Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction

Radiomics is the high-throughput extraction and analysis of quantitative image features. For non-small cell lung cancer (NSCLC) patients, radiomics can be applied to standard of care computed tomography (CT) images to improve tumor diagnosis, staging, and response assessment. The first objective of this work was to show that CT image features extracted from pre-treatment NSCLC tumors could be used to predict tumor shrinkage in response to therapy. This is important since tumor shrinkage is an important cancer treatment endpoint that is correlated with probability of disease progression and overall survival. Accurate prediction of tumor shrinkage could also lead to individually customized treatment plans. To accomplish this objective, 64 stage NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. Quantitative image features were extracted and principal component regression with simulated annealing subset selection was used to predict shrinkage. Cross validation and permutation tests were used to validate the results. The optimal model gave a strong correlation between the observed and predicted shrinkages with . The second objective of this work was to identify sets of NSCLC CT image features that are reproducible, non-redundant, and informative across multiple machines. Feature sets with these qualities are needed for NSCLC radiomics models to be robust to machine variation and spurious correlation. To accomplish this objective, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. For each machine, quantitative image features with concordance correlation coefficient values greater than 0.90 were considered reproducible. Multi-machine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering. The findings showed that image feature reproducibility and redundancy depended on both the CT machine and the CT image type (average cine 4D-CT imaging vs. end-exhale cine 4D-CT imaging vs. helical inspiratory breath-hold 3D CT). For each image type, a set of cross-machine reproducible, non-redundant, and informative image features was identified. Compared to end-exhale 4D-CT and breath-hold 3D-CT, average 4D-CT derived image features showed superior multi-machine reproducibility and are the best candidates for clinical correlation.

Comparison of Tumor Shrinkage and Cumulative Dose Distribution for Lung Cancers

Background: The physical characteristic of protons is that they deliver most of their radiation dose to the target volume and deliver no dose to the normal tissue distal to the tumor. Previously, numerous studies have shown unique advantages of proton therapy over intensity-modulated radiation therapy (IMRT) in conforming dose to the tumor and sparing dose to the surrounding normal tissues and the critical structures in many clinical sites. However, proton therapy is known to be more sensitive to treatment uncertainties such as inter- and intra-fractional variations in patient anatomy. To date, no study has clearly demonstrated the effectiveness of proton therapy compared with the conventional IMRT under the consideration of both respiratory motion and tumor shrinkage in non-small cell lung cancer (NSCLC) patients. Purpose: This thesis investigated two questions for establishing a clinically relevant comparison of the two different modalities (IMRT and proton therapy). The first question was whether or not there are any differences in tumor shrinkage between patients randomized to IMRT versus passively scattered proton therapy (PSPT). Tumor shrinkage is considered a standard measure of radiation therapy response that has been widely used to gauge a short-term progression of radiation therapy. The second question was whether or not there are any differences between the planned dose and 5D dose under the influence of inter- and intra-fractional variations in the patient anatomy for both modalities. Methods: A total of 45 patients (25 IMRT patients and 20 PSPT patients) were used to quantify the tumor shrinkage in terms of the change of the primary gross tumor volume (GTVp). All patients were randomized to receive either IMRT or PSPT for NSCLC. Treatment planning goals were identical for both groups. All patients received 5 to 8 weekly repeated 4-dimensional computed tomography (4DCT) scans during the course of radiation treatments. The original GTVp contours were propagated to T50 of weekly 4DCT images using deformable image registration and their absolute volumes were measured. Statistical analysis was performed to compare the distribution of tumor shrinkage between the two population groups. In order to investigate the difference between the planned dose and the 5D dose with consideration of both breathing motion and anatomical change, we re-calculated new dose distributions at every phase of the breathing cycle for all available weekly 4DCT data sets which resulted 50 to 80 individual dose calculations for each of the 7 patients presented in this thesis. The newly calculated dose distributions were then deformed and accumulated to T50 of the planning 4DCT for comparison with the planned dose distribution. Results: At the end of the treatment, both IMRT and PSPT groups showed mean tumor volume reductions of 23.6% ( 19.2%) and 20.9% ( 17.0 %) respectively. Moreover, the mean difference in tumor shrinkage between two groups is 3% along with the corresponding 95% confidence interval, [-8%, 14%]. The rate of tumor shrinkage was highly correlated with the initial tumor volume size. For the planning dose and 5D dose comparison study, all 7 patients showed a mean difference of 1 % in terms of target coverage for both IMRT and PSPT treatment plans. Conclusions: The results of the tumor shrinkage investigation showed no statistically significant difference in tumor shrinkage between the IMRT and PSPT patients, and the tumor shrinkage between the two modalities is similar based on the 95% confidence interval. From the pilot study of comparing the planned dose with the 5D dose, we found the difference to be only 1%. Overall impression of the two modalities in terms of treatment response as measured by the tumor shrinkage and 5D dose under the influence of anatomical change that were designed under the same protocol (i.e. randomized trial) showed similar result.

