Machine-learned analysis of the association of next-generation sequencing–based genotypes with persistent pain after breast cancer surgery

Kringel, Dario; Kaunisto, Mari A.; Kalso, Eija; Lötsch, Jörn

doi:10.1097/j.pain.0000000000001616

Cited by 11 publications

(13 citation statements)

References 144 publications

(103 reference statements)

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“…The number of trees and complexity were based on the smallest out-of-bag error rate obtained among various tested hyperparameter settings. Random forests were chosen as the classifier for the present analysis because previously they functioned similarly well on comparable genetic data as several different alternative machine learning algorithms, including adaptive boosting, k-nearest neighbors, naïve Bayes, support vector machines, and multivariate logistic regression [ 15 , 86 ].…”

Section: Methodsmentioning

confidence: 99%

Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes

Lötsch

Kringel

Geißlinger

et al. 2020

IJMS

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Genetic association studies have shown their usefulness in assessing the role of ion channels in human thermal pain perception. We used machine learning to construct a complex phenotype from pain thresholds to thermal stimuli and associate it with the genetic information derived from the next-generation sequencing (NGS) of 15 ion channel genes which are involved in thermal perception, including ASIC1, ASIC2, ASIC3, ASIC4, TRPA1, TRPC1, TRPM2, TRPM3, TRPM4, TRPM5, TRPM8, TRPV1, TRPV2, TRPV3, and TRPV4. Phenotypic information was complete in 82 subjects and NGS genotypes were available in 67 subjects. A network of artificial neurons, implemented as emergent self-organizing maps, discovered two clusters characterized by high or low pain thresholds for heat and cold pain. A total of 1071 variants were discovered in the 15 ion channel genes. After feature selection, 80 genetic variants were retained for an association analysis based on machine learning. The measured performance of machine learning-mediated phenotype assignment based on this genetic information resulted in an area under the receiver operating characteristic curve of 77.2%, justifying a phenotype classification based on the genetic information. A further item categorization finally resulted in 38 genetic variants that contributed most to the phenotype assignment. Most of them (10) belonged to the TRPV3 gene, followed by TRPM3 (6). Therefore, the analysis successfully identified the particular importance of TRPV3 and TRPM3 for an average pain phenotype defined by the sensitivity to moderate thermal stimuli.

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Section: Methodsmentioning

confidence: 99%

Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes

Lötsch

Kringel

Geißlinger

et al. 2020

IJMS

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“…In contrast, the genetic analyses for risk of persistent pain will be performed in a cohort of 70/70 women with persistent/non-persistent pain, extending a previous analysis [14]. After all, the immediate exploitation of the advanced technology which NGS provides over single variant analysis, which was still common a decade ago, is still easier to achieve in limited gene sets than in the whole genome due to technical limitations.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

“…The present set of genes complements an earlier NGS panel [20], which was successfully applied to genotype versus phenotype associations in patients who had undergone breast cancer surgery [14]. The laboratory analyses were therefore performed on the same DNA samples that were used previously, but with a non-redundant technical implementation.…”

Section: Establishment Of the Ampliseq Tm Ngs Panel 421 Dna Sample Acquisitionmentioning

confidence: 99%

“…With predominantly small effects exerted by common genetic variants [11], a breakthrough in the genetic profiling of individual risks, as occasionally expected [12], has not yet really been achieved [13]. Instead, this seems to be linked to a complex pattern of functional genetic variants [14], which is being discovered in an evolutionary rather than revolutionary way, which is supported by technical advances over the last decade [15] that allow to establish genotype versus phenotype associations for thousands of genetic variants in a still manageable small number of patients [14,16].…”

Section: Introductionmentioning

confidence: 99%

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Computational Functional Genomics-Based AmpliSeq™ Panel for Next-Generation Sequencing of Key Genes of Pain

Kringel

Malkusch

Kalso

et al. 2021

IJMS

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The genetic background of pain is becoming increasingly well understood, which opens up possibilities for predicting the individual risk of persistent pain and the use of tailored therapies adapted to the variant pattern of the patient’s pain-relevant genes. The individual variant pattern of pain-relevant genes is accessible via next-generation sequencing, although the analysis of all “pain genes” would be expensive. Here, we report on the development of a cost-effective next generation sequencing-based pain-genotyping assay comprising the development of a customized AmpliSeq™ panel and bioinformatics approaches that condensate the genetic information of pain by identifying the most representative genes. The panel includes 29 key genes that have been shown to cover 70% of the biological functions exerted by a list of 540 so-called “pain genes” derived from transgenic mice experiments. These were supplemented by 43 additional genes that had been independently proposed as relevant for persistent pain. The functional genomics covered by the resulting 72 genes is particularly represented by mitogen-activated protein kinase of extracellular signal-regulated kinase and cytokine production and secretion. The present genotyping assay was established in 61 subjects of Caucasian ethnicity and investigates the functional role of the selected genes in the context of the known genetic architecture of pain without seeking functional associations for pain. The assay identified a total of 691 genetic variants, of which many have reports for a clinical relevance for pain or in another context. The assay is applicable for small to large-scale experimental setups at contemporary genotyping costs.

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“…Past studies have successfully used ML strategies to predict chronic pain symptoms and development (e.g., post-surgical and rheumatoid arthritis) [27][28][29][30][31][32]. Lötsch and colleagues [27] used a ML approach to successfully categorize patients into persistent pain versus non-persistent pain groups after breast cancer surgery; the most relevant predictors included age, body mass index, depression, and anxiety.…”

Section: Introductionmentioning

confidence: 99%

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach

et al. 2021

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Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data.

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Machine-learned analysis of the association of next-generation sequencing–based genotypes with persistent pain after breast cancer surgery

Abstract: Please confirm the given names (pink) and surnames (blue) of authors have been identified correctly.

Cited by 11 publications

References 144 publications

Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes

Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes

Computational Functional Genomics-Based AmpliSeq™ Panel for Next-Generation Sequencing of Key Genes of Pain

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach

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