No presente trabalho foi desenvolvida uma ferramenta para tornar o ensino do câncer na graduação mais efetivo. Tal ferramenta foi criada utilizando simulações multi-agente na plataforma NetLogo e conceitos gerais do processo de formação do câncer. O modelo que serviu de base para a criação da ferramenta foi o de Hanahan & Weinberg (2011). Inicialmente, para mostrar que a ferramenta é adequada para o ensino do câncer, foram definidos conceitualmente os Sistemas Complexos e o câncer e, em seguida, foi mostrado como estes se relacionam. Para desenvolver o presente trabalho, foram utilizadas pesquisas em dados secundários, entrevistas em profundidade e participação em aulas, palestras e seminários. O resultado desse processo foi uma ferramenta com diversas aplicações capaz de ensinar sobre o câncer através de muita interatividade e experimentação. Foi elaborada uma discussão sobre a problemática do ensino no Brasil e como isso afeta a aceitação de novas metodologias de ensino conforme a que é apresentada neste trabalho. Discutiu-se sobre o ensino do câncer no Brasil e a utilidade de ferramentas da área de sistemas complexos para tal. Para finalizar o trabalho, foram sugeridas algumas formas interessantes de se estender o uso da ferramenta desenvolvida na pesquisa científica, na clínica médica, no melhoramento de exames diagnósticos, entre outras / In the current work was developed a tool to increase the effectiveness of teaching cancer in undergraduate courses. This tool was built through multi-agents simulations in NetLogo platform and using general concepts of the cancer formation process. The tool is based in Hanahan & Weinbergs (2011) model. Initially, in order to justify the adequacy of the tool in teaching cancer, the Complex Systems and the Cancer are conceptualized and then, how they are related to each other. Secondary data research, in-depth interviews and participation in classes, seminars and lectures on relevant subjects were used to develop the current work. The result of this process is a teaching tool with many applications able to teach the student through much interaction and much experimentation. There is a discussion about the education in Brazil and how it affects new methodologies in class as the one presented in this work. There was also a discussion about the teaching of cancer in Brazil and the usefulness of tools from the field of Complex Systems for this teaching. To finish the work, some interesting forms of extension of this tool are suggested

Malignant glioma is a highly lethal and destructive disease with no proper cure. We have investigated some of the hallmarks of cancer in connection to glioma and found ways to disrupt these and prevent tumor growth. The work is done within the context of a glioma subtype distinguished by activation of PDGF signaling termed the proneural subtype. In two of the studies we have investigated mechanisms regulating the glioma cells themselves, and in the other two we have focused on the tumor stroma. In the first study, glioma-initiating cells were isolated in defined serum free culture medium from PDGF-B driven murine glioma and shown to be independent of EGF and FGF2 for self-renewal and proliferation. When cultured in serum the GICs displayed an aberrant differentiation pattern that was reversible. Specific depletion of the transduced PDGF-B caused a loss of self-renewal and tumorigenicity and induced oligodendrocyte differentiation. The transcription factor S-SOX5 has previously been shown to have a tumor suppressive effect on PDGF-B induced murine glioma, and to induce cellular senescence in PDGF-B stimulated cells in vitro. We found that S-SOX5 had a negative effect on proliferation of newly established human glioma cells cultured under stem cell conditions. We also revealed a connection between alterations causing up-regulation of SOX5 with the proneural subgroup and a tendency towards co-occurrence with PDGFRA alterations. Angiogenesis, the formation of new blood vessels from existing ones, is an important hallmark for glioma malignancy. We found that the anti-angiogenic protein HRG had a negative effect on glioma progression in PDGF-B induced experimental tumors and that HRG was able to completely prevent formation of glioblastomas. Subsequently it was shown that HRG could skew pro-tumorigenic tumor associated macrophages into an anti-tumorigenic phenotype. Stromal cells had not previously been fully investigated in gliomas. We observed a correlation between tumor malignancy and increased numbers of tumor-associated macrophages as well as pericytes in PDGF-B induced gliomas. There was also a correlation between tumor grade and vessel functionality that had not previously been shown. Our results offer further understanding of gliomagenesis and present possible future therapies.

