ABSTRACT
Enfortumab vedotin (EV) is a crucial treatment for patients with metastatic urothelial carcinoma (mUC). However, a significant proportion of patients experience adverse events (AEs). The identification of biomarkers for AEs is imperative for the detection and treatment of AEs at an early stage. In this exploratory study, we aimed to identify biomarkers of AEs in patients with mUC treated with EV. We retrospectively examined 10 factors identified from the data of 116 patients with mUC treated with EV to identify biomarkers (age, body mass index, C‐reactive protein level, Eastern Cooperative Oncology Group performance status, eosinophil proportion, history of diabetes, lymphocyte proportion, neutrophil proportion, neutrophil‐to‐lymphocyte ratio, and platelet count) associated with the occurrence of AEs of any grade. The candidate biomarkers were measured at the start of EV treatment. The least absolute shrinkage and selection operator method was used to select the most useful parameters for predicting AE occurrence. Among the 10 factors, eosinophil proportion was identified as the only potential biomarker. The optimal cutoff value for eosinophil proportion against the occurrence of AEs of any grade was 2.5% (area under the curve = 0.625). Univariable logistic regression analyses showed that an eosinophil proportion of ≥ 2.5% was a risk factor for AE development (odds ratio = 4.35, 95% confidence interval = 1.35–14.0). Therefore, the results of this exploratory study indicated that an eosinophil proportion of ≥ 2.5% at the start of EV treatment may be a candidate biomarker for the occurrence of AEs of any grade.
Keywords: adverse events, biomarker, enfortumab vedotin, eosinophils, urothelial carcinoma
1. Introduction
Metastatic urothelial carcinoma (mUC) is one of the most aggressive malignant tumors, with a 5‐year survival rate of approximately 9% [1]. Recently, the utilization of enfortumab vedotin (EV), an antibody–drug conjugate targeting nectin‐4, has altered treatment strategies and significantly improved the prognosis of patients with mUC [2, 3, 4]. For instance, the EV‐301 study demonstrated that the overall survival (OS) of patients who received EV was significantly longer than that of patients who received chemotherapy (median OS [mOS], 12.88 and 8.97 months, respectively) [2]. Additionally, the EV‐302 study demonstrated that OS in patients who received EV combined with pembrolizumab was significantly longer than that in patients who received chemotherapy (mOS, 31.5 and 16.1 months, respectively) [4]. As treatments involving EV are recommended as a first‐ or late‐line treatment by the National Comprehensive Cancer Network [5], it is a crucial treatment for patients with mUC.
However, 93.9% of patients in the EV‐301 study experienced adverse events (AEs) of any grade, and 52.4% of patients experienced AEs of grade ≥ 3 [3]. AEs resulting in treatment discontinuation occurred in 16.9% of patients [3]. To ensure optimal outcomes, it is essential to promptly detect AEs, implement appropriate measures, and maintain ongoing treatments. Therefore, factors that can predict the occurrence of AEs must be identified. The EV‐301 trial showed that hyperglycemia was more frequent in patients with baseline hyperglycemia or a higher body mass index [4]. However, there are currently no known practical factors for predicting the different AEs associated with EV treatment. Therefore, in this exploratory study, we aimed to investigate the candidate biomarkers for the occurrence of AEs of any grade in patients with mUC receiving EV treatment.
2. Methods
2.1. Study Design and Treatment
We retrospectively collected the clinical data of 116 patients who had been diagnosed with mUC. Patients with mUC who had undergone ≥ 1 cycle of treatment with EV monotherapy (1.25 mg/kg on Days 1, 8, and 15 of each 28‐day cycle) between March 2021 and November 2024 at nine hospitals in Japan were included in this study. Treatment was stopped if the disease progressed, which was evaluated using the Response Evaluation Criteria in Solid Tumors, version 1.1, or if AEs that were not well‐tolerated developed. All data, including demographic information, pathological type, peripheral blood parameters, details of AEs and treatment outcomes, were obtained from the patients' medical records. All patients were followed up until March 2025. AEs were graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 5.0. OS was defined as the time from the start of EV therapy to death or the last follow‐up.
