Petr Lapcik (Brno / CZ), Klara Synkova (Brno / CZ), Lucia Janacova (Brno / CZ), Pavla Bouchalova (Brno / CZ), David Potesil (Brno / CZ), Rudolf Nenutil (Brno / CZ), Pavel Bouchal (Brno / CZ)
Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and deeper proteome coverage is needed for its molecular characterization. We present comprehensive library of targeted mass spectrometry assays specific for TNBC and demonstrate its applicability. Proteins were extracted from 105 TNBC tissues and digested. Aliquots were pooled, fractionated using hydrophilic chromatography and analyzed by LC-MS/MS in data-dependent acquisition (DDA) parallel accumulation-serial fragmentation (PASEF) mode on timsTOF Pro LC-MS system. 16 individual lysates were analyzed in data-independent acquisition (DIA)-PASEF mode. Hybrid library was generated in Spectronaut 16.0 software and covers 244,464 precursors, 168,006 peptides and 11,564 protein groups (FDR = 1%). Application of our library for pilot quantitative analysis of 16 tissues increased identification numbers in Spectronaut 18.5 and DIA-NN 1.8.1 software compared to library-free setting, with Spectronaut 18.5 achieving the best results represented by 190,310 precursors, 140,566 peptides, and 10,463 protein groups. In conclusion, we introduce assay library that offers, to our knowledge, the deepest coverage of TNBC proteome to date. The TNBC library is available via PRIDE repository (PXD047793).
This work was supported by Ministry of Health of the Czech Republic (project NU22-08-00230), all rights reserved. CIISB, Instruct-CZ Centre of Instruct-ERIC EU consortium, funded by MEYS CR infrastructure project LM2023042 and European Regional Development Fund-Project "UP CIISB" (No. CZ.02.1.01/0.0/0.0/18_046/0015974), is gratefully acknowledged for the financial support of the measurements at the CEITEC Proteomics Core Facility (by DP). Supported by the project BBMRI.CZ no. LM2023033. Supported by the project National Institute for Cancer Research (Programme EXCELES, ID Project No. LX22NPO5102)—Funded by the European Union—Next Generation EU.