Rui Qin

A phase II safety and efficacy study of the vascular endothelial growth factor receptor tyrosine kinase inhibitor pazopanib in patients with metastatic urothelial cancer

BACKGROUND Vascular endothelial growth factor (VEGF) is produced by bladder cancer cell lines in vitro and expressed in human bladder tumor tissues. Pazopanib is a vascular endothelial receptor tyrosine kinase inhibitor with anti-angiogenesis and anti-tumor activity in several preclinical models. A 2-stage phase II study was conducted to assess the activity and toxicity profile of pazopanib in patients with metastatic, urothelial carcinoma. METHODS Patients with one prior systemic therapy for metastatic urothelial carcinoma were eligible. Patients received pazopanib at a dose of 800 mg orally for a 4-week cycle. RESULTS Nineteen patients were enrolled. No grade 4 or 5 events were experienced. Nine patients experienced 11 grade 3 adverse events. Most common toxicities were anemia, thrombocytopenia, leucopenia, and fatigue. For stage I, none of the first 16 evaluable patients were deemed a success (complete response or partial response) by the Response Evaluation Criteria In Solid Tumors criteria during the first four 4-week cycles of treatment. Median progression-free survival was 1.9 months. This met the futility stopping rule of interim analysis, and therefore the trial was recommended to be permanently closed. CONCLUSIONS Pazopanib did not show significant activity in patients with urothelial carcinoma. The role of anti-VEGF therapies in urothelial carcinoma may need further evaluation in rational combination strategies.

Model-based phase I designs incorporating toxicity and efficacy for single and dual agent drug combinations: Methods and challenges

Novel therapies are challenging the standards of drug development. Agents with specific biologic targets, unknown dose-efficacy curves, and limited toxicity mandate novel designs to identify biologically optimal doses. We review two model-based designs that utilize either a proportional odds model or a continuation ratio model to identify an optimal dose of a single or two-agent combination in a Phase I setting utilizing both toxicity and efficacy data. A continual reassessment method with straightforward dose selection criterion using accumulated data from all patients treated until that time point is employed while allowing for separate toxicity and efficacy curves for each drug in a two-drug setting. The simulation studies demonstrate considerable promise, at least theoretically, in the ability of such model-based designs to identify the optimal dose. Despite such favorable operating characteristics, there are several pragmatic challenges that hinder the routine implementation of such model-based designs in practice. We review and offer practical solutions to potentially overcome some of these challenges. The acceptance and integration of these designs in practice may be quicker and easier if they are developed in concert with a clinical paradigm.

Dose‐finding designs using a novel quasi‐continuous endpoint for multiple toxicities

The aim of a phase I oncology trial is to identify a dose with an acceptable safety profile. Most phase I designs use the dose-limiting toxicity, a binary endpoint, to assess the unacceptable level of toxicity. The dose-limiting toxicity might be incomplete for investigating molecularly targeted therapies as much useful toxicity information is discarded. In this work, we propose a quasi-continuous toxicity score, the total toxicity profile (TTP), to measure quantitatively and comprehensively the overall severity of multiple toxicities. We define the TTP as the Euclidean norm of the weights of toxicities experienced by a patient, where the weights reflect the relative clinical importance of each grade and toxicity type. We propose a dose-finding design, the quasi-likelihood continual reassessment method (CRM), incorporating the TTP score into the CRM, with a logistic model for the dose–toxicity relationship in a frequentist framework. Using simulations, we compared our design with three existing designs for quasi-continuous toxicity score (the Bayesian quasi-CRM with an empiric model and two nonparametric designs), all using the TTP score, under eight different scenarios. All designs using the TTP score to identify the recommended dose had good performance characteristics for most scenarios, with good overdosing control. For a sample size of 36, the percentage of correct selection for the quasi-likelihood CRM ranged from 80% to 90%, with similar results for the quasi-CRM design. These designs with TTP score present an appealing alternative to the conventional dose-finding designs, especially in the context of molecularly targeted agents.

