scholarly journals A Patient-Derived Glioblastoma Organoid Model Maintains Intertumoral and Intratumoral Heterogeneity for Therapeutic Testing

Neurosurgery ◽  
2019 ◽  
Vol 66 (Supplement_1) ◽  
Author(s):  
Ryan Salinas ◽  
Daniel Zhang ◽  
Fadi Jacob ◽  
Phuong Nguyen ◽  
Saad Sheikh ◽  
...  

Abstract INTRODUCTION Glioblastoma remains invariably lethal due to its aggressive and invasive nature. It has been increasingly appreciated that molecular heterogeneity between tumors and within tumors likely contributes to the lack of efficacy of numerous clinical trials. METHODS In order to maintain the inherent heterogeneity of glioblastoma, we employed a novel method to rapidly culture glioblastoma organoids (named GBOs) directly from neurosurgical resection. Cultures were generated from minced glioblastoma tissue in defined media free of serum, exogenous EGF/fibroblast growth factor (FGF), and matrigel so as to minimize selection bias. Over 30 GBO lines have been generated to date. Comprehensive histologic and sequencing analyses were performed to assess similarity to primary tumors. Leveraging clinical molecular and sequencing data, selected GBOs were treated with radiation/temozolamide, EGFR inhibition (gefitinib), MEK inhibition (trametinib), and mTOR inhibition (everolimus) for 7 d and analyzed by percent KI67+ cells following treatment. Gene set enrichment and gene ontology analysis was performed from pretreated sequencing data. RESULTS Rounded GBOs form within 2 wk and maintain high similarity to the primary tumor based upon histology as well as by sequencing analysis. Treatment with radiation/temozolamide led to a decrease in the proportion of KI67+ cells in 3 of 8 tumors with some evidence of correlative clinical radiographic response. GBO response to gefitinib treatment was specific to EGFR altered tumors and enriched for EGF related gene sets. Two GBOs had downstream NF1 mutated tumors that responded to MEK inhibition with gene set enrichment for RAS signaling. Despite resistance to other experimental treatments, 1 GBO line was found to have a PI3K mutation and responded significantly to downstream mTOR inhibition. CONCLUSION This novel culturing method of GBOs maintains both intertumoral and intratumoral heterogeneity. As clinical sequencing because increasingly prevalent, GBOs may become a valuable tool for functionally testing mutation-specific treatment strategies in a patient-specific and clinically relevant time frame.

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi268-vi268
Author(s):  
Ryan Salinas ◽  
Daniel Zhang ◽  
Fadi Jacob ◽  
Phuong Nguyen ◽  
Saad Sheikh ◽  
...  

Abstract Glioblastoma treatment options remain limited due to its aggressive and invasive nature. It is increasingly appreciated that molecular heterogeneity between tumors and within tumors likely contributes to the lack of therapeutic advances. To maintain the inherent heterogeneity of glioblastoma, we employed a novel method to rapidly culture glioblastoma organoids (GBOs) directly from neurosurgical resection. GBOs are routinely generated around two weeks following initial resection. Comprehensive histologic and sequencing analyses demonstrated similarity to primary tumors. Leveraging clinical molecular and sequencing data, selected GBOs were treated with radiation/temozolamide and targeted inhibitor therapies. The effect on proliferation was measured by the percentage of KI67+ cells and gene set enrichment (GSEA) analysis was performed to compare the pre-treated expression signature amongst responsive and non-responsive tumors. Treatment of organoids with radiation/temozolamide led to a decrease in the percentage of KI67+ cells in four of eight patient-derived organoid lines with some evidence of correlative radiographic response Gene sets associated with radiation response and TNF signaling were enriched in radiation/temozolamide sensitive GBOs. GBO response to EGFR inhibition via gefitinib treatment was specific to EGFR altered tumors, whose expression also enriched for EGF signaling pathway expression. Two GBOs had downstream NF1 mutations that responded to the MEK inhibitor trametinib. On GSEA, gene expression of NF1 mutated GBOs enriched for RAS signaling. One GBO line was found to have a PI3K mutation and responded dramatically to mTOR inhibition via everolimus. Dichotomous efficacy of MEK and mTOR inhibition was also noted by tumor-specific changes in GBO diameter following treatment. This novel culturing method of GBOs maintains intertumoral and intratumoral heterogeneity and allows for therapeutic testing within two weeks of neurosurgical resection. As clinical sequencing because increasingly prevalent, GBOs may become a valuable tool to functionally test mutation-specific treatment strategies in a patient-specific manner within a clinically relevant time frame.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 11509-11509
Author(s):  
Christopher James Walker ◽  
Hua Chang ◽  
Jianjun Liu ◽  
Bruno Vincenzi ◽  
Andrea Napolitano ◽  
...  

