Sheila Reynolds

Integrated Analyses Identify a Master MicroRNA Regulatory Network for the Mesenchymal Subtype in Serous Ovarian Cancer

Integrated genomic analyses revealed a miRNA-regulatory network that further defined a robust integrated mesenchymal subtype associated with poor overall survival in 459 cases of serous ovarian cancer (OvCa) from The Cancer Genome Atlas and 560 cases from independent cohorts. Eight key miRNAs, including miR-506, miR-141, and miR-200a, were predicted to regulate 89% of the targets in this network. Follow-up functional experiments illustrate that miR-506 augmented E-cadherin expression, inhibited cell migration and invasion, and prevented TGFβ-induced epithelial-mesenchymal transition by targeting SNAI2, a transcriptional repressor of E-cadherin. In human OvCa, miR-506 expression was correlated with decreased SNAI2 and VIM, elevated E-cadherin, and beneficial prognosis. Nanoparticle delivery of miR-506 in orthotopic OvCa mouse models led to E-cadherin induction and reduced tumor growth.

Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis

We developed the transcription factor (TF)-target gene database and the Systems Genetics Network Analysis (SYGNAL) pipeline to decipher transcriptional regulatory networks from multi-omic and clinical patient data, and we applied these tools to 422 patients with glioblastoma multiforme (GBM). The resulting gbmSYGNAL network predicted 112 somatically mutated genes or pathways that act through 74 TFs and 37 microRNAs (miRNAs) (67 not previously associated with GBM) to dysregulate 237 distinct co-regulated gene modules associated with patient survival or oncogenic processes. The regulatory predictions were associated to cancer phenotypes using CRISPR-Cas9 and small RNA perturbation studies and also demonstrated GBM specificity. Two pairwise combinations (ETV6-NFKB1 and romidepsin-miR-486-3p) predicted by the gbmSYGNAL network had synergistic anti-proliferative effects. Finally, the network revealed that mutations in NF1 and PIK3CA modulate IRF1-mediated regulation of MHC class I antigen processing and presentation genes to increase tumor lymphocyte infiltration and worsen prognosis. Importantly, SYGNAL is widely applicable for integrating genomic and transcriptomic measurements from other human cohorts.

The ISB Cancer Genomics Cloud: A Flexible Cloud-Based Platform for Cancer Genomics Research

The ISB Cancer Genomics Cloud (ISB-CGC) is one of three pilot projects funded by the National Cancer Institute to explore new approaches to computing on large cancer datasets in a cloud environment. With a focus on Data as a Service, the ISB-CGC offers multiple avenues for accessing and analyzing The Cancer Genome Atlas, TARGET, and other important references such as GENCODE and COSMIC using the Google Cloud Platform. The open approach allows researchers to choose approaches best suited to the task at hand: from analyzing terabytes of data using complex workflows to developing new analysis methods in common languages such as Python, R, and SQL; to using an interactive web application to create synthetic patient cohorts and to explore the wealth of available genomic data. Links to resources and documentation can be found at www.isb-cgc.org Cancer Res; 77(21); e7-10. ©2017 AACR.

Reliability of the 7-Day Physical Activity Recall in a Biracial Group of Inactive and Active Adults

BACKGROUND Physical activity recall (PAR) reliability was estimated in a three-site sample of African American and white adults. The sample was sedentary at baseline and more varied in physical activity 24 months later. Intraclass correlation coefficients (ICCs) were used to estimate the number of PAR assessments necessary to obtain a reliability of 0.70 at both timepoints. METHODS The PAR was administered ≤ 30 d apart at baseline (n = 547) and 24 months (n = 648). Energy expenditure ICC was calculated by race, gender, and age. RESULTS Baseline reliability was low for all groups with 4-16 PARs estimated to attain reliable data. ICCs at 24 months were similar (ICC = 0.54-0.55) for race and age group, with 2-3 PARs estimated to reach acceptable reliability. At 24 months, women were more reliable reporters than men. CONCLUSION Low sample variability in activity reduced reliability, highlighting the importance of evaluating diverse groups. Despite evaluating a sample with greater physical activity variability, an estimated 2-3 PARs were necessary to obtain acceptable reliability.

