Brigatinib in NF2-Related Schwannomatosis with Progressive Tumors.

BACKGROUND: NF2-related schwannomatosis (NF2-SWN, formerly called neurofibromatosis type 2) is a tumor predisposition syndrome that is manifested by multiple vestibular schwannomas, nonvestibular schwannomas, meningiomas, and ependymomas. The condition is relentlessly progressive with no approved therapies. On the basis of preclinical activity of brigatinib (an inhibitor of multiple tyrosine kinases) in NF2-driven nonvestibular schwannoma and meningioma, data were needed on the use of brigatinib in patients with multiple types of progressive NF2-SWN tumors. METHODS: In this phase 2 platform trial with a basket design, patients who were 12 years of age or older with NF2-SWN and progressive tumors were treated with oral brigatinib at a dose of 180 mg daily. A central review committee evaluated one target tumor and up to five nontarget tumors in each patient. The primary outcome was radiographic response in target tumors. Key secondary outcomes were safety, response rate in all tumors, hearing response, and patient-reported outcomes. RESULTS: A total of 40 patients (median age, 26 years) with progressive target tumors (10 vestibular schwannomas, 8 nonvestibular schwannomas, 20 meningiomas, and 2 ependymomas) received treatment with brigatinib. After a median follow-up of 10.4 months, the percentage of tumors with a radiographic response was 10% (95% confidence interval [CI], 3 to 24) for target tumors and 23% (95% CI, 16 to 30) for all tumors; meningiomas and nonvestibular schwannomas had the greatest benefit. Annualized growth rates decreased for all tumor types during treatment. Hearing improvement occurred in 35% (95% CI, 20 to 53) of eligible ears. Exploratory analyses suggested a decrease in self-reported pain severity during treatment (-0.013 units per month; 95% CI, -0.002 to -0.029) on a scale from 0 (no pain) to 3 (severe pain). No grade 4 or 5 treatment-related adverse events were reported. CONCLUSIONS: Brigatinib treatment resulted in radiographic responses in multiple tumor types and clinical benefit in a heavily pretreated cohort of patients with NF2-SWN. (Funded by the Children's Tumor Foundation and others; INTUITT-NF2 ClinicalTrials.gov number, NCT04374305.).

Uncertainty directed factorial clinical trials.

The development and evaluation of novel treatment combinations is a key component of modern clinical research. The primary goals of factorial clinical trials of treatment combinations range from the estimation of intervention-specific effects, or the discovery of potential synergies, to the identification of combinations with the highest response probabilities. Most factorial studies use balanced or block randomization, with an equal number of patients assigned to each treatment combination, irrespective of the specific goals of the trial. Here, we introduce a class of Bayesian response-adaptive designs for factorial clinical trials with binary outcomes. The study design was developed using Bayesian decision-theoretic arguments and adapts the randomization probabilities to treatment combinations during the enrollment period based on the available data. Our approach enables the investigator to specify a utility function representative of the aims of the trial, and the Bayesian response-adaptive randomization algorithm aims to maximize this utility function. We considered several utility functions and factorial designs tailored to them. Then, we conducted a comparative simulation study to illustrate relevant differences of key operating characteristics across the resulting designs. We also investigated the asymptotic behavior of the proposed adaptive designs. We also used data summaries from three recent factorial trials in perioperative care, smoking cessation, and infectious disease prevention to define realistic simulation scenarios and illustrate advantages of the introduced trial designs compared to other study designs.

Inference in response-adaptive clinical trials when the enrolled population varies over time.

A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type-I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response-adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response-adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study.

Looking ahead in early-phase trial design to improve the drug development process: examples in oncology.

