Emily Flam

Integrated Analysis of Whole-Genome ChIP-Seq and RNA-Seq Data of Primary Head and Neck Tumor Samples Associates HPV Integration Sites with Open Chromatin Marks

Chromatin alterations mediate mutations and gene expression changes in cancer. Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has been utilized to study genome-wide chromatin structure in human cancer cell lines, yet numerous technical challenges limit comparable analyses in primary tumors. Here we have developed a new whole-genome analytic pipeline to optimize ChIP-Seq protocols on patient-derived xenografts from human papillomavirus-related (HPV+) head and neck squamous cell carcinoma (HNSCC) samples. We further associated chromatin aberrations with gene expression changes from a larger cohort of the tumor and normal samples with RNA-Seq data. We detect differential histone enrichment associated with tumor-specific gene expression variation, sites of HPV integration in the human genome, and HPV-associated histone enrichment sites upstream of cancer driver genes, which play central roles in cancer-associated pathways. These comprehensive analyses enable unprecedented characterization of the complex network of molecular changes resulting from chromatin alterations that drive HPV-related tumorigenesis. Cancer Res; 77(23); 6538-50. ©2017 AACR.

Epigenetic regulation of gene expression in cancer: techniques, resources and analysis

Abstract Cancer is a complex disease, driven by aberrant activity in numerous signaling pathways in even individual malignant cells. Epigenetic changes are critical mediators of these functional changes that drive and maintain the malignant phenotype. Changes in DNA methylation, histone acetylation and methylation, noncoding RNAs, posttranslational modifications are all epigenetic drivers in cancer, independent of changes in the DNA sequence. These epigenetic alterations were once thought to be crucial only for the malignant phenotype maintenance. Now, epigenetic alterations are also recognized as critical for disrupting essential pathways that protect the cells from uncontrolled growth, longer survival and establishment in distant sites from the original tissue. In this review, we focus on DNA methylation and chromatin structure in cancer. The precise functional role of these alterations is an area of active research using emerging high-throughput approaches and bioinformatics analysis tools. Therefore, this review also describes these high-throughput measurement technologies, public domain databases for high-throughput epigenetic data in tumors and model systems and bioinformatics algorithms for their analysis. Advances in bioinformatics data that combine these epigenetic data with genomics data are essential to infer the function of specific epigenetic alterations in cancer. These integrative algorithms are also a focus of this review. Future studies using these emerging technologies will elucidate how alterations in the cancer epigenome cooperate with genetic aberrations during tumor initiation and progression. This deeper understanding is essential to future studies with epigenetics biomarkers and precision medicine using emerging epigenetic therapies.

A Novel Functional Splice Variant of AKT3 Defined by Analysis of Alternative Splice Expression in HPV-Positive Oropharyngeal Cancers

The incidence of HPV-related oropharyngeal squamous cell carcinoma (OPSCC) has increased more than 200% in the past 20 years. Recent genetic sequencing efforts have elucidated relevant genes in head and neck cancer, but HPV-related tumors have consistently shown few DNA mutations. In this study, we sought to analyze alternative splicing events (ASE) that could alter gene function independent of mutations. To identify ASE unique to HPV-related tumors, RNA sequencing was performed on 46 HPV-positive OPSCC and 25 normal tissue samples. A novel algorithm using outlier statistics on RNA-sequencing junction expression identified 109 splicing events, which were confirmed in a validation set from The Cancer Genome Atlas. Because the most common type of splicing event identified was an alternative start site (39%), MBD-seq genome-wide CpG methylation data were analyzed for methylation alterations at promoter regions. ASE in six genes showed significant negative correlation between promoter methylation and expression of an alternative transcriptional start site, including AKT3 The novel AKT3 transcriptional variant and methylation changes were confirmed using qRT-PCR and qMSP methods. In vitro silencing of the novel AKT3 variant resulted in significant growth inhibition of multiple head and neck cell lines, an effect not observed with wild-type AKT3 knockdown. Analysis of ASE in HPV-related OPSCC identified multiple alterations likely involved in carcinogenesis, including a novel, functionally active transcriptional variant of AKT3 Our data indicate that ASEs represent a significant mechanism of oncogenesis with untapped potential for understanding complex genetic changes that result in the development of cancer. Cancer Res; 77(19); 5248-58. ©2017 AACR.

PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF

Summary: Non‐negative Matrix Factorization (NMF) algorithms associate gene expression with biological processes (e.g. time‐course dynamics or disease subtypes). Compared with univariate associations, the relative weights of NMF solutions can obscure biomarkers. Therefore, we developed a novel patternMarkers statistic to extract genes for biological validation and enhanced visualization of NMF results. Finding novel and unbiased gene markers with patternMarkers requires whole‐genome data. Therefore, we also developed Genome‐Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS), the first robust whole genome Bayesian NMF using the sparse, MCMC algorithm, CoGAPS. Additionally, a manual version of the GWCoGAPS algorithm contains analytic and visualization tools including patternMatcher, a Shiny web application. The decomposition in the manual pipeline can be replaced with any NMF algorithm, for further generalization of the software. Using these tools, we find granular brain‐region and cell‐type specific signatures with corresponding biomarkers in GTEx data, illustrating GWCoGAPS and patternMarkers ascertainment of data‐driven biomarkers from whole‐genome data. Availability and Implementation: PatternMarkers & GWCoGAPS are in the CoGAPS Bioconductor package (3.5) under the GPL license. Contact: gsteinobrien@jhmi.edu or ccolantu@jhmi.edu or ejfertig@jhmi.edu Supplementary information: Supplementary data are available at Bioinformatics online.

