Literature DB >> 35438782

Phase II Clinical and Translational Study of Everolimus ± Paclitaxel as First-Line Therapy in Cisplatin-Ineligible Advanced Urothelial Carcinoma.

Tomi Jun1,2, Noah M Hahn3, Guru Sonpavde4, Constantine Albany5, Gary R MacVicar6, Ralph Hauke7, Mark Fleming8, Theodore Gourdin9, Bagi Jana10, William K Oh1,2, Patricia Taik1, Huan Wang1, Ajay Ramakrishnan Varadarajan1, Andrew Uzilov1, Matthew D Galsky2.   

Abstract

BACKGROUND: Treatment options have been historically limited for cisplatin-ineligible patients with advanced urothelial carcinoma (UC). Given the need for alternatives to platinum-based chemotherapy, including non-chemotherapy regimens for patients with both impaired renal function and borderline functional status, in 2010 (prior to the immune checkpoint blockade era in metastatic UC), we initiated a phase II trial to test the activity of everolimus or everolimus plus paclitaxel in the cisplatin-ineligible setting.
METHODS: This was an open-label phase II trial conducted within the US-based Hoosier Cancer Research Network (ClinicalTrials.gov number: NCT01215136). Patients who were cisplatin-ineligible with previously untreated advanced UC were enrolled. Patients with both impaired renal function and poor performance status were enrolled into cohort 1; patients with either were enrolled into cohort 2. Patients received everolimus 10 mg daily alone (cohort 1) or with paclitaxel 80 mg/m2 on days 1, 8, and 15 of each 28-day cycle (cohort 2). The primary outcome was clinical benefit at 4 months. Secondary outcomes were adverse events, progression-free survival (PFS), and 1-year overall survival (OS). Exploratory endpoints included genomic correlates of outcomes. The trial was not designed for comparison between cohorts.
RESULTS: A total of 36 patients were enrolled from 2010 to 2018 (cohort 1, N = 7; cohort 2, N = 29); the trial was terminated due to slow accrual. Clinical benefit at 4 months was attained by 0 (0%, 95% confidence interval [CI] 0-41.0%) patients in cohort 1 and 11 patients (37.9%, 95% CI 20.7-57.7%) in cohort 2. Median PFS was 2.33 (95% CI 1.81-Inf) months in cohort 1 and 5.85 (95% CI 2.99-8.61) months in cohort 2. Treatment was discontinued due to adverse events for 2 patients (29%) in cohort 1 and 11 patients (38%) in cohort 2. Molecular alterations in microtubule associated genes may be associated with treatment benefit but this requires further testing.
CONCLUSION: Everolimus plus paclitaxel demonstrates clinical activity in cisplatin-ineligible patients with metastatic UC, although the specific contribution of everolimus cannot be delineated. Patients with both impaired renal function and borderline functional status may be difficult to enroll to prospective trials. (ClinicalTrials.gov Identifier NCT01215136).
© The Author(s) 2022. Published by Oxford University Press. The data published online to support this summary are the property of the authors. Please contact the authors about reuse rights of the original data.

Entities:  

Keywords:  cisplatin-ineligible; everolimus; genomic; paclitaxel; urothelial cancer

Mesh:

Substances:

Year:  2022        PMID: 35438782      PMCID: PMC9177111          DOI: 10.1093/oncolo/oyab075

Source DB:  PubMed          Journal:  Oncologist        ISSN: 1083-7159            Impact factor:   5.837


Everolimus plus paclitaxel demonstrates clinical activity in cisplatin-ineligible patients with metastatic urothelial cancer, although the contribution of everolimus is unclear. There is a need for treatment options for “chemotherapy-ineligible” patients, but these patients are challenging to enroll in prospective trials.

Discussion

Cisplatin remains the backbone of treatment for advanced UC. However, many patients are not eligible for cisplatin due to performance status or comorbidities. The subgroup of cisplatin-ineligible patients with both poor performance status and poor renal function experience increased toxicity and reduced benefit from carboplatin-based regimens necessitating novel treatment approaches. We initiated a phase II trial to test the activity of everolimus or everolimus plus paclitaxel in the cisplatin-ineligible setting shortly prior to a new era in drug development in metastatic UC. The shifting landscape, coupled with pragmatic considerations related to cohort 1, contributed to early closure due to poor accrual. Nonetheless, this trial has generated insights that may inform future treatment strategies. There was a 4-month clinical benefit rate of 37.9% associated with everolimus and paclitaxel (EVP) among patients with either poor performance status or poor renal function (cohort 2). This benefit was most likely driven by paclitaxel, which has demonstrated efficacy in this context both as a single-agent and in combinations. The EVP combination has also been studied in patients with UC progressing despite platinum-based chemotherapy with an objective response rate of 13%, similar to the response rate with paclitaxel alone, suggesting limited benefit by adding everolimus, although the specific contribution of each agent cannot be defined here. We initiated our trial in 2010 prior to the immune checkpoint blockade era and the subsequent shifts in the metastatic UC treatment landscape. Current standard first-line treatment for cisplatin-ineligible patients with metastatic UC includes carboplatin-based chemotherapy followed by switch maintenance immune checkpoint blockade or single agent immune checkpoint in patients with tumors harboring high levels of PD-L1 expression or patients who are “chemotherapy ineligible” (eg, those with poor functional status and renal function). The current trial, although performed in an earlier era and with a treatment without substantial activity, highlights the potential challenges of enrolling “chemotherapy ineligible” patients to prospective clinical trials; the median OS of patients in cohort 1 was only 4.5 months. We examined genomic data from 17 patients in cohort 2 to identify possible biomarkers of response to EVP. There were no significant associations between somatic mutations, copy number variants, or mutational signatures and response. However, power was limited. One notable, albeit non-significant, observation was the high response rates to EVP among those with mutations in either of the microtubule-associated genes MACF1 or FRY (100%; Fisher’s exact P = .24 two sided; P = .14 one sided). To our knowledge, there have not been in vitro or in vivo experiments testing the relationship between mutations in these genes and paclitaxel sensitivity. Though the use of taxanes in latter lines of therapy for metastatic UC is decreasing in the context of new treatment options, treatment selection biomarkers for these newer treatments are still lacking and biomarkers of paclitaxel benefit could still impact clinical treatment strategies and warrant further testing.

Trial Information

Additional Details of Endpoints or Study Design

Exploratory endpoints included genomic correlates of outcomes. The study included two parallel cohorts and was not designed for statistical comparison of the cohorts. Each cohort used a separate Simon’s two-stage minimax design, with one-sided α 0.05 and power 0.8. For cohort 1, the minimal activity threshold was a 4-month clinical benefit rate (CBR) of ≤10% while the substantial activity threshold was a CBR ≥30%. For cohort 2, the minimal activity threshold was a CBR ≤25% while the substantial activity threshold was a CBR ≥45%. Based on these parameters, we planned to accrue 15 evaluable patients in the first stage for cohort 1, and an additional 10 patients in the second stage. For cohort 2, we planned to enroll 17 patients in the first stage, and an additional 19 patients in the second stage. Anticipating a 10% dropout rate, the target accrual was 68 patients: 28 in cohort 1 and 40 in cohort 2. The trial opened in 2010 but was closed in 2018 due to slow accrual after having enrolled 36 patients: 7 in cohort 1 and 29 in cohort 2. All patients who received at least one dose of the trial medication were included in the final analyses for efficacy and safety.

Outcome Notes

Table 2 shows additional details of study outcome.
Table 2.

Radiographic outcomes.

Cohort 1 
(N = 7)Cohort 2 
(N = 29)Overall 
(N = 36)
Response at 4 months
Complete response, N (%)0 (0%)0 (0%)0 (0%)
Partial response, N (%)0 (0%)8 (27.6%)8 (22.2%)
Stable disease, N (%)0 (0%)3 (10.3%)3 (8.3%)
Progressive disease, N (%)4 (57.1%)9 (31%)13 (36.1%)
Not evaluable, N (%)3 (42.9%)9 (31%)12 (33.3%)
Best response
Complete response, N (%)0 (0%)1 (3.4%)1 (2.8%)
Partial response, N (%)0 (0%)13 (44.8%)13 (36.1%)
Stable disease, N (%)4 (57.1%)6 (20.7%)10 (27.8%)
Prog. disease, N (%)2 (28.6%)4 (13.8%)6 (16.7%)
Not evaluable, N (%)1 (14.3%)5 (17.2%)6 (16.7%)
Radiographic outcomes.