This study was approved by the appropriate Ethics Committee (approval number 60‐25‐0002) and conducted in accordance with the tenets of the Declaration of Helsinki. This was a retrospective study; therefore patient consent was not required. Patients were provided the opportunity to opt out of the study through an option displayed on the institutional websites.
2.2. Parameter Selection and Candidates
The least absolute shrinkage and selection operator (LASSO) method was used to select the most useful parameters for predicting AE occurrence. The LASSO method is highly effective in high‐dimensional data regression because it simultaneously performs variable selection and regularization to enhance the statistical model prediction accuracy and interpretability [6]. A 10‐fold cross‐validation was conducted within the LASSO framework to determine the optimal tuning parameter (λ), thereby serving as a strict internal validation for preventing overfitting. Referring to the literature on parameters that predict the occurrence of AEs associated with systemic chemotherapy (cytotoxic chemotherapeutic drugs, immune checkpoint inhibitors, and antibody‐drug conjugates) and selected parameter candidates [7, 8, 9, 10]. Specifically, age, body mass index, and Eastern Cooperative Oncology Group performance status (ECOG PS) were selected as fundamental indicators of general patient condition and tolerability. A history of diabetes was included due to its known clinical association with EV‐specific AEs, such as hyperglycemia. Furthermore, systemic inflammatory and immunological markers (C‐reactive protein level, platelet count, neutrophil‐to‐lymphocyte ratio, and the neutrophil, lymphocyte, and eosinophil proportions) were chosen because baseline immune status is increasingly being recognized as a critical factor influencing AEs in targeted therapies. Age, body mass index, and ECOG PS were evaluated at the start of EV treatment. Additionally, blood sampling was performed within 7 days before initial EV administration.
2.3. Statistical Analysis
Regarding missing data handling, data of patients with incomplete baseline laboratory values were excluded from the initial LASSO parameter selection process. For subsequent analyses, data of patients with missing data for any of the variables that were included in the respective models were automatically excluded. No specific imputation methods were applied. Statistical significance was set at p < 0.05. The optimal cutoff value for selected potential parameters to evaluate the occurrence of AEs was determined using receiver operating characteristic curve analysis. Univariable and multivariable logistic regression analyses were used to assess risk factors for AEs. Furthermore, to internally validate the robustness of the multivariable logistic regression model and adjust for potential overfitting, we performed a bootstrap resampling procedure with 1000 iterations to calculate the optimism‐adjusted area under the curve (AUC). OS was calculated using the Kaplan–Meier method and log‐rank test. To adjust for potential immortal time bias, a time‐dependent Cox proportional hazards regression analysis was conducted, incorporating the occurrence of AEs as a time‐varying covariate. Statistical analyses were performed using EZR software [11].
3. Results
3.1. Patient Characteristics
The patient characteristics are shown in Table 1. The median patient age was 74 years (range; 52–87). The sex distribution was 69.0% male (n = 80) and 31.0% female (n = 36). The primary tumor sites were the bladder (46.6%, n = 54) and upper urinary tract (53.4%, n = 62). The ECOG PS score was 0–1 in 63.8% (n = 74) and ≥ 2 in 36.2% (n = 43) of patients. The most common metastatic sites were the lymph nodes (75.0%, n = 87), followed by the lungs (41.4%, n = 48), bones (20.7%, n = 24), liver (13.8%, n = 16), and brain (2.59%, n = 3). The percentage of patients with a history of diabetes was 18.1% (n = 21). Radical surgery prior to EV was performed on 46.6% (n = 54) of patients. The first‐line chemotherapy regimens consisted of gemcitabine and cisplatin (61.2%, n = 71), gemcitabine and carboplatin (36.2%, n = 42), and others (2.69%, n = 3). Immune checkpoint inhibitor treatment received before EV consisted of avelumab (34.5%, n = 40), pembrolizumab (71.6%, n = 83), and nivolumab (0.86%, n = 1).
