Browsing by Subject "Oncology"

Purpose: To develop an automatic collimator setting optimization algorithm to improve dosimetric quality of pancreas Volumetric Modulated Arc Therapy (VMAT) plans for Stereotactic Body Radiation Therapy (SBRT).

Methods: Fifty-five pancreas SBRT cases were retrospectively studied. Different from the conventional practice of initializing collimator settings manually, the proposed algorithm simultaneously optimizes the collimator angles and jaw positions which are customized to the patient geometry. This algorithm includes two key steps: an iterative optimization algorithm via simulated annealing that generates a set of collimator settings candidates, and a scoring system that choose the final collimator settings based on organs-at-risk (OARs) sparing criteria and dose prescription. The scoring system penalizes 3 factors: 1) jaw opening ratio on Y direction to X direction; 2) unmodulated MLC area within the jaw aperture in a dynamic MLC sequence; 3) OAR shielding capability by MLC with MLC aperture control constraints. For validation, the other 16 pancreas SBRT cases were analyzed. Two dual-arc plans were generated for each validation case, an optimized plan (Planopt) and a conventional plan (Planconv). Each plan was generated by a same set of auxiliary planning structures and dose-volume-histogram (DVH) constraints in inverse optimization. Dosimetric results were analyzed and compared. All results were tested by Wilcoxon signed-rank tests.

Results: Both plan groups had no statistical differences in target dose coverage V95% (p=0.84) and Root Conformity Index (p=0.30). Mean doses of OARs were improved or comparable. In comparison with Planconv, Planopt reduced maximum dose (D0.03cc) to stomach (-49.5cGy, p=0.03), duodenum (-63.5cGy, p<0.01), and bowel (-62.5cGy, p=0.01). Planopt also showed lower modulation complexity score (p=0.02), which implies its higher modulation complexity of the dynamic MLC sequence.

Conclusions: The proposed collimator settings optimization algorithm successfully improved dosimetric performance for dual-arc VMAT plans in pancreas SBRT. The proposed algorithm was demonstrated with great clinical feasibility and readiness.

Controlled breathing techniques are widely used to help people manage pain, and there is growing interest in using these approaches during painful outpatient medical procedures. The outpatient MRI-guided breast biopsy is one setting where patients may particularly benefit from breathing interventions for pain. To date, however, no studies have examined interventions for pain reduction in this setting. This randomized controlled pilot study assessed the feasibility, acceptability, and efficacy of a novel audio-recorded controlled breathing intervention for reducing breast and body pain in women undergoing MRI-guided breast biopsy. Fifty-eight women undergoing MRI-guided breast biopsy were randomized to a 1) controlled breathing intervention or 2) usual care condition. Assessments of pain, anxiety, distraction from pain, relaxation, blood pressure, heart rate, pain catastrophizing, and self-efficacy for managing pain and anxiety were administered. Participants were assessed at baseline, during biopsy, immediately post-biopsy, and 24 hours post-biopsy. Results demonstrated that the intervention was feasible and acceptable. However, when compared to usual care, controlled breathing did not significantly reduce pain, increase distraction from pain or relaxation during biopsy, decrease physiological reactivity, reduce pain catastrophizing, or increase self-efficacy for pain and anxiety from pre- to post-biopsy. These findings could be used to revise the controlled breathing intervention.

Purpose: The purpose of this work is to characterize three novel formulations of a radiochromic material Presage and identify optimal imaging procedures for accurate 3D dosimetry. The dosimetric qualities of interest were studied for each formulation of Presage dosimeter in the context of accurate 3D dosimetry. The formulation of Presage showing the most promise is compared to a clinical 3D quality assurance device to investigate the accuracy of a complex state-of-the-art brain IMRT treatment.

Methods and Materials: Three novel formulations of Presage were studied for their temporal stability, sensitivity, linearity of dose response, and feasibility of absolute dose calibration in large volume dosimeters (1 kg) with small volume cuvettes (4g). Large cylindrical dosimeters with 11 cm diameter and 10 cm height were irradiated with 5 2x2 cm fields on the upper flat surface with 3 distinct dose levels (3, 6 and 9.5 Gy, representing low, medium and high). This irradiation pattern is used to determine the dosimetric characteristics mentioned above and was chosen because of its repeatability and it lends to simple measurements of linearity and sensitivity. Measurements were taken at various time points from 0 hours to 24 hours post-irradiation using the high resolution (6.45 m pixels) Duke Medium-Sized Optical-CT Scanner (DMOS) and reconstructed with a Matlab-based reconstruction GUI created in-house. Analysis of the pertinent dosimetric characteristics was performed in the GUI. A comprehensive end-to-end QA test was performed on the optimal formulation using optimal scan timing determined from the formulation studies described above. A 5-field IMRT plan was created for head treatment. The plan was delivered both to a head phantom containing a Presage insert, and to the Delta4 QA device. Comparison of both delivered distributions together with the Eclipse predicted dose distribution enabled investigation of the accuracy of the delivery, and the consistency of independent measurement devices.

Results: The DEA-1 formulation showed up to 10% variation from 0-2 hours post-irradiation, but showed excellent temporal stability (<2% variation) between 3-7 hours post irradiation, and maintained good stability until 24 hours post-irradiation (up to 3% variation). The DEA-2 also showed up to 10% variation from 0-2 hours post-irradiation. The DEA-2 formulation then showed good stability (up to 2.1% variation) from 3-7 hours, but optical density values dropped by up to 11% after 24 hours. The DX formulation did not maintain stability of optical density for any significant time with values decreasing by ~20% by the 24-hour time point and optical density decreasing at different rates for different dose levels. Linearity of dose response was good for all formulations with an R2 value > 0.99. Gamma analysis with criteria of 3%/2mm was performed on two irradiations of the 5-field pattern on DEA-1 formulation. Voxel passing rates were 96.68% and 97.96%. Comparison of the DEA-1 formulation large dosimeter was done with small volume cuvettes of the same formulation and batch. Sensitivity of the large dosimeter was less than half the sensitivity of the cuvettes. For clinical 3D QA comparison, the DEA-1 formulation was used because it had optimal performance showed the most promise for accurate 3D dosimetry. Line dose profiles showed that Presage compared very well with the Eclipse calculation and had a much better 3D gamma passing rate for 3%/3mm criteria than the Delta4 (>99% vs 75%).

Conclusions: The DEA-1 formulation shows the most promise because of its temporal stability and linearity of dose response. The optimal imaging window for this formulation was determined to be 3-24 hours post-irradiation. The DEA-2 and DX formulation also showed potential for accurate dosimetry. The optimal imaging window for the DEA-2 formulation was determined to be 2-6 hours post-irradiation. The optimal scan time for the DX formulation was determined to be immediately post-irradiation. The amount of accuracy loss depending on the scan time is dependent on the formulation and when the dosimeter is scanned. Line dose profiles and gamma analysis results from the comparison of Presage and Eclipse calculation provide strong validation of the accuracy of the IMRT treatment delivery. Comparison of Presage to the Delta4 show the Delta4 to be somewhat lacking in its ability to calculate 3D dose in the phantom/Presage geometry.

Purpose: Current Monte Carlo simulations modeling space radiation exposure typically use simplistic human phantoms with low anatomical detail and minimal variability in physical characteristics. This thesis describes the development of a GEANT4-based simulation framework (EVEREST – Evaluation of Variable-Environment Radiation Exposure during Space Travel) that incorporates highly realistic and diverse 4D extended cardiac-torso (XCAT) digital phantoms, combined with advanced NASA models of planetary atmospheres, spaceflight trajectories, and space radiation spectra, to evaluate radiation exposure in interplanetary missions and on planetary habitats.

