Gene Expression In Cell Lines Research Articles

Identification and validation of matrix metalloproteinase hub genes as potential biomarkers for Skin Cutaneous Melanoma

ObjectivesThe role of matrix metalloproteinases (MMPs) in Skin Cutaneous Melanoma (SKCM) development and progression is unclear so far. This comprehensive study delved into the intricate role of MMPs in SKCM development and progression.MethodsRT-qPCR, bisulfite sequencing, and WES analyzed MMP gene expression, promoter methylation, and mutations in SKCM cell lines. TCGA datasets validated findings. DrugBank and molecular docking identified potential regulatory drugs, and cell line experiments confirmed the role of key MMP genes in tumorigenesis.ResultsOur findings unveiled significant up-regulation of MMP9, MMP12, MMP14, and MMP16, coupled with hypomethylation of their promoters in SKCM cell lines, implicating their involvement in disease progression. Mutational analysis highlighted a low frequency of mutations in these genes, indicating less involvement of mutations in the expression regulatory mechanisms. Prognostic assessments showcased a significant correlation between elevated expression of these genes and poor overall survival (OS) in SKCM patients. Additionally, functional experiments involving gene silencing revealed a potential impact on cellular proliferation, further emphasizing the significance of MMP9, MMP12, MMP14, and MMP16 in SKCM pathobiology.ConclusionThis study identifies Estradiol and Calcitriol as potential drugs for modulating MMP expression in SKCM, highlighting MMP9, MMP12, MMP14, and MMP16 as key diagnostic and prognostic biomarkers.

Evaluation of attenuated Salmonella Typhimurium (STMΔznuABC) anticancer activity on canine mammary cancer-associated fibroblasts

Bacteria-mediated treatments gained increasing attention as alternative therapies against tumors. An attenuated mutant strain of Salmonella enterica serovar Typhimurium (STMΔznuABC) has recently been considered as a potential new anti-cancer strategy. However, it is unclear whether this activity is tumor-induced or species-specific, and no data are available regarding STMΔznuABC on canine mammary tumors (CMTs). This study aimed to investigate the ability of STMΔznuABC in modulating the response of CMTs, focusing on cancer-associated fibroblasts. Four CMT cell lines (CF33, TM51, TM52 TM53) were treated with STMΔznuABC. Then, antiproliferative activity (MTT assay), bacterial invasion, and CMT cell lines gene expression analysis (RT-qPCR) of genes involved in immune response and cancer aggressiveness were evaluated. STMΔznuABC penetrated in TM51, TM52, TM53, and CF33 cell lines, causing a significant reduction of cell viability. Moreover, the expression of several genes was significantly modulated in all CMT cell lines: STMΔznuABC infection determined a significant up-regulation of CXCL8, IL18, IL10, TLR4 and RAD51, while CD14, IL6, CXCR4, P53, PTEN, STAT5, TLR5 and TGFB were downregulated in TM53. In CF33, CXCL8 and P53 were upregulated, while MYD88, MD2, IL18, TLR4,5, TGFB were downregulated. In TM52, CXCL8, CD44 and MD2 were upregulated and PTEN was downregulated, while in TM51 CXCL8, CD44 and ErbB2 were downregulated. We demonstrated the anti-proliferative and immuno-modulatory activity of STMΔznuABC in CMTs, paving the way for potential new anti-cancer treatments.

Epigenomic, transcriptomic and proteomic characterizations of reference samples.

