Technological advances in the recent decades have enabled cancer researchers to probe the disease at multiple resolutions. This wealth of experimental data combined with computational systems biology methods is now leading to predictive models of cancer progression and response to therapy. We begin by presenting our research group’s multis-cale in silico framework for modeling cancer, whose core is a tissue-scale computational model capable of tracking the progression of tumors from a diffusion-limited avascular phase through angiogenesis, and into invasive lesions with realistic, complex morphologies. We adapt this core model to consider the delivery of systemically-administered anticancer agents and their effect on lesions once they reach their intended nuclear target. We calibrate the model parameters using in vitro data from the literature, and demonstrate through simulation that transport limitations affecting drug and oxygen distributions play a significant role in hampering the efficacy of chemotherapy; a result that has since been validated by in vitro experimentation. While this study demonstrates the capability of our adapted core model to predict distributions (e.g., cell density, pressure, oxygen, nutrient, drug) within lesions and consequent tumor morphology, nevertheless, the underlying factors driving tumor-scale behavior occur at finer scales. What is needed in our multi-scale approach is to parallel reality, where molecular signaling models predict cellular behavior, and ultimately drive what is seen at the tumor level. Models of signaling pathways linked to cell models are already beginning to surface in the literature. We next transition our research to the molecular level, where we employ data mining and bioinformatics methods to infer signaling relationships underlying a subset of breast cancer that might benefit from targeted therapy of Androgen Receptor and associated pathways. Defining the architecture of signaling pathways is a critical first step towards development of pathways models underlying tumor models, while also providing valuable insight for drug discovery. Finally, we develop an agent-based, cell-scale model focused on predicting motility in response to chemical signals in the microenvironment, generally accepted to be a necessary feature of cancer invasion and metastasis. This research demonstrates the use of signaling models to predict emergent cell behavior, such as motility. The research studies presented in this dissertation are critical steps towards developing a predictive, in silico computational model for cancer progression and response to therapy. Our Laboratory for Computational & Predictive Oncology, in collaboration with research groups throughout in the United States and Europe are following a computational systems biology paradigm where model development is fueled by biological knowledge, and model predictions are refining experimental focus. The ultimate objective is a virtual cancer simulator capable of accurately simulating cancer progression and response to therapy on a patient-specific basis. / text

A Terapia fotodinâmica (TFD) é uma conhecida opção terapêutica para diversos tipos de lesões malignas e não-malignas. A TFD age por meio de uma reação fotoquímica, formando agentes oxidativos que causam inúmeros danos às estruturas subcelulares e posterior morte da célula. Em grande parte dos casos são necessárias várias sessões da TFD para erradicação completa da lesão neoplásica. No entanto, vários estudos clínicos têm sido publicados mostrando recrescimento tumoral e um aumento na resistência do tumor às sessões posteriores da TFD. Neste estudo apresentamos um modelo teórico para descrever os efeitos causados por sucessivas sessões da TFD quando ocorre recrescimento tumoral. Para isso, considera-se uma distribuição de dose limiar que representa a variedade celular de um modelo teórico de tumor. A existência de uma variedade de células com diferentes doses limiares pode ser a causa de uma resposta parcial do tecido à terapia, implicando em recrescimento do tecido tumoral. Neste modelo, assume-se que esta distribuição de dose limiar é representada por uma distribuição Gaussiana modificada. Em termos de dose limiar, valores mais altos implicam em maior resistência à TFD. Se a distribuição é larga, o tratamento não é capaz de eliminar todas as células. A fração de células que sobrevivem promovem o recrescimento tumoral; no entanto, a população de células no tumor recrescido apresenta diferentes características quando comparada com a população de células do tumor original. Para avaliar a ocorrência da seleção das células mais resistentes foi realizada uma investigação