Analysis Of RNA Sequencing Data Research Articles

Cell-Cycle-Specific Autoencoding Improves Cluster Analysis of Cycling Cardiomyocytes.

Our previous analyses of cardiomyocyte single-nucleus RNA sequencing (snRNAseq) data from the hearts of fetal pigs and pigs that underwent apical resection surgery on postnatal day (P) 1 (ARP1), myocardial infarction (MI) surgery on P28 (MIP28), both ARP1 and MIP28 (ARP1MIP28), or controls (no surgical procedure or CTL) identified 10 cardiomyocyte subpopulations (clusters), one of which appeared to be primed to proliferate in response to MI. However, the clusters composed of primarily proliferating cardiomyocytes still contained noncycling cells, and we were unable to distinguish between cardiomyocytes in different phases of the cell cycle. Here, we improved the precision of our assessments by conducting similar analyses with snRNAseq data for only the 1646 genes included under the Gene Ontology term "cell cycle." Two cardiac snRNAseq datasets, one from mice (GEO dataset number GSE130699) and one from pigs (GEO dataset number GSE185289), were evaluated via our cell-cycle-specific analytical pipeline. Cycling cells were identified via the co-expression of 5 proliferation markers (AURKB, MKI67, INCENP, CDCA8, and BIRC5). The cell-cycle-specific autoencoder (CSA) algorithm identified 7 cardiomyocyte clusters in mouse hearts (mCM1 and mCM3-mCM8), including one prominent cluster of cycling cardiomyocytes in animals that underwent MI or Sham surgery on P1. Five cardiomyocyte clusters (pCM1, pCM3-pCM6) were identified in pig hearts, 2 of which (pCM1 and pCM4) displayed evidence of cell cycle activity; pCM4 was found primarily in hearts from fetal pigs, while pCM1 comprised a small proportion of cardiomyocytes in both fetal hearts and hearts from ARP1MIP28 pigs during the 2 weeks after MI induction, but was nearly undetectable in all other experimental groups and at all other time points. Furthermore, pseudotime trajectory analysis of snRNAseq data from fetal pig cardiomyocytes identified a pathway that began at pCM3, passed through pCM2, and ended at pCM1, whereas pCM3 was enriched for the expression of a cell cycle activator that regulates the G1/S phase transition (cyclin D2), pCM2 was enriched for an S-phase regulator (CCNE2), and pCM1 was enriched for the expression of a gene that regulates the G2M phase transition and mitosis (cyclin B2). We also identified 4 transcription factors (E2F8, FOXM1, GLI3, and RAD51) that were more abundantly expressed in cardiomyocytes from regenerative mouse hearts than from nonregenerative mouse hearts, from the hearts of fetal pigs than from CTL pig hearts, and from ARP1MIP28 pig hearts than from MIP28 pig hearts during the 2 weeks after MI induction. The CSA algorithm improved the precision of our assessments of cell cycle activity in cardiomyocyte subpopulations and enabled us to identify a trajectory across 3 clusters that appeared to track the onset and progression of cell cycle activity in cardiomyocytes from fetal pigs.

Abstract LB338: Targeted deletion of CD244 on monocytes promotes differentiation into anti-tumorigenic macrophages and potentiates PD-L1 blockade in melanoma

Abstract Background: In the myeloid compartment of the tumor microenvironment, CD244 signaling has been implicated in immunosuppressive phenotype of monocytes. However, the precise molecular mechanism and contribution of CD244 to tumor immunity in monocytes/macrophages remains elusive due to the co-existing lymphoid cells expressing CD244. Methods: To directly assess the role of CD244 in tumor-associated macrophages, monocyte-lineage-specific CD244-deficient mice were generated using cre-lox recombination and challenged with B16F10 melanoma. The phenotype and function of tumor-infiltrating macrophages along with antigen-specific CD8 T cells were analyzed by flow cytometry and single cell RNA sequencing data analysis, and the molecular mechanism underlying anti-tumorigenic macrophage differentiation, antigen presentation, phagocytosis was investigated ex vivo. Finally, the clinical feasibility of CD244-negative monocytes as a therapeutic modality in melanoma was confirmed by adoptive transfer experiments. Results: CD244fl/flLysMcre mice demonstrated a significant reduction in tumor volume (61% relative to that of the CD244fl/fl control group) 14 days after tumor implantation. Within tumor mass, CD244fl/flLysMcre mice also showed higher percentages of Ly6Clow macrophages, along with elevated gp100+IFN-γ+ CD8 T cells. Flow cytometry and RNA sequencing data demonstrated that ER stress resulted in increased CD244 expression on monocytes. This, in turn, impeded the generation of anti-tumorigenic Ly6Clow macrophages, phagocytosis and MHC-I antigen presentation by suppressing autophagy pathways. Combining anti-PD-L1 antibody with CD244-/- bone marrow-derived macrophages markedly improved tumor rejection compared to the anti-PD-L1 antibody alone or in combination with wild-type macrophages. Consistent with the murine data, transcriptome analysis of human melanoma tissue single-cell RNA-sequencing dataset revealed close association between CD244 and the inhibition of macrophage maturation and function. Furthermore, the presence of CD244-negative monocytes/macrophages significantly increased patient survival in primary and metastatic tumors. Conclusion: Our study highlights the novel role of CD244 on monocytes/macrophages in restraining anti-tumorigenic macrophage generation and tumor antigen-specific T cell response in melanoma. Importantly, our findings suggest that CD244-deficient macrophages could potentially be used as a therapeutic agent in combination with immune checkpoint inhibitors. Furthermore, CD244 expression in monocyte-lineage cells serve as a prognostic marker in cancer patients. Citation Format: Jeongsoo Kim, Tae-Jin Kim, Sehyun Chae, Hyojeong Ha, Yejin Park, Seon Ah Lim, Hyemin Lee, Younji Sim, Seyoung Oh, Geon Mo Koo, Minkyung Jo, Daehee Hwang, Kyung-Mi Lee. Targeted deletion of CD244 on monocytes promotes differentiation into anti-tumorigenic macrophages and potentiates PD-L1 blockade in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB338.