Methods: Galactic cosmic radiation spectra as a function of time and radial distance from the Sun were modeled using the Badhwar-O’Neill 2020 model, while the Van Allen belt spectra were modeled using the AE-8/AP-8 models, and solar particle event spectra could be selected from historical data. The magnetic field input to the AE-8/AP-8 model was generated using the 13th generation International Geomagnetic Reference Field. Planetary atmospheres were modeled using NASA Global Reference Atmospheric Models, which provide mean atmospheric data for any altitude, latitude, longitude, and time, and the effect of Earth’s magnetic field was accounted for using a geomagnetic cutoff rigidity algorithm. Planetary orbits, trajectories, and relative positions of objects in the Solar System were determined using the NAIF SPICE observation geometry information system. Finally, highly detailed extended cardiac-torso (XCAT) digital phantoms were integrated into EVEREST in order to accurately model radiation exposure to individual organs. XCAT phantoms model over 100 segmented structures, range in age from neonate to 78 years, and cover various combinations of height, weight, and BMI. The EVEREST framework itself was designed using a novel lookup table method, in which different stages of particle propagation were divided into separate simulations, which are then convolved in post-processing.

Results: EVEREST was validated against personal radiation dosimeter data collected by the lunar module pilot on the Apollo 15 mission and also flux data from the Mars Science Laboratory Radiation Assessment Detector (RAD). Simulation results were found to agree very well with dosimeter readings by the Apollo 15 command module pilot. Comparison of Martian surface particle fluxes simulated by EVEREST to RAD data demonstrated an agreement to within an order of magnitude, with the best agreement seen for protons, He4, Z=6-8, Z=14-24, and Z>24. Finally, as a proof of concept, EVEREST was used to evaluate radiation exposure to a population of eight XCAT phantoms (3 adult and 1 pediatric, male and female) under three different nominal shielding configurations on the surface of Mars (unshielded, 50 cm thick ice, and 50 cm thick Martian regolith) at four different timepoints during the day (12 am, 6 am, 12 pm, and 6 pm). Using the federal yearly occupational dose limit of 50 mSv (effective dose) as a metric, it was found that the phantoms evaluated would reach this limit within 70.9 – 83.8 days unshielded, 139.2 – 161.2 days with 50 cm ice shielding, and 188.1 – 235.7 days with 50 cm Martian regolith shielding, if terrestrial radiation protection standards were to be applied. The results revealed that the brain receives one of the highest organ doses in the body and that unshielded radiation exposure is lowest at midnight when analyzed across all phantoms. Based on these findings, it is recommended that extra care be taken to provide additional radiation shielding in astronauts’ helmets and that extended forays outside of the habitat be planned for late evening to reduce the biological impact of radiation exposure.

Conclusion: EVEREST is a tested and validated framework for accurate estimation of total body and organ dose in space. EVEREST’s geometric versatility makes it ideal for evaluating doses to diverse populations of XCAT phantoms within different types of planetary habitats and spacecraft, enabling optimization of mission planning with respect to radiation exposure in the near future. The model has currently been validated for Lunar and Martian missions, and the framework can be applied to any space travel mission or planetary mission where the atmospheric models for that planet are available.

Tumor angiogenesis is critical to tumor growth and metastasis, yet much is unknown about the role vascular cells play in the tumor microenvironment. A major outstanding challenge associated with studying tumor angiogenesis is that existing preclinical models are limited in their recapitulation of in vivo cellular organization in 3D. This disparity highlights the need for better approaches to study the dynamic interplay of relevant cells and signaling molecules as they are organized in the tumor microenvironment. In this thesis, we combined 3D culture of lung adenocarcinoma cells with adjacent 3D microvascular cell culture in 2-layer cell-adhesive, proteolytically-degradable poly(ethylene glycol) (PEG)-based hydrogels to study tumor angiogenesis and the impacts of neovascularization on tumor cell behavior.

In initial studies, 344SQ cells, a highly metastatic, murine lung adenocarcinoma cell line, were characterized alone in 3D in PEG hydrogels. 344SQ cells formed spheroids in 3D culture and secreted proangiogenic growth factors into the conditioned media that significantly increased with exposure to transforming growth factor beta 1 (TGF-β1), a potent tumor progression-promoting factor. Vascular cells alone in hydrogels formed tubule networks with localized activated TGF-β1. To study cancer cell-vascular cell interactions, the engineered 2-layer tumor angiogenesis model with 344SQ and vascular cell layers was employed. Large, invasive 344SQ clusters developed at the interface between the layers, and were not evident further from the interface or in control hydrogels without vascular cells. A modified model with spatially restricted 344SQ and vascular cell layers confirmed that observed 344SQ cluster morphological changes required close proximity to vascular cells. Additionally, TGF-β1 inhibition blocked endothelial cell-driven 344SQ migration.

Two other lung adenocarcinoma cell lines were also explored in the tumor angiogenesis model: primary tumor-derived metastasis-incompetent, murine 393P cells and primary tumor-derived metastasis-capable human A549 cells. These lung cancer cells also formed spheroids in 3D culture and secreted proangiogenic growth factors into the conditioned media. Epithelial morphogenesis varied for the primary tumor-derived cell lines compared to 344SQ cells, with far less epithelial organization present in A549 spheroids. Additionally, 344SQ cells secreted the highest concentration of two of the three angiogenic growth factors assessed. This finding correlated to 344SQ exhibiting the most pronounced morphological response in the tumor angiogenesis model compared to the 393P and A549 cell lines.

Overall, this dissertation demonstrates the development of a novel 3D tumor angiogenesis model that was used to study vascular cell-cancer cell interactions in lung adenocarcinoma cell lines with varying metastatic capacities. Findings in this thesis have helped to elucidate the role of vascular cells in tumor progression and have identified differences in cancer cell behavior in vitro that correlate to metastatic capacity, thus highlighting the usefulness of this model platform for future discovery of novel tumor angiogenesis and tumor progression-promoting targets.

