CELLO 2.0beta is an interactive web server to visualize curated clinical and somatic genomic information of longitudinally paired gliomas, and to explore the probability of developing treatment-induced hypermutation and grade progression using models that we trained from the collected data. The app is hosted on shinyapps.io at https://wanglab.shinyapps.io/cello/.
The server is built in R and Shiny, and is right now under active updating. In this web site, you can:
- Click Genes panel to explore genomic alterations and RNA expression of one specific gene;
- Click Cases panel to explore clinical and genetic information of patients;
- Click Prediction panel to explore the probability of developing treatment-induced hypermutation and grade progression using models that we trained from the collected data;
- Click Download panel to download the longitudinal glioma dataset.
In this page you can visualize the genomic alteration and RNA expression of one specific gene in the initial and recurrent tumors of glioma patients. It is also possible to link the changes in genomic alteration and/or gene expression with several important clinical features such as molecular subtype, pathological grade, grade progression and development of hypermutation.
Visualize genomic alteration and gene expression
In the left control panel, you can input the gene name, such as
IDH1, then click submit. At the right side of the page, you will see two figures displaying the gene expression (upper panel) and genomic alteration (lower panel) in the cohort. By default, the figures are separated into three facets representing three glioma molecular subtypes. The gene expression data are shown in scatter plot, each point represents the expression level of one sample, in the unit of log2 RPKM. The genomic alteration data are shown in a heatmap. It is clear to see that IDH1 mutation are present in all IDH-mutant-codel and IDH-mutant-non-codel patients but in none of the IDHwt patients:
Regarding genomic alteration, we included data for mutations, copy number alterations and several other important alterations such as FGFR3-TACC3 fusion, PTPRZ1-MET fusion, EGFR*vIII, *MET*ex14, *MGMT fusions and hypermutation (HM). For example, to check hypermutated samples in this cohort, you can simply type
HM in the gene name box.
Group by clinical features for comparison
In addition to molecular subtype, we can also explore other clinical features. For example, enter
MYC in the gene name box, and select to group by
HM_R, from the results we can see higher frequency of MYC gain in patients that develop hypermutation:
Similarly, we can investigate gene expression and hypermutation. Type
MGMT in the gene name panel, and select to group by
HM_R, we can see MGMT is lower in hypermutated recurrent gliomas, and is lower in initial gliomas that will develop hypermutation:
An additional useful feature is to show the longitudinal pairs in the gene expression plot. This will add a link between the initial and recurrent tumor of each patient. For example, if we check
VEGFA expression and
Grade_Progression, we will see significant elevation of VEGFA expression during grade progression: