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. 2025 Jun 17;20(6):e0324699. doi: 10.1371/journal.pone.0324699

EPHX1 and ERCC2 polymorphisms are associated with cisplatin-induced nephrotoxicity and prognosis in Thai cancer patients

Saad Ahmed 1, Jakris Eu-ahsunthornwattana 2,3, Thanaporn Thamrongjirapat 4, Aruchalean Taweewongsounton 5, Yutthana Rittavee 1,6, Nintita Sripaiboonkit Thokanit 7, Montien Ngodngamthaweesuk 8, Pitichote Hiranyatheb 9, Thanyanan Reungwetwattana 4, Nuttapong Ngamphaiboon 4, Natini Jinawath 1,10,6,*
Editor: Milad Khorasani11
PMCID: PMC12173183  PMID: 40526606

Abstract

Cisplatin is a widely used chemotherapeutic drug for various cancers. One of the common adverse effects of cisplatin is nephrotoxicity including acute kidney injury (AKI) and acute kidney disease (AKD). Single Nucleotide Polymorphisms (SNPs) can be used to identify cancer patients who are susceptible to developing cisplatin-induced nephrotoxicity (CIN). In this study, we validated the association between 6 SNPs in the drug metabolizing enzyme genes, SLC22A2 (rs316019) & EPHX1 (rs1051740), and the DNA repair genes, ERCC1 (rs11615 & rs3212986) & ERCC2 (rs13181 & rs1799793), and CIN in the 169 Thai patients with head and neck, lung, or esophageal cancer. Effect of these SNPs on cumulative incidence of AKD, progression-free survival (PFS), and overall survival (OS) was also assessed. EPHX1 rs1051740 TC genotype was significantly associated with AKD in co-dominant [OR 2.894, 95% CI 1.091–7.680; P = 0.033] and over-dominant [OR 2.793, 95% CI 1.333–5.851; P = 0.006] models, and with an increased cumulative incidence of AKD (P = 0.021). Additionally, ERCC2 rs13181 and rs1799793 were significantly associated with OS (P = 0.002 and 0.004). Our results reveal an association between EPHX1 rs1051740 and AKD, and confirms the previously reported associations between ERCC2 SNPs and OS. These findings may help in predicting CIN in Thai cancer patients.

Introduction

Cisplatin (cis-diaminedichloroplatinum; CDDP; Platinol) is a chemotherapy drug, used for treatment of multiple cancers including head and neck squamous cell carcinoma (HNSCC), lung, and esophageal. Despite being an effective anticancer drug, its usage can be delayed or terminated because of associated toxicities such as neurotoxicity, gastrointestinal toxicity, ototoxicity and nephrotoxicity [1]. Platinum could be retained in the plasma for as long as 20 years after discontinuation of cisplatin-based chemotherapy [2,3], thus increasing concerns about the long-term nephrotoxicity risks [4].

Cisplatin-induced nephrotoxicity (CIN) occurs upon cisplatin accumulation in the renal proximal tubules. Its influx into the proximal tubule of nephron is mediated through organic cationic transporter 2 (OCT2/SLC22A2). Upon entering the renal tubules, complex interactions cause cisplatin to convert into a positive electrophile, which has higher affinity to DNA, forming DNA adducts, causing the DNA double helix to distort, thus hindering DNA synthesis and replication [5]. This DNA damage is detected and repaired by DNA repair pathways, i.e., nucleotide-excision repair (NER) and base excision repair (BER). Excision repair cross-complementing group 1 (ERCC1) and excision repair cross-complementing group 2 (ERCC2), also called X-ray repair complementing defective repair (XPD), are two of several proteins involved in these DNA repair mechanisms [6].

Clinically, CIN manifests in various forms, with Acute Kidney Injury (AKI) being the most common, occurring in 20–30% of patients [7]. Animal models of CIN demonstrates increased serum creatinine (SCr) and blood urea nitrogen, along with histopathological changes [8]. Though SCr is the main marker for renal function evaluation, it needs to be considered in conjunction with other factors such as age and sex. CIN is dose-dependent, with lower doses of cisplatin causing delayed kidney damage as compared to higher doses [9]. Cisplatin dosing regimens vary by cancer type, leading to differences in nephrotoxicity incidence. In lung and esophageal cancer treatments, ≥ 60 mg/m2 every 3–4 weeks and 75 mg/m2 on days 1 and 29 are administered, respectively [10,11]. Gradually, in HNSCC, weekly 40 mg/m2 or 100mg/m2 every 3 weeks is being administered concurrently with radiotherapy [12].

AKI may occur within 7 days of cisplatin administration, presented as increase in SCr, whereas Acute Kidney Disease (AKD) develops between 7 and 90 days of cisplatin initiation [13]. Prior studies have commonly used AKI as an endpoint for CIN based on different grading criteria; while the long-term or delayed effects of CIN are not widely studied. AKD, defined as renal damage occurring within 3 months of cisplatin administration, may prove to be a valuable endpoint for studying its delayed nephrotoxic effects [1, 14].

Cisplatin treatments have varying nephrotoxic outcomes. Studies have suggested that toxic and efficacious effects of cisplatin chemotherapy may result from genetic variation such as single nucleotide polymorphisms (SNP). Certainly, SNPs in drug metabolism, transport, and DNA repair genes have been shown to cause inter-individual differences in drug response and survival outcomes. Genetic variations in the drug metabolizing enzyme genes, SLC22A2 and EPHX1, and the DNA repair genes, ERCC1 and ERCC2, have been associated with CIN [4, 15].

Organic Cation Transporter 2 (OCT2/SLC22A2), part of the Solute Carrier Transport family, facilitates uptake of various substances into tubular cells. SLC22A2 rs316019 has been linked to cisplatin nephrotoxicity [16,17]. Epoxide Hydrolase 1 (EPHX1) gene is a biotransformation enzyme involved in drug metabolism and excretion. It mainly catalyzes hydrolysis of reactive epoxides to transdihydrodiols, which may be further metabolized into polycyclic hydrocarbons diols, which are highly mutagenic. The dual roles of EPHX1 in activation and detoxification of procarcinogens make it an important drug metabolism gene [18]. Variation rs1051740 causes the enzymatic activity to decrease approximately by 50%.

ERCC1 and ERCC2 or XPD are integral part of the NER pathway. SNPs in these genes - ERCC1 rs11615 and rs3212986, and ERCC2 rs13181 and rs1799793 - are linked to changes in the DNA repair process. Variations in these genes may cause a decrease in the helicase activity and DNA repair. ERCC1 plays a role in incising the DNA damage site, and can cause a rate-limiting effect, while ERCC2, is involved in helicase unwinding the DNA at the damaged site. Hence, these SNPs may reduce the efficiency of DNA repair mechanisms, leading to greater platinum-DNA adduct formation and hindered nephron repair following cisplatin exposure [19].

Since SNP allele frequencies vary across populations, the association between these 6 SNPs and CIN in Thai cancer patients is unknown. This study aims to validate their associations with CIN in Thai patients with HNSCC, lung, and esophageal cancers.

Materials and methods

Patient selection

This study has been approved by the Human Research Ethics Committee, Faculty of Medicine Ramathibodi Hospital, Mahidol University, in compliance with the Declaration of Helsinki, protocol number COA. MURA2020/1109. Written informed consents were obtained from all participants.

Clinical and treatment data were obtained from an in-house observational head and neck cancer cohort database, Ramathibodi Tumor Registry, pharmacy database, and medical records included patient demographics, serum creatinine (SCr), estimated glomerular filtration rate (eGFR; CKD-EPI), cancer staging and comorbidities (ICD-10). All clinical information was verified by two independent medical oncologists. The clinical data were initially accessed on 15th July 2020. The authors had access to information that could identify individual participants during data collection; however, no individually identifiable data was reported in this study.

Solid tumor patients treated with cisplatin chemotherapy at the Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand were retrospectively identified. Criteria used for patient inclusion were: head & neck, lung and esophageal cancer patients, age ≥ 18 years, availability of blood samples in Ramathibodi Comprehensive Tumor Biobank collected between 1st January 2016–30th June 2020. Samples were collected from the biobank for research purposes starting from 17th March 2021. Patients with known chronic kidney disease as defined by The Kidney Disease Improving Global Outcomes (KDIGO) guideline before the start of cisplatin-based treatment were excluded [14].

For HNSCC cohort, in our prospective multidisciplinary observation study of HNSCC and NPC patients at Ramathibodi Hospital, Mahidol University, we have accrued eligible patients since 2016. The study enrolled patients with LA-HNSCC and NPC treated according to the standard of care at their treating physician’s discretion. Enrollment was consistent, except during Covid-19 pandemic when the enrollment was hold, and subsequently resumed in 2021. Similarly, lung and esophageal cancer cohorts were also enrolled since 2016.

Nephrotoxicity criteria

The study aimed to evaluate correlation of SNPs of interest and acute/subacute nephrotoxicity in Thai cancer patients who were treated with cisplatin. Acute and subacute nephrotoxicity were classified into acute kidney injury (AKI) and acute kidney disease (AKD) groups according to KDIGO criteria [14,20], which defines AKI as an increase in SCr by 50% within 7 days or 0.3 mg/dl (26.5 µmol/l) within 2 days or oliguria. AKD is defined as having AKI or GFR < 60 ml/min per1.73 m2 for < 3 months or a decrease in GFR by ≥35% or an increase in SCr by > 50% for < 3 months.

Laboratory values closest to and/or within 30 days prior to cisplatin start date were considered as the baseline to assess SCr and eGFR change [17]. Nephrotoxicity was evaluated as cases versus controls, i.e., AKI or Non-AKI and AKD or Non-AKD (Fig 1) shows the CONSORT diagram.

Fig 1. CONSORT diagram.

Fig 1

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Single nucleotide polymorphism (SNP) selection

Previous clinical studies and systematic reviews related to CIN-associated SNPs in different types of cancers and populations in PubMed from 2008–2020 were reviewed [4,15-17,21–29]. Only the candidate SNPs with minor allele frequency (MAF) ≥ 5% in Thai population were included. Finally, a total of 6 SNPs in 4 genes were selected. Allele frequencies in Thai and global populations were respectively obtained from the Thai Reference Exome Database (T-REx), which contains data from 1,092 Thai individuals (accessed on 25th February 2022), and from dbSNP (build 157) (accessed on 13th March 2025). SLC22A2 rs316019 and EPHX1 rs1051740 are involved in drug metabolism, while ERCC1 rs11615 and rs3212986, and ERCC2 rs13181 and rs1799793 play an important part in DNA repair mechanisms.

DNA extraction and genotyping analysis

Genomic DNA (gDNA) was extracted from buffy coat samples using Gene AllTM (Roche life sciences, Switzerland) for head and neck cancer cases, and GF-1 Blood DNA Extraction Kit (Vivantis Technologies, Malaysia) for lung and esophageal cancer cases, following each extraction kit’s manufacturer’s recommendation. gDNA was further quantified and qualified using NanoDropTM spectrophotometer (ThermoFisher ScientificTM, USA) followed by 1% agarose gel electrophoresis.

Genotyping was performed by TaqMan Drug Metabolizing Enzyme (DME) and Predesigned SNP genotyping assays using TaqMan™ GTXpress™ Master Mix (Applied Biosystems, USA), according to the manufacturer’s protocol. TaqMan assay probe IDs of the 6 selected SNPs are as follows: SLC22A2 (rs316019, C__3111809_20), EPHX1 (rs1051740, C__14938_30), ERCC1 (rs11615, C__2532959_20) and (rs3212986, C__2532948_10), ERCC2 (rs13181, C__3145033_10) and (rs1799793, C__3145050_10). Genotyping and allelic discrimination plot analysis was performed on a ViiATM 7 Real-Time PCR system (Applied Biosystems, USA).

Moreover, after obtaining the first set of TaqMan results, Sanger sequencing was done on randomly selected samples to confirm each genotype calls for all 6 SNPs S1 Table, which were then used as positive controls in the subsequent TaqMan runs. Primers designed and optimized by touchdown PCR protocol for Sanger sequencing are shown in the S2 Table. Sequencing chromatogram of all 6 SNPs were shown in S1 Fig.

Statistical analyses

Patient demographics and clinical variables between cases and controls were compared using Pearson’s chi-square or Fisher’s exact test. Genotype frequencies were counted directly, while Hardy-Weinberg Equilibrium deviation in control group was assessed using genhwi command in STATA program. Power calculations showed that a sample size of 169 patients, assuming 25% nephrotoxicity, would provide our study with 80% power to identify a statistically significant association between the selected SNPs and AKD, with an alpha of 0.05 and an odds ratio of 2.8.

