Dissertations / Theses on the topic 'Breast cancer prediction models'

Introducció: S'ha demostrat que el cribratge mamogràfic redueix la mortalitat per càncer de mama. Seguint les recomanacions de la Comissió Europea, els països europeus han establert programes poblacionals de cribratge que ofereixen mamografies biennals a dones d'entre 50 i 69 anys d'edat. No obstant això, el cribratge de càncer de mama no està lliure de controvèrsia ja que existeix un debat en relació a l'equilibri entre la reducció de la mortalitat i els efectes adversos. Per a millorar aquest equilibri, l'evidència científica actual dóna suport al cribratge personalitzat. Els estudis de modelització han demostrat que modificar l'interval de cribratge, la prova de cribratge o el rang d'edat de la població objectiu en funció del risc individual de les dones produeix un major benefici que les estratègies convencionals. Per tant, és necessari ampliar la informació actual sobre els factors de risc d'aquesta malaltia i crear models de predicció del risc individual mitjançant l'anàlisi de grans bases de dades poblacionals. Objectiu: L'objectiu general d'aquesta tesi és aprofundir en l'anàlisi del cribratge poblacional del càncer de mama. En concret, aquesta tesi pretén avaluar diferents factors de risc de càncer de mama per a desenvolupar i validar un model de predicció de risc individual d'aquesta malaltia. Es va analitzar com la densitat mamària afecta als diferents indicadors del cribratge en el context de la mamografia digital. A continuació, es van avaluar les diferències en el risc de càncer de mama en funció de si una lesió benigna de mama es va diagnosticar en un cribratge prevalent o un cribratge incident. També es va analitzar la interacció entre la densitat mamària i les lesions benignes en el risc de desenvolupar càncer de mama. Posteriorment, es va realitzar una revisió sistemàtica per a actualitzar l'evidència existent, dur a terme una valoració crítica i una avaluació del risc de biaix i resumir els resultats dels models de risc individualitzats que s'utilitzen per a estimar el risc de càncer de mama en les dones de la població general. Finalment, es va dissenyar un model de predicció individual del risc de càncer de mama i es va validar internament, a partir d'informació fàcilment accessible en un episodi de cribratge. Conclusions: i) Els diferents indicadors de cribratge es veuen afectats negativament per la densitat mamària, disminuint la sensibilitat i el valor predictiu positiu de la prova a mesura que augmenta la densitat mamària. ii) El risc de càncer de mama conferit per una lesió benigna difereix segons la mena de cribratge (prevalent o incident). Fins on sabem, aquest és el primer estudi que analitza l'impacte del tipus de cribratge en el pronòstic de la lesió benigna. iii) El risc de càncer de mama augmenta de manera independent amb la presència d'una lesió benigna i amb una major densitat mamària i es manté elevat durant més de 15 anys. iv) Els models de predicció són eines prometedores per a implementar polítiques de cribratge basades en el risc individualitzat. No obstant això, és un repte recomanar qualsevol d'ells per a la personalització del cribratge ja que necessiten millorar la seva qualitat i capacitat discriminatòria. v) Es va dissenyar i validar internament un model de predicció de risc capaç d'estimar el risc de càncer de mama a curt i llarg termini utilitzant la informació recollida de manera rutinària en el cribratge mamogràfic. El model inclou edat, antecedents familiars de càncer de mama, antecedents de lesió benigna i patrons mamogràfics previs, que van resultar estar relacionats amb un augment del risc de càncer de mama. El model ha de ser validat externament i actualitzat amb noves variables.
Introducción: Se ha demostrado que el cribado mamográfico reduce la mortalidad por cáncer de mama. Siguiendo las recomendaciones de la Comisión Europea, los países europeos han establecido programas poblacionales de cribado que ofrecen mamografías bienales a mujeres de entre 50 y 69 años de edad. Sin embargo, el cribado de cáncer de mama no está libre de controversia ya que existe un debate en cuanto al equilibrio entre la reducción de la mortalidad y los efectos adversos. Para mejorar este equilibrio, la evidencia científica actual apoya el cribado personalizado. Los estudios de modelización han demostrado que modificar el intervalo de cribado, la prueba de cribado o el rango de edad de la población objetivo en función del riesgo individual de las mujeres produce un mayor beneficio que las estrategias convencionales. Por lo tanto, es necesario ampliar la información actual sobre los factores de riesgo de esta enfermedad y crear modelos de predicción del riesgo individual mediante el análisis de grandes bases de datos poblacionales. Objetivo: El objetivo general de esta tesis es profundizar en el análisis del cribado poblacional del cáncer de mama. En concreto, esta tesis pretende evaluar diferentes factores de riesgo de cáncer de mama para desarrollar y validar un modelo de predicción de riesgo individual de esta enfermedad. Se analizó cómo la densidad mamaria afecta a los distintos indicadores de cribado en el contexto de la mamografía digital. A continuación, se evaluaron las diferencias en el riesgo de cáncer de mama en función de si una lesión benigna de mama se diagnosticó en un cribado prevalente o un cribado incidente. También se analizó la interacción entre la densidad mamaria y las lesiones benignas en el riesgo de cáncer de mama. Posteriormente, se realizó una revisión sistemática para actualizar la evidencia existente, llevar a cabo una valoración crítica y una evaluación del riesgo de sesgo y resumir los resultados de los modelos de riesgo individualizados que se utilizan para estimar el riesgo de cáncer de mama en las mujeres de la población general. Por último, se diseñó un modelo de predicción individual del riesgo de cáncer de mama y se validó internamente, basado en información fácilmente accesible en un episodio de cribado. Conclusiones: i) Los distintos indicadores de cribado se ven afectados negativamente por la densidad mamaria, disminuyendo la sensibilidad y el valor predictivo positivo de la prueba a medida que aumenta la densidad mamaria. ii) El riesgo de cáncer de mama conferido por una lesión benigna difiere según el tipo de cribado (prevalente o incidente). Hasta donde sabemos, este es el primer estudio que analiza el impacto del tipo de cribado en el pronóstico de la lesión benigna. iii) El riesgo de cáncer de mama aumenta de forma independiente con la presencia de una lesión benigna y con una mayor densidad mamaria y se mantiene elevado durante más de 15 años. iv) Los modelos de predicción son herramientas prometedoras para implementar políticas de cribado basadas en el riesgo individualizado. Sin embargo, es un reto recomendar cualquiera de ellos para la personalización del cribado ya que necesitan mejorar su calidad y capacidad discriminatoria. v) Diseñamos y validamos internamente un modelo de predicción de riesgo capaz de estimar el riesgo de cáncer de mama a corto y largo plazo utilizando la información recogida de forma rutinaria en el cribado mamográfico. El modelo incluye edad, antecedentes familiares de cáncer de mama, antecedentes de lesión benigna y patrones mamográficos previos, que resultaron estar relacionados con un aumento del riesgo de cáncer de mama. El modelo debe ser validado externamente y actualizado con nuevas variables.
Background: Mammographic screening has been shown to reduce mortality from breast cancer. Following the recommendations of the European Council, European countries have started population-based screening programs that offer biennial mammograms to women aged between 50 and 69 years. The results of the effectiveness of population-based screening are controversial in terms of the balance between mortality reduction and adverse effects. To improve this balance, current evidence supports personalized screening. Modeling studies have shown that modifying the screening interval, screening modality, or age range of the target population based on women's individual risk yields a greater benefit than conventional standard strategies. Several risk models have been designed to estimate women's individual breast cancer risk based on their personal characteristics. However, most of these models have not been specifically developed to estimate the risk of women targeted for breast cancer screening. There is therefore a need to broaden current information on risk factors for breast cancer and the estimation of individual risk prediction models through the analysis of large population-based databases. Aims: The general objective of the thesis is to deepen the analysis of population-based breast cancer screening. Specifically, the aim of this thesis is to assess different breast cancer risk factors in order to develop and validate an individualized breast cancer risk prediction model. We evaluated how breast density affects screening performance indicators in a digital mammography context. Then, we assessed differences in breast cancer risk across benign breast disease diagnosed at prevalent or incident screens. To our knowledge, this is the first time that such an approach has been used. We also evaluated the interaction between breast density and benign breast disease. Subsequently, we performed a systematic review to update the existing evidence, conduct a critical appraisal and risk of bias assessment and summarize the results of the individualized risk models that are used to estimate the risk of breast cancer in women in the general population. Finally, a breast cancer risk prediction model was designed and internally validated, based on information easily accessible at screening. Conclusions: i) Performance screening measures are negatively affected by breast density, with sensitivity and positive predictive value decreasing as breast density increases. ii) The risk of breast cancer conferred by benign breast disease differed according to type of screen (prevalent or incident). To our knowledge, this is the first study to analyze the impact of screening type on the prognosis of benign breast disease. iii) The risk of breast cancer independently increased with the presence of benign breast disease and with greater breast density and remained elevated for over 15 years. iv) Individualized risk prediction models are promising tools for implementing risk-based screening policies. However, it is a challenge to recommend any of them since they need further improvement in their quality and discriminatory capacity. v) We designed and internally validated a risk prediction model able to estimate short- and long-term breast cancer risk using information routinely reported at screening participation. The model included age, family history of breast cancer, benign breast disease and previous mammographic findings, which were found to be related to an increase in breast cancer risk. The model should be externally validated and updated with new variables.
Universitat Autònoma de Barcelona. Programa de Doctorat en Metodologia de la Recerca Biomèdica i Salut Pública

