Efficacy of enfortumab vedotin in advanced urothelial cancer: Analysis from the Urothelial Cancer Network to Investigate Therapeutic Experiences (UNITE) study
Enfortumab vedotin (EV) is a novel antibody-drug conjugate approved for advanced urothelial cancer (aUC) refractory to prior therapy. In the Urothelial Cancer Network to Investigate Therapeutic Experiences (UNITE) study, the authors looked at the experience with EV in patient subsets of interest for which activity had not been well defined in clinical trials.
UNITE was a retrospective study of patients with aUC treated with recently approved agents. This initial analysis focused on patients treated with EV. Patient data were abstracted from chart reviews by investigators at each site. The observed response rate (ORR) was investigator-assessed for patients with at least 1 post-baseline scan or clear evidence of clinical progression. ORRs were compared across subsets of interest for patients treated with EV monotherapy.
The initial UNITE analysis included 304 patients from 16 institutions; 260 of these patients were treated with EV monotherapy and included in the analyses. In the monotherapy cohort, the ORR was 52%, and it was >40% in all reported subsets of interest, including patients with comorbidities previously excluded from clinical trials (baseline renal impairment, diabetes, and neuropathy) and patients with fibroblast growth factor receptor 3 (FGFR3) alterations. Progression-free survival and overall survival were 6.8 and 14.4 months, respectively. Patients with a pure urothelial histology had a higher ORR than patients with a variant histology component (58% vs 42%; P = .06).
In a large retrospective cohort, responses to EV monotherapy were consistent with data previously reported in clinical trials and were also observed in various patient subsets, including patients with variant histology, patients with FGFR3 alterations, and patients previously excluded from clinical trials with an estimated glomerular filtration rate < 30 mL/min and significant comorbidities.
- Enfortumab vedotin, approved by the Food and Drug Administration in 2019, is an important new drug for the treatment of patients with advanced bladder cancer.
- This study looks at the effectiveness of enfortumab vedotin as it has been used at multiple centers since approval, and focuses on important patient populations previously excluded from clinical trials. These populations include patients with decreased kidney function, diabetes, and important mutations.
- Enfortumab vedotin is effective for treating these patients. Previously reported clinical trial data have been replicated in this real-world setting, and support the use of this drug in broader patient populations.
Advanced urothelial cancer (aUC) is an aggressive and usually incurable disease. Despite the efficacy of platinum-based chemotherapy and immune checkpoint inhibitors (ICIs), most patients with aUC invariably progress and require other systemic therapies for disease control.1-8 Enfortumab vedotin (EV) received accelerated Food and Drug Administration approval in December 2019 for patients with aUC progressing on platinum-based chemotherapy and ICIs. EV is an antibody-drug conjugate consisting of a monoclonal antibody targeting nectin-4, which is conjugated to the microtubule-disrupting agent monomethyl auristatin E.9-11 Initial Food and Drug Administration approval of EV was based on the results of the EV-201 trial, and the benefit of EV for treatment-refractory aUC was subsequently confirmed in the randomized phase 3 EV-301 trial, which led to full approval in July 2021.12, 13 EV is also being investigated in earlier treatment settings as a frontline regimen for cisplatin-ineligible aUC.14
Recent preclinical data have suggested that nectin-4 expression is both necessary and sufficient for the killing of urothelial cells by EV and that certain molecular subsets of urothelial cancer (UC) may be more likely to respond to EV.15 Additionally, patient populations with certain comorbidities common among patients with aUC were excluded from EV clinical trials. These populations included patients with significant neuropathy (grade 2 or higher), uncontrolled preexisting diabetes, and renal insufficiency (estimated glomerular filtration rate [eGFR] < 30 mL/min). Consequently, certain patient populations with aUC may be more or less likely to benefit from EV treatment according to their specific pathologic or clinical characteristics, and EV efficacy in specific patient populations of interest (eg, patients with nonurothelial histology variants, patients with certain comorbidities, and fibroblast growth factor receptor 3 [FGFR3]–altered patients) remains to be further defined.
As clinical experience with EV grows, multi-institutional, retrospective analyses can help to shed further light on these important questions and complement important information derived from clinical trials. Here we present the initial results from the Urothelial Cancer Network to Investigate Therapeutic Experiences (UNITE) study, a large, multi-institutional, retrospective cohort of patients with aUC treated with novel agents recently approved in this disease space. This initial analysis focuses on the efficacy of EV monotherapy, particularly in specific aUC patient subsets of interest. We hypothesized that EV would have robust efficacy across the different subsets of patients with aUC, including those previously excluded from clinical trials. Furthermore, we hypothesized that the efficacy of EV would be consistent in this broader population of aUC patients with what was previously reported for the more narrowly selected patients in clinical trials.