11509 Background: Patients (pts) with recurrent inoperative DDLS have a poor prognosis and limited treatment options. Selinexor is an oral, selective inhibitor of nuclear export (SINE) compound approved for previously treated pts with myeloma and diffuse large B-cell lymphoma. SEAL was a Phase 2-3 randomized, double-blind, study of selinexor versus placebo in pts with progressive DDLS and 2-5 prior systemic therapies. SEAL showed significantly prolonged progression-free survival (PFS, HR = 0.70, p = 0.0228) with well managed toxicity. A biomarker predictive of clinical activity could be used to optimize selection of pts with DDLS for selinexor. Methods: Pts were randomized 2:1 for Phase 3: 188 received twice weekly selinexor (60mg) and 97 received placebo. Three exploratory biomarker analyses (RNA sequencing of biopsies) from selinexor-treated pts were performed: discovery set of sensitive (n = 8) or resistant (n = 9) tumors; a validation set of pts with favorable (n = 19) or poor (n = 14) tumor control based on PFS, and paired lesions from a pt who harbored both a responsive and resistant lesion. Tumor biopsies from 24 pts on placebo with short ( < 5 months, n = 18) and long ( > 6 months, n = 6) PFS were RNA sequenced. Gene expressions were compared using a negative binomial distribution with DeSeq2. Pathway analyses were performed using Gene Set Enrichment Analysis (GSEA) with MSigDB Cancer Gene Neighborhoods. Results: RNA sequencing analysis comparing 17 sensitive and resistant tumors identified 114 differentially expressed genes (adjusted p-values < 0.05). Expression of CALB1, which encodes the calcium-binding protein calbindin, was significantly lower in sensitive tumors (adjusted P [Padj] = 7.5x10-20), and expression of GRM1, which encodes a metabotropic glutamate receptor that activates phospholipase C, was higher in selinexor sensitive tumors (Padj= 0.003). These findings were confirmed in an independent validation set (Padj = 0.01 – 0.02). In the pt with paired sensitive and resistant lesions, CALB1 expression was 52-fold lower in the sensitive tumor. In a comparison of placebo-treated pts, neither CALB1 or GRM1 was differentially expressed between pts with short or long PFS, indicating they are markers of response to selinexor treatment, rather than general markers of disease aggressiveness. Gene set enrichment analyses revealed that selinexor sensitive tumors in the discovery and validation sets showed upregulation of cancer genes related to SNRK and the netrin 1 receptor tumor suppressor DCC. The resistant tumors showed upregulated EIF3S2 translation initiator-related genes. Conclusions: Selinexor sensitive DDLS tumors showed low expression of CALB1 and high GRM1. If validated, pts with DDLS whose tumors match this expression profile are especially likely to benefit from selinexor. Clinical trial information: NCT02606461.


2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii64-iii64
Author(s):  
S Berendsen ◽  
D Dalemans ◽  
K Draaisma ◽  
P A Robe ◽  
T J Snijders

Abstract BACKGROUND Involvement of the subventricular zone (SVZ) in GBM is associated with poor prognosis and suggested to associate with specific tumor-biological characteristics. The SVZ microenvironment can influence gene expression and migration in GBM cells in preclinical models. We aimed to investigate whether the SVZ microenvironment has any influence on intratumoral gene expression patterns in GBM patients. MATERIAL AND METHODS The publicly available Ivy GBM database contains clinical, radiological and whole exome sequencing data from multiple regions from en bloc resected GBMs. SVZ involvement of the various tissue samples was evaluated on MRI scans. In the tumors that contacted the SVZ, we performed gene expression analyses and gene set enrichment analyses to compare gene (set) expression in tumor regions within the SVZ to tumor regions outside the SVZ, within the same tumors. We also compared these samples to GBMs that made no contact with the SVZ. RESULTS Within GBMs that contacted the SVZ, tissue samples within the SVZ showed enrichment of gene sets involved in (epithelial-)mesenchymal transition, NF-κB and STAT3 signaling, angiogenesis and hypoxia, compared to the samples outside of the SVZ region from the same tumors (p<0.05, FDR<0.25). Comparison of GBM samples within the SVZ region to samples from tumors that did not contact the SVZ yielded similar results. In contrast, we observed no difference in gene set enrichment when comparing the samples outside of the SVZ from SVZ-contacting GBMs with samples from GBMs that did not contact the SVZ at all. CONCLUSION GBM samples in the SVZ region associate with increased (epithelial-)mesenchymal transition and angiogenesis/hypoxia signaling, possibly mediated by the SVZ microenvironment.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1402-1402 ◽  
Author(s):  
Haitham Abdelhakim ◽  
Ahmad Elkhanany ◽  
Mohammad Telfah ◽  
Tara L. Lin ◽  
Andrew K Godwin

