Sage Journals: Discover world-class research

Abstract

Background

The major dose-limiting toxicity of paclitaxel, one of the most commonly used drugs to treat breast cancer, is peripheral neuropathy (paclitaxel-induced peripheral neuropathy). Paclitaxel-induced peripheral neuropathy, which persists into survivorship, has a negative impact on patient’s mood, functional status, and quality of life. Currently, no interventions are available to treat paclitaxel-induced peripheral neuropathy. A critical barrier to the development of efficacious interventions is the lack of understanding of the mechanisms that underlie paclitaxel-induced peripheral neuropathy. While data from preclinical studies suggest that disrupting cytoskeleton- and axon morphology-related processes are a potential mechanism for paclitaxel-induced peripheral neuropathy, clinical evidence is limited. The purpose of this study in breast cancer survivors was to evaluate whether differential gene expression and co-expression patterns in these pathways are associated with paclitaxel-induced peripheral neuropathy.

Methods

Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology were identified between survivors who received paclitaxel and did (n = 25) or did not (n = 25) develop paclitaxel-induced peripheral neuropathy.

Results

Pathway impact analysis identified four significantly perturbed cytoskeleton- and axon morphology-related signaling pathways. Weighted gene co-expression network analysis identified three co-expression modules. One module was associated with paclitaxel-induced peripheral neuropathy group membership. Functional analysis found that this module was associated with four signaling pathways and two ontology annotations related to cytoskeleton and axon morphology.

Conclusions

This study, which is the first to apply systems biology approaches using circulating whole blood RNA-seq data in a sample of breast cancer survivors with and without chronic paclitaxel-induced peripheral neuropathy, provides molecular evidence that cytoskeleton- and axon morphology-related mechanisms identified in preclinical models of various types of neuropathic pain including chemotherapy-induced peripheral neuropathy are found in breast cancer survivors and suggests pathways and a module of genes for validation and as potential therapeutic targets.

Keywords

Chemotherapy peripheral neuropathy breast cancer survivor paclitaxel gene expression axon morphology cytoskeleton signaling pathway pathway impact analysis gene co-expression network analysis

Introduction

Chronic chemotherapy-induced peripheral neuropathy (CIPN) is one of the most common adverse effects of neurotoxic chemotherapy (CTX), with prevalence rates that range from 30% to 70% in cancer survivors.¹ While initially described as a reversible condition, a growing body of evidence suggests that CIPN persists long into survivorship.² In fact, in 2014, CIPN was added to the National Comprehensive Cancer Network’s Clinical Practice Guideline for Survivorship^3,4 because of its substantial negative impact on survivors’ functional status and quality of life.^5–10

Paclitaxel is one of the most commonly used neurotoxic drugs to treat breast cancer.¹¹ While it is well known to stabilize microtubules with resultant impairments in axonal transport, a recent review suggested that the pathophysiologic mechanisms for paclitaxel-induced peripheral neuropathy (PIPN) extend beyond microtubule impairment¹² and may involve mitochondrial damage^13,14 and alterations in immune function.¹⁵ We recently described perturbations in mitochondrial dysfunction-related pathways that were associated with PIPN in a sample of breast cancer survivors.¹³

The anti-tumor activity of paclitaxel occurs as a result of its ability to alter normal regulatory mechanisms that control microtubule dynamics and microtubule-based transport that are required for a cell’s survival.^16,17 Preclinical evidence suggests that the administration of paclitaxel results in microtubule polymerization and stabilization in cancer cells.¹⁸ Within peripheral neurons, paclitaxel binds to β-tubulin in polymerized microtubules, which causes a conformational change and renders these microtubules less dynamic.^18,19 Microtubule hyper-stabilization primarily occurs on the most distal portion of the axon and may contribute to the “dying back” phenomenon associated with PIPN.^20–24 Recent preclinical evidence suggests that the neurotoxic effects associated with the stabilization of microtubules results in defects in axonal transport,^25,26 as well as morphological (e.g., increase in myelin abnormalities,^17,27 increase in number of Schwann cell nuclei^17,27), and biochemical (e.g., long-term retention of paclitaxel) changes in peripheral nerves.^17,27 The administration of paclitaxel results in the degradation of both peripheral and central branches of dorsal root ganglia (DRG) neurons.²⁸ The mechanisms mediating axonal degeneration in PIPN remain an area of active investigation (reviewed in the study by Fukuda et al.²⁴), providing new opportunities for therapeutic interventions.²⁹ Given the preclinical evidence that paclitaxel alters cytoskeleton structure and axon morphology and that these alterations are associated with the development and maintenance of peripheral neuropathy, we provide evidence to support the suggestion that perturbations in signaling pathways and patterns of gene co-expression associated with cytoskeleton and axon morphology are also associated with chronic PIPN in breast cancer survivors.

Materials and methods

Survivors and settings

The methods for this analysis, which is part of a larger study of CIPN, are described in detail elsewhere.¹⁰ In brief, survivors were recruited from throughout the San Francisco Bay area and met pre-specified inclusion and exclusion criteria. The National Coalition for Cancer Survivorship’s definition of a cancer survivor (i.e., a person is a cancer survivor from the moment of diagnosis through the balance of life) was used in this study.³⁰ Of the 1450 survivors who were screened, 754 enrolled, and 623 completed the self-report questionnaires and study visit. Data from a randomly selected sample of breast cancer survivors with (n = 25) or without (n = 25) chronic PIPN were used in this analysis.

Study procedures

Research nurses screened and consented the survivors over the phone; sent and asked them to complete the self-report questionnaires prior to their study visit; and scheduled the in-person assessment. At this assessment, written informed consent was obtained, responses to questionnaires were reviewed for completeness, and objective measurements were obtained. Blood samples were drawn, processed, and stored for subsequent molecular analyses in PAXgene® Blood RNA tubes (Qiagen, Inc.). This study was approved by the Institutional Review Board of the University of California, San Francisco.