SNP association with cases and controls was analyzed using logistic regression. Models were adjusted by age and sex as covariates. Minor allele was considered as the risk allele and subsequently, 5 genetic inheritance models, namely co-dominant, dominant, recessive, over-dominant, and additive, were considered for analysis. For the additive and dominant genetic models, power calculations were conducted using STATA 14, power two proportions and RStudio 4.4.2 genpwr package, respectively. For the six SNPs analyzed, the estimated power varied with 53% being the highest.

Multivariate logistic regression was also performed to analyze potential factors affecting AKD. Multiple comparisons were corrected by Benjamini-Hochberg false discovery rate (FDR) corrected P-values, and additionally with Holm-Bonferroni correction.

Survival analyses focusing on overall survival (OS), progression free survival (PFS) and time-to-AKD were performed. OS was defined as the time of cancer diagnosis to death from any cause or last follow up of patients. OS was also performed in each cancer type. PFS was defined as the time of cisplatin initiation to the time of disease progression or death from any cause. PFS was performed only in head and neck cancer cohort because of limitation of data availability. Time to AKD was defined as the time of cisplatin initiation to the time of AKD appeared by KDIGO criteria. Cox regression analysis was performed to analyze the risk factors affecting mortality and time-to-AKD. The survival status was cross-checked with the National Security Death Index of Thailand on 23/3/2022.

The Kaplan-Meier curve and log-rank test were used to assess survival differences between AKD and non-AKD groups and the effect of polymorphisms on survival by dominant genetic model. Similarly, cumulative incidence of AKD by the 3 genotypes were presented using Kaplan-Meier curves. P < 0.05 was considered statistically significant. All statistical analysis and plots were performed using STATA 14.0 (College Station, TX: StataCorp LLC, USA).

Results

Patient characteristics

AKI was only developed in 4 (3.9%) out of 103 patients in our cohort, likely due to the improvement of hydration and supplementation regimens S3 Table. On the contrary, AKD occurred in 43 (25.4%) out of 169 patients. In this cohort, most patients were male (65.1%) and aged less than 65 years old (82.2%), whereas the median age being 57.4 years old (range 27.6–77.1). Hypertension was predominant among the comorbidities; 50 (29.6%), while diabetes, cerebrovascular and heart diseases were 15 (8.9%), 13 (7.7%) and 9 (5.3%), respectively. The majority of patients had head and neck cancer (72%). Squamous cell carcinoma (SCC) was the most common histologic subtype (73.4%). Fifty percent of all patients had stage IV disease followed by stage III (29%), stage II (14%), and stage I (7%) diseases. AKD patients had significantly lower mean eGFR (P = < 0.001) and higher mean SCr (P = 0.013) as compared to non-AKD patients. Detailed demographics and clinicopathological data of the AKD and non-AKD groups are shown in Table 1. This cohort was then used for SNP association and survival analyses.

Table 1. Clinical characteristics of AKD and Non-AKD patients after receiving cisplatin chemotherapy.

Factors Overall (n = 169) AKD
(n = 43) (25.4%)		 Non-AKD
(n = 126) (74.6%)		 P value
Sex
Male
Female		 110 (65.1%)
59 (34.9%)		 25 (58.1)
18 (41.9)		 85 (67.5)
41 (32.5)		 0.268a
Median Age (range)
Age
<65
≥65		 57.4 (27.6-77.1)
139 (82.2%)
30 (17.8%)		 59.8 (38.7-73.9)
34 (79.1)
9 (20.9)		 56.2 (27.6-77.1)
105 (83.3)
21 (16.7)		 0.528a
Comorbidity (ICD-10)
Hypertension
Diabetes Mellitus
Cerebrovascular Disease
Heart Disease		 50 (29.6%)
15 (8.9%)
13 (7.7%)
9 (5.3%)		 17 (39.5)
4 (9.3)
4 (9.3)
2 (4.7)		 33 (26.2)
11 (8.7)
9 (7.1)
7 (5.6)		 0.098a
1.000b
0.741b
1.000b
Cancer Type
HNSCC
Lung
Esophagus		 121 (71.6%)
39 (23.1%)
9 (5.3%)		 27 (62.8)
13 (30.2)
3 (6.9)		 94 (74.6)
26 (20.6)
6 (4.8)		 0.333a
Histology
SCC
Non-SCC		 124 (73.4%)
45 (26.6%)		 29 (67.4)
14 (32.6)		 95 (75.4)
31 (24.6)		 0.308a
Staging
I
II
III
IV		 12 (7.1%)
23 (13.6%)
49 (29.0%)
85 (50.3%)		 4 (9.3)
5 (11.6)
10 (23.3)
24 (55.8)		 8 (6.4)
18 (14.3)
39 (30.9)
61 (48.4)		 0.668a
Baseline mean eGFR (+/-SD) ml/min/1.73m 2 98.77 ± 14.78 90.90 ± 15.95 101.45 ± 13.40 <0.001*
Baseline mean SCr (+/-SD) mg/dL 0.75 ± 0.17 0.81 ± 0.20 0.73 ± 0.15 0.013*
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aCalculated using Pearson’s chi-square test. b calculating using Fisher’s exact test. * Statistically significant P value < 0.05. SCC, Squamous Cell Carcinoma. HNSCC, Head and Neck Squamous Cell Carcinoma.

Associations between the candidate SNPs and cisplatin-induced AKD across all cancer types

Genetic polymorphism association with AKD was performed in the 169 cancer patients. All candidate SNPs have minor allele frequencies ≥ 0.05 in Thai population and there was no significant deviation from Hardy-Weinberg Equilibrium (HWE) Table 2. Genetic association between each SNP and AKD was studied using logistic regression in 5 genetic models: co-dominant, dominant, recessive, over-dominant and additive. Association analysis was done for both unadjusted and adjusted models. Unadjusted model showed TC genotype of EPHX1 rs1051740 was significantly associated with increased risk of AKD in co-dominant; TC vs CC [OR 2.894, 95% CI 1.091–7.680, P = 0.033], and over-dominant genetic models; TC vs CC + TT [OR 2.793, 95% CI 1.333–5.851, P = 0.006].

Table 2. Information and allele frequencies of the 6 selected SNPs in Thai population. Allele frequencies were obtained from dbSNP (build 157; https://www.ncbi.nlm.nih.gov/snp/), and from the T-REx database [30].

Gene SNP SNP Information Global Frequencies T-REx Frequencies Data Allele Frequencies HWE
P value
SLC22A2 rs316019 Missense c.808G > T (p.270Ala > Ser) C A C A C A 0.187
0.895 0.104 0.890 0.110 0.828 0.171
EPHX1 rs1051740 Missense c.337T > C (p.Tyr113His) C T C T C T 0.331
0.295 0.704 0.504 0.496 0.503 0.496
ERCC1 rs11615 Synonymous c.354T > C (p.Asn118Asn) G A G A G A 0.482
0.414 0.585 0.697 0.303 0.600 0.400
ERCC1 rs3212986 Synonymous 3’ UTR c*197G > T C A C A C A 0.261
0.745 0.254 0.664 0.336 0.720 0.279
ERCC2 rs13181 Missense c.2251A > C (p.751lys > Gln) T G T G T G 0.910
0.642 0.000 0.901 0.099 0.910 0.089
ERCC2 rs1799793 Missense c.934G > A (p.Asp312Asn) C T C T C T 0.889
0.684 0.315 0.937 0.063 0.942 0.057
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HWE P, Hardy-Weinberg Equilibrium P value of the non-AKD (control) group.

Similarly, after adjusting the model with age and sex, TC genotype of EPHX1 rs1051740 remained significantly associated with increased AKD risk in co-dominant and over-dominant models [OR 3.354, 95% CI 1.230–9.148, P = 0.018] and [OR 3.137, 95% CI 1.450–6.785, P = 0.004], respectively. However, both associations did not remain significant after correcting for multiple comparisons using FDR. None of the other 5 SNPs were associated with AKD in any of the genetic model. Table 3 summarizes the association analysis and genotype frequencies in AKD and Non-AKD groups.

Table 3. Association of the selected genetic polymorphisms with cisplatin-induced AKD. Association analyses were performed by logistic regression using 5 genetic models. Unadjusted OR was calculated between AKD outcome and SNP. Model was then adjusted with age and sex variables.

SNP Model Genotype AKD (%) Non-AKD (%) Unadjusted OR
(95% CI)		 P value BH-FDR
P value		 Holm- Bonferroni
P value		 Adjusted OR
(95% CI)		 P value Adj BH-FDR
P value Adj		 Holm-Bonferroni P value Adj
SLC22A2 rs316019 Co-dominant CC 32 (74.4) 101 (80.1) 1 1
AC 11 (25.6) 22 (17.5) 1.578 (0.691 - 3.603) 0.279 0.721 0.003 1.532 (0.656 - 3.578) 0.323 0.738 0.003
AA 0 (0.0) 3 (2.4) – – – – – – – –
Dominant AA + AC 11 (25.6) 25 (19.8) 1.388 (0.615 - 3.131) 0.429 0.881 0.003 1.362 (0.591 - 3.139) 0.468 0.881 0.003
Recessive CC + AC 43 (100) 123 (97.6) 1 1
AA 0 (0.0) 3 (2.4) – – – – – – – –
Over-dominant CC + AA 32 (74.4) 104 (82.5) 1 1
AC 11 (25.6) 22 (17.5) 1.625 (0.712 - 3.708) 0.249 0.693 0.002 1.576 (0.675 - 3.679) 0.293 0.721 0.003
Log-Additive 1.165 (0.562 - 2.415) 0.681 0.935 0.005 1.157 (0.546 - 2.452) 0.702 0.935 0.006
EPHX1 rs1051740 Co-dominant CC 6 (14.0) 33 (26.2) 1 1
TC 30 (69.8) 57 (45.2) 2.894 (1.091 - 7.680) 0.033* 0.526 0.002 3.354 (1.230 - 9.148) 0.018* 0.384 0.002
TT 7 (16.2) 36 (28.6) 1.069 (0.325 - 3.509) 0.912 0.941 0.017 1.138 (0.336 - 3.843) 0.835 0.941 0.010
Dominant TT + TC 37 (86.0) 93 (73.8) 2.188 (0.846 - 5.655) 0.106 0.526 0.002 2.459 (0.931 - 6.493) 0.069 0.526 0.002
Recessive CC + TC 36 (83.7) 90 (71.4) 1 1
TT 7 (16.3) 36 (28.6) 0.486 (0.198 - 1.192) 0.115 0.526 0.002 0.475 (0.188 - 1.199) 0.115 0.526 0.002
Over-dominant CC + TT 13 (30.2) 69 (54.8) 1 1
TC 30 (69.8) 57 (45.2) 2.793 (1.333 - 5.851) 0.006* 0.192 0.002 3.137 (1.450 - 6.785) 0.004* 0.192 0.002
Log-Additive 0.998 (0.607 - 1.642) 0.996 0.996 0.050 1.039 (0.624 - 1.730) 0.881 0.941 0.017
ERCC1 rs11615 Co-dominant GG 21 (48.8) 59 (46.8) 1 1
AG 17 (39.5) 57 (45.2) 0.837 (0.401 - 1.748) 0.638 0.935 0.004 0.853 (0.401 - 1.815) 0.680 0.935 0.005
AA 5 (11.6) 10 (8.0) 1.404 (0.430 - 4.587) 0.574 0.935 0.004 1.081 (0.315 - 3.706) 0.900 0.941 0.025
Dominant AA + AG 22 (51.2) 67 (53.2) 0.922 (0.461 - 1.844) 0.820 0.941 0.008 0.892 (0.437 - 1.821) 0.755 0.935 0.007
Recessive GG + AG 38 (88.4) 116 (92.1) 1 1
AA 5 (11.6) 10 (7.9) 1.526 (0.490 - 4.745) 0.465 0.881 0.003 1.164 (0.357 - 3.796) 0.800 0.941 0.008
Over-dominant GG + AA 26 (60.5) 69 (54.8) 1 1
AG 17 (39.5) 57 (45.2) 0.791 (0.391 - 1.601) 0.516 0.917 0.004 0.841 (0.407 - 1.737) 0.642 0.935 0.004
Log-Additive 1.041 (0.609 - 1.779) 0.883 0.941 0.013 0.966 (0.559 - 1.667) 0.901 0.941 0.050
ERCC1 rs3212986 Co-dominant CC 20 (46.5) 62 (49.2) 1 1
CA 17 (39.5) 49 (38.8) 1.075 (0.509 - 2.270) 0.849 0.941 0.010 1.136 (0.526 - 2.451) 0.744 0.935 0.006
AA 6 (14.0) 15 (12.0) 1.240 (0.424 - 3.624) 0.694 0.935 0.006 1.329 (0.442 - 3.988) 0.612 0.935 0.004
Dominant AA + AC 23 (53.5) 64 (50.8) 1.114 (0.556 - 2.229) 0.760 0.935 0.007 1.182 (0.580 - 2.408) 0.645 0.935 0.004
Recessive CC + CA 37 (86.0) 111 (88.1) 1 1
AA 6 (14.0) 15 (11.9) 1.200 (0.433 - 3.318) 0.725 0.935 0.006 1.256 (0.442 - 3.569) 0.668 0.935 0.005
Over-dominant CC + AA 26 (60.5) 77 (61.1) 1 1
CA 17 (39.5) 49 (38.9) 1.027 (0.505 - 2.086) 0.940 0.955 0.025 1.071 (0.516 - 2.222) 0.853 0.941 0.013
Log-Additive 1.103 (0.671 - 1.810) 0.668 0.935 0.005 1.148 (0.692 - 1.905) 0.592 0.935 0.003
ERCC2 rs13181 Co-dominant TT 37 (86.0) 95 (75.4) 1 1
TG 6 (14.0) 29 (23.0) 0.531 (0.203 - 1.384) 0.195 0.594 0.002 0.470 (0.171 - 1.291) 0.143 0.551 0.002
GG 0 (0.0) 2 (1.6) – – – – – –
Dominant GG + TG 6 (14.0) 31 (24.6) 0.496 (0.191 - 1.288) 0.150 0.551 0.002 0.443 (0.162 - 1.210) 0.112 0.526 0.002
Recessive TT + TG 43 (100) 124 (98.4) 1 1
GG 0 (0.0) 2 (1.6) – – – – – –
Over-dominant TT + GG 37 (86.1) 97 (77.0) 1 1
TG 6 (13.9) 29 (23.0) 0.542 (0.208 - 1.412) 0.210 0.611 0.002 0.481 (0.175 - 1.319) 0.155 0.551 0.002
Log-Additive 0.491 (0.196 - 1.230) 0.129 0.550 0.002 0.441 (0.166 - 1.167) 0.099 0.526 0.002
ERCC2 rs1799793 Co-dominant CC 39 (90.7) 103 (81.7) 1 1
CT 3 (7.0) 22 (17.5) 0.360 (0.103 - 1.271) 0.113 0.526 0.002 0.340 (0.091 - 1.261) 0.107 0.526 0.002
TT 1 (2.3) 1 (0.8) 2.641 (0.161 - 43.265) 0.496 0.907 0.003 4.056 (0.226 - 72.726) 0.342 0.742 0.003
Dominant TT + CT 4 (9.3) 23 (18.3) 0.459 (0.149 - 1.413) 0.175 0.586 0.002 0.451 (0.140 - 1.453) 0.183 0.586 0.002
Recessive CC + CT 42 (97.7) 125 (99.2) 1 1
TT 1 (2.3) 1 (0.8) 2.976 (0.182 - 48.638) 0.444 0.881 0.003 4.786 (0.265 - 86.183) 0.288 0.721 0.002
Over-dominant CC + TT 40 (93.0) 104 (82.5) 1 1
CT 3 (7.0) 22 (17.5) 0.354 (0.100 - 1.250) 0.107 0.526 0.002 0.331 (0.089 - 1.224) 0.288 0.526 0.002
Log-Additive 0.600 (0.225 - 1.599) 0.308 0.730 0.003 0.609 (0.217 - 1.712) 0.348 0.742 0.003
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OR, Odds Ratio. 95% CI, 95% Confidence Interval. BH-FDR, Benjamini-Hochberg False Discovery Rate. Adj, adjusted P value. * Statistically significant P value < 0.05.

