Vincristine-induced peripheral neuropathy (VIPN) is a debilitating side-effect of vincristine. It remains a challenge to predict which patients will suffer from VIPN. Pharmacogenomics may explain an individuals’ susceptibility to side-effects.
1. Introduction
Vincristine is an important chemotherapeutic agent that is commonly used in treatment for pediatric cancers. It is approved by the United States Food and Drug Administration (FDA) for the treatment of acute lymphoblastic leukemia (ALL), Hodgkin and non-Hodgkin lymphoma, neuroblastoma, rhabdomyosarcoma, low-grade glioma and nephroblastoma. Furthermore, off-label uses include the treatment of Ewing sarcoma and medulloblastoma [
1,
2]. The main side-effect of vincristine is vincristine-induced peripheral neuropathy (VIPN), which often presents as a symmetric sensory-motoric neuropathy progressing distally to proximally [
1,
2]. Presenting signs include foot drop, loss of deep tendon reflexes, impaired balance, pain or tingling [
1,
2]. In addition, patients can suffer from autonomic symptoms such as constipation or orthostatic hypotension. The reported prevalence of VIPN varies, depending on assessment method and study population, but it is estimated that the majority of patients receiving vincristine will experience some form of VIPN during treatment [
1,
2,
3,
4]. Up to 30% of patients may suffer from severe VIPN, requiring dose reduction or cessation of treatment [
3,
5]. Suffering from VIPN is associated with a lower health-related quality of life, both by self- and proxy assessment and consistently when using different assessment tools for VIPN [
6]. This effect of VIPN on health-related quality of life seems to persevere after treatment, as was shown in a recent study in ALL survivors in which over 16% suffered from long-term VIPN and experienced impact on both physical health and social functioning [
7].
It is recognized that different populations might have an altered risk for VIPN [
3]. Older age has been associated with an increased risk of VIPN, although results have been inconsistent [
8,
9,
10,
11,
12]. In addition, white children appear to have a higher risk of VIPN than black children [
3,
9,
12,
13,
14,
15], which is corroborated by a recent study in Kenyan pediatric cancer patients in which only one out of 78 black patients developed severe VIPN and less than 5% developed clinically relevant VIPN, despite the use of sensitive assessment methods [
16]. Interestingly, these children are being treated at a higher vincristine dose than what is common in Western countries (2.0 mg/m
2 as opposed to 1.5 mg/m
2) [
1,
16]. Studies assessing the relationship between VIPN and vincristine pharmacokinetics (PK) have shown inconsistent results. Some studies show a correlation between VIPN and PK parameters such as area under the curve (AUC) [
17], an estimate of vincristine exposure, and intercompartmental clearance [
18], whereas others do not confirm these findings [
19,
20,
21,
22]. Therefore, potential risk factors for VIPN could be genetic variations in genes involved in vincristine PK, such as variations in the cytochrome (CYP) 450 family of enzymes. Vincristine is predominantly metabolized by CYP3A4 and CYP3A5, of which the latter has a higher intrinsic clearance [
23]. Genetic variants in both enzymes result in different metabolic activity [
23,
24]. Racial populations have different distributions of wild-type and variant CYP3A4/5 alleles [
25,
26,
27]. Combined with the observation that black patients develop less VIPN, it has led to the hypothesis that faster clearance of vincristine in black children results in a lower risk of VIPN in comparison to white patients [
14]. Indeed, several studies have described the effect of variations in CYP3A4 and CYP3A5 on the development of VIPN [
8,
13,
14,
16,
20,
28,
29,
30,
31,
32]. Differences in VIPN prevalence across populations may thus stem from variations in genetic background, which can be studied via the rapidly expanding field of pharmacogenomics.
Pharmacogenomics aims to assess the influence of genomics on an individuals’ treatment response and susceptibility to side-effects, such as VIPN [
33,
34]. Often, the effect of single nucleotide polymorphisms (SNPs) is assessed [
35,
36]. The frequency distribution of major and minor alleles varies across racial groups and study populations, which has been well characterized in large projects such as the 1000 Genomes Project and the genome Aggregation Database (gnomAD) [
37,
38]. Pharmacogenomics aims to find those SNPs or genetic variations that are biologically relevant [
35,
36]. Two main study designs have been used to assess this: candidate gene studies or population-based genome- or exome-wide association studies (GWAS or EWAS respectively) [
39,
40]. Candidate gene studies determine, a priori, a set of genes, based on available literature or mechanism of action, whose influence on a certain outcome is to be assessed [
39]. Population-based GWAS or EWAS, on the other hand, assess the whole exome or genome (by whole exome sequencing (WES) or whole genome sequencing (WGS)) for genetic variation in relation to a certain outcome measure [
39]. These studies may therefore result in previously unknown genotype—phenotype associations.