Materials and Methods
The UNITE study is a retrospective cohort study with the goal of assessing outcomes in patients with aUC treated with novel agents recently approved for this malignancy. This initial analysis focused on the outcomes of patients with aUC treated with EV. The study met the principles set forth by the Declaration of Helsinki and was approved by the institutional review board at each participating institution. Patient eligibility criteria included the following: histologically confirmed carcinoma of urothelial origin (variant histologic component of any percentage allowed), presence of locally advanced/unresectable or metastatic disease, at least 1 dose of EV administered, and available clinicopathologic and imaging data in the electronic medical record (EMR). To be considered eligible for a response assessment, a patient needed to have at least 1 scan after the initiation of EV treatment or clear evidence of clinical progression as assessed by the treating physician. Both patients treated in a clinical trial (as long as trial results were previously reported) and patients treated according to the standard of care were included, and combination regimens that included EV treatment were also allowed. All patient data were reported in a de-identified manner, with all protected health information specifically excluded. Data were collected and managed with secure REDCap electronic data capture tools hosted at the University of Michigan.16
The assessment of the observed response rate (ORR), defined as a complete response or partial response, or, alternatively, of stable disease or progressive disease was determined according to the judgment of the investigator assessing the EMR with the available information from imaging reports or clinical notes. In making these assessments, investigators were encouraged to adhere to the Response Evaluation Criteria in Solid Tumors17; however, specific tumor measurements were not collected, and a central assessment of imaging responses was not performed. Progression-free survival (PFS) was defined as the time from EV start to progression or death, and patients alive without disease progression at the time of their last follow-up were censored at the date of last follow-up. Overall survival (OS) was defined as the time from EV start until death of any cause, and patients alive at last follow-up were similarly censored.
Summary statistics were used to describe baseline patient and treatment characteristics as well as ORRs. OS and PFS curves were constructed with the Kaplan-Meier method. The primary analysis was an assessment of ORR, PFS, and OS in patients treated with EV monotherapy and a comparison of these outcomes with data previously reported in clinical trials of EV. The secondary analysis focused on comparisons of ORR and OS among specific subsets of interest (specifically patients with pure urothelial histology vs mixed/variant histology, patients whose primary tumor origin was in the bladder vs the upper tract, and patients with liver metastases vs no liver metastases) and also on the basis of the number of prior treatment lines, the tumor mutational burden (TMB) status, the programmed death ligand 1 (PD-L1) status, and other characteristics. For patients with an evaluable response, ORR comparisons were performed with χ2 tests for equality of proportions, and confidence intervals were constructed by the Wilson method.
The overall cohort included 304 patients from 16 academic institutions in the United States (Supporting Table 1). In this cohort, 260 patients were treated with EV monotherapy, and were included in the primary and secondary analyses. Table 1 shows the characteristics of the overall cohort and the EV monotherapy cohort, and it includes the tumor molecular characteristics. Notably, FoundationOne CDx was the most common next-generation sequencing (NGS) panel used (44% of patients with NGS), and approximately 20% of patients had FGFR3 alterations.