Upon adjusting the model with other clinical factors such as hypertension, diabetes, heart diseases and cerebrovascular diseases and histological types, also showed TC genotype of EPHX1 rs1051740 to be significantly associated with AKD in both co-dominant and over-dominant models, although the associations were no longer significant after FDR and Holm-Bonferroni corrections.

Additionally, a multivariate logistic regression analysis was performed using EPHX1 rs1051740 genotype, the statistically significant variables, and potential AKD-associated clinical factors to analyze their effect on AKD. We showed that the eGFR baseline cutoff <90 ml/min/1.73m2 and TC genotype of EPHX1 rs1051740 were significantly associated with an increased risk of cisplatin-induced AKD among cancer patients. However, no significant association was found between age, hypertension and diabetes mellitus, as summarized in Table 4.

Table 4. Multivariate logistic analysis to analyze potential factors affecting AKD.

Factors Multivariate Analysis
Odds Ratio 95% Confidence Interval P value
Age ≥ 65 1.40 0.50 - 3.92 0.513
Hypertension 1.51 0.65 - 3.51 0.331
Diabetes Mellitus 1.00 0.25 - 4.01 0.993
eGFR Baseline (ml/min/1.73m 2 ) <90 3.30 1.41 - 7.71 0.006*
EPHX1 rs1051740 TC 2.79 1.01 - 7.86 0.046*
TT 1.18 0.34 - 4.06 0.791
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* Statistically significant P value < 0.05.

Cumulative incidence of AKD over time in association with the candidate SNPs

Fig 2A showed the cumulative incidence of AKD over a 90-day period across our patient cohort. Cumulative incidence of AKD over time in association with each SNP (co-dominant model) was subsequently analyzed by Kaplan-Meier method (Figs 2B-2G). The only SNP that showed statistically significant association with the increased cumulative incidence of AKD over time was EPHX1 rs1051740 (P = 0.021) when comparing TC with CC genotype (Fig 2C). SLC22A2 rs316019, ERCC1 rs11615 and rs3212986, as well as ERCC2 rs13181 and rs1799793 did not exhibit significant findings when compared against major homozygote genotypes. This result was in line with our prior association analyses between the candidate SNPs and AKD. In addition, multivariate Cox regression analysis using significant variables were performed and identified age, baseline eGFR, SCr and individuals carrying TC genotypes of the EPHX1 rs1051740 SNP as significant in univariate analysis [HR 1.040, CI 95% 1.006–1.076, P = 0.019; HR 0.954, CI 95% 0.934–0.974, P = < 0.001; HR 14.490, CI 95% 2.167–96.873, P = 0.006; HR 2.433, 1.012–5.847, P = 0.047, respectively]. However, none of the variables analyzed were identified as independent factors of time-to-AKD after multivariate Cox analysis. The associated risk factors of time-to-AKD are listed in S7 Table.

Fig 2. Cumulative incidence of AKD in association with the 6 candidate SNPs.

Fig 2

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(A) Cumulative incidence of AKD event in our patient cohort. Kaplan Meier curves are shown where the y-axis denotes cumulative incidence of patients experiencing AKD while the x-axis denotes days after the start of cisplatin. Ninety-day period of AKD is shown. Cumulative incidence of AKD in association with (B) SLC22A2 rs316019, (C) EPHX1 rs1051740, (D) ERCC1 rs11615, (E) ERCC1 rs3212986, (F) ERCC2 rs13181 and (G) ERCC2 rs1799793. P < 0.05 was considered significant.

Associations between the candidate SNPs and cisplatin-induced AKD in each cancer type

Additionally, SNP association with AKD was analyzed in the head and neck cancer-only and the lung cancer-only cohorts, consisting of 121 and 39 patients, respectively. In head and neck cancer-only cohort, TC genotype of EPHX1 rs1051740 was significantly associated with an increased risk of AKD in co-dominant [OR 13.333, 95% CI 1.684–105.533, P = 0.014] and over-dominant models [OR 4.029, 95% CI 1.552–10.455, P = 0.004]. Dominant model TT + TC vs CC also showed a significantly increased risk of AKD [OR 9.941, 95% CI 1.282–77.051, P = 0.028] S4 Table. Of note, bootstrap estimation was performed for the head and neck cancer-only cohort to correct for the wide confidence interval, which could lead to overestimation of the results S4 Table.

Moreover, the same finding was obtained when combining the 9 patients with esophageal cancer with the head and neck cancer-only cohort, or adjusting the logistic model with age and sex S5 Table. In the lung cancer-only cohort, none of the 6 SNPs showed any association with AKD, similar result was observed upon adjusting the logistic models with age & sex S6 Table.

Survival analyses of the cancer patients in association with cisplatin-induced AKD or the candidate SNPs

In order to clarify whether the AKD status or the candidate CIN-associated SNPs may affect the survival outcome of the Thai cancer patients in our cohort, first, we analyzed the association between overall survival (OS) and patients with and without AKD using Kaplan-Meier curve. There was no significant difference in the OS between patients with and without AKD after a median follow-up time of 3.24 years [log-rank P = 0.094] (Fig 3A). Secondly, OS association with the 6 selected SNPs was studied under dominant model. SNPs in SLC22A2, EPHX1 and ERCC1 showed no associations with OS. Only ERCC2 rs13181 and rs1799793 were significantly associated with poor OS, i.e., genotypes GG + TG of ERCC2 rs13181 (P = 0.002) and TT + CT of ERCC2 rs1799793 (P = 0.004), as compared to the homozygote genotypes (Figs 3B-3G).

Fig 3. Overall survival analyses of the 169 cancer patients.

Fig 3

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(A) Overall survival in patients with and without AKD at a median follow-up time of 3.24 years by dominant genetic model. Overall survival in relation to SNP genotypes (B) SLC22A2 rs316019, (C) EPHX1 rs1051740, (D) ERCC1 rs11615, (E) ERCC1 rs3212986, (F) ERCC2 rs13181 and (G) ERCC2 rs1799793. P < 0.05 was considered significant.

Furthermore, no difference in OS was found between AKD and non-AKD groups when using only head and neck or lung cancer cohorts S2A and S3A Figs. For SNP association analysis, in the head and neck cancer only cohort, no significant association was observed in SLC22A2, EPHX1 and ERCC1 SNPs S2B – S2E Figs. Only genotypes GG + TG of ERCC2 rs13181 and TT + TC of ERCC2 rs1799793 were significantly associated with inferior survival outcome, similar to the finding when using all cases S2F and S2G Figs respectively. In contrast, the lung cancer only cohort did not show significant difference in OS in relation to any SNPs S3B –S3G Figs. Multivariate Cox regression was also performed to analyze the significant variables and potential risk factors for OS, and identified male, diabetes mellitus, heart disease, G allele on ERCC2 rs13181 and T allele on rs1799793 tended to have an increased risk of mortality [HR 1.815, 95% CI 1.077–3.058, P = 0.025; HR 2.219, 95% CI 1.133–4.343, P = 0.020; HR 2.674, CI 95% 1.222–5.851, P = 0.014; HR 2.080, CI 95% 1.270–3.407, P = 0.004; HR 2.107, CI 95% 1.238–3.586, P = 0.006, respectively] whereas NSCC had decreased mortality risk [HR 0.508, CI 95% 0.279–0.925, P = 0.027] in the univariate Cox regression model, but this did not retain statistical significance in the multivariate Cox proportional hazards model. List of associated factors affecting mortality are given in S8 Table.

Lastly, PFS in association with AKD and SNPs was only analyzed in the 121 patients with head and neck cancer whose progression data was available. Our results demonstrated no association between PFS and kidney function S4A Fig and no association between PFS and SLC22A2, EPHX1 and ERCC1 SNPs S4B – S4E Figs. However, there were significant associations between PFS and ERCC2 SNPs, rs13181 (P = 0.017) and rs1799793 (P = 0.023) S4F– S4G Figs.

Discussion

In this study, we analyzed 6 SNPs in drug metabolizing enzyme and DNA repair genes and assessed their associations with CIN in Thai cancer patients. The effect of these SNPs on time-to-AKD, PFS, and OS were also investigated. A recent study by Patimarattananan et al. in Thai locally advanced Head and Neck Squamous Cell Carcinoma (HNSCC) patients who received chemoradiotherapy with cisplatin reported an incidence of AKI as 13.4% and AKD as 27.9% [1]. The incidence of AKI in our retrospective cohort, which was enrolled between 2016–2020, was only 3.9%, while AKD was 25.4%. The use of higher cisplatin doses in the past regimens may be one of the reasons for a higher AKI incidence seen in Patimarattananan et al. The recent improvement of hydration and supplementation regimens may also contribute to lower AKI incidence. Additionally, our cohort consisted of multiple types of cancer patients with various staging, influencing treatment methods, i.e., systemic chemotherapy or concurrent chemoradiation with cisplatin, and the laboratory testing timepoints, which could result in the underestimation of AKI incidence by KDIGO guideline. Therefore, we focused on AKD as an endpoint of CIN in our study.

