Poster Presentation Australasian Melanoma Conference 2018

Antibody profiling to predict responses to immunotherapy in melanoma (#106)

Jessica Da Gama Duarte 1 2 , Muneerah Smith 3 , Simone Ostrouska 1 2 , Jonathan Cebon 1 2 , Jonathan Blackburn 3 , Andreas Behren 1 2
  1. Olivia Newton-John Cancer Research Institute, Heidelberg, VIC, Australia
  2. La Trobe University, School of Cancer Medicine, Heidelberg, VIC, Australia
  3. Division of Medical Biochemistry, University of Cape Town, Cape Town, South Africa

Advances in melanoma treatment include targeting key immune checkpoints, such as programmed cell death 1 (PD-1) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4). While highly effective in some, the majority of patients do not respond and immune-related adverse events (irAEs) are common and often severe. Therefore, the ability to predict treatment outcomes is of utmost value. Treatment success is often correlated with an immunological active tumour, but the majority of the commonly-used immune monitoring techniques to investigate tumour-immune engagement rely on access to patient tissue via invasive surgery or biopsies. As such, there is a clear preference for a blood-based means of assessing immune engagement, with the intent of obtaining a more accurate representation of the majority of all tumours. Tumour antigens, including tumour-specific antigens (TSAs) and tumour-associated antigens (TAAs), can enable malignant cell recognition by the immune system and subsequently lead to the production of cognate antibodies. We hypothesized that these antibodies can be informative markers of immune engagement of tumours. Here we used a novel protein array which represents a high-throughput, sensitive tool capable of profiling antibody repertoires of cancer patients using only 1μl of serum or plasma. Importantly, healthy individuals show no detectable cancer-specific antibody titres. Preliminary data (unpublished) using the array on a small subset of patients (n=15) undergoing immune checkpoint blockade with pembrolizumab (PD-1 blockade) was generated. Pre-treatment data shows a trend towards separation of clinical responders from non-responders using the number of antigen specificities and the mean antibody intensity. Furthermore, immunotherapy-treated patient antibody profiles may be useful to predict irAEs ahead of clinical evidence.