Analysis Of Gene Expression Signatures Research Articles

Evaluation of bumetanide as a potential therapeutic agent for Alzheimer's disease.

Therapeutics discovery and development for Alzheimer's disease (AD) has been an area of intense research to alleviate memory loss and the underlying pathogenic processes. Recent drug discovery approaches have utilized in silico computational strategies for drug candidate selection which has opened the door to repurposing drugs for AD. Computational analysis of gene expression signatures of patients stratified by the APOE4 risk allele of AD led to the discovery of the FDA-approved drug bumetanide as a top candidate agent that reverses APOE4 transcriptomic brain signatures and improves memory deficits in APOE4 animal models of AD. Bumetanide is a loop diuretic which inhibits the kidney Na+-K+-2Cl- cotransporter isoform, NKCC2, for the treatment of hypertension and edema in cardiovascular, liver, and renal disease. Electronic health record data revealed that patients exposed to bumetanide have lower incidences of AD by 35%-70%. In the brain, bumetanide has been proposed to antagonize the NKCC1 isoform which mediates cellular uptake of chloride ions. Blocking neuronal NKCC1 leads to a decrease in intracellular chloride and thus promotes GABAergic receptor mediated hyperpolarization, which may ameliorate disease conditions associated with GABAergic-mediated depolarization. NKCC1 is expressed in neurons and in all brain cells including glia (oligodendrocytes, microglia, and astrocytes) and the vasculature. In consideration of bumetanide as a repurposed drug for AD, this review evaluates its pharmaceutical properties with respect to its estimated brain levels across doses that can improve neurologic disease deficits of animal models to distinguish between NKCC1 and non-NKCC1 mechanisms. The available data indicate that bumetanide efficacy may occur at brain drug levels that are below those required for inhibition of the NKCC1 transporter which implicates non-NKCC1 brain mechansims for improvement of brain dysfunctions and memory deficits. Alternatively, peripheral bumetanide mechanisms may involve cells outside the central nervous system (e.g., in epithelia and the immune system). Clinical bumetanide doses for improved neurological deficits are reviewed. Regardless of mechanism, the efficacy of bumetanide to improve memory deficits in the APOE4 model of AD and its potential to reduce the incidence of AD provide support for clinical investigation of bumetanide as a repurposed AD therapeutic agent.

IMMU-01. EVALUATING THE IMPACT OF CAR DESIGN ON THE TUMOR IMMUNE MICROENVIRONMENT AND ANTI-TUMOR RESPONSE IN SYNGENEIC GLIOMA MODELS

Abstract B7-H3 CAR T-cells have limited efficacy in immunocompetent mouse models indicating a suppressive role of the tumor immune microenvironment (TIME). In this study, we set to optimize B7-H3 CAR structure and examine the effects of CAR design on brain TIME. We generated a panel of second-generation B7-H3-specific CARs with different transmembrane (CD8, CD28), costimulatory (CD28, 4-1BB), and activation (ζ, mutζ) domains. We then compared the cytolytic activity, expansion, and anti-tumor activity of T-cells expressing these CARs. Results showed that T-cells expressing B7-H3 CARs with CD28 transmembrane/costimulatory domains have superior efficacy in vitro. Yet, providing optimized costimulation and signaling did not induce superior anti-glioma efficacy of B7-H3 CAR T-cells in vivo. Additionally, incorporating 4-1BB signaling into CD28-based CAR T-cells by expressing 4-1BBL on their cell surface enhances the therapeutic efficacy and persistence of B7-H3 CAR T-cells in vitro. However, transgenic expression of 4-1BBL in CD28-based CARs was not associated with further enhanced anti-glioma efficacy of B7-H3 CAR T-cells in vivo. Next, using high-dimensional flow cytometry and spatial transcriptomic analyses, we show that suppressive tumor-associated macrophages (TAMs) infiltrate tumors in non-responder mice and they predominantly surround the tumor edges. Additionally, minimal T-cell infiltrates were observed in these tumors with predominantly exhausted and dysfunctional phenotypes. Analysis of gene-expression signatures early after CAR T-cell treatment revealed that tumors treated with CD28-based CARs differentially upregulate genes associated with inflammatory myeloid responses. While treatment with other signaling domains’ containing CARs upregulates pathways predictive of suppressive TAM and regulatory/dysfunctional T-cell responses. We show that global depletion of brain macrophages using CSF1R inhibitor exacerbates CAR T-cell anti-tumor activity. In summary, our results show that specific CAR domains induce an early inflammatory immune hub predictive of potent anti-tumor responses. Additionally, inflammatory macrophages are needed for successful CAR T-cell activation and anti-glioma responses.