AKD is a relatively new clinical classification representing patients who do not meet the definition of AKI or chronic kidney disease (CKD) and in whom pathophysiologic processes are still ongoing after AKI [31–33]. Evaluation of AKD in terms of CIN is vital for a comprehensive patient care. Patients developing AKI may progress to AKD, which poses persistent risks such as mortality and worsened kidney function [34]. Among patients with AKD post-hospital discharge, the long-term risk includes development of CKD [32]. Patients experiencing AKD along with co-morbidities may be exposed to risk factors that may enhance AKD severity. A study has shown that in-ICU and in-hospital mortality rates were significantly higher in intensive care unit patients who developed AKD than those with AKI. Similar to our patient’s cohort, male sex, and lower baseline eGFR were among AKD progression factors [35]. Furthermore, a recent meta-analysis highlighted higher mortality risk in the AKD group compared to the non-AKD group [34]. These reports also highlight the heterogeneity among studies, necessitating further research to explore AKD outcomes in different populations. Hence, these findings underscore the need for studying AKD in addition to AKI, allowing for its close monitoring, early identification, and appropriate therapeutic interventions.

We found EPHX1 rs1051740 significantly associated with cisplatin-induced AKD in co-dominant and over-dominant model, particularly in TC pattern. To our knowledge, no prior studies have performed AKD association analysis with genetic polymorphisms. Multivariate analysis, identified lower baseline eGFR less than 90 ml/min/1.73m2 as another factor influencing AKD development after cisplatin treatment. However, comorbidities including hypertension, diabetes mellitus and age did not show significant associations in our study, whereas Patimarattanan et al. suggests that hypertension was significantly associated with AKD development. In line with the same study [1], kidney function after cisplatin treatment did not impact the OS in our study.

No consensus has been reached regarding SNPs associated with CIN, partly due to significant variations in minor allele frequencies across populations. Previous studies have shown association of these 6 selected SNPS with CIN using AKI as the endpoint. Notably, the minor allele frequency of EPHX1 rs1051740 differs in the Thai population compared to other populations [36]. Most commonly the reference allele is T, whose expression as homozygous TT genotype is associated with faster metabolism than CC. However, in Thai population, T is the minor allele, with a frequency of 0.49. The EPHX1 rs1051740 (c.337T > C), is one of the 2 widely studied polymorphisms of the EPHX1 gene. This variant results in a tyrosine-to- histidine substitution at position 113, leading to a 50% reduction in enzyme activity [37]. Decreased enzymatic activity may reduce the detoxification, increasing the formation of highly reactive metabolites, making the cells more susceptible to challenges.

EPHX1 has previously been studied in various cancers. Association of rs1051740 and esophageal cancer remains inconsistent [38–40]. Previous meta-analysis studies show a significant increase in lung cancer risk among Asians with CC genotype or C allele in their subgroup analysis by ethnicity [41–43]. Lee et al. demonstrated a link between genetically lower epoxide hydrolase activity and an increased risk of tobacco related cancers among smokers [44]. Another meta-analysis found an association between rs1051740 polymorphism and HNSCC risk in population-based studies. [18]. While EPHX1 is well-studied regarding cancer risk, few studies have explored its relationship with CIN. To date, only Khrunin et al. have reported a higher frequency of CIN in ovarian cancer patients with the rs1051740 heterozygous genotype in a Russian population [15].

Our association analysis results show EPHX1 rs1051740 heterozygous genotype to be associated with AKD in our combined three-cancer-type cohort. This SNP was also significantly associated with AKD in the head and neck cancer-only cohort, although the confidence interval (CI) was wide, likely due to the smaller sample size. This association may hold significance, as AKD has not previously been studied with any SNP. This association may also be relevant because the heterozygous enzyme EPHX1 may act as an intermediate metabolizer causing delayed elimination of cisplatin from the body. Since AKD also causes a delayed effect on kidneys, it may be a scenario where delayed metabolism of cisplatin may cause a prolonged nephrotoxic outcome. AKD time period is 3 months from the start date of cisplatin, nonetheless it is possible that there may be other factors unrelated to cisplatin that affects the kidneys. Patients undergoing multiple radiological testing might have a negative affect by the dyes used. Another possibility of having heterozygous genotype association may be because of sampling error, i.e., our cohort might lack sufficient patients with CC genotype.

ERCC1 and ERCC2 are key components of the NER pathway, activated by the formation of DNA adducts. Genetic variations in these genes may influence DNA repair capacity. Synonymous variants in the 3’ UTR region such as ERCC1 rs11615 and rs3212986, can affect protein stability, folding, structure, and expression [19, 45]. Previous studies by Khrunin et al. and Tzvetkov et al. reported an increased risk of CIN in ovarian and other cancers related to ERCC1 rs11615 and rs3212986 in Caucasian [25,26]. However, Zazuli et al. found ERCC1 rs3212986 to reduce risk of CIN in Caucasian testicular cancer patients [17]. In East-Asians, Chen et al. found no significant association between CIN and ERCC1 rs11615 in Chinese lung cancer patients [46]. The association of ERCC2 with CIN has also been widely studied, but results vary due to differing endpoints and populations. Studies have shown an increased risk of CIN in Caucasian osteosarcoma and gastric cancer patients with ERCC2 rs13181 and rs1799793, respectively [27, 47], while in East-Asian lung cancer patients, Kim et al. did not find any association between CIN and ERCC2 rs13181 [48]. SLC22A2 rs316019, a nonsynonymous missense variant (p.270Ala > Ser; c.808G > T), impacts cisplatin uptake in renal proximal tubules and has been linked to CIN in multiple studies [16,17,21-24]. However, the reported effects of SLC22A2 rs316019 on CIN vary across populations and outcome definitions. Among East-Asians, Zazuli et al. suggested a protective effect of this SNP when using CTCAE-AKI criteria [17]. Notably, all these studies used AKI as the endpoint for CIN. In our study, none of the candidate SNPs from SLC22A2, ERCC1 and ERCC2 showed a significant association with AKD in any genetic model.

Since AKD by itself can lead to poor OS in cancer patients, partly due to the resulting inadequate treatment, we further investigated the effect of these candidate SNPs on OS. In our study, although the OS analysis of AKD vs non-AKD groups was not statistically significant, we observed a trend indicating that patients without AKD had numerically longer OS compared to those with AKD. OS analysis for each selected SNP revealed no association with polymorphisms in SLC22A2, EPHX1 and ERCC1 genes. However, using a dominant genetic model, OS was associated with ERCC2 rs13181 and rs1799793: patients with ERCC2 rs13181-TT genotype and rs1799793-CC genotype exhibited better OS. Notably, these ERCC2 SNPs showed no association with AKD, suggesting their effects on OS are independent of AKD status. Previously, data on survival in relation to ERCC2 gene polymorphisms have varied across different populations and cancer types, limiting their application in clinical practice. Variant genotypes of ERCC2 rs13181 and rs1799793 have been significantly associated with poor survival in Chinese NSCLC patients [49]. A meta-analysis by Yang et al. found that the rs13181 variant G and rs1799793 variant T were poorly associated with OS in NSCLC patients [50]. Among Caucasian gastric cancer patients, rs13181 was not associated with OS, whereas the rs1799793 homozygous variant AA genotype was significantly linked to poor OS [51].

Stratification of OS analysis by cancer type revealed that both SNPs of ERCC2 were associated only with HNSCC patients in our cohort under a dominant genetic model. Our findings align with recent studies reporting worse OS for HNSCC patients with the ERCC2 rs13181 homozygous minor genotype and significantly better OS for those with the rs1799793 homozygous major genotype under the same model [52,53]. OS stratification for lung cancer patients showed no significant associations in our study. However, an Asian lung cancer study recently reported that ERCC2 rs13181 TG and TG + GG genotypes were significantly linked to worse OS under a dominant model [54]. These differing results may stem from the small sample size in our lung cancer cohort. While our findings showed no OS association with ERCC1 rs11615 and rs3212986, these SNPs have been widely studied in various cancers with varying survival outcomes. The G allele of rs11615 has been significantly associated with better OS, and the A allele of rs3212986 has been linked to improved PFS in a lung cancer study [55]. Conversely, a meta-analysis by Yang et al. found that the ERCC1 rs11615 T allele was associated with poor OS in NSCLC patients [50].

While this study provides significant insights into the genetic predictors of cisplatin-induced nephrotoxicity, several limitations should be acknowledged. Although we accounted for age and sex in our logistic regression models, other potential confounding factors, such as details of the chemotherapy regimens, pre-chemotherapy hydration status during treatment, and premedication protocols, were not included in the analysis. This is primarily because our study cohort consisted of patients with various tumor types who were treated with different cisplatin-containing regimens, making it challenging to uniformly account for these variables. These factors are known to influence nephrotoxicity outcomes and may have affected our findings. Additionally, the small sample size, single-institution retrospective study design, and limited number of SNPs, all of which are common in Thais, may restrict the generalizability of our findings to broader populations, highlighting the need for validation in larger and more diverse cohorts.

In summary, we demonstrated for the first time that EPHX1 rs1051740 heterozygous genotype is significantly associated with increased risk of AKD in co-dominant and over-dominant model among Thai cancer patients who received cisplatin. Furthermore, ERCC2 rs13181 and rs1799793 homozygous major are also confirmed to be associated with better OS in Thai HNSCC patients Table 5. Despite certain limitations, these findings highlight potential prognostic value of the analyzed SNPs, helping clinicians preliminarily identify patients more prone to developing AKD during cisplatin-based treatment, particularly in cases of locally advanced HNSCC, where cisplatin remains a mainstay of concurrent chemoradiotherapy [56]. The AKD-associated SNPs can be utilized to tailor treatment protocols for patients undergoing cisplatin-based therapy, advancing personalized chemotherapy approaches Table 5. Larger prospective studies across diverse populations are necessary to validate these genetic markers for personalized treatment approaches.

Table 5. Summary of key findings and their clinical implications.

Key Findings Potential clinical implications
EPHX1
rs1051740		 A tailored treatment protocol to reduce potential AKD occurrence (personalized chemotherapy approach)
ERCC2
rs13181
rs1799793		 Prognostic biomarkers for HNSCC patients
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Supporting information

S1 Fig. Sanger sequencing results for each SNP genotype.

(TIF)

pone.0324699.s001.TIF (4.1MB, TIF)
S2 Fig. Overall survival analyses of the 121 Head and Neck cancer cohort.

(TIF)

pone.0324699.s002.TIF (1.1MB, TIF)
S3 Fig. Overall survival analyses of the 39 lung cancer cohort.

(TIF)

pone.0324699.s003.TIF (1.1MB, TIF)
S4 Fig. Progression-free survival analyses of the 121 Head and Neck cancer cohort.

(TIF)

pone.0324699.s004.TIF (1.1MB, TIF)
S1 Table. Primer sequences to perform Sanger sequencing.

(PDF)

pone.0324699.s005.pdf (12.6KB, pdf)
S2 Table. Touchdown PCR conditions for primer optimization.

(PDF)

pone.0324699.s006.pdf (68.7KB, pdf)
S3 Table. Clinical characteristics of AKI and Non-AKI patients after receiving cisplatin chemotherapy.

(PDF)

pone.0324699.s007.pdf (75.5KB, pdf)
S4 Table. Association of the selected genetic polymorphisms with cisplatin-induced AKD in Head and Neck cancer cohort.

(PDF)

pone.0324699.s008.pdf (85.6KB, pdf)
S5 Table. Association of the selected genetic polymorphisms with cisplatin-induced AKD in Head and Neck and Esophageal cancer cohort.

(PDF)

pone.0324699.s009.pdf (45.7KB, pdf)
S6 Table. Association of the selected genetic polymorphisms with cisplatin-induced AKD in lung cancer cohort.

(PDF)

pone.0324699.s010.pdf (114.8KB, pdf)
S7 Table. Associated risk factors of Time-to-AKD in this cohort.

(PDF)

pone.0324699.s011.pdf (126.3KB, pdf)
S8 Table. Risk factors of overall survival.

(PDF)

pone.0324699.s012.pdf (124.6KB, pdf)
S9 Data File. The genotyping information and clinical metadata required to replicate the reported study findings.

(XLSX)

pone.0324699.s013.xlsx (24.1KB, xlsx)

Acknowledgments

We would like to thank Dr. Fah J Sathirapongsasuti for his valuable comments on the data analysis. We are also obliged to Dr. Meng-Shin Shiao and the Ramathibodi research center staff for their input and kind assistance in laboratory work throughout. Our sincere appreciation goes out to the Ramathibodi comprehensive tumor biobank team for their support in retrieval and allocation of patient samples.

