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Article

Association of ABC Efflux Transporter Genetic Variants and Adverse Drug Reactions and Survival in Patients with Non-Small Lung Cancer

by
Cecilia Souto Seguin
1,†,
Giovana Fernanda Santos Fidelis
1,†,
Carolina Dagli-Hernandez
2,3,
Pedro Eduardo Nascimento Silva Vasconcelos
1,
Mariana Vieira Morau
1,2,
Yasmim Gabriele Matos
1,
Maurício Wesley Perroud, Jr.
1,
Eder de Carvalho Pincinato
1 and
Patricia Moriel
1,2,*
1
School of Medical Sciences, Universidade Estadual de Campinas, Campinas 13083894, Brazil
2
Faculty of Pharmaceutical Sciences, Universidade Estadual de Campinas, Campinas 13083970, Brazil
3
Department of Pharmacy, School of Pharmaceutical Sciences, Universidade de São Paulo, São Paulo 05508000, Brazil
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2025, 16(4), 453; https://doi.org/10.3390/genes16040453
Submission received: 12 March 2025 / Revised: 10 April 2025 / Accepted: 13 April 2025 / Published: 15 April 2025
(This article belongs to the Section Molecular Genetics and Genomics)
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Abstract

:
Background/Objectives: Lung cancer has a high mortality rate worldwide, with non-small cell lung cancer (NSCLC) being the most prevalent. Carboplatin and paclitaxel are key treatments for NSCLC; however, adverse drug reactions (ADRs) pose significant challenges. This study examined the impact of genetic variations in ABCB1 and ABCC2 genes on the incidence of ADRs and survival in NSCLC patients treated with carboplatin and paclitaxel. Methods: Variants were identified using RT-PCR, and ADRs classified according to the Common Toxicity Criteria for Adverse Events, Version 4.03. Results: The ABCB1 rs1128503 (c.1236C>T) CC genotype was associated with a higher chance of nausea (OR: 3.5, 95% CI 1.367–9.250, p = 0.0093), vomiting (OR: 13.553, 95% CI 1.705–107.723, p = 0.0137), and a higher risk of death in CT or TT genotypes (HR: 1.725, 95% CI 1.036–2.871, p = 0.0361). The ABCC2 rs717620 (c.-24C>T) TT genotype was associated with increased ALP levels (OR: 14.6, 95% CI 1.234–174.236, p = 0.0335). The ABCB1 rs2032582 non-CC genotypes (TT+AA+TA+CA+CT) were associated with an increased risk of death (HR: 1.922, 95% CI 1.093–3.377, p = 0.0232). Patients with hypocalcemia (HR: 2.317, 95% IC 1.353–3.967, p = 0.022), vomiting (HR: 3.047, 95% IC 1.548–5.997, p = 0.0013), and diarrhea (HR: 2.974, 95% IC 1.590–5.562, p = 0.0006) were associated with lower overall survival. Conclusions: The data suggest that ABCB1 variants may influence gastrointestinal ADRs and patient survival, highlighting the importance of pharmacogenomics in predicting ADRs and drug resistance. This approach offers more precise pharmacotherapy, reduces ADRs, and enhances the patients’ quality of life and survival.

1. Introduction

Lung cancer is the leading cause of cancer-related morbidity and mortality worldwide. The American Cancer Society estimates that approximately 340 individuals die of lung cancer every day in the United States, with a projection of 124,730 deaths in 2025 [1]. In Brazil, a total of 28.964 deaths due to lung cancer was observed in 2021 [2]. Non-small cell lung carcinoma (NSCLC) accounts for 85% of lung cancer cases and has a poor prognosis, as evidenced by an estimated 5-year survival of 26.4% [3]. The main risk factor for NSCLC development is tobacco smoking, which contributes to approximately 90% of the cases [4].
According to the guidelines published by the European Society of Medical Oncology for NSCLC, the use of carboplatin continues to be considered as the gold standard in the treatment of NSCLC [5,6]. Although carboplatin-paclitaxel is a widely used therapeutic regimen, it can cause multiple adverse drug reactions (ADRs), resulting in dose reductions or even the temporary discontinuation of chemotherapy. This can ultimately lead to treatment delays and compromise both disease control and patient survival [7,8].
Genetic variations in influx and efflux transporters involved in the pharmacokinetics of carboplatin and paclitaxel may alter their efficacy and increase the risk of ADRs associated with chemotherapy [9,10]. ATP-binding cassette (ABC) transporters comprise a superfamily of proteins that use ATP hydrolysis energy to transport endogenous and exogenous substances across the cell membrane and are expressed in the kidneys, lungs, liver, and small intestine [11]. ABCB1 and ABCC2 genes encode two drug transporters, ABCB1 and ABCC2, involved in the transport of platinum [12]. Variants in these genes may influence the absorption, distribution, and excretion of these drugs, resulting in the intracellular accumulation of platinum and variability in paclitaxel pharmacokinetics, which may, in turn, contribute to the development of ADRs [13,14].
Recently, several studies have evaluated the influence of SNVs in ABC transporter genes on the development of ADRs in response to chemotherapy [15]. ABCB1 has been extensively described in the literature, with its most studied genetic variants being rs1045642 (c.3435A>G), rs1128503 (c.1236C>T), and rs2032582 (c.2677A>C/T) [16]. These variants have been associated with altered ABCB1 expression or activity in in vitro studies [17,18,19,20,21,22,23]. Genotyping of this transporter is highly relevant in oncological treatments [10,24,25,26,27]. For example, the ABCB1 rs1128503 (c.1236C>T) genotype is strongly associated with hepatic ADRs in patients with lung cancer undergoing platinum-based doublet chemotherapy [11]. Another study reported that the heterozygous genotype AG of ABCB1 rs1045642 (c.3435A>G) was associated with a lower risk of hematological toxicity compared to homozygous CC [28]. Concerning the ABCC2 transporter, the ABCC2 rs717620 (c.24C>T) variant has been demonstrated to be associated with overall survival (OS) in patients with advanced NSCLC [29].
Given the pharmacogenetic importance of ABCB1 and ABCC2 variants, this study aimed to identify the mechanisms by which genetic variations may contribute to the induction of ADRs and impact survival in patients with NSCLC treated with carboplatin and paclitaxel.