Adverse Events: Cohort 1, All Cycles

Data shown here are the AEs observed in at least 40% of patients. Table 3 shows a detailed listing.
Table 3.

Treatment-emergent adverse events occurring in at least 5% of patients, sorted alphabetically.

Cohort 1 (N = 7)Cohort 2 (N = 29)
Adverse eventGrade 1Grade 2Grade 3Grade 4AnyGrade 1Grade 2Grade 3Grade 4Any
No.%No.%No.%No.%No.%No.%No.%No.%No.%No.%
Any adverse event71006865712297100291002897269062129100
Abdominal pain000009313102701241
Acute kidney injury00000031000310
Alanine aminotransferase increased00000271313027
Alopecia1140001141034724001345
Anemia34345745705711759165593102276
Anorexia2293430034311386211301552
Anxiety1140001146212700621
Aspartate aminotransferase increased00000131313027
Back pain000003102700414
Blood and lymphatic system disorders - Other0000013130027
Cataract000002700027
Chills00000414000414
Cholesterol high343000343828000828
Chronic kidney disease0000013132713310
Constipation3430003431448517001655
Cough229000229113813001138
Creatinine increased11411411402292731000517
Dehydration0000003103100517
Depression1140001147242700724
Diarrhea22901140343165551741401862
Dizziness114000114517000517
Dry skin000002700027
Dysgeusia34300034362131000724
Dyspepsia114000114724000724
Dyspnea343114114034312412741401552
Edema limbs114114002291241828001448
Epistaxis00000517000517
Fatigue710022911407100258610342702586
Fever2290002291138310001241
Flatulence000002700027
Fracture0000013130027
Gastrointestinal disorders - other00000272700414
Generalized muscle weakness01140011427013027
Hallucinations000002700027
Headache00000414000414
Hematuria114114114022913130027
Hyperglycemia0114114022931013130310
Hyperhidrosis000002700027
Hypertension03430034351772441401241
Hypertriglyceridemia0000082831000828
Hypoalbuminemia000003101300310
Hypocalcemia000002713131327
Hypokalemia00000310414270621
Hypomagnesemia1140001144141300414
Hyponatremia114114001143100130310
Hypophosphatemia11411400229131327027
Infections and infestations - other01141140-229133103100-517
Insomnia11400011414480001448
Lung infection0000001313027
Mucositis oral22922911402291034828001448
Myocardial infarction0000000131327
Nail infection00000131313027
Nasal congestion0000013130027
Nausea1142290034314483102701448
Neck pain000002700027
Nervous system disorders - other011411401140270027
Neutrophil count decreased00000414414517131138
Non-cardiac chest pain0000013130027
Pain34300034317597242701862
Pain in extremity00000621000621
Peripheral motor neuropathy0000027130027
Peripheral sensory neuropathy2290002291241414001448
Platelet count decreased2291140022962127130621
Pneumonitis0011401140310130414
Pruritus114114002294142700621
Rash acneiform11434300343828414130931
Rash maculo-papular000003101300310
Renal and urinary disorders - Other, specify0000013001327
Respiratory failure000000001327
Sepsis0000000131327
Sinus disorder000002700027
Sore throat000002700027
Supraventricular tachycardia0000001313027
Thromboembolic event00000031000414
Tooth infection000000270027
Urinary frequency0000041431000621
Urinary tract infection045711404570113862101345
Urinary tract pain11411400229271300310
Vomiting229000229931132701241
Weight loss1140001143102700414

Highest grade treatment-emergent adverse events occurring in at least 5% of patients, sorted alphabetically. Treatment was discontinued due to adverse events for 2 patients (29%) in cohort 1 and 11 patients (38%) in cohort 2. Treatment-emergent grades 3-4 adverse events developed in 5 patients (71%) in cohort 1 and 26 patients (90%) in cohort 2 (Table 3). The most common grades 3-4 adverse event in both cohorts was anemia (cohort 1, N = 4; cohort 2, N = 9). The most common adverse events of any grade in cohort 1 were fatigue (N = 7, 100%), anemia (N = 5, 71%), and urinary tract infections (N = 4, 57%). The most common adverse events of any grade in cohort 2 were fatigue (N = 25, 86%), anemia (N = 22, 76%), pain (N = 18, 62%), dyspnea (N = 15, 52%), and gastrointestinal symptoms eg, diarrhea (N = 18, 62%), constipation (N = 16, 55%), anorexia (N = 15, 52%), and nausea (N = 14, 48%).

Treatment-emergent adverse events occurring in at least 5% of patients, sorted alphabetically. Highest grade treatment-emergent adverse events occurring in at least 5% of patients, sorted alphabetically. Treatment was discontinued due to adverse events for 2 patients (29%) in cohort 1 and 11 patients (38%) in cohort 2. Treatment-emergent grades 3-4 adverse events developed in 5 patients (71%) in cohort 1 and 26 patients (90%) in cohort 2 (Table 3). The most common grades 3-4 adverse event in both cohorts was anemia (cohort 1, N = 4; cohort 2, N = 9). The most common adverse events of any grade in cohort 1 were fatigue (N = 7, 100%), anemia (N = 5, 71%), and urinary tract infections (N = 4, 57%). The most common adverse events of any grade in cohort 2 were fatigue (N = 25, 86%), anemia (N = 22, 76%), pain (N = 18, 62%), dyspnea (N = 15, 52%), and gastrointestinal symptoms eg, diarrhea (N = 18, 62%), constipation (N = 16, 55%), anorexia (N = 15, 52%), and nausea (N = 14, 48%).

Adverse Events: Cohort 2, All Cycles

Data shown here are AEs occurring in at least 40% of patients. Table 3 shows details.

Introduction

The standard treatment for metastatic or unresectable urothelial carcinoma (UC) is cisplatin-based chemotherapy. However, a large subset of patients with UC are considered ineligible for cisplatin due to comorbidities such as chronic renal insufficiency.[1,2] Treatment options for cisplatin-ineligible patients are limited. The EORTC 30986 trial compared the combination of gemcitabine plus carboplatin (GCa) versus methotrexate, carboplatin, plus vinblastine (M-CAVI) and demonstrated severe acute toxicity in 9.3% of patients receiving GCa and 21.2% of patients receiving M-CAVI.[3] Patients with both impaired renal function and borderline functional status experienced even higher rates of severe acute toxicity, questioning the role of platinum-based regimens in this context. Overexpression of the mTOR pathway has been observed in invasive UC and inactivation of endogenous mTOR inhibitors, such as PTEN, has been linked to UC progression.[4,5] The mTOR inhibitor everolimus has demonstrated single-agent antitumor activity in patients with tumors harboring somatic alterations associated with mTOR pathway activation.[6] Paclitaxel has single-agent activity in UC and has demonstrated safety in the treatment of cisplatin-ineligible patients with advanced UC.[7] In model systems of cancer, PI3K/AKT/mTOR pathway upregulation is associated with taxane resistance and mTOR pathway inhibition has been shown to synergize with paclitaxel.[8-10] The combination of everolimus and paclitaxel (EVP) has also demonstrated safety and activity across a variety of tumor types.[11-13] Given the need for alternatives to platinum-based chemotherapy, including nonchemotherapy regimens for patients with both impaired renal function and borderline functional status, in 2010 (prior to the immune checkpoint blockade era in metastatic UC), we initiated a phase II trial to test the activity of everolimus or everolimus plus paclitaxel in the cisplatin-ineligible setting.

Patients and Methods

Participants

Adult patients (aged 18 or older) with histologically proven UC who were ineligible for cisplatin and who had not been previously treated for metastatic disease were eligible for this study. Upper tract disease and mixed histology (with a UC component) were allowed. Cisplatin ineligibility was based on one of two criteria: (1) calculated creatinine clearance (by the Cockroft-Gault formula) <60 mL/minute, (2) Karnofsky performance status 60-70%. Patients meeting both criteria were assigned to cohort 1 while patients meeting only one criterion were assigned to cohort 2. Key exclusion criteria included active brain metastases and lack of measurable disease (per RECIST[14]). Patients were enrolled from treatment centers within the US-based Hoosier Cancer Research Network.

Trial Oversight

The protocol was approved by the Institutional Review Board of each participating institution. Written informed consent was obtained from all participants prior to enrollment. The study was performed in accordance with ethical principles originating from the Declaration of Helsinki, which are consistent with ICH/Good Clinical Practice, and applicable regulatory requirements.