Abstract 5444: Integrated analysis of gene expression signatures and AI-based detection of tertiary lymphoid structures and stromal tumor-infiltrating lymphocytes in breast cancer H&E samples

Abstract Background: Transcriptomic and morphological features of the tumor microenvironment (TME) are associated with favorable prognosis and can serve as surrogate markers for immune checkpoint inhibitor response in certain breast cancer subtypes. Tertiary lymphoid structures (TLS) and stromal tumor-infiltrating lymphocytes (sTILs) are integral components of the TME. New techniques for TLS characterization could improve reproducibility and automate the current workflow. Here, we correlated AI-based imaging platform predictions of TLS and sTILs on breast cancer H&E whole slide images (WSIs) with expression data. Design: WSIs from 390 TCGA breast cancer samples were used to detect normalized tumoral TLS and tumor stroma infiltration areas by a convolutional neural network (CNN). High and low groups were defined by IQR1 and IQR3. Gene counts were compared using differential expression analysis followed by gene set enrichment analysis (GSEA) with hallmark gene sets. Kassandra's cell type percentage predictions, functional gene signature (Fges) single-sample GSEA, and PROGENy pathway activity median-scaled scores were compared with the Mann-Whitney U test. The Fges consisted of 4 TLS and 29 TME signatures and were clustered into 4 groups: fibrotic, myeloid, T cell, and TLS. Group medians were analyzed. Somatic deep amplifications, deletions, and missense mutations of cancer pathways were compared with Fisher’s exact test. The FDR method was used to adjust p-values. Results: FDCSP, CXCL13, CD79A, CCL19, and IGLL5 were upregulated in the TLS-high group (logFC > 1.5, p < 0.003), and CXCL13, CXCL9, CCL19, CD3E, and CD79A were upregulated in the sTIL-high group (logFC > 2, p < 4e-9). GSEA identified interferon γ and α response and allograft rejection signatures in TLS- and sTIL-high groups (p = 0.006, NES > |2.5|). sTIL-high samples expressed high levels of E2F targets, while the sTIL-low group was enriched with myogenesis and epithelial-mesenchymal transition gene sets. The TLS-high group was enriched in the TLS Fges cluster (p = 1e-5) and contained high B, CD4+ T, and CD8+ T cell content (p < 2e-4). sTIL samples expressed high levels of B, CD4+ T, CD8+ T, and myeloid cells (p < 0.001) and TLS and myeloid clusters (p < 1e-5). TGFβ and VEGF pathways were more active in sTIL-low samples (p < 0.001), while JAK-STAT was most active in the sTIL-high group (p = 0.0006). In the basal subtype (p < 0.001), the TLS-high group was associated with NOTCH2 amplification; the sTIL-low group had increased NRG1 deletions; and the sTIL-high group contained BRCA2 mutations. Conclusions: RNA-based methods used were concordant with AI-based methods of TLS and sTIL detection. Our CNN-based method of identifying immune structures in the TME with morphological features on H&E WSIs may automate traditional pathology workflows with additional validation. Citation Format: Nadezhda Lukashevich, Vladimir Kushnarev, Daniil Dymov, Anastasia Zotova, Anna Belozerova, Ivan Valiev, Lev Popyvanov, Anna M. Love, Nathan Fowler, Alexander Bagaev, Ekaterina Postovalova. Integrated analysis of gene expression signatures and AI-based detection of tertiary lymphoid structures and stromal tumor-infiltrating lymphocytes in breast cancer H&E samples. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5444.

HER2-driven breast cancer suppression by the JNK signaling pathway

The HER2+ subtype of human breast cancer is associated with the malignant transformation of luminal ductal cells of the mammary epithelium. The sequence analysis of tumor DNA identifies loss of function mutations and deletions of the MAP2K4 and MAP2K7 genes that encode direct activators of the JUN NH2-terminal kinase (JNK). We report that invitro studies of human mammary epithelial cells with CRISPR-induced mutations in the MAPK and MAP2K components of the JNK pathway caused no change in growth in 2D culture, but these mutations promoted epithelial cell proliferation in 3D culture. Analysis of gene expression signatures in 3D culture demonstrated similar changes caused by HER2 activation and JNK pathway loss. The mechanism of signal transduction cross-talk may be mediated, in part, by JNK-suppressed expression of integrin α6β4 that binds HER2 and amplifies HER2 signaling. These data suggest that HER2 activation and JNK pathway loss may synergize to promote breast cancer. To test this hypothesis, we performed invivo studies using a mouse model of HER2+ breast cancer with Cre/loxP-mediated ablation of genes encoding JNK (Mapk8 and Mapk9) and the MAP2K (Map2k4 and Map2k7) that activate JNK in mammary epithelial cells. Kaplan-Meier analysis of tumor development demonstrated that JNK pathway deficiency promotes HER2+-driven breast cancer. Collectively, these data identify JNK pathway genes as potential suppressors for HER2+ breast cancer.

