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

Knowledge of the genetics of human pain gained over the last decade from next-generation sequencing

Next-generation sequencing (NGS) technologies have revolutionized pain research by providing comprehensive insights into genetic variation across the genome. Recent studies have expanded the known spectrum of mutations in genes such as SCN9A and NTRK1, which are commonly mutated in hereditary sensory neuropathies. NGS has uncovered critical alternative splicing events and facilitated single-cell transcriptomics, revealing cellular heterogeneity within tissues. An NGS-based classifier predicted extremely high opioid requirements with 80 % accuracy, highlighting the importance of tailoring opioid therapy based on genetic profiles. Key genes such as GDF5, COL11A1, and TRPV1 have been linked to osteoarthritis risk and pain sensitivity, while HLA-DRB1, TNF, and P2X7 play critical roles in inflammation and pain modulation in rheumatoid arthritis. Innovative tools, such as an atlas of the somatosensory system in neuropathic pain, have been developed based on NGS data, focusing on the dorsal root and trigeminal ganglia. This approach allows the analysis of cellular changes during the development of chronic pain. In the study of rare variants, NGS outperforms single nucleotide variant candidate studies and classical genome-wide association approaches. The complex data generated by NGS enables integrated multi-omics approaches, allowing deeper exploration of the molecular and cellular basis of pain perception. In addition, the characterization of non-coding RNAs has opened new therapeutic avenues. NGS-based pain research faces challenges related to complex data analysis and interpretation of rare genetic variants with unknown biological functions. Nevertheless, NGS offers significant potential for improving personalized pain management and highlights the need for interdisciplinary collaboration to translate findings into clinical practice.

Activation of the proton-sensing GPCR, GPR65 on fibroblast-like synoviocytes contributes to inflammatory joint pain

Inflammation is associated with localized acidosis, however, attributing physiological and pathological roles to proton-sensitive receptors is challenging due to their diversity and widespread expression. Here, agonists of the proton-sensing GPCR, GPR65, were systematically characterized. The synthetic agonist BTB09089 (BTB) recapitulated many proton-induced signaling events and demonstrated selectivity for GPR65. BTB was used to show that GPR65 activation on fibroblast-like synoviocytes (FLS), cells that line synovial joints, results in the secretion of proinflammatory mediators capable of recruiting immune cells and sensitizing sensory neurons. Intra-articular injection of BTB resulted in GPR65-dependent sensitization of knee-innervating neurons and nocifensive behaviors in mice. Stimulation of GPR65 on human FLS also triggered the release of inflammatory mediators and synovial fluid samples from human osteoarthritis patients were shown to activate GPR65. These results suggest a role of GPR65 in mediating cell-cell interactions that drive inflammatory joint pain in both mice and humans.

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review

BACKGROUND/OBJECTIVES

Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

METHODS

A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

RESULTS

Forty four studies from 2006 to 2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%).

CONCLUSION

Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

A mouse model of chronic primary pain that integrates clinically relevant genetic vulnerability, stress, and minor injury

Chronic primary pain conditions (CPPCs) affect over 100 million Americans, predominantly women. They remain ineffectively treated, in large part because of a lack of valid animal models with translational relevance. Here, we characterized a CPPC mouse model that integrated clinically relevant genetic (catechol-O-methyltransferase; COMT knockdown) and environmental (stress and injury) factors. Compared with wild-type mice, Comt+/- mice undergoing repeated swim stress and molar extraction surgery intervention exhibited pronounced multisite body pain and depressive-like behavior lasting >3 months. Comt+/- mice undergoing the intervention also exhibited enhanced activity of primary afferent nociceptors innervating hindpaw and low back sites and increased plasma concentrations of norepinephrine and pro-inflammatory cytokines interleukin-6 (IL-6) and IL-17A. The pain and depressive-like behavior were of greater magnitude and longer duration (≥12 months) in females versus males. Furthermore, increases in anxiety-like behavior and IL-6 were female-specific. The effect of COMT genotype × stress interactions on pain, IL-6, and IL-17A was validated in a cohort of 549 patients with CPPCs, demonstrating clinical relevance. Last, we assessed the predictive validity of the model for analgesic screening and found that it successfully predicted the lack of efficacy of minocycline and the CB2 agonist GW842166X, which were effective in spared nerve injury and complete Freund's adjuvant models, respectively, but failed in clinical trials. Yet, pain in the CPPC model was alleviated by the beta-3 adrenergic antagonist SR59230A. Thus, the CPPC mouse model reliably recapitulates clinically and biologically relevant features of CPPCs and may be implemented to test underlying mechanisms and find new therapeutics.