Interventions

Patients in cohort 1 were assigned to take everolimus alone (EVE) at a dose of 10 mg by mouth daily, without interruption. Medications were dispensed on an outpatient basis on day 1 of each 28-day cycle. Patients in cohort 2 were assigned to a combination of EVP. Everolimus was prescribed at the same dose and schedule as for cohort 1. Paclitaxel 80 mg/m2 was given as a 1-hour intravenous infusion on days 1, 8, and 15 of each 28-day cycle. Dose reductions were permitted in accordance with a schedule specified in the protocol. Paclitaxel could be reduced to 60 mg/m2 and everolimus could be reduced to a minimum of 5mg every other day. The study drugs were discontinued if further dose reductions were required or if treatment was interrupted for greater than 4 weeks. Treatment was continued until radiographic progression (by RECIST criteria), unacceptable toxicity, death, or discontinuation for any other reason. Cross-sectional imaging was obtained every 2 cycles until disease progression.

Outcomes

The primary objective was to evaluate CBR at 4 months from treatment initiation. Clinical benefit was defined as complete response (CR), partial response (PR), or stable disease (SD) per RECIST criteria. Secondary objectives were to evaluate the safety of EVE and EVP in this population, and to determine progression-free survival (PFS) and 1-year overall survival (OS). Exploratory objectives included identifying genomic correlates of outcomes using whole-exome and transcriptome sequencing data from archived tumor samples.

Genomic Analyses

Formalin-fixed paraffin-embedded tumor and paired blood normal samples (N = 17) were submitted for whole-exome sequencing (WES). Exome capture and sequencing library preparation were performed using the SureSelect Human All Exon V7, no UTR hybridization capture kit from Agilent (Santa Clara, CA). Libraries were sequenced on an Illumina HiSeq 4000 instrument with 100-bp paired-end reads. An in-house GATK4-based pipeline (TIGRIS) was used to analyze the WES profiles. Somatic variants with a general allelic fraction (AF) or ethnic-specific AF ≥ 0.5% in the gnomAD database were removed from analysis. Copy number variant (CNV) segmentation profiles were called using saasCNV,[15] then fed into GISTIC 2.0[16] across the entire cohort to look for significant CNV regions. Mutational signature analysis was done via R package quadprog,[17] and only samples with SNVs in exome region ≥50 at AF ≥ 5% were included, resulting a total of 14 samples. The signature fitting step was conducted using a reference catalog consisting of bladder cancer specific COSMIC v2 mutational signatures 1, 2, 5, 10, and 13.[18] RNA sequencing was performed on 8 of the 17 WES samples using SureSelect RNADirect (Agilent, Santa Clara, CA). An in-house RNaseq data processing pipeline (EUPHRATES) was used to analyze the data. UCSC’s hg19 genome build was used as the standard reference genome for all analyses. Gene annotations were derived from UCSC’s refGene table. Briefly, STAR (v2.6.1.d)[19] was used for read alignment, and featureCounts (v1.4.4)[20] was used to measure abundance of genomic features. Differential gene expression analysis was then performed using the DESeq2[21] package using read counts from the previous step. Gene fusion events were also screened for using open source fusion calling tools FusionCatcher (v1.0)[22] and FusionInspector(v2.1.0).[23]

Statistical Considerations

The study included two parallel cohorts and was not designed for statistical comparison of the cohorts. Each cohort used a separate Simon’s two-stage minimax design, with one-sided α 0.05 and power 0.8. For cohort 1, the minimal activity threshold was a 4-month CBR of ≤10% while the substantial activity threshold was a CBR ≥30%. For cohort 2, the minimal activity threshold was a CBR ≤25% while the substantial activity threshold was a CBR ≥45%. Based on these parameters, we planned to accrue 15 evaluable patients in the first stage for cohort 1, and an additional 10 patients in the second stage. For cohort 2, we planned to enroll 17 patients in the first stage, and an additional 19 patients in the second stage. Anticipating a 10% dropout rate, the target accrual was 68 patients: 28 in cohort 1 and 40 in cohort 2. The trial opened in 2010 but was closed in 2018 due to slow accrual after having enrolled 36 patients: 7 in cohort 1 and 29 in cohort 2. All patients who received at least one dose of the trial medication were included in the final analyses for efficacy and safety. Descriptive statistics were summarized using medians and ranges for continuous variables and counts and proportions for categorical variables. The primary outcome of 4-month CBR was calculated as the number of patients achieving clinical benefit at 4 months divided by the total number of patients in the cohort. 95% confidence intervals for proportions were calculated using the Clopper-Pearson exact method. Survival outcomes were estimated using the Kaplan-Meier method. Statistical analyses were conducted in R statistical software, version 4.0.0.

Results

Patient Characteristics

Of the 36 patients enrolled, the majority (75%, N = 27) were men. Seven patients with both impaired renal function and poor performance status were assigned to EVE in cohort 1. Twenty-nine patients with either impaired renal function or poor performance status were assigned to EVP in cohort 2. The median Karnofsky performance status (60% vs 80%, P < .001) and calculated creatinine clearance (36.03 vs 51.3 mL/minute, P = .12) were both numerically lower in cohort 1 compared with cohort 2 (Table 1).
Table 1.

Baseline characteristics.

Cohort 1 (N = 7)Cohort 2 (N = 29)Overall (N = 36)
Age, median (range)79 (59-90)72 (54-88)73 (54-90)
Male, N (%)5 (71.4%)22 (75.9%)27 (75%)
White, N (%)5 (71.4%)27 (93.1%)32 (88.9%)
Black, N (%)2 (28.6%)2 (6.9%)4 (11.1%)
Non-Hispanic, N (%)7 (100%)28 (96.6%)35 (97.2%)
Karnofsky 
performance status, median (range)60 (60-70)80 (60-100)80 (60-100)
Calculated creatinine clearance, median (range)36.03 (10.54-60)51.3 (22-96)50.35 (10.54-96)

Of the 36 patients enrolled, the majority (75%, N = 27) were men. Seven patients with both impaired renal function and poor performance status were assigned to EVE in cohort 1. Twenty-nine patients with either impaired renal function or poor performance status were assigned to EVP in cohort 2. The median Karnofsky performance status (60% vs 80%, 
P < .001) and calculated creatinine clearance (36.03 vs 51.3 mL/minute, 
P = .12) were both numerically lower in cohort 1 compared with cohort 2.

Baseline characteristics. Of the 36 patients enrolled, the majority (75%, N = 27) were men. Seven patients with both impaired renal function and poor performance status were assigned to EVE in cohort 1. Twenty-nine patients with either impaired renal function or poor performance status were assigned to EVP in cohort 2. The median Karnofsky performance status (60% vs 80%, 
P < .001) and calculated creatinine clearance (36.03 vs 51.3 mL/minute, 
P = .12) were both numerically lower in cohort 1 compared with cohort 2.

Efficacy

No patients (0%, 95% confidence interval [CI] 0-41.0%) in cohort 1 attained the primary outcome of clinical benefit at 4 months; 11 patients (37.9%, 95% CI 20.7-57.7%) in cohort 2 attained the primary outcome (Table 2). Twelve patients who were not evaluable for the primary outcome (due to lack of imaging) were included in this intent to treat analysis. Three patients in cohort 1 died prior to the 4-month evaluation. Nine patients in cohort 2 were not evaluable at 4 months; 4 had died prior to that time point, while the remaining 5 did not have 4-month imaging. The median PFS was 2.33 (95% CI 1.81-Inf) months in cohort 1 and 5.85 (95% CI 2.99-8.61) months in cohort 2 (Figure 1A). Median OS was 4.5 (95% CI 2.33-Inf) months in cohort 1 and 10.9 (95% CI 6.97-16.4) months in cohort 2 (Figure 1B). Overall survival at 1 year was not estimable for cohort 1 and was 43% (95% CI 28.1-65.9) in cohort 2.
Figure 1.

Kaplan-Meier curves for (A) progression-free survival and (B) overall survival, stratified by cohort. The median progression-free survival was 2.33 (95% CI 1.81-Inf) months in cohort 1 and 5.85 (95% CI 2.99-8.61) months in cohort 2. Median overall survival was 4.5 (95% CI 2.33-Inf) months in cohort 1 and 10.9 (95% CI 6.97-16.4) months in cohort 2.

Kaplan-Meier curves for (A) progression-free survival and (B) overall survival, stratified by cohort. The median progression-free survival was 2.33 (95% CI 1.81-Inf) months in cohort 1 and 5.85 (95% CI 2.99-8.61) months in cohort 2. Median overall survival was 4.5 (95% CI 2.33-Inf) months in cohort 1 and 10.9 (95% CI 6.97-16.4) months in cohort 2.