Study measures

Demographic and clinical characteristics

Breast cancer survivors provided information on demographic characteristics and completed the Alcohol Use Disorders Identification Test (AUDIT),³¹ Karnofsky Performance Status (KPS) scale,^32–34 and the Self-Administered Comorbidity Questionnaire.^35,36

Pain measures

Survivors with PIPN rated their pain intensity using a 0 to 10 numeric rating scale and completed the pain interference scale from the Brief Pain Inventory³⁷ and the Pain Quality Assessment Scale.³⁸

Acquisition and processing of gene expression data

In this study, we used two different but complementary approaches to evaluate whole-transcriptome data for patterns of gene expression associated with PIPN in pathways associated with cytoskeleton and axon morphology (Figure 1). The first approach utilized pathway impact analysis (PIA) in which pre-defined pathways are evaluated for perturbations using the magnitude and significance of gene–gene interactions from differential gene expression (DGE) data. The second approach (i.e., weighted gene correlation network analysis (WGCNA)) constructs networks of genes with shared patterns of co-expression agnostic of phenotypic characteristics, including PIPN. These networks were then evaluated for association with chronic PIPN and for biological processes related to cytoskeleton and axon morphology.

Figure 1.

An overview of the two analytic approaches used to evaluate for cytoskeleton- and axon morphology-related gene (G) expression patterns associated with paclitaxel-induced peripheral neuropathy in breast cancer survivors (S) with (C) and without (N) PIPN: [1] PIA and [2] WGCNA. The KEGG pathways and Gene Ontology categories were evaluated for cytoskeleton and axon-morphology biological processes. DGE: differential gene expression; KEGG: Kyoto Encyclopedia of Genes and Genomes; MLR: multiple logistic regression; PIA: pathway impact analysis; PPI: protein–protein interaction; WGCNA: weighted gene co-expression network analysis.

The methods for the quantification of gene expression are described in detail elsewhere.¹³ Gene expression of total RNA, isolated from peripheral whole blood, was assayed using RNA-seq. Gene expression was summarized as counts per gene and used as input for the PIA and WGCNA.

PIA of the whole transcriptome

The DGE was quantified using a general linear model as previously described.¹³ These DGE analyses were adjusted for demographic (i.e., age, employment status) and clinical (i.e., AUDIT score, body mass index (BMI), KPS score) characteristics that differed between the PIPN groups, as well as for technical variability (e.g., potential batch effects). We estimated¹³ that, at a type I error rate of 0.01, we were powered³⁹ to detect 1.5-fold changes for 83% of genes.

The DGE results were summarized as the log fold change and p value for each gene. PIA was used to evaluate for perturbations in well-defined signaling pathways as previously described.¹³ PIA is a topology-based approach to pathway analysis (reviewed in the studies by Khatri et al.⁴⁰ and Garcia-Campos et al.⁴¹). Unlike other approaches, PIA is not limited by a pre-defined set of genes, does not assume independence of genes, and pathways are considered independent. PIA includes potentially important biological factors (e.g., gene–gene interactions, flow signals in a pathway, pathway topologies), as well as the magnitude (i.e., log fold-change), and p values from the DGE analysis (reviewed in Mitrea et al.⁴²). This PIA included the results of the DGE analysis for all genes (i.e., cutoff free) to determine the probability of pathway perturbations (pPERT) using Pathway Express.⁴³ This pathway-topology impact factor analysis approach is widely used with over 1200 citations to date.⁴⁴

A total of 208 signaling pathways were defined using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.⁴⁵ Sequence loci data were annotated with Entrez gene IDs. The gene names were annotated using the Human Genome Organisation (HUGO) Gene Nomenclature Committee resource database.⁴⁶ We assessed for significance of the PIA using a strict false discovery rate (FDR) of 1 under the Benjamini–Hochberg (BH) procedure.^47,48 Finally, we evaluated these results for pathways related to cytoskeleton and axon morphology.

Gene correlation network analysis

One limitation of PIA is its dependence on pre-defined signaling pathways. By evaluating whole-transcriptome level data (i.e., not filtered by pathway) across multiple samples, patterns of gene co-expression can be identified. Gene co-expression can be identified as sets of genes (i.e., modules) which share more similar expression patterns to each other than they do to other genes in the dataset. These co-expression groups of genes are identified independent of an outcome (e.g., PIPN status) or biological organization (e.g., pathways) to provide an independent interpretation of gene expression data. These co-expression modules tend to be functionally related and co-regulated and if associated with an outcome of interest (i.e., PIPN group) may present new insights into its molecular biology. Because we were interested in identifying novel patterns of gene expression, we generated a co-expression gene network and evaluated this network for associations with chronic PIPN.⁴⁹ First, we identified modules that demonstrated co-expression patterns in our dataset. Second, we evaluated these modules for associations with chronic PIPN group membership. Third, we evaluated any module associated with chronic PIPN group membership for patterns of higher orders of biological organization. Finally, we evaluated these results for pathways related to cytoskeleton and axon morphology.

Gene level summaries of RNA abundance were estimated as the log transformed reads per mean thousand (i.e., log₂(RPMK + 1)). Modules of genes with highly correlated expression were identified from the top 5000 most variant genes using WGCNA.^50,51 We selected an empirical soft power threshold of 11, which represented a strong model fit to a scale free topology (signed R² = 0.80) to generate the signed adjacency matrix. A clustered gene tree was generated using the “average” method. The gene tree was dynamically cut (with WGCNA parameters: minModuleSize =20, method=“hybrid”, and deepSplit = 2) and merged with a dissimilarity threshold of 0.8 to construct the merged modules. Each module was assigned a color, and this color label was used for identification in all subsequent analyses. Although the performance of co-expression network analysis is dependent of sample size, WGCNA performs well on sample sizes of >20.^52,53

Multiple logistic regression analysis, which controlled for significant demographic (i.e., age, employment status) and clinical (i.e., AUDIT score, BMI, KPS score) characteristics, was used to evaluate for an association between the PIPN group and the eigengenes (i.e., the first principal component of variation among the co-expressed genes in that module) of each of the co-expression modules. A backward stepwise approach was used to create a parsimonious model. The binomial regression and the backward stepwise method were performed using the “glm()” and “step()” functions in the “stats” package of R. We assessed for significance of the regression analyses using a p value of <.05.