A variety of newly developed next-generation sequencing technologies are making their way rapidly into the research and clinical applications, for which accuracy and cross-lab reproducibility are critical, and reference standards are much needed. Our previous multicenter studies under the SEQC-2 umbrella using a breast cancer cell line with paired B-cell line have produced a large amount of different genomic data including whole genome sequencing (Illumina, PacBio, Nanopore), HiC, and scRNA-seq with detailed analyses on somatic mutations, single-nucleotide variations (SNVs), and structural variations (SVs). However, there is still a lack of well-characterized reference materials which include epigenomic and proteomic data. Here we further performed ATAC-seq, Methyl-seq, RNA-seq, and proteomic analyses and provided a comprehensive catalog of the epigenomic landscape, which overlapped with the transcriptomes and proteomes for the two cell lines. We identified >7,700 peptide isoforms, where the majority (95%) of the genes had a single peptide isoform. Protein expression of the transcripts overlapping CGIs were much higher than the protein expression of the non-CGI transcripts in both cell lines. We further demonstrated the evidence that certain SNVs were incorporated into mutated peptides. We observed that open chromatin regions had low methylation which were largely regulated by CG density, where CG-rich regions had more accessible chromatin, low methylation, and higher gene and protein expression. The CG-poor regions had higher repressive epigenetic regulations (higher DNA methylation) and less open chromatin, resulting in a cell line specific methylation and gene expression patterns. Our studies provide well-defined reference materials consisting of two cell lines with genomic, epigenomic, transcriptomic, scRNA-seq and proteomic characterizations which can serve as standards for validating and benchmarking not only on various omics assays, but also on bioinformatics methods. It will be a valuable resource for both research and clinical communities.

Transcriptome analysis of the effect of HERV-K env gene knockout in ovarian cancer cell lines.

Human endogenous retroviruses (HERVs) have been implicated in the pathogenesis of various diseases, particularly cancers. Previous investigations from our group demonstrated that targeted knockout (KO) of the HERV-K env gene led to a significant reduction in tumorigenic attributes, including proliferation, migration, and invasion of ovarian cancer cells. In this study, we aimed to elucidate the impact of HERV-K env KO on gene expression in ovarian cancer cell lines through comparative RNA sequencing (RNA-Seq) analysis with two distinct HERV-K env KO ovarian cancer cell lines, SKOV3 and OVCAR3. HERV-K env gene KO was achieved in SKOV3 and OVCAR3 ovarian cancer cell lines using the CRISPR-Cas9 system. Next-generation mRNA sequencing was employed to assess the gene expression profiles of both mock and HERV-K env KO ovarian cancer cells. Furthermore, comprehensive analyses involving gene ontology and pathway assessments were conducted. Transcriptome analysis revealed that 23 differentially expressed genes (DEGs) were upregulated and 17 DEGs were downregulated in SKOV3 cells. In OVCAR3 cells, 198 DEGs were upregulated, and 17 DEGs were downregulated. Notably, 53 DEGs exhibited statistically significant differences among the 1,612 DEGs identified. Our findings indicate that HERV-K env gene KO exerts a profound influence on gene expression patterns in OVCAR3 cells, while genetic alterations in expression were relatively modest in SKOV3 cells. Nevertheless, genes ND1, ND2, and CYTB displayed a common increase in expression, while ERRFI1 and NDRG1 exhibited a decrease in expression in both cell lines. Our study demonstrates that KO of the HERV-K env gene in ovarian cancer cell lines has a substantial impact on gene expression patterns and can be used to identify potential therapeutic targets for ovarian cancer and related diseases.

Inhibition of osteosarcoma metastasis in vivo by targeted downregulation of MMP1 and MMP9

Osteosarcoma (OS) mortality stems from lung metastases. Matrix metalloproteinases (MMPs) facilitate metastatic dissemination by degrading extracellular matrix components. Herein we studied the impact of targeted MMP downregulation on OS metastasis. Differential gene expression analysis of human OS cell lines revealed high MMP9 expression in the majority of OS cell lines. Furthermore, 143B, a metastatic OS cell line, exhibited increased MMP1 and MMP9 mRNA levels. Gene set enrichment analysis on metastatic and non-metastatic OS patient specimens indicated epithelial-mesenchymal transition as the most enriched gene set, with MMP9 displaying strong association to genes in this network. Using the same dataset, Kaplan-Meier analysis revealed a correlation between MMP1 expression and dismal patient survival. Hence, we undertook targeted suppression of MMP1 and MMP9 gene expression in OS cell lines. The ability of OS cells to migrate and form colonies was markedly reduced upon MMP1 and MMP9 downregulation, whereas their cell proliferation capacity remained intact. MMP9 downregulation decreased tumor growth and lung metastases area in an orthotopic mouse OS model. Consistently, human OS lung metastasis specimens displayed marked MMP9 protein expression. Our findings highlight the role of MMP1 and MMP9 in OS metastasis, warranting further exploration of simultaneous inhibition of MMPs for future OS therapeutics.

MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.

Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point cloud data, are extracted using SMILES. Secondly, for different modal data, a Bi-LSTM, gMLP, multi-head attention mechanism, and multi-scale GCNs are comprehensively applied to extract the drug feature. Then, it selects appropriate omics features from gene expression and mutation omics data of cancer cell lines to construct cancer cell line features. Finally, these features are combined to predict the synergistic anti-cancer drug combination effect. The experimental results verify that MMFSyn has significant advantages in performance compared to other popular methods, with a root mean square error of 13.33 and a Pearson correlation coefficient of 0.81, which indicates that MMFSyn can better capture the complex relationship between multimodal drug combinations and omics data, thereby improving the synergistic drug combination prediction.

PACAP binds conserved receptors and modulates cytokine gene expression and protein secretion in trout cell lines

Antimicrobial peptides (AMPs) are an alternative to antibiotics for treatment and prevention of infections with a lower risk of bacterial resistance. Pituitary adenylate cyclase activating polypeptide (PACAP) is an outstanding AMP with versatile effects including antimicrobial activity and modulation of immune responses. The objective of this research was to study PACAP immunomodulatory effect on rainbow trout cell lines infected with Aeromonas salmonicida. PACAP from Clarias gariepinus (PACAP1) and a modified PACAP (PACAP5) were tested. RT-qPCR results showed that il1b and il8 expression in RTgutGC was significantly downregulated while tgfb expression was upregulated after PACAP treatment. Importantly, the concentration of IL-1β and IFN-γ increased in the conditioned media of RTS11 cells incubated with PACAP1 and exposed to A. salmonicida. There was a poor correlation between gene expression and protein concentration, suggesting a stimulation of the translation of IL-1β protein from previously accumulated transcripts or the cleavage of accumulated IL-1β precursor. In-silico studies of PACAP-receptor interactions showed a turn of the peptide characteristic of PACAP-PAC1 interaction, correlated with the higher number of interactions observed with this specific receptor, which is also in agreement with the higher PACAP specificity described for PAC1 compared to VPAC1 and VPACA2. Finally, the in silico analysis revealed nine amino acids related to the PACAP receptor-associated functionality.

Nitric oxide has diverse effects on head and neck cancer cell proliferation and glycolysis.

Glycolysis is a key energy-providing process and one of the hallmarks of cancer. Nitric oxide (NO), a free radical molecule, regulates glycolysis in various cancers. NO can alter the cell cycle and apoptosis in head and neck squamous cell carcinoma (HNSCC) cells. However, the effect of NO on glycolysis in HNSCC cells remains unresolved. The present study investigated the effects of NO on cell proliferation, glucose transporter (GLUT) gene expression and glycolytic indicators in HNSCC cell lines. Two pairs of isogenic HNSCC cell lines, HN18/HN17 and HN30/HN31, were treated with a NO donor, diethylamine NONOate (DEA-NONOate), for 24, 48 and 72 h. Cell proliferation was assessed using MTT assay and NO concentration was measured using the Griess Reagent System. GLUT1, GLUT2, GLUT3, and GLUT4 gene expression was analyzed using reverse transcription-quantitative PCR. Furthermore, hexokinase (HK) activity and lactate production were measured in NO-treated cells using colorimetric assay. NO exhibited concentration-dependent pro- and anti-proliferative effects on the HNSCC cell lines. Lower NO concentrations (5-200 µM) had pro-proliferative effects, whereas NO >200 µM had an anti-proliferative effect on HNSCC cells. NO (5 µM) promoted proliferation and glycolysis in HN18 cells by upregulating GLUT1 and GLUT2 gene expression and increasing HK activity and lactate levels. At 5-20 µM, NO-induced HN17 and HN30 cells demonstrated enhanced proliferation and GLUT2, GLUT3 and GLUT4 gene expression, whereas the glycolytic pathway was not affected. In conclusion, the present study demonstrated distinct proliferative effects of NO on HNSCC cells. NO may promote cell proliferation by stimulating glucose consumption and the glycolytic rate in HN18 cells. The effects of NO in other cell lines may be mediated by a non-glycolysis mechanism and require further investigation.

