Abstract PS18-36: Proteomic profiling of specific tumor clones using spatially resolved mass spectrometry technologies for precision oncology

• Published in: Cancer research (Chicago, Ill.) Ill.), 2021-02, Vol.81 (4_Supplement), p.PS18-36-PS18-36
• Main Authors: Hajjaji, Nawale, Aboulouard, Soulaimane, Bertin, Delphine, Robin, Yves Marie, Fournier, Isabelle, Salzet, Michel
• Format: Article
• Language: English
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.SABCS20-PS18-36

Background Breast cancer (BC) remains a leading cause of cancer-related death among women worldwide. The complexity of this disease, especially its heterogeneity, have prevented its eradication and driven resistance to treatments. To reach precision oncology to eradicate BC, therapy needs to be specific to each tumor clone. Yielding enough molecular information from tumor clones to identify new drug targets represents a technical challenge due to sample size limitation or loss of spatial resolution. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry combined with microproteomics enables a spatially-resolved unlabeled tumor imaging of its protein distribution, thus revealing proteomic clones. Our aims were to analyze the clonal proteome of luminal breast cancers, and explore its potential to expand new drug target discovery and drug repurposing. Methods A retrospective study at the Comprehensive Cancer Centre Oscar Lambret (Lille, France) was conducted to analyze 76 FFPE luminal HER2 negative tumors: 52 primary tumors from patients with early BC and 24 BC metastases. Patients gave their informed consent and the study was approved by the local institutional review board. MALDI mass spectrometry imaging and spatially-resolved on-tissue shotgun microproteomics were performed on FFPE slides of tumor tissue to determine the proteomic profile of selected clones using nanoLC-MS & MS/MS. Protein identification was performed using MaxQuant software against the Uniprot database. Functional annotation and characterization of the identified proteins were performed using Panther software. Candidate druggable targets were searched using DrugCentral druggable genome database, and their druggability level was assessed using the classification by the Illuminating the Druggable Genome Knowledge Management Center. The clonal proteome dataset was compared to publically available TCGA, BC360, and CDx datasets. Results The clonal proteome analysis identified a total of 2868 different proteins; 780 proteins were found in more than 50% of the patients. Panther analysis showed that 22% of the proteins were classified as enzymes, 15% were related to DNA processes, 6% were structural proteins, and less than 2% were related to immunity. Panther identified 139 pathways in the clonal proteome dataset. The clonal proteome analysis yielded the highest number of pathways compared to TCGA, BC360, and CDx datasets. 41 pathways (mainly metabolic pathways) were exclu