sobre as alterações da resposta das células tumorais, após múltiplas sessões da terapia fotodinâmica. Para simular este tipo de procedimento foram realizadas sucessivas sessões da TFD em culturas de células de hepatocarcinoma (HepG2). Entre as sessões de TFD foi aguardado um intervalo de tempo suficiente para que as células sobreviventes se reproduzissem e formassem uma nova cultura celular. O fotossensibilizador utilizado nos experimentos foi o Photogem® e a iluminação realizada em 630 ± 10nm. Os resultados dos experimentos in vitro forneceram evidências do aumento da resistência das células neoplásicas da linhagem HepG2 após sucessivas aplicações da TFD. Este aumento é previsto pelo modelo teórico e pode estar relacionado com a variação das características da população celular, que é expressa neste modelo pela distribuição de dose limiar. No entanto, o aumento da resistência da população celular à TFD previsto pelo modelo teórico é mais acentuado do que o aumento observado no experimento com culturas celulares, portanto, mais estudos serão necessários para adequar o modelo à condição real. Com base na variabilidade das células tumorais, as simulações demonstraram que a dose de luz insuficiente pode induzir um aumento da resistência do tumor às posteriores sessões da TFD. Este modelo poderá ser utilizado para avaliar qual o tipo de distribuição de dose limiar pode-se encontrar em tumores reais e quais as conseqüências causadas pela atenuação da luz em função da profundidade do tumor. A idéia apresentada neste estudo motivará novos estudos para identificar a importância da distribuição de dose limiar em tumores submetidos à TFD. / Photodynamic therapy is a well known treatment option for many types of malignant and nonmalignant lesions. This technique causes cell damage through a photochemical reaction, generating oxidative agents responsible for tumor cell killing. In several cases, multiple PDT-sessions are needed to promote cancer eradication. However, several clinical studies have been reported an increase of tumor resistance after a PDT-session. We present a theoretical model to describe the effects caused by successive PDT sessions based on the consequences of a partial response caused by the threshold dose distribution within the hypothetical tumor. In this model, we assume that this threshold dose distribution is represented by a Modified Gaussian Distribution. In terms of threshold dose, higher values imply higher resistance to PDT. If the distribution is broad, the treatment cannot result in the killing of all tumor cells. The survival cell fraction promotes a tumor regrowth with different characteristics compared to the original cell population. We applied the model in a hypothetical tumor to exemplify the idea here presented. The qualitative analysis extracted from our theoretical model shows a behavior that is in agreement with results obtained in our results from in vitro experiments and several clinical observations. To investigate the occurrence of a selection of higher threshold dose cells, an experiment that evaluated the response of tumor cells after multiple sessions of photodynamic therapy was carried out. To simulate this procedure, successive sessions of PDT in hepatocellular carcinoma cells (HepG2) were performed. A time interval between PDT-sessions was respected to allow surviving cells division, resulting in a new cell culture. The photosensitizer used in the experiments was Photogem® and a 630 ± 10nm irradiation was performed. The result of in vitro experiments provided evidence of increasing resistance of HepG2 cells after successive PDT-sessions. This increase is predicted by the theoretical model and may be related to variations in the tumor cell population, which is expressed by the variation of the distribution of threshold dose, according to the model. However, the increased PDT resistance of the cell population provided by the theoretical model is more pronounced than the one experimentally observed. Based on tumor cell variability, the simulations demonstrated that insufficient light dose can induce an increase in tumor resistance to further PDT sessions. This model maybe used to evaluate which type of threshold dose distribution we can find in real tumors, and the consequences caused by light attenuation observed from the illuminated surface and deeper tumor regions. This proposed model shows relative agreement to clinical literature. However, further experimental observations shall improve the model here presented. The idea presented in this study shall motivate further studies to identify the importance of cell threshold distribution in tumors submitted to PDT techniques.