Bioinformatics-driven identification of prognostic biomarkers in kidney renal clear cell carcinoma.

Renal cell carcinoma (RCC), particularly the clear cell subtype (ccRCC), poses a significant global health concern due to its increasing prevalence and resistance to conventional therapies. Early detection of ccRCC remains challenging, resulting in poor patient survival rates. In this study, we employed a bioinformatic approach to identify potential prognostic biomarkers for kidney renal clear cell carcinoma (KIRC). By analyzing RNA sequencing data from the TCGA-KIRC project, differentially expressed genes (DEGs) associated with ccRCC were identified. Pathway analysis utilizing the Qiagen Ingenuity Pathway Analysis (IPA) tool elucidated key pathways and genes involved in ccRCC dysregulation. Prognostic value assessment was conducted through survival analysis, including Cox univariate proportional hazards (PH) modeling and Kaplan-Meier plotting. This analysis unveiled several promising biomarkers, such as MMP9, PIK3R6, IFNG, and PGF, exhibiting significant associations with overall survival and relapse-free survival in ccRCC patients. Cox multivariate PH analysis, considering gene expression and age at diagnosis, further confirmed the prognostic potential of MMP9, IFNG, and PGF genes. These findings enhance our understanding of ccRCC and provide valuable insights into potential prognostic biomarkers that can aid healthcare professionals in risk stratification and treatment decision-making. The study also establishes a foundation for future research, validation, and clinical translation of the identified prognostic biomarkers, paving the way for personalized approaches in the management of KIRC.

ABCD1 as a Novel Diagnostic Marker for Solid Pseudopapillary Neoplasm of the Pancreas.

The diagnosis of solid pseudopapillary neoplasm of the pancreas (SPN) can be challenging due to potential confusion with other pancreatic neoplasms, particularly pancreatic neuroendocrine tumors (NETs), using current pathological diagnostic markers. We conducted a comprehensive analysis of bulk RNA sequencing data from SPNs, NETs, and normal pancreas, followed by experimental validation. This analysis revealed an increased accumulation of peroxisomes in SPNs. Moreover, we observed significant upregulation of the peroxisome marker ABCD1 in both primary and metastatic SPN samples compared with normal pancreas and NETs. To further investigate the potential utility of ABCD1 as a diagnostic marker for SPN via immunohistochemistry staining, we conducted verification in a large-scale patient cohort with pancreatic tumors, including 127 SPN (111 primary, 16 metastatic samples), 108 NET (98 nonfunctional pancreatic neuroendocrine tumor, NF-NET, and 10 functional pancreatic neuroendocrine tumor, F-NET), 9 acinar cell carcinoma (ACC), 3 pancreatoblastoma (PB), 54 pancreatic ductal adenocarcinoma (PDAC), 20 pancreatic serous cystadenoma (SCA), 19 pancreatic mucinous cystadenoma (MCA), 12 pancreatic ductal intraepithelial neoplasia (PanIN) and 5 intraductal papillary mucinous neoplasm (IPMN) samples. Our results indicate that ABCD1 holds promise as an easily applicable diagnostic marker with exceptional efficacy (AUC=0.999, sensitivity=99.10%, specificity=100%) for differentiating SPN from NET and other pancreatic neoplasms through immunohistochemical staining.

The VEGF-Hypoxia Signature Is Upregulated in Basal-like Breast Tumors from Women of African Ancestry and Associated with Poor Outcomes in Breast Cancer.

