SNP-SNP Interaction Network in Angiogenesis Genes Associated with Prostate Cancer Aggressiveness

<div><p>Angiogenesis has been shown to be associated with prostate cancer development. The majority of prostate cancer studies focused on individual single nucleotide polymorphisms (SNPs) while SNP-SNP interactions are suggested having a great impact on unveiling the underlying mechanism of complex disease. Using 1,151 prostate cancer patients in the Cancer Genetic Markers of Susceptibility (CGEMS) dataset, 2,651 SNPs in the angiogenesis genes associated with prostate cancer aggressiveness were evaluated. SNP-SNP interactions were primarily assessed using the two-stage Random Forests plus Multivariate Adaptive Regression Splines (TRM) approach in the CGEMS group, and were then re-evaluated in the Moffitt group with 1,040 patients. For the identified gene pairs, cross-evaluation was applied to evaluate SNP interactions in both study groups. Five SNP-SNP interactions in three gene pairs (<i>MMP16+ ROBO1</i>, <i>MMP16+ CSF1</i>, and <i>MMP16+ EGFR</i>) were identified to be associated with aggressive prostate cancer in both groups. Three pairs of SNPs (rs1477908+ rs1387665, rs1467251+ rs7625555, and rs1824717+ rs7625555) were in <i>MMP16</i> and <i>ROBO1</i>, one pair (rs2176771+ rs333970) in <i>MMP16</i> and <i>CSF1</i>, and one pair (rs1401862+ rs6964705) in <i>MMP16</i> and <i>EGFR</i>. The results suggest that <i>MMP16</i> may play an important role in prostate cancer aggressiveness. By integrating our novel findings and available biomedical literature, a hypothetical gene interaction network was proposed. This network demonstrates that our identified SNP-SNP interactions are biologically relevant and shows that EGFR may be the hub for the interactions. The findings provide valuable information to identify genotype combinations at risk of developing aggressive prostate cancer and improve understanding on the genetic etiology of angiogenesis associated with prostate cancer aggressiveness.</p> </div

Flow chart of SNP-SNP interaction cross-evaluation.

<p>In Step 1, SNP-SNP interactions identified in the CGEMS group were re-assessed in the Moffitt group. In Step 2, all possible two-way SNP-SNP interactions of the identified gene pairs were evaluated in the Moffitt group. In Step 3, the identified SNP interactions from the Step 2 were re-evaluated in the CGEMS group.</p

SNPs involved in significant interactions associated with prostate cancer aggressiveness.

a<p>model with minimum p-value in the CGEMS (Dom: dominant, Rec: recessive, Add: additive model).</p>b<p>bald: p-value<0.05.</p>c<p>odds ratio (95% confidence interval).</p

SNP-SNP interactions in angiogenesis genes associated with prostate cancer aggressiveness in the CGEMS and Moffitt group.

a<p>SNP(major/minor allele).</p>b<p>all: all three genotypes in the SNP; others: genotype combinations of the two SNPs other than the specified genotype.</p>c1–c5<p>top 1 to top 5 identified SNP-SNP interactions using the TRM approach in CGEMS.</p>d<p>similar interaction pattern in the CGEMS and Moffitt group.</p

Model of prostate cancer aggressiveness using the CGEMS group.

a<p>standard error, based on multivariable logistic model.</p

Genetic interaction network based on five interacting gene pairs.^a.

<p>^aFive interacting gene pairs: <i>MMP16+ ROBO1, MMP16+ CSF1, CSF1+ FBLN5, CSF1+ HSPG2</i>, and <i>MMP16+ EGFR. </i>^b Nodes represents the proteins, and lines between nodes indicate interactions between proteins. Green and red lines represent the positive and negative effects, respectively. Proteins of identified genes are denoted by a circle around the nodes.</p

Prognostic Fifteen-Gene Signature for Early Stage Pancreatic Ductal Adenocarcinoma

<div><p>The outcomes of patients treated with surgery for early stage pancreatic ductal adenocarcinoma (PDAC) are variable with median survival ranging from 6 months to more than 5 years. This challenge underscores an unmet need for developing personalized medicine strategies to refine the current treatment decision-making process. To derive a prognostic gene signature for patients with early stage PDAC, a PDAC cohort from Moffitt Cancer Center (n = 63) was used with overall survival (OS) as the primary endpoint. This was further evaluated using an independent microarray cohort dataset (Stratford et al: n = 102). Technical validation was performed by NanoString platform. A prognostic 15-gene signature was developed and showed a statistically significant association with OS in the Moffitt cohort (hazard ratio [HR] = 3.26; p<0.001) and Stratford et al cohort (HR = 2.07; p = 0.02), and was independent of other prognostic variables. Moreover, integration of the signature with the TNM staging system improved risk prediction (p<0.01 in both cohorts). In addition, NanoString validation showed that the signature was robust with a high degree of reproducibility and the association with OS remained significant in the two cohorts. The gene signature could be a potential prognostic tool to allow risk-adapted stratification of PDAC patients into personalized treatment protocols; possibly improving the currently poor clinical outcomes of these patients.</p></div

Descriptive statistics of clinical predictors in the Moffitt PDAC cohort (n = 63).

<p>Descriptive statistics of clinical predictors in the Moffitt PDAC cohort (n = 63).</p

NanoString validation of the 15-gene signature.

<p><b>(A)</b> By analyzing the NanoString gene expression data, a PC1 score was generated for each patient from the subset (n = 53) of the Moffitt cohort using principal component analysis to reflect the combined expression of the 15 genes. The median dichotomized PC1 score was used to classify patients into high and low PC1 groups. <b>(B)</b> For the Stratford et al cohort, the PC1 score was generated for each patient using the loading coefficients of the first principal component in the Moffitt cohort. High and low PC1 groups were determined by the cutoff at the first quartile of the PC1 score to adjust for the distribution of the N staging. Kaplan–Meier curves of overall survival are shown in the two groups. A statistically significant difference of the Kaplan–Meier survival curves between the high and low PC1 groups was determined by the two-sided log-rank test. The number of patients at risk is listed below the survival curves.</p

Association of the 15-gene signature with overall survival in the Moffitt cohort.

<p>A PC1 score was generated for each patient from the Moffitt cohort (n = 63) by principal component analysis to reflect the combined expression of the 15 genes. High and low PC1 groups were determined on the basis of a median split. Kaplan–Meier curves of overall survival are shown in the two groups. A statistically significant difference of the Kaplan–Meier survival curves between the high and low PC1 groups was determined by the two-sided log-rank test. The number of patients at risk is listed below the survival curves.</p