Proteomic Analysis of Cell communication in Tumors
Cancer is an enormous biomedical challenge, in part due to the complexity of cancer cell dynamics. The combination of intrinsic genetic alterations and cues from the tumour microenvironment impact the cancer phenotype and create h...
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Información proyecto ProACCT
Duración del proyecto: 63 meses
Fecha Inicio: 2022-04-11
Fecha Fin: 2027-07-31
Descripción del proyecto
Cancer is an enormous biomedical challenge, in part due to the complexity of cancer cell dynamics. The combination of intrinsic genetic alterations and cues from the tumour microenvironment impact the cancer phenotype and create heterogeneous tumours that evolve in space and time. Cancer cells develop distinct phenotypes in diverse tumour regions and different metastatic locations. In addition, cancer progression, metastatic dissemination, and development of therapeutic resistance affect cellular interactions over time. Therefore, understanding tumour dynamics is critical for the development of novel therapeutic approaches.
Although tumour heterogeneity has been thoroughly investigated at the genomic and transcriptomic levels, limited studies have investigated heterogeneity at the proteomic level. Changes in protein expression are central determinants of cancer phenotypes; developing a detailed understanding of proteome dynamics would be an enormous scientific advancement in the cancer field. However, technological challenges, and specifically, the challenge of analysing single cells and small groups of cells, have delayed progress along these lines. Here, I propose to combine my cutting-edge clinical proteomic expertise and extensive experience in cancer biology to study the spatial and temporal heterogeneity of cancer at the proteomic level.
We will push the boundaries of the technology towards assaying thousands of proteins from single cells and small groups of cells from in-vivo samples. We will combine microfluidic probe technology to map primary tumors and metastases at high spatial resolution, and study mouse models of melanoma and breast cancer to follow temporal changes in metastatic growth and treatment response. This breakthrough in proteomic analysis of cancer dynamics will provide the basis for targeting treatment-resistant metastatic cell populations towards advanced personalized treatment.