The American Cancer Society reported an estimated 300,000 new cases of breast cancer and 44,000 new breast-cancer related deaths in 2022 in the United States alone. With each new successfully treated primary tumor, there is a subsequent risk of disease recurrence. Recurrence poses a risk to 10% of patients within the first 5 years post treatment and a lifetime risk of 30% across all patients. While new tools are being developed to better understand and mitigate the risk of recurrence, triple negative breast cancers, which exhibit no targetable surface markers, offer little in the way of recurrence prediction or treatment. It is understood that tumor heterogeneity is a driving force in tumor recurrence. Temporal heterogeneity is associated with therapeutic treatment, where the administration either selects for resistance subpopulations of tumor cells that are able to recur or a de novo resistant phenotype arises that leads to recurrence. Additionally, it has been well documented that tumors vary spatially across a primary tumor. This heterogeneity takes the form of genetic, epigenetic, and phenotypic heterogeneity. One such phenotype of interest is metabolic heterogeneity. Metabolism is classified as a ‘Hallmark of Cancer’ and has been studied as a driver of tumor progression for almost a century since Otto Warburg first described the phenomenon of tumors exhibiting high rates of aerobic glycolysis. Optical imaging is well poised to study metabolic heterogeneity due to its ability to image cellular level features, to multiplex multiple endpoints, and the ability to image longitudinally. Endogenous fluorescence contrast of coenzymes NADH and FAD have been used to report on the redox state of in vivo tissue and distinguish cancerous from benign lesions. The Center for Global Women’s Health Technologies (GWHT) has employed the use of exogenous fluorescent contrast agents to provide substrate-specific metabolic information. Three fluorescent agents have been validated including: 2-[N-(7-nitrobenz-2-oxa-1, 3-diazol-4-yl) amino]-2-deoxy-D-glucose (2-NBDG), a glucose derivative that is able to report on glycolysis; Tetramethylrhodamine ethyl ester (TMRE), a cation that is selectively attracted to the charge gradient generated by the mitochondria during ATP synthesis, making it a reporter of OXPHOS; and Difluoro-5,7-Dimethyl-4-Bora-3a,4a-Diaza-s-Indacene-3-Hexadecanoic Acid (Bodipy FL C16), a long chain saturated fatty acid is taken up by the cell and undergoes beta oxidation similar to native fatty acids. More recently, GWHT has begun combing these fluorescence agents for in vivo use to provide a wholistic understanding of cancer metabolism. The work here sets out to develop a novel optical imaging platform that is capable of imaging multiplexed metabolic endpoints, for quantitative intra-image analysis of metabolic gradients. This technology is built on the use of exogenous fluorescence contrast agents to report on substrate or pathway specific axes of metabolism. By simultaneously introducing multiple contrast agents, it is possible to capture a more wholistic snapshot of tissue metabolism. To encourage the adoption of this technology, a novel low-cost instrument will also be developed. Leveraging a consumer grade CMOS camera and variable focus lens, it is possible to image over multiple length scales, capturing both bulk tumor features and also single cell features. The flexibility offered by this simple innovation will allow for metabolic imaging to be applied over a variety sample type. Three specific aims were proposed to realize this goal by developing methods of multi-parametric exogenous contrast and low-cost instrumentation for multi-scale imaging of tumor metabolic heterogeneity in preclinical models. Aim 1 validated and demonstrated a method for the simultaneous injection and measurement of Bodipy FL C16 and TMRE to report on lipid uptake and mitochondrial activity, two potentially interrelated axes of metabolism. To validate this method, three sets of experiments were performed to establish that the two probes do not exhibit chemical, optical, or biological crosstalk. Chemical compatibility was established using liquid chromatography. Briefly, high molar concentration solutions of each individual probe (Bodipy FL C16, TMRE, and 2-NBDG) were created alongside a solution of all three probes at the same concentration. Chromatograms were collected immediately upon mixing, after 1 hour and after 24 hours. The area under the curve for each probe at each time point displayed an area under the curve (AUC) within 2% of the AUC of the single probe solutions, suggesting no chemical reactions. Optical crosstalk was assessed using optical spectroscopy and tissue mimicking phantoms. Optical phantoms were created with tissue mimicking optical properties and various concentration of Bodipy FL C16, TMRE, polystyrene microspheres (tissue scattering mimic), and hemoglobin (tissue absorption mimic). Leveraging an inverse Monte Carlo algorithm, we demonstrated that accurate values for each fluorescent probe could be measured regardless of the concentration of the other optical probe or level of optical scattering or absorption, indicating optical compatibility. To address biological crosstalk, two sets of 4T1 tumor bearing mice were subject to optical spectroscopy with either 1) Bodipy FL C16 alone, 2) TMRE alone, 3) a dual injection of Bodipy FL C16 and TMRE. Fluorescence spectra were measured 2-, 4-, 6-, 8-, 10-, 20-, 30-, 40-, 50-, and 60-minutes post-injection to establish uptake kinetics. It was found that the uptake kinetics of the dual probe group were not statistically different from the single probe group, indicating biological compatibility. With no observable crosstalk between Bodipy FL C16 and TMRE, the two probes method was applied to characterize murine mammary gland and two tumor of differing metastatic potential (4T1 and 67NR). In addition, to Bodipy FL C16 and TMRE, oxygen saturation and total hemoglobin were extracted from estimates of optical absorption, and these 4 endpoints were used to attempt to cluster groups of tumor and normal tissue. Difficulty clustering tumor groups of varying metastatic potential suggest a need for imaging technology. In Aim 2 a low-cost fluorescence microscope was developed capable of performing quantitative fluorescence imaging over a variety of samples. The goal of this work was to design a system that could be adapted to image a number of different sample types include core-needle tissue biopsies, preclinical window chambers, and in vitro organoids. To accomplish this a low-cost CMOS detector was used with a variable magnification lens allowing for imaging at multiple length scales. Uniform illumination was a necessary criterion for quantitative imaging. To generate uniform illumination that could be scaled across multiple length scales, an LED coupled 1:4 fanout optical fiber was employed alongside a computational model to determine the positioning of each fiber. To automate the design of illumination, a computational model was employed where each optical fiber was modeled as a Lambertian emitter in a spherical coordinate system. To determine the ideal placement of each fiber such that the individual illumination contributions of all fibers summed to a uniform distribution, a global optimizer was employed. A genetic pattern search allowed for the selection of coordinates to produce uniform illumination that could be feasibly employed at the benchtop. This integrated system is referred to as the CapCell microscope. Using this computational approach, two uniform illumination profiles were designed, one with a high aspect ratio (length ≫ width) and one with a low aspect ratio (length = width). To demonstrate the utility of optimized illumination, core needle biopsies from 4T1 tumors were stained with a tumor-specific fluorescent contrast agent, HS-27 and imaged with either optimized or unoptimized gaussian illumination. The repeatability of intra-image features was compared for the two illumination scenarios, and it was found that uniform illumination repeatedly revealed the same fluorescent features across the sample. These features were further confirmed with standard histology. Window chamber imaging demonstrated the importance of designing application specific illumination. 4T1 mammary tumors were grown orthotopically before a window chamber was surgically implanted. Animals were injected with either Bodipy FL C16, 2-NBDG, or HS-27 and imaged with both the high AR and low AR illumination platforms. As expected, the low AR, designed for window chambers, had a higher power density at the sample site and thus increased contrast compared to the low AR images. With a method and a system in place, the goal of Aim 3 was to apply the optical imaging platform to observe spatiotemporal metabolic heterogeneity. To achieve this, the CapCell microscope was upgraded to enhance contrast and improve resolution for the visualization of capillaries and single cells. This was demonstrated using 4T1 window chamber models stained with acridine orange, a nucleus specific stain, and green light reflectance to highlight hemoglobin absorption in microvessels. Given the interplay between metabolism and vasculature it was desirable to employ a vessel segmentation approach to describe vascular features within an image. A Gabor filter and Djikstra segmentation approach was employed on metabolic images to enable metabolic and vascular comparisons across an image field of view. To test the improved CapCell system, 4T1 tumors were treated with combretastatin A-1, a vascular disrupting agent. Across the course of treatment, the CapCell was able to observe bulk changes in metabolism and vascular density. Additionally, by employing high resolution imaging, it was possible to observe relationships between each metabolic probe and vessel tortuosity. This analysis allowed for the identification of metabolically unique regions within each group of animals, demonstrating the ability of this technology to parse metabolically distinct regions of tumor. In total, the work outlined here describes the development of a novel optical imaging platform capable of quantifying intratumor metabolic heterogeneity of multiple metabolic endpoints over multiple length scales. The system expands on previous work developing methods for simultaneous measurement of exogenous fluorescent contrast agents to report on lipid uptake and mitochondrial activity. The system also introduced a novel computational approach to design uniform illumination for a low-cost microscope capable of imaging across multiple sample types. Together these technologies were used to observe metabolic heterogeneity in preclinical window chamber models following chemical perturbation. The technology introduced here, is primed for future exploration. First, it would be desirable to integrate all three exogenous contrast agents for simultaneous imaging of three axes of metabolism in vivo. Once accomplished, the sample technology could be applied to study metabolic and vascular changes associated with residual disease and tumors that are entering recurrence.

Purpose: Radiosurgery has become a widely used procedure in the treatment of both solid tumors and secondary metastases in the brain. In cases with multiple brain lesions, isocenters are typically set up for each target, a process which can take hours and become very uncomfortable for the patient. Recently, multifocal treatments with a single isocenter have emerged as a solution. With the high doses delivered to small regions during radiosurgery, the importance of treatment verification is paramount, especially when delivering high doses to regions off isocenter.

Methods: A 5-arc RapidArc radiosurgery plan with a single isocenter and 5 targets was used to treat a dosimeter placed within a RPC-type head and neck phantom. The treatment was delivered five times at varying prescription doses, depending on the sensitivity of the PRESAGE dosimeter used. The delivered dose distribution was measured using an in-house optical-CT system and compared to the Eclipse-planned dose distribution using dose volume histograms and Gamma analysis.

Results: Reasonable dose agreement was measured between the majority of the dosimeters and the Eclipse plan (80-85% pass rate at 5%/3 mm Gamma critera). The failing voxels were located on the periphery of the dosimeter at regions of extremely high or low dose, suggesting a dose dependent stability of the PRESAGE formulation. The formulation with the best temporal stability had a much higher Gamma pass rate of 98% at 3%/2mm criteria.