Background: Mutations in the nucleophosmin (NPM1) gene are associated with better responses to chemotherapy and improved survival among acute myeloid leukemia (AML) patients. However, older AML patients (≥ 60 years old) with NPM1 mutation have worse survival outcomes than younger patients (&lt;60 years old). This may be attributed to more adverse biologic features (frequent complex karyotype, FLT3 mutations) in addition to lower odds to receive intensive curative chemotherapy due to co-morbidities. We sought to compare the outcomes of older NPM1 mutated AML patients with younger NPM1 mutated patients after exclusions of patients with adverse-risk per ELN 2017 criteria. We also compared their genomic mutation profile and gene expression utilizing the Beat AML dataset. Methods: We queried the Beat AML dataset, supported in part by the Leukemia & Lymphoma Society and the OHSU Knight Cancer Institute, for pts with NPM1 gene mutations who did not have adverse-risk ELN 2017 (poor cytogenetic profile or mutations in FLT3, TP53 or ASXL1). Descriptive statistics described baseline characteristics and responses. Kaplan-Meier with log-rank test was used for survival analysis. DNA mutation data were obtained from the exome sequencing and analyzed using the beat AML data viewer (Vizome). RNA exome sequencing data were downloaded. Differential expression of raw count RNA-Seq and gene set enrichment was done using R via limma and ClusterProfiler packages. Results: Among 562 unique patients in the Beat AML umbrella trial, there were 81 patients with newly diagnosed NPM1 mutated AML after exclusion of patients with ELN 2017 adverse-risk category. Among these patients there was 49 older patients (≥ 60 years old) and 32 younger patients (&lt;60 years old). 39 (77.6%) in the older group received intensive induction chemotherapy and 30 patients (93.7%) in the younger group. 29 (59.1%) patients achieved complete morphologic responses in the older patient group compared to 28 (84.4%) in the younger patient group (OR 0.2, P=0.009). Median overall survival in the older patient group was 20.1 months compared to 25.4 months in the younger group (HR 0.52, P=0.08). Exome sequencing data were available for 43 and 30 patients from the older and younger group respectively. There was a median of 6.5 (2-20) and 7 (2-19) mutations in the older and younger group respectively (P=0.78). After exclusion of the benign mutations and variant of unknown significance, the median number of mutations was 4 in both group (P=0.28). Both groups shared only 24 (3.9%) of the gene mutations while there were 334 unique gene mutations in the older group and 262 in the younger group. Most common gene mutations were DNMT3a, TET2, NRAS, WT1, and PTPN11 with frequencies are shown (Figure 1). RNA sequencing data was available for 26 patients from the older group and 18 patients from the younger group. We explored the gene expression profile of the top 1000 differentially expressed genes in both groups after adjustment. There was distinctive clustering of the gene expression profile between the two groups (Figure 2). Gene set enrichment analysis identified multiple immune-related pathways among the highly enriched gene sets in both groups but with different functions in the two groups. There was significant gene set enrichment in the TGFβ signaling in the older patient group which is associated with immune suppression and microenvironment modulation. While the younger group showed significant enrichment in the TNFa, IL17, PI3K-AKT signaling which are associated with inflammation. Conclusion: Older AML patients with NPM1 mutations, and no adverse risk features, had lower rate of complete responses and a trend towards a worse survival compared to younger patients. Whole exome sequencing did not show increased mutational burden. However, 96% of the mutated genes were different between the two groups as were the gene expression profiles. Gene set enrichment analysis showed contrasting enriched immune-related pathways between both groups. The immunosuppressive TGFβ signaling gene set were significantly enriched in the older group while the inflammatory TNFa, IL17, PI3K-AKT signaling gene sets were significantly enriched in the younger group. Older AML patient with NPM1 mutations have distinctive genomic landscape compared to the younger patient which may explain in part the worse clinical outcomes in the absence of other adverse risk features. Disclosures Lin: Jazz Pharmaceuticals: Honoraria; Pfizer: Membership on an entity's Board of Directors or advisory committees.


Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 623
Author(s):  
Panagiotis C. Agioutantis ◽  
Heleni Loutrari ◽  
Fragiskos N. Kolisis

Hepatocellular carcinoma (HCC) is associated with high mortality due to its inherent heterogeneity, aggressiveness, and limited therapeutic regimes. Herein, we analyzed 21 human HCC cell lines (HCC lines) to explore intertumor molecular diversity and pertinent drug sensitivity. We used an integrative computational approach based on exploratory and single-sample gene-set enrichment analysis of transcriptome and proteome data from the Cancer Cell Line Encyclopedia, followed by correlation analysis of drug-screening data from the Cancer Therapeutics Response Portal with curated gene-set enrichment scores. Acquired results classified HCC lines into two groups, a poorly and a well-differentiated group, displaying lower/higher enrichment scores in a “Specifically Upregulated in Liver” gene-set, respectively. Hierarchical clustering based on a published epithelial–mesenchymal transition gene expression signature further supported this stratification. Between-group comparisons of gene and protein expression unveiled distinctive patterns, whereas downstream functional analysis significantly associated differentially expressed genes with crucial cancer-related biological processes/pathways and revealed concrete driver-gene signatures. Finally, correlation analysis highlighted a diverse effectiveness of specific drugs against poorly compared to well-differentiated HCC lines, possibly applicable in clinical research with patients with analogous characteristics. Overall, this study expanded the knowledge on the molecular profiles, differentiation status, and drug responsiveness of HCC lines, and proposes a cost-effective computational approach to precision anti-HCC therapies.


2021 ◽  
Author(s):  
Michael W. Greene ◽  
Peter T. Abraham ◽  
Peyton C. Kuhlers ◽  
Elizabeth A. Lipke ◽  
Martin J. Heslin ◽  
...  

AbstractBackgroundColorectal cancer (CRC) is the third-leading cause of cancer-related deaths in the United States and worldwide. Obesity - a worldwide public health concern - is a known risk factor for cancer including CRC. However, the mechanisms underlying the link between CRC and obesity have yet to be fully elucidated in part because of the molecular heterogeneity of CRC. We hypothesized that obesity modulates CRC in a consensus molecular subtype (CMS)-dependent manner.MethodsRNA-seq data and associated tumor and patient characteristics including body weight and height data for 232 patients were obtained from The Cancer Genomic Atlas – Colon Adenocarcinoma (TCGA-COAD) database. Tumor samples were classified into the four CMSs with the CMScaller R package; Body mass index (BMI) was calculated and categorized as normal, overweight, and obese.ResultsWe observed a significant difference in CMS categorization between BMI categories. Differentially expressed genes (DEGs) between obese and overweight samples and normal samples differed across the CMSs, and associated prognostic analyses indicated that the DEGs had differing effects on survival. Using Gene Set Enrichment Analysis, we found differences in Hallmark gene set enrichment between obese and overweight samples and normal samples across the CMSs. We constructed Protein-Protein Interaction networks and observed differences in obesity-regulated hub genes for each CMS. Finally, we analyzed and found differences in predicted drug sensitivity between obese and overweight samples and normal samples across the CMSs.ConclusionsThus, we conclude that obesity has CMS-specific effects on the CRC tumor transcriptome.


2016 ◽  
Author(s):  
Monther Alhamdoosh ◽  
Milica Ng ◽  
Nicholas J. Wilson ◽  
Julie M. Sheridan ◽  
Huy Huynh ◽  
...  