Silica nanoparticles-induced cytotoxicity and genotoxicity in A549 cell lines

Among the myriad of nanoparticles, silica nanoparticles (SiO2NPs) have gained significant attention since they are extensively produced and used across several kinds of industries. Because of its widespread usage, there has been increasing concern about the potential health effects. This study aims to evaluate the effects of SiO2NPs on Interleukin-6 (IL-6) gene expression in human lung epithelial cell lines (A549). In this study, A549 cells were exposed to SiO2NPs at concentrations of 0, 1, 10, 50, 100, and 200 µg/mL for 24 and 48 h. The IL-6 gene expression was assessed using Real-Time RT-PCR. Additionally, the impact of SiO2NPs on the viability of A549 cells was determined by MTT assay. Statistical analysis was performed using GraphPad Prism software 8.0. MTT assay results indicated a concentration-dependent impact on cell survival. After 24 h, survival decreased from 80 to 68% (1–100 µg/mL), rising to 77% at higher concentrations. After 48 h, survival dropped from 97 to 80%, decreasing to 90% at higher concentrations. RT-PCR showed a dose–response relationship in cellular toxicity up to 10 µg/mL. At higher concentrations, there was increased IL-6 gene expression, mitigating SiO2NP-induced cytotoxic effects. The study shows that the viability and proliferation of A549 cells are impacted by different SiO2NPs concentrations. There may be a potential correlation between IL-6 gene expression reduction and a mechanism linked to cellular toxicity. However, at higher concentrations, an unknown mechanism increases IL-6 gene expression, reducing SiO2NPs' cytotoxic effects. These effects are concentration-dependent and not influenced by exposure times. Further investigation is recommended to determine this mechanism's nature and implications, particularly in cancer research.

Overexpression of heat shock protein 47 is associated with increased proliferation and metastasis in gastric cancer

Here, we investigated that the heat shock protein 47 (HSP47) plays a crucial role in the progression of gastric cancer (GC). We analyzed HSP47 gene expression in GC cell lines and patient tissues. The HSP47 mRNA and protein expression levels were significantly higher in GC cell lines and tumor tissues compared to normal gastric mucosa. Using siRNA to silence the expression of HSP47 in GC cells resulted in a significant reduction in their proliferation, wound healing, migration, and invasion capacities. Additionally, we also showed that the mRNA expression of matrix metallopeptidase-7 (MMP-7), a metastasis-promoting gene, was significantly reduced in HSP47 siRNA-transfected GC cells. We confirmed that the HSP47 promoter region was methylated in the SNU-216 GC cell line expressing low levels of HSP47 and in most non-cancerous gastric tissues. It means that the expression of HSP47 is regulated by epigenetic regulatory mechanisms. These findings suggest that targeting HSP47, potentially through its promoter methylation, could be a useful new therapeutic strategy for treating GC.

Integrative transcriptomic and proteomic profiling of the effects of cell confluency on gene expression

In this study we examine the impact of cell confluency on gene expression. We focused on Argonaute (AGO) protein dynamics and associated gene and protein expression in HEK293, A375, and SHSY5Y cell lines. As a consequence of cell confluency, AGO2 protein translocates into the nucleus. Therefore, we generated transcriptomic data using RNA sequencing to compare gene expression in subconfluent versus confluent cells, which highlighted significant alterations in gene regulation patterns directly corresponding to changes in cell density. Our study also encompasses miRNA profiling data obtained through small RNA sequencing, revealing miRNA expressional changes dependent on cellular confluency, as well as cellular localization. Finally, we derived proteomic data from mass spectrometry analyses following AGO1-4 immunoprecipitation, providing a comprehensive view of AGO interactome in both nuclear and cytoplasmic compartments under varying confluency. These datasets offer a detailed exploration of the cellular and molecular dynamics, influenced by cell confluency, presenting a valuable resource for further research in cellular biology, particularly in understanding the basic mechanisms of cell density in cancer cells.

Characterization of exosome-mediated propagation of systemic inflammatory responses into the Central Nervous System.