Conclusions: The potential of accurate delivery of the complex radiosurgery plan was demonstrated with one of the three formulations of PRESAGE. While agreement was worse in the other formulations, the problem seemed to be an easily-fixable stability issue, resulting in improper scaling of doses. Replication of the most stable formulation would provide an excellent tool for verification of radiosurgery treatment delivery and other complex procedures.

Purpose: To predict post-radiotherapy overall survival group of NSCLC patients based on clinical information and radiomics analysis of simulation CT. Materials/Methods: A total of 258 non-adenocarcinoma patients who received radical radiotherapy or chemo-radiation were studied: 45/50/163 patients were identified as short(0-6mos)/mid(6-12mos)/long(12+mos) survival groups, respectively. For each patient, we first extracted 76 radiomics features within the gross tumor volume(GTV) identified in the simulation CT; these features were combined with patient clinical information (age, overall stage, and GTV volume) as a patient-specific feature vector, which was utilized by a 2-step machine learning model for survival group prediction. This model first identifies patients with long survival prediction via a supervised binary classifier; for those with otherwise prediction, a 2nd classifier further generates short/mid survival prediction. Two machine learning classifiers, explainable boosting machine(EBM) and balanced random forest(BRF), were interrogated as a comparison study. During the model training, all patients were divided into training/test sets by an 8:2 ratio, and 100-fold random sampling were applied to the training set with a 7:1 validation ratio. Model performances were evaluated by the sensitivity, accuracy, and ROC results. Results: The model with EBM demonstrated an overall ROC AUC (0.58±0.04) with limited sensitivities in short (0.02±0.04) and mid group (0.11±0.08) predictions due to imbalanced data sample distribution. In contrast, the model with BRF improved short/mid group sensitivities to 0.32±0.11/0.29±0.16, respectively, but the improvement of ROC AUC (0.60±0.04) is limited. Nevertheless, both EBM (0.46±0.04) and BRF (0.57±0.04) approaches achieved limited overall accuracy; a noticeable overlap was found in their feature lists with top 10 feature weight rankings. Conclusion: The proposed two-step machine learning model with BRF classifier possesses a better performance than the one with EBM classifier in the post-radiotherapy survival group prediction of NSCLC. Future works, preferably in the joint use of deep learning, are in demand to further improve the prediction results.

Purpose: The purpose of this study is to develop and validate two deep learning (DL) models for the management of brain metastasis (BM) patients treated with stereotactic radiosurgery (SRS). The first model is a radiomics-integrated deep learning (RIDL) model, which aims to distinguish between radionecrosis and tumor recurrence in patients with post-SRS radiographic progression. The second model, a novel dose-incorporated deep ensemble learning (DEL) model, aims to accurately predict local failure outcomes in brain metastasis patients following SRS.Materials/Methods: A total of 51 patients with post-SRS radiographic progression (37 radionecrosis, 14 recurrence) and 114 BMs (including 26 BMs that developed biopsy-confirmed local failure post-SRS) from 85 patients were included in this study. For the first aim, a radiomics-integrated deep learning (RIDL) model was developed using three steps: 1) 184 radiomics features (RFs) were extracted from the SRS planning target volume (PTV) and 60% isodose volume (V60%); 2) a deep neural network (DNN) mimicking the encoding path of U-net was trained for radionecrosis or recurrence prediction using the 3D MR volume. Prior to the binary prediction output, latent variables in the DNN were extracted as 512 deep features (DFs); and 3) all extracted features were synthesized as a multi-dimensional input for support vector machine (SVM) execution. Key features with higher linear kernel weighting factors were identified by clustering analysis and were utilized by SVM to predict radionecrosis or recurrence. During model training, 50 model versions were acquired with random validation sample assignments following an 8:2 training/test ratio, and sensitivity, specificity, accuracy, and ROC were evaluated and compared with results from a radiomics-only and a DNN-only prediction model. For the second aim, a novel dose-incorporated deep ensemble learning (DEL) model was developed. The DEL design included four VGG-19 deep encoder networks, and each sub-network utilized a different variable type as input for BM outcome prediction. The DEL's outcome was synthesized from the four sub-network results via logistic regression. For each BM, four variables were obtained, including three with different curvatures during spherical projection and one with the original planar images. The proposed DEL model was developed using an 8:2 ratio for training/test assignment, and 10 model versions were acquired with random validation sample assignments. The DEL model performance was compared based on ROC analysis to a single VGG-19 encoder and to DEL models with the same projection designs, which used T1-CE MRI as the only input. Results: The RIDL model demonstrated superior performance compared to radiomics-based and DNN-only prediction models for distinguishing radionecrosis from tumor recurrence in brain metastasis patients with post-SRS radiographic progression. The RIDL model achieved the best prediction accuracy (0.643±0.059) and sensitivity (0.650±0.122) results with 32 identified key features (3 RFs+29 DFs), and it also demonstrated superior ROC results (AUC=0.688±0.035). In addition, for patients with NSCLC primary disease, the RF joint energy extracted from V60% and one DF correlated with ALK/EGFR mutations, respectively. Moreover, the DEL model achieved an excellent ROC AUC=0.84±0.03 with high sensitivity (0.78±0.08), specificity (0.81±0.09), and accuracy (0.80±0.06) results. This outperformed the MRI-only single VGG-19 encoder (sensitivity:0.35±0.01, AUC:0.64±0.08) and the MRI-only DEL (sensitivity:0.60±0.09, AUC:0.68±0.06) models. Conclusions: The RIDL model successfully differentiates brain metastasis radionecrosis from recurrence using a single post-SRS MR scan. Integration of clinical and treatment-related features is warranted to develop a comprehensive clinico-radiomic model. Additionally, the dose-incorporated DEL model design demonstrated robust and promising performance. It could potentially improve other radiotherapy outcome models and warrant further evaluation.

A growing number of studies have investigated the role of exercise both during and after a breast cancer diagnosis. Observational data suggests that regular endurance exercise is associated with a 20-50% reduction in cancer-specific mortality in women diagnosed with early stage breast cancer, compared to inactive women; however it is unclear whether there is a differential association across breast cancer subtypes. As a pre-requisite to guide future large phase II/III clinical trials, there is a critical need to confirm the biological plausibility of the exercise association in breast cancer patients as well as elucidate the underlying mechanisms of action via utilization of preclinical models.

In the present study we investigated the systemic effects of prescribed aerobic training in cancer patients and the direct impact on breast cancer cell subtype phenotypes. In order to test the in vivo significance, we interrogated aerobic training effects on breast cancer progression and tumor biology using syngeneic breast cancer mouse models.

Our results suggest that aerobic training may alter the host availability of pro-inflammatory and growth factor cytokines in patients with solid tumors. Modulation of systemic effectors in breast cancer patients compared to controls causes a differential phenotypic response on breast cancer cell subtypes. In vivo, aerobic training has a differential response on breast tumor progression compared to controls that is mediated by Hif1-α and metabolic reprogramming of breast cancer cells.

Glioblastoma (GBM) is a uniformly lethal cancer with an overall survival of less than 15 months. Aggressive standard of care therapies fail to eradicate these tumors and are non-specific, resulting in incapacitating toxicities. In contrast to such therapies, by virtue of exploiting the inherent specificity and vigilance of the immune system, immunotherapy provides an exquisitely precise approach for safe and effective tumor treatment. Specifically, peptide vaccines offer a promising strategy for inducing potent cytotoxic glioma-specific immune responses. However, they are limited by various mechanisms of glioma-mediated immunosuppression, including low/dysfunctional antigen-presentation, an increased fraction of regulatory T cells, T cell inhibitory pathways, and cytokine dysregulation. Such challenges can be overcome by the combined use of immunomodulatory adjuvants to improve the setting in which T cells recognize and respond to glioma antigens. To this end, a clinically-relevant high-affinity human anti-human CD27 immunomodulatory antibody (αhCD27) that induces potent antitumor T cell responses through engagement of the CD27 T cell costimulatory pathway was recently developed. This antibody is efficacious as a monotherapy in preclinical tumor models and has given rise to significant clinical responses in early phase trials. Given the preliminary success of monotherapy αhCD27 in inducing endogenous antitumor immunity, the overall goal of this dissertation research was to develop a peptide vaccine platform that employs αhCD27 as a vaccine adjuvant for its translation as a novel brain tumor immunotherapeutic.

