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Prognostic indicators, measured at diagnosis, are important in breast cancer. They help clinicians select optimal treatment, provide rational bases for stratification of treatment trials and assist analysis of response to treatment. Univariate statistical survival curves have identified many such indicators. However, they do not explain why some patients, classified as favoured by one or other factor(s), experience early treatment failure, nor why a substantial number with unfavourable signs remain recurrence-free many years later. This study was undertaken to identify independent prognostic factors with the use of multivariate regression. A Cox proportional hazards model of disease-specific survival was based on 1184 primary breast cancer patients referred to the Cancer Control Agency of B.C. between 1975 and 1981 (median follow-up 60 months). Significant univariate associations with overall survival were found for estrogen receptor concentration ([ER]), axillary nodal status (NO, Nl-3, N4+), clinical stage (TNM I, II, III, IV), histologic differentiation and confluent tumor necrosis (minimal, marked). These factors were assessed at primary diagnosis. A subset of 859 patients with complete data on these variables and also histologic type, menopausal status, age, tumor size and treatment was used to fit the multivariate model. Nodal status was the most important independent factor but three others, TNM stage, [ER] and tumor necrosis, were needed to make adequate predictions. A derived Hazard Index defined risk groups with 8-fold variation in survival. Five-year predicted survival ranged from 36% (N4+, loge[ER]=0, marked necrosis) to 96% (NO, loge[ER]=6, no necrosis) with TNM I and 0% to 70% for the same categories in TNM IV. This wide variation occurred across all stages. Study of post-recurrence survival (369 patients) yielded a model with only three independent predictors: [ER], nodal status and tumor necrosis. Survival - overall, recurrence-free and post-recurrent - is predictable by modelling a few factors measureable at diagnosis. Use of ER concentration, rather than the more common ER status (+ or -), greatly strengthens the model. Presence of ER was also shown to be increasingly important as 'protective', attenuating the effect of other factors, as risk of mortality increases. / Medicine, Faculty of / Pathology and Laboratory Medicine, Department of / Graduate

the majority of patients with early breast cancer receive some form of systemic adjuvant therapy (chemo-, endocrine, and/or targeted therapy). Despite the increase in adjuvant therapy prescription, little progress has been made with respect to assisting oncologists to determine which breast cancer patients, particularly those deemed at “lower risk” of relapse, require chemotherapy or other systemic therapy and which women can safely be treated with loco-regional treatment alone. For these reasons, the identification of prognostic and predictive markers that will assist the clinician in selecting the most suitable form of medical therapy has become very high priority as well as a real challenge in translational research. <p>\ / Doctorat en Sciences biomédicales et pharmaceutiques / info:eu-repo/semantics/nonPublished

Contexte : Les inégalités sociales de santé représentent un problème de santé publique considérable. Dans le cadre du cancer du sein, la précarité est associée au pronostic. En effet, une relation entre précarité géographique et stade au diagnostic a été établie dans la littérature. Cependant, à ce jour, aucune étude n'a encore analysé l'association de ce dernier à la précarité individuelle.Objectifs : Les objectifs de ce travail de recherche sont (1) d'estimer le risque de cancer du sein de stade avancé associé à la précarité individuelle, (2) d'étudier l'impact des facteurs pouvant modifier ce risque, (3) d'évaluer la robustesse de l'association face au choix de la mesure de précarité.Population et méthode : Les données sont issues d'une étude cas-témoins. Les Cas et les Témoins de l'étude ont été recrutés parmi les patientes de l'Hérault atteintes de cancers du sein invasifs diagnostiqués entre 2011 et 2012. Les Cas correspondent aux patientes présentant un cancer du sein de mauvais