|Characteristic||All Patients Receiving EV (n = 304)||Patients Receiving EV Monotherapy (n = 260)|
|Median age at enrollment, y||70||71|
|Gender||Men: 239 (79%)||Men: 205 (79%)|
|Women: 65 (21%)||Women: 55 (21%)|
|Race/ethnicity||White: 262 (86%)||White: 224 (86%)|
|Black: 12 (4%)||Black: 11 (4%)|
|Asian: 9 (3%)||Asian: 8 (3%)|
|Hispanic: 12 (4%)||Hispanic: 9 (4%)|
|Other: 9 (3%)||Other: 8 (3%)|
|Smoking history||Current/former smoker: 198 (65%)||Current/former smoker: 169 (65%)|
|Never smoker: 102 (34%)||Never smoker: 87 (34%)|
|Unknown: 4 (1%)||Unknown: 4 (2%)|
|ECOG PS||0: 88 (30%)||0: 74 (29%)|
|1: 148 (50%)||1: 127 (50%)|
|2: 45 (15%)||2: 39 (15%)|
|3: 13 (4%)||3: 13 (5%)|
|4: 1 (0.3%)||4: 1 (0.4%)|
|BMI||<18 kg/m2: 8 (3%)||<18 kg/m2: 8 (3%)|
|18-25 kg/m2: 119 (39%)||18-25 kg/m2: 102 (39%)|
|25-30 kg/m2: 97 (32%)||25-30 kg/m2: 87 (34%)|
|>30 kg/m2: 71 (23%)||>30 kg/m2: 57 (22%)|
|Unknown: 9 (3%)||Unknown: 6 (2%)|
|Location of primary tumor||Bladder: 215 (71%)||Bladder: 189 (73%)|
|Upper tract: 81 (27%)||Upper tract: 65 (25%)|
|Urethra: 1 (0.3%)||Urethra: 1 (0.4%)|
|Unknown: 7 (2%)||Unknown: 5 (2%)|
|Histology||Pure urothelial: 211 (69%)||Pure urothelial: 177 (68%)|
|Mixed urothelial predominant: 77 (25%)||Mixed urothelial predominant: 69 (27%)|
|Mixed variant predominant: 7 (2%)||Mixed variant predominant: 6 (2%)|
|Pure variant: 2 (1%)||Pure variant: 2 (1%)|
|Unknown: 7 (2%)||Unknown: 6 (2%)|
|Prior definitive surgery||174 (57%)||144 (55%)|
|Pathologic T stage (only for patients who had definitive surgery)||pT0: 5 (3%)||pT0: 5 (4%)|
|pTa/CIS: 10 (6%)||pTa/CIS: 5 (4%)|
|pT1: 16 (9%)||pT1: 15 (10%)|
|pT2: 38 (22%)||pT2: 31 (22%)|
|pT3: 79 (45%)||pT3: 65 (45%)|
|pT4: 19 (11%)||pT4: 17 (12%)|
|pTx: 7 (4%)||pTx: 6 (4%)|
|Pathologic N stage (only for patients who had definitive surgery)||pN0: 89 (51%)||pN0: 71 (49%)|
|pN1: 24 (14%)||pN1: 21 (15%)|
|pN2-3: 39 (22%)||pN2-3: 35 (24%)|
|pNx: 22 (13%)||pNx: 17 (12%)|
|Neoadjuvant chemotherapy (for patients who had definitive surgery)||Yes: 81 (47%)||Yes: 69 (48%)|
|No: 93 (53%)||No: 75 (52%)|
|Adjuvant therapy (for patients who had definitive surgery)||Chemotherapy only: 32 (18%)||Chemotherapy only: 31 (22%)|
|Radiation only: 3 (2%)||Radiation only: 2 (1%)|
|Chemotherapy/RT: 3 (2%)||Chemotherapy/RT: 2 (1%)|
|No treatment: 136 (78%)||No treatment: 109 (76%)|
|No. of therapy lines for metastatic disease before EV||None: 44 (15%)||None: 13 (5%)|
|1 line: 79 (26%)||1 line: 73 (28%)|
|2 lines: 113 (37%)||2 lines: 110 (42%)|
|3 lines: 47 (16%)||3 lines: 47 (18%)|
|>3 lines: 18 (6%)||> 3 lines: 17 (7%)|
|Unknown: 3 (1%)|
|EV treatment as SOC vs clinical trial||SOC: 209 (69%)||SOC: 202 (78%)|
|Trial: 91 (30%)||Trial: 57 (22%)|
|Unknown: 4 (1%)||Unknown: 1 (0.4%)|
|Metastatic disease sites|
|LN and/or locoregional recurrence only||61 (20%)||52 (20%)|
|Liver metastases||95 (31%)||84 (32%)|
|Nonliver visceral metastases||148 (49%)||124 (48%)|
|Patient molecular characteristics|
|Available NGS results||184 (61%)||160 (62%)|
|PD-L1 status available||119 (39%)||101 (39%)|
|MSI status available||157 (52%)||139 (53%)|
|TMB available||127 (42%)||113 (43%)|
|PD-L1 status (CPS ≥ 10 considered positive)||Positive: 59 (50%)||Positive: 54 (53%)|
|Negative: 60 (50%)||Negative: 47 (47%)|
|MSI-high status||3/157 (2%)||3/139 (2%)|
|FGFR3 alterations presenta||36/184 (20%)||33/160 (21%)|
|TMB||Median = 6.19 Mut/mb||Median = 6.08 Mut/mb|
|Range = 0-48 Mut/mb||Range = 0-48 Mut/mb|
|≥10 Mut/mb: 32/127 (25%)||≥10 Mut/mb: 24/113 (21%)|
- Abbreviations: BMI, body mass index; CIS, carcinoma in situ; CPS, combined positive score; ECOG, Eastern Cooperative Oncology Group; EV, enfortumab vedotin; FGFR3, fibroblast growth factor receptor 3; LN, lymph node; MSI, microsatellite instability; NGS, next-generation sequencing; PD-L1, programmed death ligand 1; PS, performance status; SOC, standard of care; TMB, tumor mutational burden; UNITE, Urothelial Cancer Network to Investigate Therapeutic Experiences.
- Percentages in some categories add up to more than 100% because of rounding.