Chapter 1 provides an overview of brain tumor immunotherapy, including the evolution of the field to date, various genres of treatment modalities, and ongoing progress and challenges. Chapter 2 discusses the approach of T cell immunomodulation, an emerging field in cancer treatment, including the clinical development of various FDA-approved antibodies and their relevance to brain tumors, synergy with current brain tumor standard of care, and emerging immunomodulatory targets. Chapter 3 provides the rationale for targeting the CD27 costimulatory molecule in particular and includes preliminary data that serves as the basis for the preclinical development of αhCD27 as an immunotherapy for brain tumors. Chapter 4 shows the systematic approach for optimizing αhCD27 as a vaccine adjuvant in a murine model of intracranial melanoma alongside a vaccine targeting a model tumor antigen. Lastly, Chapter 5 explores the use of αhCD27 to combat tumor-mediated immunosuppression, an important aspect of its adjuvant activity and the basis for two upcoming phase I clinical trials for malignant glioma.

This dissertation comprises original research as well as figures and illustrations from previously published material used to exemplify distinct concepts in immunotherapy for cancer. These published examples were reproduced with permission in accordance with journal and publisher policies described in the Appendix.

In summary, this work 1) identifies costimulatory T cell immunomodulation as a promising strategy for brain tumor immunotherapy, 2) explores and optimizes the potential for an agonist CD27 to enhance the tumor immune response when combined with a vaccine, 3) has opened up a new line of investigation into the role of CD27 in tumor-mediated immunosuppression, and 4) provides future prospects of utilizing an agonist CD27 antibody as a vaccine adjuvant for the treatment of brain tumors. Together, these studies hold great promise to improve the clinical outlook for brain tumor patients.

Radiation therapy (RT) has been a standard-of-care treatment for many localized cancers for decades. Despite being an effective treatment modality for many clinical presentations, the efficacy of RT against cancer can be limited due to local recurrence, metastatic spread, and radiation resistance from tumor hypoxia. These limitations provide opportunity for innovative approaches to enhance the overall efficacy of RT. This thesis explores the potential novel approach to enhancing RT through the paradigm changing approach of adding a phototherapeutic component initiated simultaneously with RT. X-ray Psoralen Activated Cancer Therapy (X-PACT) is one such approach, where diagnostics-energy kilovoltage (kV) x-ray coupled with energy modulators (phosphors) converts kV photon to ultraviolet (UV) light, which in turn activates psoralen. Radiotherapy Enhanced with Cherenkov photo-Activation (RECA) is another approach, where therapeutic megavoltage (MV) x-ray generates UV light via Cherenkov phenomenon. Both approaches could increase local control in RT, increase treatment effectiveness in hypoxic tumors, and amplify anti-cancer systemic response. The overarching hypothesis that drives this dissertation is that X-PACT and RECA can activate psoralen to enhance cytotoxicity in-vitro and tumor growth control in-vivo compared to RT alone. In line with this hypothesis, this work explores the feasibility of both X-PACT and RECA via in-vitro and in-vivo verification as well as optimization of radiation techniques to maximize the therapeutic benefit of the approach.

X-PACT and RECA in-vitro / in-vivo studies indicate radiotherapy enhancement is plausible with psoralens activated by secondary UV light production from radiation, though further investigation is required to establish feasibility of RECA in-vivo. For X-PACT in-vitro, a substantial reduction in cell viability and increase in apoptosis was observed in various murine cancer cells (4T1, KP-B, and CT2A) when treated with a combination of 50µg/mL phosphor, 10µM psoralen (8-MOP), and 1Gy of 80kVp x-ray (viability < 20%), compared to any of these components alone (viability > 70%). This suggests a synergistic interaction between the components congruent with the X-PACT scheme, where x-ray induces phosphor UV emission, which in turn activates psoralen. The X-PACT in-vivo mice study showed improved survival with X-PACT versus saline control with flank 4T1 tumors (30.7 days for X-PACT vs. 21.6 days for saline) for survival criteria of 1000, 1500, and 2000mm3, respectively. For RECA, in-vitro results seem promising, where reductions in viability of 20% and 9.5% were observed for 4T1 and B16 murine cancer cell lines treated with RECA (radiation + trioxsalen, a potent psoralen derivative) versus radiation alone. A substantial increase in MHC I expression was observed for B16 cells treated with RECA versus those treated with radiation alone. A small RECA in-vivo pilot study using 8-MOP was inconclusive. Further in-vivo trials with a greater number mice per arm of are required to establish the RECA feasibility to enhance radiotherapy.

Feasibility of treatment optimization for both X-PACT and RECA were demonstrated with kV and MV beams respectively, by optimization of optical output per radiation dose delivered. It was found that in both X-PACT and RECA scheme, the energy of the photon radiation beam (i.e. tube voltage and LINAC energy settings) affected optical output the most. With kV beams for X-PACT, accurate beam delivery within the target volume to reduce normal tissue damage typically expected of kV beams was demonstrated with a 3D-printing-based preclinical irradiation scheme, which is expected to help X-PACT translation into the clinics. In addition, for X-PACT, novel MV-responding phosphors were characterized under MV radiation beam, suggesting the possibility of MV-radiation-mediated X-PACT. Immediate future studies should investigate the efficacy of the optimized X-PACT and RECA, as well as MV X-PACT in-vitro and in-vivo. Studies beyond these immediate ones should investigate X-PACT and RECA efficacy against hypoxic and metastatic tumor sites, where radiation can traditionally fail.

Conventional therapy for malignant glioma (MG) fails to specifically target tumor cells. In contrast, immunotherapy offers an exquisitely precise approach, and substantial evidence indicates that if appropriately redirected, T cells can eradicate large, well-established tumors. Even the latest generation of redirected T cell therapies are limited, however, in that they require a centralized manufacturing infrastructure with heavily trained laboratory personnel to genetically modify each patient’s own T cells, use viral transduction which poses uncertain risks, are limited to the initial subset of T cells manipulated and infused, and still face uncertainty as to the optimal T cell phenotype to infuse. This dissertation reports the rational development and clinical translation of a fully-human, bispecific antibody (hEGFRvIII-CD3 bi-scFv) that overcomes these limitations through a recombinant antibody approach that effectively redirects any human T cell to lyse MG cells expressing a tumor-specific mutation of the epidermal growth factor receptor (EGFRvIII).

Chapters one, two and three provide an overview of T cell based immunotherapy of cancer and advances in antibody engineering. Also included is a discussion of the current standard-of-care therapy for MG, other immunotherapeutic approaches for MG, and relevant targets and their therapeutic potential for the treatment of MG.

Chapter four details the rational development of a fully-human, anti-human bispecific antibody, hEGFRvIII-CD3 bi-scFv, for immunotherapy of MG. By generating a panel of fully human bispecific single chain variable fragments (bi-scFvs) and testing their specificity through successive stages of screening and refinement, a highly-expressed and easily purified construct with high-affinity to both CD3 and EGFRvIII target antigens was obtained (hEGFRvIII-CD3 bi-scFv). In vitro, hEGFRvIII-CD3 bi-scFv re-directed naïve human T cells to upregulate cell surface activation markers, secrete pro-inflammatory cytokines, and proliferate in response to antigen-bearing targets. Each of these anti-tumor effects were robust and occurred exclusively in the presence of target antigen, illustrating the specificity of the approach. Using MG cell lines expressing EGFRvIII and patient derived MG with endogenous drivers and levels of EGFRvIII expression, bispecific antibody induced specific lysis was assessed. In each case, hEGFRvIII-CD3 bi-scFv was both potent and antigen-specific, mediating significant target-specific lysis at exceedingly low antibody concentrations. Tumor growth and survival was assessed in xenogenic subcutaneous and orthotopic models of human MG, respectively. In both these models, well-engrafted, patient-derived MG was effectively treated. Intravenous administration of hEGFRvIII-CD3 bi-scFv resulted in significant regression of tumor burden in the subcutaneous models and significantly extended survival in the orthotopic models.