Black women experience the highest breast cancer mortality rate compared with women of other racial/ethnic groups. To gain a deeper understanding of breast cancer heterogeneity across diverse populations, we examined a VEGF-hypoxia gene expression signature in breast tumors from women of diverse ancestry. We developed a NanoString nCounter gene expression panel and applied it to breast tumors from Nigeria (n = 182) and the University of Chicago (Chicago, IL; n = 161). We also analyzed RNA sequencing data from Nigeria (n = 84) and The Cancer Genome Atlas (TCGA) datasets (n = 863). Patient prognosis was analyzed using multiple datasets. The VEGF-hypoxia signature was highest in the basal-like subtype compared with other subtypes, with greater expression in Black women compared with White women. In TCGA dataset, necrotic breast tumors had higher scores for the VEGF-hypoxia signature compared with non-necrosis tumors (P < 0.001), with the highest proportion in the basal-like subtype. Furthermore, necrotic breast tumors have higher scores for the proliferation signature, suggesting an interaction between the VEGF-hypoxia signature, proliferation, and necrosis. T-cell gene expression signatures also correlated with the VEGF-hypoxia signature when testing all tumors in TCGA dataset. Finally, we found a significant association of the VEGF-hypoxia profile with poor outcomes when using all patients in the METABRIC (P < 0.0001) and SCAN-B datasets (P = 0.002). These data provide further evidence for breast cancer heterogeneity across diverse populations and molecular subtypes. Interventions selectively targeting VEGF-hypoxia and the immune microenvironment have the potential to improve overall survival in aggressive breast cancers that disproportionately impact Black women in the African Diaspora.

Prognostic model based on B cell marker genes for NSCLC patients under neoadjuvant immunotherapy by integrated analysis of single-cell and bulk RNA-sequencing data.

Neoadjuvant immunotherapy has evolved as an effective option to treat non-small cell lung cancer (NSCLC). B cells play essential roles in the immune system as well as cancer progression. However, the repertoire of B cells and its association with clinical outcomes remains unclear in NSCLC patients receiving neoadjuvant immunotherapy. Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data for LUAD samples were accessed from the TCGA and GEO databases. LUAD-related B cell marker genes were confirmed based on comprehensive analysis of scRNA-seq data. We then constructed the B cell marker gene signature (BCMGS) and validated it. In addition, we evaluated the association of BCGMS with tumor immune microenvironment (TIME) characteristics. Furthermore, we validated the efficacy of BCGMS in a cohort of NSCLC patients receiving neoadjuvant immunotherapy. A BCMGS was constructed based on the TCGA cohort and further validated in three independent GSE cohorts. In addition, the BCMGS was proven to be significantly associated with TIME characteristics. Moreover, a relatively higher risk score indicated poor clinical outcomes and a worse immune response among NSCLC patients receiving neoadjuvant immunotherapy. We constructed an 18-gene prognostic signature derived from B cell marker genes based on scRNA-seq data, which had the potential to predict the prognosis and immune response of NSCLC patients receiving neoadjuvant immunotherapy.

Comparative single-cell RNA sequencing analysis of immune response to inactivated vaccine and natural SARS-CoV-2 infection.

Uncovering the immune response to an inactivated SARS-CoV-2 vaccine (In-Vac) and natural infection is crucial for comprehending COVID-19 immunology. Here we conducted an integrated analysis of single-cell RNA sequencing (scRNA-seq) data from serial peripheral blood mononuclear cell (PBMC) samples derived from 12 individuals receiving In-Vac compared with those from COVID-19 patients. Our study reveals that In-Vac induces subtle immunological changes in PBMC, including cell proportions and transcriptomes, compared with profound changes for natural infection. In-Vac modestly upregulates IFN-α but downregulates NF-κB pathways, while natural infection triggers hyperactive IFN-α and NF-κB pathways. Both In-Vac and natural infection alter T/B cell receptor repertoires, but COVID-19 has more significant change in preferential VJ gene, indicating a vigorous immune response. Our study reveals distinct patterns of cellular communications, including a selective activation of IL-15RA/IL-15 receptor pathway after In-Vac boost, suggesting its potential role in enhancing In-Vac-induced immunity. Collectively, our study illuminates multifaceted immune responses to In-Vac and natural infection, providing insights for optimizing SARS-CoV-2 vaccine efficacy.

The Workflow for Computational Analysis of Single-cell RNA-sequencing Data

The Workflow for Computational Analysis of Single-cell RNA-sequencing Data

CTEC: a cross-tabulation ensemble clustering approach for single-cell RNA sequencing data analysis.