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores – Sistemas Digitais e Percepcionais pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia
There are several clinical prognostic models in the medical field. Prior to clinical use, the outcome models of longitudinal cohort data need to undergo a multi-centre evaluation of their predictive accuracy. This thesis evaluates the possible gain in predictive accuracy in multicentre evaluation of a flexible model with Bayesian regularisation, the (PLANN-ARD), using a reference data set for breast cancer, which comprises 4016 records from patients diagnosed during 1989-93 and reported by the BCCA, Canada, with follow-up of 10 years. The method is compared with the widely used Cox regression model. Both methods were fitted to routinely acquired data from 743 patients diagnosed during 1990-94 at the Christie Hospital, UK, with follow-up of 5 years following surgery. Methodological advances developed to support the external validation of this neural network with clinical data include: imputation of missing data in both the training and validation data sets; and a prognostic index for stratification of patients into risk groups that can be extended to non-linear models. Predictive accuracy was measured empirically with a standard discrimination index, Ctd, and with a calibration measure, using the Hosmer-Lemeshow test statistic. Both Cox regression and the PLANN-ARD model are found to have similar discrimination but the neural network showed marginally better predictive accuracy over the 5-year followup period. In addition, the regularised neural network has the substantial advantage of being suited for making predictions of hazard rates and survival for individual patients. Four different approaches to stratify patients into risk groups are also proposed, each with a different foundation. While it was found that the four methodologies broadly agree, there are important differences between them. Rules sets were extracted and compared for the two stratification methods, the log-rank bootstrap and by direct application of regression trees, and with two rule extraction methodologies, OSRE and CART, respectively. In addition, widely used clinical breast cancer prognostic indexes such as the NPI, TNM and St. Gallen consensus rules, were compared with the proposed prognostic models expressed as regression trees, concluding that the suggested approaches may enhance current practice. Finally, a Web clinical decision support system is proposed for clinical oncologists and for breast cancer patients making prognostic assessments, which is tailored to the particular characteristics of the individual patient. This system comprises three different prognostic modelling methodologies: the NPI, Cox regression modelling and PLANN-ARD. For a given patient, all three models yield a generally consistent but not identical set of prognostic indices that can be analysed together in order to obtain a consensus and so achieve a more robust prognostic assessment of the expected patient outcome.

Personalised medicine is replacing the one-drug-fits-all approach with many prognostic models incorporating biomarkers available for risk stratifying patients. Evidence has been emerging that the effects of biomarkers change over time and therefore violate the assumption of proportional hazards when performing Cox regression. Analysis using the Cox model when the assumptions are invalid can result in misleading conclusions. This thesis reviews existing approaches for the analysis of non-proportional effects with respect to survival data. A number of well-developed approaches were identified but to date their uptake in practice has been limited. There is a need for more widespread use of flexible modelling to move away from standard analysis using a Cox model when the assumption of proportional hazards is violated. Two novel approaches were applied to investigate the impact of follow-up duration on two residual risk models, IHC4 and Mammostrat, for predicting risk in early breast cancers using two studies with different lengths of follow up; the Edinburgh Breast Conservation Series (BCS) and the Tamoxifen versus Exemestane Adjuvant Multinational (TEAM) trial. Similar results were observed between the two approaches that were considered, the multivariable fractional polynomial time (MFPT) approach and Royston-Parmer flexible parametric models, with their respective advantages and disadvantages being discussed. The analyses identified a strong time-varying effect of IHC4 score with the prognostic effect of IHC4 score on time-to distant recurrence decreasing with increasing follow-up time. Mammostrat score identified a group of patients with an increased risk of distant recurrence over full follow-up in the TEAM and Edinburgh BCS cohorts. The results suggest a combined IHC4 and Mammostrat risk score could provide information on the risk of recurrence and warrants further study.

Contexte : Le cancer est la seconde cause de mortalité chez la femme en France, et la première chez les femmes âgées de 35 à 84 ans. Le cancer du sein est le cancer le plus fréquemment diagnostiqué, représentant 35 % des cas chez les femmes en France en 2012. De multiples facteurs, comportementaux et non-Comportementaux, augmentant le risque de cancer, tant en incidence qu’en mortalité, ont été identifiés dans la littérature, tandis que leur influence conjointe est très peu évaluée. Dans le cas du cancer du sein, certains facteurs diffèrent selon le statut ménopausique des femmes, suggérant une étiologie différente entre les cancers du sein diagnostiqués avant et après la ménopause. Objectif : Les données de la cohorte prospective française E3N ont été utilisées pour évaluer l’influence des facteurs comportementaux et non-Comportementaux sur le risque de cancer et de mortalité chez les femmes avant et après la ménopause. Nous avons également cherché à estimer leur impact relatif sur la population et à identifier les facteurs à forts pouvoirs prédictifs.Résultats : Nos résultats suggèrent que le mode de vie a une influence modeste sur le risque de cancer et de mortalité lors de l’adhésion à une seule recommandation de santé publique. En revanche, elle est conséquente lors d’une adhésion conjointe à plusieurs recommandations. Les facteurs comportementaux jouent ainsi un rôle non négligeable dans la survenue de cancer et sur le risque de décès prématuré. Dans le cas du cancer du sein, ces facteurs influencent particulièrement le risque après la ménopause, tandis qu’avant la ménopause leur impact est plus faible que les facteurs qui ne relèvent pas du mode de vie ou de choix personnels. Ces observations sont retrouvées lorsque l’on cherche à prédire le risque de cancer du sein avant et après la ménopause. En effet, la prédiction du risque de cancer du sein en préménopause s’établit principalement à partir de facteurs non-Comportementaux, alors que la prédiction du risque en postménopause est également déterminée par des facteurs comportementaux.Conclusion : Nous avons montré que l’étiologie du cancer du sein diffère selon la nature de la tumeur, et en particulier selon le statut ménopausique des femmes. À tout âge, le mode de vie a une influence sur le risque de cancer et de mortalité prématurée, particulièrement après la ménopause lorsque leur impact est supérieur à celui des facteurs non-Comportementaux. Ces résultats demandent, cependant, à être reproduits dans des études prospectives portant sur des femmes plus jeunes
Background: Cancer is the second leading cause of mortality among women in France, and the leading cause of mortality among women aged between 35 and 84. Breast cancer is the most frequently diagnosed cancer, with 35% of cases among women in France in 2012. Multiple behavioural and non-Behavioural factors have been associated with increases in cancer incidence and mortality. However, the literature about their combined impact is scarce. Regarding breast cancer, some risk factors differed according to the menopausal status, suggesting a different etiology between premenopausal and postmenopausal breast cancers.Objectives: Data from the E3N prospective cohort of French women were used to evaluate the influence of behavioural and non-Behavioural factors on cancer risk before and after the menopause and on mortality. In addition, we aimed at estimating their relative impact on the population and identifying factors with the highest predictive power.Results: Our results suggest a modest influence of the lifestyle on cancer risk and mortality when adhering to only one public health recommendation. However, the influence is substantial with a combined adherence to several recommendations. Behavioural factors play a key role in the occurrence of cancer and mortality risk. Regarding breast cancer, these factors influence particularly the risk after the menopause, while before, their impact is lower than non-Behavioural factors. These observations were retrieved when aiming at predicting breast cancer risk according to menopausal status. Prediction was established by non-Behavioural factors in premenopause, while the prediction in postmenopause was driven by behavioural factors.Conclusion: We have shown that the etiology of breast cancer differs according to the nature of the tumour, and particularly according to the menopausal status of women. Whatever the age, lifestyle influence the risk of cancer and mortality, especially after the menopause when their impact is higher than the non-Behavioural factors’ one. New results from prospective study on younger women are warranted to confirm the results

Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2008.
Title from electronic title page (viewed Sep. 16, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum of Genetics and Molecular Biology." Discipline: Genetics and Molecular Biology; Department/School: Medicine.