Classification and survival prediction in early-stage cirrhosis by gene expression profiling.

Liver cirrhosis has been increasingly diagnosed at an early stage owing to the non-invasive diagnostic techniques. However, it is difficult to identify patients at high risk of disease progression. Screening cirrhotic patients with poor prognosis who are most in need of surveillance is still challenging. Gene expression data GSE15654 and GSE14520 were downloaded for performing unsupervised clustering analysis. The prognostic differences between the different clusters were explored by Cox regression. Integrative analysis of gene expression signature, immune cell enrichments and clinical characterization was performed for different clusters. Two distinctive subclasses were identified in HCV-related GSE15654, and Kaplan-Meier analysis indicated that subtype 2 had lower survival rates than subtype 1 (p=0.0399). Further analysis revealed subtype 2 had a higher density of follicular T helper cells, resting natural killer cells and M0, M2 macrophages while subtype 1 with a higher fraction of naive B cells, memory B cells, resting memory CD 4T cells, activated natural killer cells and monocytes. 226 differentially expressed genes were identified between the two subtypes, and Reactome analysis showed the mainly enriched pathways were biological oxidations and fatty acid metabolism. Five hub genes (AKT1, RPS16, CDC42, CCND1 and PCBP2) and three significant modules were extracted from the PPI network. The results were validated in HBV-related GSE14520 cohort. We identified two subtypes of patients with different prognosis for hepatitis C-related early-stage liver cirrhosis. Bioinformatics analysis of the gene expression and immune cell profile may provide fresh insight into understanding the prognosis difference.

Gene expression signature analysis of peripheral blood mononuclear cells from patients with for high altitude pulmonary hypertension and value for potential drug selection

Objective: To investigate the gene expression characteristics of peripheral blood mononuclear cells from patients with high altitude pulmonary hypertension (HAPH) in Naxi residents living in Lijiang, Yunnan, and to explore the underlying pathogenesis and value for potential drug selection. Methods: This is a case-control study. Six patients with HPAH (HPAH group) and 4 normal subjects (control group) were selected from the Naxi residents who originally lived in Lijiang, Yunnan Province. The general clinical data of the two groups were collected, and the related indexes of pulmonary artery pressure were collected. Peripheral blood mononuclear cells of the subjects were collected for RNA sequencing. The differences on gene expression, regulatory network of transcription factors and drug similarity between the two groups were compared. The results were compared with the public data of idiopathic pulmonary arterial hypertension (IPAH). Biological processes and signal pathways were analyzed and compared between HPAH and IPAH patients. Results: The age of 6 patients with HAPH was (68.1±8.3) years old, and there were 2 males (2/6). The age of 4 subjects in the control group was (62.3±10.9) years old, and there were 2 males (2/4). Tricuspid regurgitation velocity, tricuspid pressure gradient and pulmonary systolic pressure in HAPH group were significantly higher than those in control group (all P<0.05). The results of RNA sequencing showed that compared with the control group, 174 genes were significantly upregulated and 169 genes were downregulated in peripheral blood mononuclear cells of HAPH group. These differentially expressed genes were associated with 220 biological processes, 52 molecular functions and 23 cell components. A total of 21 biological processes and 2 signal pathways differed between HPAH and IPAH groups, most of which were related to inflammation and immune response. ZNF384, SP1 and STAT3 were selected as highly correlated transcription factors by transcription factor prediction analysis. Trichostatin A and vorinostat were screened out as potential drugs for the treatment of HAPH by drug similarity analysis. Conclusions: There are significant differences in gene expression in peripheral blood monocytes between HAPH patients and normal population, and inflammation and immune dysfunction are the main pathogenic factors. Trichostatin A and Vorinostat are potential drugs for the treatment of HAPH.

Th17.1 lymphocytes: emerging players in the orchestra of immune-mediated inflammatory diseases.