Cell-type clustering is a crucial first step for single-cell RNA-seq data analysis. However, existing clustering methods often provide different results on cluster assignments with respect to their own data pre-processing, choice of distance metrics, and strategies of feature extraction, thereby limiting their practical applications. We propose Cross-Tabulation Ensemble Clustering (CTEC) method that formulates two re-clustering strategies (distribution- and outlier-based) via cross-tabulation. Benchmarking experiments on five scRNA-Seq datasets illustrate that the proposed CTEC method offers significant improvements over the individual clustering methods. Moreover, CTEC-DB outperforms the state-of-the-art ensemble methods for single-cell data clustering, with 45.4% and 17.1% improvement over the single-cell aggregated from ensemble clustering method (SAFE) and the single-cell aggregated clustering via Mixture model ensemble method (SAME), respectively, on the two-method ensemble test. The source code of the benchmark in this work is available at the GitHub repository https://github.com/LWCHN/CTEC.git.

Construction and analysis of a network of exercise-induced mitochondria-related non-coding RNA in the regulation of diabetic cardiomyopathy.

Diabetic cardiomyopathy (DCM) is a major factor in the development of heart failure. Mitochondria play a crucial role in regulating insulin resistance, oxidative stress, and inflammation, which affect the progression of DCM. Regular exercise can induce altered non-coding RNA (ncRNA) expression, which subsequently affects gene expression and protein function. The mechanism of exercise-induced mitochondrial-related non-coding RNA network in the regulation of DCM remains unclear. This study seeks to construct an innovative exercise-induced mitochondrial-related ncRNA network. Bioinformatic analysis of RNA sequencing data from an exercise rat model identified 144 differentially expressed long non-coding RNA (lncRNA) with cutoff criteria of p< 0.05 and fold change ≥1.0. GSE6880 and GSE4745 were the differentially expressed mRNAs from the left ventricle of DCM rat that downloaded from the GEO database. Combined with the differentially expressed mRNA and MitoCarta 3.0 dataset, the mitochondrial located gene Pdk4 was identified as a target gene. The miRNA prediction analysis using miRanda and TargetScan confirmed that 5 miRNAs have potential to interact with the 144 lncRNA. The novel lncRNA-miRNA-Pdk4 network was constructed for the first time. According to the functional protein association network, the newly created exercise-induced ncRNA network may serve as a promising diagnostic marker and therapeutic target, providing a fresh perspective to understand the molecular mechanism of different exercise types for the prevention and treatment of diabetic cardiomyopathy.

ScEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data.

With the emergence of large amount of single-cell RNA sequencing (scRNA-seq) data, the exploration of computational methods has become critical in revealing biological mechanisms. Clustering is a representative for deciphering cellular heterogeneity embedded in scRNA-seq data. However, due to the diversity of datasets, none of the existing single-cell clustering methods shows overwhelming performance on all datasets. Weighted ensemble methods are proposed to integrate multiple results to improve heterogeneity analysis performance. These methods are usually weighted by considering the reliability of the base clustering results, ignoring the performance difference of the same base clustering on different cells. In this paper, we propose a high-order element-wise weighting strategy based self-representative ensemble learning framework: scEWE. By assigning different base clustering weights to individual cells, we construct and optimize the consensus matrix in a careful and exquisite way. In addition, we extracted the high-order information between cells, which enhanced the ability to represent the similarity relationship between cells. scEWE is experimentally shown to significantly outperform the state-of-the-art methods, which strongly demonstrates the effectiveness of the method and supports the potential applications in complex single-cell data analytical problems.

Unsupervised Gene-Cell Collective Representation Learning with Optimal Transport

Cell type identification plays a vital role in single-cell RNA sequencing (scRNA-seq) data analysis. Although many deep embedded methods to cluster scRNA-seq data have been proposed, they still fail in elucidating the intrinsic properties of cells and genes. Here, we present a novel end-to-end deep graph clustering model for single-cell transcriptomics data based on unsupervised Gene-Cell Collective representation learning and Optimal Transport (scGCOT) which integrates both cell and gene correlations. Specifically, scGCOT learns the latent embedding of cells and genes simultaneously and reconstructs the cell graph, the gene graph, and the gene expression count matrix. A zero-inflated negative binomial (ZINB) model is estimated via the reconstructed count matrix to capture the essential properties of scRNA-seq data. By leveraging the optimal transport-based joint representation alignment, scGCOT learns the clustering process and the latent representations through a mutually supervised self optimization strategy. Extensive experiments with 14 competing methods on 15 real scRNA-seq datasets demonstrate the competitive edges of scGCOT.

Machine Learning Analysis Using RNA Sequencing to Distinguish Neuromyelitis Optica from Multiple Sclerosis and Identify Therapeutic Candidates