2. Materials and Methods

2.1. Study Design and Set-Up

This prospective cohort study was conducted at the Onco-pneumology Ambulatory Hospital of the Universidade Estadual de Campinas (Campinas, São Paulo, Brazil). The patients were recruited from March 2018 to October 2022. Patients diagnosed with NSCLC were followed-up to assess the occurrence of hematological, renal, hepatic, and gastrointestinal ADRs during the first 21-day cycle of chemotherapy with carboplatin and paclitaxel. Clinical and demographic data were collected via interviews and medical charts reviews. Hematological and biochemical tests were performed before (D0) and 21 days after (D20) the first chemotherapy session.
This study was approved by the Ethics Committee of the Universidade Estadual de Campinas (protocol code: 83196318.8.0000.5404) and written informed consent was obtained from each patient.

2.2. Patients

Patients aged ≥18 years with histologically confirmed NSCLC, a Karnofsky Performance Status (KPS) ≥ 60%, and who were scheduled to receive their first 21-day chemotherapy cycle with paclitaxel (200 mg/m2) in combination with carboplatin (AUC = 5/6 according to Calvert’s formula) were included. Only genetically unrelated individuals were included in the study. Patients were excluded if they had already received prior chemotherapy; received the first paclitaxel and carboplatin cycle at another hospital; were diagnosed with hepatitis, HIV, or a psychiatric disorder that would limit their communication; had undergone any change in the chemotherapy protocol during the treatment; or refused to participate in the study.
Sample size was calculated using the Raosoft® sample size calculator http://www.raosoft.com/samplesize.html (accessed on 5 April 2025). For this analysis, we considered a response distribution of 19%, which is the frequency of ABCC2 rs717620 (c.-24C>T), a power of 80%, and margin of error of 5%, resulting in a minimum sample size of 101 patients.

2.3. Data Collection

Demographic and clinical data were collected through interviews and medical chart reviews. Demographic data included age, sex, comorbidities, self-reported ethnicity, and continuous use of medications. Smoking status was assessed using the smoking index (SI), which was determined by multiplying the number of cigarettes smoked per day with the total number of years smoked [30] (Jindal et al., 1982). Patients were classified as non-smokers (SI = 0), light smokers (SI = 1–100), moderate smokers (SI = 101–200), and heavy smokers (SI ≥ 300). Alcohol consumption was classified according to the scale by Whitcomb et al. (2008) [31], which evaluates the number of alcohol doses consumed during the period of maximal consumption in a patient’s life [32]. Patients were classified as abstainers (<20 doses throughout their entire life), light drinkers (≤3 doses/week), moderate drinkers (women: 4–7 doses/week; men: 4–14 doses/week), heavy drinkers (women: 8–34 doses/week; men: 15–34 doses/week), and very heavy drinkers (≥35 doses/week).
Clinical data included body mass index and KPS, as determined by the medical team [33]. Tumor histological type and size, as well as the presence of metastasis, were determined by the medical team and collected from the medical charts. The tumor stage was classified using the TNM system.

2.4. Adverse Reactions Assessment

To determine the occurrence of ADRs, blood samples were collected before (D0) and after (D20) the first 21-day paclitaxel and carboplatin chemotherapy cycle. Hematological, renal, hepatic, and gastrointestinal ADRs, and their respective grades were determined using the Common Terminology Criteria for Adverse Events (CTCAE; v.4) (National Cancer Institute, 2009).
The following hematological ADRs were assessed: anemia, leukopenia, neutropenia, lymphopenia, and thrombocytopenia. For renal ADRs, the following events were recorded: hyperuricemia, hyponatremia, hypomagnesemia, hypokalemia, hypophosphatemia, hypocalcemia, increased serum creatinine levels, and reduced creatinine clearance. The evaluated hepatic parameters included hypoalbuminemia and increased aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and total bilirubin (TB). Data on gastrointestinal ADRs, nausea, vomiting, and diarrhea were collected through patient interviews and medical chart reviews.

2.5. ABCB1 and ABCC2 Genotyping

Genomic DNA was extracted from peripheral blood and purified using a Wizard® Genomic DNA Purification Kit (Promega, Madison, WI, USA). DNA concentration and integrity were verified by analyzing the A260/280 ratio with a NanoDrop-1000-Detector (Thermo Fisher Scientific, Waltham, MA, USA). Definitive quantification was performed using the Quantus fluorometerTM and the respective kit (QuantiFluorTM dsDNA System, Promega, Madison, WI, USA). All samples were identified and adjusted for 20 ng/µL and were later stored at −20 °C for further use.
ABCB1 rs1045642 (c.3435A>G), rs1128503 (c.1236C>T), rs2032582 (c.2677A>C/T), and ABCC2 rs717620 (c.-24C>T) were genotyped using Taqman® assay (Thermo Fisher Scientific, Waltham, MA, USA). The amplification reaction mixture (10 µL) contained 2 µL DNA (20 ng/µL), 5 µL TaqMan™ Genotyping Master Mix, 2.5 µL DNAse-RNAse free water, and 0.5 µL TaqMan™ SNP Genotyping assays. A real-time polymerase chain reaction (RT-PCR) was performed using the Rotor-Gene Q 5plex HRM System (Qiagen, Hilden, Germany) with an initial phase of 2 min at 60 °C to acclimatize the reagents, an enzymatic activation phase of 10 min at 95 °C, followed by 50 cycles of denaturation, annealing, and extension at 95 °C for 15 s and 60 °C for 1 min. Allelic discrimination was performed using Rotor-Gene Q series software (Version 2.3.0).

