Avian pathogenic Escherichia coli (APEC) causes colibacillosis with different clinical manifestations. The disease is associated with compromised animal welfare and results in substantial economic ...losses in poultry production worldwide. So far, immunological mechanisms of protection against colibacillosis are not comprehensively resolved. Therefore, the present study aimed to use an ex vivo model applying chicken mononuclear cells stimulated by live and inactivated APEC. For this purpose, an 8-color flow cytometry panel was set up to target viable chicken immune cells including CD45+, CD8α+, CD4+, TCR-γδ+, Bu-1+ cells and monocytes/macrophages along with the cytokines interferon gamma (IFN-γ) or interleukin 17A (IL-17A). The 8-color flow cytometry panel was applied to investigate the effect of live and two different types of inactivated APEC (formalin-killed APEC and irradiated APEC) on the cellular immune response. For that, mononuclear cells from spleen, lung and blood of 10-week-old specific pathogen-free layer birds were isolated and stimulated with live, irradiated or killed APEC. Intracellular cytokine staining and RT-qPCR assays were applied for the detection of IFN-γ and IL-17A protein level, as well as at mRNA level for spleenocytes. Ex vivo stimulation of isolated splenocytes, lung and peripheral blood mononuclear cells (PBMCs) from chickens with live, irradiated or killed APEC showed an increasing number of IFN-γ and IL-17A producing cells at protein and mRNA level. Phenotyping of the cells from blood and organs revealed that IFN-γ and IL-17A were mainly produced by CD8α+, TCR-γδ+ T cells as well as CD4+ T cells following stimulation with APEC. Expression level of cytokines were very similar following stimulation with live and irradiated APEC but lower when killed APEC were applied. Consequently, in the present study, an ex vivo model using mononuclear cells of chickens was applied to investigate the cellular immune response against APEC. The results suggest the relevance of IFN-γ and IL-17A production in different immune cells following APEC infection in chickens which needs to be further investigated in APEC primed birds.
•TCR-γδ, CD8α, CD4 cells play a role in the production of IFN-γ and IL-17A in the immune response of chickens against APEC.•IFN-γ and IL-17A expression against live and irradiated APEC are comparable, indicating a similarity in antigenic profile.•The initial T cell response to APEC might be important in the control of colibacillosis by activating the IFN-γ and IL-17A.
Abstract BACKGROUND Pediatric diffuse hemispheric glioma (DHG) is a histone-mutated (H3.3G34R/V) high-grade glioma with poor prognosis. Clinical observation and previous reports have identified that ...a subset of patients present with tumor-associated hemorrhage. Here, we present our findings from radiological review of these patients and determine genomic risk factors predictive for hemorrhage risk in this cohort. METHODS Data was abstracted through the Children’s Brain Tumor Network (CBTN) and EGAS00001004301. Transcriptomic and genomic analyses were completed in R 4.3.1 using edgeR, msigdbr, and xCell. Presence of blood products was determined on preoperative MRI by a board certified pediatric neuroradiologist. RESULTS 48 samples were available across cohorts with transcriptomic or genomic data. 10 samples (21%) had pre-operative imaging and transcriptomic data available. Initial analysis determined that 6 samples (60%) had acute/chronic hemorrhage based on radiological review. Samples with hemorrhage had increased levels of VEGFA (LFC: 3.45, p=2E-07) and CA9 (LFC: 5.97, p=8E-05) expression on the transcriptomic level suggesting that patients with high expression of these markers (AngioHi) had increased risk of hemorrhage compared to patients with low expression of these markers (AngioLo). AngioHi patients had notably higher levels of IL8 (LFC: 3.56, p=0.01) and increased macrophage populations. Genomic information was available for 15 samples (31%, 7 AngioHi, 8 AngioLo). PTEN alterations (n=4, 57%), FBXW7 alterations (n= 2, 29%), or PDGFRa driver alterations (n= 3, 43%) were found in the AngioHi cohort while none of these alterations were identified in the AngioLo cohort. Notably, at least one of these mutations was seen in all cases in the AngioHi cohort. CONCLUSIONS These findings identify potential mutational alterations predictive for tumor-associated hemorrhage in pediatric DHG. Current work is being completed to further characterize how these mutations impact vascular remodeling for patients with DHG and may offer targeted therapeutic opportunities.
Abstract
BACKGROUND
Pediatric brain tumors (PBTs) are the leading cause of cancer-related death in children. Currently, based on the RAPNO guidelines, two-dimensional (2D) measurements are used to ...detect progression. However, due to the complex radiographic appearance of many PBTs, 2D measurements may not accurately reflect tumor growth. Here, we compared 3D, 2D, and qualitative radiologists’ interpretations in a group of PBTs to determine tumor progression.