AbstractGene set enrichment (GSE) analysis allows researchers to efficiently extract biological insight from long lists of differentially expressed genes by interrogating them at a systems level. In recent years, there has been a proliferation of GSE analysis methods and hence it has become increasingly difficult for researchers to select an optimal GSE tool based on their particular data set. Moreover, the majority of GSE analysis methods do not allow researchers to simultaneously compare gene set level results between multiple experimental conditions.Results: The ensemble of genes set enrichment analyses (EGSEA) is a method developed for RNA-sequencing data that combines results from twelve algorithms and calculates collective gene set scores to improve the biological relevance of the highest ranked gene sets. redEGSEA’s gene set database contains around 25,000 gene sets from sixteen collections. It has multiple visualization capabilities that allow researchers to view gene sets at various levels of granularity. EGSEA has been tested on simulated data and on a number of human and mouse data sets and, based on biologists' feedback, consistently outperforms the individual tools that have been combined. Our evaluation demonstrates the superiority of the ensemble approach for GSE analysis, and its utility to effectively and efficiently extrapolate biological functions and potential involvement in disease processes from lists of differentially regulated genes.Availability and Implementation: EGSEA is available as an R package at http://www.bioconductor.org/packages/EGSEA/. The gene sets collections are available in the R package EGSEAdata from http://www.bioconductor.org/packages/EGSEA/.


Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 120
Author(s):  
Yiyun Sun ◽  
Dandan Xu ◽  
Chundong Zhang ◽  
Yitao Wang ◽  
Lian Zhang ◽  
...  

We previously demonstrated that proline-rich protein 11 (PRR11) and spindle and kinetochore associated 2 (SKA2) constituted a head-to-head gene pair driven by a prototypical bidirectional promoter. This gene pair synergistically promoted the development of non-small cell lung cancer. However, the signaling pathways leading to the ectopic expression of this gene pair remains obscure. In the present study, we first analyzed the lung squamous cell carcinoma (LSCC) relevant RNA sequencing data from The Cancer Genome Atlas (TCGA) database using the correlation analysis of gene expression and gene set enrichment analysis (GSEA), which revealed that the PRR11-SKA2 correlated gene list highly resembled the Hedgehog (Hh) pathway activation-related gene set. Subsequently, GLI1/2 inhibitor GANT-61 or GLI1/2-siRNA inhibited the Hh pathway of LSCC cells, concomitantly decreasing the expression levels of PRR11 and SKA2. Furthermore, the mRNA expression profile of LSCC cells treated with GANT-61 was detected using RNA sequencing, displaying 397 differentially expressed genes (203 upregulated genes and 194 downregulated genes). Out of them, one gene set, including BIRC5, NCAPG, CCNB2, and BUB1, was involved in cell division and interacted with both PRR11 and SKA2. These genes were verified as the downregulated genes via RT-PCR and their high expression significantly correlated with the shorter overall survival of LSCC patients. Taken together, our results indicate that GLI1/2 mediates the expression of the PRR11-SKA2-centric gene set that serves as an unfavorable prognostic indicator for LSCC patients, potentializing new combinatorial diagnostic and therapeutic strategies in LSCC.


GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Colin Farrell ◽  
Michael Thompson ◽  
Anela Tosevska ◽  
Adewale Oyetunde ◽  
Matteo Pellegrini

Abstract Background Bisulfite sequencing is commonly used to measure DNA methylation. Processing bisulfite sequencing data is often challenging owing to the computational demands of mapping a low-complexity, asymmetrical library and the lack of a unified processing toolset to produce an analysis-ready methylation matrix from read alignments. To address these shortcomings, we have developed BiSulfite Bolt (BSBolt), a fast and scalable bisulfite sequencing analysis platform. BSBolt performs a pre-alignment sequencing read assessment step to improve efficiency when handling asymmetrical bisulfite sequencing libraries. Findings We evaluated BSBolt against simulated and real bisulfite sequencing libraries. We found that BSBolt provides accurate and fast bisulfite sequencing alignments and methylation calls. We also compared BSBolt to several existing bisulfite alignment tools and found BSBolt outperforms Bismark, BSSeeker2, BISCUIT, and BWA-Meth based on alignment accuracy and methylation calling accuracy. Conclusion BSBolt offers streamlined processing of bisulfite sequencing data through an integrated toolset that offers support for simulation, alignment, methylation calling, and data aggregation. BSBolt is implemented as a Python package and command line utility for flexibility when building informatics pipelines. BSBolt is available at https://github.com/NuttyLogic/BSBolt under an MIT license.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A646-A647
Author(s):  
Max Meneveau ◽  
Pankaj Kumar ◽  
Kevin Lynch ◽  
Karlyn Pollack ◽  
Craig Slingluff