Structure-based predictions broadly link transcription factor mutations to gene expression changes in cancers

Thousands of unique mutations in transcription factors (TFs) arise in cancers, and the functional and biological roles of relatively few of these have been characterized. Here, we used structure-based methods developed specifically for DNA-binding proteins to systematically predict the consequences of mutations in several TFs that are frequently mutated in cancers. The explicit consideration of protein–DNA interactions was crucial to explain the roles and prevalence of mutations in TP53 and RUNX1 in cancers, and resulted in a higher specificity of detection for known p53-regulated genes among genetic associations between TP53 genotypes and genome-wide expression in The Cancer Genome Atlas, compared to existing methods of mutation assessment. Biophysical predictions also indicated that the relative prevalence of TP53 missense mutations in cancer is proportional to their thermodynamic impacts on protein stability and DNA binding, which is consistent with the selection for the loss of p53 transcriptional function in cancers. Structure and thermodynamics-based predictions of the impacts of missense mutations that focus on specific molecular functions may be increasingly useful for the precise and large-scale inference of aberrant molecular phenotypes in cancer and other complex diseases.

Recurrent tumor-specific regulation of alternative polyadenylation of cancer-related genes

BackgroundAlternative polyadenylation (APA) results in messenger RNA molecules with different 3′ untranslated regions (3’ UTRs), affecting the molecules’ stability, localization, and translation. APA is pervasive and implicated in cancer. Earlier reports on APA focused on 3’ UTR length modifications and commonly characterized APA events as 3’ UTR shortening or lengthening. However, such characterization oversimplifies the processing of 3′ ends of transcripts and fails to adequately describe the various scenarios we observe.ResultsWe built a cloud-based targeted de novo transcript assembly and analysis pipeline that incorporates our previously developed cleavage site prediction tool, KLEAT. We applied this pipeline to elucidate the APA profiles of 114 genes in 9939 tumor and 729 tissue normal samples from The Cancer Genome Atlas (TCGA). The full set of 10,668 RNA-Seq samples from 33 cancer types has not been utilized by previous APA studies. By comparing the frequencies of predicted cleavage sites between normal and tumor sample groups, we identified 77 events (i.e. gene-cancer type pairs) of tumor-specific APA regulation in 13 cancer types; for 15 genes, such regulation is recurrent across multiple cancers. Our results also support a previous report showing the 3’ UTR shortening of FGF2 in multiple cancers. However, over half of the events we identified display complex changes to 3’ UTR length that resist simple classification like shortening or lengthening.ConclusionsRecurrent tumor-specific regulation of APA is widespread in cancer. However, the regulation pattern that we observed in TCGA RNA-seq data cannot be described as straightforward 3’ UTR shortening or lengthening. Continued investigation into this complex, nuanced regulatory landscape will provide further insight into its role in tumor formation and development.

Pan-cancer analysis reveals complex tumor-specific alternative polyadenylation

Alternative polyadenylation (APA) of 3’ untranslated regions (3’ UTRs) has been implicated in cancer development. Earlier reports on APA in cancer primarily focused on 3’ UTR length modifications, and the conventional wisdom is that tumor cells preferentially express transcripts with shorter 3’ UTRs. Here, we analyzed the APA patterns of 114 genes, a select list of oncogenes and tumor suppressors, in 9,939 tumor and 729 normal tissue samples across 33 cancer types using RNA-Seq data from The Cancer Genome Atlas, and we found that the APA regulation machinery is much more complicated than what was previously thought. We report 77 cases (gene-cancer type pairs) of differential 3’ UTR cleavage patterns between normal and tumor tissues, involving 33 genes in 13 cancer types. For 15 genes, the tumor-specific cleavage patterns are recurrent across multiple cancer types. While the cleavage patterns in certain genes indicate apparent trends of 3’ UTR shortening in tumor samples, over half of the 77 cases imply 3’ UTR length change trends in cancer that are more complex than simple shortening or lengthening. This work extends the current understanding of APA regulation in cancer, and demonstrates how large volumes of RNA-seq data generated for characterizing cancer cohorts can be mined to investigate this process.

Howdah - A Flexible Pipeline Framework for Analyzing Genomic Data

The advent of new high-throughput sequencing technologies has led to a flood of genomic data which overwhelms the capabilities of single processor machines. We present a MapReduce pipeline called Howdah that supports the analysis of genomic sequence data allowing multiple tests to be plugged in to a single MapReduce job. The pipeline is used to detect chromosomal abnormalities such as insertions, deletions and translocations as well as single nucleotide polymorphisms (SNPs).