BACKGROUND: Clinical trial design must consider the specific resource constraints and overall goals of the drug development process (DDP); for example, in designing a phase I trial to evaluate the safety of a drug and recommend a dose for a subsequent phase II trial. Here, we focus on design considerations that involve the sequence of clinical trials, from early phase I to late phase III, that constitute the DDP. METHODS: We discuss how stylized simulation models of clinical trials in an oncology DDP can quantify important relationships between early-phase trial designs and their consequences for the remaining phases of development. Simulations for three illustrative settings are presented, using stylized models of the DDP that mimic trial designs and decisions, such as the potential discontinuation of the DDP. RESULTS: We describe: (1) the relationship between a phase II single-arm trial sample size and the likelihood of a positive result in a subsequent phase III confirmatory trial; (2) the impact of a phase I dose-finding design on the likelihood that the DDP will produce evidence of a safe and effective therapy; and (3) the impact of a phase II enrichment trial design on the operating characteristics of a subsequent phase III confirmatory trial. CONCLUSIONS: Stylized models of the DDP can support key decisions, such as the sample size, in the design of early-phase trials. Simulation models can be used to estimate performance metrics of the DDP under realistic scenarios; for example, the duration and the total number of patients enrolled. These estimates complement the evaluation of the operating characteristics of early-phase trial design, such as power or accuracy in selecting safe and effective dose levels.

A validation of models for prediction of pathogenic variants in mismatch repair genes.

PURPOSE: Models used to predict the probability of an individual having a pathogenic homozygous or heterozygous variant in a mismatch repair gene, such as MMRpro, are widely used. Recently, MMRpro was updated with new colorectal cancer penetrance estimates. The purpose of this study was to evaluate the predictive performance of MMRpro and other models for individuals with a family history of colorectal cancer. METHODS: We performed a validation study of 4 models, Leiden, MMRpredict, PREMM5, and MMRpro, using 784 members of clinic-based families from the United States. Predicted probabilities were compared with germline testing results and evaluated for discrimination, calibration, and predictive accuracy. We analyzed several strategies to combine models and improve predictive performance. RESULTS: MMRpro with additional tumor information (MMRpro+) and PREMM5 outperformed the other models in discrimination and predictive accuracy. MMRpro+ was the best calibrated with an observed to expected ratio of 0.98 (95% CI = 0.89-1.08). The combination models showed improvement over PREMM5 and performed similar to MMRpro+. CONCLUSION: MMRpro+ and PREMM5 performed well in predicting the probability of having a pathogenic homozygous or heterozygous variant in a mismatch repair gene. They serve as useful clinical decision tools for identifying individuals who would benefit greatly from screening and prevention strategies.

A Bayesian Multi-Outcome Analysis of Fine Particulate Matter and Cardiorespiratory Hospitalizations.

BACKGROUND: Short-term fine particulate matter (PM2.5) exposure is positively associated with acute cardiovascular and respiratory events. Understanding whether this association varies across specific cardiovascular and respiratory conditions has important biologic, clinical, and public health implications. METHODS: We conducted a time-stratified case-crossover study of hospitalizations from 2000 through 2014 among United States Medicare beneficiaries aged 65+. The outcomes were hospitalizations with any of 57 cardiovascular and 32 respiratory discharge diagnoses. We estimated associations with two-day moving average PM2.5 as a piecewise linear term with a knot at PM2.5 = 25 g/m3. We used Multi-Outcome Regression with Tree-structured Shrinkage (MOReTreeS) to identify de novo groups of related diseases such that PM2.5 associations are: (1) similar within outcome groups; but (2) different between outcome groups. We adjusted for temperature, humidity, and individual-level characteristics. We introduce an R package, moretrees. RESULTS: Our dataset included 16,007,293 cardiovascular and 8,690,837 respiratory hospitalizations. Of 57 cardiovascular diseases, 51 were grouped and positively associated with PM2.5. We observed a stronger positive association for heart failure, which formed a separate group. We observed negative associations for groups containing the outcomes other aneurysm and intracranial hemorrhage. Of 32 respiratory outcomes, 31 were grouped and were positively associated with PM2.5. Influenza formed a separate group with a negative association. CONCLUSIONS: We used a new statistical approach, MOReTreeS, to uncover variation in the association between short-term PM2.5 exposure and hospitalizations for cardiovascular and respiratory causes controlling for patient characteristics, time trends, and environmental confounders.

Integration of survival data from multiple studies.

