The imaging and dosimetric capabilities of a CT/MR-suitable, anatomically adaptive, shielded intracavitary brachytherapy applicator for the treatment of cervical cancer

Introduction. Investigations into the shortcomings of current intracavitary brachytherapy (ICBT) technology has lead us to design an Anatomically Adaptive Applicator (A3). The goal of this work was to design and characterize the imaging and dosimetric capabilities of this device. The A3 design incorporates a single shield that can both rotate and translate within the colpostat. We hypothesized that this feature, coupled with specific A3 component construction materials and imaging techniques, would facilitate artifact-free CT and MR image acquisition. In addition, by shaping the delivered dose distribution via the A3 movable shield, dose delivered to the rectum will be less compared to equivalent treatments utilizing current state-of-the-art ICBT applicators. ^ Method and materials. A method was developed to facilitate an artifact-free CT imaging protocol that used a "step-and-shoot" technique: pausing the scanner midway through the scan and moving the A 3 shield out of the path of the beam. The A3 CT imaging capabilities were demonstrated acquiring images of a phantom that positioned the A3 and FW applicators in a clinically-applicable geometry. Artifact-free MRI imaging was achieved by utilizing MRI-compatible ovoid components and pulse-sequences that minimize susceptibility artifacts. Artifacts were qualitatively compared, in a clinical setup. For the dosimetric study, Monte-Carlo (MC) models of the A3 and FW (shielded and unshielded) applicators were validated. These models were incorporated into a MC model of one cervical cancer patient ICBT insertion, using 192Ir (mHDR v2 source). The A3 shield's rotation and translation was adjusted for each dwell position to minimize dose to the rectum. Superposition of dose to rectum for all A3 dwell sources (4 per ovoid) was applied to obtain a comparison of equivalent FW treatments. Rectal dose-volume histograms (absolute and HDR/PDR biologically effective dose (BED)) and BED to 2 cc (BED2cc ) were determined for all applicators and compared. ^ Results. Using a "step-and-shoot" CT scanning method and MR compliant materials and optimized pulse-sequences, images of the A 3 were nearly artifact-free for both modalities. The A3 reduced BED2cc by 18.5% and 7.2% for a PDR treatment and 22.4% and 8.7% for a HDR treatment compared to treatments delivered using an uFW and sFW applicator, respectively. ^ Conclusions. The novel design of the A3 facilitated nearly artifact-free image quality for both CT and MR clinical imaging protocols. The design also facilitated a reduction in BED to the rectum compared to equivalent ICBT treatments delivered using current, state-of-the-art applicators. ^

Performance evaluation in Bayesian adaptive randomization

Bayesian adaptive randomization (BAR) is an attractive approach to allocate more patients to the putatively superior arm based on the interim data while maintains good statistical properties attributed to randomization. Under this approach, patients are adaptively assigned to a treatment group based on the probability that the treatment is better. The basic randomization scheme can be modified by introducing a tuning parameter, replacing the posterior estimated response probability, setting a boundary to randomization probabilities. Under randomization settings comprised of the above modifications, operating characteristics, including type I error, power, sample size, imbalance of sample size, interim success rate, and overall success rate, were evaluated through simulation. All randomization settings have low and comparable type I errors. Increasing tuning parameter decreases power, but increases imbalance of sample size and interim success rate. Compared with settings using the posterior probability, settings using the estimated response rates have higher power and overall success rate, but less imbalance of sample size and lower interim success rate. Bounded settings have higher power but less imbalance of sample size than unbounded settings. All settings have better performance in the Bayesian design than in the frequentist design. This simulation study provided practical guidance on the choice of how to implement the adaptive design. ^

Apply response adaptive randomization to group sequential with early stopping

Group sequential methods and response adaptive randomization (RAR) procedures have been applied in clinical trials due to economical and ethical considerations. Group sequential methods are able to reduce the average sample size by inducing early stopping, but patients are equally allocated with half of chance to inferior arm. RAR procedures incline to allocate more patients to better arm; however it requires more sample size to obtain a certain power. This study intended to combine these two procedures. We applied the Bayesian decision theory approach to define our group sequential stopping rules and evaluated the operating characteristics under RAR setting. The results showed that Bayesian decision theory method was able to preserve the type I error rate as well as achieve a favorable power; further by comparing with the error spending function method, we concluded that Bayesian decision theory approach was more effective on reducing average sample size.^

