Summary
Cell-to-cell heterogeneity in biological systems has been broadly studied in unicellular organisms and mammals. Furthermore, non-genetic, in addition to genetic heterogeneity has been recently proposed to support tumour growth and to induce resistance to cancer therapy. However, the molecular events on a spatial and temporal level that lead to the emergence of tumour heterogeneity are largely unknown. To address this question, I will study breast cancer, which shows the highest cancer incidence in women and is characterised by extensive intra- and inter-patient heterogeneity in cellular and molecular phenotypes. As model system, I select 3D organoid cultures, which are gaining popularity in cancer research due to their ability to reconstruct tumour-like molecular features and to recapitulate treatment response.
Here, I propose experimental and computational time-course analyses of breast cancer organoids to understand molecular and spatial determinants that underlie the emergence of heterogeneity in cancer cell phenotypes. On the experimental side, I will use imaging mass cytometry and perturbation experiments to capture and validate spatio-temporal changes in cellular phenotypes, interactions and signalling networks. Statistical modelling will quantify dynamic changes in phenotypic heterogeneity over the time-course of organoid growth. Finally, I will predict the emergence of intra-organoid heterogeneity across multiple organoid lines, which allows me to derive targeted treatment strategies.
In sum, the proposed work will disentangle and perturb the spatio-temporal emergence of phenotypic intra-tumour heterogeneity, which is characteristic of breast cancer tissues.
Here, I propose experimental and computational time-course analyses of breast cancer organoids to understand molecular and spatial determinants that underlie the emergence of heterogeneity in cancer cell phenotypes. On the experimental side, I will use imaging mass cytometry and perturbation experiments to capture and validate spatio-temporal changes in cellular phenotypes, interactions and signalling networks. Statistical modelling will quantify dynamic changes in phenotypic heterogeneity over the time-course of organoid growth. Finally, I will predict the emergence of intra-organoid heterogeneity across multiple organoid lines, which allows me to derive targeted treatment strategies.
In sum, the proposed work will disentangle and perturb the spatio-temporal emergence of phenotypic intra-tumour heterogeneity, which is characteristic of breast cancer tissues.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/892225 |
| Start date: | 01-01-2021 |
| End date: | 31-12-2022 |
| Total budget - Public funding: | 191 149,44 Euro - 191 149,00 Euro |
Cordis data
Original description
Cell-to-cell heterogeneity in biological systems has been broadly studied in unicellular organisms and mammals. Furthermore, non-genetic, in addition to genetic heterogeneity has been recently proposed to support tumour growth and to induce resistance to cancer therapy. However, the molecular events on a spatial and temporal level that lead to the emergence of tumour heterogeneity are largely unknown. To address this question, I will study breast cancer, which shows the highest cancer incidence in women and is characterised by extensive intra- and inter-patient heterogeneity in cellular and molecular phenotypes. As model system, I select 3D organoid cultures, which are gaining popularity in cancer research due to their ability to reconstruct tumour-like molecular features and to recapitulate treatment response.Here, I propose experimental and computational time-course analyses of breast cancer organoids to understand molecular and spatial determinants that underlie the emergence of heterogeneity in cancer cell phenotypes. On the experimental side, I will use imaging mass cytometry and perturbation experiments to capture and validate spatio-temporal changes in cellular phenotypes, interactions and signalling networks. Statistical modelling will quantify dynamic changes in phenotypic heterogeneity over the time-course of organoid growth. Finally, I will predict the emergence of intra-organoid heterogeneity across multiple organoid lines, which allows me to derive targeted treatment strategies.
In sum, the proposed work will disentangle and perturb the spatio-temporal emergence of phenotypic intra-tumour heterogeneity, which is characteristic of breast cancer tissues.
Status
CLOSEDCall topic
MSCA-IF-2019Update Date
28-04-2024
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