It is now well established that Th17 lymphocytes associate with myriad immune-mediated inflammatory diseases. Over the past one and a half decades, a subset of Th17 lymphocytes viz. Th17.1 lymphocytes has been identified in pre-clinical and clinical models of inflammatory rheumatic diseases. These lymphocytes secrete IL-17A (signature cytokine of Th17 lymphocytes) as well as IFN-γ (the signature cytokine of Th1 lymphocytes). They express the chemokine markers for Th1 (CXCR3) as well as Th17 (CCR6) lymphocytes. Th17.1 lymphocytes also express the drug efflux protein p-glycoprotein, which associates with resistance to corticosteroids and other immunosuppressive drugs. This narrative review overviews the evidence regarding Th17.1 lymphocytes in different inflammatory rheumatic diseases. It is now recognized that Th17.1 lymphocytes are increased in the synovial fluid of affected joints in rheumatoid arthritis (RA) and associate with poor treatment response to abatacept. Th17.1 lymphocytes from synovial fluid of RA are less responsive to immunosuppression than those from the peripheral blood. In sarcoidosis, Th17.1 lymphocytes are concentrated in mediastinal lymph nodes and alveolar lining. Such Th17.1 lymphocytes in sarcoidosis are the predominant source of IFN-γ in the sarcoid lung. Th17.1 lymphocytes are elevated in lupus and Takayasu arteritis and associate with disease activity. Future studies should evaluate isolated Th17.1 lymphocytes from peripheral blood or sites of pathology such as synovial fluid and assess their modulation with immunosuppressive therapy in vitro. The analysis of gene expression signature of isolated Th17.1 lymphocytes might enable the identification of newer therapeutic strategies specifically targeting these cell populations in inflammatory rheumatic diseases. Key Points • Th17.1 lymphocytes are a subset of Th17 lymphocytes secreting both IFN-γ and IL-17 • Th17.1 lymphocytes drive neutrophilic inflammation, granuloma formation, and corticosteroid resistance • Th17.1 lymphocytes are elevated in rheumatoid arthritis and sarcoidosis at sites of inflammation • Increased circulating Th17.1 lymphocytes have been identified in lupus and Takayasu arteritis and associate with active disease.

An integrative gene expression signature analysis identifies CMS4 KRAS-mutated colorectal cancers sensitive to combined MEK and SRC targeted therapy

BackgroundOver half of colorectal cancers (CRCs) are hard-wired to RAS/RAF/MEK/ERK pathway oncogenic signaling. However, the promise of targeted therapeutic inhibitors, has been tempered by disappointing clinical activity, likely due to complex resistance mechanisms that are not well understood. This study aims to investigate MEK inhibitor-associated resistance signaling and identify subpopulation(s) of CRC patients who may be sensitive to biomarker-driven drug combination(s).MethodsWe classified 2250 primary and metastatic human CRC tumors by consensus molecular subtypes (CMS). For each tumor, we generated multiple gene expression signature scores measuring MEK pathway activation, MEKi “bypass” resistance, SRC activation, dasatinib sensitivity, EMT, PC1, Hu-Lgr5-ISC, Hu-EphB2-ISC, Hu-Late TA, Hu-Proliferation, and WNT activity. We carried out correlation, survival and other bioinformatic analyses. Validation analyses were performed in two independent publicly available CRC tumor datasets (n = 585 and n = 677) and a CRC cell line dataset (n = 154).ResultsHere we report a central role of SRC in mediating “bypass”-resistance to MEK inhibition (MEKi), primarily in cancer stem cells (CSCs). Our integrated and comprehensive gene expression signature analyses in 2250 CRC tumors reveal that MEKi-resistance is strikingly-correlated with SRC activation (Spearman P < 10–320), which is similarly associated with EMT (epithelial to mesenchymal transition), regional metastasis and disease recurrence with poor prognosis. Deeper analysis shows that both MEKi-resistance and SRC activation are preferentially associated with a mesenchymal CSC phenotype. This association is validated in additional independent CRC tumor and cell lines datasets. The CMS classification analysis demonstrates the strikingly-distinct associations of CMS1-4 subtypes with the MEKi-resistance and SRC activation. Importantly, MEKi + SRCi sensitivities are predicted to occur predominantly in the KRAS mutant, mesenchymal CSC-like CMS4 CRCs.ConclusionsLarge human tumor gene expression datasets representing CRC heterogeneity can provide deep biological insights heretofore not possible with cell line models, suggesting novel repurposed drug combinations. We identified SRC as a common targetable node–-an Achilles’ heel–-in MEKi-targeted therapy-associated resistance in mesenchymal stem-like CRCs, which may help development of a biomarker-driven drug combination (MEKi + SRCi) to treat problematic subpopulations of CRC.

