abstract: Biomarkers find a wide variety of applications in oncology from risk assessment to diagnosis and predicting and monitoring recurrence and response to therapy. Developing clinically useful biomarkers for cancer is faced with several challenges, including cancer heterogeneity and factors related to assay development and biomarker performance. Circulating biomarkers offer a rapid, cost-effective, and minimally-invasive window to disease and are ideal for population-based screening. Circulating immune biomarkers are stable, measurable, and can betray the underlying antigen when present below detection levels or even no longer present. This dissertation aims to investigate potential circulating immune biomarkers with applications in cancer detection and novel therapies. Over 600,000 cancers each year are attributed to the human papillomavirus (HPV), including cervical, anogenital and oropharyngeal cancers. A key challenge in understanding HPV immunobiology and developing immune biomarkers is the diversity of HPV types and the need for multiplexed display of HPV antigens. In Project 1, nucleic acid programmable protein arrays displaying the proteomes of 12 HPV types were developed and used for serum immunoprofiling of women with cervical lesions or invasive cervical cancer. These arrays provide a valuable high-throughput tool for measuring the breadth, specificity, heterogeneity, and cross-reactivity of the serologic response to HPV. Project 2 investigates potential biomarkers of immunity to the bacterial CRISPR/Cas9 system that is currently in clinical trials for cancer. Pre-existing B cell and T cell immune responses to Cas9 were detected in humans and Cas9 was modified to eliminate immunodominant epitopes while preserving its function and specificity. This dissertation broadens our understanding of the immunobiology of cervical cancer and provides insights into the immune profiles that could serve as biomarkers of various applications in cancer. / Dissertation/Thesis / Doctoral Dissertation Molecular and Cellular Biology 2018

abstract: Rapid intraoperative diagnosis of brain tumors is of great importance for planning treatment and guiding the surgeon about the extent of resection. Currently, the standard for the preliminary intraoperative tissue analysis is frozen section biopsy that has major limitations such as tissue freezing and cutting artifacts, sampling errors, lack of immediate interaction between the pathologist and the surgeon, and time consuming. Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation. This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides. These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis. / Dissertation/Thesis / Doctoral Dissertation Neuroscience 2019

<p>We present a new x-ray imaging technique, Correlation Imaging (CI), for improved breast and lung cancer detection. In CI, multiple low-dose radiographic images are acquired along a limited angular arc. Information from unreconstructed angular projections is directly combined to reduce the effect of overlying anatomy - the largest bottleneck in diagnosing cancer with projection imaging. In addition, CI avoids reconstruction artifacts that otherwise limit the performance of tomosynthesis. This work involved assessing the feasibility of the CI technique, its optimization, and its implementation for breast and chest imaging.</p><p>First a theoretical model was developed to determine the diagnostic information content of projection images using a mathematical observer. The model was benchmarked for a specific application in assessing the impact of reduced dose in mammography. Using this model, a multi-factorial task-based framework was developed to optimize the image acquisition of CI using existing low-dose clinical data. The framework was further validated using a CADe processor. Performance of CI was evaluated on mastectomy specimens at clinically relevant doses and further compared to tomosynthesis. Finally, leveraging on the expected improvement in breast imaging, a new hardware capable of CI acquisition for chest imaging was designed, prototyped, evaluated, and experimentally validated.</p><p>The theoretical model successfully predicted diagnostic performance on mammographic backgrounds, indicating a possible reduction in mammography dose by as much as 50% without adversely affecting lesion detection. Application of this model on low-dose clinical data showed that peak CI performance may be obtained with 15-17 projections. CAD results confirmed similar trends. Mastectomy specimen results at higher dose revealed that the performance of optimized breast CI may exceed that of mammography and tomosynthesis by 18% and 8%, respectively. Furthermore, for both CI and tomosynthesis, highest dose setting and maximum angular span with an angular separation of 2.75o was found to be optimum, indicating a threshold in the number of projections per angular span for optimum performance. </p><p>Finally, for the CI chest imaging system, the positional errors were found to be within 1% and motion blur to have minimal impact on the system MTF. The clinical images had excellent diagnostic quality for potentially improved lung cancer detection. The system was found to be robust and scalable to enable advanced applications for chest radiography, including novel tomosynthesis trajectories and stereoscopic imaging.</p> / Dissertation