TABLE 1.
Clinical features of patients.
| Characteristics | All cases |
|---|---|
| Total, n (%) | 116 (100) |
| Age (range) | 74 (52–87) |
| Sex, n (%) | |
| Male | 80 (69.0) |
| Female | 36 (31.0) |
| Primary tumor site, n (%) | |
| Bladder | 54 (46.6) |
| Upper urinary tract | 62 (53.4) |
| ECOG PS, n (%) | |
| 0–1 | 74 (63.8) |
| ≥ 2 | 42 (36.2) |
| Metastasis, n (%) | |
| Lymph node | 87 (75.0) |
| Lung | 48 (41.4) |
| Bone | 36 (31.0) |
| Liver | 21 (18.1) |
| Brain | 3 (2.59) |
| Diabetes, n (%) | |
| Yes | 21 (18.1) |
| No | 95 (81.9) |
| Radical surgery prior to EV, n (%) | |
| Yes | 54 (46.6) |
| No | 62 (53.4) |
| Regimen of first‐line chemotherapy, n (%) | |
| Gemcitabine and cisplatin | 71 (61.2) |
| Gemcitabine and carboplatin | 42 (36.2) |
| Others | 3 (2.69) |
| ICI treatment prior to EV, n (%) | |
| Avelumab | 40 (34.5) |
| Pembrolizumab | 83 (71.6) |
| Nivolumab | 1 (0.86) |
Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; EV, enfortumab vedotin; ICI, immune checkpoint inhibitors.
3.2. Profile of AEs
The AE profiles are listed in Table 2. A total of 253 AEs were observed across 96 patients. There were 202 incidences of Grade 1–2 AEs (79.8%) and 51 incidences of grade ≥ 3 AEs (20.2%). The most common AEs were pruritus (14.1%, 36 events), followed by skin disorders (13%, 33 events), dysgeusia (11.9%, 30 events), peripheral sensory neuropathy (9.9%, 25 events), fatigue (8.3%, 21 events), alopecia (7.5%, 19 events), anemia (4%, 10 events), decreased white‐cell count (3.6%, 9 events), decreased appetite (3.2%, 8 events), decreased neutrophil count (2.8%, 7 events), hyperglycemia (2.8%, 7 events), gastrointestinal disorders (2.4%, 6 events), pneumonitis (1.6%, 4 events), infusion reaction (1.2%, 3 events), eye disorders (1.2%, 3 events), and decreased platelet count (0.8%, 2 events). Other AEs occurred in 11.9% (n = 30) of patients.
TABLE 2.
Profile of adverse events.
