Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine Fusion

Abstract

Objectives: Incorporation of genetic factors in psychosocial/perioperative models for predicting chronic postsurgical pain (CPSP) is key for personalization of analgesia. However, single variant associations with CPSP have small effect sizes, making polygenic risk assessment important. Unfortunately, pediatric CPSP studies are not sufficiently powered for unbiased genome wide association (GWAS). We previously leveraged systems biology to identify candidate genes associated with CPSP. The goal of this study was to use systems biology prioritized gene enrichment to generate polygenic risk scores (PRS) for improved prediction of CPSP in a prospectively enrolled clinical cohort.

Methods: In a prospectively recruited cohort of 171 adolescents (14.5 ± 1.8 years, 75.4% female) undergoing spine fusion, we collected data about anesthesia/surgical factors, childhood anxiety sensitivity (CASI), acute pain/opioid use, pain outcomes 6-12 months post-surgery and blood (for DNA extraction/genotyping). We previously prioritized candidate genes using computational approaches based on similarity for functional annotations with a literature-derived "training set." In this study, we tested ranked deciles of 1336 prioritized genes for increased representation of variants associated with CPSP, compared to 10,000 randomly selected control sets. Penalized regression (LASSO) was used to select final variants from enriched variant sets for calculation of PRS. PRS incorporated regression models were compared with previously published non-genetic models for predictive accuracy.

Results: Incidence of CPSP in the prospective cohort was 40.4%. 33,104 case and 252,590 control variants were included for association analyses. The smallest gene set enriched for CPSP had 80/1010 variants associated with CPSP (p < 0.05), significantly higher than in 10,000 randomly selected control sets (p = 0.0004). LASSO selected 20 variants for calculating weighted PRS. Model adjusted for covariates including PRS had AUROC of 0.96 (95% CI: 0.92-0.99) for CPSP prediction, compared to 0.70 (95% CI: 0.59-0.82) for non-genetic model (p < 0.001). Odds ratios and positive regression coefficients for the final model were internally validated using bootstrapping: PRS [OR 1.98 (95% CI: 1.21-3.22); β 0.68 (95% CI: 0.19-0.74)] and CASI [OR 1.33 (95% CI: 1.03-1.72); β 0.29 (0.03-0.38)].

Discussion: Systems biology guided PRS improved predictive accuracy of CPSP risk in a pediatric cohort. They have potential to serve as biomarkers to guide risk stratification and tailored prevention. Findings highlight systems biology approaches for deriving PRS for phenotypes in cohorts less amenable to large scale GWAS.

Keywords: chronic post-surgical pain; gene enrichment; genetics; polygenic risk score; systems biology.

FIGURE 1

Study flow showing steps involved with gene prioritization using systems biology followed by genetic association analyses in the clinical cohort to derive polygenic risk score based prediction model for chronic post-surgical pain.

FIGURE 2

Gene enrichment analyses for pain score at 6–12 months as outcome. Centiles represent the portion of case genes used in the genetic assocaition analysis. 0% includes the training set of gene variants, 10th percentile includes the training list plus the top 10% highest ranked genes, and so forth, vertical axis represents the number of variants. Box plots represent the cumulative number of SNPs with signficant association with pain score at 6–12 months after surgery [chronic post-surgical pain (CPSP) continuous outcome] (p < 0.05) in 10,000 runs of control gene variants. The dot indicates the cumulative number of nominal associations (p < 0.05) identified for case genes. Enrichment is indicated when a greater number of genetic associations are present in case versus control genes, that is, when the number of associations in case genes (red dot) (80 variants/1010 variants) exceeded the upper 95th percentile threshold in the 10,000 runs of the control set. For CPSP continuous outcome, we see enrichment in the training set of variants (p < 0.001). The training set incudes 80 variants showing association with CPSP (p < 0.05).

FIGURE 3

Plot of predicted probability of developing chronic postsurgical pain (CPSP) after spine surgery is presented as a function of polygenic risk score (PRS), at a childhood anxiety sensitivity index (CASI) score of 28.3 (median CASI in the model). The blue line denotes predicted probabilities from the final regression model, and dashed lines the 95% confidence interval, and circles represent observed cases (or outcomes). We see a sigmoid shaped curve with increasing probability of CPSP at PRS > 16, 50% probability at PRS = 23.06 and high probability beyond PRS = 30. Thus, higher the weighted PRS, higher the probability of CPSP.

FIGURE 4

Receiver operating characteristic curve showing the sensitivity/1-specificity for prediction of chronic post-surgical pain using the non-genetic model [including childhood anxiety sensitivity index (CASI) – dashed lines] compared with the prediction using the polygenic risk score final model (PRS and CASI – solid black lines). The area under curve for genetic model is 0.96 (95% CI: 0.92–0.99) compared to 0.70 (95% CI: 0.59–0.82) for non-genetic model (p = 0.0001).