Ce travail de thèse s’est focalisé sur le développement des approches génériques de spectrométrie de masse (MS) pour la quantification des anticorps monoclonaux (mAbs) et de leurs produits dérivés dans des études précliniques. Premièrement, le développement des protocoles de préparation d’échantillons basée sur la digestion directe à partir de sérum ou comportant une étape d’immuno-précipitation spécifique par anticorps a permis la quantification des mAbs couvrant une large gamme d'étalonnage de cinq ordres de grandeur. En outre, l'emploi de peptides provenant de la région constante du mAb a démontré la polyvalence de telles approches génériques de chromatographie liquide en tandem MS (LC-MS/MS). Deuxièmement, les instruments de MS à haute résolution (HRMS) ont étés évalués dans le cadre de cette thèse en tant qu'alternative aux spectromètres de masse de type triple quadripôle traditionnellement utilisés pour l’analyse bottom-up quantitative. L’avantage majeur de l’intégration des analyseurs de HRMS a été associé à la possibilité de l’analyse quantitative simultanée des mAbs et leurs produits associés directement au niveau de la protéine fournissant un niveau d'informations bien au-delà de celui obtenu avec des approches bottom-up. Par conséquent, l’apport essential de la HRMS pour les analyses qualitative et quantitative des protéines thérapeutiques de type mAbs et produits associés a été démontré dans cette thèse. / This PhD thesis focused on the development of generic mass spectrometry (MS)-based workflows for monoclonal antibody (mAb)-related therapeutic protein quantification in pre-clinical species. First, the development of bottom-up sample preparation protocols either based on direct serum digestion or immuno-capture allowed mAb-related therapeutic protein quantification over five orders of magnitude whereas the employment of peptides from the constant region of the mAb demonstrated the versatility of such generic liquid chromatography tandem MS (LC-MS/MS)-based approaches. Second, high-resolution MS (HRMS) instruments were evaluated as an alternative to triple quadrupole mass analyzers, traditionally utilized for bottom-up mAb quantification by LC-MS/MS. The major benefit of HRMS incorporation into the workflow was associated with the possibility to quantify simultaneously mAb-related therapeutic proteins directly at an intact level, providing an information level far beyond the one obtained with bottom-up LC-MS/MS methodologies. Hence, the pivotal role of HRMS for the qualitative and quantitative analyses of mAb-related therapeutic proteins was further outlined throughout this doctoral work.

Immunohistological (IHC) stained images occupy a fundamental role in the pathologist's diagnosis and monitoring of cancer development. The manual process of monitoring such images is a subjective, time consuming process that typically relies on the visual ability and experience level of the pathologist. A novel and comprehensive system for the automated quantification of antibody inside stained cell nuclei in immunohistochemistry images is proposed and demonstrated in this research. The system is based on a cellular level approach, where each nucleus is individually analyzed to observe the effects of protein antibodies inside the nuclei. The system provides three main quantitative descriptions of stained nuclei. The first quantitative measurement automatically generates the total number of cell nuclei in an image. The second measure classifies the positive and negative stained nuclei based on the nuclei colour, morphological and textural features. Such features are extracted directly from each nucleus to provide discriminative characteristics of different stained nuclei. The output generated from the first and second quantitative measures are used collectively to calculate the percentage of positive nuclei (PS). The third measure proposes a novel automated method for determining the staining intensity level of positive nuclei or what is known as the intensity score (IS). The minor intensity features are observed and used to classify low, intermediate and high stained positive nuclei. Statistical methods were applied throughout the research to validate the system results against the ground truth pathology data. Experimental results demonstrate the effectiveness of the proposed approach and provide high accuracy when compared to the ground truth pathology data.

Immunohistological (IHC) stained images occupy a fundamental role in the pathologist¿s diagnosis and monitoring of cancer development. The manual process of monitoring such images is a subjective, time consuming process that typically relies on the visual ability and experience level of the pathologist. A novel and comprehensive system for the automated quantification of antibody inside stained cell nuclei in immunohistochemistry images is proposed and demonstrated in this research. The system is based on a cellular level approach, where each nucleus is individually analyzed to observe the effects of protein antibodies inside the nuclei. The system provides three main quantitative descriptions of stained nuclei. The first quantitative measurement automatically generates the total number of cell nuclei in an image. The second measure classifies the positive and negative stained nuclei based on the nuclei colour, morphological and textural features. Such features are extracted directly from each nucleus to provide discriminative characteristics of different stained nuclei. The output generated from the first and second quantitative measures are used collectively to calculate the percentage of positive nuclei (PS). The third measure proposes a novel automated method for determining the staining intensity level of positive nuclei or what is known as the intensity score (IS). The minor intensity features are observed and used to classify low, intermediate and high stained positive nuclei. Statistical methods were applied throughout the research to validate the system results against the ground truth pathology data. Experimental results demonstrate the effectiveness of the proposed approach and provide high accuracy when compared to the ground truth pathology data.