- a FGFR3 alterations included all mutations or fusions considered pathogenic.
Among the 260 patients treated with EV monotherapy, the median follow-up from the initial UC diagnosis to the time of last follow-up was 35.9 months, whereas the median time from the initial diagnosis to the date of advanced disease was 10.9 months. The median time from the diagnosis of advanced disease to the EV treatment start was 12.0 months. Most patients were treated with EV after 2 or more prior lines of therapy for aUC (67%), and most received EV outside a clinical trial (78%). At the time of EV treatment initiation, 32% had liver metastases, and 80% had visceral metastasis (metastatic disease other than lymph node involvement and/or locoregional or soft tissue recurrence). At the time of analysis, the median follow-up from the start of EV was 7.2 months (interquartile range, 3.7-11.6 months), and the median treatment duration was 4.1 months (interquartile range, 1.6-6.9 months). Most patients (82%; n = 212) were evaluable for a response, and among these patients, 24% (50 of 212) were still on EV treatment at the time of analysis (71 of 260 [27%] in the overall monotherapy group). Among the 162 evaluable patients who discontinued EV treatment, the most common reasons were disease progression (64%), treatment intolerance (24%), and other (12%).
The ORR for the primary analysis among evaluable patients is shown in Table 2. The investigator-assessed ORR was 52%, which was similar to the ORR observed for the overall cohort of 304 patients (54%). Notably, only 22% of the patients had progressive disease as their best response to EV monotherapy. Among responders, the median time to a response was 1.9 months. Among the 260 patients included in the primary analysis, 135 were alive at the time of analysis, and 110 had died (15 had an unknown status). The median PFS and OS were 6.8 and 14.4 months, respectively, from the start of EV treatment (Fig. 1).
|Best Response to EV Monotherapy (n = 212)||ORR, % (95% CI)|
|CR (n = 15)||7 (0-20)|
|PR (n = 96)||45 (35-55)|
|SD (n = 54)||26 (14-37)|
|PD (n = 47)||22 (10-34)|
|ORR (n = 111)||52 (43-62)|
- Abbreviations: CI, confidence interval; CR, complete response; EV, enfortumab vedotin; ORR, observed response rate (composite of complete response and partial response); PD, progressive disease; PR, partial response; SD, stable disease.
For the secondary analysis comparing patient subsets of interest, the ORRs for evaluable patients are shown in Table 3; they were robust in most patient categories (>40%). The ORR was lower for patients whose tumors had a component of variant histology (42%; n = 66) versus pure urothelial histology (58%; n = 142; P = .056). For 77 patients treated with EV monotherapy whose tumors had a variant histology component, the histology breakdown and responses among 66 evaluable patients are shown in Table 4. Responses were seen across all variant histologies. Important subsets of patients, including those with upper tract primary tumors, those with liver metastases, heavily pretreated patients (>2 lines of therapy), and patients with comorbidities (including diminished renal function [eGFR < 30 mL/min], peripheral neuropathy, and diabetes mellitus), showed high rates of response to EV treatment. Among 28 patients whose tumors harbored FGFR3 alterations, the ORR was 57%. Responses to EV were also seen independently of the TMB and PD-L1 status or prior treatment regimens (Supporting Table 2). For the most part, no OS differences were observed in comparisons of relevant subsets of patients (Fig. 2). However, patients with liver metastases had a higher ORR (64% vs 47%; P = .04) but shorter OS (8.3 vs 15.7 months; P = .005) in comparison with patients without liver metastases.