METHODS
Six PBT patients (5 low-grade glioma, 1 ependymoma, age range at the time of baseline scan: 23-191 months) with an average of 5 imaging time points who had at least one episode of progression with subsequent surgery were included. Segmentation was performed on either T1 post-contrast or FLAIR, whichever that best identified the tumor. For 2D measurements, the two largest perpendicular diameters in a section that included the largest tumor component were measured. For the volumetric assessment, the tumors were manually segmented, and the volume was computed using the ITK-SNAP software. In multifocal tumors, only the tumor component that showed progression and underwent resection was included. A 25% and 41% increase in tumor size was considered as progression on 2D and 3D measurements, respectively. The time to progression (TTP) based on 2D vs 3D assessment as well as the official pediatric neuroradiologist interpretation based on the electronic health record report was compared.
RESULTS
In three of six patients, volumetric segmentation detected tumor progression at an earlier time point compared to 2D measurements and radiologists’ interpretation (median TTP: 134 compared to 503 for 2D and radiologist interpretation). For these three patients, the TTP based on 3D vs 2D assessment was: 252 vs 1299 days, 134 vs 503 days, and 80 vs 255 days.
CONCLUSION
Volumetric measurements can determine tumor progression earlier than the current standard method of 2D measurements and qualitative interpretation of radiologists in PBTs.
The simplest approach to convey the results of scientific analysis, which can include complex comparisons, is typically through the use of visual items, including figures and plots. These statistical ...plots play a critical role in scientific studies, making data more accessible, engaging, and informative. A growing number of visual representations have been utilized recently to graphically display the results of oncologic imaging, including radiomic and radiogenomic studies. Here, we review the applications, distinct properties, benefits, and drawbacks of various statistical plots. Furthermore, we provide neuroradiologists with a comprehensive understanding of how to use these plots to effectively communicate analytical results based on imaging data.
Abstract
INTRODUCTION
Pediatric low-grade glioma is comprised of a broad variety of histological subtypes with heterogeneous responses to standard-of-care treatments. Understanding the biological ...pathways underlying clinical radiographic appearances, or radio-phenotypes, may facilitate precision diagnostics prior to the initiation of any interventions, thereby, reducing the risk of co-morbidities while improving patient outcome. Here, we aim to comprehensively harness the high-throughput multi-modal data available through Children’s Brain Tumor Network (CBTN) to elucidate the pathways that form radio-phenotypic traits of pLGG.
METHODS
On a cohort of 165 patients with pLGG, 881 radiomic features were extracted from multiparametric MRI sequences. We applied principal component analysis (PCA) followed by K-Means clustering to the radiomic features along with clinical variables of age, gender, and tumor location to group the patients into distinct imaging subtypes. We used whole transcriptomic data from OpenTargets to compare differential expression and co-expression of network- and pathway-level properties via Gene Set Enrichment Analysis (GSEA), and Gene Sets Net Correlations Analysis (GSNCA) among the discovered imaging subtypes.
RESULTS
Three imaging subtypes of pLGG were identified, among which subtype1 tumors showed increased proliferative capacity and invasive potential. This was suggested by higher expression of cell cycle regulatory, extracellular matrix (ECM) remodeling, and cell migratory pathways in subtype1 than subtypes 2 and 3. Furthermore, subtype1 exhibited differential TNF/TNFR signaling compared to subtype2, potentially related to differential regulation of pro-tumorigenic immune pathways, and differential regulation of TGF-beta and Aurora Kinase A signaling compared to subtype3, associated with differences in proliferative pathways. Subtypes 1 and 2 exhibit higher pro-tumorigenic cytokine expression while subtype3 involves higher expression of neurotransmitter release and neuronal activity, which may be targetable.
CONCLUSIONS
Our multi-modal radiogenomic analysis unraveled molecular underpinnings that give rise to the formation of distinct imaging phenotypes, with the potential to inform personalized treatments for children with brain tumors.
Abstract
Pediatric brain tumors (PBT) exhibit significant heterogeneity, and accurate assessment of tumor response is crucial for patient management. While bidimensional measurements of tumor size ...are commonly used, they may underestimate tumor dimensions. Our group previously developed a multi-institutional, and multi-histology model on the Children’s Brain Tumor Network (CBTN) imaging dataset to segment tumor subregions, recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group. In the most recent update, we utilized a self-configuring deep learning architecture called nnU-Net on a large dataset of well-annotated PBTs (233 training, and 60 withheld internal and 46 external test sets). The model was trained and validated on multiparametric MRI sequences and differentiated various tumor subregions, including enhancing tumor (ET), nonenhancing tumor (NET), edema (ED), and cystic component (CC) and the whole tumor (WT) regions. Clinical validity was assessed by comparing the volumes of tumor subregions predicted by the model with expert manual segmentations, demonstrating strong agreement, with statistically significant Pearson’s correlation coefficients of 0.93/0.69 for ET, 0.94/0.93 for NET, 0.78/0.93 for CC, and 0.94/0.50 for ED subregions (p< 0.001) for the internal/external test cohorts. The trained model showed excellent performance for the withheld internal test set and decent accuracy for the external test set. Median Dice scores for internal/external test sets were 0.94±0.10/0.90±0.07 for WT, 0.85±0.33/0.84±0.30 for ET, 0.80±0.32/0.64±0.31 for NET, 0.79±0.37/0.67±0.33 for CC, 0.70 ±0.42/0.37±0.43 for ED, and 0.86±0.19/0.80±0.21 for all nonenhancing components (combination of NET, CC, and ED), respectively. The proposed automated segmentation method provides accurate and reproducible volumetric measurements of RAPNO-defined subregions in PBTs. It exhibits robust performance and potential generalization to external datasets and the accuracy is higher compared to the previous DeepMedic model. We will continue to improve the model by constantly adding new training data to further improve the performance and sustain model robustness.
