BAYESIAN PHASE I DOSE FINDING IN CANCER TRIALS

This dissertation explores phase I dose-finding designs in cancer trials from three perspectives: the alternative Bayesian dose-escalation rules, a design based on a time-to-dose-limiting toxicity (DLT) model, and a design based on a discrete-time multi-state (DTMS) model. We list alternative Bayesian dose-escalation rules and perform a simulation study for the intra-rule and inter-rule comparisons based on two statistical models to identify the most appropriate rule under certain scenarios. We provide evidence that all the Bayesian rules outperform the traditional ``3+3'' design in the allocation of patients and selection of the maximum tolerated dose. The design based on a time-to-DLT model uses patients' DLT information over multiple treatment cycles in estimating the probability of DLT at the end of treatment cycle 1. Dose-escalation decisions are made whenever a cycle-1 DLT occurs, or two months after the previous check point. Compared to the design based on a logistic regression model, the new design shows more safety benefits for trials in which more late-onset toxicities are expected. As a trade-off, the new design requires more patients on average. The design based on a discrete-time multi-state (DTMS) model has three important attributes: (1) Toxicities are categorized over a distribution of severity levels, (2) Early toxicity may inform dose escalation, and (3) No suspension is required between accrual cohorts. The proposed model accounts for the difference in the importance of the toxicity severity levels and for transitions between toxicity levels. We compare the operating characteristics of the proposed design with those from a similar design based on a fully-evaluated model that directly models the maximum observed toxicity level within the patients' entire assessment window. We describe settings in which, under comparable power, the proposed design shortens the trial. The proposed design offers more benefit compared to the alternative design as patient accrual becomes slower.

On Bayesian seamless phase I-II designs

Early phase clinical trial designs have long been the focus of interest for clinicians and statisticians working in oncology field. There are several standard phse I and phase II designs that have been widely-implemented in medical practice. For phase I design, the most commonly used methods are 3+3 and CRM. A newly-developed Bayesian model-based mTPI design has now been used by an increasing number of hospitals and pharmaceutical companies. The advantages and disadvantages of these three top phase I designs have been discussed in my work here and their performances were compared using simulated data. It was shown that mTPI design exhibited superior performance in most scenarios in comparison with 3+3 and CRM designs. ^ The next major part of my work is proposing an innovative seamless phase I/II design that allows clinicians to conduct phase I and phase II clinical trials simultaneously. Bayesian framework was implemented throughout the whole design. The phase I portion of the design adopts mTPI method, with the addition of futility rule which monitors the efficacy performance of the tested drugs. Dose graduation rules were proposed in this design to allow doses move forward from phase I portion of the study to phase II portion without interrupting the ongoing phase I dose-finding schema. Once a dose graduated to phase II, adaptive randomization was used to randomly allocated patients into different treatment arms, with the intention of more patients being assigned to receive more promising dose(s). Again simulations were performed to compare the performance of this innovative phase I/II design with a recently published phase I/II design, together with the conventional phase I and phase II designs. The simulation results indicated that the seamless phase I/II design outperform the other two competing methods in most scenarios, with superior trial power and the fact that it requires smaller sample size. It also significantly reduces the overall study time. ^ Similar to other early phase clinical trial designs, the proposed seamless phase I/II design requires that the efficacy and safety outcomes being able to be observed in a short time frame. This limitation can be overcome by using validated surrogate marker for the efficacy and safety endpoints.^

A simulation study of the standard design, the rolling six design, the CRM, and the modified CRM in Phase I clinical trials

The Phase I clinical trial is considered the "first in human" study in medical research to examine the toxicity of a new agent. It determines the maximum tolerable dose (MTD) of a new agent, i.e., the highest dose in which toxicity is still acceptable. Several phase I clinical trial designs have been proposed in the past 30 years. The well known standard method, so called the 3+3 design, is widely accepted by clinicians since it is the easiest to implement and it does not need a statistical calculation. Continual reassessment method (CRM), a design uses Bayesian method, has been rising in popularity in the last two decades. Several variants of the CRM design have also been suggested in numerous statistical literatures. Rolling six is a new method introduced in pediatric oncology in 2008, which claims to shorten the trial duration as compared to the 3+3 design. The goal of the present research was to simulate clinical trials and compare these phase I clinical trial designs. Patient population was created by discrete event simulation (DES) method. The characteristics of the patients were generated by several distributions with the parameters derived from a historical phase I clinical trial data review. Patients were then selected and enrolled in clinical trials, each of which uses the 3+3 design, the rolling six, or the CRM design. Five scenarios of dose-toxicity relationship were used to compare the performance of the phase I clinical trial designs. One thousand trials were simulated per phase I clinical trial design per dose-toxicity scenario. The results showed the rolling six design was not superior to the 3+3 design in terms of trial duration. The time to trial completion was comparable between the rolling six and the 3+3 design. However, they both shorten the duration as compared to the two CRM designs. Both CRMs were superior to the 3+3 design and the rolling six in accuracy of MTD estimation. The 3+3 design and rolling six tended to assign more patients to undesired lower dose levels. The toxicities were slightly greater in the CRMs.^