Background and Aims: Breast cancer is a leading cause of female mortality in the Western world. It is well established that the spread of breast cancer, first locally and later distally, is the major factor in patient prognosis. Experimental systems of breast cancer rely on cell lines usually derived from primary tumours or pleural effusions. Two major obstacles hinder this research: (i) Some known sub-types of breast cancers are not represented within current cell line collections; (ii) the influence of the tumour microenvironment is not usually taken into account. Experimental Design: We developed a three-dimensional assay prepared from freshly harvested breast cancer tissue embedded in soft rat collagen I cushions. Invasive behaviour and tumour response to tamoxifen therapy was measured. Changes in proliferation, apoptosis and tumour volume in response to tamoxifen treatment were quantified using image analysis software and optical projection tomography. Further cell line based experiments and histopathological analysis of resection specimens were subsequently investigated to investigate the role of EGFR signalling pathways in driving invasion. Results: We demonstrate a technique to culture primary breast cancer specimens of all sub-types. Within 2-3 weeks, individual and collective invasion of epithelial cells into the surrounding collagen I was observed using phase contrast light microscopy and histopathological methods. Addition of tamoxifen to preparations derived from ER+ tumours demonstrated a range of response as measured by proliferative and apoptotic markers and significant reduction in tumour volume not seen in ER-specimens. Changes in tumour volume allowed stratification into responsive and nonresponsive tumours. EGF within the culture medium appeared to drive a change in phenotype from ER+ to triple negative phenotype and acted as a driver for epithelial invasiveness. Conclusion: Here, we developed an assay to culture human breast tumours without sub-type bias and to investigate and quantify the spread of these ex vivo. This method could be used to quantify drug sensitivity in primary cancers under conditions closer to real life. This may provide a more predictive model than currently used cell lines.

A major issue in the management of breast cancer is to identify patients who are less likely to be cured after primary treatment and would benefit from adjuvant chemotherapy. Of great importance is also identification of patients with only local disease who traditionally would be given chemotherapy but would survive without. In this thesis we have validated the utility of the two-gene ratio HOXB13:IL17BR, which previously has been demonstrated to predict disease-free survival in tamoxifen-treated breast cancer patients. We have also studied the prognostic and predictive utility of a single gene as a biomarker in breast cancer medicine. We could confirm that HOXB13:IL17BR may classify patients with different treatment benefit; only patients with a low value showed benefit from prolonged duration of tamoxifen therapy, whereas for the group with high ratios, the long-term recurrence rate did not improve with longer treatment duration. The combination of HOXB13:IL17BR and the molecular grade index (MGI), another prognostic marker, has been shown to outperform either alone in predicting risk of breast cancer recurrence. We validated the prognostic utility of HOXB13:IL17BR+MGI in a large randomized patient cohort and found that this risk classification identified more than 50% of the tamoxifen-treated lymph node-negative patients as having a less than 3% risk of distant recurrence and breast cancer death. Furthermore, we developed and tested a continuous risk model of HOXB13:IL17BR+MGI called Breast Cancer Index (BCI), for estimation of recurrence risk at the individual level. Our study shows that BCI has the ability to identify more than 50% of patients with a low risk of recurrence more accurately than using traditional risk assessment. These results suggest that BCI may help clinicians to make better informed treatment decisions and spare toxic chemotherapy for a large group of breast cancer patients. The protein expression of HOXB13 was also shown to be a valuable predictor in postmenopausal patients. High expression was associated with worse outcome after tamoxifen therapy. In a premenopausal cohort, patients with hormone receptor-positive tumors showed benefit from tamoxifen regardless of HOXB13 expression. Further analysis indicated that estrogen receptor β (ERβ) modified the performance of HOXB13 as a predictor of treatment effect and should be taken into account when identifying patients less likely to respond to the therapy given. In conclusion, BCI identifies patients with a very low risk of distant recurrence. It may be utilized in the management of breast cancer patients to optimize the use of chemotherapy. HOXB13 protein expression may be used as a marker for tamoxifen benefit, but its performance in premenopausal patients might be modified by ERβ.

The heterogeneity of breast cancer (disease stage and phenotype) makes it challenging to differentiate between each subtype; luminal A, luminal B, HER2, basal-like and claudin-low, on the basis of a single gene or protein. Therefore, a collection of markers is required that can serve as a signature for diagnosing different types of breast cancer. New developments in proteomics have provided the opportunity to look at phenotype-specific breast cancer cell lines and stage-specific liquid biopsies (nipple aspirate fluid [NAF], plasma samples) to identify disease and phenotype specific signature. An 8-plex iTRAQ quantification strategy was employed to compare proteomic profiles of a range of breast cancer and ‘normal-like’ cell lines with primary breast epithelial cells. From this, 2467 proteins were identified on Orbitrap Fusion and Ultraflex II, of which 1430 were common. Matched pairs of NAF samples from four patients with different stages of breast cancer, were analysed by SCX-LC-MS and a total of 1990 unique gene products were identified. More than double the number of proteins previously published data, were detected in NAF, including 300 not detected in plasma. The NAF from the diseased patients have 138 potential phenotype biomarkers that were significantly changed compared to the healthy volunteer (7 for luminal A, 9 for luminal B, 11 for HER2, 14 for basal-like and 52 for claudin-low type). The average coefficient of variation for triplicate analyses by multiple reaction monitoring mass spectrometry (MRM-MS), was 9% in cell lines, 17 % in tissue biopsies, 22% in serum samples and 24% in NAF samples. Overall, the results provide a strong paradigm to develop a clinical assay based on proteomic changes in NAF samples for the early detection of breast cancer supplementary to established mammography programmes.

Lung cancer is a global disease that affect millions of individuals worldwide. Additionally, the disease is beset with a poor 5-year survival rate, a direct consequence of a low early stage diagnosis rate. In an attempt to improve lung cancer prognosis, individuals at high risk of developing lung cancer should be identified for periodic screening. Prediction models are devised to predict an individual’s risk of developing a disease over a specified time period. These can be used to identify high risk individuals and be made publically available to allow individuals’ to be conscience of their own risk. While prediction models have multiple uses it is imperative the models demonstrate a good standard of performance consistently when reviewed. The project conducted a systematic review, analysing previously published lung cancer prediction models. The review identified that there had been inadequate reporting of the existing models and when these models have been validated this had not been consistent across different publications. As a consequence models have not been consistently considered as a selective screening tool. The project then validated the prediction models using datasets from the International Lung Cancer Consortium. The validation identified the leading models which will allow a more targeted focus on these models in future research. This could culminate in the model being implemented as a clinical utility. The final stage reviewed methods to update a single prediction model or aggregate multiple prediction models into a meta-model. A literature review identified and evaluated the different methods, discussing how different methods can be successful in different scenarios. The methods were also reviewed for their suitability updating selected lung cancer prediction models, and appropriate methods were identified. These were then applied to create updated lung cancer models which were validated to assess which methods were successful at improving the performance and robustness of lung cancer prediction models. As lung cancer research develops, particularly into researching genetic markers that may explain lung cancer risk, these factors could be incorporated into already successful prediction models using appropriate model updating methods that were identified in our research.

Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references (leaf 72).
by Natalia V. Iliouchina.
M.Eng.and S.B.

Human breast cancer is an extremely heterogeneous disease, consisting of a number of different subtypes with varying levels of aggressiveness reflected by distinct, but largely undefined, molecular profiles. Here we have analyzed several novel mouse models for breast cancer in the context of the human subtypes, and have shown parallels between the mice and humans at numerous biologically relevant levels. In addition, we have developed a statistical framework to help elucidate the individual molecular components that are at play across a panel of human breast or murine mammary tumors. Our results indicate that, while no mouse model captures all aspects of the human disease, they each contain components that are shared by a subset of human breast tumors. Furthermore, our statistical framework provides numerous advantages over previous methodologies, in helping to reveal the individual molecular pathways that make up the biology of the tumors.
Le cancer du sein est connue pour être une maladie très hétérogène, composé d'un nombre de différents sous-types avec différents niveaux de l'agressivité et distinctes, mais indéfini, profils moléculaires. Ici, nous avons analysé plusieurs nouveaux modèles de souris pour le cancer du sein, dans le cadre des sous-types, et nous avons trouver des parallèles à un certain nombre de niveaux pertinents biologiques. En outre, nous avons développé une méthodologie statistique pour aider à élucider les différents composants moléculaires qui sont à jouer dans un groupe de tumours de sein d'humains ou mammaires murins. Nos résultats indiquent que, même si aucun modèle de souris capte tous les aspects de la maladie chez l'homme, chacun contiennent des composants qui sont partagées par un sous-ensemble de tumeurs mammaires humaines. En outre, notre outil statistique offre de nombreux avantages par rapport aux précédentes méthodes, pour aider à révéler les voies moléculaires qui composent la biologie des tumeurs.