Safety and Tolerability

The median duration of exposure to EVE in cohort 1 was 1.87 months (range 0.84-5); the median duration of exposure to EVP in cohort 2 was 2.83 months (range 0.23-20). Treatment was discontinued due to adverse events for 2 patients (29%) in cohort 1 and 11 patients (38%) in cohort 2. Treatment-emergent grades 3-4 adverse events developed in 5 patients (71%) in cohort 1 and 26 patients (90%) in cohort 2 (Table 3). The most common grades 3-4 adverse event in both cohorts was anemia (cohort 1, N = 4; cohort 2, N = 9). The most common adverse events of any grade in cohort 1 were fatigue (N = 7, 100%), anemia (N = 5, 71%), and urinary tract infections (N = 4, 57%). The most common adverse events of any grade in cohort 2 were fatigue (N = 25, 86%), anemia (N = 22, 76%), pain (N = 18, 62%), dyspnea (N = 15, 52%), and gastrointestinal symptoms, eg, diarrhea (N = 18, 62%), constipation (N = 16, 55%), anorexia (N = 15, 52%), and nausea (N = 14, 48%).

Genomic Alterations Associated with Response

Whole-exome sequencing was performed on baseline biopsy samples from 19 patients in cohort 2 (Figure 2). Of these, 17 patients were evaluable for radiographic response at 4 months. We classified these patients into 13 responders and 4 non-responders, with response defined as CR, PR, or SD with a PFS of at least 120 days. The most commonly mutated gene in the cohort was TP53 (N = 9); 8 of 9 patients with TP53 mutations were responders (Fisher’s exact P = .29). Other notable recurrent mutations included the microtubule-related genes MACF1 (N = 4) and FRY (N = 4). All 6 patients with mutations in either MACF1 or FRY were responders, although the association was not statistically significant (Fisher’s exact P = .24 two sided; P = .14 one sided). TSC1 mutations have previously been linked with everolimus sensitivity in mUC.[6] One patient had a TSC1 mutation (p.Pro17fs) and was a responder.
Figure 2.

Mutational landscape of whole-exome sequencing cohort (N = 17). Genes mutated in at least three samples in the cohort are listed. Each column represents one sample. The vertical bar plot depicts tumor mutation burden in each sample. The horizontal bar plot summarizes the number and type of mutations (by color) for each gene. The tracks along the bottom provide additional clinical context, color coding each sample according to the patient’s gender and clinical outcome. The most commonly mutated gene in the cohort was TP53 (N = 9); 8 of 9 patients with TP53 mutations were responders (Fisher’s exact P = .29). Other notable recurrent mutations included the microtubule-related genes MACF1 (N = 4) and FRY (N = 4). All 6 patients with mutations in either MACF1 or FRY were responders, although the association was not statistically significant (Fisher’s exact P =.24 two sided; P =.14 one sided).

Mutational landscape of whole-exome sequencing cohort (N = 17). Genes mutated in at least three samples in the cohort are listed. Each column represents one sample. The vertical bar plot depicts tumor mutation burden in each sample. The horizontal bar plot summarizes the number and type of mutations (by color) for each gene. The tracks along the bottom provide additional clinical context, color coding each sample according to the patient’s gender and clinical outcome. The most commonly mutated gene in the cohort was TP53 (N = 9); 8 of 9 patients with TP53 mutations were responders (Fisher’s exact P = .29). Other notable recurrent mutations included the microtubule-related genes MACF1 (N = 4) and FRY (N = 4). All 6 patients with mutations in either MACF1 or FRY were responders, although the association was not statistically significant (Fisher’s exact P =.24 two sided; P =.14 one sided). Copy number segmentation profiles were qualitatively similar between responders and non-responders (Figure 3). Due to the small number of non-responders, the two cohorts were combined to identify significantly enriched CNVs by GISTIC 2.0 (Tables 4-6). The most significant regions included 2q11.2, 2q11.1 for gains and 1p36.13, 7q22.1, 12q12, 2q11.1 for losses (Figure 4). The significantly amplified regions included several genes involved in fibroblast growth factor signaling, upstream of the PI3K/AKT/mTOR pathway: FGF3, FGF4, FGF9, FGF19, and FRS2.
Figure 3.

A heatmap of the genome-wide copy number variation (CNV) profiles based on median log2ratio, stratified by response. Individual patient samples are shown along the y-axis with amplification events in red and loss events in blue. The number of CNV events at each genomic locus for responders and nonresponders are summarized as bar plots at the top for both responders and non-responders. Copy number segmentation profiles were qualitatively similar between responders and non-responders.

Table 4.

Significant CNV gain regions called by GISTIC across all 17 WES T/N samples submitted for whole exome sequencing (WES). GISTIC is an algorithm that identified regions of the genome that are gained or lost more than expected by chance across a set of samples.

Cytoband2q11.22q11.120q11.2113q12.114q35.211q13.31q21.16p22.12p25.112q1517q12
q value1.20E−062.34E−060.00123680.00532770.00572450.0137320.0495510.0920260.0987160.0987160.098716
Residual q value1.82E−064.00E−060.00123680.00532770.00572450.0137320.0495510.0920260.0987160.0987160.098716
Wide peak boundarieschr2:97820512-97828928chr2:96604806-96610328chr20:29628342-29632831chr13:22255168-23253312chr4:189022421-190878451chr11:68512544-69949326chr1:144852237-145293666chr6:29692730-29911259chr2:8943044-10729896chr12:66990486-70918140chr17:37557600-38062138
Genes in wide peak ANKRD36 [LOC729234] FRG1B FGF9 FRG1 hsa-mir-3164 SEC22B HLA-A hsa-mir-4261 hsa-mir-1279 ERBB2
HSP90AA4P CCND1 PDE4DIP HLA-F HPCAL1 CPM GRB7
TRIML2 CPT1A NOTCH2NL HLA-G ODC1 IFNG NEUROD2
TRIML1 FGF3 NBPF10 HLA-H RRM2 LYZ PNMT
LOC401164 FGF4 HCG4 ADAM17 MDM2 MED1
IGHMBP2 HCG4B KLF11 CNOT2 TCAP
MTL5 HLA-F-AS1 ASAP2 PTPRB STARD3
FGF19 IFITM4P TAF1B RAP1B IKZF3
MYEOV LOC554223 ITGB1BP1 YEATS4 CDK12
ANO1 YWHAQ DYRK2 GSDMB
MRGPRD GRHL1 CCT2 PPP1R1B
MRGPRF CPSF3 FRS2 MIEN1
MRPL21 KIDINS220 CPSF6 FBXL20
TPCN2 NOL10 GRIP1 PGAP3
ORAOV1 MBOAT2 KCNMB4 ZPBP2
CYS1 IL22 MIR4728
IAH1 SLC35E3
C2orf48 IL26
SNORA80B CAND1
MIR4261 MDM1
NUP107
RAB3IP
BEST3
LRRC10
MIR1279
SNORA70G
MIR3913-2
MIR3913-1
LOC100507250
Table 6.

GISTIC output describing significantly amplified and deleted regions across the samples. The columns represent individual patient samples and indicate the regions gained or deleted in these samples.