| Total | Grade 1–2 | Grade ≥ 3 | |
|---|---|---|---|
| Profile of AEs, n (%) | 253 (100) | 202 (79.8) | 51 (20.2) |
| Pruritus | 36 (14.2) | 34 (94.4) | 2 (5.6) |
| Skin disorders | 33 (13) | 26 (78.8) | 7 (21.2) |
| Dysgeusia | 30 (11.9) | 29 (96.7) | 1 (3.3) |
| Peripheral sensory neuropathy | 25 (9.9) | 22 (88) | 3 (12) |
| Fatigue | 21 (8.3) | 18 (85.7) | 3 (14.3) |
| Alopecia | 19 (7.5) | 19 (100) | 0 (0) |
| Anemia | 10 (4) | 4 (40) | 6 (60) |
| Decreased white‐cell count | 9 (3.6) | 6 (66.7) | 3 (33.3) |
| Decreased appetite | 8 (3.2) | 6 (75) | 2 (25) |
| Decreased neutrophil count | 7 (2.8) | 2 (28.6) | 5 (71.4) |
| Hyperglycemia | 7 (2.8) | 4 (57.1) | 3 (42.9) |
| Gastrointestinal disorders | 6 (2.4) | 6 (100) | 0 (0) |
| Pneumonitis | 4 (1.6) | 1 (25) | 3 (75) |
| Infusion reaction | 3 (1.2) | 3 (100) | 0 (0) |
| Eye disorders | 3 (1.2) | 3 (100) | 0 (0) |
| Decreased platelet count | 2 (0.8) | 2 (100) | 0 (0) |
| Others | 30 (11.9) | 17 (56.7) | 13 (43.3) |
The number of patients who received dose modifications of EV due to their AEs is shown in Table 3. The dose of EV in 42.2% (n = 49) of patients was reduced due to AE occurrence. The cycles in which the AEs occurred for the first time are listed in Table 4. The median number of cycles until the first AE occurred was 1 (range, 1–24). Most of the first‐time AEs occurred during the first cycle (61.5%, n = 59), followed by 13.5% in the second cycle (n = 13), 5.21% in the third cycle (n = 5), and 8.33% after four or more cycles (n = 8). The exact cycle of the first AE was unknown for the remaining 11.5% (n = 11) of patients.
TABLE 3.
Dose modification of enfortumab vedotin due to adverse events.
| Number of patients with dose modification due to adverse events | |
|---|---|
| Total, n (%) | 116 (100) |
| Yes | 49 (42.2) |
| No | 60 (51.7) |
| Missing data | 7 (6.03) |
TABLE 4.
Cycle in which the adverse event first occurred.
| Number of patients experiencing adverse events for the first time | |
|---|---|
| Cycle, n (%) | 96 (100) |
| 1 | 59 (61.5) |
| 2 | 13 (13.5) |
| 3 | 5 (5.21) |
| 4 or more | 8 (8.33) |
| Unknown | 11 (11.5) |
3.3. Selection of Parameters for Assessing Association With AE Occurrence
Most of the first‐time AEs were observed in the first cycle; therefore, the data at the start of the EV treatment were used as parameter candidates for assessing association with AEs. The LASSO method was used to select the most useful parameters for predicting the occurrence of AEs. We excluded data of seven patients with missing values and conducted the analysis on the data of the remaining 109 patients. The optimal tuning parameter (λ) was obtained using 10‐fold cross‐validation. The optimal λ value of 0.0396, with log(λ) −3.2289, was chosen according to 10‐fold cross‐validation (Figure 1A). The LASSO coefficient profiles of the 10 candidate parameters evaluated are shown in Figure 1B. When the λ value was optimal, the eosinophil proportion was identified as the key parameter. The eosinophil proportion was identified as the only parameter with non‐zero coefficients in the LASSO logistic regression model. Thus, this analysis identified eosinophil proportion as the only potential biomarker of AEs associated with EV treatment. We also performed the LASSO analysis focusing on severe AEs (grade ≥ 3). However, no significant candidate variables were identified in this analysis (Figure S1).
FIGURE 1.
Parameter selection using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. (A) Tuning parameter (λ) selection using the LASSO model with 10‐fold cross‐validation. Dotted vertical lines are drawn at the optimal values using the minimum criteria. A λ value of 0.0396 with log(λ) −3.2289 was chosen according to the 10‐fold cross‐validation. (B) LASSO coefficient profiles of the 10 parameters evaluated. A blue dotted vertical line is drawn at the optimal λ value, resulting in one non‐zero coefficient. BMI, body mass index; CRP, C‐reactive protein; ECOG PS, Eastern Cooperative Oncology Group performance status; NLR, neutrophil‐to‐lymphocyte ratio.
