NMF Clustering: Accessible NMF-based Clustering Utilizing GPU Acceleration

Non-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using CuPy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePattern gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelines on high performance computing (HPC) clusters that enable reproducible in silico research for non-programmers.

The GPCR-Gαs-PKA signaling axis promotes T cell dysfunction and cancer immunotherapy failure

Immune checkpoint blockade (ICB) targeting PD-1 and CTLA-4 has revolutionized cancer treatment. However, many cancers do not respond to ICB, prompting the search for additional strategies to achieve durable responses. G-protein-coupled receptors (GPCRs) are the most intensively studied drug targets but are underexplored in immuno-oncology. Here, we cross-integrated large singe-cell RNA-sequencing datasets from CD8+ T cells covering 19 distinct cancer types and identified an enrichment of Gαs-coupled GPCRs on exhausted CD8+ T cells. These include EP2, EP4, A2AR, β1AR and β2AR, all of which promote T cell dysfunction. We also developed transgenic mice expressing a chemogenetic CD8-restricted Gαs-DREADD to activate CD8-restricted Gαs signaling and show that a Gαs-PKA signaling axis promotes CD8+ T cell dysfunction and immunotherapy failure. These data indicate that Gαs-GPCRs are druggable immune checkpoints that might be targeted to enhance the response to ICB immunotherapies.

NMFClustering: Accessible NMF-based clustering utilizing GPU acceleration

Non-negative Matrix Factorization (NME) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using Cupy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePatten gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelnes on high performance computing (HPC) culsters that enable reproducible in silco research for non-programmers.

Abstract 4281: Data driven refinement of gene signatures for enrichment analysis and cell state characterization

The use of gene expression data has been crucial to the functional characterization of changes in molecular pathway activity and for identifying targets for novel treatments. However, the interpretation of this data is complicated by its high dimensionality and the difficulty of identifying biological signals within a list of differentially expressed genes. Gene Set Enrichment Analysis (GSEA) is a standard method for identifying pathway enrichment in gene expression data by testing whether a set of genes whose expression would indicate the activity of a specific process or phenotype are coordinately up- or downregulated more than would be expected by chance. As GSEA relies on high quality gene sets with coordinately regulated member genes, we maintain the Molecular Signatures Database (MSigDB) which contains 9 collections of curated gene sets representing different biological pathways and processes. Over time, we have observed that some of the MSigDB gene sets, especially those that are manually curated or defined in a very specific biological context, may not provide a sensitive and specific enough co-regulation signature. In response, we have created a data-driven, matrix-factorization-based refinement method to build more sensitive and specific gene sets. This method incorporates large-scale datasets from multiple sources such as the Cancer Dependency Map as well as curated protein-protein interaction networks. We will present the initial results of this refinement method and our ongoing work which will yield a new collection of refined gene sets that will be made freely available in MSigDB for use with GSEA and many other applications.

Citation Format: Alexander T. Wenzel, Pablo Tamayo, Jill P. Mesirov. Data driven refinement of gene signatures for enrichment analysis and cell state characterization. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4281.

Defining Key Performance Indicators for the California COVID-19 Exposure Notification System (CA Notify)

OBJECTIVES

Toward common methods for system monitoring and evaluation, we proposed a key performance indicator framework and discussed lessons learned while implementing a statewide exposure notification (EN) system in California during the COVID-19 epidemic.

MATERIALS AND METHODS

California deployed the Google Apple Exposure Notification framework, branded CA Notify, on December 10, 2020, to supplement traditional COVID-19 contact tracing programs. For system evaluation, we defined 6 key performance indicators: adoption, retention, sharing of unique codes, identification of potential contacts, behavior change, and impact. We aggregated and analyzed data from December 10, 2020, to July 1, 2021, in compliance with the CA Notify privacy policy.

RESULTS

We estimated CA Notify adoption at nearly 11 million smartphone activations during the study period. Among 1 654 201 CA Notify users who received a positive test result for SARS-CoV-2, 446 634 (27%) shared their unique code, leading to ENs for other CA Notify users who were in close proximity to the SARS-CoV-2-positive individual. We identified at least 122 970 CA Notify users as contacts through this process. Contact identification occurred a median of 4 days after symptom onset or specimen collection date of the user who received a positive test result for SARS-CoV-2.

PRACTICE IMPLICATIONS

Smartphone-based EN systems are promising new tools to supplement traditional contact tracing and public health interventions, particularly when efficient scaling is not feasible for other approaches. Methods to collect and interpret appropriate measures of system performance must be refined while maintaining trust and privacy.

Abstract 5032: Data driven refinement of gene signatures for enrichment analysis and cell state characterization

In the study of human disease, the use of gene expression data has been crucial to the functional characterization of changes in molecular pathway activity and for identifying targets for novel treatments. However, the interpretation of this data is often complicated by its high dimensionality and the difficulty of identifying biological signals within a list of differentially expressed genes. Gene Set Enrichment Analysis (GSEA) is a community standard method for identifying pathway enrichment in gene expression data by testing whether a set of genes whose expression would indicate the activity of a specific process or phenotype are coordinately up- or downregulated more than would be expected by chance. As GSEA relies on high quality gene sets with coordinately regulated member genes, we maintain the Molecular Signatures Database (MSigDB) which contains 9 collections of curated, annotated gene sets representing different biological pathways and processes. Over time, we have observed that some of the MSigDB gene sets, especially those that are manually curated or defined in a very specific biological context, may not provide a sensitive and specific enough co-regulation signature for their corresponding phenotype. In response, we have created a data-driven, matrix-factorization-based refinement method to build more sensitive and specific gene sets. This method incorporates large-scale datasets from multiple sources including compendia such as the Cancer Dependency Map as well as curated protein-protein interaction networks. We will present the initial results of this refinement method as well as our ongoing work which will yield a new collection of refined gene sets that will be made freely available in MSigDB for use with GSEA and many other applications.