|Subgroup||Patients, No.||ORR, % (95% CI)||P|
|Pure urothelial histology||142||58 (49-66)||.06|
|Variant histology (any component)||66||42 (31-55)|
|Bladder primary tumor||151||50 (42-58)||.21|
|Upper tract primary tumor||56||61 (47-73)|
|Age ≥ 75 y||69||51 (39-63)||.85|
|Age < 75 y||139||53 (45-62)|
|BMI ≥ 30 kg/m2||48||56 (41-70)||.63|
|BMI < 30 kg/m2||161||51 (43-59)|
|Prior definitive surgery or chemotherapy/RTa||126||53 (44-62)||.93|
|No prior definitive treatment||70||51 (39-63)|
|Treatment lines before EVb||.18|
|0-2 lines of prior treatment||158||49 (41-57)|
|>2 lines of prior treatment||54||61 (47-74)|
|Liver metastases||66||64 (51-75)||.04|
|No liver metastases||146||47 (39-56)|
|Bone metastases||75||51 (39-62)||.83|
|No bone metastases||137||53 (45-62)|
|ECOG PS of 0/1||173||56 (48-63)||.18|
|ECOG PS of 2/3||34||41 (25-59)|
|Baseline neuropathy||71||62 (50-73)||.08|
|No neuropathy||139||48 (40-57)|
|Baseline diabetes mellitus||29||59 (39-76)||.60|
|No diabetes mellitus||183||51 (44-59)|
|eGFR < 30 mL/min||25||40 (22-61)||.27|
|eGFR ≥ 30 mL/min||187||54 (47-61)|
|FGFR3 altered||28||57 (37-75)||.93|
|FGFR3 wild type||102||54 (44-64)|
|PD-L1 positivec||42||50 (36-65)||.23|
|PD-L1 negative||38||66 (49-80)|
|TMB ≥ 10 Mut/mb||21||62 (39-81)||.51|
|TMB < 10 Mut/mb||75||51 (39-62)|
|Neutrophil/lymphocyte ratio < median||101||51 (40-61)||1.0|
|Neutrophil/lymphocyte ratio ≥ median||101||52 (41-62)|
|Prior platinum-based therapyd||115||55 (45-64)||.53|
|No prior platinum-based therapy||97||50 (39-60)|
- Abbreviations: BMI, body mass index; CI, confidence interval; ECOG, Eastern Cooperative Oncology; eGFR, estimated glomerular filtration rate; EV, enfortumab vedotin; FGFR3, fibroblast growth factor receptor 3; ORR, observed response rate; PD-L1, programmed death ligand 1; PS, group performance status; RT, radiation therapy; TMB, tumor mutational burden.
- a Prior definitive surgery or chemotherapy/RT included prior treatment with a curative intent.
- b Treatment lines before EV included treatment in the advanced or metastatic setting.
- c For the PD-L1 status, a combined positive score (CPS) ≥ 10 was considered positive.
- d Platinum-based therapy included at least 1 prior cisplatin or carboplatin-based regimen.
|Variant Histology||Total, No.||Evaluable, No.||CR, No.||PR, No.||ORR, %|
- Abbreviations: CR, complete response; ORR, observed response rate (composite of complete response and partial response); PR, partial response.
- Among evaluable patients, 8 had majority nonurothelial histology with either variant predominant histology (n = 6) or pure variant histology (n = 2). Among these patients, 1 PR and no CRs were observed.
- a Among 4 patients with mixed histology, the histology breakdown and responses were as follows: patient 1, micropapillary and squamous–partial response; patient 2, sarcomatoid, glandular, and neuroendocrine–stable disease; patient 3, plasmacytoid and squamous–progressive disease; and patient 4, plasmacytoid, glandular, and micropapillary–not evaluable.
The UNITE study is a multi-institutional, retrospective analysis of patients with aUC receiving novel treatment modalities, including EV. This initial report of EV efficacy in non–clinical trial patients demonstrates notable activity of EV in patients with aUC, which is consistent with data previously reported from prospective clinical trials. Moreover, EV has robust activity in clinically relevant subsets of patients with aUC previously excluded from clinical trials, including patients with a poor performance status, patients with a low eGFR, and patients with relevant medical comorbidities (eg, peripheral neuropathy and diabetes mellitus). Altogether, these results offer important insights for understanding the efficacy of EV outside the clinical trial setting and the clinical context in which this novel drug can be best used to help patients.
In EV-201 (cohort 1) and EV-301, the ORRs for patients treated with EV monotherapy after prior treatment with platinum-based chemotherapy and ICIs were 44% and 41%, respectively, and 12% and 5% of the patients achieved a complete response. In cohort 2 of EV-201, which included cisplatin-ineligible patients previously treated with ICIs but not platinum-based chemotherapy, the ORR was 52%.18 The UNITE study analysis presented here, including both platinum-pretreated and platinum-naive patients, demonstrated an ORR of 52% with a 7% complete response rate; this was consistent with previously reported clinical trial data. The median PFS and OS values in this analysis—6.8 and 14.4 months, respectively—are also comparable to the values of 5.5 and 12.9 months reported in the EV-301 trial. The slightly higher values for ORR, PFS, and OS reported in this retrospective analysis are likely reflective of the inclusion of patients treated earlier in their disease course. Furthermore, ORR and PFS may have been affected by investigators not being blinded to the outcomes of their patients and by nonadherence to the Response Evaluation Criteria in Solid Tumors in determining responses. PFS can also be affected by not having a strictly defined imaging schedule as part of this assessment. The median time to a response in the UNITE analysis of 1.9 months was also almost identical to the previously reported data from EV-201 and EV-301. The data on the median duration of response are not yet mature in the UNITE analysis. In the future, it will be important to define how long these patients can remain on EV therapy in the context of both toxicity and efficacy considerations because of the availability of other treatment options such as erdafitinib and sacituzumab govitecan.19, 20
In the UNITE study, we also examined the efficacy of EV in patient populations of interest, many of which were not included in clinical trials. Patients whose tumors had a component of variant histology had high rates of response to EV (ORR, 42%), but this was lower than the rate in patients with pure urothelial histology (ORR, 58%). Prior studies have shown lower nectin-4 expression in rare histological variants in comparison with pure UC,21, 22 whereas preclinical data have suggested that nectin-4 expression is both necessary and sufficient for a response to EV.15 Therefore, the observed responses to EV may be potentially driven by the urothelial component in the tumors. In support of this hypothesis, among the 8 patients treated with EV monotherapy in this data set who had a pure variant or variant predominant tumor histology, only 1 partial response was observed (ORR, 13%). Patients with upper urinary tract primary tumors were noted to have numerically higher responses in comparison with patients with tumors originating in the bladder (ORR, 61% vs 50%). A potential explanation is that a higher proportion of upper tract tumors may have the luminal molecular subtype, which has higher nectin-4 expression and thus may be more susceptible to EV.23 However, these numbers should be confirmed in larger cohorts.