The role of plasmacytoid dendritic cells in the regulation of adaptive immunity

Plasmacytoid dendritic cells (pDCs) selectively express TLR7 which allows them to respond to RNA viruses and TLR9 which allows them to respond to DNA viruses and CpG oligonucleotides. Upon exposure to virus pDCs produce vast amounts of type I interferon (IFN) directly inhibiting viral replication and contributing to the activation of other immune cells. The ability of pDCs to promote B and T cell differentiation through type I IFN has been well documented although the role of additional factors including tumor necrosis factor (TNF) family members has not been thoroughly addressed. Here the expression of selected TNF family members in pDCs was examined and the role of TNF receptor-ligand interactions in the regulation of B and T lymphocyte growth and differentiation by pDCs was investigated. Upon stimulation with CpG-B, pDCs exhibit strong and stable expression of CD70, a TNF family ligand that binds to its receptor CD27 on memory B cells and promotes plasma cell differentiation and Ig secretion. Using an in vitro pDC/B cell co-culture system, it was determined that CpG-B-stimulated pDCs induce the proliferation of CD40L-activated human peripheral B cells and Ig secretion. This occurs independently of IFN and residual CpG, and requires physical contact between pDCs and B cells. CpG-stimulated pDCs induce the proliferation of both naive and memory B cells although Ig secretion is restricted to the memory subset. Blocking the interaction of CD70 with CD27 using an antagonist anti-CD70 antibody reduces the induction of B cell proliferation and IgG secretion by CpG-B-stimulated pDCs. Published studies have also indicated an important role for CD70 in promoting the expansion of CD4+ and CD8+ T cells and the development of effector function. CpG-B-stimulated pDCs induce naïve CD4+ T cell proliferation and production of multiple cytokines including IFN-γ, TNF-α, IL-10, IL-4, IL-5 and IL-13. Blocking the function of CD70 with an antagonist anti-CD70 antibody significantly reduced the induction of naïve CD4+ T cell proliferation by CpG-B-stimulated pDCs and the production of IL-4 and IL-13. Collectively these data indicate an important role for CD70 in the regulation of B and T lymphocyte growth and differentiation by pDCs. ^

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.^

Cellular Uptake of Neutrohpil Elastase Links Inflammation to Adaptive Immunity

Many tumors arise from sites of inflammation providing evidence that innate immunity is a critical component in the development and progression of cancer. Neutrophils are primary mediators of the innate immune response. Upon activation, an important function of neutrophils is release of an assortment of proteins from their granules including the serine protease neutrophil elastase (NE). The effect of NE on cancer has been attributed primarily to its ability to degrade the extracellular matrix thereby promoting invasion and metastasis. Recently, it was shown that NE could be taken up by lung cancer cells leading to degradation of insulin receptor substrate-1 thereby promoting hyperactivity of the phosphatidylinositol-3 kinase (PI3K) pathway and tumor cell proliferation. To our knowledge, nobody has investigated uptake of NE by other tumor types. In addition, NE has broad substrate specificity suggesting that uptake of NE by tumor cells could impact processes regulating tumorigenensis other than activation of the PI3K pathway. Neutrophil elastase has been identified in breast cancer specimens where high levels of NE have prognostic significance. These studies have assessed NE levels in whole tumor lysates. Because the major source of NE is from activated neutrophils, we hypothesized that breast cancer cells do not have endogenous NE but may take up NE released by tumor associated neutrophils in the tumor microenvironment and that this could provide a link between the innate immune response to tumors and specific adaptive immune responses. In this thesis, we show that breast cancer cells lack endogenous NE expression and that they are able to take up NE resulting in increased generation of low molecular weight cyclin E (CCNE) and enhanced susceptibility to lysis by CCNE-specific cytotoxic T lymphocytes. We also show that after taking up NE and proteinase 3 (PR3), a second primary granule protease with significant homology to NE, breast cancer cells cross-present the NE- and PR3-derived peptide PR1 rendering them susceptible to PR1-targeted therapies. Taken together, our data support a role for NE uptake in modulating adaptive immune responses against breast cancer.

