Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses

Abstract

Next-generation sequencing (NGS) provides unrestricted access to the genome, but it produces ‘big data’ exceeding in amount and complexity the classical analytical approaches. We introduce a bioinformatics-based classifying biomarker that uses emergent properties in genetics to separate pain patients requiring extremely high opioid doses from controls. Following precisely calculated selection of the 34 most informative markers in the OPRM1, OPRK1, OPRD1 and SIGMAR1 genes, pattern of genotypes belonging to either patient group could be derived using a k-nearest neighbor (kNN) classifier that provided a diagnostic accuracy of 80.6±4%. This outperformed alternative classifiers such as reportedly functional opioid receptor gene variants or complex biomarkers obtained via multiple regression or decision tree analysis. The accumulation of several genetic variants with only minor functional influences may result in a qualitative consequence affecting complex phenotypes, pointing at emergent properties in genetics.

Machine-learned analysis of the association of next-generation sequencing–based human TRPV1 and TRPA1 genotypes with the sensitivity to heat stimuli and topically applied capsaicin

“…The limit to set “B” was found at Shannon information = 0.339. Furthermore, as implemented previously, 39 further variants unlikely to provide a suitable basis for phenotype class assignment were excluded. In the present analysis, this was approached through the effect sizes of the allelic distribution between the phenotype classes used classic χ 2 statistics.…”

Big Data’s Role in Precision Public Health

“…Big data is a component in studies that have shown new precision characteristics of such public health concerns as cholera ( 141 ), chikungunya ( 142 ), diabetes ( 143 , 144 ), diarrhea ( 145 ), heatwave ( 146 ), influenza ( 147 ), opioid epidemic ( 148 , 149 ), preterm birth ( 150 ), stunting ( 151 ), and Zika ( 152 ).…”

Machine learning in pain research

“…Moreover, predicting which patients required high opioid doses for analgesia, based on a next-generation sequencing–derived opioid receptor genotype, was achieved with a subsymbolic classifier based on k-nearest neighbors calculations. 34 Another application of subsymbolic classifiers has been implemented as neural networks. The so-called elastic net regression models and SVMs predicted pain scores measured between 40 and 120 minutes after the administration of 10 mg oxycodone from interpolated pain score values before drug administration.…”