We introduce a statistical procedure that integrates datasets from multiple biomedical studies to predict patients' survival, based on individual clinical and genomic profiles. The proposed procedure accounts for potential differences in the relation between predictors and outcomes across studies, due to distinct patient populations, treatments and technologies to measure outcomes and biomarkers. These differences are modeled explicitly with study-specific parameters. We use hierarchical regularization to shrink the study-specific parameters towards each other and to borrow information across studies. The estimation of the study-specific parameters utilizes a similarity matrix, which summarizes differences and similarities of the relations between covariates and outcomes across studies. We illustrate the method in a simulation study and using a collection of gene expression datasets in ovarian cancer. We show that the proposed model increases the accuracy of survival predictions compared to alternative meta-analytic methods.

Approximating the Operating Characteristics of Bayesian Uncertainty Directed Trial Designs.

Bayesian response adaptive clinical trials are currently evaluating experimental therapies for several diseases. Adaptive decisions, such as pre-planned variations of the randomization probabilities, attempt to accelerate the development of new treatments. The design of response adaptive trials, in most cases, requires time consuming simulation studies to describe operating characteristics, such as type I/II error rates, across plausible scenarios. We investigate large sample approximations of pivotal operating characteristics in Bayesian Uncertainty directed trial Designs (BUDs). A BUD trial utilizes an explicit metric u to quantify the information accrued during the study on parameters of interest, for example the treatment effects. The randomization probabilities vary during time to minimize the uncertainty summary u at completion of the study. We provide an asymptotic analysis (i) of the allocation of patients to treatment arms and (ii) of the randomization probabilities. For BUDs with outcome distributions belonging to the natural exponential family with quadratic variance function, we illustrate the asymptotic normality of the number of patients assigned to each arm and of the randomization probabilities. We use these results to approximate relevant operating characteristics such as the power of the BUD. We evaluate the accuracy of the approximations through simulations under several scenarios for binary, time-to-event and continuous outcome models.

Leveraging external data in the design and analysis of clinical trials in neuro-oncology.

Integration of external control data, with patient-level information, in clinical trials has the potential to accelerate the development of new treatments in neuro-oncology by contextualising single-arm studies and improving decision making (eg, early stopping decisions). Based on a series of presentations at the 2020 Clinical Trials Think Tank hosted by the Society of Neuro-Oncology, we provide an overview on the use of external control data representative of the standard of care in the design and analysis of clinical trials. High-quality patient-level records, rigorous methods, and validation analyses are necessary to effectively leverage external data. We review study designs, statistical methods, risks, and potential distortions in using external data from completed trials and real-world data, as well as data sources, data sharing models, ongoing work, and applications in glioblastoma.

Prospective Study Testing a Simplified Paclitaxel Premedication Regimen in Patients with Early Breast Cancer.

BACKGROUND: In early trials, hypersensitivity reactions (HSRs) to paclitaxel were common, thus prompting the administration of antihistamines and corticosteroids before every paclitaxel dose. We tested the safety of omitting corticosteroids after cycle 2 during the paclitaxel portion of the dose-dense (DD) doxorubicin-cyclophosphamide (AC)-paclitaxel regimen. PATIENTS, MATERIALS, AND METHODS: In this prospective, single-arm study, patients who completed four cycles of DD-AC for stage I-III breast cancer received paclitaxel 175 mg/m2 every 2 weeks for four cycles. Patients received a standard premedication protocol containing dexamethasone, diphenhydramine, and a histamine H2 blocker prior to the first two paclitaxel cycles. Dexamethasone was omitted in cycles three and four if there were no HSRs in previous cycles. We estimated the rate of grade 3-4 HSRs. RESULTS: Among 127 patients enrolled, 125 received more than one dose of protocol therapy and are included in the analysis. Fourteen (11.2%; 90% confidence interval, 6.9%-20.0%) patients had any-grade HSRs, for a total of 22 (4.5%; 3.1%-6.4%) HSRs over 486 paclitaxel cycles. Any-grade HSRs occurred in 1.6% (0.3%-5.0%), 6.5% (3.3%-11.3%), 7.4% (3.9%-12.5%), and 2.6% (0.7%-6.6%) of patients after paclitaxel cycles 1, 2, 3, and 4, respectively. Dexamethasone use was decreased by 92.8% in cycles 3 and 4. Only one patient experienced grade 3 HSR in cycles 3 or 4, for a rate of grade 3/4 HSR 0.4% (0.02%-2.0%) (1/237 paclitaxel infusions). That patient had grade 2 HSR during cycle 2, and the subsequent grade 3 event occurred despite usual dexamethasone premedication. A sensitivity analysis restricted to patients not known to have received dexamethasone in cycles 3 and 4 found that any-grade HSRs occurred in 2.7% (3/111; 0.7%-6.8%) and 0.9% (1/109; 0.05%-4.3%) of patients in cycle 3 and 4, respectively. CONCLUSION: Corticosteroid premedication can be safely omitted in cycles 3 and 4 of dose-dense paclitaxel if HSRs are not observed during cycles 1 and 2. IMPLICATIONS FOR PRACTICE: Because of the potential for hypersensitivity reactions (HSRs) to paclitaxel, corticosteroids are routinely prescribed prior to each dose, on an indefinite basis. This prospective study, including 125 patients treated with 486 paclitaxel cycles, demonstrates that corticosteroids can be safely omitted in future cycles if HSRs did not occur during cycles 1 and 2 of paclitaxel and that this strategy reduces the use of corticosteroids in cycles 3 and 4 by 92.8% relative to current standard of care.

