Analysis Of Gene Expression Data Research Articles

Analysis of the influence of microgravity and space radiation on astronauts’ gene expression: An approach using quantum simulations and fuzzy logic

An analysis of the effects of microgravity and space radiation on astronauts’ RNA expression has been developed, utilizing data from NASA’s “Cell-Free RNA Analysis of Plasma Samples Collected from Six Astronauts in JAXA Cell-Free Epigenome (CFE) Study.” The project aims to deepen our understanding of how the space environment affects gene expression, particularly in key genes such as ACTB and ACTG1, by employing quantum simulations and unconventional logic, including quantum and fuzzy logic. The results suggest that specific genes like ACTB and ACTG1 undergo significant changes in their expression under these unique conditions. This in-terdisciplinary approach allows for a detailed analysis of gene expression data and explores the potential of emerging technologies in space research and bioinformatics.

Gene Expression Profile of Benign, Intermediate, and Malignant Spitz and Spitzoid Melanocytic Lesions.

Spitz and Spitzoid lesions represent one of the most challenging melanocytic neoplasms in dermatopathology. Nosologic classification has been more recently improved by the discovery of novel molecular drivers, particularly translocations. In the current study, we aimed to use an unbiased approach to explore the gene expression profile of a group of melanocytic Spitz and Spitzoid melanocytic lesions ranging from benign lesions to melanoma, including intermediate lesions such as SPARK nevi and atypical Spitz tumors/melanocytomas. Using unsupervised analysis of gene expression data, we found some distinct hierarchical clusters of lesions, including groups characterized by ALK and NTRK translocations. Few non-ALK translocated tumors demonstrated increased ALK expression, confirmed by immunohistochemistry. Spitz tumors with overlapping features of dysplastic nevi, so-called SPARK nevi, appear to have a common gene expression profile by hierarchical clustering. Finally, weighted gene correlation network analysis identified gene modules variably regulated in subtypes of these cases. Thus, gene expression profiling of Spitz and Spitzoid lesions represents a viable instrument for the characterization of these lesions.

Effects of natural treatments on the varroa mite infestation levels and overall health of honey bee (Apis mellifera) colonies.

The survival of the honey bee (Apis mellifera), which has a crucial role in pollination and ecosystem maintenance, is threatened by many pathogens, including parasites, bacteria, fungi and viruses. The ectoparasite Varroa destructor is considered the major cause of the worldwide decline in honey bee colony health. Although several synthetic acaricides are available to control Varroa infestations, resistant mites and side effects on bees have been documented. The development of natural alternatives for mite control is therefore encouraged. The study aims at exploring the effects of cinnamon and oregano essential oils (EOs) and of a mixed fruit cocktail juice on mite infestation levels and bee colony health. A multi-method study including hive inspection, mite count, molecular detection of fungal, bacterial and viral pathogens, analysis of defensin-1, hymenoptaecin and vitellogenin immune gene expression, colony density and honey production data, was conducted in a 20-hive experimental apiary. The colonies were divided into five groups: four treatment groups and one control group. The treatment groups were fed on a sugar syrup supplemented with cinnamon EO, oregano EO, a 1:1 mixture of both EOs, or a juice cocktail. An unsupplemented syrup was, instead, used to feed the control group. While V. destructor affected all the colonies throughout the study, no differences in mite infestation levels, population density and honey yield were observed between treatment and control groups. An overexpression of vitellogenin was instead found in all EO-treated groups, even though a significant difference was only found in the group treated with the 1:1 EO mixture. Viral (DWV, CBPV and BQCV), fungal (Nosema ceranae) and bacterial (Melissococcus plutonius) pathogens from both symptomatic and asymptomatic colonies were detected.

Abstract PO3-24-06: Integrative genomic and CRISPR screen analysis identifies synthetic viability interactions in BRCA1/2 cancers