List of Abbreviations:

CIN

Cisplatin Induced Nephrotoxicity

AKI

Acute Kidney Injury

AKD

Acute Kidney Disease

SNP

Single Nucleotide Polymorphisms

OCT2

Organic Cation Transporter 2

SLC22A2

Solute Carrier Family 22 member 2

ERCC1

Excision Repair Cross-Complementing 1

ERCC2

Excision Repair Cross-Complementing 2

EPHX1

Epoxide Hydrolase 1

XPD

Xeroderma Pigmentosum group D

DME

Drug Metabolizing Enzyme

PFS

Progression-Free Survival

OS

Overall Survival

CTCAE-AKI

Common Criteria Terminology for Adverse Events - Acute Kidney Injury

KDIGO

Kidney Disease Improving Global Outcomes

ICD-10

10th Revision of International Classification of Diseases

T-Rex

Thai Reference Exome Database

MAF

Minor Allele Frequency

FDR

False Discovery Rate

BH-FDR

Benjamini-Hochberg False Discovery Rate

HWE

Hardy-Weinberg Equilibrium

Data Availability

All relevant data are within the manuscript and its supporting information files. We have removed all identifying information. The genotyping information and clinical metadata required to replicate the reported study findings can be found in S9 Data file.

Funding Statement

This research project is supported by Mahidol University, and the National Science Research and Innovation (NSRF) via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation (PMU-B) [Grant numbers B01F650006, BCG.659 and B05F650041] (NJ). SA is a recipient of Master Degree Student Research Assistantship from the Faculty of Medicine Ramathibodi hospital, and the Scholarships for Ph.D. Students from Mahidol University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Emma Campbell

9 Sep 2024

EPHX1  and ERCC2

Dear Dr. Jinawath,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewer's Responses to Questions

Comments to the Author

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Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously? -->?>

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Reviewer #3: Yes

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Reviewer #1: In their manuscript PONE-D-24-18947 entitled “EPHX1 and ERCC2 polymorphisms are associated with cisplatin induced nephrotoxicity and prognosis in Thai cancer patients”, the authors have demonstrated that Single Nucleotide Polymorphisms (SNPs) can be used to identify cancer patients who are susceptible to developing cisplatin-induced nephrotoxicity (CIN). Their results reveal an association between EPHX1 rs1051740 and AKD, and confirms the previously reported associations between ERCC2 SNPs and OS."

The manuscript is well written, results are clear and the organization of the results makes it easy to follow. The authors have also addressed both strengths and limitations of the study. The article may be suitable for publication in the PLOS ONE journal.

Minor remark: can you provide a short list of abbreviations?

Reviewer #2: The manuscript was well written. Authors presented their findings in a clear and intelligible manner that was easy to follow. Authors may consider shortening the length of the discussion section to ease readability.

A data sharing statement was not included.

Reviewer #3: This manuscript presents significant findings on the genetic predictors of cisplatin-induced nephrotoxicity in Thai cancer patients. It is well-structured, ethically compliant, and methodologically rigorous. The results have considerable potential for clinical application. By identifying genetic markers associated with nephrotoxicity, chemotherapy treatments can be personalized, potentially improving the management of adverse effects. However, there are some limitations that need to be addressed.

Although the study accounts for age and sex in its logistic regression models, other potential confounders such as the details of the chemotherapy regimen, hydration status during treatment, and concurrent medications were not considered. These factors could greatly affect nephrotoxicity outcomes and should be included in the analysis.

The study's sample is restricted to Thai cancer patients treated at a single hospital. This geographic and ethnic uniformity limits the generalizability of the findings to a wider population. Differences in SNP frequencies among various ethnic groups mean that the results may not be applicable to non-Thai populations.

A more detailed discussion of the study’s limitations, including potential biases and the constraints of the analyzed SNPs, would offer a more balanced perspective on the findings and their implications.

**********

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PLoS One. 2025 Jun 17;20(6):e0324699. doi: 10.1371/journal.pone.0324699.r003

Author response to Decision Letter 1

13 Oct 2024

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response to the requirement #1:

We have revised the formatting according to PLOS ONE’S style requirement.

2. Thank you for stating the following financial disclosure:

"This research project is supported by Mahidol University, and the National Science Research and Innovation (NSRF) via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation (PMU-B) [Grant number B01F650006]; grant numbers BCG.659 and B05F650041 (NJ)." Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." If this statement is not correct you must amend it as needed.

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

Response to the requirement #2:

Please note that the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This statement has been added in both the cover letter and the main manuscript file under the “Funding” heading on page 2. Please see the added text below.

Funding

“This research project is supported by Mahidol University, and the National Science Research and Innovation (NSRF) via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation (PMU-B) [Grant numbers B01F650006, BCG.659 and B05F650041] (NJ). SA received “Master Degree Student Research Assistantship” scholarship from the Faculty of Medicine Ramathibodi hospital, Mahidol University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

3. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager.

Response to the requirement #3:

The corresponding author’s ORCID iD has been updated in the Editorial Manager.

4. We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data. We require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository (such as Figshare or Dryad) and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data.

Response to the requirement #4:

We apologize for the overlook of this requirement. Since these data do not form core part of our research, we have revised the information in “Results” section line number 409 and 462 on page 16 and 20, respectively. Please see the revised parts below.

“Upon adjusting the model with other clinical factors such as hypertension, diabetes, heart diseases and cerebrovascular diseases and histological types, also showed TC genotype of EPHX1 rs1051740 to be significantly associated with AKD in both co-dominant and over-dominant models, although the associations were no longer significant after FDR correction“.

“To analyze clinical and genetic factors, multivariate analysis was then performed using the statistically significant variables and potential AKD-associated clinical factors.”

Reviewer #1: In their manuscript PONE-D-24-18947 entitled “EPHX1 and ERCC2 polymorphisms are associated with cisplatin induced nephrotoxicity and prognosis in Thai cancer patients”, the authors have demonstrated that Single Nucleotide Polymorphisms (SNPs) can be used to identify cancer patients who are susceptible to developing cisplatin-induced nephrotoxicity (CIN). Their results reveal an association between EPHX1 rs1051740 and AKD, and confirms the previously reported associations between ERCC2 SNPs and OS." The manuscript is well written, results are clear and the organization of the results makes it easy to follow. The authors have also addressed both strengths and limitations of the study. The article may be suitable for publication in the PLOS ONE journal.

Response to Reviewer #1:

Thank you so much for taking the time to review our manuscript. We do appreciate your constructive comments.

Specific comments:

Minor remark: can you provide a short list of abbreviations?

Response to Reviewer #1:

List of abbreviations used in our manuscript is provided herein and, in the manuscript on page 3.

o CIN: Cisplatin Induced Nephrotoxicity

o AKI: Acute Kidney Injury

o AKD: Acute Kidney Disease

o SNP: Single Nucleotide Polymorphisms

o OCT2: Organic Cation Transporter 2

o SLC22A2: Solute Carrier Family 22 member 2

o ERCC1: Excision Repair Cross-Complementing 1

o ERCC2: Excision Repair Cross-Complementing 2

o EPHX1: Epoxide Hydrolase 1

o XPD: Xeroderma Pigmentosum group D

o DME: Drug Metabolizing Enzyme

o PFS: Progression-Free Survival

o OS: Overall Survival

o CTCAE-AKI: Common Criteria Terminology for Adverse Events - Acute Kidney Injury

o KDIGO: Kidney Disease Improving Global Outcomes

o ICD-10: 10th Revision of International Classification of Diseases

o T-Rex: Thai Reference Exome Database

o MAF: Minor Allele Frequency

o FDR: False Discovery Rate

o BH-FDR: Benjamini-Hochberg False Discovery Rate

o HWE: Hardy-Weinberg Equilibrium

Reviewer #2: The manuscript was well written. Authors presented their findings in a clear and intelligible manner that was easy to follow.

Specific comments:

1. Authors may consider shortening the length of the discussion section to ease readability.

2. A data sharing statement was not included.

Response to Reviewer #2:

Thank you very much for your kind comments and pointing out these points. We have revised the text accordingly as follows.

1. The discussion part has been made more concise where applicable. Please see the revised parts below.

Line 576-580, page 24 Revised: “The use of higher cisplatin doses in the past regimens may be one of the reasons for a higher AKI incidence seen in Patimarattananan et al. The recent improvement of hydration and supplementation regimens may also contribute to lower AKI incidence”.

Line 595 – 599, page 25 Revised: “Similar to our patient’s cohort, male sex, and lower baseline eGFR were among AKD progression factors [35]. Furthermore, a recent meta-analysis identified a higher mortality risk in the AKD group compared to the non-AKD group [34]”.

Line 609 – 615, page 25 Revised: “However, comorbidities including hypertension, diabetes mellitus and age did not show significant association in our study, whereas Patimarattananan et al. suggests that hypertension was significantly associated with AKD development. In line with the same study [1], kidney function after cisplatin treatment did not impact the OS in our study”.

Line 628 – 630, page 26 Deleted: “The extent and outcome of polymorphism’s effect varies, for example, decreased activity of EPHX1 gene is linked to increased risks of having alcohol dependence, liver cirrhosis and breast cancer”.

Line 635 – 638, page 26 Revised: “Lee et al has shown an association between genetically lower epoxide hydrolase activity and an increased risk of tobacco related cancers among smokers”.

Line 656 – 659, page 27 Deleted: “Interestingly, Khrunin et al. who also found rs1051740 heterozygous genotype to be significantly associated with CIN has proposed that EPHX1 might not be acting alone to cause CIN. There may be another gene such as EPHX2 which might exert its effect on the functions of EPHX1, and hence causing CIN”.

Line 711 – 716, page 29 Deleted: “Moreover, studies have shown that both ERCC1 and ERCC2 polymorphisms may influence freedom from loco-regional relapse (ffLRR) and survival outcomes in head & neck and lung cancer patients treated with platinum-based chemotherapies. However, the results from prior studies are inconsistent and does not reach a consensus regarding their clinical use, may be due to sample size and population differences”.

2. Data sharing statement has been included on page 2 of the main manuscript as follows.

Data sharing statement:

“All relevant data are within the manuscript and its supporting information files. We have removed all identifying information. The genotyping information and clinical metadata required to replicate the reported study findings can be found in S7 Data file.”

Reviewer #3: This manuscript presents significant findings on the genetic predictors of cisplatin-induced nephrotoxicity in Thai cancer patients. It is well-structured, ethically compliant, and methodologically rigorous. The results have considerable potential for clinical application. By identifying genetic markers associated with nephrotoxicity, chemotherapy treatments can be personalized, potentially improving the management of adverse effects. However, there are some limitations that need to be addressed.

Specific comments:

1. Although the study accounts for age and sex in its logistic regression models, other potential confounders such as the details of the chemotherapy regimen, hydration status during treatment, and concurrent medications were not considered. These factors could greatly affect nephrotoxicity outcomes and should be included in the analysis.

Response to Reviewer #3:

Thank you very much for the positive feedback and valuable suggestions. We do agree with the reviewer that other potential confounders, such as chemotherapy regimen details, hydration status, and concurrent medications, could influence nephrotoxicity outcomes. However, given that our study included patients with various tumor types treated with different cisplatin-containing regimens, it was challenging to uniformly account for these variables in our analysis. Additionally, detailed data on these factors were not consistently available for all patients, and the complexity of chemotherapy regimens and premedication protocols made it difficult to incorporate them comprehensively. We have acknowledged these limitations in the revised manuscript under the ‘Discussion’ section (Lines 722-729, page 30). Please see the added paragraph below.

“While this study provides significant insights into the genetic predictors of cisplatin-induced nephrotoxicity, several limitations should be acknowledged. Although we accounted for age and sex in our logistic regression models, other potential confounding factors, such as details of the chemotherapy regimens, pre-chemotherapy hydration status during treatment, and premedication protocols, were not included in the analysis. This is primarily because our study cohort consisted of patients with various tumor types who were treated with different cisplatin-containing regimens, making it challenging to uniformly account for these variables. These factors are known to influence nephrotoxicity outcomes and may have affected our findings.”

The study's sample is restricted to Thai cancer patients treated at a single hospital. This geographic and ethnic uniformity limits the generalizability of the findings to a wider population. Differences in SNP frequencies among various ethnic groups mean that the results may not be applicable to non-Thai populations.

2. A more detailed discussion of the study’s limitations, including potential biases and the constraints of the analyzed SNPs, would offer a more balanced perspective on the findings and their implications.

Response to Reviewer #3:

We appreciate your insightfulness and your suggestions regarding the study’s limitation. We have added the potential limitations and biases in more details in the Discussion (Lines 730 -732, page 30). Please see the revised part below.