PatternMarkers and Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS) for data-driven detection of novel biomarkers via whole transcriptome Non-negative matrix factorization (NMF)

NMF algorithms associate gene expression changes with biological processes (e.g., time-course dynamics or disease subtypes). Compared with univariate associations, the relative weights of NMF solutions can obscure biomarkers identification. Therefore, we developed a novel PatternMarkers statistic to extract unique genes for biological validation and enhanced visualization of NMF results. Finding novel and unbiased gene markers with PatternMarkers requires whole-genome data. However, NMF algorithms typically do not converge for the tens of thousands of genes in genome-wide profiling. Therefore, we also developed GWCoGAPS, the first robust Bayesian NMF technique for whole genome transcriptomics using the sparse, MCMC algorithm, CoGAPS. This software contains additional analytic and visualization tools including a Shiny web application, patternMatcher, which are generalized for any NMF. Using these tools, we find granular brain-region and cell-type specific signatures with corresponding biomarkers in GTex data, illustrating GWCoGAPS and patternMarkers unique ability to detect data-driven biomarkers from whole genome data.Summary Non-negative Matrix Factorization (NMF) algorithms associate gene expression with biological processes (e.g., time-course dynamics or disease subtypes). Compared with univariate associations, the relative weights of NMF solutions can obscure biomarkers. Therefore, we developed a novel PatternMarkers statistic to extract genes for biological validation and enhanced visualization of NMF results. Finding novel and unbiased gene markers with PatternMarkers requires whole-genome data. However, NMF algorithms typically do not converge for the tens of thousands of genes in genome-wide profiling. Therefore, we also developed Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS), the first robust whole genome Bayesian NMF using the sparse, MCMC algorithm, CoGAPS. This software contains analytic and visualization tools including a Shiny web application, patternMatcher, which are generalized for any NMF. Using these tools, we find granular brain-region and cell-type specific signatures with corresponding biomarkers in GTex data, illustrating GWCoGAPS and patternMarkers ascertainment of data-driven biomarkers from whole-genome data. Availability PatternMarkers & GWCoGAPS are in the CoGAPS Bioconductor package (3.5) under the GPL license. Contact gsteinobrien@jhmi.edu; ccolantu@jhmi.edu; ejfertig@jhmi.edu

Splice Expression Variation Analysis (SEVA) for Differential Gene Isoform Usage in Cancer

Alternative splicing events (ASE) cause expression of a variable repertoire of potential protein products that are critical to carcinogenesis. Current methods to detect ASEs in tumor samples compare mean expression of gene isoforms relative to that of normal samples. However, these comparisons may not account for heterogeneous gene isoform usage in tumors. Therefore, we introduce Splice Expression Variability Analysis (SEVA) to detect differential splice variation, which accounts for tumor heterogeneity. This algorithm compares the degree of variability of junction expression profiles within a population of normal samples relative to that in tumor samples using a rank-based multivariate statistic that models the biological structure of ASEs. Simulated data show that SEVA is more robust to tumor heterogeneity and its candidates are more independent of differential expression than EBSeq and DiffSplice. SEVA analysis of head and neck tumors identified differential gene isoform usage robust in cross-study validation. Moreover, SEVA finds approximately hundreds of splice variant candidates, manageable for experimental validation in contrast to the thousands of candidates found with EBSeq or DiffSplice. Based on performance in both simulated and real data, SEVA is well suited for differential ASE analysis in RNA-sequencing data from heterogeneous primary tumor samples.

Functional characterization of alternatively spliced GSN in head and neck squamous cell carcinoma

&NA; We have recently performed the characterization of alternative splicing events (ASEs) in head and neck squamous cell carcinoma, which allows dysregulation of protein expression common for cancer cells. Such analysis demonstrated a high ASE prevalence among tumor samples, including tumor‐specific alternative splicing in the GSN gene.In vitro studies confirmed that overall expression of either ASE‐GSN or wild‐type GSN (WT‐GSN) isoform inversely correlated with cell proliferation, whereas the high ratio of ASE‐GSN to WT‐GSN correlated with increased cellular invasion. Additionally, a change in expression of either isoform caused compensatory changes in expression of the other isoform. Our results suggest that the overall expression and the balance between GSN isoforms are mediating factors in proliferation, while increased overall expression of ASE‐GSN is specific to cancer tissues. As a result, we propose ASE‐GSN can serve not only as a biomarker of disease and disease progression, but also as a neoantigen for head and neck squamous cell carcinoma treatment, for which only a limited number of disease‐specific targeted therapies currently exist.

