Poster Presentation 37th Lorne Cancer Conference 2025

Establishment of patient-derived models of metastatic appendiceal cancers (#171)

King Ho Leong 1 , Madeleine Strach 1 2 , Stephanie Alfred 1 , Kate Mahon 2 , David Croucher 1 3 , Sharissa Latham 1 3
  1. Garvan Institute of Medical Research, Darlinghurst, NEW SOUTH WALES, Australia
  2. Chris O’Brien Lifehouse, Camperdown, NSW, Australia
  3. UNSW St Vincent’s Clinical School, Darlinghurst, NSW, Australia

Appendiceal cancers (ACs), including indolent low-grade appendiceal mucinous neoplasm (LAMN) and aggressive appendiceal adenocarcinoma (AAC), are rare malignancies with a high propensity for peritoneal metastases. Whilst aggressive cytoreductive surgery with heated intra-peritoneal chemotherapy confers a survival benefit, AC recurrence is almost inevitable (~95% of cases) due to residual metastatic disease after surgery. Given there is a strong rationale to identify therapies that target metastatic AC, yet few experimental tools to study this disease, this project aims to develop clinically relevant patient-derived models of metastatic AC to accelerate treatment development for this underserved patient cohort.  

 

In the first phase of this project, we have established a systematic workflow to biobank and process fresh LAMN and AAC tissue from consenting patients undergoing cytoreductive surgery at RPAH/COBLH. Briefly, harvested tissue samples are 1) dissociated for the generation of primary in vitro cultures, 2) dissociated for intraperitoneal transplantation into BALB/c nude immunodeficient mice (P0), 3) formalin fixed and paraffin embedded for immunohistochemical analysis and tumour microarray generation, and 4) cryopreserved for downstream validation studies. In parallel, we have utilised scRNAseq analysis of patient samples to identify novel surface markers of AC tumours. Through validation studies with our primary LAMN and AAC cultures and published cell lines, we have successfully assembled a panel of cell surface receptors that distinguish between the distinct cell populations comprising AC tumours. These novel data open up alternate approaches for FACS-based AC model generation and validation.