Merged PTM and Expression NSCLC Cluster

This notebook will investigate the cluster of up-regulated PTMs and genes in NSCLC lung cancer cell lines. This cluster was isolated and saved in the notebook: CST_Data_Viz.ipynb.

In [1]:
from clustergrammer_widget import *
net = Network(clustergrammer_widget)
In [2]:
net.load_file('histology_clusters/merge_nsclc.txt')

# manually set category colors for rows and columns
net.set_cat_color('row', 1, 'Data-Type: phospho', 'red')
net.set_cat_color('row', 1, 'Data-Type: Rme1', 'purple')
net.set_cat_color('row', 1, 'Data-Type: AcK', 'blue')
net.set_cat_color('row', 1, 'Data-Type: Kme1', 'grey')
net.set_cat_color('row', 1, 'Data-Type: Exp', 'yellow')
net.set_cat_color('col', 1, 'Histology: SCLC', 'red')
net.set_cat_color('col', 1, 'Histology: NSCLC', 'blue')
net.set_cat_color('col', 2, 'Sub-Histology: SCLC', 'red')
net.set_cat_color('col', 2, 'Sub-Histology: NSCLC', 'blue')
net.set_cat_color('col', 2, 'Sub-Histology: squamous_cell_carcinoma', 'yellow')
net.set_cat_color('col', 2, 'Sub-Histology: bronchioloalveolar_adenocarcinoma', 'orange')
net.set_cat_color('col', 2, 'Sub-Histology: adenocarcinoma', 'grey')

Cluster of Up-regulated PTMs and Genes in NSCLC Cell Lines

Below we will visualize the NSCLC cluster.

In [3]:
net.cluster(views=[])
net.widget()

Above, we see that the cluster is composed of primarily expression and phosphorylation data. There are two large clusters of genes/PTMs that are generally down-regulated in SCLC cell lines and up-regulated in NSCLC cell lines.

High Levels of Keratin Expression and PTMs in NSCLC

This cluster of up-regulated PTMs/genes in NSLC cell lines contains many instances of Keratin expression, phosphorylation, methylation, and acetylation. The screenshot below shows the heatmap with rows ordered alphabetically and zoomed into Keratins. Squamous cell carcinomas are known to form 'keratin pearls' (see link) that can be seen under the microscope. We see that the two cell lines in our dataset with squamous cell carcinoma (Lou-NH91 and HCC15 with yellow column Sub-Histology category) show relatively high levels of keratin expression and PTMs.

keratin

This data implies a role for Keratins in NSCLC function, implies that Keratin PTM levels are influenced by Keratin expression level, and show broad agreement between these independent datasets.

Gene Ontology Biological Process 2015

Here we are pre-calculating enrichment for biological processes from the Gene Ontology resource using the enrichrgram method (see Clustergrammer-PY's API for more information). This will help us understand the broad biological processes occurring in this cluster of up-regulated PTMs and Genes.

In [4]:
net.enrichrgram('GO_Biological_Process_2015')
net.cluster(views=[])
net.widget()

Cell Motility and Migration

Above, we see enrichment for many cell motility related terms including:

  • cellular component movement
  • cell motility
  • cell migration
  • locomotion
  • response to wound healing
  • cell adhesion

Adhesion is important for cell movement, which is in turn important for cell migration in wound healing. This broadly agrees with prior knowledge in that NSCLC cell lines are known to form adherent monolayers while SCLC cell lines grow in aggregates (Doyle et al. 1990). Similarly, enrichment results using the KEGG library show enrichment for focal adhesion among other enrichment results including proteoglycans in cancer and neurotrophin signaling.

Disease Perturbations from GEO Up

Using the 'Disease Perturbations from GEO Up' Enrichr library we can find diseases that have up-regulated genes that are similar to our up-regulated genes/proteins. This can help us understand the disease-associations of the differntially expressed genes and proteins with differentially reglated PTMs.

In [5]:
net.enrichrgram('Disease_Perturbations_from_GEO_up')
net.cluster(views=[])
net.widget()

Above, we see that up-regulated genes and PTMs in NSCLC are similar to up-regulated genes in several pancreatic cancers and other cancers including: ovarian and thyroid.

MGI Mammalian Phenotype

To find phenotypes associated with our genes/proteins, we can use the 'MGI Phenotype Level 4' Enrichr library. This library will return phenotypes that are associated with gene knockout in mice. This can give us a less biased overview of gene/phenotype relationships.

In [6]:
net.enrichrgram('MGI_Mammalian_Phenotype_Level_4')
net.cluster(views=[])
net.widget()

Immune Phenotype Associations

Above, we see enrichment for several immune related phenotypes including:

  • abnormal innate immunity
  • abnormal antigen presenting