A5083235556- Cancer, Lipids, and Metabolism
- Ferroptosis and cancer prognosis
- Pancreatic and Hepatic Oncology Research
- Lipoproteins and Cardiovascular Health
- RNA modifications and cancer
- Cancer, Hypoxia, and Metabolism
- Cancer Genomics and Diagnostics
- Vitamin D Research Studies
- Vitamin C and Antioxidants Research
- Cancer-related Molecular Pathways
- Cancer Immunotherapy and Biomarkers
- T-cell and B-cell Immunology
- Cancer Research and Treatments
- CAR-T cell therapy research
- Metabolism, Diabetes, and Cancer
- Renal cell carcinoma treatment
- Cancer Cells and Metastasis
Pancreas Centre (Canada)
2019-2024
Vancouver General Hospital
2020
The mixture of epithelial and stromal components in pancreatic ductal adenocarcinoma (PDAC) may confound sequencing-based studies tumor gene expression. Virtual microdissection has been suggested as a bioinformatics approach to segment the aforementioned components, subsequent prognostic sets have emerged from this research. We examined signature set one such study using laser capture microdissected (LCM) samples. also matched samples determine whether findings were specific epithelium. LCM...
Abstract KRAS codon 12 mutations are among the most common hotspot in human cancer. Using a functional screening platform we set out to identify αβ T-cell receptors (TCRs) as potential targeting reagents for G12D and/or G12V neoepitopes presented by prevalent HLA-A*02:01 allele. Here describe isolation and characterization of three distinct CD8 + T cell clones from pre-treated 76 year old patient with pancreatic ductal adenocarcinoma (PDAC). One clone was reactive two were reactive. Tetramer...
<p>Heatmap showing results of consensus clustering (k=2) based on expression genes associated with normal (n=20) and active (n=22) stroma in PDAC (Moffitt et al., 2015). Robust clusters (n=167) (n=158) samples were identified. Lower heatmap shows values (z-score) each gene within sample. Metabolic subgroup is overlaid as the bottom-most track. There was no significant enrichment any metabolic subgroups among or clusters.</p>
<p>Kaplan-Meier curve demonstrating the differences in time to relapse resectable PDAC. Data for ICGC PACA-CA cohort with available data are shown (N=118).</p>
<p>Supplemental Figures and Tables</p>
<p>Bar plot depicting the proportion of glycolytic and cholesterogenic genes in each three consensus clusters different cancer types.</p>
<p>Heatmap showing median gene expression levels (z-score) of genes involved in various pathways related to cellular metabolism. For each gene, within the four metabolic subgroups, values were assessed for significant deviation from zero using Wilcoxon signed rank test (mu = 0).</p>
<p>Box plots depicting tumor content levels across the metabolic subgroups in each PDAC cohort.</p>
<p>Scatter plot depicting principal component analysis of the top 25% most variable genes across integrated cohort resectable and advanced PDACs before (left) after (right) batch correction.</p>
<p>Boxplots demonstrating expression values (z-score) for genes involved in amino acid catabolism (n=64), nucleotide metabolism (n=272) or the pentose phosphate pathway (n=100). P were calculated using multiple pairwise Wilcoxon rank sum tests (two-tailed), and subjected to Benjamini-Hochberg test correction.</p>
<p>Heatmap showing median gene expression levels (z-score) of genes involved in various pathways related to cellular metabolism. For each gene, within the four metabolic subgroups, values were assessed for significant deviation from zero using Wilcoxon signed rank test (mu = 0).</p>
<p>Heatmap showing results of consensus clustering (k=2) based on expression genes associated with normal (n=20) and active (n=22) stroma in PDAC (Moffitt et al., 2015). Robust clusters (n=167) (n=158) samples were identified. Lower heatmap shows values (z-score) each gene within sample. Metabolic subgroup is overlaid as the bottom-most track. There was no significant enrichment any metabolic subgroups among or clusters.</p>
<p>Boxplots demonstrating expression values (z-score) for genes involved in amino acid catabolism (n=64), nucleotide metabolism (n=272) or the pentose phosphate pathway (n=100). P were calculated using multiple pairwise Wilcoxon rank sum tests (two-tailed), and subjected to Benjamini-Hochberg test correction.</p>
<p>Supplemental Figures and Tables</p>
<div>AbstractPurpose:<p>Identification of clinically actionable molecular subtypes pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcome. Intertumoral metabolic heterogeneity contributes cancer survival and the balance between distinct pathways may influence PDAC We hypothesized that can be stratified into prognostic subgroups based on alterations in expression genes involved glycolysis cholesterol synthesis.</p>Experimental Design:<p>We...
<div>AbstractPurpose:<p>Identification of clinically actionable molecular subtypes pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcome. Intertumoral metabolic heterogeneity contributes cancer survival and the balance between distinct pathways may influence PDAC We hypothesized that can be stratified into prognostic subgroups based on alterations in expression genes involved glycolysis cholesterol synthesis.</p>Experimental Design:<p>We...
<p>Box plots depicting tumor content levels across the metabolic subgroups in each PDAC cohort.</p>
<p>Scatter plot depicting principal component analysis of the top 25% most variable genes across integrated cohort resectable and advanced PDACs before (left) after (right) batch correction.</p>
<p>Bar plot depicting the proportion of glycolytic and cholesterogenic genes in each three consensus clusters different cancer types.</p>
<p>Kaplan-Meier curve demonstrating the differences in time to relapse resectable PDAC. Data for ICGC PACA-CA cohort with available data are shown (N=118).</p>
Abstract Reprogramming of metabolic pathways allows cancer cells to survive and thrive in the tumor microenvironment. Glycolysis-inducing factors including oncogenic KRAS mutations, loss function TP53 hypoxia are prevalent PDAC. Cholesterol its metabolites support cell growth mevalonate pathway, which uses glycolysis products for de novo cholesterol synthesis, has been found be upregulated cancer. However, whether intertumoral heterogeneity these networks influences outcome pancreatic not...