Ras-p53 genomic cooperativity as a model to investigate mechanisms of innate immune regulation in gastrointestinal cancers

Despite increasingly thorough mechanistic understanding of the dominant genetic drivers of gastrointestinal (GI) tumorigenesis (e.g., Ras/Raf, TP53, etc.), only a small proportion of these molecular alterations are therapeutically actionable. In an attempt to address this therapeutic impasse, our group has proposed an innovative extreme outlier model to identify novel cooperative molecular vulnerabilities in high-risk GI cancers which dictate prognosis, correlate with distinct patterns of metastasis, and define therapeutic sensitivity or resistance. Our model also proposes comprehensive investigation of their downstream transcriptomic, immunomic, metabolic, or upstream epigenomic cellular consequences to reveal novel therapeutic targets in previously “undruggable” tumors with high-risk genomic features. Leveraging this methodology, our and others’ data reveal that the genomic cooperativity between Ras and p53 alterations is not only prognostically relevant in GI malignancy, but may also represent the incipient molecular events that initiate and sustain innate immunoregulatory signaling networks within the GI tumor microenvironment, driving T-cell exclusion and therapeutic resistance in these cancers. As such, deciphering the unique transcriptional programs encoded by Ras-p53 cooperativity that promote innate immune trafficking and chronic inflammatory tumor-stromal-immune crosstalk may uncover immunologic vulnerabilities that could be exploited to develop novel therapeutic strategies for these difficult-to-treat malignancies

ETV4 and ETV5 drive synovial sarcoma through cell cycle and DUX4 embryonic pathway control

Synovial sarcoma is an aggressive malignancy with no effective treatments for patients with metastasis. The synovial sarcoma fusion SS18-SSX, which recruits the SWI/SNF-BAF chromatin remodeling and polycomb repressive complexes, results in epigenetic activation of FGF receptor (FGFR) signaling. In genetic FGFR-knockout models, culture, and xenograft synovial sarcoma models treated with the FGFR inhibitor BGJ398, we show that FGFR1, FGFR2, and FGFR3 were crucial for tumor growth. Transcriptome analyses of BGJ398-treated cells and histological and expression analyses of mouse and human synovial sarcoma tumors revealed prevalent expression of two ETS factors and FGFR targets, ETV4 and ETV5. We further demonstrate that ETV4 and ETV5 acted as drivers of synovial sarcoma growth, most likely through control of the cell cycle. Upon ETV4 and ETV5 knockdown, we observed a striking upregulation of DUX4 and its transcriptional targets that activate the zygotic genome and drive the atrophy program in facioscapulohumeral dystrophy patients. In addition to demonstrating the importance of inhibiting all three FGFRs, the current findings reveal potential nodes of attack for the cancer with the discovery of ETV4 and ETV5 as appropriate biomarkers and molecular targets, and activation of the embryonic DUX4 pathway as a promising approach to block synovial sarcoma tumors

Multi-omics analysis identifies therapeutic vulnerabilities in triple-negative breast cancer subtypes

Triple-negative breast cancer (TNBC) is a collection of biologically diverse cancers characterized by distinct transcriptional patterns, biology, and immune composition. TNBCs subtypes include two basal-like (BL1, BL2), a mesenchymal (M) and a luminal androgen receptor (LAR) subtype. Through a comprehensive analysis of mutation, copy number, transcriptomic, epigenetic, proteomic, and phospho-proteomic patterns we describe the genomic landscape of TNBC subtypes. Mesenchymal subtype tumors display high mutation loads, genomic instability, absence of immune cells, low PD-L1 expression, decreased global DNA methylation, and transcriptional repression of antigen presentation genes. We demonstrate that major histocompatibility complex I (MHC-I) is transcriptionally suppressed by H3K27me3 modifications by the polycomb repressor complex 2 (PRC2). Pharmacological inhibition of PRC2 subunits EZH2 or EED restores MHC-I expression and enhances chemotherapy efficacy in murine tumor models, providing a rationale for using PRC2 inhibitors in PD-L1 negative mesenchymal tumors. Subtype-specific differences in immune cell composition and differential genetic/pharmacological vulnerabilities suggest additional treatment strategies for TNBC

ASXL1 interacts with the cohesin complex to maintain chromatid separation and gene expression for normal hematopoiesis