Orale Plattenepithelkarzinome stellen seit Jahrzehnten eine globale Herausforderung im Gesundheitswesen dar. In dieser Studie wird mit dem Optical Stretcher ein neuer diagnostischer Ansatz in der Krebserkennung der Mundhöhle untersucht und im Rahmen einer klinischen Pilotstudie evaluiert. Dabei steht die Beurteilung der viskoelastischen Eigenschaften von oralen Epithelzellen im Vordergrund. Eine entscheidende Rolle spielt hierbei vor allem das Zytoskelett einer Zelle, welches aus unterschiedlichen Faserstrukturen ein komplexes, dynamisches Gerüst bildet und für die Strukturgebung sowie für die mechanischen Eigenschaften der unterschiedlichen Zelltypen verantwortlich ist. In dieser Arbeit wurden diesbezüglich einzelne Zellen im Optical Stretcher ohne direkten mechanischen Kontakt durch zwei gegenüberliegende Laserstrahlen verformt. Dabei wurde die relative Deformation als Längenänderung entlang der Laserachse von gedehnter zu ungedehnter Zelle definiert. Die relative Deformation dient als Vergleichsparameter und unterliegt verschiedenen Einflussfaktoren. Schließlich erlauben das Maß und die Art der Deformation, welche individuell für jede Zelle sind, Rückschlüsse auf ihr biologisches Verhalten. In Kombination mit statistischen Auswertungsalgorithmen war es möglich, signifikante Unterschiede hinsichtlich der relativen Dehnung zwischen benignen und malignen oralen Zellen darzustellen. Die Ergebnisse zeigen, dass der Optical Stretcher in der Lage ist, bereits minimale Veränderungen zwischen den verschiedenen zytoskelettalen Zuständen einer Zelle zu detektieren und somit wird sich die Dehnungsfähigkeit einer Zelle zukünftig als sensibler Zellmarker zur Dignitätsbestimmung etablieren.

Objectives: Intact cell peptidome profiling (ICPP) with MALDI-ToF Mass-Spectrometry holds promise as a non invasive method to detect head and neck squamous cell carcinoma (HNSCC) objectively, which may improve the early diagnosis of oral cancer tremendously. The present study was designed to discriminate between tumour samples and non-cancer controls (healthy mucosa and oral lesions) by analysing complete spectral patterns of intact cells using MALDI-ToF MS. Material and Methods: In the first step, a data base consisting of 26 patients suffering from HNSCC was established by taking brush biopsy samples of the diseased area and of the healthy buccal mucosa of the respective contralateral area. After performing MALDI-ToF MS on these samples, classification analysis was used as a basis for further classification of the blind study composed of additional 26 samples including HNSCC, oral lesions and healthy mucosa. Results: By analyzing spectral patterns of the blind study, all cancerous lesions were defined accurately. One incorrect evaluation (false positive) occurred in the lesion cohort, leading to a sensitivity of 100%, a specificity of 93% and an overall accuracy of 96.5%. Conclusion: ICPP using MALDI-ToF MS is able to distinguish between healthy and cancerous mucosa and between oral lesions and oral cancer with excellent sensitivity and specificity, which may lead to a more impartial early diagnosis of HNSCC.

Indiana University-Purdue University Indianapolis (IUPUI) / Triple-negative breast cancers (TNBCs) are pathologically defined by the absence of estrogen, progesterone, and HER2 receptors. Compared to other breast cancers, TNBC has a relatively high mortality. In addition, TNBC patients are more likely to relapse in the first few years after treatment, and experiencing a shorter median time from recurrence to death. Detecting the presence of tumor in patients who are technically “disease-free” after neoadjuvant chemotherapy and surgery as early as possible might be able to predict recurrence of patients, and then provide timely intervention for additional therapy. To this end, I applied the analysis of “liquid biopsies” for early detection of minimal residual disease (MRD) on early-stage TNBC patients using next-generation sequencing. For the first part of this study, I focused on detecting circulating tumor DNA (ctDNA) from TNBC patients after neoadjuvant chemotherapy and surgery. First, patient-specific somatic mutations were identified by sequencing primary tumors. From these data, 82% of the patients had at least one TP53 mutation, followed by 16% of the patients having at least one PIK3CA mutation. Next, I sequenced matched plasma samples collected after surgery to identify ctDNA with the same mutations. I observed that by detecting corresponding ctDNA I was able to predict rapid recurrence, but not distant recurrence. To increase the sensitivity of MRD detection, in the second part I developed a strategy to co-detect ctDNA along with circulating tumor RNA (ctRNA). An advantage of ctRNA is its active release into the circulation from living cancer cells. Preliminary data showed that more mutant molecules were identified after incorporating ctRNA with ctDNA detection in a metastatic breast cancer setting. A validation study in early-stage TNBC is in progress. In summary, my study suggests that co-detection of ctDNA and ctRNA could be a potential solution for the early detection of disease recurrence. / 2021-08-05