Elevating the Horizon: Emerging Molecular and Genomic Targets in the Treatment of Advanced Urothelial Carcinoma

Despite recent advances in the identification of genomic alterations that lead to urothelial oncogenesis in vitro, patients with advanced urothelial carcinomas continue to have poor clinical outcomes. In the present review, we focus on targeted therapies that have yielded the most promising results alone or combined with traditional chemotherapy, including the antiangiogenesis agent bevacizumab, the human epidermal growth factor receptor 2 antibody trastuzumab, and the tyrosine kinase inhibitor cabozantinib. We also describe ongoing and developing clinical trials that use innovative approaches, including dose-dense scheduling of singular chemotherapy combinations, prospective screening of tumor tissues for mutational targets and biomarkers to predict chemosensitivity before the determination of the therapeutic regimen, and novel agents that target proteins in the immune checkpoint regulation pathway (programmed cell death protein 1 [PD-1] and anti-PD-ligand 1) that have shown significant potential in preclinical models and early clinical trials. New agents and targeted therapies, alone or combined with traditional chemotherapy, will only be validated through accrual to developing clinical trials that aim to translate these therapies into individualized treatments and improved survival rates in urothelial carcinoma.

Determining the frequency of pathogenic germline variants from exome sequencing in patients with castrate-resistant prostate cancer

Objectives To determine the frequency of pathogenic inherited mutations in 157 select genes from patients with metastatic castrate-resistant prostate cancer (mCRPC). Design Observational. Setting Multisite US-based cohort. Participants Seventy-one adult male patients with histological confirmation of prostate cancer, and had progressive disease while on androgen deprivation therapy. Results Twelve patients (17.4%) showed evidence of carrying pathogenic or likely pathogenic germline variants in the ATM, ATR, BRCA2, FANCL, MSR1, MUTYH, RB1, TSHR and WRN genes. All but one patient opted in to receive clinically actionable results at the time of study initiation. We also found that pathogenic germline BRCA2 variants appear to be enriched in mCRPC compared to familial prostate cancers. Conclusions Pathogenic variants in cancer-susceptibility genes are frequently observed in patients with mCRPC. A substantial proportion of patients with mCRPC or their family members would derive clinical utility from mutation screening. Trial registration number NCT01953640; Results.

Mutational landscapes of sequential prostate metastases and matched patient derived xenografts during enzalutamide therapy

Developing patient derived models from individual tumors that capture the biological heterogeneity and mutation landscape in advanced prostate cancer is challenging, but essential for understanding tumor progression and delivery of personalized therapy in metastatic castrate resistant prostate cancer stage. To demonstrate the feasibility of developing patient derived xenograft models in this stage, we present a case study wherein xenografts were derived from cancer metastases in a patient progressing on androgen deprivation therapy and prior to initiating pre-chemotherapy enzalutamide treatment. Tissue biopsies from a metastatic rib lesion were obtained for sequencing before and after initiating enzalutamide treatment over a twelve-week period and also implanted subcutaneously as well as under the renal capsule in immuno-deficient mice. The genome and transcriptome landscapes of xenografts and the original patient tumor tissues were compared by performing whole exome and transcriptome sequencing of the metastatic tumor tissues and the xenografts at both time points. After comparing the somatic mutations, copy number variations, gene fusions and gene expression we found that the patient’s genomic and transcriptomic alterations were preserved in the patient derived xenografts with high fidelity. These xenograft models provide an opportunity for predicting efficacy of existing and potentially novel drugs that is based on individual metastatic tumor expression signature and molecular pharmacology for delivery of precision medicine.

