Comparative proteomic analysis of breast cancer and normal tissues with deep plasma profiling using the Mag-Net™ workflow

With breast cancer being responsible for the most cancer deaths worldwide, improvements in understanding of the molecular underpinnings of the disease are paramount. Plasma proteomic profiling has emerged as an invaluable tool in the medical research field due to plasma"s accessibility as a biofluid. However, robust profiling of the plasma proteome is challenged by its high dynamic range of protein abundances and the lack of high-throughput methods with sufficient depth. Mag-Net™, a high-throughput, cost-effective workflow for deep profiling of the plasma proteome based on enrichment of membrane-bound vesicles has been developed and automated (Wu et al., 2023). Here we set out to compare protein signatures between normal and breast cancer tissue samples and subsequently, using Mag-Net™, profile the plasma proteome of breast cancer patients to find tumour reporting proteins.

Blood, tumour tissue and normal tissue samples (n=15) were collected from consenting patients diagnosed with infiltrative ductal carcinoma undergoing surgery at Kalafong Tertiary Provincial Hospital. Plasma isolation was performed directly after blood collection following the EDRN SOP and stored at -80°C. Following homogenisation of fresh frozen tumour and normal tissue samples, protein extraction, capture, clean-up and digestion were performed using a semi-automated workflow on a KingFisher Flex with hydrophilic affinity beads (MagResyn HILIC). High abundant protein depletion and capture of membrane-bound vesicles were performed on patient plasma samples using the semi-automated Mag-Net™workflow on a KingFisher Flex system with MagResyn SAX beads. Peptides were loaded onto Evotips and analysed using an Evosep One coupled to a Bruker timsTOF HT system.

We previously reported using the Mag-Net™ workflow on plasma collected from patients with pancreatic ductal adenocarcinoma. Here we showed that, compared to a neat plasma workflow, Mag-Net™ identified double the number of proteins, provided clear separation of disease versus control patient groups and identified more cancer relevant pathways. Experiments on the breast cancer patient cohort is ongoing.