Abstract Breast cancer is highly prevalent in the United States with an estimated 260,000 women diagnosed with invasive breast cancer in 2018 alone. There is a growing need to identify the molecular drivers of metastatic breast cancer as the molecular mechanisms responsible for the transition from normal mammary epithelial cells to aggressive cancer cells remain poorly understood. Understanding this transition may reveal a therapeutic target for aggressive breast cancer. Small, noncoding RNAs (ncRNA), such as microRNAs (miRNAs), have recently been discovered to promote initiation, progression, and metastasis of breast cancer. Similar in size to miRNAs, tRNA-derived small RNAs (tsRNAs) are a novel class of small ncRNA whose expression may differentiate between cancer types and cancer cell lines. TsRNAs are created during the maturation process of primary tRNA transcripts, where the 3-prime end of the tRNA is cleaved by RNaseZ, resulting in a 16-48 nucleotide long strand of RNA. Although similar in size to miRNA, the functions of tsRNA are largely unknown. Previously identified two tsRNA, ts-2 and ts-112, that are expressed at 10-fold higher levels in the MCF10CA1a aggressive breast cancer cell line than the normal-like MCF10A mammary epithelial cell line. Further, ts-2 and ts-112 are detected at similarly high levels in female human embryonic cells, displaying oncofetal expression. For these reasons we hypothesize that ts-2 and ts-112 promote breast cancer characteristics. Custom inhibitors of ts-2 and ts-112 were transfected into the aggressive breast cancer cell line MCF10CA1a in vitro. The following phenotypic assays were conducted to determine the function of ts-2 and ts-112 in the MCF10CA1a cell line: proliferation, cell cycle, and wound healing. Following transfection of ts-2 inhibitors, in the proliferation assay showed a 15-20% reduction in growth of aggressive cancer cells. Ts-2 inhibition also saw an increase in population doubling time of 7% from 12 to 14 hours. These results suggest that ts-2 may play a role in cell cycle progression. Following ts-112 inhibition in the aggressive MCF10CA1a breast cancer cell line, cell cycle analysis revealed a statistically significant decrease in the number of cells in G1 phase and an increase in S phase. Using a candidate approach we analyzed the effect of ts-2 and ts-112 inhibition on G1/S phase and S/G2 phase checkpoint markers by qPCR. The inhibition of these tsRNA showed no effect on the chosen genes. Our data are in support for ts-2 and ts-112 having a role in aggressive breast cancer and may be tumor promoting In on-going studies, the capacity of tsRNAs to act as predictive biomarkers of long-term breast cancer risk is being evaluated in serum collected from women at elevated risk. Analyzing ts-2 and ts-112 biological consequences following inhibition furthers our understanding of the molecular mechanisms that are responsible for aggressive breast cancer. Continued study of tsRNA function could produce a model of their role in aggressive breast cancer and thus metastasis. Understanding tsRNA function in metastatic breast cancer in turn could lead to their use as a possible biomarker or therapeutic target.

Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2006.
Title from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum in Genetics and Molecular Biology." Discipline: Genetics and Molecular Biology; Department/School: Medicine.

Given the recent discovery of RUNX1 somatic mutations in biopsies of breast cancer patients, the overall purpose of the present thesis consists of using different in vivo and ex vivo experimental systems in the attempt to answer two main questions: firstly, if the Runx1 gene plays any causative role in the context of breast cancer; and secondly, if its putative function is symptomatic of a tumour suppressor gene and/or of a pro-oncogene. By characterizing the effects of Runx1 deletion in two different breast cancer mouse models (i.e. the MMTV-PyMT and the Wnt/β-catenin-driven models of mammary tumourigenesis), this thesis provides the first in vivo evidence of a dualistic role played by the gene in the context of breast cancer. Runx1 would in fact appear to act as a tumour suppressor at early stages of the disease, whilst as a pro-oncogene at later stages of mammary tumourigenesis. To fully comprehend the significance of these major findings, the introduction will first provide a brief description on the RUNX family of genes, as well as on the state-of-the-art knowledge of RUNX1’s role in both mammary gland and breast cancer biology. As such, particular attention will then be given not only to the ontogeny, endocrine regulation and composition of the murine mammary gland, yet also to the high degree of heterogeneity, the putative “cell-of-origin(s)” and the different experimental models commonly used to study breast cancer. Through the aforementioned rationale, it is hoped that the introduction will serve as a platform which may hold the key for unveiling the controversial role played by RUNX1 in the context of breast cancer.

Epithelial mesenchymal plasticity in cancer generally refers to the ability of a cancer cell to transform into a different cell form, which facilitates the metastatic spread of a cancer. This thesis explores the roles of four cancer-associated genes that affect the transition of the cell state during cancer metastasis, and includes extensive research on two of the four gene targets, namely TRIM28 and TGFBI. The effects of these genes in breast cancer systems indicated great potential for improving therapeutic responses towards cancer drugs, which would alleviate the suffering of breast cancer patients.

Axillary lymph node (ALN) metastasis status is an important prognostic marker in breast cancer and is widely employed for tumour staging and defining an adjuvant therapy. In an attempt to avoid invasive procedures which are currently employed for the diagnosis of nodal metastasis, several markers have been identified and tested for the prediction of ALN metastasis status in recent years. However, the nonlinear and complex relationship between these markers and nodal status has inhibited the effectiveness of conventional statistical methods as classification tools for diagnosing metastasis to ALNs. The aim of this study is to propose a reliable artificial neural network (ANN) based decision support system for ALN metastasis status prediction. ANNs have been chosen in this study for their special characteristics including nonlinear modelling, robustness to inter-class variability and having adaptable weights which makes them suitable for data driven analysis without making any prior assumptions about the underlying data distributions. To achieve this aim, the probabilistic neural network (PNN) evaluated with the .632 bootstrap is investigated and proposed as an effective and reliable tool for prediction of ALN metastasis. For this purpose, results are compared with the multilayer perceptron (MLP) neural network and two network evaluation methods: holdout and cross validation (CV). A set of six markers have been identified and analysed in detail for this purpose. These markers include tumour size, oestrogen receptor (ER), progesterone receptor (PR), p53, Ki-67 and age. The outcome of each patient is defined as metastasis or non-metastasis, diagnosed by surgery. This study makes three contributions: firstly it suggests the application of the PNN as a classifier for predicting the ALN metastasis, secondly it proposes a the .632 bootstrap evaluation of the ANN outcome, as a reliable tool for the purpose of ALN status prediction, and thirdly it proposes a novel set of markers for accurately predicting the state of nodal metastasis in breast cancer. Results reveal that PNN provides better sensitivity, specificity and accuracy in most marker combinations compared to MLP. The results of evaluation methods’ comparison demonstrate the high variability and the existence of outliers when using the holdout and 5-fold CV methods. This variability is reduced when using the .632 bootstrap. The best prediction accuracy, obtained by combining ER, p53, Ki-67 and age was 69% while tumour size and p53 were the most significant individual markers. The classification accuracy of this panel of markers emphasises their potential for predicting nodal spread in individual patients. This approach could significantly reduce the need for invasive procedures, and reduce post-operative stress and morbidity. Moreover, it can reduce the time lag between investigation and decision making in patient management.

Different forms of cancer have been widely studied and documented in various studies across the world. However, there have not been many similar studies in the developing countries - particularly those on the African continent (Parkin, et al., 2005). This thesis seeks to uncover the geo-demographic occurrence patterns of the disease by applying three Data mining Techniques, namely Logistic Regression (LR), Neural Networks (NNs) and Decision Trees (DTs), to learn the underlying rules in the overall behaviour of breast cancer. The data, 3,057 observations on 29 variables obtained from four cancer treatment centres in Libya (2004-2008), were interrogated using multiple K-folds cross validation. The predictive strategy yielded a list of breast cancer predictor factors ordered according to their importance in predicting the disease. Comparison between our results and those obtainable from conventional LR, NN and DT models shows that our strategy out-performs the conventional variable selection. It is expected that the findings from this thesis will provide an input into comparative geo-ethnic studies of cancer and provide informed intervention guidelines in the prevention and cure of the disease, not only in Libya but also in other parts of the world.

