Bayesian adaptive designs for drug combinations in early phase clinical trials

Treating patients with combined agents is a growing trend in cancer clinical trials. Evaluating the synergism of multiple drugs is often the primary motivation for such drug-combination studies. Focusing on the drug combination study in the early phase clinical trials, our research is composed of three parts: (1) We conduct a comprehensive comparison of four dose-finding designs in the two-dimensional toxicity probability space and propose using the Bayesian model averaging method to overcome the arbitrariness of the model specification and enhance the robustness of the design; (2) Motivated by a recent drug-combination trial at MD Anderson Cancer Center with a continuous-dose standard of care agent and a discrete-dose investigational agent, we propose a two-stage Bayesian adaptive dose-finding design based on an extended continual reassessment method; (3) By combining phase I and phase II clinical trials, we propose an extension of a single agent dose-finding design. We model the time-to-event toxicity and efficacy to direct dose finding in two-dimensional drug-combination studies. We conduct extensive simulation studies to examine the operating characteristics of the aforementioned designs and demonstrate the designs' good performances in various practical scenarios.^

Bayesian Adaptive Designs for Early Phase Clinical Trials

My dissertation focuses mainly on Bayesian adaptive designs for phase I and phase II clinical trials. It includes three specific topics: (1) proposing a novel two-dimensional dose-finding algorithm for biological agents, (2) developing Bayesian adaptive screening designs to provide more efficient and ethical clinical trials, and (3) incorporating missing late-onset responses to make an early stopping decision. Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which toxicity and efficacy monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a phase I/II trial design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships. Trials of combination therapies for the treatment of cancer are playing an increasingly important role in the battle against this disease. To more efficiently handle the large number of combination therapies that must be tested, we propose a novel Bayesian phase II adaptive screening design to simultaneously select among possible treatment combinations involving multiple agents. Our design is based on formulating the selection procedure as a Bayesian hypothesis testing problem in which the superiority of each treatment combination is equated to a single hypothesis. During the trial conduct, we use the current values of the posterior probabilities of all hypotheses to adaptively allocate patients to treatment combinations. Simulation studies show that the proposed design substantially outperforms the conventional multi-arm balanced factorial trial design. The proposed design yields a significantly higher probability for selecting the best treatment while at the same time allocating substantially more patients to efficacious treatments. The proposed design is most appropriate for the trials combining multiple agents and screening out the efficacious combination to be further investigated. The proposed Bayesian adaptive phase II screening design substantially outperformed the conventional complete factorial design. Our design allocates more patients to better treatments while at the same time providing higher power to identify the best treatment at the end of the trial. Phase II trial studies usually are single-arm trials which are conducted to test the efficacy of experimental agents and decide whether agents are promising to be sent to phase III trials. Interim monitoring is employed to stop the trial early for futility to avoid assigning unacceptable number of patients to inferior treatments. We propose a Bayesian single-arm phase II design with continuous monitoring for estimating the response rate of the experimental drug. To address the issue of late-onset responses, we use a piece-wise exponential model to estimate the hazard function of time to response data and handle the missing responses using the multiple imputation approach. We evaluate the operating characteristics of the proposed method through extensive simulation studies. We show that the proposed method reduces the total length of the trial duration and yields desirable operating characteristics for different physician-specified lower bounds of response rate with different true response rates.

Bayesian adaptive designs in early phase clinical trials

There are two practical challenges in the phase I clinical trial conduct: lack of transparency to physicians, and the late onset toxicity. In my dissertation, Bayesian approaches are used to address these two problems in clinical trial designs. The proposed simple optimal designs cast the dose finding problem as a decision making process for dose escalation and deescalation. The proposed designs minimize the incorrect decision error rate to find the maximum tolerated dose (MTD). For the late onset toxicity problem, a Bayesian adaptive dose-finding design for drug combination is proposed. The dose-toxicity relationship is modeled using the Finney model. The unobserved delayed toxicity outcomes are treated as missing data and Bayesian data augment is employed to handle the resulting missing data. Extensive simulation studies have been conducted to examine the operating characteristics of the proposed designs and demonstrated the designs' good performances in various practical scenarios.^

Optimal, minimax, and Bayesian two-stage designs in two-dose phase II clinical trials

