This thesis describes observations, techniques and strategies, which contribute towards the development of nanoparticle based detection and treatment of cancer. Quantum dots and biorecognition molecules were studied towards applications in detection and microgels were used in the rational design of a targeted drug delivery vehicle. The fluorescence intensity of quantum dots was examined in buffers commonly used in molecular biology. The fluorescence intensity of ZnS-capped CdSe quantum dots (QDs) was found to vary significantly, depending on the amount of ZnS capping on the QDs or the concentration, pH and type of buffer the QDs were in. Since fluorescence cannot reliably be used to quantify QDs, an alternative quantification method was developed, which does not rely on their fluorescence. This method employs phage display to identify nanoparticle-specific bacteriophage which were then applied in an assay to quantify QDs in environments where absorbance or fluorescence spectroscopy are ineffective. Biorecognition molecules, which can direct nanoparticles to a molecular target, were also identified through phage display. Phage display on whole cells was used to identify a peptide, which was conjugated with QDs to stain HeLa (cervical cancer) cells. A high-throughput phage display screening strategy was also developed, which could enable the simultaneous identification of multiple biorecognition molecules from a single library. QD-encoded microbead barcodes were conjugated to protein targets and then used to screen a phage display library. The beads and the binding phage were then separated using flow cytometry and fluorescence assisted cell sorting. Finally, biorecognition molecules were combined with nanoparticles to create drug delivery vehicles, which were designed to protect, deliver and then release chemotherapeutic drugs through an intracellular pH trigger. PolyNIPAAm and chitosan hydrogels, under 200 nm in diameter, were loaded with chemotherapeutic drugs, conjugated to transferrin and tested in vitro on HeLa cells. These projects demonstrate the great potential in this growing field as well as some of the many challenges that have yet to be overcome.

Quantum Dot (QD) nanobeacons distinguish themselves from molecular beacons with the promise of non-linear activation, tunability, and multi-functionality. These unique features make them highly attractive for cancer detection imaging with opportunities for increased signal-to-background ratio and tunable sensitivity. In this thesis, a nanobeacon was designed to target matrix metalloproteinase-7 (MMP-7), known to be over-expressed by a wide array of tumours. The nanobeacon is normally dark until specifically activated by MMP-7. The overall design strategy links single QDs to multiple energy acceptors by GPLGLARK peptides that can be cleaved specifically by MMP-7. However, design details such as the choice of energy acceptor and conjugation method was found to drastically alter the function of the nanobeacon. Studies of nanobeacons synthesized with Black Hole Quencher-1 or Rhodamine Red by either covalent conjugation or electrostatic self-assembly revealed that peptide conformation and bonding flexibility are both important considerations in nanobeacon design due to QD sterics.

This thesis describes observations, techniques and strategies, which contribute towards the development of nanoparticle based detection and treatment of cancer. Quantum dots and biorecognition molecules were studied towards applications in detection and microgels were used in the rational design of a targeted drug delivery vehicle. The fluorescence intensity of quantum dots was examined in buffers commonly used in molecular biology. The fluorescence intensity of ZnS-capped CdSe quantum dots (QDs) was found to vary significantly, depending on the amount of ZnS capping on the QDs or the concentration, pH and type of buffer the QDs were in. Since fluorescence cannot reliably be used to quantify QDs, an alternative quantification method was developed, which does not rely on their fluorescence. This method employs phage display to identify nanoparticle-specific bacteriophage which were then applied in an assay to quantify QDs in environments where absorbance or fluorescence spectroscopy are ineffective. Biorecognition molecules, which can direct nanoparticles to a molecular target, were also identified through phage display. Phage display on whole cells was used to identify a peptide, which was conjugated with QDs to stain HeLa (cervical cancer) cells. A high-throughput phage display screening strategy was also developed, which could enable the simultaneous identification of multiple biorecognition molecules from a single library. QD-encoded microbead barcodes were conjugated to protein targets and then used to screen a phage display library. The beads and the binding phage were then separated using flow cytometry and fluorescence assisted cell sorting. Finally, biorecognition molecules were combined with nanoparticles to create drug delivery vehicles, which were designed to protect, deliver and then release chemotherapeutic drugs through an intracellular pH trigger. PolyNIPAAm and chitosan hydrogels, under 200 nm in diameter, were loaded with chemotherapeutic drugs, conjugated to transferrin and tested in vitro on HeLa cells. These projects demonstrate the great potential in this growing field as well as some of the many challenges that have yet to be overcome.

