Research models for understanding and treating cancer

In silico and mathematical models: understanding and predicting

In silico approaches rely on the computational analysis of massive biological datasets (genomics, transcriptomics, proteomics, imaging). They enable the identification of molecular signatures, the reconstruction of regulatory networks, and a better understanding of tumor heterogeneity. These models are often integrated with artificial intelligence and machine learning methods, opening up new possibilities in personalized medicine.

Mathematical models, in turn, provide a theoretical framework for describing and simulating biological dynamics: tumor growth, the spread of cancer cells, interactions with the microenvironment, and response to treatments. By combining experimental data with mathematical formalization, these approaches make it possible to test hypotheses, predict complex biological behaviors, and optimize therapeutic strategies. They thus play a crucial role in synthesizing knowledge and supporting decision-making.

In vitro models: exploring cellular mechanisms

In vitro models, based on cell culture in the laboratory, are a central tool for deciphering the molecular and cellular mechanisms of cancer. They range from established tumor cell lines to primary cultures derived from patients, as well as organoids—three-dimensional structures that replicate certain characteristics of the original tissues.

These approaches allow for precise control of experimental conditions and facilitate the study of interactions between cancer cells and their immediate microenvironment. 3D models, in particular, provide a more accurate representation of tissue architecture and physicochemical gradients, thereby enhancing the relevance of the results obtained. In vitro systems are also widely used for drug screening and the assessment of treatment sensitivity.
 

In vivo models: capturing the complexity of living organisms

In vivo models, which involve the study of biological processes within living organisms, are essential for understanding the complexity of cancer in their physiological context. They allow researchers to explore the interactions between tumors and the organism as a whole, including the immune system, metabolism, and blood circulation. Thus, invertebrate models (such as fruit flies, worms, or sea anemones) enable the rapid study of the fundamental mechanisms of tumor transformation, while vertebrate models (such as zebrafish or mice), which are closer to human physiology, remain essential for studying the complexity of cancer and developing new therapeutic strategies.

These in vivo models play a key role in validating hypotheses derived from in vitro and in silico studies, as well as in evaluating the efficacy and tolerability of therapeutic approaches. They constitute an essential step in translational research, prior to the transition to clinical trials in humans.
 

Complementarity at the heart of translational research

The strength of research at Institut Curie lies in the integration of these different models within multidisciplinary approaches. No single model, taken in isolation, can fully account for the complexity of cancer: it is their complementarity that enables the transition from fundamental mechanisms to therapeutic applications.

By combining technological innovation, scientific rigor, and an integrated approach, Institut Curie harnesses all of these models to better understand cancer and accelerate the development of more effective and personalized therapies.