Pharmacogenomics can serve as a guidance tool for precision therapy in which a priori a patients’ genetic susceptibility for side-effects or therapeutic efficacy is determined. Although this has been implemented in clinical practice for some drugs, such as thiopurine methyltransferase (TPMT), this is currently not possible for vincristine [
41,
42]. Especially since there is a lack of understanding of what causes variability in VIPN across patients, pharmacogenomics can provide valuable insight into the pathogenesis of VIPN. If genes affecting vincristine PK are implicated, this may emphasize the potential of therapeutic drug monitoring. Moreover, since it is unlikely that VIPN is caused by differences in PK alone, variation in cellular sensitivity to vincristine and in neuronal pathways could be contributing factors. The implication of genes related to neuronal pathways, the cytoskeleton or cellular integrity with VIPN might then help guiding clinicians in deciding a priori if patients have a high chance of being developing (clinically relevant) VIPN and thus if patients should be monitored more closely than others, or even given an adapted vincristine dosage. In contrast, other patients might be identified who tolerate higher levels of vincristine and might thus not benefit from the generally applied dose capping at 1.5 mg/m
2. Ultimately, the goal would be to develop a protocol for vincristine in which patients are stratified based on the presence of genetic polymorphisms and given a dosage that limits the risk of severe VIPN while maintaining the highest possible therapeutic efficacy.
2. Pharmacogenomics of Vincristine-Induced Peripheral Neuropathy in Children with Cancer
2.1. Association between Pharmacogenomic Parameters and VIPN
Table 2 and Table 3 show an overview of all SNPs found to have a statistically significant and non-significant association with VIPN, respectively. Figure 2 shows a schematic overview of the function of genes associated with VIPN. Sixteen SNPs in three ATP-binding cassette transporter genes (ABCB1, ABCC1, ABCC2) and one SNP in an miRNA targeting ABCC1/RalA binding protein 1 (RALPB1) were described to be significantly associated with VIPN (Table 2). Ten SNPs were associated with a protective effect against VIPN, whereas seven SNPs were associated with an increased risk of VIPN. Of note, the strongest protective associations with high precision were reported for SNPs rs3740066 and rs12826 in ABCC2 (OR 0.23, 95% CI 0.10−0.53, and 0.24, 95% CI 0.10−0.54 respectively). The strongest risk association with acceptable precision was reported for rs3784867 in ABCC1 (OR 4.91, 95% CI 1.99−12.10).
Figure 2. Schematic overview of the function of genes associated with VIPN. Red: described SNPs in this gene are associated with a higher risk of VIPN; green: described SNPs in this gene are associated with a lower risk of VIPN, brown: described SNPs in this gene are associated with both a higher and lower risk of VIPN (different per SNP). Created with BioRender.com.
In terms of metabolism-associated genes, a deletion in glutathione S-transferase mu 1 (GSTM1) and an SNP in vitamin D receptor (VDR) were implicated with a heightened and a decreased risk to VIPN, respectively (
Table 2) [
13]. Furthermore, six SNPs in cytoskeleton-associated genes or in miRNAs targeting those were associated with VIPN (microtubule associated protein tau (MAPT), targeting tubulin beta 2B class IIB (TUBB2), actin gamma 1 (ACTG1), capping actin protein gelsolin like (CAPG) and spectrin repeat containing nuclear envelope protein 2 (SYNE2)) (
Table 2). Of those, two SNPs were related to microtubules (MAPT and TUBB2) and associated with a protective effect and an increased risk of VIPN, respectively (
Table 2) [
49]. The four other SNPs were located in cytoskeleton-associated genes (ACTG1, CAPG, and SYNE2) and associated with a CTCAE grade 3−4 VIPN (
Table 2) [
8,
52]. The latter passed the stringent significance threshold for multiple comparisons, but the results could not be confirmed in a replication cohort [
52]. The strongest protective association was noted for SNP rs3770102 in CAPG with an effect size of 0.1, although the uncertainty was high (95% CI 0.01−0.8). One SNP in a gene associated with hereditary neuropathies (solute carrier family 5 member 7 (SLC5A7)) resulted in an increased susceptibility to VIPN (
Table 2) [
51]. The reported effect size was large, but the size of the confidence interval indicated relatively high uncertainty (OR 8.60, 95% CI 1.68−44.15) Except for the SNP in SYNE2, all aforementioned SNPS were solely assessed in a discovery cohort and no replication studies were performed for any of those associations [
52].
able 2. Single-nucleotide polymorphisms that were significantly associated with vincristine-induced peripheral neuropathy in the pediatric oncology population.
