Investigating the contribution of hyaluronan to the breast tumour microenvironment using multiparametric MRI and MR elastography.
Emma L ReevesJin LiKonstantinos Zormpas-PetridisJessica K R BoultJames SullivanCraig CummingsBarbara BlouwDavid KangRalph SinkusJeffrey C BamberYann JaminSimon P RobinsonPublished in: Molecular oncology (2023)
Hyaluronan (HA) is a key component of the dense extracellular matrix in breast cancer, and its accumulation is associated with poor prognosis and metastasis. Pegvorhyaluronidase alfa (PEGPH20) enzymatically degrades HA and can enhance drug delivery and treatment response in preclinical tumour models. Clinical development of stromal-targeted therapies would be accelerated by imaging biomarkers that inform on therapeutic efficacy in vivo. Here, PEGPH20 response was assessed by multiparametric magnetic resonance imaging (MRI) in three orthotopic breast tumour models. Treatment of 4T1/HAS3 tumours, the model with the highest HA accumulation, reduced T 1 and T 2 relaxation times and the apparent diffusion coefficient (ADC), and increased the magnetisation transfer ratio, consistent with lower tissue water content and collapse of the extracellular space. The transverse relaxation rate R 2 * increased, consistent with greater erythrocyte accessibility following vascular decompression. Treatment of MDA-MB-231 LM2-4 tumours reduced ADC and dramatically increased tumour viscoelasticity measured by MR elastography. Correlation matrix analyses of data from all models identified ADC as having the strongest correlation with HA accumulation, suggesting that ADC is the most sensitive imaging biomarker of tumour response to PEGPH20.
Keyphrases
- diffusion weighted imaging
- contrast enhanced
- magnetic resonance imaging
- diffusion weighted
- poor prognosis
- extracellular matrix
- drug delivery
- magnetic resonance
- computed tomography
- long non coding rna
- high resolution
- stem cells
- bone marrow
- cancer therapy
- artificial intelligence
- combination therapy
- breast cancer cells
- deep learning
- cell proliferation
- liver fibrosis
- big data