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.

Modified 3+3 design for phase I clinical trials

Phase I clinical trial is mainly designed to determine the maximum tolerated dose (MTD) of a new drug. Optimization of phase I trial design is crucial to minimize the number of enrolled patients exposed to unsafe dose levels and to provide reliable information to the later phases of clinical trials. Although it has been criticized about its inefficient MTD estimation, nowadays the traditional 3+3 method remains dominant in practice due to its simplicity and conservative estimation. There are many new designs that have been proven to generate more credible MTD estimation, such as the Continual Reassessment Method (CRM). Despite its accepted better performance, the CRM design is still not widely used in real trials. There are several factors that contribute to the difficulties of CRM adaption in practice. First, CRM is not widely accepted by the regulatory agencies such as FDA in terms of safety. It is considered to be less conservative and tend to expose more patients above the MTD level than the traditional design. Second, CRM is relatively complex and not intuitive for the clinicians to fully understand. Third, the CRM method take much more time and need statistical experts and computer programs throughout the trial. The current situation is that the clinicians still tend to follow the trial process that they are comfortable with. This situation is not likely to change in the near future. Based on this situation, we have the motivation to improve the accuracy of MTD selection while follow the procedure of the traditional design to maintain simplicity. We found that in 3+3 method, the dose transition and the MTD determination are relatively independent. Thus we proposed to separate the two stages. The dose transition rule remained the same as 3+3 method. After getting the toxicity information from the dose transition stage, we combined the isotonic transformation to ensure the monotonic increasing order before selecting the optimal MTD. To compare the operating characteristics of the proposed isotonic method and the other designs, we carried out 10,000 simulation trials under different dose setting scenarios to compare the design characteristics of the isotonic modified method with standard 3+3 method, CRM, biased coin design (BC) and k-in-a-row design (KIAW). The isotonic modified method improved MTD estimation of the standard 3+3 in 39 out of 40 scenarios. The improvement is much greater when the target is 0.3 other than 0.25. The modified design is also competitive when comparing with other selected methods. A CRM method performed better in general but was not as stable as the isotonic method throughout the different dose settings. The results demonstrated that our proposed isotonic modified method is not only easily conducted using the same procedure as 3+3 but also outperforms the conventional 3+3 design. It can also be applied to determine MTD for any given TTL. These features make the isotonic modified method of practical value in phase I clinical trials.^

Phase I clinical trials in 56 patients with thyroid cancer: the M. D. Anderson Cancer Center experience.

INTRODUCTION: Thyroid cancer is the most common endocrine malignancy. The outcomes of patients with relapsed thyroid cancer treated on early-phase clinical trials have not been systematically analyzed. PATIENTS AND METHODS: We reviewed the records of consecutive patients with metastatic thyroid cancer referred to the Phase I Clinical Trials Program from March 2006 to April 2008. Best response was assessed by Response Evaluation Criteria in Solid Tumors. RESULTS: Fifty-six patients were identified. The median age was 55 yr (range 35-79 yr). Of 49 patients evaluable for response, nine (18.4%) had a partial response, and 16 (32.7%) had stable disease for 6 months or longer. The median progression-free survival was 1.12 yr. With a median follow-up of 15.6 months, the 1-yr survival rate was 81%. In univariate analysis, factors predicting shorter survival were anaplastic histology (P = 0.0002) and albumin levels less than 3.5 g/dl (P = 0.05). Among 26 patients with tumor decreases, none died (median follow-up 1.3 yr), whereas 52% of patients with any tumor increase died by 1 yr (P = 0.0001). The median time to failure in our phase I clinical trials was 11.5 months vs. 4.1 months for the previous treatment (P = 0.04). CONCLUSION: Patients with advanced thyroid cancer treated on phase I clinical trials had high rates of partial response and prolonged stable disease. Time to failure was significantly longer on the first phase I trial compared with the prior conventional treatment. Patients with any tumor decrease had significantly longer survival than those with any tumor increase.