Oncotype DX testing in node-positive breast cancer strongly impacts chemotherapy use at a comprehensive cancer center.

PURPOSE: In 2016, we initiated standardized "reflex" Oncotype DX Recurrence Score (RS) testing for patients ≤ 65 years with pT1-2N0-1 HR+/HER2- breast cancer. Here, we examine RS testing patterns, RS distribution, and factors associated with chemotherapy use in patients with pN1 breast cancer. METHODS: Patients with stage I-III HR+/HER2- pN1 breast cancer treated with upfront surgery from February 2016 to March 2019 were identified. Clinical characteristics were compared between patients meeting reflex RS testing criteria, those with RS ordered outside of reflex criteria, and those without RS testing. RS was categorized as low (< 18), intermediate (18-30), and high (≥ 31). Multivariate logistic regression was performed to identify factors associated with adjuvant chemotherapy receipt. We examined 3-year recurrence-free survival (RFS) and overall survival (OS) stratified by chemotherapy use. RESULTS: We identified 347 HR+/HER2- pN1 patients; 272 (78.4%) received RS testing, and 194 (71.3%) met reflex criteria. RS was < 18 in 164 (61.4%) patients, 18-30 in 89 (32.7%) patients, and ≥ 31 in 16 (5.9%) patients. On multivariate analysis, RS < 18 (OR 0.47, 95% CI 0.24-0.92) was associated with lower odds of chemotherapy use, whereas presence of lymphovascular invasion (OR 1.77, 95% CI 1.03-3.07) and lobular subtype (OR 2.40, 95% CI 1.21-4.78) were associated with higher odds. No differences in 3-year RFS (p = 0.97) or OS (p = 0.19) based on chemotherapy receipt were observed. CONCLUSION: Most RS-tested HR+/HER2- pN1 patients at our center had low genomic risk. A low RS independently influenced chemotherapy omission and in RS-tested patients, short-term outcomes were excellent. Our study demonstrates increased use of RS in guiding adjuvant treatment decisions in node-positive disease.

Optimality of testing procedures for survival data in the nonproportional hazards setting.

Most statistical tests for treatment effects used in randomized clinical trials with survival outcomes are based on the proportional hazards assumption, which often fails in practice. Data from early exploratory studies may provide evidence of nonproportional hazards, which can guide the choice of alternative tests in the design of practice-changing confirmatory trials. We developed a test to detect treatment effects in a late-stage trial, which accounts for the deviations from proportional hazards suggested by early-stage data. Conditional on early-stage data, among all tests that control the frequentist Type I error rate at a fixed α level, our testing procedure maximizes the Bayesian predictive probability that the study will demonstrate the efficacy of the experimental treatment. Hence, the proposed test provides a useful benchmark for other tests commonly used in the presence of nonproportional hazards, for example, weighted log-rank tests. We illustrate this approach in simulations based on data from a published cancer immunotherapy phase III trial.

A phase II study of cabozantinib alone or in combination with trastuzumab in breast cancer patients with brain metastases.