Unique nameDescriptorWide peak limitsPeak limitsRegion limitsq valuesResidual q values after removing segments shared with higher peaksAmplitude thresholdGP034-tumorGP019-tumorGP003-tumorGP018-tumorGP023-tumorGP043-tumorGP016-tumorGP030-tumorGP011-tumorGP007-tumorGP026-tumorGP013-tumorGP009-tumorGP024-tumorGP005-tumorGP012-tumorGP025-tumor
Amplification peak 11q21.1chr1:144852237-145293666(probes 61189:64677)chr1:145109662-145115657(probes 63931:64118)chr1:145109662-145115809(probes 63931:64121)0.0495510.0495510: t < 0.85; 1: 0.85 < t < 0.9; 2: t > 0.920000022010020000
Amplification peak 22p25.1chr2:8943044-10729896(probes 112423:113242)chr2:8946569-10729896(probes 112424:113241)chr2:8946569-10729896(probes 112424:113244)0.0987160.0987160: t < 0.85; 1: 0.85 < t < 0.9; 2: t > 0.900000010000200020
Amplification Peak 32q11.1chr2:96604806-96610328(probes 140471:140516)chr2:96604811-96607047(probes 140472:140515)chr2:96604811-96610758(probes 140472:140589)2.34E−064.00E−060: t < 0.85; 1: 0.85 < t < 0.9; 2: t > 0.922222022000202020
Amplification Peak 42q11.2chr2:97820512-97828928(probes 142142:142325)chr2:97820635-97827942(probes 142143:142295)chr2:97817579-97829924(probes 142113:142399)1.20E−061.82E−060: t < 0.85; 1: 0.85 < t < 0.9; 2: t > 0.902222022220002220
Amplification Peak 54q35.2chr4:189022421-190878451(probes 290591:290876)chr4:189022508-190878451(probes 290592:290875)chr4:189022508-190878451(probes 290592:290878)0.00572450.00572450: t < 0.85; 1: 0.85 < t < 0.9; 2: t > 0.902220022000000020
Amplification Peak 66p22.1chr6:29692730-29911259(probes 346349:346874)chr6:29910483-29910864(probes 346455:346697)chr6:29910483-29910985(probes 346455:346700)0.0920260.0920260: t < 0.85; 1: 0.85 < t < 0.9; 2: t > 0.900200020000200202
Amplification Peak 711q13.3chr11:68512544-69949326(probes 624468:624919)chr11:68527601-69949326(probes 624469:624918)chr11:68527601-69949326(probes 624469:624921)0.0137320.0137320: t < 0.85; 1: 0.85 < t < 0.9; 2: t > 0.900002000000200000
Amplification Peak 812q15chr12:66990486-70918140(probes 683150:683997)chr12:69048132-70672148(probes 683373:683974)chr12:69048132-70672148(probes 683373:683977)0.0987160.0987160: t < 0.85; 1: 0.85 < t < 0.9; 2: t > 0.902000000020000002
Amplification Peak 913q12.11chr13:22255168-23253312(probes 706758:706956)chr13:22255302-23243644(probes 706759:706876)chr13:22255302-23253391(probes 706759:706958)0.00532770.00532770: t < 0.85; 1: 0.85 < t < 0.9; 2: t>0.900200200000000200
Amplification Peak 1017q12chr17:37557600-38062138(probes 876351:876621)chr17:37558370-38061277(probes 876352:876620)chr17:37558370-38062195(probes 876352:876623)0.0987160.0987160: t < 0.85; 1: 0.85 < t < 0.9; 2: t > 0.920020000000000020
Amplification Peak 1120q11.21chr20:29628342-29632831(probes 1016094:1016357)chr20:29632520-29632826(probes 1016171:1016345)chr20:29449510-30142483(probes 1015484:1016377)0.00123680.00123680: t < 0.85; 1: 0.85 < t < 0.9; 2: t > 0.922222022200000220
Deletion Peak 11p36.13chr1:16903823-16913583(probes 17147:17600)chr1:16905719-16913583(probes 17152:17599)chr1:16890673-16913583(probes 16646:17602)2.21E−082.21E−080: t > −0.74; 1: −0.74>t> −1.3; 2: t < −1.300112001010111001
Deletion Peak 21q21.3chr1:151862662-152285860(probes 70611:71079)chr1:152187589-152285860(probes 70612:71078)chr1:152187589-152285929(probes 70612:71081)0.00199670.00199670: t>−0.74; 1: −0.74>t> −1.3; 2: t < −1.301001000000110000
Deletion Peak 32q11.1chr2:96604592-96606984(probes 140356:140472)chr2:96604731-96606943(probes 140376:140471)chr2:96604592-96606984(probes 140356:140474)2.95E−052.95E−050: t>−0.74; 1: −0.74>t> −1.3; 2: t < −1.300010001001100010
Deletion Peak 47q22.1chr7:100638857-100642454(probes 435615:435646)chr7:100639055-100642454(probes 435616:435645)chr7:100638857-100647874(probes 435615:435933)2.21E−082.21E−080: t>−0.74; 1: −0.74>t> −1.3; 2: t < −1.301002001121220022
Deletion Peak 512q12chr12:40879121-40897247(probes 667577:667888)chr12:40879178-40897247(probes 667578:667887)chr12:40837332-40897247(probes 667398:667890)2.21E−082.21E−080: t>−0.74; 1: −0.74>t> -1.3; 2: t < -1.302010011100020010
Deletion Peak 617p11.2chr17:21217587-21318587(probes 868540:868629)chr17:21217597-21318585(probes 868541:868628)chr17:21217597-21318587(probes 868541:868631)0.00161590.00161590: t>−0.74; 1: −0.74>t> -1.3; 2: t < -1.321011001111221110
Deletion Peak 717q21.2chr17:39253750-39432561(probes 878741:879305)chr17:39383074-39406408(probes 879136:879249)chr17:39383074-39406408(probes 879136:879252)0.0542620.0542620: t>−0.74; 1: −0.74>t> -1.3; 2: t < -1.300010001000010010
Deletion Peak 819q13.2chr19:40368281-40373890(probes 975747:975836)chr19:40368331-40373888(probes 975748:975826)chr19:40368331-40373890(probes 975748:975838)0.0014110.0014110: t>−0.74; 1: −0.74>t> −1.3; 2: t < −1.301000100101100010
Amplification Peak 1 - CN values1q21.1chr1:144852237-145293666(probes 61189:64677)chr1:145109662-145115657(probes 63931:64118)chr1:145109662-145115809(probes 63931:64121)0.0495510.049551Actual copy change given4.57970.691030.79530.537310.0954710.202892.38263.69750.476190.880430.0027178-0.0795431.12780.122610.402860.824950
Amplification Peak 2 - CN values2p25.1chr2:8943044-10729896(probes 112423:113242)chr2:8946569-10729896(probes 112424:113241)chr2:8946569-10729896(probes 112424:113244)0.0987160.098716Actual copy change given−0.0293330.04285700.40765−0.0594970.0944270.860.0628120.518520.21129−0.0108560.1783−0.004628502.6817−0.12104
Amplification Peak 3 - CN values2q11.1chr2:96604806-96610328(probes 140471:140516)chr2:96604811-96607047(probes 140472:140515)chr2:96604811-96610758(probes 140472:140589)2.34E−064.00E−06Actual copy change given1.64853.67350.966432.37691.13260.355961.22822.61820.356390.594830.300525.0601−0.803291.241901.65880.031842
Amplification Peak 4 - CN values2q11.2chr2:97820512-97828928(probes 142142:142325)chr2:97820635-97827942(probes 142143:142295)chr2:97817579-97829924(probes 142113:142399)1.20E−061.82E−06Actual copy change given0.77773.88890.966432.51241.09650.0184771.06052.50962.65954.45450.300520.072293-1.14641.05024.1010.978250.031842
Amplification Peak 5 - CN values4q35.2chr4:189022421-190878451(probes 290591:290876)chr4:189022508-190878451(probes 290592:290875)chr4:189022508-190878451(probes 290592:290878)0.00572450.0057245Actual copy change given0.332632.92543.10222.3359−0.0051105−0.152622.04613.20150.82050−0.040664−0.10707−0.139090.186880.480850.97708−0.42871
Amplification Peak 6 - CN values6p22.1chr6:29692730-29911259(probes 346349:346874)chr6:29910483-29910864(probes 346455:346697)chr6:29910483-29910985(probes 346455:346700)0.0920260.092026Actual copy change given−0.80453−0.193553.8034−0.207540.082220.206042.8335−0.461710.020576−0.50571−0.0198791.74210.024452−0.210151.12990.191992.5654
Amplification Peak 7 - CN values11q13.3chr11:68512544-69949326(probes 624468:624919)chr11:68527601-69949326(probes 624469:624918)chr11:68527601-69949326(probes 624469:624921)0.0137320.013732Actual copy change given−0.78822−0.305480.19049−0.1010160.043358−0.43478−0.0227140.498080.0277780.009232160.0037594−0.079159−0.131520.663110.65257
Amplification Peak 8 - CN values12q15chr12:66990486-70918140(probes 683150:683997)chr12:69048132-70672148(probes 683373:683974)chr12:69048132-70672148(probes 683373:683977)0.0987160.098716Actual copy change given0.395843.88390.310160.246060.059919−0.193680.16120.023994−0.476191.3660.012851−0.0824430.126660.21429−0.0215050.236534.0643
Amplification Peak 9 - CN values13q12.11chr13:22255168-23253312(probes 706758:706956)chr13:22255302-23243644(probes 706759:706876)chr13:22255302-23253391(probes 706759:706958)0.00532770.0053277Actual copy change given0.414270.3030360.19410.0483641.547−0.15−0.0285770.53333−0.0967740.004384−0.06446−0.0956160.167036−0.170630.42759
Amplification Peak 10 - CN values17q12chr17:37557600-38062138(probes 876351:876621)chr17:37558370-38061277(probes 876352:876620)chr17:37558370-38062195(probes 876352:876623)0.0987160.098716Actual copy change given2.71390.174760.4852160.53333−0.0565680.381030.13340.727410.449490.00872880.168650.41472−0.00728830.104961.23830.15732
Amplification Peak 11 - CN values20q11.21chr20:29628342-29632831(probes 1016094:1016357)chr20:29632520-29632826(probes 1016171:1016345)chr20:29449510-30142483(probes 1015484:1016377)0.00123680.0012368Actual copy change given1.45583.34223.03391.26991.21050.161431.382.35591.3580.310440.0341560.739320.345860.713692.3361.94770.030591
Deletion Peak 1 - CN values1p36.13chr1:16903823-16913583(probes 17147:17600)chr1:16905719-16913583(probes 17152:17599)chr1:16890673-16913583(probes 16646:17602)2.21E−082.21E−08Actual copy change given−0.63903−0.44118−0.80967-1.2147-1.3043−0.37043−0.41538−0.76366−0.38567−0.85185−0.62946-1.0867-1.1864−0.76243−0.6107−0.013426−0.77209
Deletion Peak 2 - CN values1q21.3chr1:151862662-152285860(probes 70611:71079)chr1:152187589-152285860(probes 70612:71078)chr1:152187589-152285929(probes 70612:71081)0.00199670.0019967Actual copy change given0.61226-1.16720.79530.13654−0.76930.587671.004−0.10414−0.663470.363640.0027178-1.2965−0.91416−0.0580360.527760.297520.74062
Deletion Peak 3 - CN values2q11.1chr2:96604592-
96606984(probes
140356:140472)chr2:96604731-
96606943(probes
140376:140471)chr2:96604592-
96606984(probes
140356:140474)2.95E−052.95E−05Actual copy change given−0.0115−0.531530.96643−0.95155−0.059497−0.67375−0.62034-1.15270.356390.59483−0.83263-1.0643−0.70605−0.231960-1.1970.031842
Deletion Peak 4 - CN values7q22.1chr7:100638857-100642454(probes 435615:435646)chr7:100639055-100642454(probes 435616:435645)chr7:100638857-100647874(probes 435615:435933)2.21E−082.21E−08Actual copy change given0.24656-1.1321−0.695760.21496-1.5−0.069574−0.26788−0.80776-1.101-1.3636-1.2313-1.5-1.5−0.60593−0.04892-1.5-1.3881
Deletion Peak 5 - CN values12q12chr12:40879121-40897247(probes 667577:667888)chr12:40879178-40897247(probes 667578:667887)chr12:40837332-40897247(probes 667398:667890)2.21E−082.21E−08Actual copy change given0.34613-1.458−0.30748−0.979930.0599190.13048-1.2019-1.173-1.2852−0.50110.0128510.053358-1.5−0.64087−0.021505−0.841810.025247
Deletion Peak 6 - CN values17p11.2chr17:21217587-21318587(probes 868540:868629)chr17:21217597-21318585(probes 868541:868628)chr17:21217597-21318587(probes 868541:868631)0.00161590.0016159Actual copy change given-1.5-1.18050.9029-1.0398−0.95596−0.24475−0.5611−0.80132−0.91292-1.0783-1.1403-1.3682-1.5-1.0251-1.2051−0.97453−0.30211
Deletion Peak 7 - CN values17q21.2chr17:39253750-39432561(probes 878741:879305)chr17:39383074-39406408(probes 879136:879249)chr17:39383074-39406408(probes 879136:879252)0.0542620.054262Actual copy change given−0.1487−0.407412.2584−0.8090.53333−0.0565680.38103-1.04230.727410.449490.00872880.16865-1.215−0.00728830.10496−0.839012.2532
Deletion Peak 8 - CN values19q13.2chr19:40368281-40373890(probes 975747:975836)chr19:40368331-40373888(probes 975748:975826)chr19:40368331-40373890(probes 975748:975838)0.0014110.001411Actual copy change Given−0.48534-1.02821.54990.13636−0.68163−0.885890.28891−0.45791−0.89906−0.075668-1.1579-1.2162−0.19894−0.200030.84998-1.1049−0.38052
Figure 4.