Chapter five discusses challenges associated with intratumoral heterogeneity and details two mechanisms by which bispecific antibodies like hEGFRvIII-CD3 bi-scFv can induce epitope spreading, or an immunological response against tumor antigens other than those initially targeted. These mechanisms include: 1) re-activation of pre-existing T cell clones that have specificity for the tumor but fail to mount an immune response prior to bispecific antibody induced stimulation and 2) tumor cell death that results in release of tumor antigens and subsequent antigen uptake, processing and presentation by antigen presenting cells (APCs) leading to a secondary immune response. The chapter concludes with a discussion of a novel class of recombinant antibody molecules developed as part of this dissertation work, Bispecific Activators of Myeloid Cells (BAMs), that function to enhance phagocytosis and antigen presentation. BAM molecules may be useful in conjunction with other immunotherapeutic modalities to induce epitope spreading and combat intratumoral heterogeneity.

Chapter six describes research examining hEGFRvIII-CD3 bi-scFv in a unique human CD3 transgenic murine model. These studies have furthered the rationale for continued clinical translation of hEGFRvIII-CD3 bi-scFv as a safe and effective therapy for MG and have led to the discovery of a novel mechanism of drug delivery to brain tumors. The transgenic murine model was advantageous given that the CD3 binding portion of the fully-human bispecific antibody binds only to human CD3. Accordingly, the model provides a platform where the same molecule to be advanced to human studies can be tested pre-clinically in a pharmacologically responsive, fully-immunocompetent, syngeneic, murine glioma model. In vitro, hEGFRvIII-CD3 bi-scFv induced potent human CD3 transgenic T cell activation, pro-inflammatory cytokine secretion and proliferation exclusively in the presence of the highly-invasive and aggressive murine glioma, CT-2A, bearing EGFRvIII antigen (CT-2A-EGFRvIII). hEGFRvIII-CD3 bi-scFv mediated significant lysis of CT-2A-EGFRvIII at exceedingly low antibody concentrations. In vivo, hEGFRvIII-CD3 bi-scFv significantly reduced tumor growth in human CD3 transgenic mice with well-established, subcutaneous tumors and extended survival of human CD3 transgenic mice with well-established, orthotopic, MG. In the orthotopic setting, adoptive transfer of pre-activated human CD3 transgenic T cells significantly increased efficacy compared to human CD3 transgenic mice treated with hEGFRvIII-CD3 bi-scFv alone.

This led to the hypothesis that activated T cells, known to cross the blood-brain barrier (BBB) to perform routine immunosurveillance of the central nervous system (CNS), may bind to hEGFRvIII-CD3 bi-scFv intravascularly, via its CD3 receptor, and carry or “hitchhike” the large CD3 binding macromolecule to tumors located behind the BBB. Indeed, studies have revealed that adoptive transfer of activated T cells significantly increases the biodistribution of intravenously administered hEGFRvIII-CD3 bi-scFv to orthotopic glioma. Furthermore, blocking T cell extravasation, using natalizumab, for example, a drug used clinically to prevent the migration of T cells to the CNS in patients with multiple sclerosis, completely abrogates the increase in efficacy observed with the adoptive transfer of activated T cells. This newly uncovered hitchhiking mechanism of drug delivery to the CNS provides an important tool to enhance the immunotherapy of brain tumors and has potentially far-reaching consequences for the treatment of other CNS disorders, such as Alzheimer’s or Parkinson’s disease, where issues regarding drug delivery to the CNS are relevant. To begin to study this mechanism of drug delivery in disorders where the blood-brain barrier is intact, we have developed a novel transgenic murine model that expresses EGFRvIII at very low levels within neurons in the brain and have demonstrated that intravenously administered EGFRvIII-targeted recombinant antibody can accumulate in the CNS parenchyma, even in the presence of an intact BBB.

On the basis of these results, a series of clinical research development activities were conducted that have led to the initiation of a clinical study to test the hitchhiking mechanism of drug delivery in patients and ultimately to translate hEGFRvIII-CD3 bi-scFv therapy as a safe and effective treatment for patients with MG. These activities have resulted in a foundation in pre-clinical toxicology, clinical grade biologic manufacturing, clinical protocol development, and regulatory processes necessary to safely translate hEGFRvIII-CD3 bi-scFv therapy to the clinic.

This has involved conducing an extended single-dose toxicity study of hEGFRvIII-CD3 bi-scFv in animals to support studies in humans, the results of which are detailed in chapter seven. To assess for toxicity, human CD3 transgenic mice were administered hEGFRvIII-CD3 bi-scFv or vehicle as a control. Animals were observed for 14 days post-dosing with an interim necropsy on day two. Endpoints evaluated included clinical sings, body weights, feed consumption, clinical chemistries, hematology, urinalysis, and histopathology. There were no clinical observations, evidence of experimental autoimmune encephalomyelitis (EAE), or change in body weight or feed consumption noted during the study that would be associated with toxicity. Furthermore, no statistical difference was observed between drug- and control-receiving cohorts in hematological parameters or urinalysis and no pathological findings related to EGFRvIII-CD3 bi-scFv administration were observed. Statistical differences were observed between drug-treated and control-treated cohorts for some of the clinical chemistries assessed, such as hematocrit, calcium and phosphorus among the female, 14-day analysis cohorts.

To produce hEGFRvIII-CD3 bi-scFv and autologous activated T cells to be administered to patients for clinical study, chemistry, manufacturing and control protocols for the production of clinical grade hEGFRvIII-CD3 bi-scFv and autologous activated T cells were developed and implemented. The data presented in chapter eight describe optimized manufacturing processes and rationale for the selection and implementation of in-process and release analytical methods. This work includes the generation of a stable Chinese hamster ovary (CHO) cell line that expresses high levels of hEGFRvIII-CD3 bi-scFv, the generation and certification of a current Good Manufacturing Practice (cGMP) master cell bank (MCB), optimization and scale up of upstream and downstream manufacturing procedures, and development of standard operating procedures (SOPs) for the manufacture and assessment of clinical grade hEGFRvIII-CD3 bi-scFv and autologous activated T cells. Together, these have allowed for the production of clinical grade antibody and autologous patient derived cells within Duke University Medical Center. The production of recombinant antibodies for use in the clinic is a complex endeavor often performed in industry with teams of highly skilled scientists who test and optimize manufacturing protocols using a large, well-established manufacturing infrastructure. The successful production of clinical grade recombinant antibody at an academic center, therefore, represents a significant achievement and would likely be of interest to other academic-based researchers and clinicians embarking on similar clinical endeavors.

Chapter nine describes a clinical protocol for a phase 0 study of hEGFRvIII-CD3 bi-scFv in patients with recurrent EGFRvIII-positive glioblastoma (GBM). The protocol details intravenous administration of single doses of radiolabeled hEGFRvIII-CD3 bi-scFv with and without pre-administration of radiolabeled autologous activated T cells in a given patient. This will allow for imaging studies that will reveal the pharmacokinetics of the recombinant antibody both with and without adoptive transfer of autologous activated T cells. Endpoints include an assessment of the: intracerebral tumor localization of 124iodine (I)-labeled hEGFRvIII-CD3 bi-scFv with and without prior administration of 111indium (In)-labeled autologous T cells; percentage of patients with unacceptable toxicity; percentage of patients alive or alive without disease progression six months after study drug infusion; median progression-free survival; 111-In-autologous T cell intracerebral tumor localization; and percentage of patients who are EGFRvIII-positive at recurrence.