Background: For most cytotoxic and biologic anti-cancer agents, the response rate of the drug is commonly assumed to be non-decreasing with an increasing dose. However, an increasing dose does not always result in an appreciable increase in the response rate. This may especially be true at high doses for a biologic agent. Therefore, in a phase II trial the investigators may be interested in testing the anti-tumor activity of a drug at more than one (often two) doses, instead of only at the maximum tolerated dose (MTD). This way, when the lower dose appears equally effective, this dose can be recommended for further confirmatory testing in a phase III trial under potential long-term toxicity and cost considerations. A common approach to designing such a phase II trial has been to use an independent (e.g., Simon's two-stage) design at each dose ignoring the prior knowledge about the ordering of the response probabilities at the different doses. However, failure to account for this ordering constraint in estimating the response probabilities may result in an inefficient design. In this dissertation, we developed extensions of Simon's optimal and minimax two-stage designs, including both frequentist and Bayesian methods, for two doses that assume ordered response rates between doses. ^ Methods: Optimal and minimax two-stage designs are proposed for phase II clinical trials in settings where the true response rates at two dose levels are ordered. We borrow strength between doses using isotonic regression and control the joint and/or marginal error probabilities. Bayesian two-stage designs are also proposed under a stochastic ordering constraint. ^ Results: Compared to Simon's designs, when controlling the power and type I error at the same levels, the proposed frequentist and Bayesian designs reduce the maximum and expected sample sizes. Most of the proposed designs also increase the probability of early termination when the true response rates are poor. ^ Conclusion: Proposed frequentist and Bayesian designs are superior to Simon's designs in terms of operating characteristics (expected sample size and probability of early termination, when the response rates are poor) Thus, the proposed designs lead to more cost-efficient and ethical trials, and may consequently improve and expedite the drug discovery process. The proposed designs may be extended to designs of multiple group trials and drug combination trials.^

Intrinsic oxidative stress in cancer cells: A biochemical basis for therapeutic selectivity using ROS -generating agents

A major goal of chemotherapy is to selectively kill cancer cells while minimizing toxicity to normal cells. Identifying biological differences between cancer and normal cells is essential in designing new strategies to improve therapeutic selectivity. Superoxide dismutases (SOD) are crucial antioxidant enzymes required for the elimination of superoxide (O2·− ), a free radical produced during normal cellular metabolism. Previous studies in our laboratory demonstrated that 2-methoxyestradiol (2-ME), an estradiol derivative, inhibits the function of SOD and selectively kills human leukemia cells without exhibiting significant cytotoxicity in normal lymphocytes. The present work was initiated to examine the biochemical basis for the selective anticancer activity of 2-ME. Investigations using two-parameter flow cytometric analyses and ROS scavengers established that O2·− is a primary and essential mediator of 2-ME-induced apoptosis in cancer cells. In addition, experiments using SOD overexpression vectors and SOD knockout cells found that SOD is a critical target of 2-ME. Importantly, the administration of 2-ME resulted in the selective accumulation of O 2·− and apoptosis in leukemia and ovarian cancer cells. The preferential activity of 2-ME was found to be due to increased intrinsic oxidative stress in these cancer cells versus their normal counterparts. This intrinsic oxidative stress was associated with the upregulation of the antioxidant enzymes SOD and catalase as a mechanism to cope with the increase in ROS. Furthermore, oxygen consumption experiments revealed that normal lymphocytes decrease their respiration rate in response to 2-ME-induced oxidative stress, while human leukemia cells seem to lack this regulatory mechanism. This leads to an uncontrolled production of O2·−, severe accumulation of ROS, and ultimately ROS-mediated apoptosis in leukemia cells treated with 2-ME. The biochemical differences between cancer and normal cells identified here provide a basis for the development of drug combination strategies using 2-ME with other ROS-generating agents to enhance anticancer activity. The effectiveness of such a combination strategy in killing cancer cells was demonstrated by the use of 2-ME with agents/modalities such as ionizing radiation and doxorubicin. Collectively, the data presented here strongly suggests that 2-ME may have important clinical implications for the selective killing of cancer cells. ^