This study aims to identify RNA biomarkers distinguishing neuromyelitis optica (NMO) from relapsing-remitting multiple sclerosis (RRMS) and explore potential therapeutic applications leveraging machine learning (ML). An ensemble approach was developed using differential gene expression analysis and competitive ML methodologies, interrogating total RNA sequencing datasets from peripheral whole blood of treatment-naïve RRMS and NMO patients and healthy individuals. Pathway analysis of candidate biomarkers informed the biological context of disease, transcription factor activity, and small-molecule therapeutic potential. ML models differentiated between NMO and RRMS patients, with the performance of certain models exceeding 90% accuracy. RNA biomarkers driving model performance were associated with ribosomal dysfunction and viral infection. Regulatory networks of kinases and transcription factors informed biological context and identified potential therapeutic targets. Small-molecule candidates capable of reversing perturbed gene expression were uncovered. Two small molecules identified, mitoxantrone and vorinostat, reinforced discovered expression signatures and highlighted the potential to identify new therapeutic candidates, as their use in NMO patients and experimental autoimmune encephalomyelitis have been previously described. Putative RNA biomarkers were identified that accurately distinguish NMO from RRMS and healthy individuals. The application of multivariate approaches in analysis of RNA sequencing data enhances the discovery of unique RNA biomarkers, accelerating the development of new methods for disease detection, monitoring, and therapeutics. Integrating biological understanding enhances detection of disease-specific signatures and possible therapeutic targets.

Abstract 3051: JunAP, a peptide antagonist against the activator protein 1 transcription factor complex, demonstrates cancer cell cytotoxicity and reduced invasion in vitro and tumor regression in vivo in TNBC models

Abstract The activator protein 1 (AP-1) transcription factor is a dimeric complex consisting of proteins from the JUN, FOS, MAF, and ATF families. AP-1 complexes have been identified as novel therapeutic targets in cancer due to their role in tumor growth, invasion, metastasis, angiogenesis and chemoresistance. Dimerization of components of the AP-1 complex via leucine zipper domain interactions is required for DNA binding and subsequent transcriptional activity. We designed a peptide (Jun antagonizing peptide, JunAP) targeting the basic leucine zipper motif of AP-1 to antagonize AP-1 complex formation and prevent associated activity. Fluorescence polarization and DNA ELISA assays demonstrate target binding and selectivity of JunAP towards AP-1 family members, co-immunoprecipitation experiments reveal that JunAP blocks protein-protein interactions between cJun and its key binding partners, and reporter assays confirm inhibition of AP-1 transcriptional activity in vitro. Pathway analysis of RNA sequencing data confirmed by qPCR analysis shows that JunAP disrupts key pathways required for cell survival and migration. Corresponding functional in vitro assays reveal that JunAP exhibits an inhibitory effect on invasion in Boyden chamber assays and demonstrates potent cytotoxicity in Jun-dependent cells. Further, in a BT549 triple negative breast cancer subcutaneous xenograft model, administration of JunAP results in tumor regression, demonstrating the anti-cancer potential of targeting the AP-1 complex. In summary, these data support JunAP as a potent peptide antagonist of the AP-1 transcription factor family that warrants further development as a potential therapeutic option for AP-1 driven tumors. Citation Format: Karen Mendelson, Zachary F. Mattes, Siok Leong, Ricardo Ramirez, Mark Koester, Claudio Scuoppo, Julia Diehl, Erin Gallagher, Jerel Gonzales, Franco Abbate, Lila Ghamsari, Gene Merutka, Barry J. Kappel, Abi Vainstein-Haras, Jim A. Rotolo. JunAP, a peptide antagonist against the activator protein 1 transcription factor complex, demonstrates cancer cell cytotoxicity and reduced invasion in vitro and tumor regression in vivo in TNBC models [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 3051.

Abstract 5668: Long non-coding RNA LINC01614 knockdown decreases carcinogenic behavior of papillary thyroid cancer in vitro

Abstract Papillary thyroid cancer (PTC) accounts for the vast majority of thyroid cancer cases with increasing incidence over the past few decades. PTC has a significantly higher prevalence in females in their childbearing years. Despite high survival rates, disease recurrence and metastasis remain prominent issues, thus highlighting the critical need for molecular biomarker detection. A newer class of molecular regulators, known as long non-coding RNAs (lncRNAs), have demonstrated differential expression patterns in a multitude of cancers. The largest subclass of these RNA molecules in the human genome consists of long intergenic non-coding RNAs (lincRNAs) with many of their functions yet to be identified. Known functional roles of lncRNAs center on their ability to modulate gene expression and drive carcinogenic behavior, deeming them attractive biomarkers and/or therapeutic targets for study. Bioinformatic analysis of RNA sequencing data from our patient sample biobank identified LINC01614 as being significantly upregulated (~12 fold increase) in PTC, when compared to normal, matched thyroid tissue. Further, it was also upregulated ~30-fold in male PTC compared to ~5.5 fold increase in female PTC, highlighting a potential sex correlation for study. Thus, we are studying LINC01614 as a potential PTC biomarker. Gene expression analysis of LINC01614 in vitro was found to be upregulated in various thyroid cancer cell lines, specifically two PTC cell lines: TPC1 (~5 fold; RET/PTC rearrangement; female) and K1 (~2 fold; BRAFV600E; male) when compared to the immortalized “normal” thyroid cell line (Nthy-ori-3-1). For phenotypic evaluation, we utilized the CRISPRi technique to employ transcriptional repression of LINC01614 in both TPC1 and K1. LINC01614 knockdown in TPC1 and K1 resulted in decreased healing capacity (~20%), clonogenicity (~50% and ~34%, respectively), and proliferation (~55% and ~45%, respectively). Studies are currently underway to further assess invasion, morphology, and RNA localization within the cell in vitro, as well as the identification of potential protein interactions and mechanisms of action of LINC01614 in PTC. Studying lncRNAs and their regulatory roles in cancer cell function is critical in the identification of various diagnostic and prognostic biomarkers for therapeutic intervention. Our data support the potential biological role of LINC01614 in driving PTC growth and metastatic potential. Further elucidation of how LINC01614 impacts PTC development and progression is a promising avenue of exploration. Citation Format: Danielle Quaranto, Michelle Carnazza, Nicole R. DeSouza, Sina Dadafarin, Augustine Moscatello, Humayun K. Islam, Raj K. Tiwari, Jan Geliebter. Long non-coding RNA LINC01614 knockdown decreases carcinogenic behavior of papillary thyroid cancer in vitro [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 5668.

