Apelin: A putative novel predictive biomarker for bevacizumab response in colorectal cancer

Abstract

Bevacizumab (bvz) is currently employed as an anti-angiogenic therapy across several cancer indications. Bvz response heterogeneity has been well documented, with only 10-15% of colorectal cancer (CRC) patients benefitting in general. For other patients, clinical efficacy is limited and side effects are significant. This reinforces the need for a robust predictive biomarker of response. To identify such a biomarker, we performed a DNA microarray-based transcriptional profiling screen with primary endothelial cells (ECs) isolated from normal and tumour colon tissues. Thirteen separate populations of tumour-associated ECs and 10 of normal ECs were isolated using fluorescence-activated cell sorting. We hypothesised that VEGF-induced genes were overexpressed in tumour ECs; these genes could relate to bvz response and serve as potential predictive biomarkers. Transcriptional profiling revealed a total of 2,610 differentially expressed genes when tumour and normal ECs were compared. To explore their relation to bvz response, the mRNA expression levels of top-ranked genes were examined using quantitative PCR in 30 independent tumour tissues from CRC patients that received bvz in the adjuvant setting. These analyses revealed that the expression of MMP12 and APLN mRNA was significantly higher in bvz non-responders compared to responders. At the protein level, high APLN expression was correlated with poor progression-free survival in bvz-treated patients. Thus, high APLN expression may represent a novel predictive biomarker for bvz unresponsiveness.

Keywords: VEGF; apelin; bevacizumab response; biomarker; colorectal cancer.

Figure 1. Enriched gene sets within TEC signature (GSEA)

(A) Enrichment plots generated by the GSEA tool of the three most enriched gene sets of the MSigDb “hallmark gene set”. These are a priori defined gene sets that represent specific well-defined biological states or processes, i.e. from left to right, DNA repair, epithelial-mesenchymal transition and angiogenesis. In each analysis, genes within pre-defined gene sets are ranked and scored based on the position within the TEC-specific signature. In each plot, “1” corresponds to NEC and “2” to TEC. Each vertical line of the barcode represents a gene. Most genes on the left positively correlate with TEC samples, most genes on the right correlate negatively with TEC samples. Lines in between represent genes that are not differentially expressed between TECs and NECs. As vertical lines of the barcode are overrepresented at the left side of the graph, this means many of the genes within the pre-defined gene set are positively correlated/enriched within TECs. The score relating to this enrichment (enrichment score or ES) is indicated by the green line. (B) GSEA enrichment plots show enrichment of genes involved in VEGF signalling in TEC samples. The first plot corresponds to genes assigned to the VEGF pathway by the Biocarta database, the second and third plot correspond to published data of genes up-or downregulated after treatment of HUVEC cells with VEGFA (Pubmed 12197474 respectively Pubmed 15516835) [64, 65].

Figure 2. TEC-specific signature

(A) Heatmap showing hierarchical clustering of the 200 most significantly differentially expressed probe sets between TEC and NEC. Samples are depicted on the horizontal axis, genes on the vertical axis. “L” means liver metastasis. Within the heatmap, red is highly expressed and blue is lowly expressed. (B) Table representing microarray Limma fold changes and FDR-corrected p-value of TEC-specific candidate genes comparing TEC with NEC samples, as well as Limma fold changes and FDR-corrected p-value comparing whole tumour and normal tissues.

Figure 3. mRNA expression of bvz response-predicting genes

(A) Relative APLN (p=0.0001) and (B) MMP12 (p=0.0140) mRNA expression between 15 bvz-responding and 15 non-responding patients. Data is represented as mean ± SD. (C) and (D) APLN and MMP12 mRNA expression, respectively, in different cell fractions: normal tissue (n=4), tumour tissue (n=4), NEC (n=1), TEC (APLN n=4, MMP12 n=3), isolated normal epithelial cells (n=4) and tumour epithelial cell samples (n=4). Data is represented as mean ± SD.

Figure 4. APLN protein expression in CRC tissues

(A) (a and b) represent images of low and high APLN protein expression in tumour tissue sections, as determined with immunohistochemistry. Subfigure (c) illustrates vascular APLN expression, denoted by arrows. (B) Box plot (median, 25th and 75th quartiles) demonstrating the distribution of quantitative APLN data (H-score) obtained from the automated image analysis data of 10 bvz responders and 10 non-responders. P value corresponds to difference in median value of H-scores between the non-responders and responders. (C) Kaplan–Meier survival plot showing progression-free survival. Patients are categorised based on APLN expression. Y-axis shows cumulative (cum) survival, X-axis shows time (days). Patients with low APLN levels (n=10) are indicated in blue, patients with high APLN levels (n=10) are indicated in green. Log-rank p-value is shown.