L'objectif de cette thèse est d'étudier la sensibilité des lignes cellulaires du cancer du sein BTCL aux agents stabilisants des microtubules (taxanes et ixabépilone) afin de: 1 - identifier la pharmaco-génomique prédictif de la réponse (résistance / sensibilité) comme une signature moleculaire, et de valider cette signature sur d'autres études dont les données génomiques sont disponibles en ligne, donc mis l'expression des gènes prédictifs de GES pour Tax- sensibilité (333 gènes ) et Ixa-sensibilité (79 gènes) ont été définis, et les Taxanes prédicateurs GES a considérablement prédit Pac-sensibilité dans BTCL, et pathologiques réponse complète à base de Pac-chimiothérapie néoadjuvante chez les patients du cancer du sein. 2 - étudier le rôle des cellules souches du cancer (ALDH +) sur la réponse thérapeutique aux Taxanes et donc, Nous identifions quatre lignes BTCL qui présentent un enrichissement significative dans le pourcentage et le nombre absolu de ALDELFUOR cellules positives dans chacun de ces quatre BTCLs après 5 jours de traitement par le paclitaxel, en contraste avec les résultats précédents, nous avons constaté que dans ces autres 3 BTCLs le phénomène est inversé avec la diminution significative du pourcentage et le nombre absolu de cellules positives ALDELFUOR trouve dans chacun de ces trois BTCLs après 5 jours du traitement par le paclitaxel. Une signature moléculaire de SCC résistant / sensible de 243 pb avec 179 gènes dont 152 gènes sont régulés à la hausse et 27 gènes régulés à la baisse au CSC résistantes au paclitaxel, une sorte prédicteurs génomiques pour Tax - sensibilité au CSC résistantes au paclitaxel peut être dérivée à partir BTCL et peut être utile pour mieux comprendre les mécanismes de résistance aux taxanes et de l'implication de la CSC dans cette résistance, afin de mieux sélectionner des traitements cytotoxiques chez les patients du cancer du sein et l'identification des d'autres marqueurs potentiels de thérapies ciblées dans l'avenir. 3 - Nous avons testé l'impact de l'altération des paramètres génomiques et protéiques ou les mutations de certains gènes comme tau (MAPT), K-alpha tubuline (TUB A1B) tubuline alpha-6 (A1C TUB) tubuline beta 3 (TUBB3) et stathmine (STMN1), malheureusement nous n'avions jamais identifier une mutation pour être corrélée à la réponse des BTCL aux Taxanes. 4 - Nous essayons d'étudier au niveau de protéines par immunohistochimie sur le tissu de micro-array et cyto-micro-array pour certains paramètres qui ont été déjà prouvé (in vitro) pour être corrélée à la réponse aux Taxanes, (cette partie est en fait en cours)
The aim of this thesis is to study the sensitivity of breast cancer cell lines BTCL to microtubule-stabilizing agents (Taxanes and ixabepilone) in order to:1- identify pharmaco-genomic predictor of response (resistance /sensitivity) as a molecular signature, and to validate this signature on others studies of which the genomic data are available on line, so gene expression set GES predictors for Tax-sensitivity (333 genes) and Ixa-sensitivity (79 genes) were defined, and the Taxanes GES predictors has significantly predicted Pac-sensitivity in BTCL, and pathological complete response to Pac-based neo-adjuvant chemotherapy in BC patients.2- study the role of cancer stem cell (ALDH+) on the therapeutic response to Taxanes and their we identify 4 BTCLs which present a significant enrichment in the percentage and the absolute numbers of ALDELFUOR-positive cells found in each of these 4 BTCLs after 5 days of treatment by Paclitaxel , In contrast to the previous results we found that in others 3 BTCLs these phenomenon is inversed with the significant decrease of the percentage, and the absolute numbers of ALDELFUOR-positive cells found in each of these 3 BTCLs after 5 days of treatment by Paclitaxel.A molecular signature of CSC resistant /sensitive of 243 pb with 179 genes of which 152 genes are up- regulated and 27 genes down-regulated in CSC resistant to Paclitaxel, so a genomic predictors for Tax-sensitivity in CSC resistant to Paclitaxel can be derived from BTCL and may be helpful for better understanding the mechanisms of resistance to Taxanes and the implication of CSC in this resistance in order to better select of cytotoxic treatment in breast cancer patients and identification of others potential markers for targeted therapies in the future .3- we tested the impact of the alteration of genomic and proteic parameters or the mutations of some genes like tau (MAPT),Tubulin K- ALPHA (TUB A1B) Tubulin alpha-6 (TUB A1C) Tubulin beta 3 (TUBB3) and Stathmin (STMN1), unfortunately we did'nt identify a mutations to be correlated to BTCL response to Taxanes .4- we try to study at the level of proteins by immunohistochemistry on the tissue micro- array and cyto-micro-array for some parameters which have been already proved (in vitro) to be correlated with response to Taxanes , ( this part is actually ongoing)

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 59-60).
Discriminative models for breast cancer risk prediction are needed in order to provide personalized patient care. Existing breast cancer risk models incorporate information about breast tissue using imaging biomarkers such as density scores. However, these imaging biomarkers are limited in that they suffer from variability in radiologists' assessments and they reduce the rich information contained in the image down to a single number. In this thesis, I present deep learning models that predict breast cancer risk directly from full images, specifically breast MRIs and mammograms. Our image-based deep learning models out-perform existing breast cancer risk models and our own risk-factor-only models. These results demonstrate that full images contain subtle but significant indicators of risk not captured by traditional risk factors, and that deep learning models can learn these patterns directly from the data.
by Tally E. Portnoi.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

Natural history models for breast cancer describe the unobservable disease progression. These models can either be fitted using likelihood-based estimation to data on individual tumour characteristics, or calibrated to fit statistics at a population level. Likelihood-based inference using individual level data has the advantage of ensuring model parameter identifiability. However, the likelihood function can be computationally heavy to evaluate or even intractable. In this thesis likelihood-free estimation using Approximate Bayesian Computation (ABC) will be explored. The main objective is to investigate whether ABC can be used to fit models to data collected in the presence of mammography screening. As a background, a literature review of ABC is provided. As a first step an ABC-MCMC algorithm is constructed for two simple models both describing populations in absence of mammography screening, but assuming different functional forms of tumour growth. The algorithm is evaluated for these models in a simulation study using synthetic data, and compared with results obtained using likelihood-based inference. Later, it is investigated whether ABC can be used for the models in presence of screening. The findings of this thesis indicate that ABC is not directly applicable to these models. However, by including a sub-model for tumour onset and assuming that all individuals in the population have the same screening attendance it was possible to develop an ABC-MCMC algorithm that carefully takes individual level data into consideration in the estimation procedure. Finally, the algorithm was tested in a simple simulation study using synthetic data. Future research is still needed to evaluate the statistical properties of the algorithm (using extended simulation) and to test it on observational data where previous estimates are available for reference.
Natural history models för bröstcancer är statistiska modeller som beskriver det dolda sjukdomsförloppet. Dessa modeller brukar antingen anpassas till data på individnivå med likelihood-baserade metoder, eller kalibreras mot statistik för hela populationen. Fördelen med att använda data på individnivå är att identifierbarhet hos modellparametrarna kan garanteras. För dessa modeller händer det dock att det är beräkningsintensivt eller rent utav omöjligt att evaluera likelihood-funktionen. Huvudsyftet med denna uppsats är att utforska huruvida metoden Approximate Bayesian Computation (ABC), som används för skattning av statistiska modeller där likelihood-funktionen inte är tillgänglig, kan implementeras för en modell som beskriver bröstcancer hos individer som genomgår mammografiscreening. Som en del av bakgrunden presenteras en sammanfattning av modern ABC-forskning. Metoden består av två delar. I den första delen implementeras en ABC-MCMC algoritm för två enklare modeller. Båda dessa modeller beskriver tumörtillväxten hos individer som ej genomgår mammografiscreening, men modellerna antar olika typer av tumörtillväxt. Algoritmen testades i en simulationsstudie med syntetisk data genom att jämföra resultaten med motsvarande från likelihood-baserade metoder. I den andra delen av metoden undersöks huruvida ABC är kompatibelt med modeller för bröstcancer hos individer som genomgår screening. Genom att lägga till en modell för uppkomst av tumörer och göra det förenklande antagandet att alla individer i populationen genomgår screening vid samma ålder, kunde en ABC-MCMC algoritm utvecklas med hänsyn till data på individnivå. Algoritmen testades sedan i en simulationsstudie nyttjande syntetisk data. Framtida studier behövs för att undersöka algoritmens statistiska egenskaper (genom upprepad simulering av flera dataset) och för att testa den mot observationell data där tidigare parameterskattningar finns tillgängliga.