Functional analysis of the gene correlation network modules

To evaluate for higher orders of biological organization of genes in a module associated with PIPN, we utilized two different approaches (Figure 1). First, we performed a functional enrichment analysis of gene ontology (GO) annotations^54,55 and KEGG pathways using ToppFunn and all of the genes in the brown module.⁵⁶ We assessed for significance using an FDR of 5 under the BH procedure.^47,48 Second, we evaluated connectivity among differentially expressed genes (DEG) in a given module and evaluated for functional enrichment of Reactome pathways⁵⁷ using Search Tool for the Retrieval of Interacting Genes (STRING).⁵⁸ DEGs were identified at an FDR of 10 under the BH procedure.^47,48 We assessed for significance of the DEG protein–protein interaction (PPI) network enrichment using a p value of <.05. We also assessed for functional enrichment of DEGs in the Reactome pathways using an FDR of 10 under the BH procedure.^47,48

Results

Differences in demographic, clinical, and pain characteristics

Sample characteristics were reported previously.¹³ In brief, breast cancer survivors with chronic PIPN were significantly older (p = .006) and were more likely to be unemployed (p = .022) (Supplementary Table 1). In terms of clinical characteristics, survivors with PIPN had a lower AUDIT score (p = .012), a higher BMI (p = .011), and a lower KPS score (p < .001) (Supplementary Table 2). Of note, no between-group differences were found in the total dose of paclitaxel received or in the percentage of patients who had a dose reduction or delay due to PIPN. The worst pain severity was reported as 6.3 (±2.1) and the duration of PIPN was 3.8 (±3.9) years (Supplementary Table 3).

PIA of the whole transcriptome

Of the 11,487 genes identified in the DGE analysis, 11,174 unique genes were successfully annotated with Entrez IDs and included in the PIA. Fifty-three KEGG signaling pathways were significantly perturbed between the PIPN groups at a strict FDR of 1.¹³ Of these 53 pathways, four were associated with cytoskeleton and axon morphology (Figure 1 and Table 1).

Table 1.

Differential perturbation of pathways associated with cytoskeleton and axon morphology in PIPN.

Pathway	Name	totalPert	totalPertNorm	pPert (FDR)
hsa04810	Regulation of actin cytoskeleton	18.92	7.09	0.005
hsa04360	Axon guidance	12.87	4.91	0.005
hsa04066	HIF-1 signaling pathway	6.61	6.98	0.004
hsa04510	Focal adhesion	8.83	4.22	0.017

FDR: false discovery rate; PIPN: paclitaxel-induced peripheral neuropathy; totalPert: total perturbation score; totalPertNorm: total normalized perturbation score; pPert: p value of perturbation; HIF-1: hypoxia-inducible factor 1.

Gene co-expression network analysis

Using WGCNA, gene co-expression profiling of the 5000 genes with the most variable expression clustered into three modules (i.e., brown, n = 1207; green, n = 1726; dark red, n = 2067; Figure 2). To evaluate the association between PIPN group membership and each of these co-expression modules, we fit multiple logistic regression models to predict the PIPN group membership using the module eigengenes, AUDIT score, BMI, and KPS score. Of these three modules, PIPN group membership was significantly associated only with the brown module’s eigenenes (Figure 1 and Table 2).

Figure 2.

Cluster dendrogram and module colors showing the module assignments for the gene co-expression data. The color rows underneath the cluster dendrogram show the module assignment from the unmerged Dynamic Tree Cut method and the final module set from the Merged dynamic method.

Table 2.

Multiple logistic regression analysis for the brown co-expression module and PIPN.

Predictor	Estimate	Odds ratio	Standard error	95% CI	Z	p
Eigengene	−13.241	1.77 × 10⁻⁰⁶	5.643	2.788 × 10⁻¹², 1.984 × 10⁻⁰²	−2.34	.019
KPS score	−0.278	0.751	0.100	0.591, 0.872	−2.97	.003
Age	0.087	1.091	0.046	1.001, 1.209	1.99	.058
Overall model fit:	r = 0.76, p = 2.02 × 10⁻¹⁰

CI: confidence interval; KPS: Karnofsky Performance Status; r: Pearson’s product moment correlation coefficient; PIPN: paclitaxel-induced peripheral neuropathy.

In our functional analyses of the brown module using ToppFunn, 49 GO annotations (n = 10 biological processes, n = 36 cellular component, n = 3 molecular function) and 27 KEGG signaling pathways were found that were significantly enriched for genes in this module. Two GO categories and three KEGG pathways were related to cytoskeleton and axon morphology (Table 3). To evaluate for enrichment of PPI of DEGs in the brown module, we used STRING. We identified 253 DEGs in the brown module, of which 242 were annotated for HUGO symbol and used for further functional analysis. The resulting network of 242 DEGs in the brown module had significantly more interactions than expected for a random set of proteins of similar size drawn from the genome (number of edges = 726, average node degree = 6, clustering coefficient = 0.438, PPI enrichment p value <1.0 × 10⁻¹⁶). Thirty-eight Reactome pathways were found to be significantly enriched for DEGs in the brown module. One enriched Reactome pathway, axon guidance (HSA-422475), was related to cytoskeleton and axon morphology (Table 3 and Figure 3).

Table 3.

Functionally enriched pathways associated with cytoskeleton and axon morphology from genes in the brown co-expression module in PIPN.

ID	Name	Source	FDR	N_brown	N_pathway
TopFunn – all brown module genes
GO:0005925	Focal adhesion	GO:cellular component	0.0004	13	393
GO:0044327	Dendritic spine head	GO:cellular component	0.0202	2	9
hsa04510	Focal adhesion	KEGG	0.0061	8	199
hsa04810	Regulation of actin cytoskeleton	KEGG	0.0269	7	212
hsa04540	Gap junction	KEGG	0.0120	5	88
STRING – differentially expressed brown module genes
R-HSA-422475	Axon guidance	Reactome	5.67 × 10⁻⁵	58	557

GO: gene ontology; PIPN: paclitaxel-induced peripheral neuropathy; KEGG: Kyoto Encyclopedia of Genes and Genomes; FDR: false discovery rate; N_brown: count of genes in the brown module in the pathway; N_pathway: count of genes in the pathway.

Figure 3.

STRING connectivity network demonstrating a protein–protein interaction network of predicted functional partners for differentially expressed genes in the brown module. Nodes represent all proteins produced by a single protein coding gene locus. Edges represent specific or meaningful associations. Node color: axon guidance pathway (KEGG hsa04360) genes (red), second shell of interactors (white). Node size: protein of unknown three-dimensional (3D) structure (small), protein of known or predicted 3D structure (large). Color of the edges connecting the nodes represents the types of evidence supporting the connections: predicted gene neighborhood (green), predicted gene fusions (red), known interactions from experimental evidence (pink), co-expression (black), and text-mining (green). Disconnected nodes in the network are not displayed.