PURPOSE: To analyze the efficacy and tolerability of cabozantinib-a small molecule inhibitor of MET and VEGFR2-alone or with trastuzumab in patients with breast cancer brain metastases (BCBM). METHODS: This single-arm phase II study enrolled patients with new or progressive measurable BCBM into 3 cohorts: Cohort 1 (HER2-positive), Cohort 2 (hormone receptor-positive/HER2-negative), and Cohort 3 (triple-negative). Patients received cabozantinib 60-mg daily on a 21-day cycle. Cohort 1 added trastuzumab every 3 weeks and had a primary objective of central nervous system (CNS) objective response rate (ORR) by RECIST 1.1. Secondary objectives for all cohorts were progression-free survival, overall survival, toxicity, and changes in vascular parameters and circulating biomarkers. Cohorts 2 and 3 also had CNS ORR as a secondary objective. RESULTS: Thirty-six BCBM patients enrolled (cohort 1, n = 21; cohort 2, n = 7; cohort 3, n = 8), with a median age of 50. Patients had a median of 3 prior lines for metastatic disease (range 1-9). Treatments prior to enrollment included craniotomy (n = 4), whole brain radiation (n = 24) and stereotactic radiosurgery (n = 11). CNS ORR was 5% in cohort 1, 14% in cohort 2, and 0% in cohort 3. Most common grade 3/4 adverse events included elevations in lipase (11%), AST (8%), ALT (6%), hyponatremia (8%), and hypertension (6%). Cabozantinib increased plasma concentrations of CA-IX, soluble (s)MET, PlGF, sTIE-2, VEGF, and VEGF-D, and decreased sVEGFR2 and TNF-α and total tumor blood volume. CONCLUSIONS: Cabozantinib had insufficient activity in heavily pretreated BCBM patients. Biomarker analysis showed that cabozantinib had antiangiogenic activity and increased tissue hypoxia. TRIAL REGISTRATION: Clinicaltrial.gov registration: NCT02260531.

Efficient computation of the joint probability of multiple inherited risk alleles from pedigree data.

The Elston-Stewart peeling algorithm enables estimation of an individual's probability of harboring germline risk alleles based on pedigree data, and serves as the computational backbone of important genetic counseling tools. However, it remains limited to the analysis of risk alleles at a small number of genetic loci because its computing time grows exponentially with the number of loci considered. We propose a novel, approximate version of this algorithm, dubbed the peeling and paring algorithm, which scales polynomially in the number of loci. This allows extending peeling-based models to include many genetic loci. The algorithm creates a trade-off between accuracy and speed, and allows the user to control this trade-off. We provide exact bounds on the approximation error and evaluate it in realistic simulations. Results show that the loss of accuracy due to the approximation is negligible in important applications. This algorithm will improve genetic counseling tools by increasing the number of pathogenic risk alleles that can be addressed. To illustrate we create an extended five genes version of BRCAPRO, a widely used model for estimating the carrier probabilities of BRCA1 and BRCA2 risk alleles and assess its computational properties.

Adding experimental arms to platform clinical trials: randomization procedures and interim analyses.

Multi-arm clinical trials use a single control arm to evaluate multiple experimental treatments. In most cases this feature makes multi-arm studies considerably more efficient than two-arm studies. A bottleneck for implementation of a multi-arm trial is the requirement that all experimental treatments have to be available at the enrollment of the first patient. New drugs are rarely at the same stage of development. These limitations motivate our study of statistical methods for adding new experimental arms after a clinical trial has started enrolling patients. We consider both balanced and outcome-adaptive randomization methods for experimental designs that allow investigators to add new arms, discuss their application in a tuberculosis trial, and evaluate the proposed designs using a set of realistic simulation scenarios. Our comparisons include two-arm studies, multi-arm studies, and the proposed class of designs in which new experimental arms are added to the trial at different time points.

Prognostic role of circulating exosomal miRNAs in multiple myeloma.

