Phyllodes tumours (PT) are rare fibroepithelial neoplasms accounting for less than 1% of all breast tumours in Western countries and up to 7% amongst Asian populations. Currently, no established standard therapy exists for malignant PT, an aggressive chemoresistant subtype with high metastatic potential. We hypothesized that patient-derived xenograft (PDX) and cell line models created in “real-time” may identify effective therapies to mirror a patient’s treatment trajectory.
Malignant PT from a chest wall mass was subcutaneously inoculated onto female NSG™ mice with serial transplantation to establish a PDX and cell line model (designated MPT-S1). In vitro cell viability and cell cycle analyses were performed following drug exposures.
The affected patient was diagnosed with metastatic malignant PT affecting the chest wall and lungs. Histology of the chest wall mass showed a high grade malignant tumour composed of markedly pleomorphic spindle cells, interspersed with osteoclast-like multinucleated giant cells. On immunohistochemistry, tumour cells were positive for p63, negative for MNF116 and showed high proliferative index on Ki-67. Whole exome sequencing followed by Sanger sequencing confirmed mutations in TP53, PRB, MED12 and KMT2D. Immunohistochemistry and genomic profiles of the patient’s tumour, PDX, and cell line were consistent. Interestingly, despite primary resistance to conventional chemotherapies including doxorubicin, gemcitabine and docetaxel, the patient achieved partial response to off-label treatment with pazopanib, a multi-targeted receptor tyrosine kinase inhibitor (TKI). Correspondingly, drug susceptibility testing in vitro showed that pazopanib reduced cell viability in a dose-dependent manner (IC50 6.37 μM), accompanied by induction of S-phase arrest and apoptosis. Other TKIs including sorafenib, sunitinib and axitinib elicited similar effects (all IC50 <5 μM).
We established MPT-S1, a new PDX and cell line model of malignant PT, and provided initial evidence for the clinical utility of such models for identifying therapeutic vulnerabilities of rare cancers in real-time.
The authors.
SingHealth, Duke-NUS.
All authors have declared no conflicts of interest.