3.4. Eosinophil Proportion May Be a Candidate Biomarker for AE Occurrence
Receiver operating characteristic curve analysis revealed that the optimal cutoff value for eosinophil proportion at the start of EV treatment as a biomarker for the occurrence of AEs was 2.5% (AUC, 0.625; 95% confidence interval [CI] = 0.50–0.75; sensitivity, 0.53; specificity, 0.78; Figure 2). Univariable and multivariable logistic regression analyses showed that an eosinophil proportion of ≥ 2.5% at the start of EV treatment was an independent risk factor for AE development (univariable: odds ratio [OR] = 4.35, 95% CI = 1.35–14.0; p < 0.05; multivariable: OR = 6.53, 95% CI = 1.56–27.3; p < 0.05; Table 5). Internal validation using 1000 bootstrap resamples confirmed the robustness of the multivariable logistic regression analysis. The bootstrapping procedure yielded an apparent AUC of 0.74, and the average optimism was 0.12, resulting in an optimism‐adjusted AUC of 0.62. These data suggested that the multivariable model retained acceptable predictive performance after correction for optimism. Subgroup analysis showed that a baseline eosinophil proportion of ≥ 2.5% had heterogeneous effects on the occurrence of AEs (Figure S2).
FIGURE 2.
Optimal cutoff value for eosinophil proportion. Receiver operating characteristic curve analysis of eosinophil proportion for the occurrence of any adverse events.
TABLE 5.
Univariate and multivariate logistic regression analysis of risk factors for the occurrence of adverse events.
| Univariable | Multivariable | |||||
|---|---|---|---|---|---|---|
| OR | 95% CI | p | OR | 95% CI | p | |
| Proportion of eosinophils: ≥ 2.5% | 4.35 | 1.35–14.00 | < 0.05 | 6.53 | 1.56–27.30 | < 0.05 |
| Age: ≥ 75 | 1.17 | 0.44–3.11 | 0.76 | 0.93 | 0.28–3.10 | 0.91 |
| BMI: ≥ 25 | 1.08 | 0.28–4.15 | 0.91 | 1.81 | 0.28–11.80 | 0.53 |
| CRP: ≥ ULN (0.14) | 0.46 | 0.10–2.16 | 0.32 | 0.39 | 0.06–2.48 | 0.32 |
| ECOG PS: ≥ 2 | 0.50 | 0.19–1.32 | 0.16 | 0.66 | 0.22–2.01 | 0.46 |
| History of diabetes: yes | 0.86 | 0.26–2.90 | 0.81 | 0.89 | 0.20–4.06 | 0.88 |
| NLR: ≥ 3 | 1.21 | 0.42–3.56 | 0.72 | 1.99 | 0.54–7.30 | 0.30 |
| Prior treatment lines: ≥ 2 | 1.80 | 0.57–5.70 | 0.32 | 3.12 | 0.81–12.00 | 0.10 |
Abbreviations: BMI, body mass index; CI, confidence interval; CRP, C‐reactive protein; ECOG PS, Eastern Cooperative Oncology Group performance status; NLR, neutrophil‐to‐lymphocyte ratio; OR, odds ratio; ULN, upper limit of normal.
3.5. High Eosinophil Proportion Was Associated With Improved OS
Finally, we examined the association of OS with eosinophil proportion, AE, and dose modification. An eosinophil proportion of ≥ 2.5% at the start of EV treatment was correlated with improved mOS (19.84 vs. 10.02 months, p < 0.01, Figure 3). The mOS of patients who experienced AEs tended to be longer than that of patients who did not (15.97 vs. 10.11 months, p = 0.08; Figure S3A). However, after adjusting for immortal time bias using a time‐dependent Cox regression model, this tendency disappeared (hazard ratio = 0.93, 95% CI = 0.48–1.81, p = 0.83). The mOS of patients who experienced EV dose modification was significantly longer than that of patients who did not experience dose modification (19.35 vs. 10.02 months, p < 0.01, Figure S3B).
FIGURE 3.