Quantum Dot (QD) nanobeacons distinguish themselves from molecular beacons with the promise of non-linear activation, tunability, and multi-functionality. These unique features make them highly attractive for cancer detection imaging with opportunities for increased signal-to-background ratio and tunable sensitivity. In this thesis, a nanobeacon was designed to target matrix metalloproteinase-7 (MMP-7), known to be over-expressed by a wide array of tumours. The nanobeacon is normally dark until specifically activated by MMP-7. The overall design strategy links single QDs to multiple energy acceptors by GPLGLARK peptides that can be cleaved specifically by MMP-7. However, design details such as the choice of energy acceptor and conjugation method was found to drastically alter the function of the nanobeacon. Studies of nanobeacons synthesized with Black Hole Quencher-1 or Rhodamine Red by either covalent conjugation or electrostatic self-assembly revealed that peptide conformation and bonding flexibility are both important considerations in nanobeacon design due to QD sterics.

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.

Surface-Enhanced Raman Scattering (SERS) has the potential to be a rapid disease diagnostic platform. SERS is a well-known ultrasensitive, label-free method for the detection and identification of molecules at low concentrations. The Raman cross-sections are primarily enhanced by plasmonic effects for molecules close to (< 5 nm) the surface of nanostructured metal substrates. Due to the unique Raman vibration features that provide molecular signatures, we have shown that SERS can provide a rapid (< one hour), label-free, sensitive and specific diagnosis for a number of diseases. This work demonstrates the capability of SERS to be an effective optical diagnostic approach, in particular, for bacterial infectious diseases such as urinary tract infections (UTI) and sexually transmitted diseases (STD), and cancer cell identification. More specifically, this work demonstrates the ability of SERS to distinguish different vegetative bacterial cells with species and strain specificity based on their intrinsic SERS molecular signatures. With the exception of C. trachomatis - the causative agent of chlamydia - whose SERS molecular signatures are found to be aggregated proteins on the cell membrane, all bacterial SERS molecular signatures are due to purine molecules resulting from nucleic acid metabolism as part of the rapid onset of the starvation response of these pathogens. The differences in relative contribution of different purine metabolites for each bacterium gives rise to the SERS strain and species specificity. The ability of SERS to distinguish cancer and normal cells grown in vitro based on changes of SERS spectral feature as a function of time after sample processing is also demonstrated. Furthermore, the difference of spectral features on the gold and silver SERS substrate of the same bacteria can be used as additional attribute for identification. This work demonstrate the potential of SERS platform to provide antibiotic-specific diagnostics in clinical settings within one hour when combined with a portable Raman microscopy instrument, an effective enrichment procedure, multivariate data analysis and an expendable SERS reference library with drug-susceptibility profile for each bacterial strain determined a priori, as well as the ability of SERS platform as a powerful bioanalytical probe for learning about near cell membrane biochemical processes.

The introduction of clinical Positron Emission Tomography (PET) in oncology in the 1990s has substantially changed the management of patients with cancer and become one of the diagnostic modalities with the fastest growth worldwide (Buck et al., 2010). The major hurdle delaying the proliferation of PET was partly due to its high initial investment and insufficient third-party reimbursement (Keppler & Conti, 2001). Hong Kong, a region with about half the economic strength of Germany, was able to sustain a higher ratio of PET-CT scanners than that of Germany. Through the study of the PET utilization in Hong Kong, this research is to (i) explore the factors contributing to this phenomenon; and (ii) find out if those factors are applicable to other developing countries. The key factors found contributed to a higher ratio of PET-CT scanners in Hong Kong were: 1) medical expertise in a regionally profound disease; 2) the direct payment culture which enables an economically efficient and a cost-effective operation; 3) the influx of patients from neighboring countries; and 4) the reputation of its medical services. Applying these factors, citizens in developing countries were able to have access to the latest and expensive medical technology.

