the genomic location and dosage of sub-clonal events; the pervasive nature of genetic heterogeneity of chromosomal amplifications; and the association of copy number heterogeneity with clinical and biological parameters such as polyploidy and estrogen receptor negative status. Our data highlight the power of single-cell genomics in dissecting, in its many forms, intra-tumoral genetic heterogeneity of CNAs, the magnitude with which CNA heterogeneity affects the genomes of breast cancers, and the potential importance of CNA heterogeneity in phenomena such as therapeutic resistance and disease relapse.

Cells in the body remain healthy by tightly preventing and repairing random changes, or mutations, in their genetic material. In cancer cells, however, these mechanisms can break down. When these cells grow and multiply, they can then go on to accumulate many mutations. As a result, cancer cells in the same tumor can each contain a unique combination of genetic changes. This genetic heterogeneity has the potential to affect how cancer responds to treatment, and is increasingly becoming appreciated clinically. For example, if a drug only works against cancer cells carrying a specific mutation, any cells lacking this genetic change will keep growing and cause a relapse. However, it is still difficult to quantify and understand genetic heterogeneity in cancer. Copy number alterations (or CNAs) are a class of mutation where large and small sections of genetic material are gained or lost. This can result in cells that have an abnormal number of copies of the genes in these sections. Here, Baslan et al. set out to explore how CNAs might vary between individual cancer cells within the same tumor. To do so, thousands of individual cancer cells were isolated from human breast tumors, and a technique called single-cell genome sequencing used to screen the genetic information of each of them. These experiments confirmed that CNAs did differ ­ sometimes dramatically ­ between patients and among cells taken from the same tumor. For example, many of the cells carried extra copies of well-known cancer genes important for treatment, but the exact number of copies varied between cells. This heterogeneity existed for individual genes as well as larger stretches of DNA: this was the case, for instance, for an entire section of chromosome 8, a region often affected in breast and other tumors. The work by Baslan et al. captures the sheer extent of genetic heterogeneity in cancer and in doing so, highlights the power of single-cell genome sequencing. In the future, a finer understanding of the genetic changes present at the level of an individual cancer cell may help clinicians to manage the disease more effectively.

Brief-exposure to preoperative bevacizumab reveals a TGF-ß 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-ß activity signature in every single breast tumor that achieved pCR (p ≤ 0.004). The TGF-ß signature was confirmed to be a tumor-specific read-out of the canonical TGF-ß 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-ß 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-ß 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.

Identification of a novel PARP14-TFE3 gene fusion from 10-year-old FFPE tissue by RNA-seq.

Xp11 (TFE3) translocation renal cell carcinoma (RCC) is officially recognized as a distinct subtype of RCC in the 2004 WHO classification. This neoplasm is characterized by several chromosomal translocations between the TFE3-involving Xp11.2 breakpoint and various fusion partners. To date, five partner genes have been identified, that is, PRCC in 1q21, PSF in 1q34, ASPL in 17q25, CLTC in 17q23, and NONO in Xq12; and three additional translocations have been reported with no partner gene being defined: t(X;3)(p11;q23), t(X;10)(p11;q23), and t(X;19)(p11;q13). Here, we report the identification of a novel TFE3 fusion partner, PARP14 in chromosome band3q21. We used RNA-seq on a 10-year-old FFPE (formalin-fixed, paraffin-embedded) tissue sample, which carried t(X;3)(p11;q23) as detected in the original cytogenetic study. The fusion transcript connected the 5'-end of the first two exons of PARP14 to the 3'-end of five exons of TFE3, which was verified by reverse transcription PCR (RT-PCR) and Sanger sequencing. Similar to other TFE3 fusions previously reported, the predicted PARP14-TFE3 product retains the nuclear localization and DNA-binding domains of TFE3. This finding expands the list of TFE3 translocation partner genes and re-emphasizes the essential oncogenic role of TFE3 fusion proteins in this tumor. Our result also clearly demonstrated the feasibility of identifying chromosomal translocation by RNA-seq in clinical FFPE, which are easily accessible and associated with valuable clinical information. © 2015 Wiley Periodicals, Inc.

Optimizing sparse sequencing of single cells for highly multiplex copy number profiling.

Genome-wide analysis at the level of single cells has recently emerged as a powerful tool to dissect genome heterogeneity in cancer, neurobiology, and development. To be truly transformative, single-cell approaches must affordably accommodate large numbers of single cells. This is feasible in the case of copy number variation (CNV), because CNV determination requires only sparse sequence coverage. We have used a combination of bioinformatic and molecular approaches to optimize single-cell DNA amplification and library preparation for highly multiplexed sequencing, yielding a method that can produce genome-wide CNV profiles of up to a hundred individual cells on a single lane of an Illumina HiSeq instrument. We apply the method to human cancer cell lines and biopsied cancer tissue, thereby illustrating its efficiency, reproducibility, and power to reveal underlying genetic heterogeneity and clonal phylogeny. The capacity of the method to facilitate the rapid profiling of hundreds to thousands of single-cell genomes represents a key step in making single-cell profiling an easily accessible tool for studying cell lineage.

Non-linear Bayesian framework to determine the transcriptional effects of cancer-associated genomic aberrations.