Kaplan–Meier curves showing overall survival. Patients were divided into two groups based on whether their eosinophil proportion was 2.5% or higher (eosinophil proportion ≥ 2.5%; n = 54, and eosinophil proportion < 2.5%; n = 62). Log‐rank test.
4. Discussion
In this exploratory study, we found that an eosinophil proportion of ≥ 2.5% at the start of EV treatment may be a candidate biomarker for the occurrence of AEs. Several studies have reported factors or patient characteristics related to skin disorders in patients treated with EV for mUC [12, 13]. Furubayashi et al. [12] revealed that the incidence of skin disorders associated with EV is significantly higher in patients with immune checkpoint inhibitor‐related skin disorders. Additionally, Vlachou et al. [13] revealed that skin disorders were more common in patients with higher body weights and body mass index. However, these studies only mentioned specific AEs, and practical biomarkers for the various types of AEs are still lacking. Previously, we focused on the use of eosinophil proportion as a biomarker for immune‐related AEs [14, 15, 16]. Notably, the present findings suggest that eosinophil proportion may also be a biomarker of AEs induced by EV treatment. To our knowledge, this is the first study to demonstrate an association between a high eosinophil proportion at the start of EV treatment and the occurrence of AEs in patients with mUC.
EV exerts its primary pharmacological activity, such as anti‐tumor effects and AEs, through the targeted delivery of Monomethyl Auristatin E to tumor and normal cells expressing Nectin‐4 [17]. In preclinical studies, secondary mechanisms, such as the induction of early signs of immunogenic cell death, including the release of damage‐associated molecular patterns (DAMPs), have also been identified. DAMPs are recognized by innate and adaptive immune cells, ultimately leading to the uptake of tumor cells by antigen‐presenting cells and subsequent cross‐presentation of tumor antigens to cluster of differentiation (CD) 8+ T cells [18, 19, 20]. This mechanism also explains the drastic outcomes demonstrated in the EV‐302 trial, which combined EV with immune checkpoint inhibitors, such as pembrolizumab [4]. Moreover, eosinophils play a major role in immune system modulation and may contribute to the anti‐tumor response as immunomodulatory cells [21, 22, 23]. Eosinophils produce chemokines (C‐C motif chemokine ligand 5, C‐X‐C motif chemokine ligand [CXCL] 9, and CXCL10) and have been shown to promote the mobilization and activation of tumor‐reactive CD8+ T cells that mediate tumor rejection [21, 22, 23]. Thus, eosinophils and EV may be closely associated with immune responses, particularly tumor rejection by CD8+ T cells. The involvement of eosinophils and EV in tumor rejection by CD8+ cells may explain the increased likelihood of AEs in patients with a high eosinophil proportion. However, this study relied solely on clinical observations and lacked immunological or translational data. Therefore, the proposed biological mechanisms linking eosinophils, EV activity, and AEs remain speculative and require further analysis.
The EV‐301 trial showed that the median time to the first onset of skin disorders was 0.43 (range: 0.03–12.68) months [3]. Post‐marketing drug safety surveillance of EV showed that the median time to onset of all EV‐related AEs was 14 (range: 7–49) days and that 66.5% of AEs occurred within 30 days of EV administration [24]. Consistent with clinical trials and large‐scale post‐marketing drug safety surveillance, this study showed that 61.5% of patients experienced their first AEs during the first cycle (Table 4). Therefore, identification of biomarkers for AEs based on a patient's condition and blood data prior to or at the initiation of treatment is imperative. Notably, we demonstrated that the proportion of eosinophils at the start of the EV treatment was associated with the occurrence of AEs. Therefore, focusing on eosinophils at the start of the EV treatment may help to prevent AEs and enable early detection.
Patients who developed skin disorders following treatment with EV have been reported to have a favorable prognosis [25, 26, 27]. Consistent with these reports, our initial analysis indicated a tendency toward a better prognosis in patients who experienced AEs. However, as demonstrated in our time‐dependent Cox regression analysis, this tendency disappeared after adjusting for immortal time bias. This suggests that the occurrence of AEs may not independently prolong OS.
