Sarah-Maria Fendt, VIB-Katholieke Universiteit Leuven
Exploiting metabolic intra-tumor heterogeneity of primary breast tumors to predict metastasis risk
Metastasis formation is the leading cause of death in breast cancer patients. Nearly 30% of patients will develop metastases. Unfortunately, we currently cannot predict which patient will develop metastases. This greatly reduces the application of personalized follow up screening and preventative measures.
We discovered that heterogeneous protein expression of a metabolic enzyme in primary tumors of breast cancer patients indicates metastasis. Thus, we hypothesize that comprehensively identifying intra-tumor metabolic heterogeneity in primary breast tumors will enable us to predict metastasis risk. Current technologies such as single cell RNA sequencing allow the quantification of heterogeneity at the transcript level. However, the limitation of identifying metabolic heterogeneity at this level is that there is no linear relationship between mRNA, protein and metabolic functionality. Thus, we propose to study metabolic heterogeneity of primary breast tumors with the beyond state-of-the-art technology of spatial metabolomics. In particular, we will define metabolic heterogeneity of highly and low/non-metastatic primary PDX breast tumors with spatial metabolomics and single cell RNA sequencing. We expect that defining the metabolome is advantageous over the transcriptome when predicting metastasis risk.
Thus, this project will pave the ground for predicting metastasis risk of breast cancer patients which will ultimately lead to improved care.