Integrated genomic profiling identifies microRNA-92a regulation of IQGAP2 in locally advanced rectal cancer

Abstract

Locally advanced rectal cancer (LARC) is treated with chemoradiation prior to surgical excision, leaving residual tumors altered or completely absent. Integrating layers of genomic profiling might identify regulatory pathways relevant to rectal tumorigenesis and inform therapeutic decisions and further research. We utilized formalin-fixed, paraffin-embedded pre-treatment LARC biopsies (n=138) and compared copy number, mRNA, and miRNA expression with matched normal rectal mucosa. An integrative model was used to predict regulatory interactions to explain gene expression changes. These predictions were evaluated in vitro using multiple colorectal cancer cell lines. The Cancer Genome Atlas (TCGA) was also used as an external cohort to validate our genomic profiling and predictions. We found differentially expressed mRNAs and miRNAs that characterize LARC. Our integrative model predicted the upregulation of miR-92a, miR-182, and miR-221 expression to be associated with downregulation of their target genes after adjusting for the effect of copy number alterations. Cell line studies using miR-92a mimics and inhibitors demonstrate that miR-92a expression regulates IQGAP2 expression. We show that endogenous miR-92a expression is inversely associated with endogenous KLF4 expression in multiple cell lines, and that this relationship is also present in rectal cancers of TCGA. Our integrative model predicted regulators of gene expression change in LARC using pre-treatment FFPE tissues. Our methodology implicated multiple regulatory interactions, some of which are corroborated by independent lines of study, while others indicate new opportunities for investigation.

Trial registration: ClinicalTrials.gov NCT00335816.

© 2016 Wiley Periodicals, Inc.

Figures

Figure 1

Sample allocation and genomic profiling of locally advanced rectal cancers from pre-treatment biopsies and normal rectal mucosa from surgical resection. The genomic profiles are then evaluated together, pooling the normal samples, using a regression model to integrate the profiles. CNA – copy number alteration.

Figure 2

A – Clustering analysis of Rectal Cancer vs. Normal Rectum mRNA expression across all “Timing trial” samples using signature of 248 genes (FDR < 0.01, |logFC| > 2); B – Successive ranked subsets of our DE genes and their corresponding median in a ranked list of DE genes from TCGA; our most significant DE genes are more significantly differentially expressed in TCGA, and as we move down our list, we similarly move down in the list in TCGA; C – Correlation of log fold change for mRNAs differentially expressed in both “Timing trial” and TCGA; D) Hypergeometric test out of 13,630 shared miRNAs across “Timing trial” and TCGA. DE – differentially expressed, TCGA – The Cancer Genome Atlas, * our DE mRNA list includes 217 genes, but only 138 overlap with genes present in TCGA platform.

Figure 3

A – Clustering analysis of Rectal Cancer vs. Normal Rectum miRNA expression across all “Timing trial” samples using signature of 55 DE miRNAs (FDR < 0.01, |logFC| > 1, base mean > 30); B – Successive ranked bins of our DE miRNAs and their corresponding median in a ranked list of DE miRNAs from TCGA; our selected signature of DE miRNAs are more significantly differentially expressed in TCGA, and as we move down our list, successive bins have a lower rank with respect to differential expression in TCGA; C – Correlation of log fold change for miRNAs differentially expressed in both “Timing trial” and TCGA; D) Hypergeometric test of our signature across the 464 shared miRNAs across “Timing trial” and TCGA. DE – differentially expressed, TCGA – The Cancer Genome Atlas. * our DE miRNA list includes 55 miRNAs, but only 45 overlap with miRNAs present in TCGA platform.

Figure 4

Comparison of Copy Number Alterations as measured by array CGH across all samples in “Timing trial” samples and TCGA samples. The correlation between copy number alterations between cohorts is statistically significant (p < 0.001, Supplementary Figure S3).

Figure 5

Circus plot showing copy number variations in LARC samples compared with normal rectal mucosa (outer track), upregulated miRNAs in LARC that are predicted to regulate gene expression changes (middle track), and downregulated mRNAs in LARC that can be bound by those upregulated miRNAs (inner track). LARC – locally advanced rectal cancer.

Figure 6

RT/qPCR expression of miR-92a and IQGAP2 in (left) DLD-1 cells (low endogenous miR-92a expression) and (right) SW620 cells (high endogenous miR-92a expression) with either a mimic or inhibitor. **, p

Figure 7

RT/qPCR expression of miR-92a and KLF4 across 6 cell lines. These 6 cell lines were selected from 10 colorectal cancer cell lines based on having the 3 highest and lowest endogenous levels of KLF4 expression. Representative of 3 separate experiments.