The mechanisms through which systemic inflammation exerts its effect on the CNS are still not completely understood. Exosomes are small (30 to 100 nanometers) membrane-bound extracellular vesicles released by most of the mammalian cells. Exosomes play a vital role in cell-to-cell communication. This includes regulation of inflammatory responses by shuttling mRNAs, miRNAs, and cytokines both locally and systemically to the neighboring as well as distant cells to further modulate their transcriptional and/or translational states and affect the functional phenotype of those cells that have taken up these exosomes. The role of circulating blood exosomes leading to neuroinflammation during systemic inflammatory conditions was further characterized. Serum-derived exosomes from LPS-challenged mice (SDEL) were freshly isolated from the sera of the mice that were earlier treated with LPS and used to study SDEL effects on neuroinflammation. Exosomes isolated from the sera of the mice injected with saline were used as a control. In-vitro studies showed that the SDEL upregulate pro-inflammatory cytokine gene expression in the murine cell lines of microglia (BV-2), astrocytes (C8-D1A), and cerebral microvascular endothelial cells (bEnd.3). To further study their effects in-vivo, SDEL were intravenously injected into normal adult mice. Elevated mRNA expression of pro-inflammatory cytokines was observed in the brains of SDEL recipient mice. Proteomic analysis of the SDEL confirmed the increased expression of inflammatory cytokines in them. Together, these results further demonstrate and strengthen the novel role of peripheral circulating exosomes in causing neuroinflammation during systemic inflammatory conditions.

#2485 CellMatchR—RNA-seq based matching of kidney cell lines to kidney reference cell types

Abstract Background and Aims Cultured kidney cell lines are broadly used to model the physiology and pathophysiology of the kidney. Due to immortalization, passaging and culture conditions, a cell line's gene expression profile might differ greatly from the primary cells it was originally derived from. This might be further enhanced by experimental treatments with compounds or genetic modifications. It is therefore a relevant question if a given cell culture system is still the appropriate model for a certain disease or scientific investigations. Reasoning that RNA sequencing (RNA-seq) based transcriptomes are generated as part of many experimental setups and hence may be used to address this question, we developed and tested an RNA-seq based approach, CellMatchR. This approach matches kidney cell lines, primary cells and even tissue specimen to known kidney reference cell types to determine which primary kidney cell type is the most similar and to what degree global gene expression or the expression of selected marker genes differs. Method CellMatchR uses published murine and human kidney single cell- and tubule-level transcriptomic datasets from healthy mice and human donors as references. Single cell datasets were further processed to pseudobulk references for each of the contained cell types. Then RNA-seq data from cell lines or tissues of interest (test data) was compared to the reference (pseudo)bulk data with Spearman correlations of gene counts-per-million values (CPM) or Euclidean distance using log-transformed CPM values. Both approaches were systematically tested for various combinations of test and reference datasets across different species and using global gene expression (i.e. all genes measured in reference and test datasets) and a set of 315 manually curated, kidney cell type marker genes. Results We sequentially used different kidney tissue types, primary cells and cell lines as positive controls and matched them to our reference datasets. Spearman correlations of gene expression rankings showed the highest correlation coefficients with biologically correct reference cell types (examples in Fig. 1A-C). We found Spearman correlations to be superior to Euclidean distances as method of comparison. Analyses based on global gene expression compared to our curated set of 315 kidney cell type marker genes yielded similar results. Notably, correlation coefficients were higher and showed less variation between reference cell types when using the global gene set. Matchings across species, i.e. using murine test and human reference data or vice versa still yielded mostly correct results. However, correlation coefficients were generally lower (i.e. rho = 0.9 vs. 0.6) and varied more across reference datasets if comparisons were performed across species. Using published RNA-seq data for different cell lines of the proximal tubule (e.g. human HK-2 and opossum OKH cells) we observed low similarities with proximal tubule cells (Fig. 1D), whereas two tested cell lines of the collecting duct (mIMCD-3 and mpkCCD cells) plausibly showed the highest similarity to medullary cell types like collecting duct and Loop of Henle cells. The most similar reference cell types did not change in mIMCD-3 cells when kept in 2-dimensional vs. 3-dimensional culture conditions, and in mpkCCD cells when the osmolality of the culture medium was changed from 300 to 600 mosmol/kg. Also, a Pkd1 knockout in mIMCD-3 cells did not change the most similar reference cell type in our analyses (Fig. 1E and F). Conclusion Our CellMatchR approach uses publicly available kidney single cell and bulk RNA-seq datasets and combines these with simple, computationally fast yet effective statistical methods to determine similarities of kidney cell lines and tissue samples to reference cell types of interest. It can easily be implemented by trained users or integrated into online resources for usage with a visual interface. It relies on RNA-seq data from the cell lines of interest and hence presents a feasible complementary method to check the general similarity of cell culture models to kidney cell types and assess their stability across experimental conditions.