Notably, among 28 evaluable patients with FGFR3 alterations, EV also had significant activity with an ORR of 57%. Responses were also observed in a subset of these patients previously treated with the FGFR3 inhibitor erdafitinib (a partial response in 2 of 5 patients), and this suggests that these drugs can successfully be used sequentially for patients with aUC and FGFR3 alterations. The optimal sequence of EV and other available therapies for treatment-refractory patients remains to be further defined.24 Additional future data from the UNITE study and future prospective studies could help to answer these important questions as treatment paradigms for aUC continue to evolve.
Finally, the UNITE study shows the efficacy of EV in patient subsets typically associated with a poor prognosis, including patients with liver metastases, a high disease burden, and multiple lines of prior treatment. It should be noted that although patients with liver metastases had a higher ORR, they still had inferior OS in comparison with patients without liver metastases; this suggests a limited durability of benefit with EV in this patient population. Additionally, patient subsets with aUC that were previously excluded from clinical trials were shown to benefit from EV. They included patients with a poor performance status (Eastern Cooperative Oncology Group performance status of 2/3), as well as patients with relevant comorbidities, such as peripheral neuropathy, diabetes mellitus, and impaired renal function (eGFR < 30 mL/min). These comorbidities may affect the duration of treatment with EV as well as treatment-related adverse events, which will be further explored in future UNITE analyses.
The strengths of our study include the use of data from routine oncologic practice across multiple institutions and the relatively large sample size. This study had a number of important limitations inherent to the retrospective cohort design, including a lack of randomization or matched-control groups, potential missing data, and other selection and confounding biases. We did not report safety or toxicity data, which will be provided in future analyses of the UNITE study. Furthermore, there was no central radiology or pathology review, which may affect the interpretation of efficacy results and the association with histology variants. There may have been practice-related variability in disease monitoring and follow-up periods, which could affect the ascertainment of response and progression. Molecular diagnostics, such as NGS, PD-L1, TMB, and microsatellite instability status, were obtained from various heterogeneous platforms/assays and relied on EMR review. The study was limited to academic sites, which may not reflect the patterns of EV use in community practice settings and may, therefore, make this study's conclusions less generalizable to community oncology practices. Despite these limitations, this analysis provides important preliminary data regarding EV efficacy in aUC and complements and builds on published clinical trial data.
In conclusion, this initial, large, retrospective analysis from the UNITE study, which included patients with aUC treated with EV, has shown the treatment efficacy of EV to be consistent with what was previously reported in the clinical trials that led to the approval of this drug. Importantly, this includes robust activity in clinically relevant patient subsets, such as patients with FGFR3 alterations and patients previously excluded from clinical trials of EV (eg, patients with significantly diminished renal function, uncontrolled diabetes mellitus, and peripheral neuropathy). EV is also effective for patients with mixed/variant histologies, although the ORR is lower in comparison with patients with pure urothelial histology.
The University of Texas MD Anderson Cancer Center is supported by the National Institutes of Health (grant P30 CA016672).