Abstract
INTRODUCTION
Tumor immune microenvironment (TIME) plays a key role in response to anti-tumor therapies. Therefore, detailed characterization of TIME is crucial for patient stratification and ...enrollment onto emerging immunotherapies. As standard-of-care treatments are not feasible or confer devastating long-term morbidity for pediatric low-grade glioma (pLGG), non-invasive quantification of the TIME using radiomic analysis, may guide treatment decisions. On the largest available transcriptomic and imaging data in pLGGs, provided through Children’s Brain Tumor Network (CBTN), this data-driven study aims to discover immunological profiles of pLGG that are meaningfully related to biomarkers of tumor immune response, and radioimmunomic signatures that are predictive of immunological profiles.
METHODS
Enrichment of immune and stromal cell types and pathways in 502 subjects were inferred using xCell algorithm applied to transcriptomic data and the tumors were then clustered via consensus clustering approach. Tumor inflammation signature (TIS), as a biomarker of response to anti-PD-1 blockade, was compared across the immunological clusters. Quantitative radiomic features were extracted from the segmented tumor regions on multiparametric MRI scans of 155/502 patients. Cross-validated support vector machines with forward feature selection were trained on radiomic features using one-versus-the-rest approach to predict the immunological groups.
RESULTS
Three immunological clusters, i.e., immune-cold, -altered, and -hot were found, with different levels of enrichment in immune and microenvironment scores, CD4 T-cells, and macrophages. Significantly higher TIS values were found in immune-hot and -altered compared to immune-cold tumors (p= 1.2e-8 and p= 7e-16, respectively). Radioimmunomic models yielded average AUC of 0.75 for one-versus-the-rest differentiation of the immunological clusters.
CONCLUSIONS
The proposed analysis revealed three distinct immunological groups in pLGGs, with immune-hot tumors more likely to respond favorably to immunotherapies. Furthermore, non-invasive radioimmunomic signatures were shown to potentially stratify the patients based on their TIME, which upon further development and validation, could inform treatments for pLGGs.
Abstract
There is mounting evidence that tumor microenvironmental pressure selects for somatic genetic alterations that contribute to the formation of distinct morphological characteristics, captured ...on radiology scans as radio-phenotypes. Such phenotypic variations are a source of heterogeneity in clinical manifestation of tumors of the same histology across the patients, and in part, their heterogeneous responses to therapies. Deeper understanding of the associations between genotype and radio-phenotype in pediatric low-grade glioma (pLGG), the most histologically diverse childhood brain tumor, may facilitate precision diagnostic and therapeutic approaches. Here, we categorize pLGGs into distinct and relatively homogeneous imaging subtypes based on radiomic features and further explore the associations of these imaging subtypes with genotype. From multiparametric MRI scans of 167 pLGGs from the Children’s Brain Tumor Network (CBTN), 881 radiomic features and clinical variables (tumor location, age, and gender) were extracted. After dimensionality reduction using principal component analysis (PCA), K-Means clustering was applied on 19 principal components to group the patients into three imaging subtypes. Using whole transcriptomic data from OpenTargets, differential expression and co-expression of network- and pathway-level and immune-related signaling were compared among these three imaging subtypes. Gene Set Enrichment Analysis (GSEA) revealed differentially higher expression of cell cycle regulatory, extracellular matrix (ECM) remodeling, and cell migratory pathways in imaging subtype1 than subtypes 2 and 3, and upregulation of ECM and immune-related pathways in subtypes 1 and 2 compared to subtype3. Based on Gene Sets Net Correlations Analysis (GSNCA), subtype1 exhibited differential co-regulation of TNF/TNFR1 signaling compared to subtype2, and differential co-regulation of RHOG GTPase and TGFB1pathways when compared against subtype3. Subtype2 showed differential co-regulation in NOTCH1 signaling and transcriptional regulatory pathways. Our proposed multi-disciplinary radiomic-genomic analysis approach elucidates the molecular and biological processes in the genotype of the tumors that are associated with emergence of distinct imaging subtypes in pLGG.