Use of Echogenic Immunoliposomes for Delivery of both Drug and Stem Cells for Inhibition of Atheroma Progression

Use of Echogenic Immunoliposomes for Delivery of both Drug and Stem Cells for Inhibition of Atheroma Progression By Ali K. Naji B.S. Advisor: Dr. Melvin E. Klegerman PhD Background and significance: Echogenic liposomes can be used as drug and cell delivery vehicles that reduce atheroma progression. Vascular endothelial growth factor (VEGF) is a signal protein that induces vasculogenesis and angiogenesis. VEGF functionally induces migration and proliferation of endothelial cells and increases intracellular vascular permeability. VEGF activates angiogenic transduction factors through VEGF tyrosine kinase domains in high-affinity receptors of endothelial cells. Bevacizumab is a humanized monoclonal antibody specific for VEGF-A which was developed as an anti-tumor agent. Often, anti-VEGF agents result in regression of existing microvessels, inhibiting tumor growth and possibly causing tumor shrinkage with time. During atheroma progression neovasculation in the arterial adventitia is mediated by VEGF. Therefore, bevacizumab may be effective in inhibiting atheroma progression. Stem cells show an ability to inhibit atheroma progression. We have previously demonstrated that monocyte derived CD-34+ stem cells that can be delivered to atheroma by bifunctional-ELIP ( BF-ELIP) targeted to Intercellular Adhesion Molecule-1 (ICAM-1) and CD-34. Adhesion molecules such as ICAM-1 and vascular cell adhesion molecule-1 (VCAM-1) are expressed by endothelial cells under inflammatory conditions. Ultrasound enhanced liposomal targeting provides a method for stem cell delivery into atheroma and encapsulated drug release. This project is designed to examine the ability of echogenic liposomes to deliver bevacizumab and stem cells to inhibit atheroma progression and neovasculation with and without ultrasound in vitro and optimize the ultrasound parameters for delivery of bevacizumab and stem cells to atheroma. V Hypotheses: Previous studies showed that endothelial cell VEGF expression may relate to atherosclerosis progression and atheroma formation in the cardiovascular system. Bevacizumab-loaded ELIP will inhibit endothelial cell VEGF expression in vitro. Bevacizumab activity can be enhanced by pulsed Doppler ultrasound treatment of BEV-ELIP. I will also test the hypothesis that the transwell culture system can serve as an in vitro model for study of US-enhanced targeted delivery of stem cells to atheroma. Monocyte preparations will serve as a source of CD34+ stem cells. Specific Aims: Induce VEGF expression using PKA and PKC activation factors to endothelial cell cultures and use western blot and ELISA techniques to detect the expressed VEGF.  Characterize the relationship between endothelial cell proliferation and VEGF expression to develop a specific EC culture based system to demonstrate BEV-ELIP activity as an anti-VEGF agent. Design a cell-based assay for in vitro assessment of ultrasound-enhanced bevacizumab release from echogenic liposomes.  Demonstrate ultrasound delivery enhancement of stem cells by applying different types of liposomes on transwell EC culture using fluorescently labeled monocytes and detect the effect on migration and attachment rate of these echogenic liposomes with and without ultrasound in vitro.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

ATXA -dependent gene expression in Bacillus anthracis

The Bacillus anthracis toxin genes, cya, lef , and pag, can be viewed as a regulon, in which transcription of all three genes is activated in trans by the same regulatory gene, atxA, in response to the same signal, CO2. I determined that several phenotypes are associated with the atxA gene. In addition to being toxin-deficient, an atxA -null mutant grows poorly on minimal media and sporulates early compared to the parent strain. Furthermore, an atxA-null mutant has an altered 2-D gel protein profile. I used a genetic approach to find additional atxA-regulated genes. Random transcriptional lacZ fusions were generated in B. anthracis using transposon Tn 917-LTV3. Transposon-insertion libraries were screened for mutants expressing increased β-galactosidase activity in 5% CO2. Introduction of an atxA-null mutation in these mutants revealed that 79% of the CO2-regulated fusions were also atxA-dependent. DNA sequence analysis of transposon insertion sites in mutants carrying CO 2/atxA-regulated fusions revealed ten mutants harboring transposon insertions in loci distinct from the toxin genes. The majority of the tcr (toxin co-regulated) loci mapped within the pXO1 pathogenicity island. These results indicate a clear association of atxA with CO2-enhanced gene expression in B. anthracis and provide evidence that atxA regulates genes other than the structural genes for the anthrax toxin proteins. ^ Characterization of one tcr locus revealed a new regulatory gene, pagR. The pagR gene (300 nt) is located downstream of pag. pagR is cotranscribed with pag and is responsible for autogenous control of the operon. pagR also represses expression of cya and lef. Repression of toxin gene expression by pagR may be mediated by atxA. The steady state level of atxA mRNA is increased in a pagR mutant. Recombinant PagR protein purified from Escherichia coli did not specifically bind the promoter regions of pagA or atxA. An unidentified factor in B. anthracis crude extracts, however, was able to bind the atxA promoter in the absence of PagR or AtxA. These investigations increase our knowledge of virulence regulation in B. anthracis and ultimately will lead to a better understanding of anthrax disease. ^