Significantly enriched amplification (red) and deletion (blue) events in the overall cohort of 17 samples, using GISTIC 2.0. Annotated cytobands indicate significant calls (FDR < 0.1) with the peaks corresponding to the significance value on the x-axis. The most significant regions included 2q11.2, 2q11.1 for gains and 1p36.13, 7q22.1, 12q12, 2q11.1 for losses. The significantly amplified regions included several genes involved in fibroblast growth factor signaling, upstream of the PI3K/AKT/mTOR pathway: FGF3, FGF4, FGF9, FGF19, and FRS2.

Significant CNV gain regions called by GISTIC across all 17 WES T/N samples submitted for whole exome sequencing (WES). GISTIC is an algorithm that identified regions of the genome that are gained or lost more than expected by chance across a set of samples. Significant CNV loss regions called by GISTIC across all 17 samples submitted for whole-exome sequencing. GISTIC is an algorithm which identifies regions of the genome that are gained or lost more than expected by chance across a set of samples. GISTIC output describing significantly amplified and deleted regions across the samples. The columns represent individual patient samples and indicate the regions gained or deleted in these samples. A heatmap of the genome-wide copy number variation (CNV) profiles based on median log2ratio, stratified by response. Individual patient samples are shown along the y-axis with amplification events in red and loss events in blue. The number of CNV events at each genomic locus for responders and nonresponders are summarized as bar plots at the top for both responders and non-responders. Copy number segmentation profiles were qualitatively similar between responders and non-responders. Significantly enriched amplification (red) and deletion (blue) events in the overall cohort of 17 samples, using GISTIC 2.0. Annotated cytobands indicate significant calls (FDR < 0.1) with the peaks corresponding to the significance value on the x-axis. The most significant regions included 2q11.2, 2q11.1 for gains and 1p36.13, 7q22.1, 12q12, 2q11.1 for losses. The significantly amplified regions included several genes involved in fibroblast growth factor signaling, upstream of the PI3K/AKT/mTOR pathway: FGF3, FGF4, FGF9, FGF19, and FRS2. Mutational signature analysis revealed two clusters of samples based on their signature decomposition results (Figure 5, Table 7). Cluster 1 (N = 8) was dominated by signatures 2 and 13, which are associated with activity of APOBEC cytidine deaminases.[18] Cluster 2 (N = 6) was characterized by the dominance of signature 5, which has been associated with ERCC2 mutations.[24] Mutational signature clusters were not associated with response (Fisher’s exact P = 1 two sided).
Figure 5.

Mutational signature analysis revealed that samples fell into two clusters with distinct patterns of single-nucleotide alterations throughout the genome. Cluster 1 (N = 8) was dominated by COSMIC v2 signatures 2 and 13, which are associated with APOBEC cytidine deaminases. Cluster 2 (N = 6) was characterized by dominance of signature 5, which has been associated with ERCC2 mutations. Mutational signature clusters were not associated with response (Fisher’s exact P = 1 two-sided).

Table 7.

Mutational signatures in each sample (N = 15 samples from cohort 2 with whole-exome sequencing), based on bladder-specific signatures in the COSMIC v2 database. Mutational signatures represent global patterns in the types of single-nucleotide changes throughout the genome and are thought to reflect distinct underlying mutational processes. The numbers indicate the percent of mutations attributed to each signature within each sample.

SampleSignature.1Signature.2Signature.5Signature.10Signature.13
GP0120.019432570.366608020.1498790.024865390.43921502
GP0430.010959830.331357120.342259690.020938940.29448443
GP0033.28E-180.052266910.901551300.04618179
GP0250.03919490.433564560.146956890.009351190.37093247
GP0230.178844310.288885860.174627840.045107760.31253424
GP0110.016905640.313564150.2745807100.39494949
GP0160.047554210.098757410.8464440200.00724437
GP0340.022339310.2024520.61821754-8.67E-190.15699115
GP0070.011400290.439523210.093741510.031987360.42334762
GP0180.003929370.544353740.079430010.031599050.34068783
GP0320.090881630.055478020.7684621600.08517819
GP0130.039541190.003253780.5957206500.36148437
GP0050.003700140.17255240.5344498100.28929765
GP0300.048760710.256370490.545551920.055209810.09410708
GP00900.224470060.3099450.034862230.4307227
Mutational signatures in each sample (N = 15 samples from cohort 2 with whole-exome sequencing), based on bladder-specific signatures in the COSMIC v2 database. Mutational signatures represent global patterns in the types of single-nucleotide changes throughout the genome and are thought to reflect distinct underlying mutational processes. The numbers indicate the percent of mutations attributed to each signature within each sample. Mutational signature analysis revealed that samples fell into two clusters with distinct patterns of single-nucleotide alterations throughout the genome. Cluster 1 (N = 8) was dominated by COSMIC v2 signatures 2 and 13, which are associated with APOBEC cytidine deaminases. Cluster 2 (N = 6) was characterized by dominance of signature 5, which has been associated with ERCC2 mutations. Mutational signature clusters were not associated with response (Fisher’s exact P = 1 two-sided). RNA sequencing was performed on 8 of the WES samples; 1 patient was not evaluable for response, leaving 7 evaluable patients with RNA expression data. Clustering of transcriptomic profiles showed no clear separation between responders (N = 3) versus non-responders (N = 4; Figure 6). Differential expression analyses identified 9 differentially expressed genes between responders versus non-responders (at adj. P cutoff of .05). None of these were associated with mTOR signaling or microtubule function (Table 8).
Figure 6.