A BAYESIAN APPROACH TO DOSE-RESPONSE ASSESSMENT AND DRUG-DRUG INTERACTION ANALYSIS: APPLICATION TO IN VITRO STUDIES

The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis. The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method (Chou and Talalay, 1984). The Median-Effect Principle/Combination Index method leads to inefficiency by ignoring important sources of variation inherent in dose-response data and discarding data points that do not fit the Median-Effect Principle. Previous work has shown that the conventional method yields a high rate of false positives (Boik, Boik, Newman, 2008; Hennessey, Rosner, Bast, Chen, 2010) and, in some cases, low power to detect synergy. There is a great need for improving the current methodology. We developed a Bayesian framework for dose-response modeling and drug-drug interaction analysis. First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the case that parametric dose-response models do not fit the data, we developed a practical and flexible nonparametric regression method for meta-analysis of independently repeated dose-response experiments. Third, and lastly, we developed a method, based on Loewe additivity that allows one to quantitatively assess interaction between two agents combined at a fixed dose ratio. The proposed method makes a comprehensive and honest account of uncertainty within drug interaction assessment. Extensive simulation studies show that the novel methodology improves the screening process of effective/synergistic agents and reduces the incidence of type I error. We consider an ovarian cancer cell line study that investigates the combined effect of DNA methylation inhibitors and histone deacetylation inhibitors in human ovarian cancer cell lines. The hypothesis is that the combination of DNA methylation inhibitors and histone deacetylation inhibitors will enhance antiproliferative activity in human ovarian cancer cell lines compared to treatment with each inhibitor alone. By applying the proposed Bayesian methodology, in vitro synergy was declared for DNA methylation inhibitor, 5-AZA-2'-deoxycytidine combined with one histone deacetylation inhibitor, suberoylanilide hydroxamic acid or trichostatin A in the cell lines HEY and SKOV3. This suggests potential new epigenetic therapies in cell growth inhibition of ovarian cancer cells.

An Innovative Phase I Trial Design Allowing for the Identification of Multiple Potential Maximum Tolerated Doses with Combination Therapy of Targeted Agents

Treatment for cancer often involves combination therapies used both in medical practice and clinical trials. Korn and Simon listed three reasons for the utility of combinations: 1) biochemical synergism, 2) differential susceptibility of tumor cells to different agents, and 3) higher achievable dose intensity by exploiting non-overlapping toxicities to the host. Even if the toxicity profile of each agent of a given combination is known, the toxicity profile of the agents used in combination must be established. Thus, caution is required when designing and evaluating trials with combination therapies. Traditional clinical design is based on the consideration of a single drug. However, a trial of drugs in combination requires a dose-selection procedure that is vastly different than that needed for a single-drug trial. When two drugs are combined in a phase I trial, an important trial objective is to determine the maximum tolerated dose (MTD). The MTD is defined as the dose level below the dose at which two of six patients experience drug-related dose-limiting toxicity (DLT). In phase I trials that combine two agents, more than one MTD generally exists, although all are rarely determined. For example, there may be an MTD that includes high doses of drug A with lower doses of drug B, another one for high doses of drug B with lower doses of drug A, and yet another for intermediate doses of both drugs administered together. With classic phase I trial designs, only one MTD is identified. Our new trial design allows identification of more than one MTD efficiently, within the context of a single protocol. The two drugs combined in our phase I trial are temsirolimus and bevacizumab. Bevacizumab is a monoclonal antibody targeting the vascular endothelial growth factor (VEGF) pathway which is fundamental for tumor growth and metastasis. One mechanism of tumor resistance to antiangiogenic therapy is upregulation of hypoxia inducible factor 1α (HIF-1α) which mediates responses to hypoxic conditions. Temsirolimus has resulted in reduced levels of HIF-1α making this an ideal combination therapy. Dr. Donald Berry developed a trial design schema for evaluating low, intermediate and high dose levels of two drugs given in combination as illustrated in a recently published paper in Biometrics entitled “A Parallel Phase I/II Clinical Trial Design for Combination Therapies.” His trial design utilized cytotoxic chemotherapy. We adapted this design schema by incorporating greater numbers of dose levels for each drug. Additional dose levels are being examined because it has been the experience of phase I trials that targeted agents, when given in combination, are often effective at dosing levels lower than the FDA-approved dose of said drugs. A total of thirteen dose levels including representative high, intermediate and low dose levels of temsirolimus with representative high, intermediate, and low dose levels of bevacizumab will be evaluated. We hypothesize that our new trial design will facilitate identification of more than one MTD, if they exist, efficiently and within the context of a single protocol. Doses gleaned from this approach could potentially allow for a more personalized approach in dose selection from among the MTDs obtained that can be based upon a patient’s specific co-morbid conditions or anticipated toxicities.

