Oral Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

Intra- and inter-individual variation in the proteome of high-grade serous ovarian cancer (#142)

Maiken M Espersen 1 , Srikanth Manda 2 , Rohan Shah 2 , Steve Williams 2 , Natasha Lucas 2 , Dylan Xavier 2 , Sadia Mahboob 2 , Andrew Robinson 2 , Peter G Hains 2 , Brett Tully 2 , Roger Reddel 2 , Phil J Robinson 2 , Qing Zhong 2 , Anna deFazio 1 , Rosemary Balleine 2
  1. Centre for Cancer Research, Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW, Australia
  2. ProCan®, Children’s Medical Research Institute, The University of Sydney, Westmead, NSW, Australia

High-grade serous ovarian cancer (HGSOC) is a disease with persistently poor survival rates. Targeted therapeutics are beginning to show improved outcomes for selected patients. There is an urgent need for predictive biomarkers that can be reliably measured in small tissue samples. The aim of this study was to characterize variation in the HGSOC proteome.

Proteomic profiles from 447 frozen samples, taken from primary (n=11) and matched metastatic (n=10) tumour tissues were generated by DIA on SCIEX 6600 triple TOF instruments (7-49 samples per tissue). Data were analysed with OpenSWATH followed by Diffacto to compile a protein matrix including over 3,000 quantified proteins.

The data were highly structured with closest similarity between samples from the same tissue, followed by similarity between primary and metastatic tissues from the same individual. Around 15% of proteins were detected in all samples, with ribosomal proteins being the top ranked category, consistent with their housekeeping role. There were ~20 proteins absent from all samples in some patients, whilst uniformly present in all samples of others. This pattern of stable intra- / variable inter-individual expression demonstrates features that could form a basis for robust sub-classification.

The HGSOC proteome is highly individualized with marked spatial variation. Global proteomic profiling can facilitate biomarker discovery by screening for discriminative features associated with treatment response and clinical outcome.


*MM Espersen and S Manda contributed equally.