Despite significant improvements in medical sciences over the last decade, cancer still continues to be a major cause of death in humans throughout the world. Parallel to the efforts of understanding the intricacies of cancer biology, researchers are continuously striving to develop effective cancer detection and treatment strategies. Use of nanotechnology in the modern era opens up a wide range of possibilities for diagnostics, therapies and preventive measures for cancer management. Although, existing strategies of cancer detection and treatment, using nanoparticles, have been proven successful in case of cancer imaging and targeted drug deliveries, they are often limited by poor sensitivity, lack of specificity, complex sample preparation efforts and inherent toxicities associated with the nanoparticles, especially in case of in-vivo applications. Moreover, the detection of cancer is not necessarily integrated with treatment. X-rays have long been used in radiation therapy to kill cancer cells and also for imaging tumors inside the body using nanoparticles as contrast agents. However, X-rays, in combination with nanoparticles, can also be used for cancer diagnosis by detecting cancer biomarkers and circulating tumor cells. Moreover, the use of nanoparticles can also enhance the efficacy of X-ray radiation therapy for cancer treatment. This dissertation describes a novel in vitro technique for cancer detection and treatment using X-ray radiation and nanoparticles. Surfaces of synthesized metallic nanoparticles have been modified with appropriate ligands to specifically target cancer cells and biomarkers in vitro. Characteristic X-ray fluorescence signals from the X-ray irradiated nanoparticles are then used for detecting the presence of cancer. The method enables simultaneous detection of multiple iv cancer biomarkers allowing accurate diagnosis and early detection of cancer. Circulating tumor cells, which are the primary indicators of cancer metastasis, have also been detected where the use of magnetic nanoparticles allows enrichment of rare cancer cells prior to detection. The approach is unique in that it integrates cancer detection and treatment under one platform, since, X-rays have been shown to effectively kill cancer cells through radiation induced DNA damage. Due to high penetrating power of X-rays, the method has potential applications for in vivo detection and treatment of deeply buried cancers in humans. The effect of nanoparticle toxicity on multiple cell types has been investigated using conventional cytotoxicity assays for both unmodified nanoparticles as well as nanoparticles modified with a variety of surface coatings. Appropriate surface modifications have significantly reduced inherent toxicity of nanoparticles, providing possibilities for future clinical applications. To investigate cellular damages caused by X-ray radiation, an on-chip biodosimeter has been fabricated based on three dimensional microtissues which allows direct monitoring of responses to X-ray exposure for multiple mammalian cell types. Damage to tumor cells caused by X-rays is known to be significantly higher in presence of nanoparticles which act as radiosensitizers and enhance localized radiation doses. An analytical approach is used to investigate the various parameters that affect the radiosensitizing properties of the nanoparticles. The results can be used to increase the efficacy of nanoparticle aided X-ray radiation therapy for cancer treatment by appropriate choice of X-ray beam energy, nanoparticle size, material composition and location of nanoparticle with respect to the tumor cell nucleus.