Cancer researchers and oncologists benefit greatly from text mining major knowledge sources in biomedicine such as PubMed. Fundamentally, text mining depends on accurate text classification. In conventional natural language processing (NLP), this requires experts to annotate scientific text, which is costly and time consuming, resulting in small labelled datasets. This leads to extensive feature engineering and handcrafting in order to fully utilise small labelled datasets, which is again time consuming, and not portable between tasks and domains. In this work, we explore emerging neural network methods to reduce the burden of feature engineering while outperforming the accuracy of conventional pipeline NLP techniques. We focus specifically on the cancer domain in terms of applications, where we introduce two NLP classification tasks and datasets: the first task is that of semantic text classification according to the Hallmarks of Cancer (HoC), which enables text mining of scientific literature assisted by a taxonomy that explains the processes by which cancer starts and spreads in the body. The second task is that of the exposure routes of chemicals into the body that may lead to exposure to carcinogens. We present several novel contributions. We introduce two new semantic classification tasks (the hallmarks, and exposure routes) at both sentence and document levels along with accompanying datasets, and implement and investigate a conventional pipeline NLP classification approach for both tasks, performing both intrinsic and extrinsic evaluation. We propose a new approach to classification using multilevel embeddings and apply this approach to several tasks; we subsequently apply deep learning methods to the task of hallmark classification and evaluate its outcome. Utilising our text classification methods, we develop and two novel text mining tools targeting real-world cancer researchers. The first tool is a cancer hallmark text mining tool that identifies association between a search query and cancer hallmarks; the second tool is a new literature-based discovery (LBD) system designed for the cancer domain. We evaluate both tools with end users (cancer researchers) and find they demonstrate good accuracy and promising potential for cancer research.

Breast cancer is the second most common type of cancer among women in the US and the second leading cause of cancer death. HER2+ breast cancers represent ~20% of all cancer types, are highly invasive, and can be treated by using targeted therapies against the HER2 receptor. However, these therapies are challenged by the development of drug resistance, often induced by the presence of mutations in the cell-membrane proteins and receptors and/or by alternative signaling pathways that cross-talk with- or transactivate HER2+ triggered signaling. This study was aimed at investigating the cell membrane proteome of SKBR3 cells, representative of HER2+ breast cancers, and the signaling landscape and cellular responses elicited by the cell membrane receptors when the cells are stimulated with either growth factors or therapeutic drugs. It was hypothesized that the identification of a broad range of cell membrane proteins with roles in cancer progression and signaling crosstalk will lead to a more comprehensive understanding of the biological processes that sustain the proliferation of cancer cells, and will guide the selection of more efficient drug targets. The project was conceptualized in three stages: (1) profiling the cell membrane proteins of SKBR3 cells, (2) determining the functional role of the detected cell membrane proteins in the context of cancer hallmarks and exploring their mutational profile, and (3) analyzing the cellular events that occur in response to treatment with a single therapeutic agent or a combination of drugs. Mass spectrometry technologies were used for performing proteomic and phosphoproteomic profiling of SKBR3 cells, detecting changes in the abundance of the detected proteins, and identifying the presence of mutations in the cell membrane proteins. Orthogonal enrichment methods were developed for profiling the low-abundance cell membrane proteins, for generating a rich landscape of cell membrane receptors with various functional roles and relevance to the cancer hallmarks, and for enabling the detection of potentially new drivers of aberrant proliferation. The analysis of serum-starved, stimulated (with growth factors), or inhibited (with kinase inhibitors) cells revealed alternative protein players and crosstalk activities that determine the fate of cells, and that may fuel the development of resistance to treatment with drugs. The proteome profiles that were generated in this project expand the opportunities for targeting cancer-relevant processes beyond proliferation, which is commonly attempted, broadening the landscape to also include apoptosis, invasion, and metastasis. Altogether, the findings that emerged from this work will lay the ground for future studies that aim at developing more complex and effective targeted cancer treatment approaches. / Doctor of Philosophy / Breast cancer is one of the most common cancers among women in the US and the second major contributor to cancer-related deaths. Several therapies that have been developed for the treatment of cancer target the HER2 receptor, which is overexpressed in ~20% of breast cancers and results in a highly invasive cancer phenotype. However, most patients receiving these therapies observe cancer reoccurrence within a year due to the development of resistance to the therapeutic drug. The current challenge stands in identifying novel protein targets, and in developing new therapies that can be used in combination with the existing approaches to eradicate cancer. Research has indicated that proteins located at the cell membrane play crucial roles in cancer progression and invasion due to their involvement in cell response to stimuli and in initiating signaling cascades within the cell. Knowledge about the cell membrane proteins of HER2+ breast cancer cells is limited due to the challenges associated with their isolation. Therefore, this project was aimed at profiling the cell membrane proteins of HER2+ breast cancer cells, and their intra-cellular signaling activity, to provide insights into the behavior of these cells and to support the identification of potentially novel drug targets. The three objectives of the work were to (1) isolate the cell membrane proteins through various approaches using cell culture conditions that would encourage or discourage cancer cell growth, (2) identify the cancer-relevant signaling pathways and processes represented by the detected cell membrane proteins, and (3) investigate the behavior of cancer cells when treated with drugs. To approach these objectives, a powerful analytical technology, called mass spectrometry, was utilized. Mass spectrometry can accurately and simultaneously detect the presence of the proteins in a biological sample. Our study identified cell membrane proteins that are involved in cancer progression through various signaling pathways, and how these proteins interact with each other to drive the behavior of cells. The study also provided insights into how cancer cells respond when they are treated with various drugs, uncovering to the scientific community a variety of proteins with potential therapeutic value. Lastly, this study sheds light on the complex biology of breast cancer and highlights the importance of continued research to develop more effective treatments.