Citation Format: Alexander T. Wenzel, Pablo Tamayo, Jill P. Mesirov. Data driven refinement of gene signatures for enrichment analysis and cell state characterization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5032.

KITlow Cells Mediate Imatinib Resistance in Gastrointestinal Stromal Tumor

Gastrointestinal stromal tumor (GIST) is commonly driven by oncogenic KIT mutations that are effectively targeted by imatinib (IM), a tyrosine kinase inhibitor (TKI). However, IM does not cure GIST, and adjuvant therapy only delays recurrence in high-risk tumors. We hypothesized that GIST contains cells with primary IM resistance that may represent a reservoir for disease persistence. Here, we report a subpopulation of CD34+KITlow human GIST cells that have intrinsic IM resistance. These cells possess cancer stem cell-like expression profiles and behavior, including self-renewal and differentiation into CD34+KIThigh progeny that are sensitive to IM treatment. We also found that TKI treatment of GIST cell lines led to induction of stem cell-associated transcription factors (OCT4 and NANOG) and concomitant enrichment of the CD34+KITlow cell population. Using a data-driven approach, we constructed a transcriptomic-oncogenic map (Onco-GPS) based on the gene expression of 134 GIST samples to define pathway activation during GIST tumorigenesis. Tumors with low KIT expression had overexpression of cancer stem cell gene signatures consistent with our in vitro findings. Additionally, these tumors had activation of the Gas6/AXL pathway and NF-κB signaling gene signatures. We evaluated these targets in vitro and found that primary IM-resistant GIST cells were effectively targeted with either single-agent bemcentinib (AXL inhibitor) or bardoxolone (NF-κB inhibitor), as well as with either agent in combination with IM. Collectively, these findings suggest that CD34+KITlow cells represent a distinct, but targetable, subpopulation in human GIST that may represent a novel mechanism of primary TKI resistance, as well as a target for overcoming disease persistence following TKI therapy.

Model-based assessment of mammalian cell metabolic functionalities using omics data

Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie).

Abstract 4411: A gene set enrichment analysis approach in single-cells along pseudotime trajectories reveals the dynamic activity of oncogenic pathways

Single cell RNA-sequencing (scRNA-seq) has advanced studies of heterogeneous tissues and cell populations in both healthy and disease states. Dimensionality reduction methods leverage this highly granular data to identify cellular states not captured by “bulk” gene expression or to organize cells along ‘pseudotime' trajectories related to biological processes such as cell cycle or cellular differentiation. Cellular states and trajectories are often annotated using individual marker genes and not gene sets or pathways, despite their more reliable and interpretable association to biological processes. Gene Set Enrichment Analysis (GSEA) was designed to test the enrichment of gene sets in bulk transcriptomic data, but due to the sparsity of the expression data, has not been adapted to single cell analysis. We propose a method to compute gene set enrichment for scRNA-seq data, which calculates an enrichment score for each cell in the dataset. We apply this method to a set of matched ovarian cancer cell lines with acquired resistance to carboplatin. After aligning the cells to a cell-cycle derived pseudotime trajectory, the activity of pathways which were dysregulated in bulk expression profiles appeared to change with the cell cycle progression. The analysis further identified interferon alpha signaling induction in resistant cells, independent of differences in cell cycle progression. Hence, by quantifying and analyzing pathway-level transcriptional activity in the context of a single-cell pseudotime trajectory, the proposed method provides more interpretable annotation of biological processes altered in cancer progression and treatment.

Citation Format: Alexander T. Wenzel, Devora Champa, Stephen B. Howell, Jill P. Mesirov, Olivier Harismendy. A gene set enrichment analysis approach in single-cells along pseudotime trajectories reveals the dynamic activity of oncogenic pathways [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4411.

An accessible, interactive GenePattern Notebook for analysis and exploration of single-cell transcriptomic data

Single-cell RNA sequencing (scRNA-seq) has emerged as a popular method to profile gene expression at the resolution of individual cells. While there have been methods and software specifically developed to analyze scRNA-seq data, they are most accessible to users who program. We have created a scRNA-seq clustering analysis GenePattern Notebook that provides an interactive, easy-to-use interface for data analysis and exploration of scRNA-Seq data, without the need to write or view any code. The notebook provides a standard scRNA-seq analysis workflow for pre-processing data, identification of sub-populations of cells by clustering, and exploration of biomarkers to characterize heterogeneous cell populations and delineate cell types.

Nonoverlapping Clinical and Mutational Patterns in Melanomas from the Female Genital Tract and Atypical Genital Nevi

Genital melanomas (GM) are the second most common cancer of the female external genitalia and may be confused with atypical genital nevi (AGN), which exhibit atypical histological features but have benign behavior. In this study, we compared the clinical, histological, and molecular features of 19 GM and 25 AGN. We described chromosomal copy number aberrations and the mutational status of 50 oncogenes and tumor suppressor genes in both groups. Our study showed that a pigmented lesion occurring in mucosal tissue, particularly in postmenopausal women, was more likely to be a melanoma than a nevus. GM had high levels of chromosomal instability, with many copy number aberrations. Furthermore, we found a completely nonoverlapping pattern of oncogenic mutations when comparing GM and AGN. In GM, we report somatic mutations in KIT and TP53. Conversely, AGN had frequent BRAF V600E mutations, which were not seen in any of the GM. Our results show that GM and AGN have distinct clinical and molecular changes and that GM have a different mutational pattern compared with AGN.