2.6. Statistical Analysis

Categorical variables were expressed as number and frequency. Continuous variables were expressed as mean ± standard deviation. Fisher’s exact test, univariate logistic regression, and Cox regressions were conducted to analyze the associations between clinical and demographic variables (age, sex, ethnicity, smoking, alcohol consumption, and presence of comorbidities), ABCB1 and ABCC2 variants, and ADRs. The Hardy–Weinberg equilibrium (HWE) was tested using the χ2 goodness-of-fit test (p ≥ 0.05). Significant associations (p < 0.05) were further tested in multivariate logistic analyses, using the “Stepwise regression” method, for the calculation of adjusted odds ratios (ORadjusted) and respective 95% confidence intervals (95% CI). Univariate and multivariate logistic regression analyses were performed using the recessive and dominant models. Relevant clinical variables, such as genetic variants, age, sex, ethnicity, smoking, alcoholism, and comorbidities, were considered covariates in the multivariate analysis. The dominant (AA vs. AG+GG; CC vs. CT+TT) and recessive (GG vs. AG+AA; TT vs. CT+CC) models were utilized to target ABCB1 rs1045642 (c.3435A>G), ABCB1 rs1128503 (c.1236C>T), and ABCC2 rs717620 (c.-24C>T). For rs2032582 (c.2677A>C/T), the models were designed as CC vs. non-CC (TT+AS+TA+CA+CT), and AA vs. non-AA (TT+CC+AT+CA+CT). Individual features (clinical and histopathological variables and genotypes) were evaluated for their association with the occurrence of adverse reactions using the Chi-square test or Fisher’s exact test (categorical data). Survival and OS were estimated using a Kaplan–Meier analysis, and factors associated with survival were estimated using a multiple Cox regression analysis. OS was measured from the time of diagnosis until death or the last follow-up. The results were considered statistically significant at p < 0.05. Statistical tests were performed using SAS v. 9.4 (SAS Institute Inc., 2002–2012, Cary, NC, USA).

3. Results

3.1. Demographic and Clinical Characteristics of Patients

One hundred and seventy-seven (177) NSCLC patients who qualified through the inclusion criteria for this study started their first chemotherapy cycle with the carboplatin and paclitaxel protocol. Out of 177, 65 patients were excluded because of several reasons, such as death before the initiation of protocol (15), a change in protocol (19), chemotherapy suspension (14), patient withdrawal (3), and incomplete data collection due to the COVID-19 pandemic (14), totaling 113 patients to be included for ADR analysis. Furthermore, blood samples from three patients were not available for DNA extraction, reducing the total number of patients who were eligible for ABCB1 and ABCC2 genotyping to 110.
The patients were predominantly males (53.1%), with a mean age of 63 years, whites (82%), light drinkers (34.5%), and heavy smokers (38.1%) (Table 1). Most patients had no comorbidities (57.5%) and had not undergone previous surgery (96.5%). Adenocarcinoma was found to be the most common histological type (58.4%). According to the TNM classification, the tumors were mostly categorized as T4 (69.9%), N2 (47.8%), and M1 (48.7%) (Table 2).

3.2. Clinical Parameters

The clinical parameters for the assessment of hematological, renal, and hepatic ADRs are depicted in Table S1. Statistically significant reductions were observed between baseline (D0) and D20 for the hematological parameters, including hemoglobin, leukocytes, neutrophils, and platelets. Reductions were also observed in the renal parameters of calcium, magnesium, and urea, as well as in the hepatic parameters of TB and total proteins.

3.3. Occurrence and Severity of ADRs

In this cohort, 93 (82.3%) patients experienced ADRs. Of these, 60 (53.1%) had renal ADRs, 59 (52.2%) exhibited hematological reactions, 39 (34.5%) suffered from gastrointestinal events, and 36 (31.9%) showed symptoms of hepatic ADRs.
The observed ADRs and their grades, according to the CTCAE classification (CTCAE; v.4), are described in Table 3. The most frequent ADRs were anemia (39.8%), nausea (26.5%), hypocalcemia (26.3%), and reduced creatinine clearance (25.2%), whereas the most severe ADR was grade 4 leukopenia, which affected one (0.9%) patient. However, most events were classified as grade 1 or 2.

3.4. Frequency of ABCB1 and ABCC2 Variants

The frequencies of ABCB1 rs1045642 (c.3435A>G), rs1128503 (c.1236C>T), rs2032582 (c.2677A>C/T), and ABCC2 rs717620 (c.-24C>T) genotypes and alleles are shown in Table S2. The distributions of all genotypes were in HWE (p ≥ 0.05).
Regarding ABCB1 rs1128503 (c.1236C>T), 45.5% of the patients were homozygous for the variant C allele (CC), whereas for ABCB1 rs1045642 (c.3435A>G), the majority were heterozygous for AG (46.2%). Most patients were heterozygous carriers of the AC, CT, or AT genotypes (45.4%) of the ABCB1 triallelic variant, rs2032582 (c.2677A>C/T). C allele was the most frequent (65.7%), with 44.4% homozygous carriers (CC) (Table S2).
For the ABCC2 rs717620 c.-24C>T variant, most patients were homozygous for the reference allele C (CC, 70.0%). The allele frequency observed in this study closely corresponded to that reported for the Brazilian population. Statistically significant differences (p < 0.0001) were found between the global and Brazilian allele frequency of ABCB1 gene variants (Table S2).