abstract: Cancer is one of the most serious global diseases. We have focused on cancer immunoprevention. My thesis projects include developing a prophylactic primary and metastatic cancer vaccines, early cancer detection and investigation of genes involved in tumor development. These studies were focused on frame-shift (FS) antigens. The FS antigens are generated by genomic mutations or abnormal RNA processing, which cause a portion of a normal protein to be translated out of frame. The concept of the prophylactic cancer vaccine is to develop a general cancer vaccine that could prevent healthy people from developing different types of cancer. We have discovered a set of cancer specific FS antigens. One of the FS candidates, structural maintenance of chromosomes protein 1A (SMC1A) FS, could start to accumulate at early stages of tumor and be specifically exposed to the immune system by tumor cells. Prophylactic immunization with SMC1A-FS could significantly inhibit primary tumor development in different murine tumor models and also has the potential to inhibit tumor metastasis. The SMC1A-FS transcript was detected in the plasma of the 4T1/BALB/c mouse tumor model. The tumor size was correlated with the transcript ratio of the SMC1A-FS verses the WT in plasma, which could be measured by regular RT-PCR. This unique cancer biomarker has a practical potential for a large population cancer screen, as well as clinical tumor monitoring. With a set of mimotope peptides, antibodies against SMC1A-FS peptide were detected in different cancer patients, including breast cancer, pancreas cancer and lung cancer with a 53.8%, 56.5% and 12.5% positive rate respectively. This suggested that the FS antibody could be a biomarker for early cancer detection. The characterization of SMC1A suggested that: First, the deficiency of the SMC1A is common in different tumors and able to promote tumor initiation and development; second, the FS truncated protein may have nucleolus function in normal cells. Mis-control of this protein may promote tumor development. In summary, we developed a systematic general cancer prevention strategy through the variety immunological and molecular methods. The results gathered suggest the SMC1A-FS may be useful for the detection and prevention of cancer. / Dissertation/Thesis / Ph.D. Molecular and Cellular Biology 2012

abstract: Despite significant advances in digital pathology and automation sciences, current diagnostic practice for cancer detection primarily relies on a qualitative manual inspection of tissue architecture and cell and nuclear morphology in stained biopsies using low-magnification, two-dimensional (2D) brightfield microscopy. The efficacy of this process is limited by inter-operator variations in sample preparation and imaging, and by inter-observer variability in assessment. Over the past few decades, the predictive value quantitative morphology measurements derived from computerized analysis of micrographs has been compromised by the inability of 2D microscopy to capture information in the third dimension, and by the anisotropic spatial resolution inherent to conventional microscopy techniques that generate volumetric images by stacking 2D optical sections to approximate 3D. To gain insight into the analytical 3D nature of cells, this dissertation explores the application of a new technology for single-cell optical computed tomography (optical cell CT) that is a promising 3D tomographic imaging technique which uses visible light absorption to image stained cells individually with sub-micron, isotropic spatial resolution. This dissertation provides a scalable analytical framework to perform fully-automated 3D morphological analysis from transmission-mode optical cell CT images of hematoxylin-stained cells. The developed framework performs rapid and accurate quantification of 3D cell and nuclear morphology, facilitates assessment of morphological heterogeneity, and generates shape- and texture-based biosignatures predictive of the cell state. Custom 3D image segmentation methods were developed to precisely delineate volumes of interest (VOIs) from reconstructed cell images. Comparison with user-defined ground truth assessments yielded an average agreement (DICE coefficient) of 94% for the cell and its nucleus. Seventy nine biologically relevant morphological descriptors (features) were computed from the segmented VOIs, and statistical classification methods were implemented to determine the subset of features that best predicted cell health. The efficacy of our proposed framework was demonstrated on an in vitro model of multistep carcinogenesis in human Barrett's esophagus (BE) and classifier performance using our 3D morphometric analysis was compared against computerized analysis of 2D image slices that reflected conventional cytological observation. Our results enable sensitive and specific nuclear grade classification for early cancer diagnosis and underline the value of the approach as an objective adjunctive tool to better understand morphological changes associated with malignant transformation. / Dissertation/Thesis / Ph.D. Electrical Engineering 2013