This research study investigated the association of microRNA related single nucleotide polymorphisms (miRSNPs) with breast cancer susceptibility in Australian Caucasian women. The thesis then progressed with developing an in silico methodology for miRNA-target identification followed by the validation of miRNA-target relationships regarding the distinctive molecular subtypes of human breast cancers.

"Cancer is the second leading cause of mortality worldwide, despite the extraordinary advances in the last two decades due to the development of targeted therapies. These target particular molecules required for cell growth and tumorigenesis; nonetheless, de novo or acquired resistance to therapy often lead to patient relapse and disease progression. There is cumulating evidence supporting the importance of tumor microenvironment (TME)-driven mechanisms in cancer progression and drug resistance. Therefore, there is a need for cancer models in which critical components of the TME, such as the non-malignant cell types and the extracellular matrix (ECM), are represented and tissue architecture is maintained. (...)"
N/A

Cellular redox potential is incredibly important for the control and regulation of a vast number of processes occurring in cells. Disruption of the fine redox balance within cells is has been associated with disease. Of particular interest to my research is the redox gradient that develops in cancer tumours, in which the internal regions are further from vascular blood supply and therefore become starved of oxygen and hypoxic. This makes treatment of these areas a lot more challenging, as radiotherapy approaches rely on the presence of oxygen and, with a poor vascular blood supply, drugs delivered through the blood stream will have poor access to these regions. Currently, there is limited knowledge regarding the quantitative nature of this redox gradient in cancerous tumours. To aid the development of drugs and therapies to overcome this problem, a system that enables quantitative mapping of redox potential through a tumour would be a vital tool. In this work redox sensitive molecules attached to gold nanoparticles (NPs) are delivered to cells and give signals using surface enhanced Raman scattering (SERS). Redox potential changes are monitored quantitatively by ratiometric changes in signal intensity of selected signals in the SER spectra acquired. Multicellular tumour spheroids (MTS) are used as a three dimensional (3D) in vitro tumour model, in which the 3D architecture and gradients observed in tumours in vivo develop. As redox potential is pH dependent and pH is another important physiological characteristic in its own right, a SERS pH sensor was developed and ultimately a system that multiplexes intracellular pH and redox measurement by SERS. Initially, simultaneous redox potential and pH measurements were performed in monolayer culture before extending this to MTS. Photothermal optical coherence tomography (OCT) was used to investigate overall 3D NP distribution in the MTS models. It was possible to control NP delivery to MTS to localise NPs to various regions. Redox potential and pH could then be measured using a fibre optic Raman probe, and spatial response to drug treatment monitored. Intracellular NP localisation was investigated using transmission electron microscopy (TEM), scanning electron microscopy (SEM), helium ion microscopy (HIM) and confocal fluorescence microscopy (CFM) and attempts were made to control NP delivery to particular intracellular compartments.

This thesis focuses on the modelling and prediction of factors affecting attendance to screening invitations of the NHS Breast Screening Programme. The analysis is based on data collected by the Warwickshire, Solihull and Coventry Breast Screening Unit from 1989 up to 2001 with respect to invitation to screening for the prevention of breast cancer in non-symptomatic women. Using a novel approach to the analysis of the data, from the perspective of the screening episode of each woman, rather than the usual analysis from the perspective of the screening round of the units, a statistical analysis is carried out on the whole registered population for the first time. Amendments to the current formulae for coverage calculations, the introduction of a new parameter (invitation rate) and the proposal for a reduction of the invitation period (period of time between two consecutive invitations) follows from the analysis. A preliminary analysis of predictive methodologies, including traditional statistical methods and artificial intelligent methods, gives the foundation to the formulation of two new algorithms; the first, for the prediction of attendance of women to screening invitations, and the second for the prediction of occurrence of screening variation (change of appointment dates) of women to invitations. Both algorithms are based on neural network generated models able to learn from the previous screening behaviour history of the woman, a technique not previously explored for the prediction of attendance. The accuracy of the new proposed algorithm for the prediction of attendance to invitation is tested on a blind study using data not previously seen by the predictive system, and for which results were unknown at the time when the predictions were made. From the obtained results, it is concluded to recommend the implementation by the NHS Breast Screening Unit of the two algorithms proposed for the prediction of the women’s attendance and screening variation to their invitation for screening. With these predictions, women likely not to attend, or change appointment date, can be identified and appropriately targeted with the aim of increasing their attendance in the short term, and in the long term, reducing breast cancer mortality.

The growth and development of the breast is to a large extent regulated by oestrogens through the oestrogen receptor (ER). Activation of the ERα triggers transcription of genes that are important for cell proliferation and stimulates entry into the G1 phase of the cell cycle. In breast cancer the ERα is often upregulated and is therefore a suitable target for adjuvant therapies such as tamoxifen. Although tamoxifen is an effective treatment in most cases, tumours sometimes acquire resistance to the drug. The aim of this thesis was to investigate the impact of G1 phase proteins and 11q13 amplification on prognosis and treatment response in breast cancer. The material used was from a clinical trial in which postmenopausal breast cancer patients were randomised to chemotherapy or radiotherapy and tamoxifen or no adjuvant treatment. We studied the expression of cyclin D1, cyclin E and Rb with immunohisochemistry and amplification of CCND1 and PAK1 with real time PCR. We found that among patients with high tumour expression of cyclin D1, overexpression of ErbB2 was associated with reduced recurrence-free survival. Both cyclin D1 and cyclin E overexpression were associated with reduced tamoxifen response. High expression of cyclin D1 has been found to induce ligand independent activation of ERα in breast cancer cells and might also switch tamoxifen from acting as an antagonist to an agonist. Overexpression of cyclin E has been shown to be associated with expression of low molecular weight isoforms of the protein that possess an increased kinase activity and are insensitive to p21 and p27 inhibition. Furthermore, amplification of 11q13, and in particular the gene PAK1, was a strong predictor of tamoxifen resistance. The pak1 protein is involved in phosphorylation and ligand independent activation of the ERα. We also found that lost expression of either p53 or Rb reduced the patients benefit from radiotherapy compared with patients with normal expression of both proteins. Normally, ionizing radiation upregulates p53 resulting in G1 arrest or apoptosis. If either functional p53 or Rb is missing the cells can proceed from G1 to the S phase despite damaged DNA. The expression of the microRNA, miR-206, was analysed with real time PCR, and the results showed that high expression of miR-206 correlated to low expression of ERα and 11q13 amplification. In vitro studies have shown that miR-206 negatively regulates the expression of ERα. Taken together the G1 regulators and amplification of 11q13 seem to have an important role in predicting the patient’s response to adjuvant therapy.

Breast cancer is the main cause of cancer death in women. Improved treatments, prevention programs and earlier detection are reducing the rate of death; however, there is still a high percentage of mortality by this cancer. Identification of novel targets to predict response to specific treatments is a key goal for personalizing breast cancer therapy and to improve survival. Few years ago, PARP inhibitors appeared as a promising therapy, particularly in BRCA-mutated cancers. However, there was a clear need to conduct further preclinical and translational work to improve the rational development of PARP inhibition in breast cancer. In this work we described PARP1 expression in breast tumour samples and characterized the effects of its inhibition in preclinical models. We found that nuclear PARP1 protein overexpression was associated with malignant transformation and poor prognosis in breast cancer. PARP1 overexpression was more common in triple negative subtype, but was also detectable in small subsets of estrogen receptor positive and HER2 positive breast cancers. In preclinical models, PARP1 played distinct roles in different molecular subtypes of breast cancer. Moreover, we described that olaparib (novel PARP inhibitor) had antitumour effects in different breast cancer subtypes, and its combination with trastuzumab (anti-HER2 antibody) enhanced the antitumour effects of this therapy.
El càncer de mama és la principal causa de mort per càncer en dones. La millora dels tractaments i la detecció precoç estan reduint la taxa de mort, però segueix sent elevada. Identificar noves dianes per predir la resposta a tractaments és clau per millorar les teràpies contra aquest càncer i la supervivència. Els inhibidors de PARP van aparèixer com una teràpia prometedora, particularment en càncers BRCA-mutants, però, cal dur a terme més estudis preclínics i translacionals per fomentar un desenvolupament racional d’aquesta teràpia en càncer de mama. Aquest treball descriu l’expressió de PARP1 en mostres de tumors mamaris i caracteritza els efectes de la seva inhibició a models preclínics. Vam observar que la sobreexpressió nuclear de la proteïna PARP1 fou associada amb: la transformació maligna; mal pronòstic en càncer de mama; i fou més freqüent al subtipus triple-negatiu, però també es va detectar en un subgrup de càncers de mama receptors d’estrogen positius i HER2 positius. En models preclínics, PARP1 va exercir rols diferents als diferents subtipus de càncer de mama. Per altra banda, vam descriure que olaparib (inhibidor de PARP) té efectes antitumorals en els diversos subtipus, i combinat amb trastuzumab (anticòs anti-HER2) potencia els efectes antitumorals d’aquesta teràpia.