Global analysis of gene expression signature and diagnostic/prognostic biomarker identification of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world. The landscape of HCC's molecular alteration signature has been explored over the last few decades. Even so, more comprehensive research is still needed to improve understanding of tumorigenesis and progression of HCC, as well as to identify potential biomarkers for the malignancy. In this research, a comprehensive bioinformatics analysis was conducted based on the publicly available databases from both the Cancer Genome Atlas (TCGA) program and the gene expression omnibus (GEO) database. R/Bioconductor was used to analyze differentially expressed genes (DEGs) between HCC tumor and normal control (NC) samples, and then a protein-protein interaction (PPI) network of DEGs was established through the STRING platform. Finally, the application of specific candidate genes as diagnostic or prognostic biomarkers of HCC was explored and evaluated by ROC and survival analysis. A total of 310 DEGs were detected in the HCC tumor samples. Thirty-six hub DEGs in the PPI network and 10 candidates of the 36 genes showed significant alterations in tumor expression, including CDKN3, TOP2A, UBE2C, CDC20, PBK, ASPM, KIF20A, NCAPG, CCNB2, CYP3A4. The 10-gene signature had relatively significant effects when distinguishing tumors from normal samples (sensitivity >70%, specificity >70%, AUC >0.8, p < 0.001). Eight candidate genes were negatively correlated with the overall survival rate of the patients (p < 0.05) and were all up-regulated in HCC tumor samples. The age and gender factors had no significant impact on the overall survival rate of HCC patients (p > 0.05), and the TNM stage status factor had a significant negative prognosis correlation (p < 0.05). This research provides evidence for a better understanding of tumorigenesis and progression of HCC and helps to explore candidate targets for disease diagnosis and treatment.

LINCS gene expression signature analysis revealed bosutinib as a radiosensitizer of breast cancer cells by targeting eIF4G1.

Radioresistance is the predominant cause for radiotherapy failure and disease progression, resulting in increased breast cancer‑associated mortality. Using gene expression signature analysis of the Library of Integrated Network‑Based Cellular Signatures (LINCS) and Gene Expression Omnibus (GEO), the aim of the present study was to systematically identify potential candidate radiosensitizers from known drugs. The similarity of integrated gene expression signatures between irradiated eukaryotic translation initiation factor 4γ1(eIF4G1)‑silenced breast cancer cells and known drugs was measured using enrichment scores(ES). Drugs with positive ES were selected as potential radiosensitizers. The radiosensitizing effects of the candidate drugs were analyzed in breast cancer cell lines (MCF‑7, MX‑1 and MDA‑MB‑231) using CCK‑8 and colony formation assays following exposure to ionizing radiation. Cell apoptosis was measured using flow cytometry. The expression levels of eIF4G1 and DNA damage response (DDR) proteins were analyzed by western blotting. Bosutinib was identified as a promising radiosensitizer, as its administration markedly reduced the dosage required both for the drug and for ionizing radiation, which may be associated with fewer treatment‑associated adverse reactions. Moreover, combined treatment of ionizing radiation and bosutinib significantly increased cell killing in all three cell lines, compared with ionizing radiation or bosutinib alone. Among the three cell lines, MX‑1 cells were identified as the most sensitive to both ionizing radiation and bosutinib. Bosutinib markedly downregulated the expression of eIF4G1 in a dose‑dependent manner and also reduced the expression of DDR proteins (including ATM, XRCC4, ATRIP, and GADD45A). Moreover, eIF4G1 was identified as a key target of bosutinib that may regulate DNA damage induced by ionizing radiation. Thus, bosutinib may serve as a potential candidate radiosensitizer for breast cancer therapy.

Descriptive and functional characterization of epidermal growth factor‑like domain 8 in mouse cortical thymic epithelial cells by integrated analysis of gene expression signatures and networks.