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Background/objective

Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer.

Methods

A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies.

Results

This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%).

Conclusion

Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

The incidence, impact, and risk factors for moderate to severe persistent pain after breast cancer surgery: a prospective cohort study

BACKGROUND

Few Australasian studies have evaluated persistent pain after breast cancer surgery.

OBJECTIVE

To evaluate the incidence, impact, and risk factors of moderate to severe persistent pain after breast cancer surgery in a New Zealand cohort.

DESIGN

Prospective cohort study.

METHODS

Consented patients were reviewed at 3 timepoints (preoperative, 2 weeks and 6 months postoperative). Pain incidence and interference, psychological distress and upper limb disability were assessed perioperatively. Clinical, demographic, psychological, cancer treatment-related variables, quantitative sensory testing, and patient genotype (COMT, OPRM1, GCH1, ESR1, and KCNJ6) were assessed as risk factors using multiple logistic regression.

RESULTS

Of the 173 patients recruited, 140 completed the 6-month follow-up. Overall, 15.0% (n = 21, 95% CI: 9.5%-22.0%) of patients reported moderate to severe persistent pain after breast cancer surgery with 42.9% (n = 9, 95% CI: 21.9%-66.0%) reporting likely neuropathic pain. Pain interference, upper limb dysfunction and psychological distress were significantly higher in patients with moderate to severe pain (P < .004). Moderate to severe preoperative pain (OR= 3.60, 95% CI: 1.13-11.44, P = .03), COMT rs6269 GA genotype (OR = 5.03, 95% CI: 1.49-17.04, P = .009) and psychological distress at postoperative day 14 (OR= 1.08, 95% CI: 1.02-1.16, P = .02) were identified as risk factors. Total intravenous anesthesia (OR= 0.31, 95% CI: 0.10 - 0.99, P = .048) was identified as protective.

CONCLUSION

The incidence of moderate to severe persistent pain after breast cancer surgery is high with associated pain interference, physical disability, and psychological distress. Important modifiable risk factors were identified to reduce this important condition.

Dopamine and dopamine receptor D1 as a novel favourable biomarker for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) remains one of the most common malignant tumours worldwide. Therefore, the identification and development of sensitivity- genes as novel diagnostic markers and effective therapeutic targets is urgently needed. Dopamine and dopamine receptor D1 (DRD1) are reported to be involved in the progression of various cancers. However, the crucial role of DRD1 in HCC malignant activities remains unclear.

Methods

We enrolled 371 patients with liver hepatocellular carcinoma (LIHC) from The Cancer Genome Atlas (TCGA) to detect the expression and functions of DRD1. The Tumour Immune Estimation Resource (TIMER), UALCAN database, Kaplan–Meier plotter, cBioPortal database, and LinkedOmics database were utilized for the systematic investigation of DRD1 expression and related clinical features, coexpressed genes, functional pathways, mutations, and immune infiltrates in HCC.

Results

In this study, we determined that DRD1 expression was decreased in HCC tumour tissues versus normal tissues and that low DRD1 expression indicated a poor prognosis. The significance of DRD1 expression varied among different tumour samples. The somatic mutation frequency of DRD1 in the LIHC cohort was 0.3%. The biological functions of DRD1 were detected and validated, and DRD1 was shown to be involved in various functional activities, including metabolism, oxidation, mitochondrial matrix-related processes and other related signaling pathways. In addition, out study indicated that DRD1 had significant correlations with the infiltration of macrophages, B cells and CD+ T cells in HCC.

Conclusions

These findings demonstrated the rationality of the potential application of DRD1 function as a novel biomarker for HCC diagnosis and a therapeutic target for HCC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02298-9.

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

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.

[Machine learning in anesthesiology]

The application of artificial intelligence (AI) is currently changing very different areas of life. Artificial intelligence involves the emulation of human behavior with the aid of methods from mathematics and informatics. Machine learning (ML) represents a subdivision of AI. Algorithms for ML have the potential to optimize patient care, in that they can be utilized in a supportive way in personalized medicine, decision making and risk prediction. Although the majority of the applications in medicine are still limited to data analysis and research, it is certain that ML will become increasingly more important in scientific and clinical aspects in this supportive function. Therefore, it is necessary for clinicians to have at least a basic understanding of the functional principles, strengths and weaknesses of ML.

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

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.

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

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.