Modulating bioreductive drug activity with antivascular agents

Modulation of tumor hypoxia to increase bioreductive drug antitumor activity was investigated. The antivascular agent 5,6-dimethylxanthenone acetic acid (DMXAA) was used in combination studies with the bioreductive drugs Tirapazamine (TPZ) and Mitomycin C (MMC). Blood perfusion studies with DMXAA showed a maximal reduction of 66% in tumor blood flow 4 hours post drug administration. This tumor specific decrease in perfusion was also found to be dose-dependent, with 25 and 30 mg/kg DMXAA yielding greater than 50% reduction in tumor blood flow. Increases in antitumor activity with combination therapy (bioreductive drugs $+$ DMXAA) were significant over individual therapies, suggesting an increased activity due to increased hypoxia induced by DMXAA. Combination studies yielded the following significant tumor growth delays over control: MMC (5mg/kg) $+$ DMXAA (25mg/kg) = 20 days, MMC (2.5mg/kg) $+$ DMXAA (25 mg/kg) = 8 days, TPZ (21.4mg/kg) $+$ DMXAA (17.5mg/kg) = 4 days. The mechanism of interaction of these drugs was investigated by measuring metabolite production and DNA damage. 'Real time' microdialysis studies indicated maximal metabolite production at 20-30 minutes post injection for individual and combination therapies. DNA double strand breaks induced by TPZ $\pm$ DMXAA (20 minutes post injection) were analyzed by pulsed field gel electrophoresis (PFGE). Southern blot analyses and quantification showed TPZ induced DNA double strand breaks, but this effect was not evident in combination studies with DMXAA. Based on these data, combination studies of TPZ $+$ DMXAA showed increased antitumor activity over individual drug therapies. The mechanism of this increased activity, however, does not appear to be due to an increase in TPZ bioreduction at this time point. ^

A cross sectional examination of risky sexual behaviors among a population of drug users

This study is a secondary data analysis that assesses the relationship between risky sexual behaviors and sexually transmitted infections (STIs) among drug users. This study analyzes data collected from drug users in the Houston Metropolitan area during 2004 and through August 2005, by researchers with the DASH (Drugs, AIDS, STDs and Hepatitis) project at The University of Texas at Houston School of Public Health. Specifically, the sexually transmitted infections that will be of interest in this proposed study are Human Immunodeficiency Virus (HIV) and Hepatitis B Virus (HBV). Risky sexual behaviors that will be examined include lack of condom use, sexual orientation, trading sex for drugs, trading sex for money, and number of male and female sexual partners in the last 4 weeks. ^ Unadjusted, gender, sexual orientation, number of recent male and female sex partners, and a history of injection drug use were all found to be significant independent variables that increased the odds of STI status. When included in an overall model, these variables significantly increased the odds of STI status, including HBV infection, HIV infection, and HBV/HIV co-infection. History of injection drug use was significant for both HBV and HBV/HIV co-infection, whereas a gay sexual orientation was significant for both HIV and HBV/HIV co-infection. Additionally, having excessive female sex partners was significant for HIV infection. This significant association increases the need for implementation of stronger intervention programs tailored to suit this population's needs such as a combination of drug and sexually transmitted disease (STD) treatment. ^ The importance of these findings is that they establish the strength of associations between the previously mentioned risky sexual behaviors and STI status among drug users. This is crucial for assessing future risk of infection as well as for serving as a necessary component in intervention and treatment programs both for drug use and STIs. ^

Maximizing efficacy of the hypomethylating drug 5-aza-2'-deoxycytidine in human leukemia