3.5. Association of Clinical and Demographic Data with ADRs

A univariate logistic regression was performed to analyze the associations between clinical characteristics and ADR development. For this analysis, only CTCAE grade ≥1 ADRs that occurred in more than 10% of the patients were included. The following clinical and demographic variables were analyzed: age, sex, ethnicity, smoking status, alcohol consumption, and presence of comorbidities.
All univariate logistic regression analyses are reported in Supplementary Materials (Tables S3–S6). Women were more likely than men to experience nausea (OR: 2.489, 95% CI 1.051–5.894, p = 0.0381). Advanced age increased the frequency of hepatic ADR hypoalbuminemia (OR: 1.091, 95% CI 1.011–1.178, p = 0.0250) and renal ADR hypocalcemia (OR: 1.102, 95% CI 1.025–1.185, p = 0.0087). Non-smokers also had more hepatic reactions, observed by ALP increase (OR: 4.6, 95% CI 1.409–15.258, p = 0.0116). No association was found between the remaining ADRs and demographic and clinical variables. These significant associations (p < 0.05) were analyzed using a multivariate regression analysis; however, no statistically significant associations were observed.

3.6. Association of ABCB1 and ABCC2 Variants with ADRs

Univariate logistic regression analyses were performed to verify the association between ADRs and ABCB1 and ABCC2 variants (Supplementary Tables S7–S10).
The ABCC2 rs717620 (c.-24C>T) genotype demonstrated a significant association with hepatic ADRs in a univariate logistic regression analysis (Table S7). In the recessive model (TT vs. CT+CC), patients carrying the TT genotype were more likely to show an increase in ALP (OR: 14.3, 95% CI: 1.211–169.086, p = 0.0347), compared to CT and CC genotype carriers. This association remained significant after a multivariate analysis (OR: 14.6, 95% CI 1.234–174.236; p = 0.0335) (Table 4).
Variants in ABCB1 were associated with gastrointestinal ADRs (Table S8). Individuals carrying ABCB1 rs1128503 (c.1236C>T) CC+CT genotypes had a higher risk of developing nausea (OR: 3.5, 95% CI 1.367–9.250, p = 0.0093) and vomiting (OR: 13.553, 95% CI 1.705–107.723, p = 0.0137) than those with the TT genotype. Additionally, CT+TT genotypes were associated with an increased risk of experiencing diarrhea (p = 0.038); however, none of the individuals with CC reported having diarrhea. Similarly, ABCB1 rs2032582 (c.2677A>C/T) non-AA genotypes (TT+CT+AT+CA+CT) were associated with nausea (p = 0.0341); however, none of the individuals with AA reported nausea. Since no cases were found for either of the aforementioned variants, the odds ratios could not be estimated. Despite these notable associations identified in the univariate logistic regression analyses, no significant association was found in the multivariate analysis.
Other ADRs, such as hematological and renal ADRs, were not associated with ABCB1 and ABCC2 variants in this analysis (Supplementary Tables S9 and S10).

3.7. Survival Analysis

A total of 65 (57.5%) patients had died by the end of the study period. The average OS was 1.68 ± 0.15 years, equivalent to approximately 1 year and 8 months. The probability of survival was 2.58 years (approximately 2 years and 7 months) for 75%, 1.21 years (1 year and 2 months) for 50%, and 0.64 years (8 months) for 25% of the patients (Figure S1).
OS was assessed using a univariate Cox regression analysis, examining the association between clinical and demographic data or ADRs and survival. Only ADRs that occurred in more than 10% of patients with grade ≥1 were included in the analysis.
The results of the univariate Cox regressions associating clinical and demographic data with survival are elucidated in Table S11. The analysis showed that smokers had a 5.048 times greater risk of death than non-smokers (95% CI 2.004–12.711, p = 0.0006). The other clinical and demographic parameters were not associated with survival.
The risk of death associated with ADRs is presented in Table S12. Patients with hypocalcemia showed a 2.317 times higher risk of death than those without hypocalcemia (95% CI 1.353–3.967, p = 0.022). Additionally, patients who experienced vomiting and diarrhea showed 3.047 (95% CI 1.548–5.997, p = 0.013) and 2.974 (95% CI 1.590–5.562, p = 0.0006) times greater risks of death, respectively. The other ADRs were not associated with survival (Table S12).
The risk of death associated with ABCB1 and ABCC2 variants is described in Table S13. Death associated with ABCB1 variants is shown in Figure 1 and Figure 2. Patients with ABCB1 rs2032582 non-CC (TT+AA+TA+CA+CT) genotypes had a higher risk of death when compared with patients carrying the CC genotype (HR: 1.795, 95% CI: 0.836–3.784, p = 0.0265). The multivariate analysis, which adjusted for genetic variants, age, sex, ethnicity, smoking, alcoholism, and comorbidities, still revealed an increased risk of death (HR: 1.922, 95% CI 1.093–3.377, p = 0.0232) (Table 5). Likewise, patients carrying ABCB1 rs1128503 CT or CC genotypes had a higher risk of death than those carrying the TT genotype (HR: 1.725, 95% CI 1.036–2.871, p = 0.0361). However, no association was found in the multivariate regression analysis. Other genotypes and variants showed no significant associations with survival (Table S13).