Discussion

This study is the first to provide molecular evidence that suggests that a number of cytoskeleton- and axon morphology-related mechanisms identified in various pre-clinical models of neuropathic pain^25–27,59 are associated with chronic PIPN in breast cancer survivors. Of note, the regulation of actin cytoskeleton, focal adhesion, and axon guidance pathways was identified using both analytical methods.

The structural organization and dynamic remodeling of the neuronal cytoskeleton are responsible for cell migration and proliferation, as well as neuronal polarization and the establishment of a synaptic network.⁶⁰ In terms of the regulation of cytoskeleton and focal adhesion pathways, both are involved in intracellular signaling and in the regulation of cell motility.⁶¹ Focal adhesions are large, dynamic protein complexes that enable the cytoskeleton to connect to the extracellular matrix of a cell.⁶² At these specialized structures, integrin receptors cluster together to connect the extracellular matrix on the outside of the cell with the actin cytoskeleton within the cell.⁶³ These complexes transmit force or tension at adhesion sites to maintain strong attachments to the extracellular matrix, act as signaling centers for numerous intracellular pathways (e.g., processes involved in the reorganization of the actin cytoskeleton), modulate growth cones, and regulate axon regeneration of peripheral neurons.^64,65 While no studies of PIPN were found, in a mouse model of oxaliplatin-induced peripheral neuropathy,⁶⁶ the absence of the advillin-containing focal adhesion protein (i.e., a sensory neuron specific actin binding protein) was associated with significant increases in cold allodynia. In addition, in a study of gene expression changes in patients with intractable neuropathic pain following spinal cord injury,⁶⁷ the focal adhesion pathway was one of the enriched pathways identified when these patients were compared to control patients without pain.

While originally identified as instructive cues to guide embryonic axons, axon guidance proteins have numerous functions including the control of synaptic plasticity.⁶⁸ Emerging evidence suggests that axon guidance mechanisms regulate the neuronal remodeling (e.g., dying back, axonal pruning) that occurs when peripheral nerves are injured.^69,70 Consistent with our identification of an association between PIPN and perturbations and co-expression among genes in the axon guidance signaling pathway, in a recent pre-clinical model of PIPN,⁷¹ the administration of paclitaxel acted directly on sensory neurons to alter inositol 1,4,5-triphosphate activity and initiated axonal degeneration. The authors concluded that paclitaxel-induced degradation differs from developmental degradation in the initiation of the degradation cascade and suggested that axon pruning molecules may be potential targets to prevent PIPN.

The hypoxia-inducible factor 1 (HIF-1) signaling pathway, a master regulator of cellular responses to hypoxia,⁷² plays an important role in axon regeneration following peripheral nerve injury.⁷³ It has been found to mediate both processes that protect against axonal degeneration⁷⁴ and stimulate axon regeneration.⁷⁵ While no studies have reported on an association between PIPN and this pathway, findings from preclinical studies suggest that this transcription factor plays a significant role in the development of bortezomib-induced peripheral neuropathy,⁷⁶ diabetic peripheral neuropathy,⁷⁷ and sciatic nerve injury.⁷⁸

In our study, the co-expression of genes in the gap junctions signaling pathway was associated with PIPN. Gap junctions contain intracellular channels that facilitate cell-to-cell communications through direct exchange of intracellular messages.⁷⁹ The major gap junction proteins are from the connexin family (e.g., connexin 32).⁸⁰ Of note, connexin 32 is a fundamental protein in the peripheral nervous system that is associated with the x-linked form of Charcot-Marie-Tooth Disease, the second most common form of hereditary sensory and motor neuropathy.⁸¹ While no studies were found using paclitaxel, in an ex vivo mouse sciatic nerve injury model,⁸² prolonged exposure of the nerve to oxaliplatin caused a forced and persistent opening of connexin 32 channels and connexin 29 hemichannels in peripheral myelinated neurons which resulted in a disruption of axonal potassium homeostasis. This prolongation was almost completely blocked by the gap junction inhibitor octanol. The authors suggested that gap junction proteins may serve as neuroprotectants.

In terms of the enrichment of genes identified in the dendritic spine head GO annotated cellular component, dendritic spines are small, thin, specialized protrusions localized on excitatory synapses. They are primarily composed of polymerized actin or filamentous actin which undergo morphological changes in response to a stimulus.⁸³ While best characterized for their role in learning and memory,⁸⁴ emerging evidence suggests that the remodeling of dendritic spines may contribute to neuropathic pain by reorganizing nociceptive processing pathways⁸⁵ and abnormal dendritic spine structure following disease or injury may represent the “molecular memory” for maintaining chronic pain.⁸⁶ While no studies of CIPN were identified, findings from preclinical studies suggest that dendritic spine dysgenesis is involved in chronic pain associated with spinal cord injury,^87,88 burns,^89,90 and diabetes.⁹¹ In terms of PIPN, because microtubules are thought to play a role in dendritic spine plasticity,⁹² paclitaxel-induced stabilization of microtubules may adversely restrict morphological changes of dendritic spines and result in significant neurotoxicity.

Several limitations warrant consideration. While our sample size was relatively small, we have an extremely well-characterized sample of breast cancer survivors with and without PIPN. Future research with larger sample sizes may improve the resolution of the co-expression networks. Of note, no differences were found in the total cumulative dose of paclitaxel that the two groups of survivors received. Consistent with previous reports,^93,94 we evaluated for differences in RNA expression from peripheral blood rather than from DRG neurons. Therefore, we can only infer that these findings are consistent with changes in the peripheral nervous system. Finally, our findings need to be replicated before they their translational impact can be explored. Part of the translational genomics⁹⁵ agenda to improve health⁹⁶ is to integrate high throughput molecular data with rich demographic and clinical data to evaluate mechanisms that underlie clinical conditions like PIPN.⁹⁷ Given the limits of pre-clinical models of pain,^98,99 the identification of patterns of co-expression and perturbed pathways in survivors with PIPN that are related to mechanisms previously identified in pre-clinical models provides support for the translational value of this approach (e.g., findings such as these can help guide the development and selection of future pre-clinical models,^98,99 or identify molecular features as targets¹⁰⁰ for pharmacological interventions¹⁰¹ or as diagnostic biomarkers¹⁰²).