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.^

Uptake And Metabolism Of 5’-AMP In The Erythrocyte Play Key Roles In The 5’-AMP Induced Model Of Deep Hypometabolism

UPTAKE AND METABOLISM OF 5’-AMP IN THE ERYTHROCYTE PLAY KEY ROLES IN THE 5’-AMP INDUCED MODEL OF DEEP HYPOMETABOLISM Publication No. ________ Isadora Susan Daniels, B.A. Supervisory Professor: Cheng Chi Lee, Ph.D. Mechanisms that initiate and control the natural hypometabolic states of mammals are poorly understood. The laboratory developed a model of deep hypometabolism (DH) initiated by uptake of 5’-adenosine monophosphate (5’-AMP) into erythrocytes. Mice enter DH when given a high dose of 5’-AMP and the body cools readily. Influx of 5’-AMP appears to inhibit thermoregulatory control. In a 15°C environment, mice injected with 5’-AMP (0.5 mg/gw) enter a Phase I response in which oxygen consumption (VO2) drops rapidly to 1/3rd of euthermic levels. The Phase I response appears independent of body temperature (Tb). This is followed by gradual body temperature decline that correlates with VO2 decline, called Phase II response. Within 90 minutes, mouse Tb approaches 15°C, and VO2 is 1/10th of normal. Mice can remain several hours in this state, before gradually and safely recovering. The DH state translates to other mammalian species. Our studies show uptake and metabolism of 5’-AMP in erythrocytes causes biochemical changes that initiate DH. Increased AMP shifts the adenylate equilibrium toward ADP formation, consequently decreasing intracellular ATP. In turn, glycolysis slows, indicated by increased glucose and decreased lactate. 2,3-bisphosphoglycerate levels rise, allosterically reducing oxygen affinity for hemoglobin, and deoxyhemoglobin rises. Less oxygen transport to tissues likely triggers the DH model. The major intracellular pathway for AMP catabolism is catalyzed by AMP deaminase (AMPD). Multiple AMPD isozymes are expressed in various tissues, but erythrocytes only have AMPD3. Mice lacking AMPD3 were created to study control of the DH model, specifically in erythrocytes. Telemetric measurements demonstrate lower Tb and difficulty maintaining Tb under moderate metabolic stress. A more dramatic response to lower dose of 5’-AMP suggests AMPD activity in the erythrocyte plays an important role in control of the DH model. Analysis of adenylates in erythrocyte lysate shows 3-fold higher levels of ATP and ADP but similar AMP levels to wild-type. Taken together, results indicate alterations in energy status of erythrocytes can induce a hypometabolic state. AMPD3 control of AMP catabolism is important in controlling the DH model. Genetically reducing AMP catabolism in erythrocytes causes a phenotype of lower Tb and compromised ability to maintain temperature homeostasis.

A dosimetric study of radionuclide therapy for bone marrow ablation

In a phase I clinical trial, six multiple myeloma patients, who were non-responsive to conventional therapy and were scheduled for bone marrow transplantation, received Holmium-166 ($\sp{166}$Ho) labeled to a bone seeking agent, DOTMP (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetramethylene-phosphonic acid), for the purpose of bone marrow ablation. The specific aims of my research within this protocol were to evaluate the toxicity and efficacy of $\sp{166}$Ho DOTMP by quantifying the in vivo pharmacokinetics and radiation dosimetry, and by correlating these results to the biologic response observed. The reproducibility of pharmacokinetics from multiple injections of $\sp{166}$Ho DOTMP administered to these myeloma patients was demonstrated from both blood and whole body retention. The skeletal concentration of $\sp{166}$Ho DOTMP was heterogenous in all six patients: high in the ribs, pelvis, and lumbar vertebrae regions, and relatively low in the femurs, arms, and head.^ A novel technique was developed to calculate the radiation dose to the bone marrow in each skeletal ROI, and was applied to all six $\sp{166}$Ho DOTMP patients. Radiation dose estimates for the bone marrow calculated using the standard MIRD "S" factors were compared with the average values derived from the heterogenous distribution of activity in the skeleton (i.e., the regional technique). The results from the two techniques were significantly different; the average of the dose estimates from the regional technique were typically 30% greater. Furthermore, the regional technique provided a range of radiation doses for the entire marrow volume, while the MIRD "S" factors only provided a single value. Dose volume histogram analysis of data from the regional technique indicated a range of dose estimates that varied by a factor of 10 between the high dose and low dose regions. Finally, the observed clinical response of cells and abnormal proteins measured in bone marrow aspirates and peripheral blood samples were compared with radiation dose estimates for the bone marrow calculated from the standard and regional technique. The results showed the regional technique values correlated more closely to several clinical response parameters. (Abstract shortened by UMI.) ^