4. Discussion

The carboplatin–paclitaxel regimen remains a cornerstone of chemotherapy in adjuvant, neoadjuvant, and palliative settings for NSCLC. Since its introduction over two decades ago, it has been extensively documented in clinical trials and case reports [34]. Despite significant advances in new therapies for NSCLC, platinum-based chemotherapy remains the standard treatment, recommended in international guidelines, such as the European Society for Medical Oncology for advanced/metastatic NSCLC guideline [6]. However, the success of this therapy may be compromised by the development of chemotherapy-induced ADRs [35].
In our study, 52.2% of ADRs were of hematological origin, with anemia being the most frequent, and grade 4 leukopenia being the most severe. Hematological toxicity was the most frequent ADR in other clinical studies where patients received carboplatin–paclitaxel treatment, with the all-grade anemia frequency ranging from 50 to 100% [33,36,37,38]. However, no association was identified between hematological toxicity and ABCB1 or ABCC2 variants, aligning with the findings of an Indian cohort [39].
Chemotherapy can also induce nausea and vomiting, which affect approximately 40% of patients [40], mostly women [41]. In our study, nausea was the second most frequent ADR (26.5%) and was more prevalent in women, corroborating previous studies [42,43]. Gastrointestinal events are caused by oxidative stress generated by platinum-based compounds, which damage the enterochromaffin cells of the small intestine [44]. This leads to the release of emetic neurotransmitters, including serotonin, dopamine, and prostaglandins, that can directly affect specific receptors in the enteric nervous system and intestinal smooth muscle. Alternatively, these neurotransmitters can indirectly affect these receptors by activating emetic nuclei in the central nervous system [45].
In our study, patients with the ABCB1 rs1128503 (c.1236C>T) CC+CT genotypes had a higher risk of developing nausea and vomiting. A previous study reported different results, as the CT genotype was associated with a reduced risk of nausea or vomiting compared to the TT genotype [39]. Furthermore, ABCB1 rs1128503 CT and CC genotypes were associated with lower OS in our cohort. Similarly, other studies found that the TT genotype, alone or in a haplotype, was associated with longer OS [46,47,48,49,50]. Additionally, variants in the ABCB1 gene have been associated with the response to taxane-based treatment. In particular, the ABCB1 rs2032582 and ABCB1 rs1045642 variants have been correlated with a partial response to these agents, suggesting a potential functional impact of these variants on therapeutic efficacy [10].
Notably, while ABCB1 rs1128503 was associated with nausea, vomiting, and a lower OS, vomiting itself was also associated with lower OS in our cohort. These results suggest a possible chain of events: ABCB1 rs1128203 may increase the risk of vomiting with carboplatin and paclitaxel treatment, which in turn leads to a lower OS in patients with NSCLC. Thus, we propose a mechanism for the induction of nausea and vomiting caused by the interplay between carboplatin, paclitaxel, and the ABCB1 rs1128503 variant. Given that previous studies have shown that rs1128503 T allele might decrease ABCB1 activity [51,52], normal ABCB1 activity, due to the presence of the C (reference) allele, may result in higher efflux, and, as a result, higher plasma levels of carboplatin and paclitaxel, activating emetic pathways. Emetic neurotransmitters that trigger nausea and vomiting can compromise the effectiveness of the treatment, as high degrees of nausea and vomiting can lead to dose reduction and/or the temporary suspension of treatment. This may lead to disease progression, and consequently, a reduction in survival. Additionally, the development of nausea and vomiting affects electrolyte balance, causing dehydration and malnutrition, and significantly impacting the physical and psychological well-being of the patients [51]. Consequently, this event potentially has a substantial impact on the quality of life and effectiveness of the treatment [52].
For the ABCB1 rs2032582 (c.2677A>C/T) variant, our study revealed an association between non-AA genotypes (namely, the AT/CC/CA/CT genotypes) and worse OS, which was further confirmed in the multivariate analysis. Likewise, the c.2677AA genotype was associated with a longer OS in Belgian patients with ovarian cancer treated with paclitaxel and carboplatin [53]. In Korean and Indian cohorts, CC/CT/TT genotypes were associated with a lower progression-free survival (PFS) compared to AA/AC/AT genotypes, but not with OS [27,36]. Therefore, the contribution of ABCB1 rs2032582 to the risk of chemotherapy-induced ADRs needs to be further investigated.
ABCC2 encodes the ABCC2 transporter or multi-drug resistance protein 2 (MRP2), which is a well-established transporter of platinum and taxanes. Genetic variations in ABCC2 activity may affect plasma drug levels and clearance, ultimately affecting drug response [27,54]. Patients carrying the TT genotype of ABCC2 rs717620 had a 14-fold higher risk of developing hepatic ADR associated with elevated ALP levels. To the best of our knowledge, this is the first study to associate this variant with the risk of hepatic ADRs. Further studies are required to confirm these findings.
This study has two significant limitations. First, owing to challenges in maintaining patient follow-up during the COVID-19 pandemic, only the first chemotherapy cycle was evaluated. However, all patients included in this study were diagnosed by the same medical team, which minimized the potential for bias in the study population. The second limitation concerns the analysis of genetic variants. Only the impact of these variants on the development of ADRs was considered, without accounting for additional genes that could influence the outcomes. Addressing this would require a different type of analysis such as a genome-wide association study, which would require a substantially larger patient cohort to achieve the desired level of statistical significance [55]. Nonetheless, despite not accounting for these variables, this study identified a clear association between the investigated variants and ADRs, which was further confirmed through the multivariate analysis.