ASXL1 is frequently mutated in a spectrum of myeloid malignancies with poor prognosis. Loss of Asxl1 leads to myelodysplastic syndrome-like disease in mice; however, the underlying molecular mechanisms remain unclear. We report that ASXL1 interacts with the cohesin complex, which has been shown to guide sister chromatid segregation and regulate gene expression. Loss of Asxl1 impairs the cohesin function, as reflected by an impaired telophase chromatid disjunction in hematopoietic cells. Chromatin immunoprecipitation followed by DNA sequencing data revealed that ASXL1, RAD21, and SMC1A share 93% of genomic binding sites at promoter regions in Lin-cKit+ (LK) cells. We have shown that loss of Asxl1 reduces the genome binding of RAD21 and SMC1A and alters the expression of ASXL1/cohesin target genes in LK cells. Our study underscores the ASXL1-cohesin interaction as a novel means to maintain normal sister chromatid separation and regulate gene expression in hematopoietic cells

Understanding Evolution of Gene Expression by Comparative Analysis

During the course of evolution, phenotypic adaptations can arise from changes in both gene function and gene expression. Evolution of gene function is well-documented in comparative genomics through the study of protein and DNA sequences. But the evolution of gene expression is not well-understood. The first objective of the thesis is to evaluate the methods for quantifying divergence in gene expression and examine conservation of gene expression based on a set of DNA microarray data. The second objective is to explore the correlations between divergence of gene expression and various genetic factors. Pairwise comparison of gene expression across species has been often used in study of evolution of gene expression. We showed that the existing methods for quantifying divergence of gene expression may give unreliable results. We proposed to modify the Pearson’s distance method by adding a stabilizing factor to avoid overestimation of expression differences due to small variation of gene expression. We showed that the modification improves the estimation of expression divergence. We applied the proposed method to quantify gene expression divergence across 9 corresponding tissues of human, mouse, and rat, based on gene expression measured by species-specific whole-genome DNA microarrays. We demonstrated that gene expression diverges rapidly but conservation can be observed in more than 30% of human-rodent orthologous genes, and 70% of the mouse-rat orthologs. Moreover, we showed that expression of a significant portion of genes do not evolve strictly according to the neutral model, suggesting strong influence of stabilizing selection. We used a linear regression to systematically investigate effects of multiple factors on expression divergence, and found that level of tissue specificity is the most important predictor for expression divergence. Expression of tissue-specific genes is more conserved as compared to genes selectively-expressed in multiple tissues. Ninetyeight percent of orthologs whose expression found in only one tissue are expressed in the same tissue between any two species. In summary, we demonstrated that conservation of mammalian gene expression can be detected based on comparison of genome-wide gene expression data. By studying the relationship between divergence in gene expression and genomic data, we gained more insights into evolution of gene expression

Investigating microbial co-occurrence patterns based on metagenomic compositional data

Motivation: The high-throughput sequencing technologies have provided a powerful tool to study the microbial organisms living in various environments. Characterizing microbial interactions can give us insights into how they live and work together as a community. Metagonomic data are usually summarized in a compositional fashion due to varying sampling/sequencing depths from one sample to another. We study the co-occurrence patterns of microbial organisms using their relative abundance information. Analyzing compositional data using conventional correlation methods has been shown prone to bias that leads to artifactual correlations. Results: We propose a novel method, regularized estimation of the basis covariance based on compositional data (REBACCA), to identify significant co-occurrence patterns by finding sparse solutions to a system with a deficient rank. To be specific, we construct the system using log ratios of count or proportion data and solve the system using the l(1)-norm shrinkage method. Our comprehensive simulation studies show that REBACCA (i) achieves higher accuracy in general than the existing methods when a sparse condition is satisfied; (ii) controls the false positives at a pre-specified level, while other methods fail in various cases and (iii) runs considerably faster than the existing comparable method. REBACCA is also applied to several real metagenomic datasets. Availability and implementation: The R codes for the proposed method are available at http://faculty.wcas.northwestern.edu/∼hji403/REBACCA.htm Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Identification of metagenes and their Interactions through Large-scale Analysis of <it>Arabidopsis</it> Gene Expression Data