5-aza-2'-deoxycytidine (DAC) is a cytidine analogue that strongly inhibits DNA methylation, and was recently approved for the treatment of myelodysplastic syndromes (MDS). To maximize clinical results with DAC, we investigated its use as an anti-cancer drug. We also investigated mechanisms of resistance to DAC in vitro in cancer cell lines and in vivo in MDS patients after relapse. We found DAC sensitized cells to the effect of 1-β-D-Arabinofuranosylcytosine (Ara-C). The combination of DAC and Ara-C or Ara-C following DAC showed additive or synergistic effects on cell death in four human leukemia cell lines in vitro, but antagonism in terms of global methylation. RIL gene activation and H3 lys-9 acetylation of short interspersed elements (Alu). One possible explanation is that hypomethylated cells are sensitized to cell killing by Ara-C. Turning to resistance, we found that the IC50 of DAC differed 1000 fold among and was correlated with the dose of DAC that induced peak hypomethylation of long interspersed nuclear elements (LINE) (r=0.94, P<0.001), but not with LINE methylation at baseline (r=0.05, P=0.97). Sensitivity to DAC did not significantly correlate with sensitivity to another hypomethylating agent 5-azacytidine (AZA) (r=0.44, P=0.11). The cell lines most resistant to DAC had low dCK, hENT1, and hENT2 transporters and high cytosine deaminase (CDA). In an HL60 leukemia cell line, resistance to DAC could be rapidly induced by drug exposure, and was related to a switch from monoallelic to biallelic mutation of dCK or a loss of wild type DCK allele. Furthermore, we showed that DAC induced DNA breaks evidenced by histone H2AX phosphorylation and increased homologous recombination rates 7-10 folds. Finally, we found there were no dCK mutations in MDS patients after relapse. Cytogenetics showed that three of the patients acquired new abnormalities at relapse. These data suggest that in vitro spontaneous and acquired resistance to DAC can be explained by insufficient incorporation of drug into DNA. In vivo resistance to DAC is likely due to methylation-independent pathways such as chromosome changes. The lack of cross resistance between DAC and AZA is of potential clinical relevance, as is the combination of DAC and Ara-C. ^

Prevalence and risk factors for human immunodefiency virus and hepatitis C virus co-infection among drug users in innercity neighborhoods in Houston, Texas

Background. There are 200,000 HIV/HCV co-infected people in the US and IDUs are at highest risk of exposure. Between 52-92% of HIV infected IDUs are chronically infected with HCV. African Americans and Hispanics bear the largest burden of co-infections. Furthermore HIV/HCV co-infection is associated with high morbidity and mortality if not treated. The present study investigates the demographic, sexual and drug related risk factors for HIV/HCV co-infection among predominantly African American injecting and non-injecting drug users living in two innercity neighborhoods in Houston, Texas. ^ Methods. This secondary analysis used data collected between February 2004 and June 2005 from 1,889 drug users. Three case-comparison analyses were conducted to investigate the risk factors for HIV/HCV co-infection. HIV mono-infection, HCV mono-infection and non-infection were compared to HIV/HCV co-infection to build multivariate logistic regression models. Race/ethnicity and age were forced into each model regardless of significance in the univariate analysis. ^ Results. The overall prevalence of HIV/HCV co-infection was 3.9% while 39.8% of HIV infected drug users were co-infected with HCV and 10.7% of HCV infected drug users were co-infected with HIV. Among HIV infected IDUs the prevalence of HCV was 71.7% and among HIV infected NIDUs the prevalence of HCV was 24%. In the multivariate analysis, HIV/HCV co-infection was associated with injecting drug use when compared to HIV mono-infection, with MSM when compared to HCV mono-infection and with injecting drug use as well as MSM when compared to non-infection. ^ Conclusion. HIV/HCV co-infection was associated with a combination of sexual and risky injecting practices. More data on the prevalence and risk factors for co-infection among minority populations is urgently needed to support the development of targeted interventions and treatment options. Additionally there should be a focus on promoting safer sex and injecting practices among drug users as well as the expansion of routine testing for HIV and HCV infections in this high risk population.^

Immune response to hepatitis B vaccination in drug using populations: A systematic review and meta-regression analysis