Clustering of samples by transcriptomic profile using principal components analysis failed to identify separate clusters based on paclitaxel treatment responsiveness. R: responders; P: nonresponders.

Table 8.

Differentially expressed genes between responders (N = 3) and nonresponders (N = 4) (including only those with successful RNA sequencing). Negative log2 foldchange values indicate decreased expression in responders compared with nonresponders, while positive log2 foldchange values indicate increased expression. All patients for this analysis were from Cohort 2.

Gene IDApproved SymbolLog2 foldchangeAdj P
HGNC:19133HS6ST2−4.176714.008258125
HGNC:1047BHMT−5.999643.008258125
HGNC:21226LRFN2−7.754684.008258125
HGNC:26731C8orf31−4.328898.0206424
HGNC:7423MTCP1−2.920986.032091609
HGNC:21923STEAP4−2.754969.03327653
HGNC:32406IQCJ−6.218168.038737815
HGNC:23596KRTAP5-14.892141.038737815
HGNC:4020FUT9−6.352472.040669222
Differentially expressed genes between responders (N = 3) and nonresponders (N = 4) (including only those with successful RNA sequencing). Negative log2 foldchange values indicate decreased expression in responders compared with nonresponders, while positive log2 foldchange values indicate increased expression. All patients for this analysis were from Cohort 2. Clustering of samples by transcriptomic profile using principal components analysis failed to identify separate clusters based on paclitaxel treatment responsiveness. R: responders; P: nonresponders. No recurrent gene fusions were identified after false positives were eliminated by manual review (Tables 9 and 10).
Table 9.

Gene fusions in responders.

Fusion namePatient count
ZNF137P:ZNF831
ADGRE5:ADGRE2 (FALSE POSITIVE)3
ADGRE2:ADGRE5 (FALSE POSITIVE)3
SMG1:NPIPB51
KANSL1:ARL17A1
KANSL1:ARL17B1
SCNN1A:TNFRSF1A1
PSMD14:ZNF6381
ANK2:CAMK2D1
PIP4K2A:RAB181
STX16:NPEPL11
STX16:STX16-NPEPL11
ACLY:DNAJC71
ZNF486:GATAD2A1
TBCEL:TECTA1
STX16-NPEPL1:NPEPL11
STX16-NPEPL1:STX16-NPEPL11
CYTIP:ERMN1
Table 10.

Gene fusions in non-responders.

Fusion namePatient count
ADGRE5:ADGRE2 (FALSE POSITIVE)3
ADGRE2:ADGRE5 (FALSE POSITIVE)2
SMG1:NPIPB51
NAIP:OCLN1
CLTC:VMP11
KANSL1:ARL17A1
KANSL1:ARL17B1
EIF3K:ACTN41
PTPN1:PPTC71
Gene fusions in non-responders. Gene fusions in responders. Cisplatin remains the backbone of treatment for advanced UC. However, many patients are not eligible for cisplatin due to performance status or comorbidities. The subgroup of cisplatin-ineligible patients with both poor performance status and poor renal function experience increased toxicity and reduced benefit from carboplatin-based regimens necessitating novel treatment approaches. We initiated a phase II trial to test the activity of everolimus or everolimus plus paclitaxel in the cisplatin-ineligible setting shortly prior to a new era in drug development in metastatic UC. Novel regimens such as enfortumab vedotin alone or combined with pembrolizumab have demonstrated promising activity in cisplatin-ineligible patients.[25,26] The shifting landscape, coupled with pragmatic considerations related to cohort 1, contributed to early closure due to poor accrual. Nonetheless, this trial has generated insights that may inform future treatment strategies. We observed a 4-month CBR of 37.9% associated with EVP among patients with either poor performance states or poor renal function (cohort 2). This benefit was most likely driven by paclitaxel, which has demonstrated efficacy in this context both as a single-agent and in combinations.[7,27,28] This degree of activity is similar to that observed with carboplatin-based regimens in the phase II/III EORTC 30986 trial[3] and reinforces the value of single-agent paclitaxel in the cisplatin-ineligible setting. However, treatment was still associated with a notable adverse event burden in this population. The EVP combination has also been studied in patients with UC progressing despite platinum-based chemotherapy with an objective response rate of 13%,[29] similar to the response rate with paclitaxel, suggesting limited benefit by adding everolimus although the specific contribution of each agent cannot be defined here. Notably, everolimus monotherapy was disappointing across different solid tumors selected for genomic alterations predicted to confer vulnerability, despite previous promising case reports.[6,30,31] We initiated our trial in 2010 prior to the immune checkpoint blockade era and the subsequent shifts in the metastatic UC treatment landscape.[32] Current standard first-line treatment for cisplatin-ineligible patients with metastatic UC includes carboplatin-based chemotherapy followed by switch maintenance immune checkpoint blockade or single agent immune checkpoint in patients with tumors harboring high levels of PD-L1 expression or patients who are “chemotherapy ineligible” (in certain regions of the world). Notably, such regimens have the potential for durable disease control in a subset of patients not observed in the current study. Patients for whom the risks of any platinum-based chemotherapy outweigh the potential benefits of treatment are complicated to define in both clinical care and for the purposes of trial design. However, the results of EORTC 30986 suggest that patients with both impaired renal function and borderline functional status suffer excessive toxicity from carboplatin-based chemotherapy and this definition of “chemotherapy ineligibility” was reinforced in a recent survey of oncologists.[33] The current trial, although performed in an earlier era and with a treatment without substantial activity, highlights the potential challenges of enrolling “chemotherapy ineligible” patients to prospective clinical trials; the median OS of patients in Cohort 1 was only 4.5 months. We examined genomic data from 17 patients in cohort 2 to identify possible biomarkers of response to EVP. There were no significant associations between somatic mutations, copy number variants, or mutational signatures and response. However, power was limited by the number of samples and an imbalance of responders (N = 13) and nonresponders (N = 4). One notable, albeit non-significant, observation was the high response rates to EVP among those with mutations in either of the microtubule-associated genes MACF1 or FRY (100%; Fisher’s exact P = .24 two sided; P = .14 one sided). MACF1 is a microtubule binding protein that bridges cytoskeletal elements and has roles in cellular migration, adhesion, and intracellular transport.[34,35]FRY also binds microtubules and regulates the mitotic spindle during cell division.[36] To our knowledge, there have not been in vitro or in vivo experiments testing the relationship between mutations in these genes and paclitaxel sensitivity. Though the use of taxanes in latter lines of therapy for metastatic UC is decreasing in the context of new treatment options, treatment selection biomarkers for these newer treatments are still lacking and biomarkers that might define patients deriving most benefit from paclitaxel could still impact clinical treatment strategies and warrant further testing.