Gene |
SNP |
Allele, Major/Minor |
Author and Year of Publication |
MAF (%) |
Number of Patients (n) |
Method Effect Size |
Effect Size with 95% CI (If Applicable) |
Effect |
Cases of VIPN * |
Controls * |
Transport |
ABCB1 |
rs4728709 |
C/T |
Ceppi et al., 2014 [8] |
TT/TC: 17.1 CC: 82.9 |
63 (grade 1–2) |
214 (grade 0) |
Dominant OR |
0.3 (0.1–0.9) |
Protective 1 |
|
rs10244266 |
T/G |
Lopez-Lopez et al., 2016 [11] |
14.3 |
46 (WHO grade 1–4) |
103 (WHO grade 0) |
Dominant OR |
2.60 (1.16–5.83) |
Risk 2 |
|
rs10268314 |
T/C |
Lopez-Lopez et al., 2016 [11] |
14.3 |
27 (WHO grade 1–2) |
103 (WHO grade 0) |
Dominant OR |
3.19 (1.23–8.25) |
Risk 2 |
|
rs10274587 |
G/A |
Lopez-Lopez et al., 2016 [11] |
14.6 |
27 (WHO grade 1–2) |
103 (WHO grade 0) |
Dominant OR |
3.48 (1.36–8.86) |
Risk 2 |
ABCC1 |
rs1967120 |
T/C |
Lopez-Lopez et al., 2016 [11] |
27.3 |
18 (WHO grade 3–4) |
103 (WHO grade 0) |
Dominant OR |
0.29 (0.09–0.99) |
Protective 2 |
|
rs3743527 |
C/T |
Lopez-Lopez et al., 2016 [11] |
19.7 |
46 (WHO grade 1–4) |
103 (WHO grade 0) |
Dominant OR |
0.32 (0.13–0.79) |
Protective 2 |
|
rs3784867 |
C/T |
Wright et al., 2019 [51] |
32.0 |
170 (grade 2–4) |
57 (grade 0) |
Additive OR |
4.91 (1.99–12.10) |
Risk 3 |
|
rs11642957 |
T/C |
Lopez-Lopez et al., 2016 [11] |
48.1 |
46 (WHO grade 1–4) |
103 (WHO grade 0) |
Dominant OR |
0.43 (0.19–0.98) |
Protective 2 |
|
rs11864374 |
G/A |
Lopez-Lopez et al., 2016 [11] |
24.4 |
46 (WHO grade 1–4) |
103 (WHO grade 0) |
Dominant OR |
0.35 (0.15–0.79) |
Protective 2 |
|
rs12923345 |
T/C |
Lopez-Lopez et al., 2016 [11] |
15.4 |
46 (WHO grade 1–4) |
103 (WHO grade 0) |
Dominant OR |
2.39 (1.08–5.25) |
Risk 2 |
|
rs17501331 |
A/G |
Lopez-Lopez et al., 2016 [11] |
13.2 |
46 (WHO grade 1–4) |
103 (WHO grade 0) |
Dominant OR |
2.50 (1.10–5.68) |
Risk 2 |
ABCC2 |
rs12826 |
G/A |
Lopez-Lopez et al., 2016 [11] |
42.6 |
46 (WHO grade 1–4) |
103 (WHO grade 0) |
Dominant OR |
0.24 (0.10–0.54) |
Protective |
|
rs3740066 |
G/A |
Lopez-Lopez et al., 2016 [11] |
36.2 |
46 (WHO grade 1–4) |
103 (WHO grade 0) |
Dominant OR |
0.23 (0.10–0.53) |
Protective |
|
rs2073337 |
A/G |
Lopez-Lopez et al., 2016 [11] |
45.8 |
18 (WHO grade 3–4) |
103 (WHO grade 0) |
Dominant OR |
0.35 (0.10–1.24) |
Protective |
|
rs4148396 |
C/T |
Lopez-Lopez et al., 2016 [11] |
42.1 |
46 (WHO grade 1–4) |
103 (WHO grade 0) |
Dominant OR |
0.36 (0.16–0.81) |
Protective |
|
rs11190298 |
G/A |
Lopez-Lopez et al., 2016 [11] |
45.0 |
46 (WHO grade 1–4) |
103 (WHO grade 0) |
Recessive OR |
2.44 (1.01–5.86) |