Abstract Background Pathogenic BRCA1 and BRCA2 (BRCA1/2) germline mutations contribute to hereditary breast cancer and are linked with increased risk of other cancers. Paradoxically, bi-allelic inactivation of BRCA1/2 (bBRCA1/2) is embryonically lethal. Although TP53 loss can at least in part mitigated the deleterious impact of Brca1 deficiency in mice, this is insufficient in human cells to compensate for decreased proliferative capacity. What other compensatory mechanisms facilitate oncogenesis remains unclear. Methods We performed an integrative analysis of cancer genomes from bBRCA1/2 tumors and CRISPR/Cas9 screens from cellular models to identify novel gene candidates that facilitate synthetic viability. First, we analyzed cancer genomes from ovarian and breast cancer derived from TCGA and identified genomic loci, which were enriched for recurrent copy number alterations (CNAs) in bBRCA1/2 tumors. We subsequently identified genes from these CNA loci that had corresponding changes in gene expression. Then, we used high-throughput genome-wide CRISPR/Cas9 knockout screens in BRCA1/2−/− cell lines (BRCA1−/- in RPE1 cells, BRCA2−/− in DLD1 cells) to identify which of these genes increased cell proliferation in vitro. Lastly, we experimentally validated these synthetic viable interactions using an orthogonal MCF12a system to evaluate changes in colony formation with a clonogenic cell survival assay. Results In human cancer genomes, we identified that bBRCA1/2 tumors harbored recurrent large-scale genetic deletions significantly more frequent than histologically matched control tumors. We identified 148 cytobands (23 distinct genomic loci) frequently deleted in bBRCA1/2 ovarian cancers (N=92), and 90 cytobands (15 distinct genomic loci) in bBRCA1/2 ER+ breast cancers (N=30). Subsequent computational analysis of gene expression data identified transcriptionally decreased genes in the frequently deleted loci. Cross-referencing with CRISPR/Cas9 screens in BRCA1/2−/− cells, we ultimately identified 277 and 218 genes that were altered in human tumors and putatively enhanced viability in BRCA1/2-null models but not in isogenic wild-type cell lines in BRCA1 and BRCA2 models, respectively. We subsequently selected 17 genes that either increased viability in BRCA1, BRCA2, or both contexts for experimental validation. Of these, about 40% (7 of 17) were experimentally validated (RIC8A, GNA12, ATMIN, IPO7, ATXN2, KDM1A, and NUP98) and had evidence of synthetic viability in this cellular context. Interestingly, RIC8A and GNA12 interact with each other, are both involved in G protein-coupled signaling pathway, and are synthetically viable with both BRCA1−/− and BRCA2−/− cells. In a re-analysis of a recently published genomic dataset on metastatic breast cancer, we found that homologous recombination deficient tumors developed additional loss of function mutations in RIC8A in the metastatic setting. Conclusions This study provides insights into the oncogenesis of BRCA1/2 malignancies and describes a high-throughput framework to identify synthetic viability interactions and causal driver genes affected by large-scale CNAs in human cancers. Citation Format: Yingjie Zhu, Xin Pei, Ardijana Novaj, Jeremy Setton, Fatemeh Derakhshan, Pier Selenica, Niamh McDermott, Sonali Sinha, Atif Khan, Simon Powell, Jorge Reis-Filho, Nadeem Riaz. Integrative genomic and CRISPR screen analysis identifies synthetic viability interactions in BRCA1/2 cancers [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-24-06.

Unlocking the genetic tapestry of autoimmune diseases: Unveiling common genes across multiple conditions.

This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune-associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain-Barré syndrome. Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour-MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions. Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players. This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine.

Prognostic and immunotherapeutic implications of bilirubin metabolism-associated genes in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a major subtype of non-small-cell lung cancer and accompanies high mortality rates. While the role of bilirubin metabolism in cancer is recognized, its specific impact on LUAD and patient response to immunotherapy needs to be elucidated. This study aimed to develop a prognostic signature of bilirubin metabolism-associated genes (BMAGs) to predict outcomes and efficacy of immunotherapy in LUAD. We analysed gene expression data from The Cancer Genome Atlas (TCGA) to identify survival-related BMAGs and construct a prognostic model in LUAD. The prognostic efficacy of our model was corroborated by employing TCGA-LUAD and five Gene Expression Omnibus datasets, effectively stratifying patients into risk-defined cohorts with marked disparities in survival. The BMAG signature was indeed an independent prognostic determinant, outperforming established clinical parameters. The low-risk group exhibited a more favourable response to immunotherapy, highlighted by increased immune checkpoint expression and immune cell infiltration. Further, somatic mutation profiling differentiated the molecular landscapes of the risk categories. Our screening further identified potential drug candidates preferentially targeting the high-risk group. Our analysis of critical BMAGs showed the tumour-suppressive role of FBP1, highlighting its suppression in LUAD and its inhibitory effects on tumour proliferation, migration and invasion, in addition to its involvement in cell cycle and apoptosis regulation. These findings introduce a potent BMAG-based prognostic indicator and offer valuable insights for prognostication and tailored immunotherapy in LUAD.