Conclusions

Paclitaxel demonstrated activity comparable to historical reports of carboplatin-based regimens in cisplatin-ineligible patients with metastatic UC in this small cohort, although is not associated with durable responses that occur in a subset of patients treated with modern regimens. Everolimus did not demonstrate obvious additivity in this combination regimen. Outcomes in “chemotherapy ineligible” patients remain suboptimal and enrollment of such patients to prospective trials is challenging.
Diseasebladder cancer
Stage of disease/treatmentmetastatic/advanced
Prior therapynone
Type of studyphase II
Primary endpointclinical benefit rate at 4 months from treatment initiation
Secondary endpointtoxicity, safety, correlative endpoint, other
Investigator’s analysisactive but results overtaken by other developments
Everolimus
Generic/Working nameEverolimus
Drug TypeSmall molecule
Drug Classm-TOR
Dose10 mg per flat dose
Routeoral (p.o.)
Schedule of administrationdaily
Everolimus
Generic/working nameeverolimus
Drug typesmall molecule
Drug classm-TOR
Dose10 mg per flat dose
Routeoral (p.o.)
Schedule of administrationdaily
Paclitaxel
Generic/working namepaclitaxel
Drug typechemotherapy
Drug classtaxane
Dose80 mg/m²
Routei.v.
Schedule of administration: days 1, 8, and 15 of each 28-day cycle
Number of patients, male5
Number of patients, female2
AgeMedian (range): 79 (59-90) years
Number of prior systemic therapiesMedian (range): 0
Performance status: ECOG0–0
1–0
2–0
3–0
Unknown–0
OtherKarnofsky performance status, Median (range)
60 (60-70)
Calculated creatinine clearance, median (range)
36.03 (10.54-60)
Number of patients, male22
Number of patients, female7
AgeMedian (range): 72 (54-88 years)
Number of prior systemic therapies
Performance status: ECOG0–0
1–0
2–0
3–0
Unknown–0
OtherKarnofsky performance status, median (range):
80 (60-100)
Calculated creatinine clearance, median (range):
51.3 (22-96)
TitleResponse at 4 months
Number of patients screened0
Number of patients enrolled7
Number of patients evaluable for toxicity7
Number of patients evaluated for efficacy4
Evaluation methodRECIST 1.1
Response assessment CR n = 0 (0%)
Response assessment PR n = 0 (0%)
Response assessment SD n = 0 (0%)
Response assessment PD n = 4 (57.1%)
Response assessment other n = 3 (42.9%)
TitleRadiographic response at 4 months
Number of patients screened0
Number of patients enrolled29
Number of patients evaluable for toxicity29
Number of patients evaluated for efficacy20
Evaluation methodRECIST 1.1
Response assessment CR n = 0 (0%)
Response assessment PR n = 8 (27.6%)
Response assessment SD n = 3 (10.3%)
Response assessment PD n = 9 (31%)
Response assessment other n = 9 (31%)
Name*NC/NA12345All grades
Anemia29%14%0%57%0%0%71%
Anorexia57%0%43%0%0%0%43%
Cholesterol high57%43%0%0%0%0%43%
Constipation57%43%0%0%0%0%43%
Diarrhea57%29%0%14%0%0%43%
Dysgeusia57%43%0%0%0%0%43%
Dyspnea57%14%14%14%0%0%43%
Fatigue0%57%29%14%0%0%100%
Hypertension57%0%43%0%0%0%43%
Nausea57%14%29%0%0%0%43%
Rash acneiform57%0%43%0%0%0%43%
Urinary tract infection43%0%43%14%0%0%57%
Name*NC/NA12345All grades
Abdominal pain59%24%10%7%0%0%41%
Alopecia55%21%24%0%0%0%45%
Anemia24%17%28%31%0%0%76%
Anorexia48%28%21%3%0%0%52%
Constipation45%38%17%0%0%0%55%
Diarrhea38%38%10%14%0%0%62%
Dyspnea48%31%7%14%0%0%52%
Fatigue14%52%28%7%0%0%86%
Fever59%31%10%0%0%0%41%
Hypertension59%14%14%14%0%0%41%
Insomnia52%48%0%0%0%0%48%
Mucositis oral52%21%28%0%0%0%48%
Nausea52%34%7%7%0%0%48%
Peripheral sensory neuropathy52%34%14%0%0%0%48%
Urinary tract infection55%0%24%21%0%0%45%
Vomiting59%31%3%7%0%0%41%
Completiondid not fully accrue
Investigator’s assessmentactive but results overtaken by other developments
Table 5.

Significant CNV loss regions called by GISTIC across all 17 samples submitted for whole-exome sequencing. GISTIC is an algorithm which identifies regions of the genome that are gained or lost more than expected by chance across a set of samples.

Cytoband 1p36.137q22.112q122q11.119q13.217p11.21q21.317q21.2
q value 2.21E-082.21E-082.21E-082.95E-050.0014110.00161590.00199670.054262
Residual q value 2.21E-082.21E-082.21E-082.95E-050.0014110.00161590.00199670.054262
Wide peak boundaries chr1:16903823-16913583chr7:100638857-100642454chr12:40879121-40897247chr2:96604592-96606984chr19:40368281-40373890chr17:21217587-21318587chr1:151862662-152285860chr17:39253750-39432561
Genes in wide peak NBPF1MUC12[LRRK2][LOC729234]FCGBPKCNJ12FLGKRTAP9-9
MAP2K3S100A10KRTAP4-6
KCNJ18S100A11KRTAP4-12
TCHHKRTAP9-2
THEM4KRTAP9-3
TCHHL1KRTAP9-8
RPTNKRTAP4-4
HRNRKRTAP9-4
KRTAP4-1
KRTAP4-5
KRTAP4-3
KRTAP4-2
KRTAP4-11
KRTAP4-8
KRTAP9-1
KRTAP4-9
  31 in total

1.  featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

Authors:  Yang Liao; Gordon K Smyth; Wei Shi
Journal:  Bioinformatics       Date:  2013-11-13       Impact factor: 6.937

2.  Impact of renal impairment on eligibility for adjuvant cisplatin-based chemotherapy in patients with urothelial carcinoma of the bladder.

Authors:  Atreya Dash; Matthew D Galsky; Andrew J Vickers; Angel M Serio; Theresa M Koppie; Guido Dalbagni; Bernard H Bochner
Journal:  Cancer       Date:  2006-08-01       Impact factor: 6.860

3.  Furry promotes acetylation of microtubules in the mitotic spindle by inhibition of SIRT2 tubulin deacetylase.

Authors:  Tomoaki Nagai; Masanori Ikeda; Shuhei Chiba; Shin-Ichiro Kanno; Kensaku Mizuno
Journal:  J Cell Sci       Date:  2013-07-25       Impact factor: 5.285

4.  Randomized phase II/III trial assessing gemcitabine/ carboplatin and methotrexate/carboplatin/vinblastine in patients with advanced urothelial cancer "unfit" for cisplatin-based chemotherapy: phase II--results of EORTC study 30986.

Authors:  Maria De Santis; Joaquim Bellmunt; Graham Mead; J Martijn Kerst; Michael Leahy; Pablo Maroto; Iwona Skoneczna; Sandrine Marreaud; Ronald de Wit; Richard Sylvester
Journal:  J Clin Oncol       Date:  2009-09-28       Impact factor: 44.544

5.  New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).

Authors:  E A Eisenhauer; P Therasse; J Bogaerts; L H Schwartz; D Sargent; R Ford; J Dancey; S Arbuck; S Gwyther; M Mooney; L Rubinstein; L Shankar; L Dodd; R Kaplan; D Lacombe; J Verweij
Journal:  Eur J Cancer       Date:  2009-01       Impact factor: 9.162

6.  Phase II Study of Pazopanib and Paclitaxel in Patients With Refractory Urothelial Cancer.

Authors:  Sujata Narayanan; Anthony Lam; Ulka Vaishampayan; Lauren Harshman; Alice Fan; Russell Pachynski; Shermeen Poushnejad; Denise Haas; Shufeng Li; Sandy Srinivas
Journal:  Clin Genitourin Cancer       Date:  2016-03-12       Impact factor: 2.872

7.  Interaction between p230 and MACF1 is associated with transport of a glycosyl phosphatidyl inositol-anchored protein from the Golgi to the cell periphery.

Authors:  Takumi Kakinuma; Haruo Ichikawa; Yoshito Tsukada; Takashi Nakamura; Ban-Hock Toh
Journal:  Exp Cell Res       Date:  2004-08-15       Impact factor: 3.905

8.  Inhibition of the mammalian target of rapamycin (mTOR) by rapamycin increases chemosensitivity of CaSki cells to paclitaxel.

Authors:  L S Faried; A Faried; T Kanuma; T Nakazato; T Tamura; H Kuwano; T Minegishi
Journal:  Eur J Cancer       Date:  2006-03-15       Impact factor: 9.162

9.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.

Authors:  Michael I Love; Wolfgang Huber; Simon Anders
Journal:  Genome Biol       Date:  2014       Impact factor: 13.583

10.  Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors.

Authors:  Jaegil Kim; Kent W Mouw; Paz Polak; Lior Z Braunstein; Atanas Kamburov; David J Kwiatkowski; Jonathan E Rosenberg; Eliezer M Van Allen; Alan D'Andrea; Gad Getz
Journal:  Nat Genet       Date:  2016-04-25       Impact factor: 38.330

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