Ovarian Cancer is the 5th most common cause of cancer death in women and the second most common gynaecological cancer in the UK. Worldwide, around 152,000 women were estimated to have died from ovarian cancer in 2012. Survival rates for women with epithelial ovarian cancer have not significantly changed since platinum-based treatment was introduced over 30 years ago. This is particularly disconcerting considering the fact that there is a less than 5% five year survival rate for patients diagnosed with late stage high grade serous ovarian cancer. This thesis examines the role of BMP signalling in ovarian cancer using in vitro cancer cell models. It builds upon the initial published work by the Inman lab identifying autocrine BMP9 as a promoter of ovarian cancer cell proliferation. The findings of Chapters 3-5 provide strong evidence indicating BMP9 as a context specific modulator of ovarian cancer cell proliferation. This significantly builds upon on the sole pro-proliferative BMP9 growth response previously described. Responding cell lines were subjected to a microarray with and without BMP9 treatment In order to determine early BMP target genes which were subsequently transiently knocked down in order to determine their role in the aetiology of said growth phenotype. ID1 gene expression was found to significantly contribute to the BMP9 proproliferative phenotype. Moreover several other BMP genes identified significantly alter basal cell proliferation. It was subsequently determined that BMP9 implemented a cell growth phenotype by negating apoptosis. .Excitingly, preliminary evidence suggests a marked reduction in detectable levels of a recently described Bax isoform, Bax β that coincide with BMP9 addition and the resultant anti-apoptotic phenotype observed. This is very interesting as no prior evidence correlating the BMP family and Bax β currently exists. These findings provide an enhanced understanding of BMP9s contribution to ovarian cancer pathogenesis that may result in the development of effective and targeted therapeutic interventions upon further stratification of the contextuality of the BMP induced growth response.

Viscoelasticity and its alteration in time and space has turned out to act as a key element in fundamental biological processes in living systems, such as morphogenesis and motility. Based on experimental and theoretical findings it can be proposed that viscoelasticity of cells, spheroids and tissues seems to be a collective characteristic that demands macromolecular, intracellular component and intercellular interactions. A major challenge is to couple the alterations in the macroscopic structural or material characteristics of cells, spheroids and tissues, such as cell and tissue phase transitions, to the microscopic interferences of their elements. Therefore, the biophysical technologies need to be improved, advanced and connected to classical biological assays. In this review, the viscoelastic nature of cytoskeletal, extracellular and cellular networks is presented and discussed. Viscoelasticity is conceptualized as a major contributor to cell migration and invasion and it is discussed whether it can serve as a biomarker for the cells’ migratory capacity in several biological contexts. It can be hypothesized that the statistical mechanics of intra- and extracellular networks may be applied in the future as a powerful tool to explore quantitatively the biomechanical foundation of viscoelasticity over a broad range of time and length scales. Finally, the importance of the cellular viscoelasticity is illustrated in identifying and characterizing multiple disorders, such as cancer, tissue injuries, acute or chronic inflammations or fibrotic diseases.

Gene expression can serve as a powerful predictor for disease progression and other phenotypes. Consequently, microarrays, which capture gene expression genome-wide, have been used widely over the past two decades to derive biomarker signatures for tasks such as cancer grading, prognosticating the formation of metastases, survival, and others. Each of these signatures was selected and optimized for a very specific phenotype, tissue type, and experimental set-up. While all of these differences may naturally contribute to very heterogeneous and different biomarker signatures, all cancers share characteristics regardless of particular cell types or tissue as summarized in the hallmarks of cancer. These commonalities could give rise to biomarker signatures, which perform well across different phenotypes, cell and tissue types. Here, we explore this possibility by employing a network-based approach for pan-cancer biomarker discovery. We implement a random surfer model, which integrates interaction, expression, and phenotypic information to rank genes by their suitability for outcome prediction. To evaluate our approach, we assembled 105 high-quality microarray datasets sampled from around 13,000 patients and covering 13 cancer types. We applied our approach (NetRank) to each dataset and aggregated individual signatures into one compact signature of 50 genes. This signature stands out for two reasons. First, in contrast to other signatures of the 105 datasets, it is performant across nearly all cancer types and phenotypes. Second, It is interpretable, as the majority of genes are linked to the hallmarks of cancer in general and proliferation specifically. Many of the identified genes are cancer drivers with a known mutation burden linked to cancer. Overall, our work demonstrates the power of network-based approaches to compose robust, compact, and universal biomarker signatures for cancer outcome prediction.