Availability of receptors for advanced glycation end-products (RAGE) influences differential transcriptome expression in mice exposed to chronic secondhand smoke

The receptor for advanced glycation end-products (RAGE) has a central function in orchestrating inflammatory responses in multiple disease states. RAGE is a transmembrane pattern recognition receptor with particular interest in lung disease due to its naturally abundant pulmonary expression. Our previous research demonstrated an inflammatory role for RAGE following acute exposure to secondhand smoke (SHS). However, chronic inflammatory mechanisms associated with RAGE remain unclear. In this study, we assessed transcriptional outcomes in mice exposed to chronic smoke in the context of RAGE expression. RAGE knockout (RKO) and wild type (WT) mice were exposed to SHS five times weekly via a nose-only delivery system (Scireq Scientific, Montreal, Canada) for six months and compared to mice exposed to room air (RA) only. Total lung RNA was isolated using the Direct-zol RNA MiniPrep kit (Zymo Research, Irvine, CA) and mRNA was purified using poly-T oligo-attached magnetic beads. Synthesis of cDNA, library construction, and sequencing was performed using standard approaches. We specifically compared the phenotypic and environmental conditions from WT+RA, WT+SHS, and RKO+SHS mice. Preprocessing and analysis of RNA-sequencing gene expression data included read trimming, mapping and quantifying the reads to transcripts, and calculating significant differentially expressed genes. The results of these analyses were summarized and compared via Venn diagrams, volcano plots, and functional gene cluster enrichment analysis. Notable gene clusters were specific to cytoskeletal elements, inflammatory signaling, and ciliogenesis. Finally, gene ontologies (GO) demonstrated significant biological pathways that were differentially impacted by the presence of RAGE. These data collectively identify several opportunities to further dissect RAGE signaling in the context of SHS exposure and foreshadow possible therapeutic modalities. This work was supported by funding from the National Institutes of Health (NIH 1R15-HL152257). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Availability of Receptors for Advanced Glycation End-Products (RAGE) Influences Differential Transcriptome Expression in Lungs from Mice Exposed to Chronic Secondhand Smoke (SHS).

The receptor for advanced glycation end-products (RAGE) has a central function in orchestrating inflammatory responses in multiple disease states including chronic obstructive pulmonary disease (COPD). RAGE is a transmembrane pattern recognition receptor with particular interest in lung disease due to its naturally abundant pulmonary expression. Our previous research demonstrated an inflammatory role for RAGE following acute exposure to secondhand smoke (SHS). However, chronic inflammatory mechanisms associated with RAGE remain ambiguous. In this study, we assessed transcriptional outcomes in mice exposed to chronic SHS in the context of RAGE expression. RAGE knockout (RKO) and wild-type (WT) mice were delivered nose-only SHS via an exposure system for six months and compared to control mice exposed to room air (RA). We specifically compared WT + RA, WT + SHS, RKO + RA, and RKO + SHS. Analysis of gene expression data from WT + RA vs. WT + SHS showed FEZ1, Slpi, and Msln as significant at the three-month time point; while RKO + SHS vs. WT + SHS identified cytochrome p450 1a1 and Slc26a4 as significant at multiple time points; and the RKO + SHS vs. WT + RA revealed Tmem151A as significant at the three-month time point as well as Gprc5a and Dynlt1b as significant at the three- and six-month time points. Notable gene clusters were functionally analyzed and discovered to be specific to cytoskeletal elements, inflammatory signaling, lipogenesis, and ciliogenesis. We found gene ontologies (GO) demonstrated significant biological pathways differentially impacted by the presence of RAGE. We also observed evidence that the PI3K-Akt and NF-κB signaling pathways were significantly enriched in DEGs across multiple comparisons. These data collectively identify several opportunities to further dissect RAGE signaling in the context of SHS exposure and foreshadow possible therapeutic modalities.

Clustering algorithm based on DINNSM and its application in gene expression data analysis.