Fibroblast growth factor pathway promotes glycolysis by activating LDHA and suppressing LDHB in a STAT1-dependent manner in prostate cancer

BackgroundThe initiation of fibroblast growth factor 1 (FGF1) expression coincident with the decrease of FGF2 expression is a well-documented event in prostate cancer (PCa) progression. Lactate dehydrogenase A (LDHA) and LDHB are essential metabolic products that promote tumor growth. However, the relationship between FGF1/FGF2 and LDHA/B-mediated glycolysis in PCa progression is not reported. Thus, we aimed to explore whether FGF1/2 could regulate LDHA and LDHB to promote glycolysis and explored the involved signaling pathway in PCa progression.MethodsIn vitro studies used RT‒qPCR, Western blot, CCK-8 assays, and flow cytometry to analyze gene and protein expression, cell viability, apoptosis, and cell cycle in PCa cell lines. Glycolysis was assessed by measuring glucose consumption, lactate production, and extracellular acidification rate (ECAR). For in vivo studies, a xenograft mouse model of PCa was established and treated with an FGF pathway inhibitor, and tumor growth was monitored.ResultsFGF1, FGF2, and LDHA were expressed at high levels in PCa cells, while LDHB expression was low. FGF1/2 positively modulated LDHA and negatively modulated LDHB in PCa cells. The depletion of FGF1, FGF2, or LDHA reduced cell proliferation, induced cell cycle arrest, and inhibited glycolysis. LDHB overexpression showed similar inhibitory effect on PCa cells. Mechanistically, we found that FGF1/2 positively regulated STAT1 and STAT1 transcriptionally activated LDHA expression while suppressed LDHB expression. Furthermore, the treatment of an FGF pathway inhibitor suppressed PCa tumor growth in mice.ConclusionThe FGF pathway facilitates glycolysis by activating LDHA and suppressing LDHB in a STAT1-dependent manner in PCa.

Identification of CSNK1D and KLK6 as two common upregulated genes present in BRCA1 mutated triple-negative breast cancer and ovarian epithelial carcinoma

Deficiency in the breast cancer type 1 (BRCA1) gene expression predisposes to triple-negative breast cancer (TNBC) and ovarian cancer (OC). We previously identified by Comparative Genomic Hybridization (CGH) array a gain in the 17q25.3 genomic region in 90% of the BRCA1 mutated TNBC tissues, where 17 genes were up-regulated. A second region (Chr19_45681759_54221324) was identified as the second most frequent gain in the BRCA1-mutated population and has not yet been described in the context of BRCA1 mutation. We thus aimed to validate the expression of the Casein kinase 1 delta (CSNK1D) gene of Chr17 in TNBC and OC cell lines and to investigate the expression of genes of Chr19 in TNBC cell lines and tissues as well as in OC cell lines. Expression level of the genes of the 17q25.3, 19q13.32,13.33 and 13.41 chromosomal regions was analyzed using RT-PCR in BRCA1 deficient TNBC and OC cell lines, as well as in 10 BRCA1-mutated TNBC tissues versus 10 wild type carriers. Our results revealed a significant upregulation of CSNK1D gene expression in BRCA1 deficient TNBC and OC cell lines when compared to control ones, and a significant aberration in the expression of the other six genes of Chr19 was observed. Interestingly, upregulation of kallikrein-related peptidase 6 (KLK6) was detected among the BRCA1 deficient TNBC (cell lines and tissues) and OC cell lines. In conclusion, our results suggested that CSNK1D and KLK6 expression levels could be very promising in the search for biomarkers for BRCA1 deficient TNBC and OC.