Exosomes, secreted by several cell types, including cancer cells, can be isolated from the peripheral blood and have been shown to be powerful markers of disease progression in cancer. In this study, we examined the prognostic significance of circulating exosomal microRNAs (miRNAs) in multiple myeloma (MM). A cohort of 156 patients with newly diagnosed MM, uniformly treated and followed, was studied. Circulating exosomal miRNAs were isolated and used to perform a small RNA sequencing analysis on 10 samples and a quantitative reverse transcription polymerase chain reaction (qRT-PCR) array on 156 samples. We studied the relationship between miRNA levels and patient outcomes, including progression-free survival (PFS) and overall survival (OS). We identified miRNAs as the most predominant small RNAs present in exosomes isolated from the serum of patients with MM and healthy controls by small RNA sequencing of circulating exosomes. We then analyzed exosomes isolated from serum samples of 156 patients using a qRT-PCR array for 22 miRNAs. Two of these miRNAs, let-7b and miR-18a, were significantly associated with both PFS and OS in the univariate analysis and were still statistically significant after adjusting for the International Staging System and adverse cytogenetics in the multivariate analysis. Our findings support the use of circulating exosomal miRNAs to improve the identification of patients with newly diagnosed MM with poor outcomes. The results require further validation in other independent prospective MM cohorts.

Multi-study factor analysis.

We introduce a novel class of factor analysis methodologies for the joint analysis of multiple studies. The goal is to separately identify and estimate (1) common factors shared across multiple studies, and (2) study-specific factors. We develop an Expectation Conditional-Maximization algorithm for parameter estimates and we provide a procedure for choosing the numbers of common and specific factors. We present simulations for evaluating the performance of the method and we illustrate it by applying it to gene expression data in ovarian cancer. In both, we clarify the benefits of a joint analysis compared to the standard factor analysis. We have provided a tool to accelerate the pace at which we can combine unsupervised analysis across multiple studies, and understand the cross-study reproducibility of signal in multivariate data. An R package (MSFA), is implemented and is available on GitHub.

Frequentist operating characteristics of Bayesian optimal designs via simulation.

Bayesian adaptive designs have become popular because of the possibility of increasing the number of patients treated with more beneficial treatments, while still providing sufficient evidence for treatment efficacy comparisons. It can be essential, for regulatory and other purposes, to conduct frequentist analyses both before and after a Bayesian adaptive trial, and these remain challenging. In this paper, we propose a general simulation-based approach to compare frequentist designs with Bayesian adaptive designs based on frequentist criteria such as power and to compute valid frequentist p-values. We illustrate our approach by comparing the power of an equal randomization (ER) design with that of an optimal Bayesian adaptive (OBA) design. The Bayesian design considered here is the dynamic programming solution of the optimization of a specific utility function defined by the number of successes in a patient horizon, including patients whose treatment will be affected by the trial's results after the end of the trial. While the power of an ER design depends on treatment efficacy and the sample size, the power of the OBA design also depends on the patient horizon size. Our results quantify the trade-off between power and the optimal assignment of patients to treatments within the trial. We show that, for large patient horizons, the two criteria are in agreement, while for small horizons, differences can be substantial. This has implications for precision medicine, where patient horizons are decreasing as a result of increasing stratification of patients into subpopulations defined by molecular markers.

Phase I/II trial of the CXCR4 inhibitor plerixafor in combination with bortezomib as a chemosensitization strategy in relapsed/refractory multiple myeloma.

We tested the hypothesis that using CXCR4 inhibition to target the interaction between the tumor cells and the microenvironment leads to sensitization of the tumor cells to apoptosis. Eligibility criteria included multiple myeloma (MM) patients with 1-5 prior lines of therapy. The purposes of the phase I study were to evaluate the safety and maximal-tolerated dose (MTD) of the combination. The treatment-related adverse events and response rate of the combination were assessed in the phase II study. A total of 58 patients were enrolled in the study. The median age of the patients was 63 years (range, 43-85), and 78% of them received prior bortezomib. In the phase I study, the MTD was plerixafor 0.32 mg/kg, and bortezomib 1.3 mg/m2 . The overall response rate for the phase II study was 48.5%, and the clinical benefit rate 60.6%. The median disease-free survival was 12.6 months. The CyTOF analysis demonstrated significant mobilization of plasma cells, CD34+ stem cells, and immune T cells in response to plerixafor. This is an unprecedented study that examines therapeutic targeting of the bone marrow microenvironment and its interaction with the tumor clone to overcome resistance to therapy. Our results indicate that this novel combination is safe and that the objective response rate is high even in patients with relapsed/refractory MM. ClinicalTrials.gov, NCT00903968.