BackgroundVaccines are a promising therapeutic for patients with advanced cancer, but achieving robust T-cell responses remains a challenge. Melanoma-associated antigen-A3 (MAGE-A3) in combination with adjuvant AS15 (a formulation of Toll-Like-Receptor (TLR)-4 and 9 agonists and a saponin), induced systemic CD4+ T-cell responses in 50% of patients when given subcutaneously/intradermally. Little is known about the transcriptional landscape of the vaccine-site microenvironment (VSME) of patients with systemic T-cell responses versus those without. We hypothesized that patients with systemic T-cell responses to vaccination would exhibit increased immune activation in the VSME, higher dendritic cell (DC) activation/maturation, TLR-pathway activation, and enhanced Th1 signatures.MethodsBiopsies of the VSME were obtained from participants on the Mel55 clinical trial (NCT01425749) who were immunized with MAGE-A3/AS15. Biopsies were taken 8 days after immunization. T-cell response to MAGE-A3 was assessed in PBMC after in-vitro stimulation with recMAGE-A3, by IFNγ ELISPOT assay. Gene expression was assessed by RNAseq using DESeq2. Comparisons were made between immune-responders (IR), non-responders (NR), and normal skin controls. FDR p<0.01 was considered significant.ResultsFour IR, four NR, and three controls were evaluated. The 500 most variable genes were used for principal component analysis (PCA). Two IR samples were identified as outliers on PCA and excluded from further analysis. There were 882 differentially expressed genes (DEGs) in the IR group vs the NR group (figure 1A). Unsupervised clustering of the top 500 DEGs revealed clustering according to the experimental groups (figure 1B). Of the 10 most highly upregulated DEGs, 9 were immune-related (figure 1C). Gene-set enrichment analysis revealed that immune-related pathways were highly enriched in IRs vs NRs (figure 1D). CD4 and CD8 expression did not differ between IR and NR (figure 2A), though both were higher in IR compared to control. Markers of DC activation/maturation were higher in IR vs NR (figure 2B), as were several Th1 associated genes (figure 2C). Interestingly, markers of exhaustion were higher in IR v NR (figure 2D). Expression of numerous TLR-pathway genes was higher in IR vs NR, including MYD88, but not TICAM1 (figure 2E).Abstract 611 Figure 1Gene expression profiling of vaccine site samples from patients immunized with MAGE-A3/AS15. (A) Volcano plots showing the distribution of differentially expressed genes (DEGs) between immune responders (IR) and non-responders (NR), IR and control, and NR and control. (B) Heatmap of the top 500 most differentially expressed genes demonstrating hierarchical clustering of sequenced samples according to IR, NR, and control. (C) Table showing the 10 most highly up and down-regulated genes in IR compared to NR. 9 of the top 10 most highly up-regulated genes are related to the immune response. (D) Enrichment plots from a gene set enrichment analysis highlighting the upregulation of immune related pathways in IR compared to NR. Gene set enrichment data was generated from the Reactome gene set database and included all expressed genes. Significance was set at FDR p <0.01Abstract 611 Figure 2Expression of T-cell markers in IR vs NR vs Control samples in the vaccine site microenvironment (VSME). (A) T-cell markers showing similar expression in IR vs NR but higher expression in IR vs control. (B) Markers of dendritic cell activation and maturation in the VSME which are higher in IR vs control but not IR vs NR. (B) Transcription factors and genes associated with Th1/Th2 responses within the VSME. (D) Genes associated with T-cell exhaustion at the VSME. (E) Expression of TLR pathway genes in the VSME. Expression data is provided in terms of normalized counts. Bars demonstrate median and interquartile range. N=9. IR = immune responder, NR = non-responder, TLR = Toll-like Receptor. * = <0.01, ** < 0.001, *** <0.0001, **** < 0.00001ConclusionsThese findings suggest a unique immune-transcriptional landscape in the VSME is associated with circulating T-cell responses to immunization, with differences in DC activation/maturation, Th1 response, and TLR signaling. Thus, immunologic changes in the VSME are useful predictors of systemic immune response, and host factors that modulate immune-related signaling at the vaccine site may have concordant systemic effects on promoting or limiting immune responses to vaccines.Trial RegistrationSamples for this work were collected from patients enrolled on the Mel55 clinical trial NCT01425749.Ethics ApprovalThis work was completed after approval from the UVA institutional review board IRB-HSR# 15398.


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