From cell lines to cancer patients: personalized drug synergy prediction.

Combination drug therapies are effective treatments for cancer. However, the genetic heterogeneity of the patients and exponentially large space of drug pairings pose significant challenges for finding the right combination for a specific patient. Current in silico prediction methods can be instrumental in reducing the vast number of candidate drug combinations. However, existing powerful methods are trained with cancer cell line gene expression data, which limits their applicability in clinical settings. While synergy measurements on cell line models are available at large scale, patient-derived samples are too few to train a complex model. On the other hand, patient-specific single-drug response data are relatively more available. In this work, we propose a deep learning framework, Personalized Deep Synergy Predictor (PDSP), that enables us to use the patient-specific single drug response data for customizing patient drug synergy predictions. PDSP is first trained to learn synergy scores of drug pairs and their single drug responses for a given cell line using drug structures and large scale cell line gene expression data. Then, the model is fine-tuned for patients with their patient gene expression data and associated single drug response measured on the patient ex vivo samples. In this study, we evaluate PDSP on data from three leukemia patients and observe that it improves the prediction accuracy by 27% compared to models trained on cancer cell line data. PDSP is available at https://github.com/hikuru/PDSP.

Impact of Chemotherapeutic Agents on PD-L1, CTLA-4, and VISTA Gene Expression in Cervical Cancer Cell Lines: An In Vitro Study

Impact of Chemotherapeutic Agents on PD-L1, CTLA-4, and VISTA Gene Expression in Cervical Cancer Cell Lines: An In Vitro Study

Herpesvirus Entry Mediator as an Immune Checkpoint Target and a Potential Prognostic Biomarker in Myeloid and Lymphoid Leukemia.

Herpesvirus entry mediator (HVEM) is a molecular switch that can modulate immune responses against cancer. The significance of HVEM as an immune checkpoint target and a potential prognostic biomarker in malignancies is still controversial. This study aims to determine whether HVEM is an immune checkpoint target with inhibitory effects on anti-tumor CD4+ T cell responses in vitro and whether HVEM gene expression is dysregulated in patients with acute lymphocytic leukemia (ALL). HVEM gene expression in tumor cell lines and peripheral blood mononuclear cells (PBMCs) from ALL patients and healthy controls was measured using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Tumor cells were left untreated (control) or were treated with an HVEM blocker before co-culturing with CD4+ T cells in vitro in a carboxyfluorescein succinimidyl ester (CFSE)-dependent proliferation assay. HVEM expression was upregulated in the chronic myelogenous leukemia cell line (K562) (FC = 376.3, p = 0.086) compared with normal embryonic kidney cells (Hek293). CD4+ T cell proliferation was significantly increased in the HVEM blocker-treated K562 cells (p = 0.0033). Significant HVEM differences were detected in ALL PBMCs compared with the controls, and these were associated with newly diagnosed ALL (p = 0.0011) and relapsed/refractory (p = 0.0051) B cell ALL (p = 0.0039) patients. A significant differentiation between malignant ALL and the controls was observed in a receiver operating characteristic (ROC) curve analysis with AUC = 0.78 ± 0.092 (p = 0.014). These results indicate that HVEM is an inhibitory molecule that may serve as a target for immunotherapy and a potential ALL biomarker.

Interleukin 33 supports squamous cell carcinoma growth via a dual effect on tumour proliferation, migration and invasion, and T cell activation