Selecting an appropriate similarity measurement method is crucial for obtaining biologically meaningful clustering modules. Commonly used measurement methods are insufficient in capturing the complexity of biological systems and fail to accurately represent their intricate interactions. This study aimed to obtain biologically meaningful gene modules by using the clustering algorithm based on a similarity measurement method. A new algorithm called the Dual-Index Nearest Neighbor Similarity Measure (DINNSM) was proposed. This algorithm calculated the similarity matrix between genes using Pearson's or Spearman's correlation. It was then used to construct a nearest-neighbor table based on the similarity matrix. The final similarity matrix was reconstructed using the positions of shared genes in the nearest neighbor table and the number of shared genes. Experiments were conducted on five different gene expression datasets and compared with five widely used similarity measurement techniques for gene expression data. The findings demonstrate that when utilizing DINNSM as the similarity measure, the clustering results performed better than using alternative measurement techniques. DINNSM provided more accurate insights into the intricate biological connections among genes, facilitating the identification of more accurate and biological gene co-expression modules.

ScGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data

BackgroundAccurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH.ResultsWe introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal.ConclusionsJoint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

Evaluation of network-guided random forest for disease gene discovery.

Gene network information is believed to be beneficial for disease module and pathway identification, but has not been explicitly utilized in the standard random forest (RF) algorithm for gene expression data analysis. We investigate the performance of a network-guided RF where the network information is summarized into a sampling probability of predictor variables which is further used in the construction of the RF. Our simulation results suggest that network-guided RF does not provide better disease prediction than the standard RF. In terms of disease gene discovery, if disease genes form module(s), network-guided RF identifies them more accurately. In addition, when disease status is independent from genes in the given network, spurious gene selection results can occur when using network information, especially on hub genes. Our empirical analysis on two balanced microarray and RNA-Seq breast cancer datasets from The Cancer Genome Atlas (TCGA) for classification of progesterone receptor (PR) status also demonstrates that network-guided RF can identify genes from PGR-related pathways, which leads to a better connected module of identified genes. Gene networks can provide additional information to aid the gene expression analysis for disease module and pathway identification. But they need to be used with caution and validation on the results need to be carried out to guard against spurious gene selection. More robust approaches to incorporate such information into RF construction also warrant further study.

Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study

BackgroundMale-pattern baldness (MPB) is the most common cause of hair loss in men. It can be categorized into three types: type 2 (T2), type 3 (T3), and type 4 (T4), with type 1 (T1) being considered normal. Although various MPB-associated genetic variants have been suggested, a comprehensive study for linking these variants to gene expression regulation has not been performed to the best of our knowledge.ResultsIn this study, we prioritized MPB-related tissue panels using tissue-specific enrichment analysis and utilized single-tissue panels from genotype-tissue expression version 8, as well as cross-tissue panels from context-specific genetics. Through a transcriptome-wide association study and colocalization analysis, we identified 52, 75, and 144 MPB associations for T2, T3, and T4, respectively. To assess the causality of MPB genes, we performed a conditional and joint analysis, which revealed 10, 11, and 54 putative causality genes for T2, T3, and T4, respectively. Finally, we conducted drug repositioning and identified potential drug candidates that are connected to MPB-associated genes.ConclusionsOverall, through an integrative analysis of gene expression and genotype data, we have identified robust MPB susceptibility genes that may help uncover the underlying molecular mechanisms and the novel drug candidates that may alleviate MPB.

(363) - Unsupervised Analysis of Gene Expression Data to Identify Lung Transplant Rejection Phenotypes

(363) - Unsupervised Analysis of Gene Expression Data to Identify Lung Transplant Rejection Phenotypes

Grouped Spherical Data Modeling Through Hierarchical Nonparametric Bayesian Models and Its Application to fMRI Data Analysis.

Recently, spherical data (i.e., L2 normalized vectors) modeling has become a promising research topic in various real-world applications (such as gene expression data analysis, document categorization, and gesture recognition). In this work, we propose a hierarchical nonparametric Bayesian model based on von Mises-Fisher (VMF) distributions for modeling spherical data that involve multiple groups, where each observation within a group is sampled from a VMF mixture model with an infinite number of components allowing them to be shared across groups. Our model is formulated by employing a hierarchical nonparametric Bayesian framework known as the hierarchical Pitman-Yor (HPY) process mixture model, which possesses a power-law nature over the distribution of the components and is particularly useful for data distributions with heavy tails and skewness. To learn the proposed HPY process mixture model with VMF distributions, we systematically develop a closed-form optimization algorithm based on variational Bayes (VB). The merits of the proposed hierarchical Bayesian nonparametric model for modeling grouped spherical data are demonstrated through experiments on both synthetic data and a real-world application about resting-state functional magnetic resonance imaging (fMRI) data analysis.