5. Conclusions

The findings of our study indicate that variants in the ABCB1 gene may be associated with nausea and vomiting. Consequently, patients who experienced episodes of vomiting exhibited reduced survival rates. This finding suggests the importance of antiemetic therapy before and after chemotherapy sessions for preventing gastrointestinal ADRs.
Furthermore, this study demonstrates the potential of ABCB1 variants as pharmacogenetic markers of ADRs in patients with NSCLC treated with carboplatin and paclitaxel. Pharmacogenetic testing before chemotherapy may reduce the risk of ADRs, potentially increasing treatment adherence and efficiency, and ultimately leading to better OS in these patients.
To the best of our knowledge, this is the first study to investigate the association between ABC transporter genetic variants and chemotherapy outcomes in lung cancer patients treated with carboplatin and paclitaxel in the Brazilian population. Given the highly heterogenous nature of the Brazilian population and its underrepresentation in pharmacogenetic studies [56,57,58], research in this group is essential for advancing pharmacogenetics implementation in countries with admixed populations. Further studies should explore the impact of genetic variants in genes involved in the pharmacokinetics and pharmacodynamics of carboplatin and paclitaxel to better elucidate their role in ADRs and OS, particularly in the Brazilian population. Additionally, larger sample sizes are needed to validate these findings in future research.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes16040453/s1, Figure S1: Overall survival as a function of time in years in non-small cell lung cancer patients treated with carboplatin and paclitaxel; Table S1: Clinical parameters used for evaluation of hematological, renal, and hepatic adverse reactions in non-small cell lung cancer patients treated with carboplatin and paclitaxel; Table S2: Frequency of ABCB1 and ABCC2 genotypes and alleles in non-small cell lung cancer patients treated with carboplatin and paclitaxel; Table S3: Univariate logistic regression associations between hematological adverse drug reactions, clinical and demographic data of non-small cell lung cancer patients treated with carboplatin and paclitaxel; Table S4: Univariate logistic regression associations between hepatic adverse drug reactions, clinical, and demographic data of non-small cell lung cancer patients treated with carboplatin and paclitaxel; Table S5: Univariate logistic regression associations between renal adverse drug reactions, clinical, and demographic data of non-small cell lung cancer patients treated with carboplatin and paclitaxel; Table S6: Univariate logistic regression associations between gastrointestinal adverse drug reactions, clinical, and demographic data of non-small cell lung cancer patients treated with carboplatin and paclitaxel; Table S7: Univariate logistic regression analysis associating hepatic adverse drug reactions with ABCB1 and ABCC2 variants in non-small cell lung cancer patients treated with carboplatin and paclitaxel; Table S8: Univariate logistic regression analysis associating gastrointestinal adverse drug reactions with ABCB1 and ABCC2 variants in non-small cell lung cancer patients treated with carboplatin and paclitaxel; Table S9: Univariate logistic regression analysis associating hematological adverse drug reactions with ABCB1 and ABCC2 variants in non-small cell lung cancer patients treated with carboplatin and paclitaxel; Table S10: Univariate logistic regression analysis associating renal adverse drug reactions with ABCB1 and ABCC2 variants in non-small cell lung cancer patients treated with carboplatin and paclitaxel; Table S11: Survival analysis associated with demographic and clinical data of non-small cell lung cancer patients treated with carboplatin and paclitaxel; Table S12: Univariate Cox regressions between adverse drug reactions and death of non-small cell lung cancer patients treated with carboplatin and paclitaxel; Table S13: Survival analysis associated with ABCB1 and ABCC2 variants in non-small cell lung cancer patients treated with carboplatin and paclitaxel.

Author Contributions

Conceptualization, P.M.; methodology, P.M.; formal analysis, C.S.S., G.F.S.F., C.D.-H., E.d.C.P. and P.M.; investigation, C.S.S., G.F.S.F., P.E.N.S.V., M.V.M., E.d.C.P., M.W.P.J. and Y.G.M.; resources, E.d.C.P. and P.M.; data curation, C.S.S., G.F.S.F., C.D.-H. and P.M.; writing—original draft preparation, C.S.S., G.F.S.F. and C.D.-H.; writing—review and editing, C.D.-H., E.d.C.P. and P.M.; visualization, C.S.S., G.F.S.F. and C.D.-H.; supervision, P.M.; project administration, P.M.; funding acquisition, E.d.C.P. and P.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Council for Scientific and Technological Development (CNPq) (14030/2019-4; 444090/2023); the São Paulo Research Foundation (FAPESP) (2020/06703-7; 2023/06280-7; 2023/16093-0); the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES) (Finance Code 001); the Pharmaceutical Security Nucleus Project, object of Agreement no. 895688/2019, the result of a partnership between the Ministry of Justice and Public Security of Brazil, through the Fund for the Defense of Diffuse Rights and the Universidade Estadual de Campinas. The content does not necessarily reflect the official position of the Ministry or the Fund on the subject under consideration.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Universidade Estadual de Campinas (protocol code: 83196318.8.0000.5404, 7 March 2018).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors would like to thank all patients who agreed to participate, the oncopneumology and thoracic surgery team at the Universidade Estadual de Campinas clinical hospital for all the support offered to conduct this study, and professors Marcelo Lancelloti and Gabriel Forato Anhê who provided the use of equipment in their laboratories.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
ADRsAdverse Drug Reactions
ALTAlanine Aminotransferase
ALPAlkaline Phosphatase
ASTAspartate Aminotransferase
ABCATP-Binding Cassette Transporters
BMIBody Mass Index
CTCAECommon Terminology Criteria for Adverse Events
ESMOEuropean Society of Medical Oncology
GWASGenome-Wide Association Study
HWEHardy–Weinberg Equilibrium
KPSKarnofsky Performance Status
NSCLCNon-Small Cell Lung Carcinoma
OSOverall Survival
P-gpP-glycoprotein
PFSProgression-Free Survival
SNVsSingle Nucleotide Variants
TBTotal Bilirubin