Germline predictors of androgen deprivation therapy response in advanced prostate cancer

OBJECTIVE To evaluate whether germline variations in genes involved in sex steroid biosynthesis and metabolic pathways predict time to treatment failure for patients with advanced prostate cancer undergoing androgen deprivation therapy (ADT), because there are few known clinical predictors of response. PATIENTS AND METHODS In a cohort of 304 patients with advanced prostate cancer undergoing ADT, we genotyped 746 single-nucleotide polymorphisms (SNPs) from 72 genes from germline DNA (680 tagSNPs from 58 genes and 66 candidate SNPs from 20 genes [6 genes common in both]). Association with the primary end point of time to ADT failure was assessed using proportional hazards regression models at the gene level (for genes with tagging SNPs) and at the SNP level. False discovery rates (FDRs) of 0.10 or less were considered noteworthy to account for multiple testing. RESULTS At the gene level, TRMT11 showed the strongest association with time to ADT failure (P<.001; FDR=0.008). Two of 4 TRMT11 tagSNPs were associated with time to ADT failure. Median time to ADT failure for rs1268121 (A>G) was 3.05 years for the AA, 4.27 years for the AG, and 6.22 years for the GG genotypes (P=.002), and for rs6900796 (G>A), it was 2.42 years for the GG, 3.52 years for the AG, and 4.18 years for the AA genotypes (P<.001). No other gene level or SNP level tests had an FDR of 0.10 or less. CONCLUSION Genetic variation in TRMT11 was associated with time to ADT failure. Confirmation of these preliminary findings in an independent cohort is needed.

Biomarker-Based Targeting of the Androgen-Androgen Receptor Axis in Advanced Prostate Cancer

Recent therapeutic advances for managing advanced prostate cancer include the successful targeting of the androgen-AR axis with several new drugs in castrate resistant prostate cancer including abiraterone acetate and enzalutamide (MDV3100). This translational progress from “bench to bed-side” has resulted in an enlarging repertoire of novel and traditional drug choices now available for use in advanced prostate cancer therapeutics, which has had a positive clinical impact in prolonging longevity and quality of life of advanced prostate cancer patients. In order to further the clinical utility of these drugs, development of predictive biomarkers guiding individual therapeutic choices remains an ongoing challenge. This paper will summarize the potential in developing predictive biomarkers based on the pathophysiology of the androgen-AR axis in tumor tissue from patients with advanced prostate cancer as well as inherited variation in the patients genome. Specific examples of rational clinical trial designs incorporating potential predictive biomarkers from these pathways will illustrate several aspects of pharmacogenetic and pharmacogenomic predictive biomarker development in advanced prostate cancer therapeutics.

Pharmacogenetics- and pharmacogenomics-based rational clinical trial designs in oncology

The rapid evolution of molecular technologies that can identify genetic markers and lead to dissecting the inherent variance of individual cancer biology has had a tangible impact on trial designs in oncology. Rational trial designs based on molecular marker expression coupled with drug-marker interactions have started to be adopted, challenging the previous paradigms of morphology-based, single-arm efficacy studies. This review summarizes novel trials being developed based on molecular predictive factor therapeutics and the potential impact these novel trial designs will have on the practice of oncology in future. A variety of clinical trial designs based on tumor and drug-host genetic interactions are discussed and the example of advanced prostate cancer is used to illustrate the changing landscape of clinical trial designs in cancer.

A Bayesian dose-finding design incorporating toxicity data from multiple treatment cycles

Phase I oncology trials are designed to identify a safe dose with an acceptable toxicity profile. The dose is typically determined based on the probability of severe toxicity observed during the first treatment cycle, although patients continue to receive treatment for multiple cycles. In addition, the toxicity data from multiple types and grades are typically summarized into a single binary outcome of dose-limiting toxicity. A novel endpoint, the total toxicity profile, was previously developed to account for the multiple toxicity types and grades. In this work, we propose to account for longitudinal repeated measures of total toxicity profile over multiple treatment cycles, accounting for cumulative toxicity during dosing-finding. A linear mixed model was utilized in the Bayesian framework, with addition of Bayesian risk functions for decision-making in dose assignment. The performance of this design is evaluated using simulation studies and compared with the previously proposed quasi-likelihood continual reassessment method (QLCRM) design. Twelve clinical scenarios incorporating four different locations of maximum tolerated dose and three different time trends (decreasing, increasing, and no effect) were investigated. The proposed repeated measures design was comparable with the QLCRM when only cycle 1 data were utilized in dose-finding; however, it demonstrated an improvement over the QLCRM when data from multiple cycles were used across all scenarios. Copyright