Towards precision medicine for pain: diagnostic biomarkers and repurposed drugs

A B Niculescu, H Le-Niculescu, D F Levey, K Roseberry, K C Soe, J Rogers, F Khan, T Jones, S Judd, M A McCormick, A R Wessel, A Williams, S M Kurian, F A White, A B Niculescu, H Le-Niculescu, D F Levey, K Roseberry, K C Soe, J Rogers, F Khan, T Jones, S Judd, M A McCormick, A R Wessel, A Williams, S M Kurian, F A White

Abstract

We endeavored to identify objective blood biomarkers for pain, a subjective sensation with a biological basis, using a stepwise discovery, prioritization, validation, and testing in independent cohorts design. We studied psychiatric patients, a high risk group for co-morbid pain disorders and increased perception of pain. For discovery, we used a powerful within-subject longitudinal design. We were successful in identifying blood gene expression biomarkers that were predictive of pain state, and of future emergency department (ED) visits for pain, more so when personalized by gender and diagnosis. MFAP3, which had no prior evidence in the literature for involvement in pain, had the most robust empirical evidence from our discovery and validation steps, and was a strong predictor for pain in the independent cohorts, particularly in females and males with PTSD. Other biomarkers with best overall convergent functional evidence for involvement in pain were GNG7, CNTN1, LY9, CCDC144B, and GBP1. Some of the individual biomarkers identified are targets of existing drugs. Moreover, the biomarker gene expression signatures were used for bioinformatic drug repurposing analyses, yielding leads for possible new drug candidates such as SC-560 (an NSAID), and amoxapine (an antidepressant), as well as natural compounds such as pyridoxine (vitamin B6), cyanocobalamin (vitamin B12), and apigenin (a plant flavonoid). Our work may help mitigate the diagnostic and treatment dilemmas that have contributed to the current opioid epidemic.

Conflict of interest statement

ABN is listed as inventor on a patent application being filed by Indiana University, and is a co-founder of MindX Sciences. The other authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Steps 1–3: Discovery, prioritization, and validation. a Cohorts used in study, depicting flow of discovery, prioritization, and validation of biomarkers from each step. b Discovery cohort longitudinal within-subject analysis. Phchp### is study ID for each subject. V# denotes visit number. c Discovery of possible subtypes of Pain based on High Pain visits in the discovery cohort. Subjects were clustered using measures of mood and anxiety (Simplified Affective State Scale (SASS)), as well as psychosis (PANNS Positive). d Differential gene expression in the Discovery cohort—number of genes identified with differential expression (DE) and absent-present (AP) methods with an internal score of 2 and above. Red—increased in expression in High Pain, blue—decreased in expression in High Pain. At the discovery step probesets are identified based on their score for tracking pain with a maximum of internal points of 6 (33% (2 pt), 50% (4 pt), and 80% (6 pt)). e Prioritization with CFG for prior evidence of involvement in pain. In the prioritization step, probesets are converted to their associated genes using Affymetrix annotation and GeneCards. Genes are prioritized and scored using CFG for pain evidence with a maximum of 12 external points. Genes scoring at least six points out of a maximum possible of 18 total internal and external scores points are carried to the validation step. f Validation in an independent cohort of psychiatric patients with co-morbid pain disorders and severe subjective and functional pain ratings. In the validation step biomarkers are assessed for stepwise change from the discovery groups of subjects with Low Pain, to High Pain, to Clinically Severe Pain disorder, using ANOVA. N = number of testing visits. Five biomarkers were nominally significant, MFAP3 and PIK3CD were the most significant, and 68 biomarkers were stepwise changed
Fig. 2
Fig. 2
Best single biomarkers predictors. From the long list (n = 65). Those on short list (n = 5) are bolded. Bar graph shows best predictive biomarkers in each group. *Nominally significant p < 0.05. **Bonferroni significant for the 65 biomarkers tested. Table underneath the figures displays the actual number of biomarkers for each group whose ROC AUC p-values (a, b) and Cox odds ratio p-values (c) are at least nominally significant. Some female diagnostic group are missing from the graph as they did not have any significant biomarkers. Cross-sectional is based on levels at one visit. Longitudinal is based on levels at multiple visits (integrates levels at most recent visit, maximum levels, slope into most recent visit, and maximum slope). Dividing lines represent the cutoffs for a test performing at chance levels (white), and at the same level as the best biomarkers for all subjects in cross-sectional (gray) and longitudinal (black) based predictions. All biomarkers perform better than chance. Biomarkers performed better when personalized by gender and diagnosis
Fig. 3
Fig. 3
Biological roles. STRING interaction network for the top biomarkers for pain (65 probesets, 60 genes)

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