Epidermal growth factor-like domain 8 (EGFL8), a newly identified member of the EGFL family, and plays negative regulatory roles in mouse thymic epithelial cells (TECs) and thymocytes. However, the role of EGFL8 in these cells remains poorly understood. In the present study, in order to characterize the function of EGFL8, genome-wide expression profiles in EGFL8-overexpressing or -silenced mouse cortical TECs (cTECs) were analyzed. Microarray analysis revealed that 458 genes exhibited a >2-fold change in expression levels in the EGFL8-overexpressing vs. the EGFL8-silenced cTECs. Several genes involved in a number of cellular processes, such as the cell cycle, proliferation, growth, migration and differentiation, as well as in apoptosis, reactive oxygen species generation, chemotaxis and immune responses, were differentially expressed in the EGFL8-overexpressing or -silenced cTECs. WST-1 analysis revealed that that the overexpression of EGFL8 inhibited cTEC proliferation. To investigate the underlying mechanisms of EGFL8 in the regulation of cTEC function, genes related to essential cellular functions were selected. Reverse transcription-polymerase chain reaction analysis revealed that EGFL8 knockdown upregulated the expression of cluster differentiation 74 (CD74), Fas ligand (FasL), C-X-C motif chemokine ligand 5 (CXCL5), CXCL10, CXCL16, C-C motif chemokine ligand 20 (CCL20), vascular endothelial growth factor-A (VEGF-A), interferon regulatory factor 7 (Irf7), insulin-like growth factor binding protein-4 (IGFBP-4), thrombospondin 1 (Thbs1) and nuclear factor κB subunit 2 (NF-κB2) genes, and downregulated the expression of angiopoietin-like 1 (Angptl1), and neuropilin-1 (Nrp1) genes. Additionally, EGFL8 silencing enhanced the expression of anti-apoptotic molecules, such as B-cell lymphoma-2 (Bcl-2) and Bcl-extra large (Bcl-xL), and that of cell cycle-regulating molecules, such as cyclin-dependent kinase 1 (CDK1), CDK4, CDK6 and cyclin D1. Moreover, gene network analysis revealed that EGFL8 exerted negative effects on VEGF-A gene expression. Hence, the altered expression of several genes associated with EGFL8 expression in cTECs highlights the important physiological processes in which EGFL8 is involved, and provides insight into its biological functions.

Stable gene expression for normalisation and single-sample scoring.

Gene expression signatures have been critical in defining the molecular phenotypes of cells, tissues, and patient samples. Their most notable and widespread clinical application is stratification of breast cancer patients into molecular (PAM50) subtypes. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical application of thousands of existing gene signatures captured in repositories such as the Molecular Signature Database. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across thousands of samples, allowing signature scoring and supporting general data normalisation for transcriptomic data. Our new method, stingscore, quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these ‘stably-expressed genes’. We show that our list of stable genes has better stability across cancer and normal tissue data than previously proposed gene sets. Additionally, we show that signature scores computed from targeted transcript measurements using stingscore can predict docetaxel response in breast cancer patients. This new approach to gene expression signature analysis will facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.

Comparative transcriptome profiling of pomegranate genotypes having resistance and susceptible reaction to Xanthomonas axonopodis pv. punicae.

Pomegranate (Punica granatum L.) is an important fruit crop, rich in fiber, vitamins, antioxidants, minerals and source of different biologically active compounds. The bacterial blight caused by Xanthomonas axonopodispv. punicae is a serious threat to the crop leading to 60–80% yield loss under epiphytotic conditions. In this work, we have generated comparative transcriptome profile to mark the gene expression signatures during resistance and susceptible interactions. We analyzed leaf and fruits samples of moderately resistant genotype (IC 524207) and susceptible variety (Bhagawa) of pomegranate at three progressive infection stages upon inoculation with the pathogen. RNA-Seq with the Illumina HiSeq 2500 platform revealed 1,88,337 non-redundant (nr) transcript sequences from raw sequencing data, for a total of 34,626 unigenes with size >2 kb. Moreover, 85.3% unigenes were annotated in at least one of the seven databases examined. Comparative analysis of gene-expression signatures in resistant and susceptible varieties showed that the genes known to be involved in defense mechanism in plants were up-regulated in resistant variety. Gene Ontology (GO) analysis successfully annotated 90,485 pomegranate unigenes, of which 68,464 were assigned to biological, 78,107 unigenes molecular function and 44,414 to cellular components. Significantly enriched GO terms in DEGs were related to oxidations reduction biological process, protein binding and oxidoreductase activity. This transcriptome data on pomegranate could help in understanding resistance and susceptibility nature of cultivars and further detailed fine mapping and functional validation of identified candidate gene would provide scope for resistance breeding programme in pomegranate.

Association of Tumor Budding With Immune Evasion Pathways in Primary Colorectal Cancer and Patient-Derived Xenografts

Tumor budding has been found to be of prognostic significance for several cancers, including colorectal cancer (CRC). Additionally, the molecular classification of CRC has led to the identification of different immune microenvironments linked to distinct prognosis and therapeutic response. However, the association between tumor budding and the different molecular subtypes of CRC and distinct immune profiles have not been fully elucidated. This study focused, firstly, on the validation of derived xenograft models (PDXs) for the evaluation of tumor budding and their human counterparts and, secondly, on the association between tumor budding and the immune tumor microenvironment by the analysis of gene expression signatures of immune checkpoints, Toll-like receptors (TLRs), and chemokine families. Clinical CRC samples with different grades of tumor budding and their corresponding PDXs were included in this study. Tumor budding grade was reliably reproduced in early passages of PDXs, and high-grade tumor budding was intimately related with a poor-prognosis CMS4 mesenchymal subtype. In addition, an upregulation of negative regulatory immune checkpoints (PDL1, TIM-3, NOX2, and IDO1), TLRs (TLR1, TLR3, TLR4, and TLR6), and chemokine receptors and ligands (CXCR2, CXCR4, CXCL1, CXCL2, CXCL6, and CXCL9) was detected in high-grade tumor budding in both human samples and their corresponding xenografts. Our data support a close link between high-grade tumor budding in CRC and a distinctive immune-suppressive microenvironment promoting tumor invasion, which may have a determinant role in the poor prognosis of the CMS4 mesenchymal subtype. In addition, our study demonstrates that PDX models may constitute a robust preclinical platform for the development of novel therapies directed against tumor budding in CRC.