Association of Antipsychotic Drugs with Venous Thromboembolism: Data Mining of Food and Drug Administration Adverse Event Reporting System and Mendelian Randomization Analysis.

Past observational studies have reported on the association between antipsychotic drugs and venous thromboembolism (VTE); however, the conclusions remain controversial, and its mechanisms are yet to be fully understood. Thus, in this study, we aim to determine the associations of antipsychotic drugs with VTE, including deep vein thrombosis (DVT) and pulmonary embolism (PE), and their potential mechanisms. We first mined the adverse event signals of VTE, DVT, and PE caused by antipsychotic drugs in Food and Drug Administration Adverse Event Reporting System (FAERS). Next, we used two-sample Mendelian randomization (MR) method to investigate the association of antipsychotic drug target gene expression with VTE, DVT, and PE, using single-nucleotide polymorphisms as genetic instruments. We not only used the expression of all antipsychotic drug target genes as exposure to perform MR analyses but also analyzed the effect of single target gene expression on the outcomes. In the FAERS, 1694 cases of VTE events were reported by 16 drugs. However, using the MR approach, no significant association was determined between the expression of all antipsychotic target genes and VTE, DVT, or PE, either in blood or brain tissue. Although the analysis of single gene expression data showed that the expression of nine genes was associated with VTE events, these targets lacked significant pharmacological action. Adverse event mining results have supported the claim that antipsychotic drugs can increase the risk of VTE. However, we failed to find any genetic evidence for this causal association and potential mechanisms. Thus, vigilance is still needed for antipsychotic drug-related VTE despite the limited supporting evidence.

A comparative assessment of reference genes in mouse brown adipocyte differentiation and thermogenesis in vitro

ABSTRACT Adipogenic differentiation and thermogenesis in brown adipose tissue (BAT) undergo dynamic processes, altering phenotypes and gene expressions. Proper reference genes in gene expression analysis are crucial to mitigate experimental variances and ensure PCR efficacy. Unreliable reference genes can lead to erroneous gene expression quantification, resulting in data misinterpretation. This study focused on identifying suitable reference genes for mouse brown adipocyte research, utilizing brown adipocytes from the Ucp1-luciferase ThermoMouse model. Comparative analysis of gene expression data under adipogenesis and thermogenesis conditions was conducted, validating 13 housekeeping genes through various algorithms, including DeltaCq, BestKeeper, geNorm, Normfinder, and RefFinder. Tbp and Rer1 emerged as optimal references for Ucp1 and Pparg expression in brown adipogenesis, while Tbp and Ubc were ideal for the expression analysis of these target genes in thermogenesis. Conversely, certain conventional references, including Actb, Tubb5, and Gapdh, proved unstable as reference genes under both conditions. These findings stress the critical consideration of reference gene selection in gene expression analysis within specific biological systems to ensure accurate conclusions.

Abstract 4947: Uncovering the molecular basis for clinically relevant sphingolipidomic subtypes in acute myeloid leukemia