Diagnosing early formation of tumors or lumps, particularly those caused by cancer, has been a challenging problem. To help physicians detect tumors more efficiently, various imaging techniques with different imaging modalities such as computer tomography, ultrasonic imaging, nuclear magnetic resonance imaging, and mammography, have been developed. However, each of these techniques has limitations, including exposure to radiation, excessive costs, and complexity of machinery. Tissue elasticity is an important indicator of tissue health, with increased stiffness pointing to an increased risk of cancer. In addition to increased tissue elasticity, geometric parameters such as size of a tissue inclusion are also important factors in assessing the tumor. The combined knowledge of tissue elasticity and its geometry would aid in tumor identification. In this research, we present a tactile sensation imaging system (TSIS) and algorithms which can be used for practical medical diagnostic experiments for measuring stiffness and geometry of tissue inclusion. The TSIS incorporates an optical waveguide sensing probe unit, a light source unit, a camera unit, and a computer unit. The optical method of total internal reflection phenomenon in an optical waveguide is adapted for the tactile sensation imaging principle. The light sources are attached along the edges of the waveguide and illuminates at a critical angle to totally reflect the light within the waveguide. Once the waveguide is deformed due to the stiff object, it causes the trapped light to change the critical angle and diffuse outside the waveguide. The scattered light is captured by a camera. To estimate various target parameters, we develop the tactile data processing algorithm for the target elasticity measurement via direct contact. This algorithm is accomplished by adopting a new non-rigid point matching algorithm called "topology preserving relaxation labeling (TPRL)." Using this algorithm, a series of tactile data is registered and strain information is calculated. The stress information is measured through the summation of pixel values of the tactile data. The stress and strain measurements are used to estimate the elasticity of the touched object. This method is validated by commercial soft polymer samples with a known Young's modulus. The experimental results show that using the TSIS and its algorithm, the elasticity of the touched object is estimated within 5.38% relative estimation error. We also develop a tissue inclusion parameter estimation method via indirect contact for the characterization of tissue inclusion. This method includes developing a forward algorithm and an inversion algorithm. The finite element modeling (FEM) based forward algorithm is designed to comprehensively predict the tactile data based on the parameters of an inclusion in the soft tissue. This algorithm is then used to develop an artificial neural network (ANN) based inversion algorithm for extracting various characteristics of tissue inclusions, such as size, depth, and Young's modulus. The estimation method is then validated by using realistic tissue phantoms with stiff inclusions. The experimental results show that the minimum relative estimation errors for the tissue inclusion size, depth, and hardness are 0.75%, 6.25%, and 17.03%, respectively. The work presented in this dissertation is the initial step towards early detection of malignant breast tumors. / Electrical and Computer Engineering

Yes / Near field imaging using microwaves in medical applications is of great current interest for its capability and accuracy in identifying features of interest, in comparison with other known screening tools. This paper documents microwave imaging experiments on breast cancer detection, using active antenna tuning to obtain matching over a wide bandwidth. A simple phantom consisting of a plastic container with a low dielectric material emulating fatty tissue and a high dielectric constant object emulating a tumor is scanned between 4 to 8 GHz with a UWB microstrip antenna. Measurements indicate that this prototype microwave sensor is a good candidate for such imaging applications. / Yorkshire Innovation Fund, Research Development Project (RDP)

The first thermal cycling experiments of ionic self-assembled multilayer (ISAM) films have been reported examining their survivability through repeated thermal cycles from -20° C to 120° C in ambient atmospheric conditions. The films were constructed from alternating layers of Nile Blue A and gold nanoparticles which provided a strong absorbance in the optical wavelength range. No degradation of the optical characteristics of the ISAM films was observed [1]. Techniques for measuring the capacitance and resistivity of various ISAM films have also been developed allowing for a more complete electrical characterization of ISAM films. Capacitance measurements enabled a calculation of the dielectric function and breakdown field strength of the ISAM films. The capacitance measurement technique was verified by measuring the dielectric function of a spin-coated thin film PMMA, which has a well characterized dielectric function [2]. Surface-enhanced Raman spectroscopy (SERS) has been studied as a possible detection method for malignant melanoma revealing spectral differences in blood sera from healthy horses and horses with malignant melanoma. A SERS microscope system was constructed with the capability of resolving the Raman signal from biologically important molecules such as beta-carotene and blood sera. The resulting Raman signals from sera collected from horses with malignant melanoma were found to have additional peaks not found in the Raman signals obtained from sera collected from healthy horses. A systematic analysis of the combination of absorbance and fluorescence signals of blood sera collected from populations of healthy dogs and dogs with cancer has resulted in a rapid and cost-effective method for monitoring protein concentrations that could possibly be used as part of a cancer screening process. This method was developed using the absorbance and fluorescence signals from known serum proteins, the combinations of which were used to match the absorbance and fluorescence signals of blood sera allowing for an accurate determination of protein concentrations in blood sera [3]. Finally, a novel method for measuring the melting point of DNA in solution using capacitance measurements is presented. This method allows for the determination of the melting temperature as well as the melting entropy and melting enthalpy of DNA strands. Two different short strands of DNA, 5'-CAAAATAGACGCTTACGCAACGAAAAC-3' along with its complement and 5'-GGAAGAGACGGAGGA-3' along with its complement were used to validate the technique as the characteristics of these strands could be modeled using theoretical methods. This experimental technique allows for the precise determination of the melting characteristics of DNA strands and can be used to evaluate the usefulness of theoretical models in calculating the melting point for particular strands of DNA. Additionally, a micro-fluidic device has been proposed that will allow for a rapid and cost-effective determination of the melting characteristics of DNA [4]. / Ph. D.