Cancer as the Disintegration of Robustness: Population-Level Variance in Gene Expression Identifies Key Differences Between Tobacco- and HPV-Associated Oropharyngeal Carcinogenesis

Abstract

Context: Oropharyngeal squamous cell carcinoma is associated both with tobacco use and with human papillomavirus (HPV) infection. It is argued that carcinogen-driven tumorigenesis is a distinct disease from its virally driven counterpart. We hypothesized that tumorigenesis is the result of a loss of genotypic robustness resulting in an increase in phenotypic variation in tumors compared with adjacent histologically normal tissues, and that carcinogen-driven tumorigenesis results in greater variation than its virally driven counterpart.

Objectives: To examine the loss of robustness in carcinogen-driven and virally driven oropharyngeal squamous cell carcinoma samples, and to identify potential pathways involved.

Design: We used coefficients of variation for messenger RNA and microRNA expression to measure the loss of robustness in oropharyngeal squamous cell carcinoma samples. Tumors were compared with matched normal tissues, and were further categorized by HPV and patient smoking status. Weighted gene coexpression networks were constructed for genes with highly variable expression among the HPV⁻ tumors from smokers.

Results: We observed more genes with variable messenger RNA expression in tumors compared with normal tissues, regardless of HPV and smoking status, and more microRNAs with variable expression in HPV⁻ and HPV⁺ tumors from smoking patients than from nonsmokers. For both the messenger RNA and microRNA data, we observed more variance among HPV⁻ tumors from smokers compared with HPV⁺ tumors from nonsmokers. The gene coexpression network construction highlighted pathways that have lost robustness in carcinogen-induced tumors but appear stable in virally induced tumors.

Conclusions: Using coefficients of variation and coexpression networks, we identified multiple altered pathways that may play a role in carcinogen-driven tumorigenesis.

Figure 1. Tumor messenger RNA (mRNA) Expression Variation in Human Papillomavirus negative (HPV−) and Human Papillomavirus positive (HPV+) Patients

Scatterplots representing individual mRNA coefficients of variation (CV) for tumors and matched normal tissue. Genes with a CV > 1 both for tumors and for normal tissues are colored red. A. CVs plotted for 15 paired HPV− tumors from smokers. Seventy genes had greater CVs in tumor samples compared to 27 genes in the corresponding normal tissues, suggesting a greater amount of genes with high expression variation among tumors. B. CVs plotted for 4 paired HPV− tumors from non-smokers. Again, there is a greater amount of genes with high expression variation among the tumors, as demonstrated by 106 variably expressed genes in the tumors and only 8 variably expressed genes in the normal tissue. C. CVs plotted for 16 paired HPV+ tumors from smokers. There were 45 variably expressed genes in the tumors compared to 10 variably expressed genes in the normal tissue. D. CVs plotted for 4 paired HPV+ tumors from non-smokers. Similarly, 55 genes were variant in the tumors compared to 14 genes in the matched normal tissue.

Figure 2. Greater Degree of messenger RNA (mRNA) Expression Variation in Human Papillomavirus negative (HPV−) Tumors from Smokers Compared to Human Papillomavirus positive (HPV+) tumors from Non-Smokers

A. Scatterplot representing individual mRNA coefficients of variation for 20 HPV− tumors from smokers and 6 HPV+ tumors from non-smokers. Forty-three genes with high expression variation only in HPV− tumors are colored red. B. Heatmap representing the expression of the 43 genes with high expression variation in HPV− tumors. The patient sample dendrogram is above the heatmap. The 20 HPV− tumors from smokers are denoted with a red bar, and the 6 HPV+ tumors from non-smokers are indicated by the black bar. The expression values were Log2 transformed for the heatmap, and the grayscale was divided into 6 distinct colors to highlight the extreme differences in expression within the two groups of tumors.

Figure 3. Weighted Gene Co-Expression Network (WGCNA) Construction

Network construction using the WGCNA R package. A. TOM-based dissimilarity cluster dendrogram of genes with high expression variation in 19 Human Papillomavirus negative (HPV−) tumors from smokers. Gene clusters are assigned to colored modules (blue, grey, red, and green). B. Heatmap representation of the clusters with module colors. Dark red signifies high network overlap between two genes. C. Multi-dimensional scaling visualization of the colored modules. Scattered modules represent strong co-expression networks. Ends of the scattered modules represent hub genes (i.e. the genes with the most connections).

Figure 4. Weighted Gene Co-Expression Networks

Expression networks were constructed for the blue and red modules. Genes that are colored black are the hub genes (the genes with the most connections within the network). A. The network representation of the blue module. A connection weight cutoff of 0.4 was used within the VisANT software to visualize the genes with the most connections and identify the hub gene, PIGX (Phosphatidylinositol Glycan Anchor Biosynthesis, Class X). B. The network representation of the red module. A connection weight cutoff of 0.5 was used to identify two hub genes, CRIPAK (cysteine-rich PAK1 inhibitor) and FOXP4 (forkhead box P4).

Figure 5. Increased microRNA (miRNA) expression in Human Papillomavirus negative (HPV−) tumors from smokers

A–C. Scatterplots representing individual miRNA coefficients of variation (CV) for tumors and matched normal tissue. Genes with CV>1 both for tumors and for normal tissues are colored red. HPV− tumors from non-smokers are not shown due to a sample size of 2. A. CVs plotted for 14 paired HPV− tumors from smokers. Eight miRNAs had greater CVs in tumor samples compared to no miRNAs in the corresponding normal tissue, suggesting a greater amount of expression variation among tumor miRNAs. B. CVs plotted for 3 paired HPV+ tumors from non-smokers. Conversely, there were no miRNAs with high expression variation in the HPV+ tumors from the non-smoking group. C. CVs plotted for 18 paired HPV+ smokers. Only one miRNA had a high CV in the tumors, while there were no miRNAs with highly variable expression in the normal tissue. D. Scatterplot representing individual miRNA coefficients of variation for 14 HPV− tumors from smokers and 3 HPV+ tumors from non-smokers. Eighteen miRNAs with high expression variation in HPV− tumors from smokers are colored red.