Dose modification is used to manage AEs. Several studies have reported that dose modifications had no significant effect on OS [28, 29]. In this study, dose modification led to better prognoses (Figure S3B). However, this apparent association may also have been influenced by immortal‐time bias and should therefore be interpreted with caution. A phase‐I study of EV comparing safety, tolerability, pharmacokinetic profiles, and antitumor activity between 1.0 and 1.25 mg/kg showed that 1.0 mg/kg EV did not compromise antitumor activity [30]. Notably, a baseline eosinophil proportion of ≥ 2.5% remained significantly associated with favorable OS (Figure 3). As the baseline eosinophil proportion is determined before treatment initiation, this association is not subject to immortal‐time bias in the same manner as time‐dependent variables, such as AEs or dose modifications. These findings suggest that patients with elevated baseline eosinophil proportions may represent a subgroup with increased risk of AEs while potentially deriving greater clinical benefit from EV. Therefore, closer monitoring and appropriate dose modification may be considered in these patients; however, prospective studies are required to determine whether eosinophil proportion‐guided management strategies can improve clinical outcomes.
This study has some limitations. The predictive performance of the baseline eosinophil proportion was modest (AUC 0.625, sensitivity 53%, specificity 78%). Therefore, it should serve merely as an auxiliary indicator to identify patients requiring closer monitoring for AEs. The limited sample size was a key concern, as was the number of participants who did not experience any AEs. Our retrospective design did not account for patient selection bias, and AEs that were not appropriately documented may have been overlooked. There is a potential risk of overfitting because variable selection, determination of the optimal cutoff value, and the final multivariable logistic regression were all performed using the same dataset. Although we performed rigorous internal validation using a bootstrap method to minimize this risk, external validation using an independent cohort could not be conducted. Furthermore, the number of events available to perform analyses by severe or specific AEs was limited, and overlapping AEs were observed in some patients. Thus, large‐scale prospective interventional studies are required to confirm the findings of this study.
5. Conclusions
An eosinophil proportion of ≥ 2.5% at the start of EV treatment may be a candidate biomarker for the occurrence of AEs in patients with mUC. Physicians should focus on the proportion of eosinophils required for early AE detection and strategies to manage AEs, including dose modifications, to maximize the effectiveness of EV therapy.
Author Contributions
Kunihiro Odagiri: conceptualization, methodology, formal analysis, investigation, data curation, writing – original draft, writing – review and editing. Takashi Nagai: investigation, writing – review and editing. Yosuke Sugiyama: investigation, data curation, writing – review and editing. Yoshihiko Tasaki: investigation, writing – review and editing. Aya Naiki‐Ito: investigation, writing – review and editing. Toshiki Etani: investigation, writing – review and editing. Taku Naiki: conceptualization, methodology, formal analysis, investigation, data curation, writing – original draft, writing – review and editing. Toshiharu Morikawa: conceptualization, methodology, formal analysis, investigation, data curation, writing – original draft, writing – review and editing. Yusuke Noda: investigation, writing – review and editing. Maria Aoki: investigation, writing – review and editing. Nobuhiko Shimizu: investigation, writing – review and editing. Rika Banno: investigation, writing – review and editing. Keitaro Iida: investigation, writing – review and editing. Hiroki Kubota: investigation, writing – review and editing. Ryosuke Ando: investigation, writing – review and editing. Masakazu Gonda: investigation, writing – review and editing. Daiki Ishikawa: investigation, writing – review and editing. Noriyasu Kawai: investigation, writing – review and editing. Yoko Furukawa‐Hibi: investigation, writing – review and editing, supervision. Yoshihisa Mimura: investigation, writing – review and editing. Takahiro Yasui: investigation, writing – review and editing, supervision. Yukihiro Umemoto: investigation, writing – review and editing.