Abstract 1799: Regulation of epidermal stem cell proliferation and adhesion by ACSS2

Abstract Acetyl-CoA Synthetase Short-Chain Family Member 2 (ACSS2) is an enzyme that converts acetate into acetyl-CoA, a crucial metabolite required for protein acetylation and lipogenesis. Acetyl-CoA is membrane-impermeable and must be synthesized in the compartment in which it will be used. Among ACSS enzymes, ACSS2 alone is found in the nucleocytoplasmic compartment rather than the mitochondria. This enzyme shows dynamic localization dependent on cell nutrient status, with nutrient stress promoting nuclear localization where ACSS2 activity promotes cell growth and survival. Emerging evidence suggests ACSS2 may also play roles in promoting metastasis, and has been proposed as a potential therapeutic target. However, the role of ACSS2 in controlling normal tissue homeostasis is understudied. Addressing this knowledge gap is critical to ensure safety and efficacy of cancer therapeutics. As 80% to 90% of human cancers originate from epithelial tissues, we have leveraged human epidermal stem cells to further interrogate the role of ACSS2. As progenitor keratinocytes differentiate, they detach from the basal lamina and migrate from the metabolically privileged basal layer to the more challenging environment at the surface of the epidermis. Using shRNA-mediated knockdown, we first found ACSS2 knockdown dramatically decreases progenitor keratinocyte clonogenicity, abolishing proliferative capacity. Analysis of RNA sequencing data following ACSS2 knockdown with five constructs showed significant increases in expression of genes related to cell adhesion, extracellular matrix organization, collagen catabolism, and cell migration, in addition to keratinocyte differentiation. Downregulated genes were associated with cell division, DNA repair, and DNA replication. Live cell imaging revealed knockdown cells show striking differences in cell adhesion compared to control. These results indicate a role for ACSS2 in regulating keratinocyte proliferation and adhesion during the maintenance of epidermal homeostasis. Citation Format: Nicholas K. Chamberlain, Xiaomin Bao. Regulation of epidermal stem cell proliferation and adhesion by ACSS2 [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 1799.

Abstract 3269: APE1/NF-κB/PRDX2 activated by reflux conditions mediates chemoresistance in esophageal adenocarcinoma by suppressing ferroptosis

Abstract Background: Esophageal adenocarcinoma (EAC) stands out as one of the most lethal malignancies, characterized by a discouragingly low 5-year survival rate of less than 20%. Accumulating lines of evidence indicate that ferroptosis, a novel type of cell death, can regulate drug resistance. Peroxiredoxin 2 (PRDX2), an antioxidant enzyme, plays an antioxidant protective role in the cells under stress by regulating redox homeostasis. We aimed to elucidate the role of PRDX2 in regulating reactive oxygen species (ROS), ferroptosis, and chemoresistance in EAC. Methods: In this investigation, we analyzed RNA sequencing data and public databases to uncover the aberrant overexpression and potential chemoresistance function of PRDX2 in EAC. We employed transient acidic bile salts exposure (ABS) and repeated ABS exposure (rABS) to mimic gastroesophageal reflux disease, the main risk factor for EAC, in in vitro cell models. EAC cells and 3D organotypic culture (OTC) served as platforms to delve into the anti-ferroptosis function and regulatory mechanisms of PRDX2. The findings were validated using patient-derived xenografts (PDX) and human EAC tissue microarrays (TMA). Results: Elevated PRDX2 level was observed in both EAC patients and cell lines exhibiting poor responsiveness to chemotherapy. Intrinsic overexpression of PRDX2 emerged as a crucial factor in the recovery from ABS-exposure-induced lipid peroxidation. Silencing PRDX2 heightened the sensitivity of EAC cells to Oxaliplatin by inhibiting ferroptosis in a GPX4-dependent manner. Mechanistically, we found ABS activate the APE1/redox/NF-kB signal axis, leading to the upregulation of PRDX2 transcription. The co-overexpression of APE1 and PRDX2 was identified through both immunofluorescent staining in cells/OTC and immunohistochemistry staining in TMA. Moreover, the APE1-redox-specific inhibitor, APX2009, significantly sensitized EAC cells to Oxaliplatin by repressing PRDX2 and inducing ferroptosis. Remarkably, the combination therapy of Oxaliplatin and APX2009 achieved a synergistic effect in EAC-PDX tumors. Conclusion: The study unveils a novel signaling axis, APE1/NF-kB/PRDX2, linking disease-associated ROS unbalance and ferroptosis-related chemoresistance. Targeting PRDX2 signaling by APE1-redox-specific inhibition provides a potential novel strategy for combined chemotherapies in refractory EAC. Citation Format: Lei Chen, Heng Lu, Farah Ballout, Dunfa Peng, Zheng Chen, Jianwen Que, Steven Chen, Oliver McDonald, Alexander Zaika, Wael El-Rifai. APE1/NF-κB/PRDX2 activated by reflux conditions mediates chemoresistance in esophageal adenocarcinoma by suppressing ferroptosis [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 3269.