Conflict of Interest Disclosures
Vadim S. Koshkin declares consulting fees from AstraZeneca, Clovis, Janssen, Pfizer, EMD Serono, Seattle Genetics/Astellas, Dendreon, Guidepoint, and GLG and institutional research support from Endocyte, Nektar, Clovis, Janssen, and Taiho. Pedro C. Barata declares grants or contracts from Merck, Seagen, Blue Earth Diagnostics, Pfizer, and EMD Serono; consulting fees from Dendreon, Pfizer, Caris Life Sciences, Astellas, Eisai, Janssen, EMD Serono, Seattle Genetics, Bristol-Myers Squibb, Bayer, and Guardant Health; payments or honoraria from Caris Life Sciences, Bayer, and Pfizer; and participation on boards for Bristol-Myers Squibb, Seagen, Astellas, Eisai, Janssen, EMD Serono, Dendreon, Pfizer, Seattle Genetics, Bayer, and Guardant Health. Arnab Basu declares grants or contracts from Natera; consulting fees from Bristol-Myers Squibb, Astellas, Pfizer, Cardinal Health, and EMD Serono; payments or honoraria from Eisai and Natera; and stock or stock options in BioNTech. Yousef Zakharia declares participation on boards for Amgen, Roche Diagnostics, Novartis, Janssen, Eisai, Exelixis, Castle Bioscience, and Pfizer and institutional research support from Pfizer, Exelixis, and Eisai. Chelsea K. Osterman was supported by a National Research Service Award Post-Doctoral Traineeship from the Agency for Healthcare Research and Quality. Ali Raza Khaki declares support for attending meetings and/or travel from the American Society of Clinical Oncology, the Bladder Cancer Advocacy Network, and the San Antonio Breast Cancer Society; participation on an advisory board for Seagen/Astellas; and stock in Merck and Sanofi. Christopher T. Su declares support from the National Cancer Institute. Terence W. Friedlander declares institutional research funding from Seagen and Roche and participation on boards for Seagen, Janssen, and EMD Serono. Mehmet A. Bilen declares consulting fees from Exelixis, Bayer, Bristol-Myers Squibb, Eisai, Pfizer, AstraZeneca, Janssen, Calithera Biosciences, Genomic Health, Nektar, EMD Serono, Seagen, and Sanofi and institutional research support from Merck, Xencor, Bayer, Bristol-Myers Squibb, Genentech/Roche, Seagen, Incyte, Nektar, AstraZeneca, Tricon Pharmaceuticals, Genome & Company, AAA, Peloton Therapeutics, and Pfizer for work performed outside the current study. Matthew T. Campbell declares grants or contracts from Exelixis, Pfizer/EMD Serono, Janssen, AstraZeneca, Aveo, and Apricity Health; consulting fees from Apricity Health, Exelixis, Astellas, and Seagen; payments or honoraria from Bristol-Myers Squibb, Roche, Pfizer/EMD Serono, Curio Sciences, and Medscape; and participation on boards for Eisai, EMD Serono, Pfizer, Genentech, and AstraZeneca. Hamid Emamekhoo declares speaking fees from Bristol-Myers Squibb, Seattle Genetics, Exelixis, and Bayer; institutional research funding from Bristol-Myers Squibb; travel support from Dava Oncology; and participation on boards for Exelixis, Bayer, Bristol-Myers Squibb, and Cardinal Health. Christopher Hoimes declares consulting/speaker fees from Merck, Seagen, and Astellas; honoraria from Seagen, Astellas, Merck, Eisai, and Genentech; grants or contracts from Seagen, Astellas, and Merck; and participation on boards for Seagen, Astellas, and Merck. Nancy B. Davis declares institutional research support from Seagen/Astellas, Immunomedics/Gilead, Incyte, HCRN, AstraZeneca, Mirati Therapeutics, Merck, Bristol-Myers Squibb, Calithera, Jounce Therapeutics, Exelixis, Hoffman-LaRoche, and Pfizer; support for attending meetings and/or travel from Bristol-Myers Squibb and Taris Biomedical; and participation on a board for Janssen Biotech. Matthew I. Milowsky declares institutional research support from Seagen, Astellas Pharma, Merck, Roche/Genentech, Bristol-Myers Squibb, Inovio Pharmaceuticals, Mirati Therapeutics, Syndax, Incyte, Clovis, Constellation, X4Pharmaceuticals, Innocrin, and Johnson & Johnson; consulting fees from Loxo/Lilly; stock or stock options in Pfizer, Merck, and Gilead Sciences; and funds paid to him from Elsevier. Petros Grivas declares consulting fees from AstraZeneca, Astellas Pharma, Bayer, Bristol-Myers Squibb, Clovis Oncology, Dyania Health, Driver, EMD Serono, Exelixis, Foundation Medicine, Genentech/Roche, Genzyme, GlaxoSmithKline, Guardant Health, Heron Therapeutics, Immunomedics/Gilead, Infinity Pharmaceuticals, Janssen, Merck & Co, Mirati Therapeutics, Pfizer, QED Therapeutics, Regeneron Pharmaceuticals, Seattle Genetics, Urogen, and 4D Pharma PLC; support for attending meetings and/or travel from AstraZeneca and Clovis Oncology; and institutional research support from Bavarian Nordic, Bristol-Myers Squibb, Clovis Oncology, Debiopharm, EMD