Funding
This study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 25K18657 (Y. Tasaki) and Nitto Foundation.
Ethics Statement
This study was approved by the Ethics Committee of Nagoya City University Hospital (approval number 60‐25‐0002) and was conducted in accordance with the tenets of the Declaration of Helsinki.
Consent
As this was a retrospective study, patient consent was not required. Patients could also choose to opt out of the study by using the authors' institutional websites.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Figure S1: Parameter selection using the least absolute shrinkage and selection operator (LASSO) method binary logistic regression model for severe adverse events. (A) Tuning parameter (λ) selection using the LASSO model with the 10‐fold cross‐validation. Dotted vertical lines were drawn at the optimal values using the minimum criteria. A λ value of 0.0501, with log(λ) −2.994, was chosen according to the 10‐fold cross‐validation. (B) LASSO coefficient profiles of the 10 parameters to be evaluated. A blue dotted vertical line was drawn at the optimal λ value. BMI, Body mass index; CRP, C‐reactive protein; ECOG PS, Eastern Cooperative Oncology Group performance status; NLR, neutrophil‐to‐lymphocyte ratio.
Figure S2: Association between an elevated baseline eosinophil proportion and specific adverse events. The forest plot displays the ORs and 95% CIs for the occurrence of specific AEs in patients with an eosinophil proportion ≥ 2.5% compared to those with < 2.5%. The squares represent the point estimates of the ORs, and the horizontal lines indicate the corresponding 95% CIs. The vertical dashed line at an OR of 1 represents the null value (no association). Univariable logistic regression. AE, adverse event; CI, confidence interval; OR, odds ratio.
Figure S3: Kaplan–Meier curves showing overall survival. (A) Patients were divided into two groups based on whether they experienced AEs or not (Experienced AEs; n = 96, and not experienced AEs; n = 20). (B) Patients were divided into two groups based on whether they underwent dose modification or not (Dose modification; n = 49, and not dose modification; n = 60). A–B log‐rank test. AE, adverse event.
Acknowledgments
The authors have nothing to report.
Data Availability Statement
Detailed data are available upon request from the corresponding author (T. Naiki).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Figure S1: Parameter selection using the least absolute shrinkage and selection operator (LASSO) method binary logistic regression model for severe adverse events. (A) Tuning parameter (λ) selection using the LASSO model with the 10‐fold cross‐validation. Dotted vertical lines were drawn at the optimal values using the minimum criteria. A λ value of 0.0501, with log(λ) −2.994, was chosen according to the 10‐fold cross‐validation. (B) LASSO coefficient profiles of the 10 parameters to be evaluated. A blue dotted vertical line was drawn at the optimal λ value. BMI, Body mass index; CRP, C‐reactive protein; ECOG PS, Eastern Cooperative Oncology Group performance status; NLR, neutrophil‐to‐lymphocyte ratio.
Figure S2: Association between an elevated baseline eosinophil proportion and specific adverse events. The forest plot displays the ORs and 95% CIs for the occurrence of specific AEs in patients with an eosinophil proportion ≥ 2.5% compared to those with < 2.5%. The squares represent the point estimates of the ORs, and the horizontal lines indicate the corresponding 95% CIs. The vertical dashed line at an OR of 1 represents the null value (no association). Univariable logistic regression. AE, adverse event; CI, confidence interval; OR, odds ratio.
Figure S3: Kaplan–Meier curves showing overall survival. (A) Patients were divided into two groups based on whether they experienced AEs or not (Experienced AEs; n = 96, and not experienced AEs; n = 20). (B) Patients were divided into two groups based on whether they underwent dose modification or not (Dose modification; n = 49, and not dose modification; n = 60). A–B log‐rank test. AE, adverse event.
Data Availability Statement
Detailed data are available upon request from the corresponding author (T. Naiki).