The role of CRH and CRH related peptides in mediating HPA axis and various pathophysiological processes is well known. Over the last decade the role of CRH in mediating cancer has been widely studied and considered to exert variable functions. This study was undertaken to study the biological role of CRH in ER(+) and ER(-) breast cancer cell lines. Since estrogen is related to normal as well as the neoplastic growth, the CRH effects were studied with and without estrogen in ER (+) MCF7 and ER (-) SKBR3 cell lines. The role of the stress hormone CRH in breast cancer is complex, and its abundance and biological activity may be modulated by estrogen. The signaling mechanisms were investigated by employing phosphokinase assay to identify protein kinases activated by CRH in ER(+) MCF7 cells. CRH activated numerous kinases and downstream effectors, at least some of which were mediated by the CRH receptor type 1 (CRH-R1). MAPK, GSK3β and Akt were further investigated to study downstream effects. The analysis of the spatiotemporal characteristics of MAPK activation suggested that CRH mediated p38 response is strong compared to ERK1/2 which is inhibited by the CRH in MCF7 cells. UCN II also showed a similar response, but the extent of p38 response is not as strong as with CRH. The MAPK effects were studied in SKBR3 cells and interestingly, the CRH and UCN II mediated effect were inhibited by 24 hrs of estrogen treatment. CRH also increased the transcription of many genes that encode effectors, transcriptional targets, or regulators associated with estrogen signaling. CRH mediates its effects by activating two types of CRH receptors, i.e R1 and R2. The tissue sensitivity to agonists is determined by the presence of receptors in the plasma membrane and signal activation. This project was undertaken to investigate cellular expression of CRH-R and CRH-R1 splicing variants (α,β,c,d) and their internalization characteristics. CRH-R has been confirmed in both the cell lines. One of the goals of this project was to identify the expression of CRH-R1 splicing variants in both the cell lines with and without estrogen. CRH-R1α and R1d expression was confirmed in the ER (+) cells. However the CRH-R1d expression was lacking in SKBR3 cells. The CRH mediated splicing of CRH-R1 receptor was dose dependent. This splicing event is regulated by the splicing factors, thus in silico analysis was performed to identify splicing factor, with high affinity binding at exon 12 which is missing in CRH-R1d. It was hypothesized that Estrogen regulated these effect downregulating serine/arginine-rich splicing factor 55 (SRp55) expression resulting in generation of CRH-R1d. The results show that E2 is a driving factor influencing CRH-R1 gene exon 12 splicing and causing an increase in the expression of CRH-R1d. Immunofluorescence demonstrated that CRH treatment initiates the internalization of receptor inside the cytoplasm but this effect is lost when cells were treated for 24 hrs with E2, probably due to generation of CRH-R1d which loses internalization properties. This effect is lost in the absence of E2 receptors (SKBR3 cells).

This study was undertaken to evaluate some important prognostic factors in human breast cancer. The prognostic value of accepted clinicopathological factors such as the presence of axillary lymph node métastasés, histologic grade, clinical and pathological stage was confirmed. In a cohort of stage T3,T4,M0 breast cancer with 91 patients (paper I) DNA ploidy by static cytometry (SCM) turned out to be the most important prognostic factor. In a cohort of stage T2,M0 breast cancer with 99 patients (paper III) the presence of involved axillary nodes and low histologic grade were independent prognostic factors. According to life-table analyses DNA ploidy by flow cytometty (FCM) and SCM were significant prognostic predictors for survival but S-phase fraction (SPF) was not. The significant discrimination between euploid and aneuploid tumours was seen also among the node-negative patients. In a patient material with 158 tumours of predominantly low stages (73% T0,T1, papers IV and V) and calculated mammographie tumour volume doubling time (DT) DNA ploidy by FCM gave no significant prognostic information. A computer program was used to calculate SPF from the histograms obtained by FCM. SPF with a cut-off value of 7.5% between tumours with high and low proliferation rate was a highly significant and independent prognostic factor for survival. The other independent prognostic predictors were low histologic grade, the presence of involved axillary nodes and stage II and III (versus stage I). DT values for 158 patients (papers IV and V) varied between 0.6 and 65.8 months (mean 10.9 months) and 11 tumours showed no growth at all between mammographies. The median value of 9.0 months was chosen as cut-off point between slow and fast growing tumours. The prognostic power of DT was however low, and the difference between slow and fast growing tumours was significant only for distant disease-free survival. Seventy-one of the 158 tumours were detected by mammographie screening. The screening detected carcinomas with predominantly long DT:s were discovered at an early stage and showed favourable characteristics concerning DNA ploidy and SPF. FCM was a rapid and reliable method for DNA analysis with a better prognostic discrimination between euploid and aneuploid groups than SCM (papers II and III). SPF, DNA ploidy and histologic grade are significantly correlated to one another but show no strong correlation to the presence of axillary lymph node métastasés. There is also a significant correlation between DT on one hand and DNA ploidy and SPF on the other hand. In conclusion the classic prognostic factors are still valuable. DNA ploidy as a single prognostic factor seems to have a relatively low prognostic power and seems to be of limited clinical value. SPF is a highly significant prognostic predictor for breast cancer of low stage, but the clinical value is not defined.

S. 3-38: sammanfattning, s. 39-94: 5 uppsatser

digitalisering@umu

EGF receptors are key regulators of cell survival and growth in normal and transformed tissues. Ligand binding results in formation of homo/hetero dimers of these receptors, followed by activation of the kinase activity and subsequent tyrosine phosphorylation of many downstream molecules. The activation of these receptors is not only mediated by the binding of their cognate ligands, but by transactivaton by other molecules as well. Recent studies have identified an oncogenic glycoprotein MUC1 as a binding partner for EGFR and that MUC1 expression can potentiate EGFR-dependent signal transduction. After receptor activation, EGFR is typically downregulated via an endocytic pathway that results in receptor degradation or recycling. We report here that MUC1 expression inhibits the degradation of ligand-activated erbB1. In addition, MUC1 expression results in prolonged activation of Akt, but not ERK1,2 MAPKinase. The MUC1-mediated protection against degradation occurs with a decrease in EGF-stimulated ubiquitination of erbB1, and an increase in erbB1 recycling. We then utilized the WAP-TGFα transgenic mouse model of breast cancer and determined that a loss of Muc1 expression dramatically alters mammary tumor progression. While 100% of WAP-TGFα/Muc1^(+/+) mice form mammary gland tumors, only 37% of WAP-TGFα/Muc1^(-/-) form tumors. Furthermore, expression of cyclin D1 expression is significantly suppressed in tumors derived from WAPTGFα/Muc1^(-/-) animals, and loss of Muc1 expression resulted in a significant inhibition in the formation of hyperplastic lesions in the mammary gland. We also observed metastatic pulmonary adenocarcinoma (1/29) and perivascular lymphoma of unknown origin (28/29) in the WAP-TGFα transgenic mice but not in the WAP TGFα/Muc1^(-/-) animals. To determine the effects of Muc1 expression on metastasis in a model lacking perivascular lymphoma, we crossed MMTV-Wnt-1 and MMTV-MUC1 transgenic mice and evaluated interactions between Muc1 and EGFR. Although the MMTV-Wnt-1 mice are non-metastatic, a majority (6/10) of the bitransgenic MMTVWnt- 1/MMTV-MUC1 formed pulmonary metastases. Furthermore, overexpression of MUC1 increases the breast cancer cell invasion in vitro. The MUC1 induced increase in invasion is found to be EGF and EGFR-kinase dependent. Collectively, these data indicate that MUC1 expression contributes to many of the hallmarks of cancer and in addition, is an important modulator of EGFR-associated mammary tumor progression.