pronostic (taille de tumeur supérieure à 5cm, ou atteinte ganglionnaire ou atteinte métastatique) et les Témoins aux patientes présentant des cancers de bon pronostic (taille de tumeur inférieure à 5cm et aucune atteinte ganglionnaire et aucune atteinte métastatique). Au total 604 patientes ont été incluses : 173 Cas et 431 Témoins. L'exposition à la précarité a été recueillie par un questionnaire standardisé.Résultats : Les patientes précaires ont, toutes variables égales par ailleurs, 2 fois plus de risque d'avoir un cancer de stade avancé comparée aux patientes non précaires. La précarité n'est associée à aucun autre facteur biologique (grade SBR, types histologique et moléculaire). Chez les patientes asymptomatiques (diagnostiquées suite à un dépistage) les patientes précaires ont plus de risque d'avoir des cancers de stade avancé. Chez les femmes avec un antécédent familial de cancer du sein tout comme chez les femmes vivant dans une zone géographique favorisée, les patientes précaires et non-précaires ont le même risque de cancer de stade avancé. Comparé aux autres mesures de l'environnement socio-économique (classe sociale, précarité géographique…), le score EPICES semble la méthode de mesure la plus adaptée pour étudier l'association entre précarité et stade au diagnostic.Conclusion : Nos résultats suggèrent que les écarts observés entre les patientes précaires et les patientes non-précaire semblent être plutôt liés à retard au diagnostic plutôt qu'à des différences biologiques entre les tumeurs. Ce retard au diagnostic semble dépendre de composantes individuelles mais aussi collectives. De plus, une meilleure connaissance du cancer du sein pourrait permettre de réduire les barrières supplémentaires vécues par les précaires. / Context: Socio-economic inequalities in health represent a significant public health problem. In the breast cancer context, socio-economic deprivation is associated with prognosis. Indeed, a relationship between area-based deprivation and diagnostic stages was already described in the international literature. However, the association between individual deprivation and diagnostic stages was not study so far.Objectives: Our aim was to (1) estimate the risk of advanced breast cancer associated with individual socio-economic deprivation, (2) study the impact of modifying factors, (3) evaluate the strength of this association according to the method used to measure deprivation.Population and methods: Data were collected from a Case-Control study. Cases and Controls were recruited among invasive breast cancer patients diagnosed between 2011 and 2012 in the Hérault. Cases were defined as patients with poor prognosis breast cancer (with tumor size over 5cm, or with lymph node involvement, or with metastasis). Controls were defined as patients with good prognosis breast cancer (with tumor size under 5cm, and without lymph node involvement, and without metastasis). A total number of 604 patients were included: 173 Cases and 431 Controls. The exposition to deprivation was measured by a standardized questionnaire.Results: Deprived patients, with all other variables remaining constant, have a two-fold risk of having advanced breast cancer compared to non-deprived patients. Deprivation was not associated with the other biological factors (SBR grade, histologic and molecular type). Among asymptomatic patients (diagnosed after a mammographic screening), deprived patients have a higher risk of advanced breast cancer. Among women with family history of breast cancer so as women living in affluent geographic areas, deprived and non-deprived patients have the same risk of advanced breast cancer. Compared to other measures of socio-economic environment (social class, area-based deprivation…), EPICES score seems to be the most adapted method to study the association between deprivation and breast cancer diagnostic stages.Conclusion: Our results suggest that the gap observed between deprived and non-deprived patients seem to be associated with delayed diagnosis more than biological differences between tumors. This delayed diagnosis seems depend on individual and geographic components. Moreover, a better knowledge of breast cancer could allow a reduction of the barrier experienced by deprived women.