Role of activation of the protein tyrosine kinase, Src, in colorectal cancer chemoresistance

Advances in therapy for colorectal cancer have been hampered by development of resistance to chemotherapy. The Src family of protein tyrosine kinases has been associated with colorectal cancer development and progression. Activation of the prototypic member of the family, Src, occurs in advanced colorectal cancer and is associated with a worse outcome. This work tests the hypotheses that Src activation contributes to chemoresistance in some colon tumors and that this resistance can be overcome by use of Src inhibitors. The aims of the proposal were to (1) determine if constitutive Src activation is sufficient to induce oxaliplatin resistance; (2) evaluate the role of reactive oxygen species (ROS) in the activation of Src after oxaliplatin treatment; (3) determine the frequency of Src activation in liver metastases after oxaliplatin treatment; and (4) evaluate the safety, preliminary efficacy, and pharmacodynamics of the combination of dasatinib with oxaliplatin-based therapy in patients with metastatic colorectal cancer. ^ Using a panel of colon cancer cell lines and murine models, I demonstrate that administration of oxaliplatin, a commonly utilized chemotherapy for colorectal cancer, results in an increased activation of Src. The activation occurs acutely in some, but not all, colorectal carcinoma cell lines. Cell lines selected for oxaliplatin resistance are further increased in Src activity. Treatment of cell lines with dasatinib, a non-selective pharmacologic inhibitor of the Src family kinases synergistically killed some, but not all cell lines. Cell lines with the highest acute activation of Src after oxaliplatin administration were the most sensitive to the combination therapy. Previous work demonstrated that siRNA to Src increased sensitivity to oxaliplatin, suggesting that the effects of dasatinib are primarily due to its ability to inhibit Src in these cell lines. ^ To examine the mechanism underlying these results, I examined the effects of reactive oxygen species (ROS), as previous studies have demonstrated that platinum chemotherapeutics result in intracellular oxidative stress. I demonstrated that oxaliplatin-induced reactive oxygen species were higher in the cell lines with Src activation, relative to those in which Src was not activated. This oxaliplatin-induced Src activation was blocked by the administration of anti-oxidants, thereby demonstrating that synergistic killing between dasatinib and oxaliplatin was associated with the ability of the latter to generate ROS. ^ In a murine model of colorectal cancer metastasis to the liver, the combination of dasatinib and oxaliplatin was more effective in reducing tumor volume than either agent alone. However, when oxaliplatin resistant cell lines were treated with a combination of oxaliplatin and AZD0530, an inhibitor in the clinic with increased specificity for Src, no additional benefit was seen, although Src was activated by oxaliplatin and Src substrates were inhibited. The indolent growth of oxaliplatin-resistant cells, unlike the growth of oxaliplatin resistant tumors in patients, precludes definitive interpretation of these results. ^ To further explore Src activation in patients with oxaliplatin exposure and resistance, an immunohistochemistry analysis of tumor tissue from resected liver metastases of colorectal cancer was performed. Utilizing a tissue microarray, staining for phosphorylated Src and FAK demonstrated strong staining of tumor relative to stromal and normal liver. In patients recently exposed to oxaliplatin, there was increased FAK activation, supporting the clinical relevance of the prior preclinical studies. ^ To pursue the potential clinical benefit of the combination of Src inhibition with oxaliplatin, a phase IB clinical trial was completed. Thirty patients with refractory metastatic colorectal cancer were treated with a combination of 5-FU, oxaliplatin, an epidermal-growth factor receptor monoclonal antibody, and dasatinib. The recommended phase II dose of dasatinib was established, and toxicities were quantified. Pharmacodynamic studies demonstrated increased phosphorylation of the Src substrate paxillin after dasatinib therapy. Tumor biopsies were obtained and Src expression levels were quantitated. Clinical benefit was seen with the combination, including a response rate of 20% and disease control rate of 56%, prompting a larger clinical study. ^ In summary, although Src is constitutively activated in metastatic colorectal cancer, administration of oxaliplatin chemotherapy can further increase its activity, through a reactive oxygen species dependent manner. Inhibition of Src in combination with oxaliplatin provides additional benefit in vitro, in preclinical animal models, and in the clinic. Further study of Src inhibition in the clinic and identification of predictive biomarkers of response will be required to further advance this promising therapeutic target. ^

