Carbon nanotubes micro-arrays: characterization and application in biosensing of free proteins and label-free capture of breast cancer cells

Circulating tumor cells (CTCs) are cells released into the bloodstream from primary tumors and are suspected to be one of the main causes behind metastatic spreading of cancer. The ability to capture and analyze circulating tumor cells in clinical samples is of great interest in prevailing patient prognosis and clinical management of cancer. Carbon nanotubes, individual rolled-up graphene sheets, have emerged as exciting materials for probing the biomolecular interactions. With diameter of about 1 nm, they can attach themselves to cell surface receptors through specific antibodies and hold a great potential for diagnostic cellular profiling. Carbon nanotubes can be either semiconducting or metallic, and the electronic properties of either type rivals the best known materials. Small size of nanotubes and the ability to functionalize their surface using 1-Pyrenebutanoic Acid, Succinimidyl Ester (PASE), enables a versatile probe for developing a platform for capture and analysis of cancer biomarkers and circulating tumor cells. Although nanotubes have previously been used to electrically detect a variety of molecules and proteins, here for the first time we demonstrate the label free capture of spiked breast cancer cells using ultra-thin carbon nanotube film micro-array devices in a drop of buffy coat and blood. A new statistical approach of using Dynamic Time Warping (DTW) was used to classify the electrical signatures with 90% sensitivity and 90% specificity in blood. These results suggest such label free devices could potentially be useful for clinical capture and further analysis of circulating tumor cells. This thesis will go in-depth the properties of carbon nanotubes, device fabrication and characterization methodologies, functionalization protocols, and experiments in buffy coats and in blood. Combination of nano and biological materials, functionalization protocols and advanced statistical classifiers can potentially enable clinical translation of such devices in the future

Tumor vasculature and microenvironment during progression and treatment : insights from optical microscopy

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2010.Vita. Cataloged from PDF version of thesis.Includes bibliographical references.In addition to cancer cells, solid tumors consist of a variety of cell types and tissues defining a complex microenvironment that influences disease progression and response to therapy. To fully characterize and probe the tumor microenvironment, new tools are needed to quantitatively assess microanatomical and physiological changes during tumor growth and treatment. Particularly important, is the metabolic microenvironment defined in tumors by hypoxia (low p02) and acidity (low pH). These parameters have been shown to influence response to radiation therapy and chemotherapy. However, very little is known about spatio-temporal changes in p02 and pH during tumor progression and therapy. By modifying the technique of intravital multiphoton microscopy (MPM) to perform phosphorescence quenching microscopy, I developed a non-invasive method to quantify oxygen tension (p02) in living tissue at high three-dimensional resolution. To probe functional changes in the metabolic microenvironment, I measured in vivo P02 during tumor growth and antiangiogenic (vascular targeted) treatment in preclinical tumor models. Nanotechnology is rapidly emerging as an important source of biocompatible tools that may shape the future of medical practice. Fluorescent semiconductor nanocrystals (NCs), also known as quantum dots, are a powerful tool for biological imaging, cellular targeting and molecular sensing.(cont.) I adapted novel fluorescence resonance energy transfer (FRET) -based nanocrystal (NC) biosensors for use with MPM to qualitatively measure in vivo extracellular pH in tumors at high-resolution. While intravital multiphoton microscopy demonstrates utility and adaptability in the study of cancer and response to therapy, the requisite high numerical aperture and exogenous contrast agents result in a limited capacity to investigate substantial tissue volumes or probe dynamic changes repeatedly over prolonged periods. By applying optical frequency domain imaging (OFDI) as an intravital microscopic tool, the technical limitations of multiphoton microscopy can be circumvented providing unprecedented access to previously unexplored, critically important aspects of tumor biology. Using entirely intrinsic mechanisms of contrast within murine tumor models, OFDI is able to simultaneously, rapidly, and repeatedly probe the microvasculature, lymphatic vessels, and tissue microstructure and composition over large volumes. Using OFDI-based techniques, measurements of tumor angiogenesis, lymphangiogenesis, tissue viability and both vascular and cellular responses to therapy were demonstrated, thereby highlighting the potential of OFDI to facilitate the exploration of pathophysiological processes and the evaluation of treatment strategies.by Ryan M. Lanning.Ph.D

Priorización de genes y búsqueda de dianas terapéuticas por medio de herramientas informáticas y técnicas de aprendizaje automatizado en cáncer de mama