Chapter 10 concludes with a discussion of ongoing and anticipated future pre-clinical and clinical research. Together, these data presented in this dissertation have been submitted to the US Food and Drug Administration (FDA) in support of an Investigational New Drug (IND) application permit for clinical studies of hEGFRvIII-CD3 bi-scFv at Duke University Medical Center. This clinical study of the hitchhiking mechanism of drug delivery and the pharmacokinetics of hEGFRvIII-CD3 bi-scFv may have far reaching implications for disorders of the CNS where drug access past the BBB is relevant and will advance our understanding of hEGFRvIII-CD3 bi-scFv therapy in patients, guiding future clinical study of the molecule as a safe and effective form of immunotherapy for patients with EGFRvIII-positive GBM and other cancers.

Cancer is a major threat to human health and results in 1 in 6 deaths globally. Despite an extraordinary amount of effort and money spent, eradication or control of advanced disease has not yet been achieved. Understanding cancer from an evolutionary point of view may provide new insight to more effective control and treatment of the disease. Cancer as a disease of dynamic, stochastic somatic genomic evolution was first described by Nowell in 1976, and since then researchers have identified clonal expansions and genetic heterogeneity within many different types of neoplasms. The advancement in sequencing technology, especially single-cell sequencing, has open up new frontier by bringing the study of genomes to the cellular level. Phylogenetic analysis, which is a powerful tool inferring evolutionary relationships among various biological species or other entities based upon similarities and differences in their physical or genetic characteristics, has recently been applied to cancer studies and start to show promises in deciphering cancer evolution. However, new challenges have also arisen in experimental design, methodology and interpretation regarding to phylogeny of cancer cells. The overarching theme of this dissertation is to bring phylogenetic analysis to the context of cancer evolution. By using in silico simulations, I show the advantages and disadvantages of different sampling designs for phylogenetic analysis. Although bulk sequencing can hardly recover the topology of phylogenetic trees, I then developed a new method to infer sub-clone spatial distribution utilizing phased haplotypes from bulk sequencing. And lastly, I demonstrate the usage of phylogenetic analysis in breast cancer with multi-regional bulk sequencing and lung cancer with single cell sequencing.

Artificial intelligence (AI) has been rapidly developing in various fields, featuring automation in complex tasks with superior efficiency. This feature meets the urgent need for the automation of resource-intensive tasks in clinics. In radiation oncology, AI has been investigated for almost every process in patient management and treatment. Among these, radiotherapy treatment planning is one of the most time-consuming and labor-intensive processes. This dissertation work focuses on AI-based planning agents for intensity-modulated radiation therapy (IMRT) for various treatment sites. Fluence map prediction for prostate simultaneous integrated boost (SIB) or Stereotactic Body Radiotherapy (SBRT) cases was selected for a feasibility study. Prostate cases have one of the most consistent anatomic geometries and dosimetric constraints among all treatment sites. The developed prostate AI planning agent employed a customized convolutional neuro network (CNN), Dense-Res Hybrid Network (DRHN). DRHN was trained to predict optimal fluence maps from patient anatomic information. The proposed method avoids the time-consuming inverse planning process and thus could make fluence map predictions in seconds and generate IMRT plans in a few minutes. The resulting AI plan quality met institutional clinical guidelines. This preliminary study demonstrated the feasibility of the proposed AI strategy in automatic treatment planning and provided a solid foundation for the following studies. As a step forward, a more sophisticated AI agent for oropharyngeal cases was developed based on the prostate AI agent. This AI agent had the following two upgrades to adapt to the much more complex geometry in head-and-neck (H&N) treatment site: 1) conditional generative adversarial networks (cGAN) training architecture; 2) the generator, PyraNet, was a customized CNN network with more complicated network structure design in the shape of pyramids. This H&N AI agent demonstrated encouraging plan quality, especially that organs-at-risk (OAR) dosimetric outcomes achieved expectations. A graphical user interface (GUI) was developed and commissioned to make this AI tool available for clinical implementation. In summary, a DL-based fluence map prediction was developed for prostate and H&N cases. The H&N AI agent was implemented for clinical use, and more related research and applications are around the corner.

Purpose: To investigate the inter-patient and inter-sequence variation in liver tumor contrast in MRI and the feasibility of improving the liver tumor contrast by using an in-house developed multi-source adaptive fusion method for use in MRI-based treatment planning.

Methods and Materials: MR-images from 29 patients were retrospectively reviewed in this study. The imaging sequences acquired by a 1.5T GE and 3T Siemens MR scanner consisted of T1-w, T1-w, Post C, T2-w, T2/T1-w, and DWI. Using an in-house developed MSAF algorithm, we created fused images for a smaller subset of 12 patients using T1-w, T2-w, T2/T1-w, and DWI as inputs. Two fusion-images were obtained for each patient by implementing either an input-driven or output-driven fusion optimization method. Once a fusion-image was obtained an analysis was performed on each original image, and the fusion-image for each patient to calculate the tumor-to-tissue contrast-to-noise ratio(CNR) by contouring the tumor and a liver background-region(BG) in a homogeneous region of the liver using this in-house algorithm. CNR was calculated by (Itum-IBG)/SDBG, where Itum and IBG are the mean values of the tumor and the BG respectively, and SDBG is the standard deviation of the BG. To assess variation in tumor to tissue CNR for each image type an inter-patient coefficient-of-variation(CV) was calculated across all patients, as well as an inter-sequence CV. CV was calculated using the following: CV = σ/µ, where σ and µ are the standard deviation, and mean CNR for a single image sequence, respectively. These values were calculated for the original sequence types and fusion-images and compared.

Results: Our results from the 29 patients showed large inter-patient and inter-sequence variability, ranging from 86.90% to 67.03%, and 134.67% to 1.22% respectively. The T1-w, T1-w, Post Contrast, T2-w, T2/T1-w, DWI, and CT CV was 85.25%, 84.11%, 67.03%, 81.78%, 86.90%, and 74.30% respectively. Tumor CNR ranged from 0.95 to 4.47 with mean (± SD) CNR for T1-w, T1-w, Post Contrast, T2-w, T2/T1-w, DWI, and CT of 1.90 (±1.60), 2.12 (±1.42), 3.59 (±2.94), 1.95 (±1.70), 4.47 (±3.32), and 0.95 (±0.81) respectively. In the smaller subset of 12 patients, our results show a reduction in the inter-patient CV when using the in-house algorithm to obtain a tumor enhanced – fusion image. The inter-patient CV for T1-w, T2-w, T2/T1-w, DWI, Balanced Anatomy – Fusion, and Tumor Enhanced – Fusion was 94.16%, 112.73%, 105.69%, 124.23%, and 67.94% respectively. Tumor-CNR was significantly enhanced for each patient when using the in-house algorithm to obtain a tumor-enhanced image. The mean (± SD) CNR for T1-w, T2-w, T2/T1-w, Balanced Anatomy – Fusion, and Tumor Enhanced – Fusion was 2.11 (±1.99), 3.89 (±4.38), 3.71 (±3.92), 5.73 (±7.12), and 17.01 (±11.55) respectively.

Conclusion: The in-house multi-source adaptive fusion algorithm has the potential to increase the liver tumor contrast, as well as, improve the consistency for use in MRI based radiation therapy treatment planning.

B cells and humoral immunity are critical components of an effective immune response. However, B cells are also a significant driver of a variety of autoimmune diseases and can also become malignant. Antibody-mediated B cell depletion is now regularly used in the clinic to treat both B cell-derived cancers and B-cell driven autoimmunity, and while depletion itself is effective in some patients, removal of B cells is not often curative for patients and may present additional, unforeseen risks. The overall goal of this dissertation was therefore to determine the impact of B cell depletion on T cell homeostasis and function during infection and to elucidate the genetic factors that determine the effectiveness of antibody-mediated therapy.

In Chapter 3 of this dissertation, the role of B cells in promoting T cell homeostasis was investigated by depleting mature B cells using CD20 monoclonal antibody (mAb). Acute B cell depletion in adult mice significantly reduced spleen and lymph node T cell numbers, including naïve, activated, and cytokine-producing cells, as well as Foxp3+ regulatory T cells, whereas chronic B cell depletion in aged mice resulted in a profound decrease in activated and cytokineproducing T cell numbers. To determine the significance of this finding, B cell-depleted adult mice were infected with acute lymphocytic choriomeningitis virus (LCMV). Despite their expansion, activated and cytokine-producing T cell numbers were still significantly reduced one week later. Moreover, viral peptide-specific T cell numbers and effector cell development were significantly reduced in mice lacking B cells, while LCMV titers were dramatically increased. Thus, B cells are required for optimal T cell homeostasis, activation, and effector development in vivo, particularly during acute viral infection.