The role of post-translation regulation of Twist expression in the tumor progression of squamous cell carcinoma of head and neck

Squamous cell carcinoma of head and neck (SCCHN) is the tenth most common cancer in the world. Unfortunately, the survival of patients with SCCHN has not improved in the last 40 years. Therefore new targets for therapy are needed, and to this end we are studying signaling pathways activated by IL-6 which we have found stimulates cell migration and soft agar growth in SCCHN. Our data show that IL-6 increases TWIST expression in a transcription-independent mechanism in many SCCHN cell lines. Further investigation reveals TWIST can be phosphorylated upon IL-6 treatment. By computation prediction (http://scansite.mit.edu/motifscan_seq.phtml ), we found that TWIST has a putative phosphorylation site for casein kinase 2 (CK2) suggesting that this kinase could serve as a link between IL-6 stimulation and Twist stability. To test this hypothesis, we used a CK2 inhibitor and shRNA to CK2 and found that these interventions inhibited IL-6 stimulation of TWIST stability. In addition, mutation of the putative CK2 phosphorylation site (S18/S20A) in TWIST decreased the amount of phospho-ATP incorporated by TWIST in an in vitro kinase assay, and altered TWIST stability. In Boyd chamber migration assay and wound-healing assay, the CK2 inhibitor, DMAT, was found to decrease the motility of IL-6 stimulated SCCHN cells and over expression of either a wild-type or the hyperphosphorylated mimicking mutant S18/20D –Twist rather than the hypo-phosphorylated mimicking mutant S18/20A-Twist can promote SCCHN cell motility.To our knowledge, this is the first report to identify the importance of IL-6 stimulated CK2 phosphorylation of TWIST in SCCHN. As CK2 inhibitors are currently under phase I clinical trials, our findings indicate that CK2 may be a viable therapeutic target in SCCHN. Therefore, further pre-clinical studies of this inhibitor are underway.

A quality of life subscale for terminally ill cancer patients

A subscale was developed to assess the quality of life of cancer patients with a life expectancy of six months or less. Phase I of this study identified the major concerns of 74 terminally ill cancer patients (19 with breast cancer, 19 with lung cancer, 18 with colorectal cancer, 9 with renal cell cancer, 9 with prostate cancer), 39 family caregivers, and 20 health care professionals. Patients interviewed were being treated at the University of Texas M. D. Anderson Cancer Center or at the Hospice at the Texas Medical Center in Houston. In Phase II, 120 patients (30 with breast cancer, 30 with lung cancer, 30 with colorectal cancer, 15 with prostate cancer, and 15 with renal cell cancer) rated the importance of these concerns for quality of life. Items retained for the subscale were rated as "extremely important" or "very important" by at least 60% of the sample and were reported as being applicable by at least two-thirds of the sample. The 61 concerns that were identified were formatted as a questionnaire for Phase III. In Phase III, 356 patients (89 with breast cancer, 88 with lung cancer, 88 with colorectal cancer, 44 with prostate cancer, and 47 with renal cell cancer) were interviewed to determine the subscale's reliability and sensitivity to change in clinical status. Both factor analysis and item response theory supported the inclusion of the same 35 items for the subscale. Internal consistency reliability was moderate to high for the subscale's domains: spiritual (0.87), existential (0.76), medical care (0.68), symptoms (0.67), social/family (0.66), and emotional (0.61). Test-retest correlation coefficients also were high for the domains: social/family (0.86), emotional (0.83), medical care (0.83), spiritual (0.75), existential (0.75), and symptoms (0.81).^ In addition, concurrent validity was supported by the high correlation between the subscale's symptom domain and symptom items from the European Organization for Research and Treatment of Cancer (EORTC) scale (r = 0.74). Patients' functional status was assessed with the Eastern Cooperative Oncology Group (ECOG) Performance status rating. When ECOG categories were compared to subscale domains, patients who scored lower in functional status had lower scores in the spiritual, existential, social/family, and emotional domains. Patients who scored lower in physical well-being had higher scores in the symptom domain. Patient scores in the medical care domain were similar for each ECOG category. The results of this study support the subscale's use in assessing quality of life and the outcomes of palliative treatment for cancer patients in their last six months of life. ^