A Terapia fotodinâmica (TFD) é uma conhecida opção terapêutica para diversos tipos de lesões malignas e não-malignas. A TFD age por meio de uma reação fotoquímica, formando agentes oxidativos que causam inúmeros danos às estruturas subcelulares e posterior morte da célula. Em grande parte dos casos são necessárias várias sessões da TFD para erradicação completa da lesão neoplásica. No entanto, vários estudos clínicos têm sido publicados mostrando recrescimento tumoral e um aumento na resistência do tumor às sessões posteriores da TFD. Neste estudo apresentamos um modelo teórico para descrever os efeitos causados por sucessivas sessões da TFD quando ocorre recrescimento tumoral. Para isso, considera-se uma distribuição de dose limiar que representa a variedade celular de um modelo teórico de tumor. A existência de uma variedade de células com diferentes doses limiares pode ser a causa de uma resposta parcial do tecido à terapia, implicando em recrescimento do tecido tumoral. Neste modelo, assume-se que esta distribuição de dose limiar é representada por uma distribuição Gaussiana modificada. Em termos de dose limiar, valores mais altos implicam em maior resistência à TFD. Se a distribuição é larga, o tratamento não é capaz de eliminar todas as células. A fração de células que sobrevivem promovem o recrescimento tumoral; no entanto, a população de células no tumor recrescido apresenta diferentes características quando comparada com a população de células do tumor original. Para avaliar a ocorrência da seleção das células mais resistentes foi realizada uma investigação sobre as alterações da resposta das células tumorais, após múltiplas sessões da terapia fotodinâmica. Para simular este tipo de procedimento foram realizadas sucessivas sessões da TFD em culturas de células de hepatocarcinoma (HepG2). Entre as sessões de TFD foi aguardado um intervalo de tempo suficiente para que as células sobreviventes se reproduzissem e formassem uma nova cultura celular. O fotossensibilizador utilizado nos experimentos foi o Photogem® e a iluminação realizada em 630 ± 10nm. Os resultados dos experimentos in vitro forneceram evidências do aumento da resistência das células neoplásicas da linhagem HepG2 após sucessivas aplicações da TFD. Este aumento é previsto pelo modelo teórico e pode estar relacionado com a variação das características da população celular, que é expressa neste modelo pela distribuição de dose limiar. No entanto, o aumento da resistência da população celular à TFD previsto pelo modelo teórico é mais acentuado do que o aumento observado no experimento com culturas celulares, portanto, mais estudos serão necessários para adequar o modelo à condição real. Com base na variabilidade das células tumorais, as simulações demonstraram que a dose de luz insuficiente pode induzir um aumento da resistência do tumor às posteriores sessões da TFD. Este modelo poderá ser utilizado para avaliar qual o tipo de distribuição de dose limiar pode-se encontrar em tumores reais e quais as conseqüências causadas pela atenuação da luz em função da profundidade do tumor. A idéia apresentada neste estudo motivará novos estudos para identificar a importância da distribuição de dose limiar em tumores submetidos à TFD. / Photodynamic therapy is a well known treatment option for many types of malignant and nonmalignant lesions. This technique causes cell damage through a photochemical reaction, generating oxidative agents responsible for tumor cell killing. In several cases, multiple PDT-sessions are needed to promote cancer eradication. However, several clinical studies have been reported an increase of tumor resistance after a PDT-session. We present a theoretical model to describe the effects caused by successive PDT sessions based on the consequences of a partial response caused by the threshold dose distribution within the hypothetical tumor. In this model, we assume that this threshold dose distribution is represented by a Modified Gaussian Distribution. In terms of threshold dose, higher values imply higher resistance to PDT. If the distribution is broad, the treatment cannot result in the killing of all tumor cells. The survival cell fraction promotes a tumor regrowth with different characteristics compared to the original cell population. We applied the model in a hypothetical tumor to exemplify the idea here presented. The qualitative analysis extracted from our theoretical model shows a behavior that is in agreement with results obtained in our results from in vitro experiments and several clinical observations. To investigate the occurrence of a selection of higher threshold dose cells, an experiment that evaluated the response of tumor cells after multiple sessions of photodynamic therapy was carried out. To simulate this procedure, successive sessions of PDT in hepatocellular carcinoma cells (HepG2) were performed. A time interval between PDT-sessions was respected to allow surviving cells division, resulting in a new cell culture. The photosensitizer used in the experiments was Photogem® and a 630 ± 10nm irradiation was performed. The result of in vitro experiments provided evidence of increasing resistance of HepG2 cells after successive PDT-sessions. This increase is predicted by the theoretical model and may be related to variations in the tumor cell population, which is expressed by the variation of the distribution of threshold dose, according to the model. However, the increased PDT resistance of the cell population provided by the theoretical model is more pronounced than the one experimentally observed. Based on tumor cell variability, the simulations demonstrated that insufficient light dose can induce an increase in tumor resistance to further PDT sessions. This model maybe used to evaluate which type of threshold dose distribution we can find in real tumors, and the consequences caused by light attenuation observed from the illuminated surface and deeper tumor regions. This proposed model shows relative agreement to clinical literature. However, further experimental observations shall improve the model here presented. The idea presented in this study shall motivate further studies to identify the importance of cell threshold distribution in tumors submitted to PDT techniques.