Abstract 3268: A novel signaling axis of APE1-redox-sensitive SOX9 activation underlying reflux-condition-associated chemoresistance in esophageal adenocarcinoma

Abstract Chronic gastroesophageal reflux disease (GERD), characterized by the reflux of acidic bile salts (ABS) into the esophagus, stands as the primary risk factor for esophageal adenocarcinoma (EAC). EAC, known for its aggressive nature and limited response to therapy, exhibits a disheartening 5-year survival rate of less than 20%. Overcoming drug resistance remains a pressing challenge in achieving optimal therapeutic outcomes for EAC patients. Previous studies from our team have unveiled that exposure to ABS leads to the overexpression of Apurinic/apyrimidinic endonuclease (APE1), which orchestrates various oncogenic pathways in EAC cells. In this study, we delve into the influence of ABS in driving APE1-redox-mediated activation of the SOX9 pathway, thus contributing to the chemoresistance observed in EAC. To identify the enrichment of the SOX9 pathway and the heightened downstream target genes in EAC, we analyzed RNA sequencing data and public databases. Cell models, patient tissue arrays, pL2-IL1β transgenic mice, and de-identified xenografts derived from EAC patients (PDXs) were employed to investigate the signaling axis of APE1-redox-SOX9 in oxaliplatin resistance of EAC. Our analysis of public databases unveiled a significant enrichment of the SOX9 pathway in EAC patients. RNA sequencing data from EAC cells exposed to ABS, with or without APE1-knockdown, suggested APE1's involvement in the GERD-activated SOX9 signaling pathway. Immunohistochemistry staining of human patient tissue arrays, along with immunofluorescent staining of L2-IL1ß transgenic mice, confirmed the co-overexpression of APE1, SOX9, and ALDH1A1 in EAC. Furthermore, APE1 silencing led to the downregulation of SOX9 and exhibited synergistic effects with oxaliplatin in chemo-resistant EAC cells. Mechanistically, we uncovered that APE1's redox function is crucial for AKT-GSK3ß-mediated SOX9 stability. Treatment with the APE1-redox-specific inhibitor, APX2009, sensitized patient-derived xenograft (PDX) tumors to oxaliplatin by repressing SOX9-regulated chemoresistance genes. This study sheds light on a novel signaling axis involving APE1-redox-AKT-GSK3ß-SOX9 in Barrett’s carcinogenesis. The pharmacological inhibition of APE1-redox function presents itself as a promising therapeutic approach to target the previously intractable SOX9 signals, potentially overcoming chemoresistance in EAC. Citation Format: Heng Lu, Lei Chen, Farah Ballout, Dunfa Peng, Zheng Chen, Jianwen Que, Steven Chen, Oliver Gene McDonald, Alexander Zaika, Wael El-Rifai. A novel signaling axis of APE1-redox-sensitive SOX9 activation underlying reflux-condition-associated chemoresistance in esophageal adenocarcinoma [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 3268.