Interleukin (IL)-33 is an important cytokine in the tumour microenvironment; it is known to promote the growth and metastasis of solid cancers, such as gastric, colorectal, ovarian and breast cancer. Our group demonstrated that the IL-33/ST2 pathway enhances the development of squamous cell carcinoma (SCC). Conversely, other researchers have reported that IL-33 inhibits tumour progression. In addition, the crosstalk between IL-33, cancer cells and immune cells in SCC remains unknown. The aim of this study was to investigate the effect of IL-33 on the biology of head and neck SCC lines and to evaluate the impact of IL-33 neutralisation on the T cell response in a preclinical model of SCC. First, we identified epithelial and peritumoural cells as a major local source of IL-33 in human SCC samples. Next, in vitro experiments demonstrated that the addition of IL-33 significantly increased the proliferative index, motility and invasiveness of SCC-25 cells, and downregulated MYC gene expression in SCC cell lines. Finally, IL-33 blockade significantly delayed SCC growth and led to a marked decrease in the severity of skin lesions. Importantly, anti-IL-33 monoclonal antibody therapy increase the percentage of CD4+IFNγ+ T cells and decreased CD4+ and CD8+ T cells secreting IL-4 in tumour-draining lymph nodes. Together, these data suggest that the IL-33/ST2 pathway may be involved in the crosstalk between the tumour and immune cells by modulating the phenotype of head and neck SCC and T cell activity. IL-33 neutralisation may offer a novel therapeutic strategy for SCC.

Abstract LB008: Integrative dynamic MultiOMICs analysis identifies cell cycle and DNA damage response phenotypes as downstream consequences of using the PRMT5 inhibitor AZD3470

Abstract Background: PRMT5 is an epigenetic enzyme that catalyzes symmetric di-methylation of arginine (SDMA) of multiple substrates that regulate biological processes including RNA splicing and cell cycle. The role of PRMT5 in controlling chromatin accessibility has been investigated in some cancer contexts, but its role in non-small cell lung cancer (NSCLC) global chromatin regulation is not known. AstraZeneca has developed an MTA-cooperative PRMT5 inhibitor (AZD3470) that selectively inhibits PRMT5 in MTAP-null tumors and currently is in Phase I clinical trial (NCT06130553, NCT06130553). Here we applied integrative dynamic MultiOMICs analysis upon AZD3470 treatment in six MTAP-null NSCLC cancer cell lines to inform on PRMT5’s function in maintaining tumor cell growth and survival. Methods: Utilizing an integrative approach, our study explored the effects of AZD3470 on human MTAP-deleted NSCLC cells. MultiOMICs analysis including SDMA post-translational modification (PTM) scans, ATAC-seq, RNA-seq, and mass spectrometry proteomics were employed to delineate the immediate effects and downstream phenotypes of AZD3470 PRMT5 inhibition. Results: AZD3470 induced significant SDMA modification of splicing factors consistent with known roles for PRMT5. PRMT5 inhibition by AZD3470 led to changes in global chromatin accessibility in NSCLC cells. Specific changes in genes involved in the cell cycle pathway were observed including CDK6. Integrative analysis of transcriptomics and proteomics data consistently showed alterations in cell cycle, DNA damage repair and splicing related pathways upon treatment, consistent with the established biological roles of PRMT5. Alternative splicing analysis showed that AZD3470 treatment induced a unique retained intron within the ATM gene and resulted in reduced ATM protein levels. Conclusion: Here we identify AZD3470-induced changes to protein post-translational modification, chromatin accessibility, gene expression and RNA splicing in MTAP-deficient NSCLC cancer cell lines. Different area of MultiOMICs data together pointed to changes in cell cycle and DNA repair pathways. Further, we identified alternative splicing of ATM as a consequence of AZD3470 PRMT5i treatment resulting in significant reduction of ATM protein expression. Keywords: AZD3470, NSCLC, PRMT5 Inhibition, Chromatin Accessibility, Alternative Splicing, DNA Repair, ATM Gene. Citation Format: Ted Hong, Steven Criscione, Andrew Jarnuczak, Jelena Urosevic, Stephanie Ashenden, Ghaith Hamza, Praveen Kumar, Nevena Cvetesic, Lauren Bradshaw, Shaun Moore, Daniel Karl, Tianhui Zhang, Abel Sousa, Jonathan Cairns, Anthony Iannetta, Andrew Zhang, Eric Miele, John Reicha, Chris Chambers, Ramy Elgendy, Julia Lindgren, Daniel Jachimowicz, Maryam Clausen, Graham Belfield, Tony Cheung, Michael Grondine, James T. Lynch, Ho Man Chan, Susan Critchlow, Emma Dean. Integrative dynamic MultiOMICs analysis identifies cell cycle and DNA damage response phenotypes as downstream consequences of using the PRMT5 inhibitor AZD3470 [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 LB008.