While the tumorigenic effects of specific recurrent mutations in known cancer driver-genes is well-characterized, not much is known about the functional relevance of the vast majority of recurrent mutations observed across cancers. Prior studies have attempted to identify functional genomic aberrations by integrating multi-omics measurements in cancer samples with community-curated biological pathway networks. However, the majority of these approaches overlook the following biological considerations: i) signaling pathway networks are highly tissue-specific and their regulatory interactions differ across tissue types; ii) regulatory factors exhibit heterogeneous influence on downstream gene transcription; iii) epigenetic and genomic alterations exhibit nonlinear impact on gene transcription. In order to accommodate these biological effects, we propose a hybrid Bayesian method to learn tissue-specific pairwise influence models amongst genes and to predict a gene's expression level as a nonlinear-function of its epigenetic and regulatory influences. We employ a novel tree-based depth-penalization mechanism in order to capture the higher regulatory impact of closer neighbors in the regulatory network. Using a breast cancer multi-omics dataset (N=1190), we show that our proposed method has superior prediction power over optimization-based regression models, with the additional advantage of revealing gene deregulations potentially driven by somatic mutations.

A novel saliva-based microRNA biomarker panel to detect head and neck cancers.

BACKGROUND: MicroRNAs (miRNAs) are known to play an important role in cancer development by post-transcriptionally affecting the expression of critical genes. The aims of this study were two-fold: (i) to develop a robust method to isolate miRNAs from small volumes of saliva and (ii) to develop a panel of saliva-based diagnostic biomarkers for the detection of head and neck squamous cell carcinoma (HNSCC). METHODS: Five differentially expressed miRNAs were selected from miScript™ miRNA microarray data generated using saliva from five HNSCC patients and five healthy controls. Their differential expression was subsequently confirmed by RT-qPCR using saliva samples from healthy controls (n = 56) and HNSCC patients (n = 56). These samples were divided into two different cohorts, i.e., a first confirmatory cohort (n = 21) and a second independent validation cohort (n = 35), to narrow down the miRNA diagnostic panel to three miRNAs: miR-9, miR-134 and miR-191. This diagnostic panel was independently validated using HNSCC miRNA expression data from The Cancer Genome Atlas (TCGA), encompassing 334 tumours and 39 adjacent normal tissues. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic capacity of the panel. RESULTS: On average 60 ng/µL miRNA was isolated from 200 µL of saliva. Overall a good correlation was observed between the microarray data and the RT-qPCR data. We found that miR-9 (P <0.0001), miR-134 (P <0.0001) and miR-191 (P <0.001) were differentially expressed between saliva from HNSCC patients and healthy controls, and that these miRNAs provided a good discriminative capacity with area under the curve (AUC) values of 0.85 (P <0.0001), 0.74 (P < 0.001) and 0.98 (P < 0.0001), respectively. In addition, we found that the salivary miRNA data showed a good correlation with the TCGA miRNA data, thereby providing an independent validation. CONCLUSIONS: We show that we have developed a reliable method to isolate miRNAs from small volumes of saliva, and that the saliva-derived miRNAs miR-9, miR-134 and miR-191 may serve as novel biomarkers to reliably detect HNSCC.

Integrative prediction of gene function and platinum-free survival 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 discover genes critical to the development, progression, and therapeutic resistance 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 bioinformatics analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. 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 identify changes in DNA methylation and expression specifically for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic gene function from integrative analysis of three modalities: copy number variation, DNA methylation, and gene expression. Our method (1) calculates the extent of genomic and epigenetic alterations of defined tumor suppressor and oncogenic features for the functional prediction of significant ovarian cancer gene candidates and (2) identifies the functional activity or inactivity of known tumor suppressors and oncogenes in ovarian cancer. We applied our protocol on 42 primary serous ovarian cancer samples using MOMA-ROMA representational array assays. Additionally, we provide the basis for incorporating epigenetic profiles of ovarian tumors for the purposes of platinum-free survival prediction in the context of TCGA data.

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.

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.

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.

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.

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.

Expression-based network biology identifies alteration in key regulatory pathways of type 2 diabetes and associated risk/complications.

Type 2 diabetes mellitus (T2D) is a multifactorial and genetically heterogeneous disease which leads to impaired glucose homeostasis and insulin resistance. The advanced form of disease causes acute cardiovascular, renal, neurological and microvascular complications. Thus there is a constant need to discover new and efficient treatment against the disease by seeking to uncover various novel alternate signalling mechanisms that can lead to diabetes and its associated complications. The present study allows detection of molecular targets by unravelling their role in altered biological pathways during diabetes and its associated risk factors and complications. We have used an integrated functional networks concept by merging co-expression network and interaction network to detect the transcriptionally altered pathways and regulations involved in the disease. Our analysis reports four novel significant networks which could lead to the development of diabetes and other associated dysfunctions. (a) The first network illustrates the up regulation of TGFBRII facilitating oxidative stress and causing the expression of early transcription genes via MAPK pathway leading to cardiovascular and kidney related complications. (b) The second network demonstrates novel interactions between GAPDH and inflammatory and proliferation candidate genes i.e., SUMO4 and EGFR indicating a new link between obesity and diabetes. (c) The third network portrays unique interactions PTPN1 with EGFR and CAV1 which could lead to an impaired vascular function in diabetic nephropathy condition. (d) Lastly, from our fourth network we have inferred that the interaction of beta-catenin with CDH5 and TGFBR1 through Smad molecules could contribute to endothelial dysfunction. A probability of emergence of kidney complication might be suggested in T2D condition. An experimental investigation on this aspect may further provide more decisive observation in drug target identification and better understanding of the pathophysiology of T2D and its complications.