Abstract 4738: Overcoming therapy resistance in rhabdomyosarcoma by targeting the PI3K-MAPK pathway

Abstract Rhabdomyosarcoma (RMS) is a devastating pediatric cancer. There has not been any significant change in the treatment options for the last 4 decades, especially for relapsed or advanced disease. There is an urgent need to identify alternative treatment options to improve survival outcomes of RMS patients. RMS is comprised of two major subtypes as defined by the presence or absence of PAX3/PAX7-FOXO1 gene fusion. Fusion-negative RMS (FN RMS) is characterized by alterations in the RAS-PI3K-MAPK pathway in &amp;gt;90% of the cases. However, the role of the PI3K-MAPK pathway in therapy resistance and disease relapse remains poorly characterized. We have generated two FN-RMS lines, RD and SMS-CTR, with resistance to the standard-of-care chemotherapeutic agent, vincristine, through long-term culturing in incrementally increased concentrations of the drug. By RNA sequencing, vincristine-resistant RD and SMS-CTR cells demonstrated increased expression of genes in the RAS-PI3K-MAPK pathway compared to the parental lines. We hypothesize that increased activity of the PI3K-MAPK pathway contributes to therapy resistance in FN RMS. To test the hypothesis, we first showed that targeted disruption of PIK3CA by shRNA inhibited the cell growth of vincristine-resistant FN RMS cells. The same cells also showed increased sensitivity to the MEK inhibitor, trametinib, as well as the PIK3Cα inhibitor, alpelisib, compared to the parental cells. We then showed that zebrafish FN-RMS tumors treated with the combination of vincristine and alpelisib significantly inhibited tumor growth compared to the treatment with each agent alone. Through integrative analysis of RNA sequencing and ATAC-seq data, we identified MYOD1 and NFATC2 as among the top candidate upstream regulators. Targeted disruption of either MYOD1 or NFATC2 by CRISPR/Cas9 reduced cell growth of vincristine-resistant FN RMS cells and altered expression of the genes in the PI3K-MAPK pathway, implicating MYOD1 and NFATC2 in driving abnormal activity of this pathway. To assess the relevance of our findings in the clinical settings, we showed that selective components in the PI3K-MAPK pathways are up-regulated in 3 matched primary-recurrence/metastasis paraffinized tissue samples of human FN RMS by the NanoString GeoMx Digital Spatial Profiler (DSP) platform. Our findings indicate that targeting the PI3K-MAPK pathway is a promising targeted therapy option for overcoming therapy resistance in RMS. Citation Format: Yadong Wang, Eleanor Chen. Overcoming therapy resistance in rhabdomyosarcoma by targeting the PI3K-MAPK pathway [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 4738.

Abstract 2268: CREDAC: Copy number-based reference-free expression deconvolution analysis of cancers

Abstract Introduction: Analyses of bulk RNA sequencing data are central to most large-scale tumor sequencing studies. Bulk expression data represent population averages and its interpretation is confounded by both normal cell contamination and somatic copy number alterations. Several computational methods to deconvolve have been developed recently. However, these methods often rely on a set of cell-type-specific reference signatures and ignore the effect of copy number changes. Methods: To address these issues, we have developed a method that formalizes the relationship between allele-specific copy number, expression and sample purity to deconvolve the expression profiles of tumor and normal cells from bulk RNA-seq in an unbiased manner. Our method was applied to sequencing data produced by the TRACERx consortium, a longitudinal study with multi-region whole-exome and RNA-seq of non-small-cell lung cancers. A total of 414 primary tumor regions and 140 adjacent normal tissue samples from 140 patients with matched DNA and RNA-seq data were processed. Results: We were able to directly deconvolve a median of ~2,000 genes per sample and indirectly infer tumor and normal expression profiles of ~10,000 genes using an scaled and weighted mean approach. The accuracy of the deconvolution was validated using (1) in-silico mixtures of patient-derived tumor and normal cells, (2) pseudo-bulk scRNAseq and (3) in regions with loss of heterogeneity (LOH) directly on the bulk sequencing data, where the total fraction of expression attributed to tumor cells can be computed directly using somatic mutations. CREDAC allows for single sample bulk-level tumor-normal differential expression analysis and revealed a strong and constitutive genome-wide overexpression in cancer compared to admixed normal cells, with a more pronounced overexpression in lung squamous cell carcinoma than lung adenocarcinoma (p&amp;lt;0.001). CREDAC adjusts for variations in copy number, thus facilitating the investigation of cancer-specific dosage compensatory responses triggered by aneuploidy. We revealed that a majority of genes (~65%) exhibit proportionality to copy number alterations, while a substantial proportion (~35%) shows anti-scaling or dosage compensation strategies. We show an enrichment of genes involved in cell cycle and expression processes within the dosage compensated group and revealed methylation as a key mechanism driving this compensatory process. Conclusion: Overall, our results suggest that CREDAC is able to accurately disentangle the expression of tumor and normal cells from bulk RNA-seq without any previous knowledge. It has potential applications in many studies that include matched RNA-seq and copy number data and can provide new insights functional characterization, the taxonomy of cancer, and tumor evolution. Citation Format: Carla Castignani, Jonas Demeulemeester, Oriol Pich, Tom Lesluyes, Robert E. Hynds, David R. Pearce, Elizabeth Larose Cadieux, Stefan C. Dentro, TRACERx Consortium, Nnenna Kanu, Charles Swanton, Peter Van Loo. CREDAC: Copy number-based reference-free expression deconvolution analysis of cancers [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 2268.