Risk |
ABCC1/RALPB1: miR–3117 |
rs12402181 |
G/A |
Gutierrez–Camino et al., 2017 [48] |
14.8 |
19 (WHO grade 3–4) |
128 (WHO grade 0) |
Dominant OR |
0.13 (0.02–0.99) |
Protective 2 |
Vincristine metabolism |
CYP3A4 |
rs2740574 |
A/G(*1B) |
Aplenc et al., 2003 [28] |
8.6 |
28 (CCG grade 3–4) |
505 (CCG grade 0–2) |
Allelic OR |
0 (0–0.75) |
Protective 2 |
|
|
|
Guilhaumou et al., 2011 [20] |
6.3 |
Nr of neurotoxicity events |
Chi–square |
p = 1.00 |
Not significant |
|
|
|
Kishi et al., 2007 [13] |
AA: 79.6 AG + GG: 20.4 |
30 (grade 2–4) |
210 (grade 0–1) |
Dominant OR |
1.37 (0.57–3.29) |
Not significant |
GSTM1 |
Deletion |
Non–null/null |
Kishi et al., 2007 [13] |
Non–null: 57.5 Null: 42.5 |
30 (grade 2–4) |
210 (grade 0–1) |
OR |
0.46 (0.22–0.94) |
Protective2 |
VDR |
rs1544410 |
G/A |
Kishi et al., 2007 [13] |
GG: 45.8 AA and AG: 54.2 |
30 (grade 2–4) |
210 (grade 0–1) |
Recessive OR |
2.22 (1.06–4.67) |
Risk |
Cytoskeleton–associated |
ACTG1 |
rs1135989 |
G/A |
Ceppi et al., 2014 [8] |
36.5 |
38 (grade 3–4) |
214 (grade 0) |
Dominant OR |
2.8 (1.3–6.3) |
Risk 1 |
CAPG |
rs2229668 |
G/A |
Ceppi et la. 2014 [8] |
12.6 |
39 (grade 3–4) |
214 (grade 0) |
Dominant OR |
2.1 (1.1–3.7) |
Risk 1 |
|
rs3770102 |
C/A |
Ceppi et al., 2014 [8] |
41.4 |
39 (grade 3–4) |
214 (grade 0) |
Dominant OR |
0.1 (0.01–0.8) |
Protective 1 |
CEP72 |
rs924607 |
C/T |
Diouf et al., 2015—St. Jude cohort [9] |
36.7 |
64 (grade 2–4) |
158 (grade 0) |
Recessive OR |
5.5 (2.5–12.2) |
Risk |
|
|
|
Diouf et al., 2015—COG cohort [9] |
36.4 |
22 (grade 2–4) |
74 (grade 0) |
Recessive OR |
3.8 (1.3–11.4) |
Risk |
|
|
|
Gutierrez–Camino et al., 2016 [10] |
39.4 |
36 (WHO grade 2–4) |
106 (WHO grade 0–1) |
Recessive OR |
0.7 (0.2–2.4) |
Not significant |
|
|
|
Wright et al., 2019 [51] |
TT: 13.5 CT and CC: 86.5 |
156 (grade 2–4) |
56 (grade 0) |
Recessive OR |
3.4 (0.9–12.6) |
Not significant |
|
|
|
Zgheib et al., 2018 [50] |
36.9 |
23 (grade 2–4) |
107 (grade 0–1) |
Recessive OR |
1.04 (0.32–3.43) |
Not significant |
MAPT |
rs11867549 |
A/G |
Martin–Guerrero et al., 2019 [49] |
22.5 |
18 (WHO grade 3–4) |
103 (WHO grade 0) |
Dominant OR |
0.21 (0.04–0.96) |
Protective 2 |
SYNE2 |
rs2781377 |
G/A |
Abaji et al., 2018—QcALL cohort [52] |
7.8 |
35 (grade 3–4) |
201 (grade 0) |
Additive OR |
2.5 (1.2–5.2) |
Risk |
TUBB2B: miR–202 |
rs12355840 |
T/C |
Martin–Guerrero et al., 2019 [49] |
23.4 |
27 (WHO grade 1–2) |
103 (WHO grade 0) |
Dominant OR |
2.88 (1.07–7.72) |
Risk |
Hereditary neuropathy |
SLC5A7 |
rs1013940 |
T/C |