This study provides molecular evidence that a number of cytoskeleton- and axon morphology-related mechanisms identified in preclinical models of various types of neuropathic pain, including CIPN,^19,103,104 are preferentially found in cancer survivors with persistent PIPN. The perturbations and co-expression patterns in these processes suggest persistent damage and/or changes in the regulation of the cytoskeleton and axon morphology in the peripheral nervous system of these survivors. Future studies need to evaluate for differences in epigenetic changes (i.e., methylation, microRNA) between survivors with and without PIPN, which may reflect changes in regulation patterns. In addition, studies are warranted that evaluate for common and distinct cytoskeleton- and axon morphology-related mechanisms associated with other neurotoxic CTX drugs (e.g., platinum, platinum and taxane combination).

Supplemental Material

MPX878088 Supplemental Material1 - Supplemental material for Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology in breast cancer survivors with chronic paclitaxel-induced peripheral neuropathy

Supplemental material, MPX878088 Supplemental Material1 for Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology in breast cancer survivors with chronic paclitaxel-induced peripheral neuropathy by Kord M Kober, Mark Schumacher, Yvette P Conley, Kimberly Topp, Melissa Mazor, Marilynn J Hammer, Steven M Paul, Jon D Levine and Christine Miaskowski in Molecular Pain

Supplemental Material

MPX878088 Supplemental Material2 - Supplemental material for Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology in breast cancer survivors with chronic paclitaxel-induced peripheral neuropathy

Supplemental material, MPX878088 Supplemental Material2 for Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology in breast cancer survivors with chronic paclitaxel-induced peripheral neuropathy by Kord M Kober, Mark Schumacher, Yvette P Conley, Kimberly Topp, Melissa Mazor, Marilynn J Hammer, Steven M Paul, Jon D Levine and Christine Miaskowski in Molecular Pain

Supplemental Material

MPX878088 Supplemental Material3 - Supplemental material for Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology in breast cancer survivors with chronic paclitaxel-induced peripheral neuropathy

Supplemental material, MPX878088 Supplemental Material3 for Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology in breast cancer survivors with chronic paclitaxel-induced peripheral neuropathy by Kord M Kober, Mark Schumacher, Yvette P Conley, Kimberly Topp, Melissa Mazor, Marilynn J Hammer, Steven M Paul, Jon D Levine and Christine Miaskowski in Molecular Pain

Supplemental Material

MPX878088 Supplemental Material4 - Supplemental material for Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology in breast cancer survivors with chronic paclitaxel-induced peripheral neuropathy

Supplemental material, MPX878088 Supplemental Material4 for Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology in breast cancer survivors with chronic paclitaxel-induced peripheral neuropathy by Kord M Kober, Mark Schumacher, Yvette P Conley, Kimberly Topp, Melissa Mazor, Marilynn J Hammer, Steven M Paul, Jon D Levine and Christine Miaskowski in Molecular Pain

Footnotes

Acknowledgments

Recruitment was facilitated by Dr. Susan Love Research Foundation’s Army of Women® Program. Anatol Sucher managed the storage and processing of the biospecimens.

Declaration of Conflicting Interests

The author(s) declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the National Cancer Institute (NCI, CA151692) and the American Cancer Society (ACS, IRG-97–150-13). Dr Miaskowski is supported by grants from the ACS and NCI (CA168960). This project was also supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through UCSF-CTSI Grant Number UL1 TR000004. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Supplemental Material

Supplemental material for this article is available online.

References

Banach

Juranek

Zygulska

AL.

Chemotherapy-induced neuropathies-a growing problem for patients and health care providers. Brain Behav 2017; 7: e00558.

Cavaletti

Alberti

Marmiroli

Chemotherapy-induced peripheral neurotoxicity in cancer survivors: an underdiagnosed clinical entity?

Am Soc Clin Oncol Educ Book 2015; 35: e553–e560.

Denlinger

Ligibel

Are

Baker

Demark-Wahnefried

Dizon

Friedman

Goldman

Jones

King

Kvale

Langbaum

Leonardi-Warren

McCabe

Melisko

Montoya

Mooney

Morgan

Moslehi

O’Connor

Overholser

Paskett

Peppercorn

Raza

Rodriguez

Syrjala

Urba

Wakabayashi

Zee

McMillian

Freedman-Cass

DA.

Survivorship: screening for cancer and treatment effects, version 2.2014. J Natl Compr Canc Netw 2014; 12: 1526–1531.

Kvale

Urba

SG.

NCCN guidelines for survivorship expanded to address two common conditions. J Natl Compr Canc Netw 2014; 12: 825–827.

Mols

Beijers

Vreugdenhil

van de Poll-Franse

Chemotherapy-induced peripheral neuropathy and its association with quality of life: a systematic review. Support Care Cancer 2014; 22: 2261–2269.

Tofthagen

Donovan

Morgan

Shibata

Yeh

Oxaliplatin-induced peripheral neuropathy's effects on health-related quality of life of colorectal cancer survivors. Support Care Cancer 2013; 21: 3307–3313.

Tofthagen

Visovsky

Berry

DL.

Strength and balance training for adults with peripheral neuropathy and high risk of fall: current evidence and implications for future research. Oncol Nurs Forum 2012; 39: E416–E424.

Ezendam

Pijlman

Bhugwandass

Pruijt

Mols

Vos

Pijnenborg

van de Poll-Franse

LV.

Chemotherapy-induced peripheral neuropathy and its impact on health-related quality of life among ovarian cancer survivors: results from the population-based PROFILES registry. Gynecol Oncol 2014; 135: 510–517.

Gewandter

Fan

Magnuson

Mustian

Peppone

Heckler

Hopkins

Tejani

Morrow

Mohile

SG.

Falls and functional impairments in cancer survivors with chemotherapy-induced peripheral neuropathy (CIPN): a University of Rochester CCOP study. Support Care Cancer 2013; 21: 2059–2066.

10.

Miaskowski

Mastick

Paul

Topp

Smoot

Abrams

Chen

Kober

Conley

Chesney

Bolla

Mausisa

Mazor

Wong

Schumacher

Levine

JD.