In Chapter 4 of this dissertation, lymphoma genetic changes that conferred either sensitivity or resistance to CD20 mAb therapy were examined in a preclinical mouse lymphoma model. An examination of primary lymphomas and extensive lymphoma families demonstrated that sensitivity to CD20 mAb was not regulated by differences in CD20 expression, prior exposure to CD20 mAb, nor serial in vivo passage. An unbiased forward genetic screen of CD20 mAb-resistant and -sensitive lymphomas identified galectin-1 as a significant factor driving CD20 mAb therapy resistance. As lymphomas acquired therapy resistance following serial in vivo passage, galectin-1 expression also increased. Furthermore, inducing lymphoma galectin-1 expression within the tumor microenvironment ablated lymphoma sensitivity to CD20 mAb. Therefore, lymphoma acquisition of galectin-1 expression confers CD20 mAb therapy resistance.

In Chapter 5 of this dissertation, the distinct germline components that control the efficacy of host CD20 mAb-dependent B cell and lymphoma depletion were evaluated using genetically distinct lab mouse strains. Variations in B cell depletion by CD20 mAb among several lab mouse strains were observed, where 129 mice had significantly impaired mAb-dependent depletion of endogenous B cells and primary lymphomas relative to B6 mice. An unbiased forward genetic screen of mice revealed that a 1.5 Mbp region of Chromosome 12 that contains mycn significantly altered CD20 mAb-dependent lymphoma depletion. Elevated mycn expression enhanced mAb-dependent B cell depletion and lymphoma phagocytosis and correlated with higher macrophage numbers. Thus, host genetic variations in mycn expression in macrophages alter the outcome of Ab-dependent depletion of endogenous and malignant cells.

These studies collectively demonstrate that B cells are required for effective cellular immune responses during infection and identified factors that alter the effectiveness of mAb-dependent B cell depletion. This research also established and validated an unbiased forward genetics approach to identify the totality of host and tumor-intrinsic factors that influence mAb therapy in vivo. The findings of these studies ultimately urge careful consideration in the clinical application of B cell depletion therapies.

Purpose: To investigate the effect of incorporating a beam spreading parameter in a beam angle optimization algorithm and to evaluate its efficacy for creating coplanar IMRT lung plans in conjunction with machine learning generated dose objectives.

Methods: Fifteen anonymized patient cases were each re-planned with ten values over the range of the beam spreading parameter, k, and analyzed with a Wilcoxon signed-rank test to determine whether any particular value resulted in significant improvement over the initially treated plan created by a trained dosimetrist. Dose constraints were generated by a machine learning algorithm and kept constant for each case across all k values. Parameters investigated for potential improvement included mean lung dose, V20 lung, V40 heart, 80% conformity index, and 90% conformity index.

Results: With a confidence level of 5%, treatment plans created with this method resulted in significantly better conformity indices. Dose coverage to the PTV was improved by an average of 12% over the initial plans. At the same time, these treatment plans showed no significant difference in mean lung dose, V20 lung, or V40 heart when compared to the initial plans; however, it should be noted that these results could be influenced by the small sample size of patient cases.

Conclusions: The beam angle optimization algorithm, with the inclusion of the beam spreading parameter k, increases the dose conformity of the automatically generated treatment plans over that of the initial plans without adversely affecting the dose to organs at risk. This parameter can be varied according to physician preference in order to control the tradeoff between dose conformity and OAR sparing without compromising the integrity of the plan.

Malignant gliomas are the most common primary brain tumor in adults, with an incidence of five cases per 100,000 persons per year. Grade IV glioblastoma is the most aggressive form and prognosis remains poor despite the current gold-standard first-line treatment – maximal safe resection and combination of radiotherapy with temozolomide chemotherapy – resulting in a median survival of approximately 20 months. Tumor recurrence occurs in virtually all glioblastoma patients, and there currently exists no accepted treatment for these patients. Recent advances in novel directed therapeutics are showing efficacy and have entered clinical trials. This work spans the pre-clinical and clinical development of a bispecific antibody – EGFRvIII:CD3 bi-scFv – for the treatment of malignant gliomas.

Chapter 1 reviews current front-line immunotherapy research in the fields of antibodies, including BiTEs and checkpoint inhibitors, and tumor vaccinations, including peptide and dendritic cell vaccinations. Furthermore, challenges specific to high-grade gliomas as well as opportunities for combination therapies are discussed. Chapter 2 introduces the architecture of the novel bispecific antibody EGFRvIII:CD3 bi-scFv and provides an overview of the molecule’s efficacy in various models. EGFRvIII:CD3 bi-scFv is a truncated antibody with dual specificity. One arm targets the epidermal growth factor receptor mutation variant III (EGFRvIII), a tumor-specific antigen found on glioblastoma. The other arm targets the human CD3 receptor on T cells. As an obligate bispecific antibody, simultaneous binding of both receptors by multiple EGFRvIII:CD3 bi-scFv’s results in the crosslinking of CD3 receptor, activation of T cells, and release of perforin/granzyme which lyses the proximal EGFRvIII-expressing tumor cells. EGFRvIII:CD3 bi-scFv effectively treats orthotopic patient-derived malignant glioma and syngeneic glioblastoma.

Chapter 3 outlines the in-house development of a scalable clinical production process using a WAVE (GE) bioreactor and describes the cGMP-compliant clinical production of EGFRvIII:CD3 bi-scFv. The 250-liter cGMP-production run yielded more than four grams of clinical drug material.

Chapter 4 demonstrates that EGFRvIII:CD3 bi-scFv produced using the cGMP development process is efficacious in both in vitro and in vivo models of glioblastoma. The chapter also describes the approach used to calculate the starting dose for the upcoming first-in-human clinical trial. First-in-human clinical trials require careful selection of a safe yet biologically relevant starting dose. Typically, such starting doses are selected based on toxicity studies in a pharmacologically relevant animal model. However, with the advent of target-specific and highly active immunotherapeutics, both the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have provided guidance that recommend determining a safe starting dose based on a minimum anticipated biological effect level (MABEL) approach. In order to establish a first-in-human dose, as advised by the FDA for bispecific antibodies, this work uses a MABEL approach to select a safe starting dose for EGFRvIII:CD3 bi-scFv, based on a combination of in vitro data, in vivo animal studies, and theoretical human receptor occupancy modeling. Using the most conservative approach to the MABEL assessment, a dose of 57.4 ng EGFRvIII:CD3 bi-scFv/kg body weight was selected as a safe starting dose for a first-in-human clinical study.

Chapter 5 describes the pharmacokinetic properties of EGFRvIII:CD3 bi-scFv, a necessary step in the drug development process. Using microflow liquid chromatography coupled to high resolution parallel reaction monitoring mass spectrometry, and data analysis in Skyline, the chapter first describes the development of a bottom-up proteomic assay for quantification of EGFRvIII:CD3 bi-scFv in both plasma and whole blood. Importantly, a protein calibrator, along with stable isotope-labeled EGFRvIII:CD3 bi-scFv protein, was used for absolute quantification. A PK analysis in a CD3 humanized mouse revealed that EGFRvIII:CD3 bi-scFv in plasma and whole blood has an initial half-life of ~8 minutes and a terminal half-life of ~2.5 hours. These results establish a sensitive, high-throughput assay for direct quantification of EGFRvIII:CD3 bi-scFv without the need for immunoaffinity enrichment. Moreover, these pharmacokinetic parameters will guide drug optimization and dosing regimens in future IND-enabling and Phase I studies of EGFRvIII:CD3 bi-scFv.

Finally, Chapter 6 provides an outlook of the future development of cancer therapeutics for treating malignant gliomas.