Wright et al., 2019 [51] |
15.2 |
170 (grade 2–4) |
57 (grade 0) |
Additive OR |
8.60 (1.68–44.15) |
Risk 3 |
Other (GWAS/EWAS studies) |
BAHD1 |
rs3803357 |
C/A |
Abaji et al., 2018—QcALL cohort [52] |
41.7 |
35 (grade 3–4) |
201 (grade 0) |
Dominant OR |
0.35 (0.2–0.7) |
Protective |
COCH |
rs1045466 |
T/G |
Li et al., 2020—POG cohort [53] |
38 |
Maximum neuropathy score |
Dominant HR |
0.27 (0.16–0.50) |
Protective |
|
|
|
Li et al., 2020—ADVANCE cohort [53] |
33 |
|
|
Linear regression |
−3.56 (−5.45;−1.67) |
Protective |
Chromosome 12/ chemerin |
rs7963521 |
T/C |
Li et al., 2020—POG cohort [53] |
41 |
Maximum neuropathy score |
Additive HR |
2.23 (1.49–3.35) |
Risk |
|
|
|
Li et al., 2020—ADVANCE cohort [53] |
43 |
|
|
Additive HR |
2.16 (0.53–3.70) |
Not significant |
ETAA1 |
rs17032980 |
A/G |
Diouf et al., 2015—St. Jude cohort [9] |
26.6 |
64 (grade 2–4) |
158 (grade 0) |
Allelic OR |
3.17 (1.95–5.17) |
Risk |
|
|
|
Diouf et al., 2015—COG cohort [9] |
19.2 |
22 (grade 2–4) |
74 (grade 0) |
Allelic OR |
10.4 (2.97–36.15) |
Risk |
MRPL4 |
rs10513762 |
C/T |
Abaji et al., 2018—QcALL cohort [52] |
7.0 |
35 (grade 3–4) |
202 (grade 0) |
Dominant OR |
3.3 (1.4–7.7) |
Risk |
MTNR1B |
rs12786200 |
C/T |
Diouf et al., 2015—St. Jude cohort [9] |
22.7 |
64 (grade 2–4) |
158 (grade 0) |
Allelic OR |
0.23 (0.13–0.40) |
Protective |
|
|
|
Diouf et al., 2015—COG cohort [9] |
20.7 |
22 (grade 2–4) |
74 (grade 0) |
Allelic OR |
0.24 (0.08–0.76) |
Protective |
|
|
|
Zgheib et al., 2018 [50] |
18.1 |
23 (grade 2–4) |
107 (grade 0–1) |
Dominant OR |
0.59 (0.22–1.62) |
Not significant |
NDUFAF6 |
rs7818688 |
C/A |
Diouf et al., 2015—St. Jude cohort [9] |
12.6 |
64 (grade 2–4) |
158 (grade 0) |
Allelic OR |
4.26 (2.45–7.42) |
Risk |
|
|
|
Diouf et al., 2015—COG cohort [9] |
14.1 |
22 (grade 2–4) |
74 (grade 0) |
Allelic OR |
4.59 (1.35–15.59) |
Risk |
TMEM215 |
rs4463516 |
C/G |
Diouf et al., 2015—St. Jude cohort [9] |
33.6 |
64 (grade 2–4) |
158 (grade 0) |
Allelic OR |
3.17 (1.95–5.17) |
Risk |
|
|
|
Diouf et al., 2015—COG cohort [9] |
24.2 |
22 (grade 2–4) |
74 (grade 0) |
Allelic OR |
4.94 (1.65–14.79) |
Risk |
miRNA |
miR–4481 |
rs7896283 |
T/C |
Gutierrez–Camino et al., 2017 [48] |
37.5 |
19 (WHO grade 3–4) |
128 (WHO grade 0) |
Dominant OR |
4.69 (1.43–15.43) |
Risk 2 |
miR–6076 |
rs35650931 |
G/C |
Gutierrez–Camino et al., 2017 [48] |
8.7 |
47 (WHO grade 1–4) |
128 (WHO grade 0) |
Dominant OR |
0.22 (0.05–0.97) |
Protective 2 |
This entry is adapted from the peer-reviewed paper 10.3390/cancers14030612