Adaptive clinical trial design for targeted therapy development

The development of targeted therapy involve many challenges. Our study will address some of the key issues involved in biomarker identification and clinical trial design. In our study, we propose two biomarker selection methods, and then apply them in two different clinical trial designs for targeted therapy development. In particular, we propose a Bayesian two-step lasso procedure for biomarker selection in the proportional hazards model in Chapter 2. In the first step of this strategy, we use the Bayesian group lasso to identify the important marker groups, wherein each group contains the main effect of a single marker and its interactions with treatments. In the second step, we zoom in to select each individual marker and the interactions between markers and treatments in order to identify prognostic or predictive markers using the Bayesian adaptive lasso. In Chapter 3, we propose a Bayesian two-stage adaptive design for targeted therapy development while implementing the variable selection method given in Chapter 2. In Chapter 4, we proposed an alternate frequentist adaptive randomization strategy for situations where a large number of biomarkers need to be incorporated in the study design. We also propose a new adaptive randomization rule, which takes into account the variations associated with the point estimates of survival times. In all of our designs, we seek to identify the key markers that are either prognostic or predictive with respect to treatment. We are going to use extensive simulation to evaluate the operating characteristics of our methods.^

Phenotypic evolution in a fossil gastropod species lineage: evidence for adaptive radiation?

Detecting speciation in the fossil record is a particularly challenging matter. Palaeontologists are usually confronted with poor preservation and limited knowledge on the palaeoenvironment. Even in the contrary case of adequate preservation and information, the linkage of pattern to process is often obscured by insufficient temporal resolution. Consequently, reliable documentations of speciation in fossils with discussions on underlying mechanisms are rare. Here we present a well-resolved pattern of morphological evolution in a fossil species lineage of the gastropod Melanopsis in the long-lived Lake Pannon. These developments are related to environmental changes, documented by isotope and stratigraphical data. After a long period of stasis, the ancestral species experiences a phenotypic change expressed as shift and expansion of the morphospace. The appearance of several new phenotypes along with changes in the environment is interpreted as adaptive radiation. Lake-level high stands affect distribution and availability of habitats and, as a result of varied functional demands on shell geometry, the distribution of phenotypes. The on-going divergence of the morphospace into two branches argues for increasing reproductive isolation, consistent with the model of ecological speciation. In the latest phase, however, progressively unstable conditions restrict availability of niches, allowing survival of one branch only.

Adaptive variability to low-pH river discharges in Acartia tonsa and stress responses to high PCO2 conditions

Environmental transitions leading to spatial physical-chemical gradients are of ecological and evolutionary interest because they are able to induce variations in phenotypic plasticity. Thus, the adaptive variability to low-pH river discharges may drive divergent stress responses [ingestion rates (IR) and expression of stress-related genes such as Heat shock protein 70 (Hsp70) and Ferritin] in the neritic copepod Acartia tonsa facing changes in the marine chemistry associated to ocean acidification (OA). These responses were tested in copepod populations inhabiting two environments with contrasting carbonate system parameters (an estuarine versus coastal area) in the Southern Pacific Ocean, and assessing an in situ and 96-h experimental incubation under conditions of high pressure of CO2 (PCO2 1200 ppm). Adaptive variability was a determining factor in driving variability of copepods' responses. Thus, the food-rich but colder and corrosive estuary induced a traits trade-off expressed as depressed IR under in situ conditions. However, this experience allowed these copepods to tolerate further exposure to high PCO2 levels better, as their IRs were on average 43% higher than those of the coastal individuals. Indeed, expression of both the Hsp70 and Ferritin genes in coastal copepods was significantly higher after acclimation to high PCO2 conditions. Along with other recent evidence, our findings confirm that adaptation to local fluctuations in seawater pH seems to play a significant role in the response of planktonic populations to OA-associated conditions. Facing the environmental threat represented by the inter-play between multiple drivers of climate change, this biological feature should be examined in detail as a potential tool for risk mitigation policies in coastal management arrangements.

Long-term dynamics of adaptive evolution in a globally important coccolithophore to ocean acidification

Recent evolution experiments have revealed that marine phytoplankton may adapt to global change, for example to ocean warming or acidification. Long-term adaptation to novel environments is a dynamic process and phenotypic change can take place thousands of generations after exposure to novel conditions. Using the longest evolution experiment performed in any marine species to date (4 yrs, = 2100 generations), we show that in the coccolithophore Emiliania huxleyi, long-term adaptation to ocean acidification is complex and initial phenotypic responses may revert for important traits. While fitness increased continuously, calcification was restored within the first 500 generations but later reduced in response to selection, enhancing physiological declines of calcification in response to ocean acidification. Interestingly, calcification was not constitutively reduced but revealed rates similar to control treatments when transferred back to present-day CO2 conditions. Growth rate increased with time in controls and adaptation treatments, although the effect size of adaptation assessed through reciprocal assay experiments varied. Several trait changes were associated with selection for higher cell division rates under laboratory conditions, such as reduced cell size and lower particulate organic carbon content per cell. Our results show that phytoplankton may evolve phenotypic plasticity that can affect biogeochemically important traits, such as calcification, in an unforeseen way under future ocean conditions.