Hepatitis B virus (HBV) is a significant cause of liver diseases and related complications worldwide. Both injecting and non-injecting drug users are at increased risk of contracting HBV infection. Scientific evidence suggests that drug users have subnormal response to HBV vaccination and the seroprotection rates are lower than that in the general population; potentially due to vaccine factors, host factors, or both. The purpose of this systematic review is to examine the rates of seroprotection following HBV vaccination in drug using populations and to conduct a meta-analysis to identify the factors associated with varying seroprotection rates. Seroprotection is defined as developing an anti-HBs antibody level of ≥ 10 mIU/ml after receiving the HBV vaccine. Original research articles were searched using online databases and reference lists of shortlisted articles. HBV vaccine intervention studies reporting seroprotection rates in drug users and published in English language during or after 1989 were eligible. Out of 235 citations reviewed, 11 studies were included in this review. The reported seroprotection rates ranged from 54.5 – 97.1%. Combination vaccine (HAV and HBV) (Risk ratio 12.91, 95% CI 2.98-55.86, p = 0.003), measurement of anti-HBs with microparticle immunoassay (Risk ratio 3.46, 95% CI 1.11-10.81, p = 0.035) and anti-HBs antibody measurement at 2 months after the last HBV vaccine dose (RR 4.11, 95% CI 1.55-10.89, p = 0.009) were significantly associated with higher seroprotection rates. Although statistically nonsignificant, the variables mean age>30 years, higher prevalence of anti-HBc antibody and anti-HIV antibody in the sample population, and current drug use (not in drug rehabilitation treatment) were strongly associated with decreased seroprotection rates. Proportion of injecting drug users, vaccine dose and accelerated vaccine schedule were not predictors of heterogeneity across studies. Studies examined in this review were significantly heterogeneous (Q = 180.850, p = 0.000) and factors identified should be considered when comparing immune response across studies. The combination vaccine showed promising results; however, its effectiveness compared to standard HBV vaccine needs to be examined systematically. Immune response in DUs can possibly be improved by the use of bivalent vaccines, booster doses, and improving vaccine completion rates through integrated public programs and incentives.^

PIM KINASE INHIBITION: SINGLE AGENT AND COMBINATION APPROACHES IN B-CELL LYMPHOMA

Proviral integration site for Moloney murine leukemia virus (Pim) kinases are Ser/Thr/Tyr kinases. They modulate B-cell development but become oncoproteins and promote cancer development once overexpressed. Containing three isoforms, Pim-1, -2 and -3 are known to phosphorylate various substrates that regulate transcription, translation, cell cycle, and survival pathways in both hematological and solid tumors. Mantle cell lymphoma (MCL) is an aggressive B-cell lymphoma. Elevated Pim kinase levels are common in MCL, and it negatively correlates with patient outcome. SGI-1776 is a small molecule inhibitor selective for Pim-1/-3. We hypothesize that SGI-1776 treatment in MCL will inhibit Pim kinase function, and inhibition of downstream substrates phosphorylation will disrupt transcriptional, translational, and cell cycle processes while promoting apoptosis. SGI-1776 treatment induced moderate to high levels of apoptosis in four MCL cell lines (JeKo-1, Mino, SP-53 and Granta-519) and peripheral blood mononuclear cells (PBMCs) from MCL patients. Phosphorylation of transcription and translation regulators, c-Myc and 4E-BP1 declined in both model systems. Additionally, levels of short-lived Mcl-1 mRNA and protein also decreased and correlated with decline of global RNA synthesis. Collectively, our investigations highlight Pim kinases as viable drug targets in MCL and emphasize their roles in transcriptional and translational regulation. We further investigated a combination strategy using SGI-1776 with bendamustine, an FDA-approved DNA-damaging alkylating agent for treating non-Hodgkin’s lymphoma. We hypothesized this combination will enhance SGI-1776-induced transcription and translation inhibition, while promoting bendamustine-triggered DNA damage and inducing additive to synergistic cytotoxicity in B-cell lymphoma. Bendamustine alone resulted in moderate levels of apoptosis induction in MCL cell lines (JeKo-1 and Mino), and in MCL and splenic marginal zone lymphoma (a type of B-cell lymphoma) primary cells. An additive effect in cell killing was observed when combined with SGI-1776. Expectedly, SGI-1776 effectively decreased global RNA and protein synthesis levels, while bendamustine significantly inhibited DNA synthesis and generated DNA damage response. In combination, intensified inhibitory effects in DNA, RNA and protein syntheses were observed. Together, these data suggested feasibility of using Pim kinase inhibitor in combination with chemotherapeutic agents such as bendamustine in B-cell lymphoma, and provided foundation of their mechanism of actions in lymphoma cells.