“Additionally, the small sample size, single-institution retrospective study design, and limited number of SNPs analyzed, all of which are common in Thais may limit the generalizability of our findings to a wider population.”

Additionally, we have revised the last paragraph of the Discussion (Lines 734-743, page 30) to better summarize our findings and their potential application, as shown below:

“In summary, we demonstrated for the first time that EPHX1 rs1051740 heterozygous genotype is significantly associated with increased risk of AKD in co-dominant and over-dominant model among Thai cancer patients who received cisplatin. Furthermore, ERCC2 rs13181 and rs1799793 homozygous major are also confirmed to be associated with better OS in Thai HNSCC patients. Despite certain limitations, these findings highlight potential prognostic value of the analyzed SNPs, helping clinicians preliminarily identify patients more prone to developing AKD during cisplatin-based treatment, particularly in cases of locally advanced HNSCC, where cisplatin remains a mainstay of concurrent chemoradiotherapy (56). Larger prospective studies across diverse populations are necessary to validate these genetic markers for personalized treatment approaches.”

Reference:

56. Ngamphaiboon, N., et al., Evolving role of novel radiosensitizers and immune checkpoint inhibitors in (chemo)radiotherapy of locally advanced head and neck squamous cell carcinoma. Oral Oncol, 2023. 145: p. 106520.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0324699.s014.docx (40.6KB, docx)

Decision Letter 1

Milad Khorasani

27 Nov 2024

EPHX1  and ERCC2

Dear Dr. Jinawath,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #4: (No Response)

Reviewer #5: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #4: Yes

Reviewer #5: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #4: No

Reviewer #5: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #4: Yes

Reviewer #5: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #4: No

Reviewer #5: Yes

**********

Reviewer #4: The authors report the results of the study “EPHX1 and ERCC2 polymorphisms are associated with cisplatin-induced nephrotoxicity and prognosis in Thai cancer patients”. The authors have evaluated the effects of polymorphisms of six SNPs in the drug-metabolizing enzyme genes, SLC22A2 (rs316019) & EPHX1 (rs1051740), and the DNA repair genes, ERCC1 (rs11615 & rs3212986) & ERCC2 (rs13181 & rs1799793), and CIN in the 169 Thai patients with head and neck, lung, or esophageal cancer and whether they could on predict the cumulative incidence of AKD, progression-free survival (PFS), and overall survival (OS).

While acknowledging the research problem's significance and the contextualisation of results within the existing literature, it is essential to address certain ambiguities in the manuscript. The following significant concerns warrant the authors' attention:

A significant area for improvement of the manuscript is regarding the suitability of the study population to reach the study goal. The sample needs to be more important to achieve the primary objectives. Second, the manuscript is written poorly– which complicates the reading and the precise interpretation of the data.

I have significant comments for this manuscript:

1) The manuscript's language is acceptable but needs to be re-revised; there are word errors on the paper, so it needs to be controlled and corrected.

2) What was the enrollment period? Were all consecutive patients enrolled, and when were the data and follow-up censored?

3) Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection

4) How were the sample size and power calculated for this study?

5) A CONSORT diagram would help better understand which patients were removed from analyses for various reasons.

6) What were the patients' side effects after receiving the platinum chemotherapy? Please elaborate

7) Since chemotherapy is associated with drug toxicity, how dose delay or discontinuance was accounted for?

8) The authors have calculated the odds ratio to calculate the risk. Did the authors adjust the odds with other clinical covariates? Adjusting the odds with different covariates impacts clinical parameters and is crucial.

9) The therapeutic response and survival depend on tumour grade and stage; how was this factor accounted for in the data analysis

10) Cox regression analysis results should be shown (in tables, at least as supplementary data).

11) Another validation cohort should be used to validate the discovery cohort.

12) Insert in the conclusion a sentence or a short sentence that summarises the relevance of the results found in this study. It is essential to emphasise the significance of the results obtained from the objectives proposed by the authors.

13) No attempts have been made to collect information on the other confounding factors which would have influenced for genotyping variants.

14) Some limitations of the study should be discussed.

Reviewer #5: General Comments:

The manuscript, titled "EPHX1 and ERCC2 polymorphisms are associated with cisplatin-induced nephrotoxicity and prognosis in Thai cancer patients", explores an important clinical issue regarding genetic predisposition to cisplatin-induced nephrotoxicity. The study is well-structured and provides significant findings, particularly concerning the association of EPHX1 rs1051740 and ERCC2 SNPs with nephrotoxicity and survival outcomes.

The following comments address the review questions and include additional observations:

Specific Comments on the Manuscript:

Clarity of Hypothesis and Objectives:

The manuscript effectively outlines its objectives and hypotheses, focusing on SNPs linked to nephrotoxicity in Thai cancer patients. However, a clearer differentiation between novel findings and confirmation of existing knowledge would enhance the manuscript's impact.

Data Availability and Sharing:

While the authors have addressed data sharing requirements, ensuring that the data and supplementary files (e.g., S7 Data file) are easily accessible is crucial. Clarification regarding the repository details would improve transparency.

Methods:

The methods are detailed and appropriate for the study's aims. However, the choice of SNPs and their allele frequencies should be briefly discussed in the context of Thai population genetics.

Missing details on hydration regimens, concurrent medications, and cisplatin dosing specifics should be acknowledged as potential confounders.

Results:

The results are systematically presented, but some sections could benefit from additional context:

Highlight the clinical relevance of the significant findings, especially the EPHX1 rs1051740 association with AKD.

The Kaplan-Meier curves and survival analyses are insightful, though more detailed figures or supplementary visuals might aid interpretation.

Discussion:

While the discussion is comprehensive, consider further condensing to maintain focus on the key findings.

Address the implications of the findings for clinical practice, particularly regarding personalized chemotherapy approaches.

Study Limitations:

The authors acknowledge the study's limitations, including geographic and ethnic constraints. Emphasizing the need for validation in diverse cohorts would strengthen the discussion.

Ethical and Publication Concerns:

Ethical Compliance:

The manuscript states approval from the Human Research Ethics Committee and adherence to the Declaration of Helsinki. This is sufficient, though explicit mention of informed consent obtained for SNP analysis would reinforce compliance.

Publication Ethics:

There are no indications of dual publication or ethical concerns.

Recommendations for Improvement:

Include a concise abbreviation list in the manuscript's main body to improve readability.

Ensure that all cited data is either included in the manuscript or accessible through provided links.

Provide a summary table of key findings with clinical implications to improve accessibility for non-specialist readers.

Final Evaluation:

The manuscript is well-written and presents valuable findings that contribute to the field of oncology and pharmacogenomics. After addressing the aforementioned comments, the manuscript will be suitable for publication in PLOS ONE.

**********

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Reviewer #4: No

Reviewer #5: Yes:  Kazuo Kobayashi

**********

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PLoS One. 2025 Jun 17;20(6):e0324699. doi: 10.1371/journal.pone.0324699.r005

Author response to Decision Letter 2

1 Jan 2025

Reviewer’s Comments:

Reviewer #4: The authors report the results of the study “EPHX1 and ERCC2 polymorphisms are associated with cisplatin-induced nephrotoxicity and prognosis in Thai cancer patients”. The authors have evaluated the effects of polymorphisms of six SNPs in the drug-metabolizing enzyme genes, SLC22A2 (rs316019) & EPHX1 (rs1051740), and the DNA repair genes, ERCC1 (rs11615 & rs3212986) & ERCC2 (rs13181 & rs1799793), and CIN in the 169 Thai patients with head and neck, lung, or esophageal cancer and whether they could on predict the cumulative incidence of AKD, progression-free survival (PFS), and overall survival (OS).

While acknowledging the research problem's significance and the contextualisation of results within the existing literature, it is essential to address certain ambiguities in the manuscript. The following significant concerns warrant the authors' attention:

A significant area for improvement of the manuscript is regarding the suitability of the study population to reach the study goal. The sample needs to be more important to achieve the primary objectives. Second, the manuscript is written poorly– which complicates the reading and the precise interpretation of the data.

I have significant comments for this manuscript:

1) The manuscript's language is acceptable but needs to be re-revised; there are word errors on the paper, so it needs to be controlled and corrected.

Response to the Reviewer # 4:

Thank you for your kind suggestion. We have thoroughly checked the grammar and corrected the writing throughout the manuscript, where applicable:

Page 5 – Lines 126-130: “Despite being an effective anticancer drug, its usage can be delayed or terminated because of associated toxicities such as neurotoxicity, gastrointestinal toxicity, ototoxicity and nephrotoxicity [1]. Platinum could be retained in the plasma for as long as 20 years after discontinuation of cisplatin-based chemotherapy [2, 3], thus increasing concerns about the long-term nephrotoxicity risks [4].”

Pages 5-6 – Lines 143-152: “Clinically, CIN manifests in various forms, with Acute Kidney Injury (AKI) being the most common, occurring in 20-30% of patients [7]. Animal models of CIN demonstrates increased serum creatinine (SCr) and blood urea nitrogen, along with histopathological changes [8]. Though SCr is the main marker for renal function evaluation, it needs to be considered in conjunction with other factors such as age and sex. CIN is dose-dependent, with lower doses of cisplatin causing delayed kidney damage as compared to higher doses [9]. Cisplatin dosing regimens vary by cancer type, leading to differences in nephrotoxicity incidence. In lung and esophageal cancer treatments, ≥ 60 mg/m2 every 3 - 4 weeks and 75 mg/m2 on days 1 and 29 are administered, respectively [10, 11]. Gradually, in HNSCC, weekly 40 mg/m2 or 100mg/m2 every 3 weeks is being administered concurrently with radiotherapy [12].”

Page 6 – Line 154-159: “AKI may occur within 7 days of cisplatin administration, presented as increase in SCr, whereas Acute Kidney Disease (AKD) develops between 7 and 90 days of cisplatin initiation [13]. Prior studies have commonly used AKI as an endpoint for CIN based on different grading criteria; while the long-term or delayed effects of CIN are not widely studied. AKD, defined as renal damage occurring within 3 months of cisplatin administration, may prove to be a valuable endpoint for studying its delayed nephrotoxic effects [1, 14].”

Page 7 – Lines 178-185: “ERCC1 and ERCC2 or XPD are integral part of the NER pathway. SNPs in these genes - ERCC1 rs11615 and rs3212986, and ERCC2 rs13181 and rs1799793 - are linked to changes in the DNA repair process. Variations in these genes may cause a decrease in the helicase activity and DNA repair. ERCC1 plays a role in incising the DNA damage site, and can cause a rate-limiting effect, while ERCC2, is involved in helicase unwinding the DNA at the damaged site. Hence, these SNPs may reduce the efficiency of DNA repair mechanisms, leading to greater platinum-DNA adduct formation and hindered nephron repair following cisplatin exposure [19].”

Page 25 – Lines 576-580: “We found EPHX1 rs1051740 significantly associated with cisplatin-induced AKD in co-dominant and over-dominant model, particularly in TC pattern. To our knowledge, no prior studies have performed AKD association analysis with genetic polymorphisms. Multivariate analysis, identified lower baseline eGFR less than 90 ml/min/1.73m2 as another factor influencing AKD development after cisplatin treatment.”

Page 25 – Lines 586-596: “No consensus has been reached regarding SNPs associated with CIN, partly due to significant variations in minor allele frequencies across populations. Previous studies have shown association of these 6 selected SNPS with CIN using AKI as the endpoint. Notably, the minor allele frequency of EPHX1 rs1051740 differs in the Thai population compared to other populations [36]. Most commonly the reference allele is T, whose expression as homozygous TT genotype is associated with faster metabolism than CC. However, in Thai population, T is the minor allele, with a frequency of 0.49. The EPHX1 rs1051740 (c.337T>C), is one of the 2 widely studied polymorphisms of the EPHX1 gene. This variant results in a tyrosine-to- histidine substitution at position 113, leading to a 50% reduction in enzyme activity [37]. Decreased enzymatic activity may reduce the detoxification, increasing the formation of highly reactive metabolites, making the cells more susceptible to challenges.”

Page 26 – Lines 603-607: “Another meta-analysis found an association between rs1051740 polymorphism and HNSCC risk in population-based studies. [18]. While EPHX1 is well-studied regarding cancer risk, few studies have explored its relationship with CIN. To date, only Khrunin et al. have reported a higher frequency of CIN in ovarian cancer patients with the rs1051740 heterozygous genotype in a Russian population [15].”