Discovery and development of differentially methylated regions in human papillomavirus-related oropharyngeal squamous cell carcinoma: Discovery and development of differentially methylated regions

Human papillomavirus (HPV)‐related oropharyngeal squamous cell carcinoma (OPSCC) exhibits a different composition of epigenetic alterations. In this study, we identified differentially methylated regions (DMRs) with potential utility in screening for HPV‐positive OPSCC. Genome wide DNA methylation was measured using methyl‐CpG binding domain protein‐enriched genome sequencing (MBD‐seq) in 50 HPV‐positive OPSCC tissues and 25 normal tissues. Fifty‐one DMRs were defined with maximal methylation specificity to cancer samples. The Cancer Genome Atlas (TCGA) methylation array data was used to evaluate the performance of the proposed candidates. Supervised hierarchical clustering of 51 DMRs found that HPV‐positive OPSCC had significantly higher DNA methylation levels compared to normal samples, and non‐HPV‐related head and neck squamous cell carcinoma (HNSCC). The methylation levels of all top 20 DNA methylation biomarkers in HPV‐positive OPSCC were significantly higher than those in normal samples. Further confirmation using quantitative methylation specific PCR (QMSP) in an independent set of 24 HPV‐related OPSCCs and 22 controls showed that 16 of the 20 candidates had significant higher methylation levels in HPV‐positive OPSCC samples compared with controls. One candidate, OR6S1, had a sensitivity of 100%, while 17 candidates (KCNA3, EMBP1, CCDC181, DPP4, ITGA4, BEND4, ELMO1, SFMBT2, C1QL3, MIR129–2, NID2, HOXB4, ZNF439, ZNF93, VSTM2B, ZNF137P and ZNF773) had specificities of 100%. The prediction accuracy of the 20 candidates rang from 56.2% to 99.8% by receiver operating characteristic analysis. We have defined 20 highly specific DMRs in HPV‐related OPSCC, which can potentially be applied to molecular‐based detection tests and improve disease management.

Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer

Motivation Current bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches. Results We introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g. tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVAs performance against EBSeq, DiffSplice and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVA in identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery. These results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data. Availability and implementation SEVA is implemented in the R/Bioconductor package GSReg. Contact bahman@jhu.edu or favorov@sensi.org or ejfertig@jhmi.edu. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract 2880: The discovery of novel GSN alternative splicing in head and neck squamous cell carcinoma

Alternative splice events (ASES) are significant components of potential oncogenic pathways alterations and play a critical role in malignant cell transformation in a variety of solid and liquid tumors, including head and neck squamous cell carcinoma (HNSCC). However, high throughput analyses performed to date have not considered ASEs. Therefore, they have detected a limited number of genetic alterations for HNSCC, which incompletely describe the HNSCC specific pathway alterations. The heterogeneous nature of these alterations has made the discovery of reliable HNSCC biomarkers and therapeutic targets for this disease challenging. We performed alternative splice events (ASEs) analysis to enhance our understanding of HNSCC biology. To define ASEs specific to HNSCC we designed a novel bioinformatics pipeline from RNA-sequencing data of HNSCC tumors and independent normal samples. Evaluating the top scoring candidates, we have found several highly promising ASE candidates, including GSN, Gelsolin, an actin-binding protein, a key regulator of actin filament assembly and disassembly. Previously published literature proposes that GSN demonstrates tumor-suppressor properties by reducing cell proliferation in vivo and in vitro via suppression of protein kinase C (PKC, part of the PI3K pathway which is altered in HNSCC). The alternative splicing event involves an insertion of 110 bp from the 14th intron. This insertion contains a stop codon in frame, and the splice variant gives rise to a truncated (562 amino acids(aa)) protein with only 4 Gelsolin domains (instead of the full-length 731 aa protein with 6 Gelsolin domains). Using RNA-Seq data we demonstrated that 40% of tumor samples harbor the GSN-ASE. QRT-PCR confirmed that while total expression of GSN is decreased in HNSCC samples, GSN is expressed in the alternative truncated form only in HNSCC tumors and not normal tissues. Accordingly, total GSN expression is also seen to be downregulated in breast, lung and colon cancers. Evaluation of TCGA data confirmed the pre-dominant expression of the truncated GSN isoform over the wt GSN in HNSCC, bladder urothelial carcinoma, colon adenocarcinoma, lung SCC, breast invasive carcinoma, cervical SCC and endocervical adenocarcinoma. Moreover, we confirmed that that the expression of the truncated GSN is important for the migration and invasion of the cancer cells in vitro. These data suggest that alternative splicing plays an important role in the GSN gene for multiple tumor types. Citation Format: Daria A. Gaykalova, Dylan Kelley, Theresa Guo, Craig Bohrson, Ilse Tiscareno, Veronika Zizkova, Michael Considine, Ludmila Danilova, Emily Flam, Justin Bishop, Julie Ahn, Samantha Merritt, Marla Goldsmith, Chi Zhang, Wayne Koch, William Westra, Zubair Khan, Michael Ochs, Sarah Wheelan, Elana Fertig, Joseph Califano. The discovery of novel GSN alternative splicing in head and neck squamous cell carcinoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2880.