Introduction: Cancer cells are affected by paracrine signalling from surrounding stromal cells. Here we investigate the role of kinases in this signalling using a model breast cancer (BC) co-culture system to identify novel paracrine signalling mechanisms supporting the growth and survival of tumour cells and potentially modulating epithelial to mesenchymal transition (EMT). Methods: The influence of paracrine signalling of the MSCs in the growth of luminal and basal-like breast cancer cells after the knock-down of human kinases, selected through a screen of a siRNA library, was investigated using a co-culture system that involves the culture of these transfected cells with or without human bone marrow-derived MSCs. An in silico analysis was also performed to investigate potential clinical relevance of these molecules. Results: The screen of the human kinase siRNA library in the MCF-7 cells co-cultured with MSCs revealed a number of kinases that seemed to be involved in the regulation of tumour growth. The knock-down of a subset, including GKAP1, CALM2, NEK7, MAPK7 and PI3KC2G, in the MCF-7, MDA-MB-231 and BT-549 cells growing alone or with MSCs resulted in the modulation of growth through autocrine or paracrine pathways and some may also be involved in the activation of the EMT pathways. Conclusion: There is a need for novel cancer biomarkers and targets to treat some forms of tumours such as the triple-negative BC, lacking a targeted therapy, or those that are resistant to the available ones. Here, the importance of the tumour microenvironment (TME) in the response to the inhibition of the targets in the cells was demonstrated. Potential therapeutic targets and pathways were presented as novel candidates for new treatments that need further investigation.

TNF-Related Apoptosis-Inducing Ligand (TRAIL) belongs to the TNF cytokine family and can signal to apoptosis by binding to either of two membrane-bound death receptors, TRAIL-R1 or TRAIL-R2. Using ligands specific for TRAIL-R1 (R1L) or TRAIL-R2 (R2L), our laboratory has previously shown that combining a histone deacetylase inhibitor with R1L, but not R2L, induces apoptosis in primary chronic lymphocytic leukaemia cells. The aim of this project was to extend the profiling of TRAIL-Receptor signalling to breast cancer, using breast cancer cell lines and importantly primary breast tumours as model systems. A 3-dimensional explant culturing technique was employed to maintain the primary tumour architecture and mimic the breast tumour microenvironment. In addition, tumour-initiating cells from advanced metastatic breast cancer patients were also tested for their sensitivity to TRAIL. The results obtained from breast cancer cell lines, primary mucinous carcinomas and advanced metastatic breast cancer cells suggest that in breast cancer, TRAIL-R1 is the predominant functional TRAIL death receptor independent of oestrogen receptor status. In contrast, invasive ductal/lobular carcinomas (IDC/ILC) were resistant to TRAIL-induced apoptosis and required the breast cancer chemotherapeutic, doxorubicin as a sensitising agent. Studies using the TRAIL-resistant cell line, T47D, demonstrated that doxorubicin sensitised tumour cells to TRAIL-induced apoptosis via enhanced TRAIL DISC formation. Importantly, in primary tumour explants, the combination of doxorubicin and TRAIL signalled to apoptosis exclusively in the tumour cells, but not in normal cells. Significantly, in four IDC/ILC tumours, doxorubicin sensitised breast tumour cells to R1L more efficiently than R2L. Therefore, using R1L in combination with sub-lethal doses of chemotherapeutic agents could improve the benefit of conventional therapy whilst reducing drug-associated side-effects and potential TRAIL-mediated cell proliferation/survival in apoptosis-resistant tumour cells. My data suggest that using a TRAIL-R1-selective agonist with an appropriate sensitising agent (example, doxorubicin), offers a promising therapeutic approach for treatment of breast cancer.

碩士
高雄醫學大學
醫學研究所
101
Background and Purpose Breast Cancer is the most common cancer in the world women. Also, the breast cancer is causing the highest mortality rate the main reason. The breast cancer is the number one killer for women and become the highest ranking in Asia. This study is therefore comparing artificial neural network (ANN) and logical regression (LR) prediction models to find the best of important effect factors. The purposes of this research are as follows: Ⅰ、To investigate long-term trend analysis of the breast cancer patient after surgery in 5-year mortality; Ⅱ、To compare the accuracy of different predict models for breast cancer patients after surgery in 5-year mortality; Ⅲ、To conduct the global sensitivity analyze and to estimate the significant predictors for breast cancer patients after surgery in 5-year mortality. Research Methods This study subject of "National Health Insurance Research Database" is the research framework. The study design used the retrospective method. The study period is from 1996 to 2010. The study subjects of the breast cancer patients are above sixteen years old after surgery. The samples of study are total 3,632 people. The use of the diagnostic codes ICD-9-CM174x (174.0-174.9) and disposal code 85.20-23,85.33-36,85.4 x, 85.5x, 85.6x, 85.7x, 85.8x, 85.95 find the predictor factors of meaningful. Investigate the important effect factors are by the breast cancer patient analysis after surgery in 5-year mortality and use of the patient''s characteristics, hospital characteristics and time characteristics, separately. In addition, the significant predictors are used in the ANN and LR to build model and to compare the accuracy. SPSS 19.0 statistical software was employed for the data collection and analysis. The main statistical methods include: descriptive statistics and inferential statistics (trend analysis, university analysis, and multivariate analysis - including logistic regression and neural networks and sensitivity analysis). Results In this study, the model build are used data mining technology of ANN and LR and seven important variables (age, charlson comorbidity index (CCI), hospital level, hospital volume, surgeon volume, chemotherapy, radiotherapy and hormone therapy). The results show that ANN model is better than LR model. The sensitivity of ANN and LR is 5.66% and 3.77%, respectively. The 1-specificity, positive predictive value, negative predictive value and accuracy are good performance. The AU-ROC curve is 0.70 and 0.52, respectively. Generally, the performance of ANN is better than LR model. The first three important predictors of ANN are surgeon volume, chemotherapy and age. The LR model are the surgeon volume, age, charlson comorbidity index (CCI). Conclusions and Recommendations The research results found that the distribution trend for the breast cancer patient after surgery in 5-year mortality is a significant change with time in patients’ characteristics and hospital characteristics. The means show that the correlation significant factors are worth applied in clinically and become improvement factors even if as standard of treatment guidelines. To compare the different predict models, found that the neural network can be used to expand predict variable items. The various diseases are easy to analyze and investigate, systematically. The death predicts model is use of appropriate research methods with development. In the future, this prediction model can applied to other cancers. In addition, the most important factor is surgeon volume. So that, how to enable health authorities to assist and train surgeons relevant experience reference. Secondary the impact factors such as age are increased with mortality rate. Therefore, the breast cancer should repeated think the relation with age and pay attention in how to promote and improve screening rates issues and to reach early detection and early treatment. Finally, this study expect can provide clinical reference value for medical staff and integrate medical treatment and to establish a predict model of medical decision.

Finding suitable ways to develop models for predicting unknown data classes is a challenging task in data mining and machine learning. The improvement of the quality of data sets and combining AdaBoost with a weak learner is an important contribution to the development of these prediction models. The objectives of this thesis are to build accurate, stable and effective breast cancer survivability prediction models using breast cancer data obtained from the Srinagarind Hospital in Thailand. To achieve these objectives, five approaches were proposed including: 1) £-means and RELIEF to improve accuracy and stability of prediction models generated from AdaBoost algorithms; 2) C-Support Vector Classification Filtering (CSVCF) to identify and eliminate outliers; 3) a combination of C-SVCF and oversampling approaches to handle both outliers and imbalanced data problems; 4) a hybrid AdaBoost and Random Forests to build stronger prediction models; and 5) C4.5 to form breast cancer survivability decision trees and rules. To illustrate capability, performance and effectiveness of these approaches, extensive experimental studies have been conducted using W E K A version 3.5.6, AdaBoost M A T L A B Toolbox, L I B S V M and C4.5 program.

Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra
Breast Cancer (BC) is the second most frequently diagnosed cancer and the fth cause of cancer mortality worldwide. Among women, it is the leading cause of cancer deaths, with more than 500 000 registered deaths in 2012, and Portugal also re ects that reality. Survival prediction plays a crucial role in diseases with associated high mortality rates, since it has the power to help clinicians to de ne each patient's prognosis, thus allowing to personalize the corresponding treatments. Particularly for BC, prognosis is related to the patterns of recurrence (cancer that reappears after treatment), and it even di ers depending on the local involved. This work analyses the data of a cohort of 97 patients, with a total of 27 characteristics, more than 50% of them incomplete. Therefore, the rst step is to handle Missing Data (Imputation or Deletion), to perform Classi cation afterwards. The purpose is to study the prognostic factors that de ne recurrence of female BC, to try to build a model that accurately predicts recurrence patterns, which would create the possibility of more targeted treatments. The application of machine learning algorithms to the prediction of recurrence in di erent sites seems to be a novel application of these methodologies, and the results can lead the way to a better understanding of the pathways of BC recurrence.