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-12-01T14:33:08Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_Kamila_Queiroz.pdf: 2031256 bytes, checksum: 11934292ef99e7d010a7bdd8c3309096 (MD5) / Made available in DSpace on 2016-12-01T14:33:08Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_Kamila_Queiroz.pdf: 2031256 bytes, checksum: 11934292ef99e7d010a7bdd8c3309096 (MD5) Previous issue date: 2016-02-19 / CNPQ / A inspeção termográfica tem emergido como um método potencial para melhorar a eficiência da detecção precoce do câncer de mama. A técnica não utiliza radiação ionizante e possui a vantagem de facilitar a realização de exames de mama em homens e detectar alterações nas mamas de mulheres mais jovens. Sistemas de diagnóstico auxiliado por computador (CAD) são métodos importantes no subsídio à decisão médica e são usados para melhorar a consistência da interpretação das imagens. Normalmente, estes sistemas são associados a interfaces gráficas para facilitar o trabalho dos usuários e tornar o programa desenvolvido acessível a pesquisadores e/ou médicos ligados à área. O objetivo desta dissertação é desenvolver uma interface gráfica de usuário (GUI – Graphical User Interface) prática e que possibilite a detecção de anormalidades a partir de termogramas de mamas. Para isto foram implementados sistemas de CAD baseados em classificadores estatísticos, além de análises relacionadas ao quantitativo de casos clínicos e sua relação com a idade das pacientes. As regiões de interesse foram segmentadas tanto de forma semiautomática quanto de forma automática, as quais estão associadas, respectivamente, ao classificador SVM (Support Vector Machine) e ao classificador baseado na distância de Mahalanobis. Com o intuito de identificaras anormalidades das mamas, participaram noventa e oito pacientes do Hospital das Clínicas da Universidade Federal de Pernambuco, as quais serviram para construir a base de dados para a classificação individual de determinada paciente. A eficácia da classificação para esta amostra foi medida através da sensibilidade e da especificidade ao grupo Maligno, e das taxas de acerto das classes Benigno, Cisto e Normal. A GUI desenvolvida foi avaliada através do estudo das imagens termográficas de cinco pacientes pertencentes às diferentes classes. No presente trabalho, apresentam-se resultados para o classificador Mahalanobis e para o classificador SVM, além de suas variações. / The infrared thermography has emerged as a potential method to improve the efficiency of the early detection of the breast cancer. This technique does not use ionizing radiation and is also suitable to breast screening in men, as well as is able to detect changes in the younger women’s breasts. Computer-aided diagnosis (CAD) systems are important to medical decision and are used to improve image interpretation. Typically, these systems are associated with graphical interfaces to facilitate users’ work. Furthermore the developed framework can be an important tool (GUI - Graphical User Interface) for the people interested in breast abnormalities detection. In the sense, the CAD systems were implemented based on statistical classifiers. Some statistical analyses associated to quantitative clinical cases were performed. The relation to patients’ age was also analyzed. The regions of interest were segmented in automatic and semiautomatic manners, which are respectively associated with the SVM (Support Vector Machine) classifier and the Mahalanobis classifier. Ninety eight patients images from the Hospital das Clínicas (HC) of Federal University of Pernambuco (UFPE) participated in the tests. The classification efficiency for this sample was measured using the sensitivity and the specificity to the malignant group, and to the accuracy of classifying the classes: Benign, Cyst and Normal. The GUI created was evaluated through the study of thermographic images of five patients with the different referred classes. In the present work, the results for the Mahalanobis classifier and SVM classifier are presented.