Chemotherapy-induced neuropathy in cancer survivors. J Pain Symptom Manage 2017; 54: 204–218.e2.

11.

Arbuck

Dorr

Friedman

MA.

Paclitaxel (Taxol) in breast cancer. Hematol Oncol Clin North Am 1994; 8: 121–140.

12.

De Iuliis

Taglieri

Salerno

Lanza

Scarpa

Taxane induced neuropathy in patients affected by breast cancer: literature review. Crit Rev Oncol Hematol 2015; 96: 34–45.

13.

Kober

Olshen

Conley

Schumacher

Topp

Smoot

Mazor

Chesney

Hammer

Paul

Levine

Miaskowski

Expression of mitochondrial dysfunction-related genes and pathways in paclitaxel-induced peripheral neuropathy in breast cancer survivors. Mol Pain 2018; 14: 174480691881646.

14.

Duggett

Griffiths

Flatters

Paclitaxel-induced painful neuropathy is associated with changes in mitochondrial bioenergetics, glycolysis, and an energy deficit in dorsal root ganglia neurons. Pain 2017; 158: 1499–1508.

15.

Makker

Duffy

Lees

Perera

Tonkin

Butovsky

Park

Goldstein

Moalem-Taylor

Characterisation of immune and neuroinflammatory changes associated with chemotherapy-induced peripheral neuropathy. PLoS One 2017; 12: e0170814.

16.

Argyriou

Koltzenburg

Polychronopoulos

Papapetropoulos

Kalofonos

HP.

Peripheral nerve damage associated with administration of taxanes in patients with cancer. Critical Reviews in Oncology/Hematology 2008; 66: 218–228.

17.

Cook

Wozniak

Proctor

Bromberg

Slusher

Littlefield

Jordan

Wilson

Feinstein

SC.

Differential morphological and biochemical recovery from chemotherapy-induced peripheral neuropathy following paclitaxel, ixabepilone, or eribulin treatment in mouse sciatic nerves. Neurotox Res 2018; 34: 677–692.

18.

Weaver

BA.

How Taxol/paclitaxel kills cancer cells. Mol Biol Cell 2014; 25: 2677–2681.

19.

Starobova

Vetter

Pathophysiology of chemotherapy-induced peripheral neuropathy. Front Mol Neurosci 2017; 10: 174.

20.

Gornstein

Schwarz

TL.

Neurotoxic mechanisms of paclitaxel are local to the distal axon and independent of transport defects. Exp Neurol 2017; 288: 153–166.

21.

Sahenk

Barohn

New

Mendell

JR.

Taxol neuropathy. Electrodiagnostic and sural nerve biopsy findings. Arch Neurol 1994; 51: 726–729.

22.

Gornstein

Schwarz

TL.

The paradox of paclitaxel neurotoxicity: mechanisms and unanswered questions. Neuropharmacology 2014; 76 Pt A: 175–183.

23.

Authier

Gillet

Fialip

Eschalier

Coudore

Description of a short-term Taxol-induced nociceptive neuropathy in rats. Brain Res 2000; 887: 239–249.

24.

Fukuda

Segal

RA.

A mechanistic understanding of axon degeneration in chemotherapy-induced peripheral neuropathy. Front Neurosci 2017; 11: 481.

25.

LaPointe

Morfini

Brady

Feinstein

Wilson

Jordan

MA.

Effects of eribulin, vincristine, paclitaxel and ixabepilone on fast axonal transport and kinesin-1 driven microtubule gliding: implications for chemotherapy-induced peripheral neuropathy. Neurotoxicology 2013; 37: 231–239.

26.

Shemesh

Spira

ME.

Paclitaxel induces axonal microtubules polar reconfiguration and impaired organelle transport: implications for the pathogenesis of paclitaxel-induced polyneuropathy. Acta Neuropathol 2010; 119: 235–248.

27.

Wozniak

Vornov

Liu

Carozzi

Rodriguez-Menendez

Ballarini

Alberti

Pozzi

Semperboni

Cook

Littlefield

Nomoto

Condon

Eckley

DesJardins

Wilson

Jordan

Feinstein

Cavaletti

Polydefkis

Slusher

BS.

Peripheral neuropathy induced by microtubule-targeted chemotherapies: insights into acute injury and long-term recovery. Cancer Res 2018; 78: 817–829.

28.

Tasnim

Rammelkamp

Slusher

Wozniak

Slusher

Farah

MH.

Paclitaxel causes degeneration of both central and peripheral axon branches of dorsal root ganglia in mice. BMC Neurosci 2016; 17: 47.

29.

DiAntonio

Axon degeneration: mechanistic insights lead to therapeutic opportunities for the prevention and treatment of peripheral neuropathy. Pain 2019; 160: S17–S22.

30.

Clark

EJ.

Teamwork – the cancer patient's guide to talking with your doctor. 5th ed. Silver Springs, MD: National Coalition for Cancer Survivorship, 2011.

31.

Bohn

Babor

Kranzler

HR.

The Alcohol Use Disorders Identification Test (AUDIT): validation of a screening instrument for use in medical settings. J Stud Alcohol 1995; 56: 423–432.

32.

Karnofsky

Performance scale. New York, NY: Plenum Press, 1977.

33.

Karnofsky

Abelmann

Craver

Burchenal

JH.

The use of nitrogen mustards in the palliative treatment of carcinoma. Cancer 1948; 1: 634–656.

34.

Schnadig

Fromme

Loprinzi

Sloan

Mori

Beer

TM.

Patient-physician disagreement regarding performance status is associated with worse survivorship in patients with advanced cancer. Cancer 2008; 113: 2205–2214.

35.

Brunner

Bachmann

Weber

Kessels

Perez

Marinus

Kissling

Complex regional pain syndrome 1–the Swiss cohort study. BMC Musculoskelet Disord 2008; 9: 92.

36.

Cieza

Geyh

Chatterji

Kostanjsek

Ustun

Stucki

Identification of candidate categories of the International Classification of Functioning Disability and Health (ICF) for a Generic ICF Core Set based on regression modelling. BMC Med Res Methodol 2006; 6: 36.

37.

Daut

Cleeland

Flanery

RC.

Development of the Wisconsin Brief Pain Questionnaire to assess pain in cancer and other diseases. Pain 1983; 17: 197–210.

38.