L’imagerie moléculaire avec des biomarqueurs radiomarqués par des émetteurs de positons est couramment utilisée dans les cancers mammaires infiltrants pour la stadification initiale ou en cas de récidive, et également pour l’évaluation de la réponse tumorale en situation néoadjuvante et métastatique. Après quelques notions générales sur les cancers mammaires et le rôle alloué à l’imagerie moléculaire, les résultats des travaux axés sur l’imagerie moléculaire des cancers mammaires que j’ai encadrés et auxquels j’ai contribués au sein de l’équipe de Médecine Nucléaire de l’Institut Curie - Saint-Cloud sont présentés. Ces études ont évalué la valeur de la Tomographie par Emission de Positons au FluoroDesoxyGlucose (TEP-FDG) en cas de suspicion biologique de récidive, ou pour la stadification initiale et l’évaluation de la réponse tumorale à la Chimiothérapie NéoAdjuvante (CNA) des cancers mammaires inflammatoires. Notre équipe a également étudié la valeur pronostique de la réponse tumorale à l’hormonothérapie par la TEP-FDG, en situation métastatique. La problématique de l’évaluation précoce de la réponse tumorale à la CNA par l'imagerie moléculaire m’a amené à étudier un modèle murin transgénique de cancer mammaire avec d’autres biomarqueurs que le FDG, à savoir la FLuoroThymidine (FLT) et le pertechnetate. Des outils de quantification de la charge tumorale ont pu être développés et ont permis d’évaluer la réponse tumorale à des drogues cytotoxiques lors d’un suivi longitudinal, mais ce travail s’est heurté aux limites de l’imagerie préclinique. Les résultats des études cliniques sur l’évaluation précoce de la réponse tumorale à la CNA sont ensuite exposés, avec le FDG comme biomarqueur en insistant sur l’influence du phénotype moléculaire sur les paramètres définissant une réponse métabolique. L’état des connaissances et les perspectives avec d’autres biomarqueurs que le FDG, à savoir la FLT et le FluoroEStradiol (FES), pour étudier respectivement la prolifération cellulaire tumorale et la présence et la fonctionnalité des récepteurs aux estrogènes, puis l’imagerie en développement de l’Human Epidermal Growth Factor Receptor-2 (HER2), destinés à la fois à la sélection de patients candidats à des thérapies innovantes et à la prédiction et l’évaluation de la réponse tumorale aux thérapies sont discutées. / Molecular imaging with biomarkers radiolabeled by positron emitters is commonly used in invasive breast cancers for initial staging or restaging, and for assessment of tumor response in neoadjuvant and metastatic settings. After general concepts of breast cancers and of the place of molecular imaging, results of studies I supervised and to which I contributed within the nuclear medicine team from Curie Institute - Saint-Cloud focused on molecular imaging in breast cancers are presented. These studies assessed the value of Positron Emission Tomography using FluoroDeoxyGlucose (FDG-PET) in recurrence suspected on tumor marker rising and in staging and assessment of tumor response to neoadjuvant chemotherapy (NACT) in inflammatory breast cancers. Moreover our team has studied the prognostic value of tumor response to endocrine therapy by FDG-PET in metastatic breast cancers. The problem of early assessment of tumor response to NACT by molecular imaging has led me to study a transgenic mouse model with other biomarkers than FDG, namely FLuoroThymidine (FLT) and pertechnetate. Quantification tools of tumor burden have been developed and allowed assessment of tumor response to cytotoxic drugs during a longitudinal follow-up of this model, but several limitations of preclinical imaging were encountered. Results of clinical studies on early assessment of tumor response to NACT with FDG as a biomarker are exposed, with emphasis on the influence of the molecular subtype on parameters defining a metabolic response. State of the art and perspectives with FLT and FluoroEStradiol (FES), as biomarkers of proliferation and of estrogen receptors respectively, then imaging of Human Epidermal Growth Factor Receptor-2 (HER2), for both selection of patients to innovative therapies and prediction and assessment of tumor response to systemic therapies are discussed.