<p>Abstract</p> <p>Background</p> <p>Many plant genes have been identified through whole genome and deep transcriptome sequencing and other methods; yet our knowledge on the function of many of these genes remains limited. The integration and analysis of large gene-expression datasets gives researchers the ability to formalize hypotheses concerning the functionality and interaction between different groups of correlated genes.</p> <p>Results</p> <p>We applied the non-negative matrix factorization (NMF) algorithm to the AtGenExpress dataset which consists of 783 microarray samples (29 separate experimental series) conducted on the model plant <it>Arabidopsis thaliana</it>. We identified 15 metagenes, which are groups of genes with correlated expression. Functional roles of these metagenes are established by observing the enriched gene ontology (GO) categories using gene set enrichment analyses (GSEA). Activity levels of these metagenes in various experimental conditions are also analyzed to associate metagenes with stimuli/conditions. A metagene correlation network, constructed based on the results of NMF analysis, revealed many new interactions between the metagenes. Comparison of these metagenes with an earlier large-scale clustering analysis indicates many statistically significant overlaps.</p> <p>Conclusions</p> <p>This study identifies a network of correlated metagenes composed of <it>Arabidopsis</it> genes acting in a highly correlated fashion across a broad spectrum of experimental stimuli, which may shed some light on the function of many of the un-annotated genes.</p

PathwaySplice: an R package for unbiased pathway analysis of alternative splicing in RNA-Seq data

Abstract Summary Pathway analysis of alternative splicing would be biased without accounting for the different number of exons or junctions associated with each gene, because genes with higher number of exons or junctions are more likely to be included in the 'significant' gene list in alternative splicing. We present PathwaySplice, an R package that (i) Performs pathway analysis that explicitly adjusts for the number of exons or junctions associated with each gene; (ii) visualizes selection bias due to different number of exons or junctions for each gene and formally tests for presence of bias using logistic regression; (iii) supports gene sets based on the Gene Ontology terms, as well as more broadly defined gene sets (e.g. MSigDB) or user defined gene sets; (iv) identifies the significant genes driving pathway significance and (v) organizes significant pathways with an enrichment map, where pathways with large number of overlapping genes are grouped together in a network graph. Availability and implementation https://bioconductor.org/packages/release/bioc/html/PathwaySplice.html Supplementary information Supplementary data are available at Bioinformatics online

The Implementation of an Online Mathematics Placement Exam and its Effects on Student Success in Precalculus and Calculus

Engineering education research on the impact of freshman engineering courses reveals a close connection between graduation rate and first semester GPA.1 The same research also explains the importance of first-semester math placement, so as to provide students with the necessary background for success. For example, students at Purdue University that earned a grade of A in a pre-calculus course in the first semester had the same engineering retention rate as students who earned a B in the first semester calculus class.1 Yet, if those same students are placed based on their SAT math scores, such students would probably fail calculus if taken in their first semester.1 A recent study on parameters that affect student success indicated that the grade earned in a student’s first college level mathematics class was significantly correlated to whether or not those students persisted in engineering, while the level at which they began mathematics study at the university was not.2 French, et al. conclude in their study of indicators of engineering students’ success and persistence, that achievement of good grades at the student’s university is an indicator of persistence, and suggests that retention programs focus on academic achievement.3 These studies highlight the importance of timely and accurate student placement in mathematics in terms of success in engineering programs

Conserved expression of natural antisense transcripts in mammals

BACKGROUND: Recent studies had found thousands of natural antisense transcripts originating from the same genomic loci of protein coding genes but from the opposite strand. It is unclear whether the majority of antisense transcripts are functional or merely transcriptional noise. RESULTS: Using the Affymetrix Exon array with a modified cDNA synthesis protocol that enables genome-wide detection of antisense transcription, we conducted large-scale expression analysis of antisense transcripts in nine corresponding tissues from human, mouse and rat. We detected thousands of antisense transcripts, some of which show tissue-specific expression that could be subjected to further study for their potential function in the corresponding tissues/organs. The expression patterns of many antisense transcripts are conserved across species, suggesting selective pressure on these transcripts. When compared to protein-coding genes, antisense transcripts show a lesser degree of expression conservation. We also found a positive correlation between the sense and antisense expression across tissues. CONCLUSION: Our results suggest that natural antisense transcripts are subjected to selective pressure but to a lesser degree compared to sense transcripts in mammals