Jensen

Gammaitoni

Olaleye

Oleka

Nalamachu

Galer

BS.

The pain quality assessment scale: assessment of pain quality in carpal tunnel syndrome. J Pain 2006; 7: 823–832.

39.

Hart

Therneau

Zhang

Poland

Kocher

JP.

Calculating sample size estimates for RNA sequencing data. J Comput Biol 2013; 20: 970–978.

40.

Khatri

Sirota

Butte

AJ.

Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 2012; 8: e1002375.

41.

Garcia-Campos

Espinal-Enriquez

Hernandez-Lemus

Pathway analysis: state of the art. Front Physiol 2015; 6: 383.

42.

Mitrea

Taghavi

Bokanizad

Hanoudi

Tagett

Donato

Voichiţa

Drăghici

Methods and approaches in the topology-based analysis of biological pathways. Front Physiol 2013; 4: 278.

43.

Draghici

Khatri

Tarca

Amin

Done

Voichita

Georgescu

Romero

A systems biology approach for pathway level analysis. Genome Res 2007; 17: 1537–1545.

44.

Nguyen

Mitrea

Draghici

Network-based approaches for pathway level analysis. Curr Protoc Bioinformatics 2018; 61: 21–28.

45.

Aoki-Kinoshita

Kanehisa

Gene annotation and pathway mapping in KEGG. Methods Mol Biol 2007; 396: 71–91. [18025687]

46.

Gray

Daugherty

Gordon

Seal

Wright

Bruford

EA.

Genenames.org: the HGNC resources in 2013. Nucleic Acids Res 2012; 41: D545–D552.

47.

Hochberg

Benjamini

More powerful procedures for multiple significance testing. Statist Med 1990; 9: 811–818.

48.

Benjamini

Hochberg

Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B (Methodol) 1995; 57: 289–300.

49.

van Dam

Vosa

van der Graaf

Franke

de Magalhaes

JP.

Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform 2018; 19: 575–592.

50.

Langfelder

Horvath

Fast R functions for robust correlations and hierarchical clustering. J Stat Softw 2012; 46: pii: i11. PMID 23050260.

51.

Langfelder

Horvath

WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008; 9: 559.

52.

Ballouz

Verleyen

Gillis

Guidance for RNA-seq co-expression network construction and analysis: safety in numbers. Bioinformatics 2015; 31: 2123–2130.

53.

Allen

Xie

Chen

Girard

Xiao

Comparing statistical methods for constructing large scale gene networks. PLoS One 2012; 7: e29348.

54.

Ashburner

Ball

Blake

Botstein

Butler

Cherry

Davis

Dolinski

Dwight

Eppig

Harris

Hill

Issel-Tarver

Kasarskis

Lewis

Matese

Richardson

Ringwald

Rubin

Sherlock

Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000; 25: 25–29.

55.

The Gene Ontology Consortium. The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res 2019; 47: D330–D338.

56.

Chen

Bardes

Aronow

Jegga

AG.

ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 2009; 37: W305–W311.

57.

Joshi-Tope

Gillespie

Vastrik

D'Eustachio

Schmidt

de Bono

Jassal

Gopinath

Matthews

Lewis

Birney

Stein

Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 2004; 33: D428–D432.

58.

Szklarczyk

Franceschini

Wyder

Forslund

Heller

Huerta-Cepas

Simonovic

Roth

Santos

Tsafou

Kuhn

Bork

Jensen

von Mering

STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 2015; 43: D447–D452.

59.

Benbow

Wozniak

Kulesh

Savage

Slusher

Littlefield

Jordan

Wilson

Feinstein

SC.

Microtubule-targeting agents eribulin and paclitaxel differentially affect neuronal cell bodies in chemotherapy-induced peripheral neuropathy. Neurotox Res 2017; 32: 151–162.

60.

Malacrida

Meregalli

Rodriguez-Menendez

Nicolini

Chemotherapy-induced peripheral neuropathy and changes in cytoskeleton. Int J Mol Sci 2019; 20: pii: E2287. PMID 31075828.

61.

Welf

Haugh

JM.

Signaling pathways that control cell migration: models and analysis. Wires Syst Biol Med 2011; 3: 231–240.

62.

Geiger

Bershadsky

Pankov

Yamada

KM.

Transmembrane crosstalk between the extracellular matrix–cytoskeleton crosstalk. Nat Rev Mol Cell Biol 2001; 2: 793–805.

63.

Sastry

Burridge

Focal adhesions: a nexus for intracellular signaling and cytoskeletal dynamics. Exp Cell Res 2000; 261: 25–36.

64.

Focal adhesion: a focal point in current cell biology and molecular medicine. Cell Adh Migr 2007; 1: 13–18.

65.

Eva

Fawcett

Integrin signalling and traffic during axon growth and regeneration. Curr Opin Neurobiol 2014; 27: 179–185.

66.

Chuang

Lee

Sun

Chen

CC.

Involvement of advillin in somatosensory neuron subtype-specific axon regeneration and neuropathic pain. Proc Natl Acad Sci U S A 2018; 115: E8557–E8566.

67.

Fan

Cheng

Gene expression profiles reveal key pathways and genes associated with neuropathic pain in patients with spinal cord injury. Mol Med Rep 2017; 15: 2120–2128.

68.

Van Battum

Brignani

Pasterkamp

RJ.

Axon guidance proteins in neurological disorders. Lancet Neurol 2015; 14: 532–546.

69.

Yaron

Schuldiner

Common and divergent mechanisms in developmental neuronal remodeling and dying back neurodegeneration. Curr Biol 2016; 26: R628–R639.

70.

Vanderhaeghen

Cheng

HJ.

Guidance molecules in axon pruning and cell death. Cold Spring Harb Perspect Biol 2010; 2: a001859.

71.

Pease-Raissi

Pazyra-Murphy

Wachter

Fukuda

Fenstermacher

Barclay

Bird

Walensky

Segal

RA.

Paclitaxel reduces axonal Bclw to initiate IP3R1-dependent axon degeneration. Neuron 2017; 96: 373–386.e6.

72.

Zepeda

Pessoa

Jr Castillo

Figueroa

Pulgar

Farias

JG.

Cellular and molecular mechanisms in the hypoxic tissue: role of HIF-1 and ROS. Cell Biochem Funct 2013; 31: 451–459.

73.

Venkatesh

Blackmore

MG.