TRPM4, un canal cationique non-sélective activé par le Ca2+ intracellulaire, est un acteur moléculaire important impliqué de la régulation du signal calcique et l’activation des lymphocytes T conventionnels mais son rôle dans la fonction des lymphocytes T régulateurs (Tregs Foxp3+) reste inconnu. Dans un modèle de souris transgéniques dans lequel le gène Trpm4 a été sélectivement invalidé dans la population des Tregs Foxp3+ (souris Foxp3(YFP)Cre+Trpm4flox/flox), nous avons démontré dans différents modèles in vivo d’inflammation aiguë et chronique que TRPM4 contrôle la fonction suppressive et la mort de ces cellules. Dans le modèle de fibrosarcome induit par le méthylcholanthrène (3-MCA) ou implanté (modèle MCA205), dans lequel le rôle des Tregs est documenté, l’absence de fonction de TRPM4 induit une diminution significative de l’incidence et de la croissance tumorale. Dans l’environnent inflammatoire chronique et hypoxique de ces tumeurs, l’expression de TRPM4 protège les Tregs infiltrant la tumeur de la mort cellulaire induit par l’ATP extracellulaire et stimule ainsi le développent et la progression tumorale. L’absence d’expression de TRPM4 dans les Tregs stimule la réponse anti-tumorale médiée par l’IFNg et induit la régression des tumeurs. En conclusion, en inhibant l’entrée de Ca2+ extracellulaire, TRPM4 régule négativement les fonctions suppressives des Tregs et protège ces cellules de la mort cellulaire induite par l’activation. / TRPM4, a Ca2+-activated non-selective cation ion channel is an important regulator of Ca2+ signaling and cell activation in conventional T cells, but its role in Foxp3+ Tregs function remains unknown. Using a model in which Trpm4 gene was selectively invalidated in Foxp3+ Tregs population (Foxp3(YFP)Cre+Trpm4flox/flox mice) we have shown in different in vivo models of acute and chronic inflammation that TRPM4 is an important regulator of Tregs functions and survival. In a model of primary carcinogenesis induced by methylcholantrene (3-MCA) or implanted fibrosarcoma (MCA205 model), in which Tregs role has been documented, lack of TRPM4 expression and function induced significantly decreased incidence and tumor growth. We found that within chronic inflammatory and hypoxic tumor microenvironment, TRPM4 protected Tregs from ATP-induced cell death and therefore promoted tumor initiation and progression. In contrast, TRPM4 deficiency in Tregs favored IFN-g-mediated spontaneous anti-tumor immune response. Thus, through inhibiting Ca2+ influx, TRPM4 acts as a negative modulator of Tregs suppressive functions and protects Tregs from activation-induced cell death.

La Tomographie par Emission de Positons (TEP) au 18Fluoro-désoxyglucose (18F-FDG) est l’imagerie de référence pour la quantification in vivo du métabolisme glucidique des cellules tumorales. Elle permet, entre autre, de suivre les modifications du métabolisme tumoral en cours de chimiothérapie. Le cancer du sein regroupe différentes entités génomiques dont les comportements clinico-biologiques et la prise en charge thérapeutique divergent. L’objectif de cette thèse était d’étudier le lien entre ces diverses entités biologiques du cancer du sein et le comportement métabolique tumoral en cours de chimiothérapie néoadjuvante. Nous avons également extrait, parmi les différents paramètres métaboliques tumoraux des images TEP, les critères les plus robustes pour prédire dès la fin dès la première cure de chimiothérapie néoadjuvante la réponse histologique finale et la survie des patientes. Nous avons également appliqué un modèle de mesure de la perfusion tumorale, dérivée d’une acquisition dynamique du premier passage artériel et tumoral du 18F-FDG.Le premier article de cette thèse souligne l’impact majeur du phénotype tumoral sur le comportement métabolique en cours de chimiothérapie de la tumeur primitive mammaire. Les trois articles suivants montrent que, pour les tumeurs triple-négatives et HER2 positives, les modifications métaboliques tumorales observées par la TEP au 18F-FDG prédisent la réponse histologique complète à l’issue du traitement. Concernant le phénotype tumoral luminal/HER2 négatif, la réponse métabolique apporte surtout une information pronostique. L’imagerie TEP au 18F-FDG pourrait permettre dans un avenir proche de guider les choix thérapeutiques du clinicien, en proposant une alternative thérapeutique aux patientes non-répondeuses identifiées dès la première cure de chimiothérapie néoadjuvante. / Positron Emission Tomography (PET) with 18Fluoro-deoxyglucose (18F-FDG) is the reference imaging examination for in-vivo quantification of the glucidic metabolism of tumour cells. It allows for the monitoring of tumour metabolic changes during chemotherapy. Breast cancer comprises several distinct genomic entities with different biological characteristics and clinical behaviours, leading to different tailored treatments. The aim of this doctoral thesis was to evaluate the relationship between the different biological entities of breast cancer and the tumour metabolic behaviour during neoadjuvant chemotherapy. We have also retrieved, among the various metabolic parameters on PET images, the most reliable ones to predict, as early as after the first neoadjuvant cycle, the final tumour histologic response and patient’s outcome. We have also evaluated early changes in tumour blood flow, using a tumour first-pass model derived from an dynamic 18F-FDG-PET acquisition.The first article presented in this thesis has underlined the strong correlation between breast cancer subtypes, and the tumour metabolic behaviour during chemotherapy. The following three articles have demonstrated that tumour metabolic changes after the first neoadjuvant cycle can predict the final histologic complete response at the end of the treatment, both in triple-negative and HER2 positive tumours. Concerning the luminal/HER2 subtype, the early metabolic response mainly predicts patient’s outcome.These results should lead, in the near future, to PET-guided neoadjuvant strategies, in order to adapt the neoadjuvant treatment in poor-responding women. Such a strategy should lead to enhanced personalized medicine.