Effect of first-line spartalizumab + dabrafenib + trametinib on immunosuppressive features detected in peripheral blood and clinical outcome in patients (pts) with advanced BRAF V600–mutant melanoma.

10034 Background: Spartalizumab + dabrafenib + trametinib has previously shown a high response rate of 78% (28 of 36 pts), with a complete response (CR) rate of 42%. A correlative analysis of gene expression signatures (GES)/pathways using whole-transcriptome RNA-seq data from tissue showed that pts with a CR had significantly lower expression levels of immunosuppressive factors in the tumor microenvironment (TME) at baseline (BL). Here we analyze BL peripheral blood markers in the same cohort of pts to assess whether liquid markers can also predict response and clinical outcome to spartalizumab + dabrafenib + trametinib. Methods: The Phase III COMBI-i study (NCT02967692) is evaluating spartalizumab + dabrafenib + trametinib in pts with previously untreated BRAF V600–mutant unresectable or metastatic melanoma. In parts 1 (safety run-in; n = 9) and 2 (biomarker cohort; n = 27), blood and tissue samples were collected at BL, on treatment after 2-3 wk and 8-12 wk, and at disease progression. Lactate dehydrogenase (LDH) and other blood-based markers (including cytokine profiling [n = 45] and blood RNA-seq [114 signatures]) were assessed in all 36 pts. Pts were divided into 2 groups of 24 and 12 pts based on progression-free survival (PFS) of &gt; 1 or &lt; 1 y. Results: In addition to LDH, previously described blood markers such as neutrophil to lymphocyte ratio (NLR) and plasma IL-8 were identified among other neutrophil and immunosuppressive features as top candidates associated with PFS &gt; 1 y. Low plasma IL-8 levels were also associated with CR, and multivariate models suggested that IL-8 may add independent predictive value to LDH and NLR for PFS &gt; 1 y and CR. Pts with high IL-8 levels in the circulation were characterized by high neutrophil chemokine signaling (ρ = 0.553) and high neutrophil markers (ρ = 0.466) in the tumor as measured by RNA-seq GES levels. We observed a decrease in plasma IL-8 levels from BL upon treatment with spartalizumab + dabrafenib + trametinib. Conclusions: Our peripheral blood marker analysis confirmed recent findings from tissue samples that intratumoral immunosuppressive features may preclude a CR and are associated with poor outcomes. High BL plasma IL-8 levels may be associated with an immunosuppressive TME. Further validation is warranted; the randomized placebo-controlled part 3 of COMBI-i is ongoing. Clinical trial information: NCT02967692.

Gene expression signatures: A tool for analysis of breast cancer prognosis and therapy.

Breast Cancer is the most predominant female cancer in developed as well as developing countries. The treatment strategies of breast cancers depends on an array of factors like age at diagnosis, menstrual status, dietary pattern, immunological response, genetic variations of the cancer cells etc. Recent technological advancements in cancer diagnosis lead to the emergence of gene expression pattern for better understanding of the tumor behavior. It has not only bolstered the prognosis, but also the early diagnosis and therapy. The accuracy in disease prognosis can be boosted when gene expression signatures are combined with traditional clinicopathological features. This review explains how the evolution of gene expression signatures for breast cancers, its advantages and future prospects. In addition, an overview of currently available gene expression signature analysis tools and consolidated information on their current status and specific benefits, that can be availed for breast cancer diagnosis are also discussed.

Analysis of gene expression signatures identifies prognostic and functionally distinct ovarian clear cell carcinoma subtypes.

