Back

The extracellular matrix promotes breast cancer cell growth under amino acid starvation by promoting tyrosine catabolism

Nazemi, M.; Yanes, B.; Llanses Martinez, M.; Walker, H.; Bard, F.; Rainero, E.

2021-06-09 cancer biology Community evaluation
10.1101/2021.06.09.447520 bioRxiv
Show abstract

Breast and pancreatic tumours are embedded in a collagen I-rich extracellular matrix (ECM) network, where nutrients are scarce due to limited blood flow and elevated tumour growth. Metabolic adaptation is required for cancer cells to endure these conditions. Here, we demonstrated that the presence of ECM supported the growth of invasive breast cancer cells, but not non-transformed mammary epithelial cells, and pancreatic cancer cells under amino acid starvation, through a mechanism that required macropinocytosis-dependent ECM uptake. Importantly, we showed that this behaviour was acquired during carcinoma progression. ECM internalisation, followed by lysosomal degradation, contributed to the upregulation of the intracellular levels of several amino acids, most notably tyrosine and phenylalanine. This resulted in elevated tyrosine catabolism on ECM under starvation, leading to increased fumarate levels, potentially feeding into the tricarboxylic acid cycle. Interestingly, this pathway was required for ECM-dependent cell growth under amino acid starvation, as the knockdown of p-hydroxyphenylpyruvate hydroxylase-like protein (HPDL), the third enzyme of the pathway, opposed cell growth on ECM in both 2D and 3D systems, without affecting cell proliferation on plastic. Finally, high HPDL expression correlated with poor prognosis in breast and pancreatic cancer patients. Collectively, our results highlight that the ECM in the tumour microenvironment represents an alternative source of nutrients to support cancer cell growth, by regulating phenylalanine and tyrosine metabolism.

Matching journals

The top 12 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.