Le nombre croissant d’essais cliniques pour le traitement du cancer a conduit à la standardisation de l’évaluation de la réponse tumorale. Dans les essais cliniques de phase III des cancers avancés, la survie sans progression est souvent appliquée comme un critère de substitution pour la survie globale. Pour les tumeurs solides, la progression est généralement définie par les critères RECIST qui utilisent l’information sur le changement de taille des lésions cibles et les progressions de la maladie non-cible. Malgré leurs limites, les critères RECIST restent l’outil standard pour l’évaluation des traitements. En particulier, la taille tumorale mesurée au cours de temps est utilisée comme variable ponctuelle catégorisée pour identifier l’état d’un patient. L’approche statistique de la modélisation conjointe permet une analyse plus précise des marqueurs de réponse tumorale et de la survie. En outre, les modèles conjoints sont utiles pour les prédictions dynamiques individuelles. Dans ce travail, nous avons proposé d’appliquer un modèle conjoint trivarié pour des données longitudinales (taille tumorale), des évènements récurrents (les progressions de la maladie non-cible) et la survie. En utilisant des mesures de capacité prédictive, nous avons comparé le modèle proposé avec un modèle pour les progressions tumorales, définies selon les critères standards et la survie. Pour un essai clinique randomisé porté sur le cancer colorectal, nous avons trouvé une meilleure capacité prédictive du modèle proposé. Dans la deuxième partie, nous avons développé un logiciel en libre accès pour l’application de l’approche de modélisation conjointe proposée et les prédictions. Enfin, nous avons étendu le modèle à une analyse plus sophistiquée de l’évolution tumorale à l’aide d’un modèle mécaniste. Une équation différentielle ordinaire a été mise en œuvre pour décrire la trajectoire du marqueur biologique en tenant compte les caractéristiques biologiques de la croissance tumorale. Cette nouvelle approche contribue à la recherche clinique sur l’évaluation d’un traitement dans les essais cliniques grâce à une meilleure compréhension de la relation entre la réponse tumorale et la survie. / The increasing number of clinical trials for cancer treatments has led to standardization of guidelines for evaluation of tumor response. In phase III clinical trials of advanced cancer, progression-free survival is often applied as a surrogate endpoint for overall survival (OS). For solid tumors, progression is usually defined using the RECIST criteria that use information on the change of size of target lesions and progressions of non-target disease. The criteria remain the standard tool for treatment evaluation despite their limitations. In particular, repeatedly measured tumor size is used as a pointwise categorized variable to identify a patient’s status. Statistical approach of joint modeling allows for more accurate analysis of the tumor response markers and survival. Moreover, joint models are useful for individual dynamic predictions of death using patient’s history. In this work, we proposed to apply a trivariate joint model for a longitudinal outcome (tumor size), recurrent events (progressions of non-target disease) and survival. Using adapted measures of predictive accuracy we compared the proposed joint model with a model that considered tumor progressions defined within standard criteria and OS. For a randomized clinical trial for colorectal cancer patients, we found better predictive accuracy of the proposed joint model. In the second part, we developed freely available software for application of the proposed joint modeling and dynamic predictions approach. Finally, we extended the model to a more sophisticated analysis of tumor size evolution using a mechanistic model. An ordinary differential equation was implemented to describe the trajectory of the biomarker regarding the biological characteristics of tumor size under a treatment. This new approach contributes to clinical research on treatment evaluation in clinical trials by better understanding of the relationship between the markers of tumor response with OS.