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Genes 16 00453 g001
Figure 1. Kaplan–Meier curves illustrating the association between overall survival in different genotypes of ABCB1 rs2032582 in patients with non-small cell lung cancer who were treated with carboplatin and paclitaxel. HR, hazard ratio; ABCB1, ATP Binding Cassette Subfamily B Member 1.
Figure 1. Kaplan–Meier curves illustrating the association between overall survival in different genotypes of ABCB1 rs2032582 in patients with non-small cell lung cancer who were treated with carboplatin and paclitaxel. HR, hazard ratio; ABCB1, ATP Binding Cassette Subfamily B Member 1.
Genes 16 00453 g001
Genes 16 00453 g002
Figure 2. Kaplan–Meier curves illustrating the association between overall survival in different genotypes of ABCB1 rs1128503 in patients with non-small cell lung cancer who were treated with carboplatin and paclitaxel. HR, hazard ratio; ABCB1, ATP Binding Cassette Subfamily B Member 1.
Figure 2. Kaplan–Meier curves illustrating the association between overall survival in different genotypes of ABCB1 rs1128503 in patients with non-small cell lung cancer who were treated with carboplatin and paclitaxel. HR, hazard ratio; ABCB1, ATP Binding Cassette Subfamily B Member 1.
Genes 16 00453 g002
Table 1. Demographic data of patients with non-small cell lung cancer treated with carboplatin and paclitaxel included in the study (n = 113).
Table 1. Demographic data of patients with non-small cell lung cancer treated with carboplatin and paclitaxel included in the study (n = 113).
VariablePatients
n (%)
Sex
  Male60(53.1)
  Female53(46.9)
Age (mean ± SD, years)63.23 ± 7.31
Ethnicity
  White93(82.3)
  Non-white20(17.7)
Smoking status
  Non-smoker17(15.0)
  Light smoker38(33.6)
  Moderate smoker15(13.3)
  Heavy smoker43(38.1)
Alcoholism status
  Abstainer38(33.6)
  Light drinker39(34.5)
  Moderate drinker12(10.6)
  Heavy drinker16(14.2)
  Very heavy drinker8(7.1)
Continuous-use medications
  Losartan24(21.2)
  Formoterol + Budesonide19(16.8)
  Simvastatin16(14.2)
  Morphine15(13.3)
  Metformin14(12.4)
  Dipyrone12(10.6)
  Hydrochlorothiazide10(8.8)
  Acetylsalicylic acid9(8.0)
  Dexamethasone9(8.0)
  Amitriptyline8(7.1)
n, absolute number of patients; SD, standard deviation.
Table 2. Clinical data of patients with non-small lung cancer who were treated with carboplatin and paclitaxel included in the study (n = 113).
Table 2. Clinical data of patients with non-small lung cancer who were treated with carboplatin and paclitaxel included in the study (n = 113).
VariablePatients n (%)
BMI (mean ± SD, kg/m2)
  Overweight (25.0–29.9)		24.40 ± 5.61
45(39.8)
  Normal (18.5–24.9)62(54.9)
  Underweight (<18.5)6(5.3)
Performance status
  KPS 100%101(89.4)
  KPS 90%8(7.1)
  KPS 80%2(1.8)
  KPS 60%2(1.8)
Comorbidities
  No comorbidities65(57.5)
  Hypertension36(31.9)
  Diabetes21(18.6)
  Hypercholesterolemia15(13.3)
  Other5(4.4)
Surgical resection before treatment
  No109(96.5)
  Yes4(3.5)
Histological type
  Oat cell (small cell carcinoma)1(0.9)
  SCC (squamous cell carcinoma)38(33.6)
  Adenocarcinoma66(58.4)
  Neuroendocrine1(0.9)
  Undifferentiated7(6.2)
Primary tumor size—T
  T13(2.7)
  T29(8.0)
  T317(15.0)
  T479(69.9)
  Unidentified5(4.4)
Lymph node metastasis—N
  N011(9.7)
  N14(3.5)
  N254(47.8)
  N333(29.2)
  Unidentified11(9.7)
Presence of metastasis—M
  M034(30.1)
  M155(48.7)
  MX24(21.2)
n, absolute number of patients; SD, standard deviation; KPS, Karnofsky Performance Status; BMI, body mass index.
Table 3. Frequency and severity of adverse drug reactions in patients with non-small lung cancer who were treated with carboplatin and paclitaxel according to Common Terminology Criteria for Adverse Events (CTCAE; v.4) grade.
Table 3. Frequency and severity of adverse drug reactions in patients with non-small lung cancer who were treated with carboplatin and paclitaxel according to Common Terminology Criteria for Adverse Events (CTCAE; v.4) grade.
ADRsTotal of Patients
(n)		Total of Patients Who Experienced ADRs,
n (%)		Grade 0
n (%)		Grade 1
n (%)		Grade 2
n (%)		Grade 3
n (%)		Grade 4
n (%)
Hematological
  Anemia11345 (39.8)68 (60.2)43 (38.0)2 (1.8)--
  Leukopenia11320 (17.7)93 (82.3)11 (9.7)6 (5.3)2 (1.8)1 (0.9)
  Neutropenia1116 (5.4)105 (94.6)5 (4.5)-1 (0.9)-
  Lymphopenia1134 (3.6)109 (96.4)2 (1.8)2 (1.8)--
  Thrombocytopenia11213 (11.6)99 (88.4)13 (11.6)---
Renal
  Hyperuricemia1056 (5.7)99 (94.3)6 (5.7)---
  Hyponatremia11223 (20.5)89 (79.5)23 (20.5)---
  Hypomagnesemia8614 (16.3)72 (83.7)13 (15.1)1 (1.2)--
  Hypokalemia1134 (3.5)109 (86.5)3 (2.7)-1 (0.9)-
  Hypophosphatemia88-88 (100)----
  Hypocalcemia9926 (26.3)73 (73.7)26 (26.3)---
  Increased serum creatinine1125 (4.5)107 (95.5)5 (4.5)---
  Reduced creatinine clearance11128 (25.2)83 (74.8)27 (24.3)1 (0.9)--
Hepatic
  Hypoalbuminemia11319 (16.8)94 (83.2)18 (15.9)1 (0.9)--
  AST increase1134 (3.5)109 (96.5)3 (2.6)1 (0.9)--
  ALT increase1136 (5.3)107 (94.7)4 (3.5)-2 (1.8)-
  ALP increase11316 (14.1)97 (85.8)14 (12.4)2 (1.8)--
  TB increase113-113 (100)----
Gastrointestinal
  Nausea11330 (26.5)83 (73.4)27 (23.9)2 (1.8)1 (0.9)- *
  Vomiting11316 (14.1)97 (85.8)13 (11.5)2 (1.8)1 (0.9)-
  Diarrhea11317 (15.0)96 (84.9)16 (14.2)1 (0.9)--
n, absolute number of patients; ADR, adverse drug reaction; ALT, alanine aminotransferase; ALP, alkaline phosphatase; AST, aspartate aminotransferase; TB, total bilirubin. * There is no grade 4 nausea according to CTCAE (version 4).
Table 4. Multivariate logistic regression of adverse drug reactions in patients with non-small lung cancer who were treated with carboplatin and paclitaxel and ABCB1 and ABCC2 variants.
Table 4. Multivariate logistic regression of adverse drug reactions in patients with non-small lung cancer who were treated with carboplatin and paclitaxel and ABCB1 and ABCC2 variants.
VariableADRsp-Value *OR (95% CI)
Grade 0
(n, %)		Grade 1–4
(n, %)
Increased ALP (n = 90)
ABCC2 rs717620 (c.-24C>T)
TT (ref)1 (33.3)2 (66.7)0.033514.664 (1.234–174.236)
CT+CC93 (87.7)13 (12.3)
Statistically significant differences are in bold; * p-values were calculated using the Chi square/Fisher exact test. Stepwise variable selection criterion. N, absolute number of patients; OR, odds ratio; 95% IC, 95% confidence interval for the OR; ALP, alkaline phosphatase; ABCC2, ATP Binding Cassette Subfamily C Member 2.
Table 5. Multivariate logistic regression analysis of ABCB1 rs2032582 (c.2677A>C/T) with overall survival in non-small lung cancer patients treated with carboplatin and paclitaxel.
Table 5. Multivariate logistic regression analysis of ABCB1 rs2032582 (c.2677A>C/T) with overall survival in non-small lung cancer patients treated with carboplatin and paclitaxel.
Variablep-Value *HR (95% CI)
Overall survival (n = 91)
ABCB1 rs2032582 (c.2677A>C/T)
CC (ref)
Non-CC (TT+AA+TA+CA+CT)0.02321.922 (1.093–3.377)
Variables (genetic variant, age, sex, ethnicity, smoking, alcoholism, and comorbidities) that, together, increase the risk of death. Statistically significant differences are in bold. * p-values were calculated using the Chi square/Fisher exact test. Stepwise variable selection criterion. n, absolute number of patients; HR, hazard ratio; 95% IC, 95% confidence interval for the OR; ABCB1, ATP Binding Cassette Subfamily B Member 1.
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Seguin, C.S.; Fidelis, G.F.S.; Dagli-Hernandez, C.; Vasconcelos, P.E.N.S.; Morau, M.V.; Matos, Y.G.; Perroud, M.W., Jr.; de Carvalho Pincinato, E.; Moriel, P. Association of ABC Efflux Transporter Genetic Variants and Adverse Drug Reactions and Survival in Patients with Non-Small Lung Cancer. Genes 2025, 16, 453. https://doi.org/10.3390/genes16040453

