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Search by event  1752 | enregistrements trouvés : 7

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Post-edited  Mathematical modelling of angiogenesis
Maini, Philip (Auteur de la Conférence) | CIRM (Editeur )

Tumour vascular is highly disordered and has been the subject of intense interest both clinically (anti-angiogenesis therapies) and theoretically (many models have been proposed). In this talk, I will review aspects of modelling tumour angiogenesis and how different modelling assumptions impact conclusions on oxygen delivery and, therefore, predictions on the possible effects of radiation treatments.

93A30 ; 92C50 ; 92C37 ; 92C17 ; 65C20 ; 35Q92

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Multi angle  Where maths meet cancer
Chomienne, Christine (Auteur de la Conférence) | CIRM (Editeur )

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How combination therapies can reduce the emergence of cancer resistance? Can we exploit intra-tumoral competition to modify the effectiveness of anti-cancer treatments?
Bearing these questions in mind, we present a mathematical model of cancer-immune competition under therapies. The model consists of a system of differential equations for the dynamics of two cancer clones and T-cells. Comparisons with experimental data and clinical protocols for non-small cell lung cancer have been performed.
In silico experiments confirm that the selection of proper infusion schedules plays a key role in the success of anti-cancer therapies. The outcomes of protocols of chemotherapy and immunotherapy (separately and in combination) differing in doses and timing of the treatments are analyzed.
In particular, we highlight how exploiting the competition between cancer populations seems to be an effective recipe to limit the insurgence of resistant populations. In some cases, combination of low doses therapies could yield a substantial control of the total tumor population without imposing a massive selective pressure that would suppress the sensitive clones leaving unchecked the clonal types resistant to therapies.
How combination therapies can reduce the emergence of cancer resistance? Can we exploit intra-tumoral competition to modify the effectiveness of anti-cancer treatments?
Bearing these questions in mind, we present a mathematical model of cancer-immune competition under therapies. The model consists of a system of differential equations for the dynamics of two cancer clones and T-cells. Comparisons with experimental data and clinical protocols for ...

92D25 ; 92C37 ; 92C50 ; 37N25 ; 35Q92

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Cancer patients often respond differently to the same treatment. Precision oncology aims at predicting which treatments will be effective on a given patient. Such predictive biomarkers of drug response typically take the form of a particular somatic mutation. However, lessons from the past indicate that these single gene-drug response associations are rare and/or often fail to achieve a significant impact in clinic. In this context, Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. Our results show that combining multiple gene alterations of the tumours via ML often results in better discrimination than that provided by the corresponding single-gene marker. This approach also permits assessing which type of molecular profile is most predictive of tumour response depending on treatment and cancer type. Moreover, ML multi-gene predictors generally retrieve a much higher proportion of treatment-sensitive tumours (i.e. they have a higher recall) than the corresponding single-gene marker. The latter suggest that a higher proportion of patients could benefit from precision oncology by applying this ML methodology to existing clinical pharmacogenomics data sets.
Cancer patients often respond differently to the same treatment. Precision oncology aims at predicting which treatments will be effective on a given patient. Such predictive biomarkers of drug response typically take the form of a particular somatic mutation. However, lessons from the past indicate that these single gene-drug response associations are rare and/or often fail to achieve a significant impact in clinic. In this context, Machine ...

92C40 ; 68Uxx

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The emergence of drug-resistance is a major challenge in chemotherapy. In this talk we will present our recent mathematical models for describing the dynamics of drug-resistance in solid tumors. Our models follow the dynamics of the tumor, assuming that the cancer cell population depends on a phenotype variable that corresponds to the resistance level to a cytotoxic drug. We incorporate the dynamics of nutrients and two different types of drugs: a cytotoxic drug, which directly impacts the death rate of the cancer cells, and a cytostatic drug that reduces the proliferation rate. Through analysis and simulations, we study the impact of spatial and phenotypic heterogeneity on the tumor growth under chemotherapy. We demonstrate that heterogeneous cancer cells may emerge due to the selection dynamics of the environment. Our models predict that under certain conditions, multiple resistant traits emerge at different locations within the tumor. We show that a higher dosage of the cytotoxic drug may delay a relapse, yet, when this happens, a more resistant trait emerges. Moreover, we estimate the expansion rate of the tumor boundary as well as the time of relapse, in terms of the resistance trait, the level of the nutrient, and the drug concentration. Finally, we propose an efficient drug schedule aiming at minimizing the growth rate of the most resistant trait. By combining the cytotoxic and cytostatic drugs, we demonstrate that the resistant cells can be eliminated.
The emergence of drug-resistance is a major challenge in chemotherapy. In this talk we will present our recent mathematical models for describing the dynamics of drug-resistance in solid tumors. Our models follow the dynamics of the tumor, assuming that the cancer cell population depends on a phenotype variable that corresponds to the resistance level to a cytotoxic drug. We incorporate the dynamics of nutrients and two different types of drugs: ...

92C50 ; 92C37 ; 92C40

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