Oral Presentation 37th Lorne Cancer Conference 2025

Identifying prognostic lipid signatures associated with clinical disease relapse in patients with localised prostate cancer by spatial mass spectrometry imaging (114038)

Jacob Truong 1 2 3 , Paul Trim 1 3 , Feargal Ryan 4 , Jonas Dehairs 5 , Jessica Logan 6 , Doug Brooks 6 , Johannes Swinnen 5 , David Lynn 4 , Marten Snel 1 3 , Lisa Butler 1 2
  1. The University of Adelaide, Adelaide, SA, Australia
  2. SAiGENCI - South Australian immunoGENomics Cancer Institute, Adelaide, SA, Australia
  3. Proteomics, Metabolomics and MS Imaging Core Facilities, SAHMRI - South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
  4. Flinders Health and Medical Research Institute, Adelaide, South Australia, Australia
  5. Laboratory of Lipid Metabolism and Cancer, KU Leuven and Leuven Cancer Institute, Leuven, Flanders, Belgium
  6. Mechanisms in Cell Biology and Disease, University of South Australia, Adelaide, South Australia, Australia

Prostate cancer is the most frequently diagnosed malignancy in Australian men. Treatment for high-risk localised disease may include radical prostatectomy. However, biochemical relapse occurs in 20-40% of cases within 10 years, of which up to 35% of cases can progress to metastatic relapse [1]. Identifying more aggressive tumours at diagnosis could result in earlier intervention and improved outcomes. Prostate cancer depends on lipid metabolism for growth, progression and survival, but its role in disease progression after local therapy is unclear. This makes the tumour lipidome a promising source of potential prognostic markers and novel therapeutic targets. However, prostate cancer is a heterogeneous and multi-focal disease, necessitating the use of spatial techniques such as mass spectrometry imaging, which visualises the abundance and distribution of analytes in situ, to identify tumour specific markers. Consequently, we employed MSI in this project to analyse sections from prostate tumour specimens in the Australian Prostate Cancer BioResource (n=115). Regions of tumour and stroma were compared between non-relapsed (NR) and clinically relapsed (CR) patients by hierarchical clustering and principal component analysis followed by mixed effects models. The association of tumour lipid levels with risk of disease progression was evaluated using Cox Proportional Hazards and a classification model to predict outcome based on lipid profile was generated using sparse partial least squares discriminant analysis (sPLA-DA). We identified a lipid signature that effectively discriminated between patients based on clinical outcome. These included several species containing a 16-carbon monounsaturated fatty acid (FA 16:1). Moreover, we identified and characterised a rare lipid class not previously reported in cancer. We also confirmed that these lipids of interest were associated with risk of CR (HR p-value < 0.05) and contributed to an sPLS-DA model which classified samples with high accuracy (AUC >0.9). In summary, using MSI to image tumour specimens, we identified a group of lipids that were associated with tumours of CR patients. After further validation, these markers will be combined with clinical factors to determine if they improve patient risk stratification for disease progression, and their regulatory pathways will be studied to identify actionable therapeutic targets.

  1. Boorjian, SA, Thompson, RH, Tollefson, MK, Rangel, LJ, Bergstralh, EJ, Blute, ML & Karnes, RJ 2011, 'Long-term risk of clinical progression after biochemical recurrence following radical prostatectomy: the impact of time from surgery to recurrence', Eur Urol, vol. 59, no. 6, Jun, pp. 893-899.