Lessons from clinically relevant biological modelling of human melanoma — ASN Events

Lessons from clinically relevant biological modelling of human melanoma (#140)

Clare Fedele 1 , Samantha Boyle 1 , Elisha Wybacz 1 , Mark Shackleton 1
  1. Peter MacCallum Cancer Institute, East Melbourne, Vic, Australia

A dominant paradigm of cancer research over recent decades has involved identifying novel molecular targets in cancers and devising specific therapies for them. This led to trastuzumab for Her2-amplified breast cancer, imatinib for chronic myeloid leukemia, sunitinib for renal cancer and vemurafenib for BRAF-mutant melanoma. However, despite these successes, the vast majority of potential cancer treatment targets that appeared promising based on pre-clinical evaluation have been found not to be useful in patients. This not only highlights the limitations of current pre-clinical approaches to testing new therapies but also raises the possibility that development of more clinically relevant laboratory models of human cancer will enhance the identification and testing of targets in ways that will reduce deaths from this disease.

Towards this, we have developed a xenograft model for human melanoma progression that allows efficient formation of xenografted tumors from ~30% of single cells obtained directly from patient melanomas. In studies comparing xenografted tumor biology with clinical outcomes of patients who donated tumors for the research, the xenograft model predicted patient outcomes with 100% sensitivity and 83% specificity. This indicates that the major determinants of melanoma patient outcome are melanoma cell-intrinsic, rather than being primarily driven by patient factors. We have also observed during progression of xenografted tumors extensive evolutionary changes that reveal a remarkable capacity of this disease for intra-patient adaptation in the face of metastatic spread and anti-melanoma therapies. These findings open the way for pre-clinical studies of melanoma progression and therapy response/resistance mechanisms that are likely to be relevant to patient outcomes.