This thesis addresses the use of machine learning techniques to develop clinical diagnostic tools for breast cancer using molecular data. These tools are designed to assist physicians in their evaluation of the clinical outcome of breast cancer (referred to as prognosis).<p>The traditional approach to evaluating breast cancer prognosis is based on the assessment of clinico-pathologic factors known to be associated with breast cancer survival. These factors are used to make recommendations about whether further treatment is required after the removal of a tumor by surgery. Treatment such as chemotherapy depends on the estimation of patients' risk of relapse. Although current approaches do provide good prognostic assessment of breast cancer survival, clinicians are aware that there is still room for improvement in the accuracy of their prognostic estimations.<p>In the late nineties, new high throughput technologies such as the gene expression profiling through microarray technology emerged. Microarrays allowed scientists to analyze for the first time the expression of the whole human genome ("transcriptome"). It was hoped that the analysis of genome-wide molecular data would bring new insights into the critical, underlying biological mechanisms involved in breast cancer progression, as well as significantly improve prognostic prediction. However, the analysis of microarray data is a difficult task due to their intrinsic characteristics: (i) thousands of gene expressions are measured for only few samples; (ii) the measurements are usually "noisy"; and (iii) they are highly correlated due to gene co-expressions. Since traditional statistical methods were not adapted to these settings, machine learning methods were picked up as good candidates to overcome these difficulties. However, applying machine learning methods for microarray analysis involves numerous steps, and the results are prone to overfitting. Several authors have highlighted the major pitfalls of this process in the early publications, shedding new light on the promising but overoptimistic results. <p>Since 2002, large comparative studies have been conducted in order to identify the key characteristics of successful methods for class discovery and classification. Yet methods able to identify robust molecular signatures that can predict breast cancer prognosis have been lacking. To fill this important gap, this thesis presents an original methodology dealing specifically with the analysis of microarray and survival data in order to build prognostic models and provide an honest estimation of their performance. The approach used for signature extraction consists of a set of original methods for feature transformation, feature selection and prediction model building. A novel statistical framework is presented for performance assessment and comparison of risk prediction models.<p>In terms of applications, we show that these methods, used in combination with a priori biological knowledge of breast cancer and numerous public microarray datasets, have resulted in some important discoveries. In particular, the research presented here develops (i) a robust model for the identification of breast molecular subtypes and (ii) a new prognostic model that takes into account the molecular heterogeneity of breast cancers observed previously, in order to improve traditional clinical guidelines and state-of-the-art gene signatures./Cette thèse concerne le développement de techniques d'apprentissage (machine learning) afin de mettre au point de nouveaux outils cliniques basés sur des données moleculaires. Nous avons focalisé notre recherche sur le cancer du sein, un des cancers les plus fréquemment diagnostiqués. Ces outils sont développés dans le but d'aider les médecins dans leur évaluation du devenir clinique des patients cancéreux (cf. le pronostique).<p>Les approches traditionnelles d'évaluation du pronostique d'un patient cancéreux se base sur des critères clinico-pathologiques connus pour être prédictifs de la survie. Cette évaluation permet aux médecins de décider si un traitement est nécessaire après l'extraction de la tumeur. Bien que les outils d'évaluation traditionnels sont d'une aide importante, les cliniciens sont conscients de la nécessité d'améliorer de tels outils.<p>Dans les années 90, de nouvelles technologies à haut-débit, telles que le profilage de l'expression génique par biopuces à ADN (microarrays), ont été mises au point afin de permettre aux scientifiques d'analyser l'expression de l'entièreté du génôme de cellules cancéreuses. Ce nouveau type de données moléculaires porte l'espoir d'améliorer les outils pronostiques traditionnels et d'approfondir nos connaissances concernant la génèse du cancer du sein. Cependant ces données sont extrêmement difficiles à analyser à cause (i) de leur haute dimensionalité (plusieurs dizaines de milliers de gènes pour seulement quelques centaines d'expériences); (ii) du bruit important dans les mesures; (iii) de la collinéarité entre les mesures dûe à la co-expression des gènes.<p>Depuis 2002, des études comparatives à grande échelle ont permis d'identifier les méthodes performantes pour l'analyse de groupements et la classification de données microarray, négligeant l'analyse de survie pertinente pour le pronostique dans le cancer du sein. Pour pallier ce manque, cette thèse présente une méthodologie originale adaptée à l'analyse de données microarray et de survie afin de construire des modèles pronostiques performants et robustes. <p>En termes d'applications, nous montrons que cette méthodologie, utilisée en combinaison avec des connaissances biologiques a priori et de nombreux ensembles de données publiques, a permis d'importantes découvertes. En particulier, il résulte de la recherche presentée dans cette thèse, le développement d'un modèle robuste d'identification des sous-types moléculaires du cancer du sein et de plusieurs signatures géniques améliorant significativement l'état de l'art au niveau pronostique. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished