Gene integrated set profile analysis: a context-based approach for inferring biological endpoints

Obesity-induced galectin-9 is a therapeutic target in B-cell acute lymphoblastic leukemia

The incidence of obesity is rising with greater than 40% of the world’s population expected to be overweight or suffering from obesity by 2030. This is alarming because obesity increases mortality rates in patients with various cancer subtypes including leukemia. The survival differences between lean patients and patients with obesity are largely attributed to altered drug pharmacokinetics in patients receiving chemotherapy; whereas, the direct impact of an adipocyte-enriched microenvironment on cancer cells is rarely considered. Here we show that the adipocyte secretome upregulates the surface expression of Galectin-9 (GAL-9) on human B-acute lymphoblastic leukemia cells (B-ALL) which promotes chemoresistance. Antibody-mediated targeting of GAL-9 on B-ALL cells induces DNA damage, alters cell cycle progression, and promotes apoptosis in vitro and significantly extends the survival of obese but not lean mice with aggressive B-ALL. Our studies reveal that adipocyte-mediated upregulation of GAL-9 on B-ALL cells can be targeted with antibody-based therapies to overcome obesity-induced chemoresistance.

Obesity has been reported to promote tumourigenesis and chemoresistance but the underlying mechanisms are not completely understood. Here, the authors show that adipocytes induce Galectin-9 (GAL-9) expression in B-acute lymphoblastic leukaemia (B-ALL) cells which leads to chemoresistance and antibody-mediated blockade of GAL-9 increases survival in preclinical B-ALL murine models.

Functional Genomics Identify Distinct and Overlapping Genes Mediating Resistance to Different Classes of Heterobifunctional Degraders of Oncoproteins

Heterobifunctional proteolysis-targeting chimeric compounds leverage the activity of E3 ligases to induce degradation of target oncoproteins and exhibit potent preclinical antitumor activity. To dissect the mechanisms regulating tumor cell sensitivity to different classes of pharmacological "degraders" of oncoproteins, we performed genome-scale CRISPR-Cas9-based gene editing studies. We observed that myeloma cell resistance to degraders of different targets (BET bromodomain proteins, CDK9) and operating through CRBN (degronimids) or VHL is primarily mediated by prevention of, rather than adaptation to, breakdown of the target oncoprotein; and this involves loss of function of the cognate E3 ligase or interactors/regulators of the respective cullin-RING ligase (CRL) complex. The substantial gene-level differences for resistance mechanisms to CRBN- versus VHL-based degraders explains mechanistically the lack of cross-resistance with sequential administration of these two degrader classes. Development of degraders leveraging more diverse E3 ligases/CRLs may facilitate sequential/alternating versus combined uses of these agents toward potentially delaying or preventing resistance.

Matrix Integrative Analysis (MIA) of Multiple Genomic Data for Modular Patterns

The increasing availability of high-throughput biological data, especially multi-dimensional genomic data across the same samples, has created an urgent need for modular and integrative analysis tools that can reveal the relationships among different layers of cellular activities. To this end, we present a MATLAB package, Matrix Integration Analysis (MIA), implementing and extending four published methods, designed based on two classical techniques, non-negative matrix factorization (NMF), and partial least squares (PLS). This package can integrate diverse types of genomic data (e.g., copy number variation, DNA methylation, gene expression, microRNA expression profiles, and/or gene network data) to identify the underlying modular patterns by each method. Particularly, we demonstrate the differences between these two classes of methods, which give users some suggestions about how to select a suitable method in the MIA package. MIA is a flexible tool which could handle a wide range of biological problems and data types. Besides, we also provide an executable version for users without a MATLAB license.

shinySISPA: A web tool for defining sample groups using gene sets from multiple-omics data

As opposed to genome-wide testing of several hundreds of thousands of genes on very few samples, gene panels target as few as tens of genes and enable the simultaneous testing of many samples.  For example, some cancer gene panels test for 50 genes that can affect tumor growth and potentially identify treatment options directed against the genetic mutation.  The increasing popularity of gene panel testing has spurred the technological development of panels that test for diverse data types such as expression and mutation.   Once samples are tested, there is the desire to examine clinical associations based on the panel and for this purpose, one would like to identify, among the samples tested, which show support for a molecular profile (e.g., presence of mutation with increased expression) versus those samples that do not among the genes tested.  With user-specified molecular profile of interest, and gene panel data matrices (e.g., gene expression, variants, etc.) that define the profile, shinySISPA (Sample Integrated Set Profile Analysis) is a web-based shiny tool to define two sample groups with and without profile support based on our previously published method from which clinical associations may be readily examined. The shinySISPA can be accessed from http://shinygispa.winship.emory.edu/shinySISPA/.

shinySISPA: A web tool for defining sample groups using gene sets from multiple-omics data

As opposed to genome-wide testing of several hundreds of thousands of genes on very few samples, gene panels target as few as tens of genes and enable the simultaneous testing of many samples.  For example, some cancer gene panels test for 50 genes that can affect tumor growth and potentially identify treatment options directed against the genetic mutation.  The increasing popularity of gene panel testing has spurred the technological development of panels that test for diverse data types such as expression and mutation.   Once samples are tested, there is the desire to examine clinical associations based on the panel and for this purpose, one would like to identify, among the samples tested, which show support for a molecular profile (e.g., presence of mutation with increased expression) versus those samples that do not among the genes tested.  With user-specified molecular profile of interest, and gene panel data matrices (e.g., gene expression, variants, etc.) that define the profile, shinySISPA (Sample Integrated Set Profile Analysis) is a web-based shiny tool to define two sample groups with and without profile support based on our previously published method from which clinical associations may be readily examined. The shinySISPA can be accessed from http://shinygispa.winship.emory.edu/shinySISPA/.

shinyGISPA: A web application for characterizing phenotype by gene sets using multiple omics data combinations

While many methods exist for integrating multi-omics data or defining gene sets, there is no one single tool that defines gene sets based on merging of multiple omics data sets. We present shinyGISPA, an open-source application with a user-friendly web-based interface to define genes according to their similarity in several molecular changes that are driving a disease phenotype. This tool was developed to help facilitate the usability of a previously published method, Gene Integrated Set Profile Analysis (GISPA), among researchers with limited computer-programming skills. The GISPA method allows the identification of multiple gene sets that may play a role in the characterization, clinical application, or functional relevance of a disease phenotype. The tool provides an automated workflow that is highly scalable and adaptable to applications that go beyond genomic data merging analysis. It is available at http://shinygispa.winship.emory.edu/shinyGISPA/.

A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma

Extensive interindividual variation in response to chemotherapy is a major stumbling block in achieving desirable efficacy in the treatment of cancers, including multiple myeloma (MM). In this study, our goal was to develop a gene expression signature that predicts response specific to proteasome inhibitor (PI) treatment in MM. Using a well-characterized panel of human myeloma cell lines (HMCLs) representing the biological and genetic heterogeneity of MM, we created an in vitro chemosensitivity profile in response to treatment with the four PIs bortezomib, carfilzomib, ixazomib and oprozomib as single agents. Gene expression profiling was performed using next-generation high-throughput RNA-sequencing. Applying machine learning-based computational approaches including the supervised ensemble learning methods Random forest and Random survival forest, we identified a 42-gene expression signature that could not only distinguish good and poor PI response in the HMCL panel, but could also be successfully applied to four different clinical data sets on MM patients undergoing PI-based chemotherapy to distinguish between extraordinary (good and poor) outcomes. Our results demonstrate the use of in vitro modeling and machine learning-based approaches to establish predictive biomarkers of response and resistance to drugs that may serve to better direct myeloma patient treatment options.