Integrative genomics analysis identifies candidate drivers at 3q26-29 amplicon in squamous cell carcinoma of the lung

Abstract

Purpose: Chromosome 3q26-29 is a critical region of genomic amplification in lung squamous cell carcinomas (SCC). Identification of candidate drivers in this region could help uncover new mechanisms in the pathogenesis and potentially new targets in SCC of the lung.

Experimental design: We conducted a meta-analysis of seven independent datasets containing a total of 593 human primary SCC samples to identify consensus candidate drivers in 3q26-29 amplicon. Through integrating protein-protein interaction network information, we further filtered for candidates that may function together in a network. Computationally predicted candidates were validated using RNA interference (RNAi) knockdown and cell viability assays. Clinical relevance of the experimentally supported drivers was evaluated in an independent cohort of 52 lung SCC patients using survival analysis.

Results: The meta-analysis identified 20 consensus candidates, among which four (SENP2, DCUN1D1, DVL3, and UBXN7) are involved in a small protein-protein interaction network. Knocking down any of the four proteins led to cell growth inhibition of the 3q26-29-amplified SCC. Moreover, knocking down of SENP2 resulted in the most significant cell growth inhibition and downregulation of DCUN1D1 and DVL3. Importantly, a gene expression signature composed of SENP2, DCUN1D1, and DVL3 stratified patients into subgroups with different response to adjuvant chemotherapy.

Conclusion: Together, our findings show that SENP2, DCUN1D1, and DVL3 are candidate driver genes in the 3q26-29 amplicon of SCC, providing novel insights into the molecular mechanisms of disease progression and may have significant implication in the management of SCC of the lung.

Figure 1

Overview of the integrative genomics approach. (A) Developing a method for estimating sample-specific amplification level and prioritizing candidate driver genes using expression data alone, and applying the method to 6 independent data sets. (B) Using the order statistics technique to identify candidate driver genes that are supported by multiple data sets. (C) Using network analysis to filter for functionally related candidate drivers. (D) Experimental refinement and clinical evaluation of the identified driver genes.

Figure 2

Genomic amplification can be inferred from gene expression data. The IGV (Integrative Genomic Viewer) plot of 152 paired TCGA gene expression data (A) and copy number data. (B) X-axis represents chromosome 3 and y-axis represents TCGA samples which are in descending order according to the amplification scores. Different color shades represent the expression or amplification values of samples from blue (the minimum value) to red (the maximum value).(C) Cumulative probability distribution of spearman correlation coefficient between amplification scores of samples and expression values of genes in 3q26-29 amplicon (red) and other genes in the expression data (blue) based on 152 TCGA paired copy number and gene expression samples. (D) The scatter plot of amplification scores against average expression scores for each of the 152 TCGA SCC samples. (E) The scatter plot of correlation between gene expression and amplification score (corr_exp_amp) against correlation between gene expression and average expression score (corr_exp_ave) for 164 genes in the 3q26-29 amplicon. Previously published candidate driver genes are labeled in the scatter plot. The solid lines in (D) and (E) are fitted lines based on the data in the plots. Because the data points in (D) and (E) are distributed along the diagonals, the data series represented by the x-axis and y-axis in each figure have a high concordance index (89.4% and 96.2%).

Figure 3

Graphical representation of an inferred SCC related candidate driver network centered around four candidate driver genes in 3q26-29. Four candidate driver genes are shown in red.

Figure 4

Loss of SENP2 overexperssion leads to the downregulation of DCUN1D1 and DVL3. (A) Overexpression of SENP2, DCUN1D1, DVL3 and UBXN7 in 3q amplified SCC cell lines. (B) Knockdown of SENP2, DCUN1D1, DVL3 and UBXN7 result in decreased cell viability on H520 cells. (C) Knockdown of SENP2 expression leads to down-regulation of DCUN1D1 and DVL3 but not UBXN7 in H520 cells. (D) Knockdown DCUN1D1, DVL3 or UBXN7 fail to induce downregulation of SENP2 expression.

Figure 5

Three-gene (SENP2, DCUN1D1 and DVL3) expression signature predicts response to adjuvant chemotherapy in early stage NSCLC. Red solid and red dashed lines represent Kaplan-Meier survival curves for patients with high expression of the three-gene signature, without and with adjuvant chemotherapy, respectively; Green solid and green dashed lines represent Kaplan-Meier survival curves for patients with low expression of the three-gene signature, without and with adjuvant chemotherapy, respectively. “n” represents the number of patients in the corresponding group; “p” and “HR”represents Log-Rank test p value and hazard ratio for a pair of survival curves, respectively. Numbers in the parentheses indicate the 95% confidence interval of hazard ratio. The analysis was based on GSE14814 from the JBR.10 trial.