Abstract Acute myeloid leukemia (AML) has been extensively studied at the genomic level, resulting in various genomic classifications. Despite the progress, three key challenges remain: 1) a significant proportion of patients lack identified genomic features, 2) a discrepancy exists between genomic risk classification and clinical outcomes, and 3) many mutated targets remain non-druggable. There is an urgent need for an enhanced AML risk classification that goes beyond genomic aberrations to guide effective therapeutics. Recent research, including our own, has increasingly linked AML pathogenesis and therapeutic resistance with dysfunctional sphingolipid metabolism, a family of bioactive molecules crucial for cellular functions. In a recent manuscript, we identified two robust sphingolipidomic clusters in AML, characterized by reciprocal abundances of hexosylceramide (Hex) and sphingomyelin (SM) species (HexloSMhi, HexhiSMlo). These clusters correlate with latent transcriptional states and stratify patient groups in multiple independent datasets such as TCGA, and BeatAML, with the HexloSMhi cluster representing a high-risk subgroup associated with poor clinical outcomes. These findings underscore the role of sphingolipids in refining AML risk assessment and creating avenues for discovery and therapeutic intervention. However, the signaling states driving these sphingolipidomic clusters and the mechanisms elevating risk in the HexloSMhi cluster require further investigation. To identify regulatory genes for the AML subtypes, we constructed gene regulatory networks using differentially expressed genes. Focusing on the high-risk subtype, we identified highly connected genes ranked by connectivity, including KDM1A, GSK3β, LCK, and STAT5A. We found that higher STAT5A abundances in the HexloSMhi subtype, particularly correlated with pro-survival Bcl-xL proteins. Furthermore, we identified STAT5 protein abundance as a key variable associated with drug sensitivity from pharmacological screening with sphingolipid pathway inhibitors and AML therapeutics 30 AML cell lines and patient samples. This is encouraging as STAT5 is druggable as evidenced in the NIH-Pharos druggable genome database, and several small-molecule inhibitors are currently in clinical trials. Analysis of gene expression data suggest that STAT5 inhibition, through genetic or pharmacological means, downregulates cell cycle-related genes and significantly alters sphingolipid gene expression. Recent work by others indicates that sphingolipid-catalyzing enzymes regulate STAT5 activity in leukemias, with ceramides acting as a second messenger in cells. Taken together, this suggests a possibility of a positive feedback loop between sphingolipid homeostasis and activity of STAT5; the details of this interaction require further investigation. Citation Format: B Bishal Paudel, Su-Fern Tan, Johnson Ung, David Claxton, David J. Feith, Thomas P. Loughran, Kevin A. Janes. Uncovering the molecular basis for clinically relevant sphingolipidomic subtypes in acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4947.

Abstract 2485: A combined mregDC and Treg signature associates with antitumor efficacy of CCR4 antagonist tivumecirnon FLX475

Abstract Cancers evade immune surveillance through multiple mechanisms including the recruitment of immunosuppressive regulatory T cells (Treg). Tivumecirnon (formerly known as FLX475), a small molecule oral CCR4 antagonist under clinical evaluation, blocks the binding of CCR4 to its ligands CCL17 and CCL22 and thereby reduces Treg infiltration into the tumor microenvironment (TME). This relieves Treg-mediated immune suppression resulting in increased antitumor immunity. Our clinical study (NCT03674567) demonstrated that tivumecirnon can block the local accumulation of Treg in the TME and has clinical activity as monotherapy as well as in combination with pembrolizumab. In both mouse and human tumors, we identified a subset of tumor infiltrating dendritic cells (DC) producing high levels of CCL22, previously identified as mregDC, suggesting a potential role in the CCR4-dependent recruitment of Treg. To study the role of mregDC in the recruitment of Treg, non-clinical studies were performed. CD45+ immune cells from the TME of MB49 tumor bearing mice treated with tivumecirnon or vehicle and were analyzed by flow cytometry, scRNA-seq and CITE-seq. We confirmed that mregDC cells are the primary producer of CCL22 in the TME and that tivumecirnon treatment reduced the number of Treg cells in the TME and increased the percentage of IFN gamma and granzyme B expressing CD4+ T cells. Based on these findings we interrogated tumor biopsies from patients from our clinical study prior to (n = 33), and after approximately 6 weeks of treatment (n = 22, pre/post-treatment paired samples) with tivumecirnon +/- pembrolizumab. We performed RNAseq and compared to published biopsy data from anti-PD-1 treated patients. We found that higher mregDC + Treg signature levels at baseline significantly (p < 0.001) associated with response to tivumecirnon and pembrolizumab therapy. Importantly, analysis of published datasets revealed that higher baseline mregDC and Treg levels are not associated with response to anti-PD-1 monotherapy. Analysis of TCGA gene expression data demonstrated a positive correlation with mregDC and Treg cells across human tumor types, indicating a previously unknown relationship between these cell types. Further application of this mregDC and Treg signature to the TCGA dataset identified a subset of cancer types resistant to prior treatments that could potentially benefit from combinatorial treatment with tivumecirnon. Overall, this study demonstrated that inhibition of the CCR4 pathway using tivumecirnon can disrupt the immunosuppressive mregDC – Treg axis leading to enhanced antitumor activity. Furthermore, the levels of mregDC and Treg may be used as a biomarker for selection of patients more likely to respond to tivumecirnon in combination with anti-PD-1. Additional studies are required to confirm these results Citation Format: Adam Grant, Juraj Adamik, Jingtao Qiu, Jacob Wert, Molly Grandcolas, Scott Jacobson, Rakesh K. Goyal, William Ho, Shoji Ikeda, Marvin Au, Damian Trujillo, Michael Chisamore, Denise de Almeida Nagata, Mohsen S. Ghomi, Dirk G. Brockstedt, Paul D. Kassner, George Katibah. A combined mregDC and Treg signature associates with antitumor efficacy of CCR4 antagonist tivumecirnon FLX475 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2485.

Abstract 6430: Gene expression signature quantifies the cellular contribution of the tumor vs. the microenvironment in determining colorectal cancer prognosis and therapeutic outcome

Abstract Accumulating evidence has suggested that cancer progression and therapeutic response may depend on not only tumor cells but also their tumor microenvironment (TME). However, their contributory roles are complex and not well understood. Here we report an analysis of 2373 human colorectal cancer (CRC) tumors representing all stages, leading to identification of a 20-gene signature capable of quantifying the cellular contribution of the tumor vs. the TME in determining colorectal cancer prognosis and therapeutic outcome. We classified these tumors into the consensus molecular subtypes (CMS1-4) with distinct, variable TME cellular features defined by CIBERSORT deconvolution analysis of bulk gene expression data. Surprisingly, all the 10 positive signature genes (portending worse survival) were strongly correlated with the TME-rich CMS1 and CMS4 tumors. By contrast, all the 10 negative signature genes (portending better survival) were highly related to the TME-poor CMS2 and CMS3 tumors. A single cell expression analysis from an independent database further revealed that strikingly, all the 10 positive genes were principally expressed by the immune/stromal TME whereas all the 10 negative genes were predominantly expressed in epithelial tumor cells. These data suggest that the TME has an equally important role as do tumor cells in determining CRC prognosis and support targeting the TME to improve survival. Moreover, distinct gene expression from tumor cells versus the TME also showed a differential impact on therapeutic outcome. Retrospective analyses on two independent clinical trial datasets show that the 20-gene signature score significantly predicted progression free survival in metastatic CRC patients treated with cetuximab, an FDA-approved EGFR inhibitor therapy. These data suggest a potential for the 20-gene signature as a predictive biomarker to identify “sensitive” versus “resistant” subpopulations of CRC patients to improve outcome of EGFR-targeted therapy. Citation Format: Mingli Yang, Timothy J. Yeatman, Michael V. Nebozhyn, Michael J. Schell, Lance Pflieger, Andrey Loboda, Warren J. Pledger, Ramani Soundararajan, Michelle Maurin, Heiman Wang, Jetsen Rodriguez Silva, Ashley Alden. Gene expression signature quantifies the cellular contribution of the tumor vs. the microenvironment in determining colorectal cancer prognosis and therapeutic outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6430.

Abstract 2270: UALCAN Mobile, An app for cancer gene expression data analysis

Abstract Cancer is a complex disease effecting different organs and a major cause of death and a burden on the society. Multiple molecular alteration occur during cancer initiation and disease progression and metastasis. Recent advances in technology led to generation of large amount of molecular data including transcriptome. These large datasets can be used to analyze and identify sub-class specific cancer biomarkers and targets. However, there is a need for the development user friendly tools for its analysis and dissemination of the large scale cancer molecular data. Earlier, we had developed and upgraded web based comprehensive proteogenomic platform UALCAN (ualcan.path.uab.edu), which allows users to analyze and integrate the disparate data to better understand the gene, proteins, and pathways perturbed in cancer and make discoveries. UALCAN web platform enables intuitive analysis and discovery by cancer research community. In the current study, we describe the development of UALCAN Mobile application (App) that will provide cancer transcriptomic data obtained from The Cancer Genome Atlas (TCGA) project, to evaluate protein-coding gene expression based on various stratification including stage, grade, race, gender and molecular-subtypes across over 30 types of cancers. The UALCAN Mobile app aims at providing large cancer dataset on the go. In order to find causative gene expression changes, identify biomarkers and therapeutic targets, UALCAN Mobile will assist as the data will be easily mobile and transportable with this App. Here, we describe the development of UALCAN Mobile and its utility to the cancer research community. UALCAN Mobile App can be found at both iOS/Apple and Android play store and is free to download and use. Citation Format: Sooryanarayana Varambally, Darshan Shimoga Chandrashekar, Gopi Chand Puli, Santhosh Kumar Karthikeyan, Upender Manne, Chad J. Creighton, Sidarth Kumar. UALCAN Mobile, An app for cancer gene expression data analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2270.