Selecting optimal combinations of transcription factors to promote axon regeneration: why mechanisms matter. Neurosci Lett 2017; 652: 64–73.

74.

Keswani

Bosch-Marce

Reed

Fischer

Semenza

Hoke

Nitric oxide prevents axonal degeneration by inducing HIF-1-dependent expression of erythropoietin. Proc Natl Acad Sci U S A 2011; 108: 4986–4990.

75.

Cho

Shin

Ewan

Pita-Thomas

Cavalli

Activating injury-responsive genes with hypoxia enhances axon regeneration through neuronal hif-1alpha. Neuron 2015; 88: 720–734.

76.

Ludman

Melemedjian

OK.

Bortezomib and metformin opposingly regulate the expression of hypoxia-inducible factor alpha and the consequent development of chemotherapy-induced painful peripheral neuropathy. Mol Pain 2019; 15: 1744806919850043.

77.

Rojas

Tegeder

Kuner

Agarwal

Hypoxia-inducible factor 1alpha protects peripheral sensory neurons from diabetic peripheral neuropathy by suppressing accumulation of reactive oxygen species. J Mol Med 2018; 96: 1395–1405.

78.

Kanngiesser

Mair

Lim

Zschiebsch

Blees

Haussler

Brune

Ferreiros

Kress

Tegeder

Hypoxia-inducible factor 1 regulates heat and cold pain sensitivity and persistence. Antioxid Redox Signal 2014; 20: 2555–2571.

79.

Eugenin

Basilio

Saez

Orellana

Raine

Bukauskas

Bennett

Berman

JW.

The role of gap junction channels during physiologic and pathologic conditions of the human central nervous system. J Neuroimmune Pharmacol 2012; 7: 499–518.

80.

Evans

Martin

PE.

Gap junctions: structure and function (Review). Mol Membr Biol 2002; 19: 121–136.

81.

Bortolozzi

What's the function of connexin 32 in the peripheral nervous system?

Front Mol Neurosci 2018; 11: 227.

82.

Kagiava

Theophilidis

Sargiannidou

Kyriacou

Kleopa

KA.

Oxaliplatin-induced neurotoxicity is mediated through gap junction channels and hemichannels and can be prevented by octanol. Neuropharmacology 2015; 97: 289–305.

83.

Sala

Segal

Dendritic spines: the locus of structural and functional plasticity. Physiol Rev 2014; 94: 141–188.

84.

Stein

Zito

Dendritic spine elimination: molecular mechanisms and implications.

Neuroscientist 2019; 25: 27–47.

85.

Tan

Waxman

SG.

Dendritic spine dysgenesis in neuropathic pain. Neurosci Lett 2015; 601: 54–60.

86.

Tan

AM.

Dendritic spine dysgenesis in neuropathic pain. Prog Mol Biol Transl Sci 2015; 131: 385–408.

87.

Zhao

Hill

Liu

Chen

Bangalore

Waxman

Tan

AM.

Dendritic spine remodeling following early and late Rac1 inhibition after spinal cord injury: evidence for a pain biomarker. J Neurophysiol 2016; 115: 2893–2910.

88.

Cao

Pappalardo

Waxman

Tan

AM.

Dendritic spine dysgenesis in superficial dorsal horn sensory neurons after spinal cord injury. Mol Pain 2017; 13: 174480691668801.

89.

Guo

Benson

Hill

Henry

Effraim

Waxman

Dib-Hajj

Tan

AM.

Therapeutic potential of Pak1 inhibition for pain associated with cutaneous burn injury. Mol Pain 2018; 14: 174480691878864.

90.

Tan

Samad

Liu

Bandaru

Zhao

Waxman

SG.

Burn injury-induced mechanical allodynia is maintained by Rac1-regulated dendritic spine dysgenesis. Exp Neurol 2013; 248: 509–519.

91.

Tan

Samad

Fischer

Zhao

Persson

Waxman

SG.

Maladaptive dendritic spine remodeling contributes to diabetic neuropathic pain. J Neurosci 2012; 32: 6795–6807.

92.

Dent

Merriam

The dynamic cytoskeleton: backbone of dendritic spine plasticity. Curr Opin Neurobiol 2011; 21: 175–181.

93.

Langjahr

Schubert

Sommer

Uceyler

Increased pro-inflammatory cytokine gene expression in peripheral blood mononuclear cells of patients with polyneuropathies. J Neurol 2018; 265: 618–627.

94.

Uceyler

Rogausch

Toyka

Sommer

Differential expression of cytokines in painful and painless neuropathies. Neurology 2007; 69: 42–49.

95.

Kussmann

Kaput

Translational genomics. Appl Transl Genom 2014; 3: 43–47.

96.

Khoury

Gwinn

Bowen

Dotson

WD.

Beyond base pairs to bedside: a population perspective on how genomics can improve health. Am J Public Health 2012; 102: 34–37.

97.

Lemberger

Systems biology in human health and disease. Mol Syst Biol 2007; 3: 136.

98.

Burma

Leduc-Pessah

Fan

Trang

Animal models of chronic pain: advances and challenges for clinical translation. J Neurosci Res 2017; 95: 1242–1256.

99.

Yezierski

Hansson

Inflammatory and neuropathic pain from bench to bedside: what went wrong?

J Pain 2018; 19: 571–588.

100.

Chen

Paik

Sirota

Wei

Chua

Butte

AJ.

Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets. Nat Commun 2017; 8: 16022.

101.

Chen

Butte

AJ.

Leveraging big data to transform target selection and drug discovery. Clin Pharmacol Ther 2016; 99: 285–297.

102.

Niculescu

Le-Niculescu

Levey

Roseberry

Soe

Rogers

Khan

Jones

Judd

McCormick

Wessel

Williams

Kurian

White

FA.

Towards precision medicine for pain: diagnostic biomarkers and repurposed drugs. Mol Psychiatry 2019; 24: 501–522.

103.

Staff

Grisold

Windebank

AJ.

Chemotherapy-induced peripheral neuropathy: a current review. Ann Neurol 2017; 81: 772–781.

104.

Kavelaars

Dougherty

Heijnen

CJ.

Beyond symptomatic relief for chemotherapy-induced peripheral neuropathy: targeting the source. Cancer 2018; 124: 2289.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.11 MB

0.12 MB

0.11 MB