Indiana University-Purdue University Indianapolis (IUPUI) / Cancer stem cells (CSCs) are proposed to play a major role in tumor progression, metastasis, and recurrence. The Human Epidermal growth factor Receptor 2 (HER2) gene is amplified and/or its protein product overexpressed in approximately 20% of breast cancers. HER2 overexpression is associated with increased CSCs, which may explain the aggressive phenotype and increased likelihood of recurrence for HER2+ breast cancers. Telomerase is reactivated in tumor cells, including CSCs, but has limited activity in normal tissues, providing support for the use of telomerase inhibition in anti-cancer therapy. Telomerase inhibition via an antagonistic oligonucleotide, imetelstat (GRN163L), has been shown to be effective in limiting cell growth in vitro and limiting tumor growth. Moreover, we have previously shown imetelstat can decrease metastases to the lungs, leading us to question if this is due to imetelstat targeting the CSC population. In this thesis, we investigated the effects of imetelstat on CSC and non-CSC populations of HER2+ breast cancer cell lines, as well as a triple negative breast cancer cell line, which lacks HER2 overexpression. Imetelstat inhibited telomerase activity in both CSC and non-CSC subpopulations. Moreover, imetelstat treatment alone and in combination with trastuzumab significantly reduced the CSC fraction and inhibited CSC functional ability, as shown by a significant decrease in mammosphere counts and invasive potential. Tumor growth rate was slower in combination treated mice compared to either drug alone. Additionally, there was a trend toward decreased CSC marker expression in imetelstat treated xenograft cells compared to vehicle control. The decrease in CSC marker expression we observed occurred prior to and after telomere shortening, suggesting imetelstat acts on the CSC subpopulation in telomere length dependent and independent mechanisms. Our study suggests addition of imetelstat to trastuzumab may enhance the effects of HER2 inhibition therapy.

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

Indiana University-Purdue University Indianapolis (IUPUI) / Mammary ductal epithelial cell growth is controlled by microenvironmental signals in serum under both normal physiological settings and during breast cancer progression. Importantly, the effects of several of these microenvironmental signals are mediated by the activities of the tumor suppressor protein kinases of the Hippo pathway. Canonically, Hippo protein kinases inhibit cellular growth through the phosphorylation and inactivation of the oncogenic transcriptional co-activator Yes-Associated Protein (YAP). This study defines an alternative mechanism whereby Hippo protein kinases induce growth arrest via the phosphorylation of the long isoform of Angiomotin (Amot130). Specifically, serum starvation is found to activate the Hippo protein kinase, Large Tumor Suppressor (LATS), which phosphorylates the adapter protein Amot130 at serine-175. Importantly, wild-type Amot130 potently inhibits mammary epithelial cell growth, unlike the Amot130 serine-175 to alanine mutant, which cannot be phosphorylated at this residue. The growth-arrested phenotype of Amot130 is likely a result of its mechanistic response to LATS signaling. Specifically, LATS activity promotes the association of Amot130 with the ubiquitin ligase Atrophin-1 Interacting Protein 4 (AIP4). As a consequence, the Amot130-AIP4 complex amplifies LATS tumor suppressive signaling by stabilizing LATS protein steady state levels via preventing AIP4-targeted degradation of LATS. Additionally, AIP4 binding to Amot130 leads to the ubiquitination and stabilization of Amot130. In turn, the Amot130-AIP4 complex signals the ubiquitination and degradation of YAP. This inhibition of YAP activity by Amot130 requires both AIP4 and the ability of Amot130 to be phosphorylated by LATS. Together, these findings significantly modify the current view that the phosphorylation of YAP by Hippo protein kinases is sufficient for YAP inhibition and cellular growth arrest. Based upon these results, the inhibition of cellular growth in the absence of serum more accurately involves the stabilization of Amot130 and LATS, which together inhibit YAP activity and mammary epithelial cell growth.