Interaction among apoptosis-associated sequence variants and joint effects on aggressive prostate cancer

Abstract

Background: Molecular and epidemiological evidence demonstrate that altered gene expression and single nucleotide polymorphisms in the apoptotic pathway are linked to many cancers. Yet, few studies emphasize the interaction of variant apoptotic genes and their joint modifying effects on prostate cancer (PCA) outcomes. An exhaustive assessment of all the possible two-, three- and four-way gene-gene interactions is computationally burdensome. This statistical conundrum stems from the prohibitive amount of data needed to account for multiple hypothesis testing.

Methods: To address this issue, we systematically prioritized and evaluated individual effects and complex interactions among 172 apoptotic SNPs in relation to PCA risk and aggressive disease (i.e., Gleason score ≥ 7 and tumor stages III/IV). Single and joint modifying effects on PCA outcomes among European-American men were analyzed using statistical epistasis networks coupled with multi-factor dimensionality reduction (SEN-guided MDR). The case-control study design included 1,175 incident PCA cases and 1,111 controls from the prostate, lung, colo-rectal, and ovarian (PLCO) cancer screening trial. Moreover, a subset analysis of PCA cases consisted of 688 aggressive and 488 non-aggressive PCA cases. SNP profiles were obtained using the NCI Cancer Genetic Markers of Susceptibility (CGEMS) data portal. Main effects were assessed using logistic regression (LR) models. Prior to modeling interactions, SEN was used to pre-process our genetic data. SEN used network science to reduce our analysis from > 36 million to < 13,000 SNP interactions. Interactions were visualized, evaluated, and validated using entropy-based MDR. All parametric and non-parametric models were adjusted for age, family history of PCA, and multiple hypothesis testing.

Results: Following LR modeling, eleven and thirteen sequence variants were associated with PCA risk and aggressive disease, respectively. However, none of these markers remained significant after we adjusted for multiple comparisons. Nevertheless, we detected a modest synergistic interaction between AKT3 rs2125230-PRKCQ rs571715 and disease aggressiveness using SEN-guided MDR (p = 0.011).

Conclusions: In summary, entropy-based SEN-guided MDR facilitated the logical prioritization and evaluation of apoptotic SNPs in relation to aggressive PCA. The suggestive interaction between AKT3-PRKCQ and aggressive PCA requires further validation using independent observational studies.

Figure 1

Visualization of the interaction among apoptosis-related sequence variants using Statistical Epistasis Network Modeling (SEN)-guided MDR. The Statistical Epistasis Network (SEN) was generated using a pairwise interaction strength cut-off or 1%. Overall, the model has 91 vertices (single SNPs), 80 edges (pairwise SNPs), and 18 components ("sub-networks" of vertices and edges). The largest connected component, shown in the center and spanning the entire length of this model, has 24 vertices and 34 edges. Each number in Figure 1 corresponds with a specific apoptosis-related SNP analyzed in the current study, as summarized in Additional file 2.

Figure 2

Interaction Entropy model. This graphical model, describes the percent entropy that is explained by each apoptosis-related SNP or pairwise combination within our study population. Positive percent entropy indicates information gain (IG) or synergy; whereas, negative percent indicates redundancy or lack of information gain (IG). Schematic coloration used in the visualization tools represents a continuum from synergy (i.e. non-additive) to redundancy. The colors range from red representing a high degree of synergy (positive information gain (IG)), orange a lesser degree, and gold representing independence and a midway point between synergy and redundancy. On the other hand, green and blue represent redundancy, which is not apparent in the current analysis.