Serono, GlaxoSmithKline, Immunomedics, Kure It Cancer Research, Merck & Co, Mirati Therapeutics, Pfizer, and QED Therapeutics. Guru P. Sonpavde declares consulting/speaking fees from Bristol-Myers Squibb, Genentech, EMD Serono, Merck, Sanofi, Seattle Genetics/Astellas, AstraZeneca, Exelixis, Janssen, Bicycle Therapeutics, Pfizer, Immunomedics/Gilead, Scholar Rock, G1 Therapeutics, Eli Lilly/Loxo Oncology, Infinity Pharmaceuticals, Physicians Education Resource, OncLive, Research to Practice, Medscape, Cancer Network, Masters Lecture Series, and UpToDate; being editor of the Elsevier PracticeUpdate Bladder Cancer Center of Excellence; institutional research support from Sanofi, AstraZeneca, Immunomedics/Gilead, QED, Predicine, and Bristol-Myers Squibb; participation on a board for Mereo Pharmaceuticals; travel costs from Bristol-Myers Squibb and AstraZeneca; and participation on steering committees for Bristol-Myers Squibb, Bavarian Nordic, Seattle Genetics, QED, G1 Therapeutics, AstraZeneca, EMD Serono, and Debiopharm. Deepak Kilari declares speaking fees from Astellas. Shilpa Gupta declares payments or honoraria from Seattle Genetics and participation on the scientific advisory board and steering committee for the Bladder Cancer Advocacy Network. Ajjai S. Alva declares institutional research funding from Seattle Genetics, AstraZeneca, Progenics, Prometheus, Arcus Biosciences, Bristol-Myers Squibb, Bayer, Janssen, Mirati Therapeutics, Astellas Pharma, Celgene, Merck Sharp & Dohme, and Genentech; consulting fees from Bristol-Myers Squibb and Astellas; and participation on a board for Aveo Oncology. The other authors made no disclosures.
Vadim S. Koshkin: Conceptualization, data curation, resources, formal analysis, methodology, project administration, writing–original draft, and writing–review and editing. Nicholas Henderson: Data curation, formal analysis, methodology, and writing–review and editing. Marihella James: Data curation and writing–review and editing. Divya Natesan: Data curation and writing–review and editing. Dory Freeman: Data curation and writing–review and editing. Amanda Nizam: Data curation and writing–review and editing. Christopher T. Su: Data curation and writing–review and editing. Ali Raza Khaki: Conceptualization, data curation, and writing–review and editing. Chelsea K. Osterman: Data curation and writing–review and editing. Michael J Glover: Data curation and writing–review and editing. Ryan Chiang: Data curation and writing–review and editing. Dimitrios Makrakis: Data curation and writing–review and editing. Rafee Talukder: Data curation and writing–review and editing. Emily Lemke: Data curation and writing–review and editing. T. Anders Olsen: Data curation and writing–review and editing. Jayanshu Jain: Data curation and writing–review and editing. Albert Jang: Data curation and writing–review and editing. Alicia Ali: Data curation, project administration, and writing–review and editing. Tanya Jindal: Data curation and writing–review and editing. Jonathan Chou: Data curation and writing–review and editing. Terence W. Friedlander: Data curation, methodology, and writing–review and editing. Christopher Hoimes: Conceptualization, data curation, methodology, and writing–review and editing. Arnab Basu: Conceptualization, data curation, methodology, and writing–review and editing. Yousef Zakharia: Conceptualization, data curation, methodology, and writing–review and editing. Pedro C. Barata: Conceptualization, data curation, methodology, and writing–review and editing. Mehmet A. Bilen: Conceptualization, data curation, methodology, and writing–review and editing. Hamid Emamekhoo: Conceptualization, data curation, methodology, and writing–review and editing. Nancy B. Davis: Conceptualization, data curation, methodology, and writing–review and editing. Sumit A. Shah: Data curation, methodology, and writing–review and editing. Matthew I. Milowsky: Conceptualization, data curation, methodology, and writing–review and editing. Shilpa Gupta: Conceptualization, data curation, methodology, and writing–review and editing. Matthew T. Campbell: Conceptualization, data curation, methodology, and writing–review and editing. Petros Grivas: Conceptualization, data curation, methodology, and writing–review and editing. Guru P. Sonpavde: Conceptualization, data curation, methodology, and writing–review and editing. Deepak Kilari: Conceptualization, data curation, methodology, project administration, writing–original draft, and writing–review and editing. Ajjai S. Alva: Conceptualization, data curation, resources, methodology, project administration, writing–original draft, and writing–review and editing.
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