AMA Style

Seguin CS, Fidelis GFS, Dagli-Hernandez C, Vasconcelos PENS, Morau MV, Matos YG, Perroud MW Jr., de Carvalho Pincinato E, Moriel P. Association of ABC Efflux Transporter Genetic Variants and Adverse Drug Reactions and Survival in Patients with Non-Small Lung Cancer. Genes. 2025; 16(4):453. https://doi.org/10.3390/genes16040453

Chicago/Turabian Style

Seguin, Cecilia Souto, Giovana Fernanda Santos Fidelis, Carolina Dagli-Hernandez, Pedro Eduardo Nascimento Silva Vasconcelos, Mariana Vieira Morau, Yasmim Gabriele Matos, Maurício Wesley Perroud, Jr., Eder de Carvalho Pincinato, and Patricia Moriel. 2025. "Association of ABC Efflux Transporter Genetic Variants and Adverse Drug Reactions and Survival in Patients with Non-Small Lung Cancer" Genes 16, no. 4: 453. https://doi.org/10.3390/genes16040453

APA Style

Seguin, C. S., Fidelis, G. F. S., Dagli-Hernandez, C., Vasconcelos, P. E. N. S., Morau, M. V., Matos, Y. G., Perroud, M. W., Jr., de Carvalho Pincinato, E., & Moriel, P. (2025). Association of ABC Efflux Transporter Genetic Variants and Adverse Drug Reactions and Survival in Patients with Non-Small Lung Cancer. Genes, 16(4), 453. https://doi.org/10.3390/genes16040453

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