BackgroundOvarian clear cell carcinoma (OCCC) is a histological subtype of epithelial ovarian cancer (EOC) with distinct pathological, biological, and molecular features. OCCCs are more resistant to conventional treatment regimen of EOC and have the worst stage-adjusted prognosis amongst EOC subtypes. As the OCCC incidence rate in Asian populations has significantly increased in recent decades, it is critical to elucidate its molecular features that could lead to OCCC-tailored therapeutic strategies.MethodsGene expression profiles of 222 OCCC were analyzed by hierarchical clustering and statistical analyses.FindingsWe identified two OCCC gene expression subtypes: EpiCC—epithelial-like, which is associated with early-stage disease, with a relatively higher rate of gene mutations in the SWI/SNF complex; and MesCC—mesenchymal-like, associated with late-stage and higher enrichment of immune-related pathway activity. Genetic, copy number and transcriptomic analyses showed that both EpiCC and MesCC carried OCCC-associated aberrations. The EpiCC/MesCC classification was reproducible in validation cohorts and OCCC cell lines. MesCC tumors had a poorer progression-free survival (PFS) than EpiCC tumors (HR: 3·0, p = 0·0006). Functional assays in cell lines showed that the MesCC subtype was more proliferative and more anoikis-resistant than the EpiCC. By applying the EpiCC/MesCC classification to the TCGA renal clear cell carcinoma cohort, our results indicated interoperability of the subtyping scheme, and revealed preferential drug response of MesCC to bevacizumab.InterpretationThe EpiCC/MesCC classification shows promise for prognostic and therapeutic stratification in OCCC patients and warrants further investigation in the context of OCCC gene expression subtype-tailored treatment strategies.

Gene Expression Signature Predicts Deep Molecular Response (DMR) in Chronic Myeloid Leukemia (CML): An Exploratory Biomarker Analysis from ENESTnd

Gene Expression Signature Predicts Deep Molecular Response (DMR) in Chronic Myeloid Leukemia (CML): An Exploratory Biomarker Analysis from ENESTnd

Identification of genes and functional coexpression modules closely related to ulcerative colitis by gene datasets analysis.

BackgroundUlcerative colitis is a type of inflammatory bowel disease posing a great threat to the public health worldwide. Previously, gene expression studies of mucosal colonic biopsies have provided some insight into the pathophysiological mechanisms in ulcerative colitis; however, the exact pathogenesis is unclear. The purpose of this study is to identify the most related genes and pathways of UC by bioinformatics, so as to reveal the core of the pathogenesis.MethodsGenome-wide gene expression datasets involving ulcerative colitis patients were collected from gene expression omnibus database. To identify most close genes, an integrated analysis of gene expression signature was performed by employing robust rank aggregation method. We used weighted gene co-expression network analysis to explore the functional modules involved in ulcerative colitis pathogenesis. Besides, biological process and pathways analysis of co-expression modules were figured out by gene ontology enrichment analysis using Metascape.ResultsA total of 328 ulcerative colitis patients and 138 healthy controls were from 14 datasets. The 150 most significant differentially expressed genes are likely to include causative genes of disease, and further studies are needed to demonstrate this. Seven main functional modules were identified, which pathway enrichment analysis indicated were associated with many biological processes. Pathways such as ‘extracellular matrix, immune inflammatory response, cell cycle, material metabolism’ are consistent with the core mechanism of ulcerative colitis. However, ‘defense response to virus’ and ‘herpes simplex infection’ suggest that viral infection is one of the aetiological agents. Besides, ‘Signaling by Receptor Tyrosine Kinases’ and ‘pathway in cancer’ provide new clues for the study of the risk and process of ulcerative colitis cancerization.

CDRgator: An Integrative Navigator of Cancer Drug Resistance Gene Signatures.

Understanding the mechanisms of cancer drug resistance is a critical challenge in cancer therapy. For many cancer drugs, various resistance mechanisms have been identified such as target alteration, alternative signaling pathways, epithelial–mesenchymal transition, and epigenetic modulation. Resistance may arise via multiple mechanisms even for a single drug, making it necessary to investigate multiple independent models for comprehensive understanding and therapeutic application. In particular, we hypothesize that different resistance processes result in distinct gene expression changes. Here, we present a web-based database, CDRgator (Cancer Drug Resistance navigator) for comparative analysis of gene expression signatures of cancer drug resistance. Resistance signatures were extracted from two different types of datasets. First, resistance signatures were extracted from transcriptomic profiles of cancer cells or patient samples and their resistance-induced counterparts for >30 cancer drugs. Second, drug resistance group signatures were also extracted from two large-scale drug sensitivity datasets representing ~1,000 cancer cell lines. All the datasets are available for download, and are conveniently accessible based on drug class and cancer type, along with analytic features such as clustering analysis, multidimensional scaling, and pathway analysis. CDRgator allows meta-analysis of independent resistance models for more comprehensive understanding of drug-resistance mechanisms that is difficult to accomplish with individual datasets alone (database URL: http://cdrgator.ewha.ac.kr).