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01Tese por compendio de publicacións[Resumen] El cáncer de mama (CM) es la principal causa de muerte relacionada a neoplasias en mujeres y es el tipo de cáncer más diagnosticado a nivel mundial. CM es una enfermedad heterogénea en donde están envueltos diversos factores como alteraciones genómicas, desregulación de la expresión de proteínas, alteración de cascadas genéticas, desregulación hormonal, determinantes ambientales y etnicidad. A pesar de los grandes avances tecnológicos y científicos en los últimos años, la comprensión de los procesos moleculares, la identificación de nuevas dianas terapéuticas y la predicción de proteínas envueltas inmunoterapia, metástasis, y unión al ARN es indispensable para el desarrollo de fármacos y la aplicación de la medicina de precisión en la práctica clínica. La tesis aquí propuesta plantea el desarrollo de una estrategia consenso altamente eficiente en el reconocimiento de genes y proteínas asociadas al CM; la validación oncológica de dichos genes y proteínas priorizadas mediante la estrategia OncoOmics que consistió en el análisis de bases de datos experimentales de alta relevancia a nivel mundial; la identificación de mutaciones oncogénicas y fármacos indispensables para el desarrollo y aplicación de la medicina de precisión; y la predicción de proteínas de CM asociadas a inmunoterapia, metástasis y unión al ARN mediante diversas herramientas informáticas y métodos de inteligencia artificial. Todos los resultados se publicaron en revistas internacionales de importante factor de impacto.Abstract] Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. BC is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. Despite the technological and scientific advances in recent years, an understanding of molecular processes, the identification of new therapeutic targets and the prediction of proteins involved in immunotherapy, metastasis, and RNA binding is essential for drug development and application of precision medicine in clinical practice. The current thesis proposes the development of a high efficient consensus strategy in the recognition of genes and proteins associated with BC; the oncological validation of these prioritized genes and proteins using the OncoOmics strategy, which consisted of the analysis of outstanding experimental databases; the identification of oncogenic mutations and essential drugs for the development and application of precision medicine; and the prediction of BC proteins associated with immunotherapy, metastasis and RNA-binding using bioinformatics tools and artificial intelligence methods. All results were published in international journals with a significant impact factor.[Resumo] O cancro de mama (CM) é a principal causa de morte relacionada con enfermidades malignas en mulleres e é o tipo de cancro máis diagnosticado a nivel mundial. A CM é unha enfermidade heteroxénea onde interveñen varios factores, como alteracións xenómicas, desregulación da expresión proteica, alteración de cascadas xenéticas, desregulación hormonal, determinantes ambientais e etnia. A pesar dos grandes avances tecnolóxicos e científicos dos últimos anos, a comprensión dos procesos moleculares, a identificación de novas dianas terapéuticas e a predición de proteínas implicadas na inmunoterapia, metástase e unión ao ARN é fundamental para o desenvolvemento de fármacos e aplicación da medicina de precisión na práctica clínica. Esta tese propón o desenvolvemento dunha estratexia de consenso altamente eficiente no recoñecemento de xenes e proteínas asociadas a CM; a validación oncolóxica destes xenes e proteínas prioritarias mediante a estratexia OncoOmics, que consistiu na análise de bases de datos experimentais altamente relevantes en todo o mundo; a identificación de mutacións oncogénicas e fármacos esenciais para o desenvolvemento e aplicación da medicina de precisión; e a predición de proteínas CM asociadas á inmunoterapia, metástase e unión ao ARN usando diversas ferramentas informáticas e métodos de intelixencia artificial. Todos os resultados publicáronse en revistas internacionais cun importante factor de impacto

Characterizing SARS-CoV-2 oncogenic features from protein-protein multilayer interaction networks

In recent years, network-based approaches have become increasingly popular in the field of molecular biology and network medicine. One of the main challenges in this field is to identify the role of viruses in the development of cancer. In this thesis, we apply multilayer complex network theory to protein-protein interaction networks of different viruses, including the SARS-CoV-2 virus, to classify them as oncogenic or non-oncogenic. Specifically, we use tools from graph theory and machine learning to analyze the topology and structure of these networks, and to identify the key proteins and pathways involved in virus-induced carcinogenesis. We aim to create two classes, one for oncogenic and one for non-oncogenic viruses, and then verify in which of the two the SARS-CoV-2 virus falls. The results of this study may provide new insights into the mechanisms underlying virus-induced cancer and could lead to the development of novel therapeutic strategies

Priorización de genes y búsqueda de fármacos por medio de herramientas informáticas y técnicas de aprendizaje de máquinas en osteosarcoma