Pages 26-27 – Lines 621-640: “ERCC1 and ERCC2 are key components of the NER pathway, activated by the formation of DNA adducts. Genetic variations in these genes may influence DNA repair capacity. Synonymous variants in the 3’ UTR region such as ERCC1 rs11615 and rs3212986, can affect protein stability, folding, structure, and expression [19, 45]. Previous studies by Khrunin et al. and Tzvetkov et al. reported an increased risk of CIN in ovarian and other cancers related to ERCC1 rs11615 and rs3212986 in Caucasian [25, 26]. However, Zazuli et al. found ERCC1 rs3212986 to reduce risk of CIN in Caucasian testicular cancer patients [17]. In East-Asians, Chen et al. found no significant association between CIN and ERCC1 rs11615 in Chinese lung cancer patients [46]. The association of ERCC2 with CIN has also been widely studied, but results vary due to differing endpoints and populations. Studies have shown an increased risk of CIN in Caucasian osteosarcoma and gastric cancer patients with ERCC2 rs13181 and rs1799793, respectively [27, 47], while in East-Asian lung cancer patients, Kim et al. did not find any association between CIN and ERCC2 rs13181 [48]. SLC22A2 rs316019, a nonsynonymous missense variant (p.270Ala>Ser; c.808G>T), impacts cisplatin uptake in renal proximal tubules and has been linked to CIN in multiple studies [16, 17, 21-24]. However, the reported effects of SLC22A2 rs316019 on CIN vary across populations and outcome definitions. Among East-Asians, Zazuli et al. suggested a protective effect of this SNP when using CTCAE-AKI criteria [17]. Notably, all these studies used AKI as the endpoint for CIN. In our study, none of the candidate SNPs from SLC22A2, ERCC1 and ERCC2 showed a significant association with AKD in any genetic model.”

Pages 27-28 – Lines 642-657: “Since AKD by itself can lead to poor OS in cancer patients, partly due to the resulting inadequate treatment, we further investigated the effect of these candidate SNPs on OS. In our study, although the OS analysis of AKD vs non-AKD groups was not statistically significant, we observed a trend indicating that patients without AKD had numerically longer OS compared to those with AKD. OS analysis for each selected SNP revealed no association with polymorphisms in SLC22A2, EPHX1 and ERCC1 genes. However, using a dominant genetic model, OS was associated with ERCC2 rs13181 and rs1799793: patients with ERCC2 rs13181-TT genotype and rs1799793-CC genotype exhibited better OS. Notably, these ERCC2 SNPs showed no association with AKD, suggesting their effects on OS are independent of AKD status. Previously, data on survival in relation to ERCC2 gene polymorphisms have varied across different populations and cancer types, limiting their application in clinical practice. Variant genotypes of ERCC2 rs13181 and rs1799793 have been significantly associated with poor survival in Chinese NSCLC patients [49]. A meta-analysis by Yang et al. found that the rs13181 variant G and rs1799793 variant T were poorly associated with OS in NSCLC patients [50]. Among Caucasian gastric cancer patients, rs13181 was not associated with OS, whereas the rs1799793 homozygous variant AA genotype was significantly linked to poor OS [51].”

Page 28 – Lines 659-670: “Stratification of OS analysis by cancer type revealed that both SNPs of ERCC2 were associated only with HNSCC patients in our cohort under a dominant genetic model. Our findings align with recent studies reporting worse OS for HNSCC patients with the ERCC2 rs13181 homozygous minor genotype and significantly better OS for those with the rs1799793 homozygous major genotype under the same model [52, 53]. OS stratification for lung cancer patients showed no significant associations in our study. However, an Asian lung cancer study recently reported that ERCC2 rs13181 TG and TG+GG genotypes were significantly linked to worse OS under a dominant model [54]. These differing results may stem from the small sample size in our lung cancer cohort. While our findings showed no OS association with ERCC1 rs11615 and rs3212986, these SNPs have been widely studied in various cancers with varying survival outcomes. The G allele of rs11615has been significantly associated with better OS, and the A allele of rs3212986 has been linked to improved PFS in a lung cancer study [55].”

Reviewer#4:

2) What was the enrollment period? Were all consecutive patients enrolled, and when were the data and follow-up censored?

Response to the Reviewer # 4:

Thank you for giving us the opportunity to elaborate the enrollment period further, please see the revised parts in the manuscript and as follows:

Pages 8-9 – Lines 218-223: “For HNSCC cohort, in our prospective multidisciplinary observation study of HNSCC and NPC patients at Ramathibodi Hospital, Mahidol University, we have accrued eligible patients since 2016. The study enrolled patients with LA-HNSCC and NPC treated according to the standard of care at their treating physician’s discretion. Enrollment was consistent, except during Covid-19 pandemic when the enrollment was hold, and subsequently resumed in 2021. Similarly, lung and esophageal cancer cohorts were also enrolled since 2016.”

Page 11 – Lines 300-301: “The survival status was cross-checked with the National Security Death Index of Thailand on 23/03/2022.”

Reviewer#4:

3) Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection

Response to the Reviewer # 4:

We appreciate the reviewer for their insightful comment. All enrolled patients were treated with cisplatin at Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand and were retrospectively identified with their available blood samples in Ramathibodi Comprehensive Tumor Biobank collected between 1st January 2016 to 30th June 2020. We also described the details in the “Patient Selection” section on Pages 8-9 – Lines 200-223 and here as follows:

“Clinical and treatment data were obtained from an in-house observational head and neck cancer cohort database, Ramathibodi Tumor Registry, pharmacy database, and medical records included patient demographics, serum creatinine (SCr), estimated glomerular filtration rate (eGFR; CKD-EPI), cancer staging and comorbidities (ICD-10). All clinical information was verified by two independent medical oncologists. The clinical data were initially accessed on 15th July 2020. The authors had access to information that could identify individual participants during data collection; however, no individually identifiable data was reported in this study.

Solid tumor patients treated with cisplatin chemotherapy at the Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand were retrospectively identified. Criteria used for patient inclusion were: head & neck, lung and esophageal cancer patients, age ≥18 years, availability of blood samples in Ramathibodi Comprehensive Tumor Biobank collected between 1st January 2016 to 30th June 2020. Samples were collected from the biobank for research purposes starting from 17th March 2021. Patients with known chronic kidney disease as defined by The Kidney Disease Improving Global Outcomes (KDIGO) guideline before the start of cisplatin-based treatment were excluded [14].

For HNSCC cohort, in our prospective multidisciplinary observation study of HNSCC and NPC patients at Ramathibodi Hospital, Mahidol University, we have accrued eligible patients since 2016. The study enrolled patients with LA-HNSCC and NPC treated according to the standard of care at their treating physician’s discretion. Enrollment was consistent, except during Covid-19 pandemic when the enrollment was hold, and subsequently resumed in 2021. Similarly, lung and esophageal cancer cohorts were also enrolled since 2016. The study enrolled patients with LA-HNSCC and NPC treated according to the standard of care at their treating physician’s discretion. Enrollment was consistent, except during Covid-19 pandemic when the enrollment was hold, and subsequently resumed in 2021. Similarly, lung and esophageal cancer cohorts were also enrolled since 2016.”

Reviewer#4:

4) How were the sample size and power calculated for this study?

Response to the Reviewer # 4:

Thank you for your comment. In our study, sample size and power were calculated using the STATA program, assuming 25% nephrotoxicity, which indicated 80% power in 169 patients. This allowed us to identify a statistically significant association between selected SNPs and AKD.

We have also mentioned power and sample size calculation on Page 11 – Lines 282 - 284 and here as follows:

“Power calculation showed that 169 patients assuming 25% nephrotoxicity would give our study 80% power to identify statistically significant association between selected SNPs and AKD with an alpha of 0.05 and odds ratio of 2.8.”

Reviewer#4:

5) A CONSORT diagram would help better understand which patients were removed from analyses for various reasons.

Response to the Reviewer # 4:

Thank you for your kind suggestion, we have added a CONSORT diagram as Figure 1 in the main figures and as follows:

Reviewer#4:

6) What were the patients' side effects after receiving the platinum chemotherapy? Please elaborate

Response to the Reviewer # 4:

We sincerely thank the reviewer for their insightful comment regarding the side effects experienced by patients after receiving platinum chemotherapy. In this study, we specifically focused on SNPs associated with cisplatin-induced nephrotoxicity. Consequently, the only toxicity data collected and analyzed pertained to nephrotoxicity. Unfortunately, information on other toxicities was not available in this dataset.

We acknowledge the importance of capturing a broader spectrum of chemotherapy-related toxicities and will consider this aspect for future research to provide a more comprehensive analysis.

Reviewer#4:

7) Since chemotherapy is associated with drug toxicity, how dose delay or discontinuance was accounted for?

Response to the Reviewer # 4:

Thank you for your valuable comment. Consistently accounting for this potential confounder was challenging due to the varying dosing regimens across tumor types. We acknowledge it’s imp

Attachment

Submitted filename: Response_to_Reviewers_auresp_2.docx

pone.0324699.s015.docx (263KB, docx)

Decision Letter 2

Milad Khorasani

11 Mar 2025

EPHX1  and ERCC2

Dear Dr. Jinawath,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Please submit your revised manuscript by Apr 25 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Milad Khorasani, PhD

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #5: All comments have been addressed

Reviewer #6: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #5: Yes

Reviewer #6: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #5: Yes

Reviewer #6: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #5: Yes

Reviewer #6: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #5: Yes

Reviewer #6: Yes

**********

Reviewer #5: The manuscript, titled "EPHX1 and ERCC2 polymorphisms are associated with cisplatin-induced nephrotoxicity and prognosis in Thai cancer patients," addresses a clinically relevant issue and presents robust findings on the association of genetic polymorphisms with cisplatin-induced nephrotoxicity and survival outcomes. The study is well-structured, with clear objectives, detailed methods, and comprehensive results.

Strengths:

The study investigates a significant clinical problem and fills gaps in existing knowledge regarding the association of SNPs with nephrotoxicity and survival in Thai cancer patients.

The inclusion of SNPs relevant to drug metabolism and DNA repair pathways enhances the study's translational potential.

The manuscript demonstrates clear implications for personalized chemotherapy, particularly in tailoring cisplatin-based treatments.

Recommendations:

The authors have adequately addressed the initial concerns and incorporated suggestions to improve the manuscript's clarity, focus, and presentation.

The addition of a CONSORT diagram and a key findings table significantly enhances the manuscript's accessibility for readers.

The discussion of limitations is transparent, and the authors have acknowledged the need for validation in larger, diverse cohorts.

Conclusion:

The manuscript is scientifically sound, addresses all reviewer concerns comprehensively, and meets the journal's standards for publication. It provides valuable insights that are likely to advance personalized medicine in oncology.

I recommend accepting the manuscript for publication in its current form.

Reviewer #6: Your study makes a significant contribution to understanding the genetic factors associated with AKD and demonstrates a well-thought-out approach to data analysis. Overall, your study is valuable and deserves attention within the research community. However, certain aspects, such as the choice of correction methods and inheritance models, could be further discussed to refine the conclusions.

1. Table 2 (L404) presents a comparison of allele frequencies between the T-REx database and the current study (based on the frequencies observed in non-AKD patients). Additionally, for better comparison, global MAF frequencies should be included in the table.

2. False Discovery Rate (FDR) was used for multiple comparisons, which is commonly applied in large-scale studies such as GWAS. However, given the small sample size and a limited number of comparisons (169 samples and 6 SNPs), Bonferroni correction is preferable to minimize false positives. In this case, a more balanced approach could be the Holm correction, which is less conservative than Bonferroni but provides better Type I error control than FDR.

3. Since the researchers applied five genetic models (codominant, dominant, recessive, overdominant, and additive), power analysis was conducted for each of them. However, the additive model is the most justified approach, as it is widely used in SNP association studies due to its general applicability and suitability for small sample sizes. Given the study parameters (n = 169, 6 SNPs), the additive model remains the optimal choice for power calculations, as it accounts for all possible genotypes and provides better sensitivity. However, if the minor allele frequency is particularly low, the dominant model should also be considered.

4. L482 (R2): The TC genotype of EPHX1 rs1051740 was significantly associated with an increased risk of AKD in the codominant model [OR 13.333, 95% CI 1.684 - 105.533, P = 0.014].

The confidence interval (CI) of 1.684 - 105.533 is extremely wide, indicating high uncertainty (low precision) in the effect estimation. This result cannot be considered reliable. Its interpretation should be approached with caution, as the large uncertainty makes the odds ratio (OR) estimate questionable. It is essential to check for the potential influence of outliers and conduct bootstrap estimation of the CI to assess its stability.

6.Were other factors, such as hydration status, concurrent medications, and chemotherapy regimens, considered in the study? Their incomplete accounting could introduce bias into the results.

