The identification of clinically relevant biomarkers represents an important challenge in oncology. This can be addressed with biomarker discovery studies in formalin-fixed paraffin-embedded (FFPE) tissues. However, reliably measuring large sets of proteins in FFPE tissues remains challenging. Here we demonstrate the use of liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) as a promising technique for such applications. An LC-MS/MS method was developed to simultaneously quantify hundreds of peptides extracted from FFPE samples. 200 proteins were measured in 48 triple-negative (TNBC), 19 HER2-overexpressing, and 20 luminal A breast tumors. Quantitative information was obtained for 185 proteins, including known markers such as HER2, hormone receptors, ki-67, or inflammation-related proteins. MS results for these proteins matched IHC or CISH data. In addition, several proteins representing potential biomarkers were identified as differentially expressed in TNBC samples. Comparison of our results with data from the literature showed that MS assays can reliably and simultaneously measure many proteins in FFPE tissues using the analysis of surrogate peptides. Therefore LC-MS/MS is a powerful tool for the relative quantification of proteins in FFPE tissues and for biomarker discovery.

An SRM assay was developed for 200 proteins, which are relevant for TNBC and/or basal-like breast cancer. Protein selection was published previously (Steiner et al., Methods Mol Biol. 2019;1959:185-203). The assay development included several screening steps as well as a linearity experiment to select the best peptide / fragment candidates. Synthetic heavy-labeled peptides were used as internal standards for all 480 peptides monitored. Samples were reconstituted in buffer and analyzed using the developed SRM assay on a Vantage triple stage quadrupole mass spectrometer. The assay was split in two different acquisition methods in order to guarantee sufficient data points across all chromatographic peaks. Data was imported and reviewed in Skyline and peptides with poor quality signal were annotated. A reference library was also created using the heavy-labeled peptides and was used for data review. The reviewed data was exported to Tibco Spotfire for normalization against the internal standard and for visualization of the sample groups. Selected proteins were tested using the non-parametric Kruskal-Wallis test, as well as Dunn's multiple comparison test in order to find significant differences between tumor groups.

50 TNBC, 20 Luminal A and 20 HER2 FFPE breast cancer samples were selected retrospectively from the sample repository of the Geneva University Hospitals. Exclusion criteria were male gender and administration of a neo-adjuvant treatment. Tissue slices (10 µm) from these 90 samples were cut and mounted on glass slides. The first and the last slices were H&E stained and used as dissection templates. 2 x 10 µm tissue slices were used for peptide extraction. The tumor area was macrodissected according to the H&E template and subsequently deparaffinated and rehydrated. Peptides were extracted from the collected tissues using a procedure based on antigen-retrieval and tryptic digestion. The large number of samples were processed in separate batches of approx. 20 samples. Samples were randomized prior to tumor dissection, prior to peptide extraction and again prior to LC-MS/MS analysis. All FFPE tissue samples were extracted and analyzed in duplicate using two independent tissue slices. Sample Nomenclature, Example: TN_150410_E1_B3_H26_M1_02; TN: common to all samples in triple-negative FFPE study; 150410: date of sample analysis (YYMMDD); E1: tissue extraction replicate 1 (in oppostion to E2 for replicate 2); B3: MS analysis batch number 3 (each extraction replicate comprises 4 batches; B1-B4); H26: sample identifier (T=TNBC, L=LuminalA, H=Her2 followed by sample number); M1: acquisition method number 1 (in opposition to M2 for method number 2); 01: sample run (most samples were analyzed only once, a few samples were rerun because of a technical problem and therefore are labeled as "_02")