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01Tese por compendio de publicacións[Resumen] El osteosarcoma es el subtipo más común de cáncer de hueso primario y afecta principalmente a adolescentes. En los últimos años, varios estudios se han centrado en dilucidar los mecanismos moleculares de este sarcoma; sin embargo, su etiología molecular aún no se ha determinado con precisión. Por otro lado, su diagnóstico clínico es generalista y sus terapias no han cambiado en las últimas décadas. Aunque hoy en día las tasas de supervivencia a 5 años pueden alcanzar hasta el 60-70%, las complicaciones agudas y los efectos tardíos del tratamiento del osteosarcoma son dos de los factores limitantes de los tratamientos. Así, el objetivo de esta tesis doctoral es desarrollar una estrategia de priorización que permita la identificación de genes asociados con la patogenicidad del osteosarcoma y explicar de forma más completa la etiología de esta enfermedad. Por otro lado, se busca desarrollar algoritmos de predicción de fármacos basados en aprendizaje de máquinas que permitan proponer nuevos agentes terapéuticos para el tratamiento de esta enfermedad. Todos los resultados obtenidos se publicaron en revistas científicas internacionales con importante factor de impacto JCR.[Abstract] Osteosarcoma is the most common subtype of primary bone cancer, affecting mainly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has not yet been accurately determined. On the other hand, the clinical diagnosis is generalist and therapies have not changed in recent decades. Although nowadays 5-year survival rates can reach up to 60-70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. Thus, the objective of this doctoral thesis is to develop a prioritization strategy that allows the identification of genes associated with the pathogenicity of osteosarcoma, and to explain more fully the etiology of this disease. On the other hand, it seeks to develop drug prediction algorithms based on machine learning techniques that allow proposing new therapeutic agents for the treatment of this disease. All the results obtained in this research were published in international scientific journals with an important JCR impact factor.[Resumo] O osteosarcoma é o subtipo máis común de cancro óseo primario, que afecta principalmente a adolescentes. Nos últimos anos, varios estudos centráronse en dilucidar os mecanismos moleculares deste sarcoma; con todo, a súa etioloxía molecular aínda non foi determinada con precisión. Por outra banda, o seu diagnóstico clínico é xeralista e as súas terapias non cambiaron nas últimas décadas. Aínda que hoxe as taxas de supervivencia a 5 anos poden chegar ata o 60- 70%, as complicacións agudas e os efectos tardíos do tratamento con osteosarcoma son dous dos factores limitantes dos tratamentos. Deste xeito, o obxectivo desta tese de doutoramento é desenvolver unha estratexia de priorización que permita a identificación de xenes asociados á patoxenicidade do osteosarcoma e explicar máis plenamente a etioloxía desta enfermidade. Por outra banda, buscamos desenvolver algoritmos de predición de medicamentos baseados na aprendizaxe automática que permitan propoñer novos axentes terapéuticos para o tratamento desta enfermidade. Todos os resultados obtidos publicáronse en revistas científicas internacionais cun importante factor de impacto JCR

Doctor of Philosophy

dissertationThe overall objective of this project is to develop methods that can help us to understand the movement of drugs and carriers along their routes inside solid tumors. The origins and current paradigm of targeted drug delivery offer a lot of promising strategies. However, the carriers often struggle with challenges in optimizing their own characteristics against that of the tumor's. Ultimately, they struggle with translation into the clinical setting. It is apparent that solid tumors pose a unique challenge in drug delivery. Many drug carrier characteristics are designed to take advantage of the pathophysiology of the tumor environment. However, this passive delivery and accumulation is constrained to partial distribution within the tumor. Many uncertainties remain regarding how nanoparticles enter and travel through the tumor environment. The barriers to intratumoral distribution are still currently being probed. The research herein identified transport barriers using human fibroid tumors known to have impaired drug transport. After perfusing human uteri containing fibroids with stains, probe distribution was found to correlate with features of the pathophysiology such as blood vessel characteristics, tissue and collagen density, interstitial fluid pressure, and solid stress. Methods, including custom MATLAB code, were developed to analyze the spatiotemporal distribution of two uniquely fluorescent nanoparticle doses in xenograft mice. It shows how three-dimensional distance measurements of nanoparticles from nearest blood vessels are more precise than two-dimensional measurements. Colocalization analysis on the fluorescent signals showed the two different doses (administered hours apart from each other) did not accumulate in the same locations with the tumor. Furthermore, intravital imaging showed that some vessels of the tumor would only provide access to the first dose of nanoparticles. Future work suggests further analysis of multidose interdependence and implementing these methods to screen strategies in the literature of modifying drug carriers and the tumor environment to improve intratumoral distribution of cancer drugs. The more understanding we have of the solid tumor environment and its barriers, the better we can navigate treatments to reach the tumor