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Reviewer #5: Yes:  Kazuo Kobayashi

Reviewer #6: Yes:  Alisher Abdullaev

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PLoS One. 2025 Jun 17;20(6):e0324699. doi: 10.1371/journal.pone.0324699.r007

Author response to Decision Letter 3

27 Mar 2025

Reviewer’s Comments:

Reviewer #6:

1) Table 2 (L404) presents a comparison of allele frequencies between the T-REx database and the current study (based on the frequencies observed in non-AKD patients). Additionally, for better comparison, global MAF frequencies should be included in the table.

Response to the Reviewer # 6:

Thank you for your valuable suggestion. Adding global MAF would certainly provide a better comparison. we have added the revised Table 2 on Page 16 and here:

Gene SNP SNP Information Global Frequencies T-REx Frequencies Data Allele Frequencies HWE P value

SLC22A2 rs316019 Missense c.808G>T (p.270Ala > Ser) C A C A C A 0.187

0.895 0.104 0.890 0.11 0.8289 0.1711

EPXH1 rs1051740 Missense c.337T>C (p.Tyr113His) C T C T C T 0.331

0.295 0.704 0.504 0.496 0.5036 0.4964

ERCC1 rs11615 Synonymous c.354T>C (p.Asn118Asn) G A G A G A 0.482

0.414 0.585 0.697 0.303 0.6 0.4

ERCC1 rs3212986 Synonymous 3’ UTR c*197G>T C A C A C A 0.261

0.745 0.254 0.664 0.336 0.7205 0.2795

ERCC2 rs13181 Missense c.2251A>C (p.751lys > Gln) T G T G T G 0.91

0.642 0.000 0.901 0.099 0.9108 0.0892

ERCC2 rs1799793 Missense c.934G>A (p.Asp312Asn) C T C T C T 0.889

0.684 0.315 0.937 0.063 0.9422 0.0578

Global frequencies were obtained from dbSNP (build 157), accessed on 13th March 2025.

The following sentences “Allele frequencies in Thai and global populations were respectively obtained from the Thai Reference Exome Database (T-REx), which contains data from 1,092 Thai individuals (accessed on 25th February 2022), and from dbSNP (build 157) (accessed on 13th March 2025) & “Allele frequencies were obtained from dbSNP (build 157; https://www.ncbi.nlm.nih.gov/snp/), and from the T-REx database [30]. are also added in the Materials and Methods (Line 248-251, pages 9-10) and table legend (Line 405-406, page 16), respectively.

2) False Discovery Rate (FDR) was used for multiple comparisons, which is commonly applied in large-scale studies such as GWAS. However, given the small sample size and a limited number of comparisons (169 samples and 6 SNPs), Bonferroni correction is preferable to minimize false positives. In this case, a more balanced approach could be the Holm correction, which is less conservative than Bonferroni but provides better Type I error control than FDR.

Response to the Reviewer # 6:

Thank you for your insightful comment. We have performed the Holm-Bonferroni correction for our main results and revised Table 3 accordingly in the manuscript and here as follows:

These sentences “Multiple comparisons were corrected by Benjamini-Hochberg false discovery rate (FDR) corrected P-values, and additionally with Holm-Bonferroni correction” & “although the associations were no longer significant after FDR and Holm-Bonferroni corrections” were also added in the Materials and Methods (Line 296-297, page 11) and Results (Line 421-422, page 17), respectively.

3) Since the researchers applied five genetic models (codominant, dominant, recessive, overdominant, and additive), power analysis was conducted for each of them. However, the additive model is the most justified approach, as it is widely used in SNP association studies due to its general applicability and suitability for small sample sizes. Given the study parameters (n = 169, 6 SNPs), the additive model remains the optimal choice for power calculations, as it accounts for all possible genotypes and provides better sensitivity. However, if the minor allele frequency is particularly low, the dominant model should also be considered.

Response to the Reviewer # 6

Thank you for your suggestion. Previously, we have calculated the power for the overall study regardless of the genetic model (Lines 283 - 286, Page 11). As recommended, we have performed power calculation for additive and dominant model. Details of genetic model power calculation were added in the manuscript (Lines 291 - 294, Page 11) and here. Results varied across the two models, 53% being the highest. While it is below the conventional threshold of 80%, suggesting limited statistical power to detect significant association due to small size, we acknowledged this as a study limitation in the manuscript (Lines 686 - 689, Page 29) and here.

Lines 283 - 286, Page 11: “Power calculations showed that a sample size of 169 patients, assuming 25% nephrotoxicity, would provide our study with 80% power to identify a statistically significant association between the selected SNPs and AKD, with an alpha of 0.05 and an odds ratio of 2.8.

Lines 291 - 294, Page 11 “For the additive and dominant genetic models, power calculations were conducted using STATA 14, power two proportions and RStudio 4.4.2 genpwr package, respectively. For the six SNPs analyzed, the estimated power varied with 53% being the highest”.

Lines 686 - 689, Page 29: “Additionally, the small sample size, single-institution retrospective study design, and limited number of SNPs, all of which are common in Thais, may restrict the generalizability of our findings to broader populations, highlighting the need for validation in larger and more diverse cohorts”.

4) L482 (R2): The TC genotype of EPHX1 rs1051740 was significantly associated with an increased risk of AKD in the codominant model [OR 13.333, 95% CI 1.684 - 105.533, P = 0.014].

The confidence interval (CI) of 1.684 - 105.533 is extremely wide, indicating high uncertainty (low precision) in the effect estimation. This result cannot be considered reliable. Its interpretation should be approached with caution, as the large uncertainty makes the odds ratio (OR) estimate questionable. It is essential to check for the potential influence of outliers and conduct bootstrap estimation of the CI to assess its stability.

Response to the Reviewer # 6

Table 4.1: Genotype frequency of EPHX1 rs1051740 under the codominant model in the HNSCC-only cohort

Codominant model No AKD AKD Total

CC 26 (96.3) 1 (3.7) 27 (100)

TC 39 (66.1) 20 (33.9) 59 (100)

TT 29 (82.86) 6 (17.14) 35 (100)

Total 94 (77.69) 27 (22.31) 121 (100)

We appreciate your observations regarding wide confidence interval (CI) of SNP rs1051740 and the reliability of this result.

Comparing to the combined three-cancer-type cohort, the sample size of our head and neck cancer-only cohort is smaller, and only has one case with the “CC” EPHX1 rs1051740 genotype (Table 4.1). This by itself could lead to the overestimation of results and contribute to a wider CI. To address this concern, we performed a bootstrap analysis with 1,000 repetitions. The bootstrap estimates, including the odds ratio (OR) and CI, are now included in the updated Supplementary Table S4A. Also, we added the sentences here: “Of note, bootstrap estimation was performed for the head and neck cancer-only cohort to correct for the wide confidence interval, which could lead to overestimation of the results S4A Table”. & “Our association analysis results show EPHX1 rs1051740 heterozygous genotype to be associated with AKD in our combined three-cancer-type cohort. This SNP was also significantly associated with AKD in the head and neck cancer-only cohort, although the confidence interval (CI) was wide, likely due to the smaller sample size” in our Results (Lines 489-491, Page 22) & Discussion (Lines 611 - 614, Page 26), respectively.

Additionally, we conducted a comparison between 1,000 and 10,000 bootstrap repetitions for this specific SNP. We found that the effect size (OR) remained consistent across both bootstrap iterations, and the improved confidence intervals were comparable when both repetitions were

used (Table 4.2). Based on these findings, we opted to use 1,000 bootstrap repetitions for all other SNPs and models as shown in the revised Supplementary Table S4A.

Furthermore, the model demonstrated good reliability and validity, with an AUC of 0.77 (Figure 4.1) and a non-significant Hosmer-Lemeshow test (p = 0.571) (Table 4.3) after adjusting for age and sex. These results suggest a good model fit and support the robustness of our findings.

Table 4.2: Bootstrap estimation comparison between 1,000 and 10,000 repetitions in EPHX1 rs1051740 co-dominant model in HNSCC-only cohort.

Bootstrap estimation repetitions

Co-dominant AKD Non-AKD 1,000 repetitions P value 10,000 repetitions P value

CC 1 (3.7) 26 (27.7) 1 1

TC 20 (74.1) 39 (41.5) 17.319 (5.131-58.460) <0.001 17.319 (5.131-58.460) <0.001

TT 6 (22.2) 29 (30.8) 5.824 (1.357-24.985) 0.018 5.824 (1.408 - 24.080) 0.015

Figure 4.1: AUC for the EPHX1 rs1051740

Table 4.3: Hosmer-Lemeshow test for EPHX1 rs1051740 in HNSCC-only cohort shows a non-significant result

Logistic model for AKD event goodness-of-fit test:

Number of observations 121

Number of covariate patterns 119

Pearson chi2(114) 110.64

Prob > chi2 0.5715

5) Were other factors, such as hydration status, concurrent medications, and chemotherapy regimens, considered in the study? Their incomplete accounting could introduce bias into the results.

Response to the Reviewer # 6:

Thank you for raising this valid concern. We agree that these factors could introduce bias into the results. However, we acknowledge these as limitations of our study and addressed this in the manuscript on Line 678 – 685, Page 29 and here as follows:

“While this study provides significant insights into the genetic predictors of cisplatin-induced nephrotoxicity, several limitations should be acknowledged. Although we accounted for age and sex in our logistic regression models, other potential confounding factors, such as details of the chemotherapy regimens, pre-chemotherapy hydration status during treatment, and premedication protocols, were not included in the analysis. This is primarily because our study cohort consisted of patients with various tumor types who were treated with different cisplatin-containing regimens, making it challenging to uniformly account for these variables. These factors are known to influence nephrotoxicity outcomes and may have affected our findings. “

Attachment

Submitted filename: Response_to_Reviewers_auresp_3.docx

pone.0324699.s016.docx (97.7KB, docx)

Decision Letter 3

Milad Khorasani

30 Apr 2025

EPHX1  and ERCC2 polymorphisms are associated with cisplatin induced nephrotoxicity and prognosis in Thai cancer patients

PONE-D-24-18947R3

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Reviewers' comments:

Acceptance letter

Milad Khorasani

PONE-D-24-18947R3

PLOS ONE

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Sanger sequencing results for each SNP genotype.

    (TIF)

    pone.0324699.s001.TIF (4.1MB, TIF)
    S2 Fig. Overall survival analyses of the 121 Head and Neck cancer cohort.

    (TIF)

    pone.0324699.s002.TIF (1.1MB, TIF)
    S3 Fig. Overall survival analyses of the 39 lung cancer cohort.

    (TIF)

    pone.0324699.s003.TIF (1.1MB, TIF)
    S4 Fig. Progression-free survival analyses of the 121 Head and Neck cancer cohort.

    (TIF)

    pone.0324699.s004.TIF (1.1MB, TIF)
    S1 Table. Primer sequences to perform Sanger sequencing.

    (PDF)

    pone.0324699.s005.pdf (12.6KB, pdf)
    S2 Table. Touchdown PCR conditions for primer optimization.

    (PDF)

    pone.0324699.s006.pdf (68.7KB, pdf)
    S3 Table. Clinical characteristics of AKI and Non-AKI patients after receiving cisplatin chemotherapy.

    (PDF)

    pone.0324699.s007.pdf (75.5KB, pdf)
    S4 Table. Association of the selected genetic polymorphisms with cisplatin-induced AKD in Head and Neck cancer cohort.

    (PDF)

    pone.0324699.s008.pdf (85.6KB, pdf)
    S5 Table. Association of the selected genetic polymorphisms with cisplatin-induced AKD in Head and Neck and Esophageal cancer cohort.

    (PDF)

    pone.0324699.s009.pdf (45.7KB, pdf)
    S6 Table. Association of the selected genetic polymorphisms with cisplatin-induced AKD in lung cancer cohort.

    (PDF)

    pone.0324699.s010.pdf (114.8KB, pdf)
    S7 Table. Associated risk factors of Time-to-AKD in this cohort.

    (PDF)

    pone.0324699.s011.pdf (126.3KB, pdf)
    S8 Table. Risk factors of overall survival.

    (PDF)

    pone.0324699.s012.pdf (124.6KB, pdf)
    S9 Data File. The genotyping information and clinical metadata required to replicate the reported study findings.

    (XLSX)

    pone.0324699.s013.xlsx (24.1KB, xlsx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0324699.s014.docx (40.6KB, docx)
    Attachment

    Submitted filename: Response_to_Reviewers_auresp_2.docx

    pone.0324699.s015.docx (263KB, docx)
    Attachment

    Submitted filename: Response_to_Reviewers_auresp_3.docx

    pone.0324699.s016.docx (97.7KB, docx)

    Data Availability Statement

    All relevant data are within the manuscript and its supporting information files. We have removed all identifying information. The genotyping information and clinical metadata required to replicate the reported study findings can be found in S9 Data file.

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