In Vivo computation - Where computing meets nanosytem for smart tumor biosensing

According to World Health Organization, 13.1 million people will die in the world just because of cancer by 2030. Early tumor detection is very crucial to saving the world from this alarming mortality rate. However, it is an insurmountable challenge for the existing medical imaging techniques with limited imaging resolution to detect microscopic tumors. Hence, the need of the hour is to explore novel cross-disciplinary strategies to solve this problem. The rise of nanotechnologies provides a strong belief to solve complex medical problems such as early tumor detection. Nanoparticles with sizes ranging between 1-100 nanometers can be used as contrast agents. Their small sizes enable them to leak out of blood vessels and accumulate within tumors. Moreover, their chemical, optical, magnetic and electronic properties also change at nanoscale, which make them an ideal probing agent to spatially highlight the tumor site. Though, using nanoparticles to target malignant tumors is a promising concept, only 0.7% of the injected nanoparticles reach the tumor according to the statistical results of last 10 years. In PhD work, we proposed novel in vivo computational frameworks for fast, accurate and robust nanobiosensing. Specifically, the peritumoral region corresponds to the “objective function”; the tumor is the “global optimum”; the region of interest is the “domain” of the objective function; and the nanoswimmers are the “computational agents” (i.e., guesses or optimization variables). First, in externally manipulable in vivo computation, nanoswimmers are used as contrast agents to probe the region of interest. The observable characteristics of these nanoswimmers, under the influence of tumor-induced biological gradients, are utilized by the external tracking system to steer nanoswimmers towards the possible tumor direction. To take it one step ahead, we provide solutions to the real-life constraints of in vivo natural computation such as uniformity of the external steering force and finite life span of the nanoswimmers. To overcome these challenges, we propose a multi-estimate-fusion strategy to obtain a common steering direction for the swarm of nanoswimmers and an iterative memory-driven gradient descent optimization strategy for faster tumor sensitization. Next, we proposed a parallel framework called autonomous in vivo computation, where the tumor sensitization is highly scalable and tracking-free. We demonstrate that the tumor-triggered biophysical gradients can be leveraged by nanoparticles to collectively move toward the potential tumor hypoxic regions without the aid of any external intervention. Although individual nanoparticles have no target-directed locomotion ability due to limited communication and computation capability, we showed that once passive collaboration is achieved, they can successfully avoid obstacles and detect the tumor. Finally, to address the respective limitations of externally manipulable and autonomous settings such as constant monitoring and slow detection, we proposed a semi-autonomous in vivo computational framework. We showed that the spot sampling strategy for an autonomous swarm of nanoswimmers can achieve faster tumor sensitization in complex environments. This approach makes the swarm highly scalable along with giving it the freedom from constant monitoring. The performance of the aforementioned tumor sensitization frameworks is evaluated through comprehensive in silico experiments that mimic the realistic targeting processes in externally manipulable, self-regulatable and semi-autonomous settings. The efficacies of the proposed frameworks are demonstrated through numerical simulations that incorporate various physical constraints with respect to controlling and steering of computational agents, their motion in discretized vascular networks and their motion under the influence of disturbance and noise

Antitumoral Properties of Natural Products

Cancer is one of the major causes of death worldwide. It is a multifactorial heterogeneous disease characterized by the transformation of normal cells into malignant cells, which acquire an uncontrolled growth, immortality, invasiveness, and ability to form distant metastasis. Natural bioactive molecules may interfere with these processes and inhibit the carcinogenesis process. In this book, new molecules and extracts, mainly derived from plants, have been described as being able to alter tumor cell behavior and target several abnormal molecular pathways in cancer cells. Among different cancer cells, the more studied include those derived from glioblastoma, osteosarcoma, lung, breast and gastric cancer. These natural products could be an attractive source for the development of new preventative and therapeutic agents against cancer. They may be more selective and have weaker adverse effects compared to conventional chemotherapy drugs that are actually used for cancer treatment. Clinical trials are necessary to demonstrate whether the in vitro and in vivo animal data are reproduced in humans before the application of natural products in cancer prevention and treatment