| Popis |
Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype. As heterogeneity of TNBC tumors on transcript and protein levels was indicated, there is a need for improved molecular characterization of TNBC, for which deeper coverage of TNBC proteome is essential. The data-independent acquisition (DIA) mass spectrometry-based proteomics offers comprehensive and reproducible analysis of proteome. Several approaches for processing of complex DIA data were developed, with Spectronaut and DIA-NN software as widely used tools that can employ experimentally derived spectral libraries for identification and quantification of peptide precursors, or can be operated in library-free setting with in-silico generated spectral libraries. Here we present a comprehensive library of targeted mass spectrometry assays specific for TNBC and compare performance of several DIA data analysis approaches. Proteins were extracted from 105 TNBC tissues of patients treated at Masaryk Memorial Cancer Institute and digested with trypsin. 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. Individual lysates were analyzed in diaPASEF mode. Hybrid library was generated in Spectronaut 16.0 software. The library that covers 262,351 precursors, 175,744 peptides and 11,739 protein groups (FDR = 1%) is used for quantitative data extraction from 16 DIA runs using the latest Spectronaut 18.0 and DIA-NN 1.8.1 software, and the results are compared with the directDIA data extraction in Spectronaut 18.0 software and the library-free data extraction in the DIA-NN 1.8.1 software. We demonstrate that use of our library results in higher identification numbers in both tools represented by 204,792 and 176,678 precursors, 149,014 and 132,834 peptides, and 10,882 and 10,659 protein groups in Spectronaut 18.0 and DIA-NN 1.8.1, respectively. On the other hand, library-free setting leads to identification of 155,182 and 133,887 precursors, 117,705 and 112,923 peptides, and 9,860 and 10,633 protein groups, in Spectronaut 18.0 and DIA-NN 1.8.1, respectively. In conclusion, we introduce an assay library that offers the deepest coverage of TNBC proteome to date and represents a basis for further characterization of TNBC molecular profiles at protein level. We also reveal that use of our library for quantitative data extraction outperforms library-free approaches in newest versions of Spectronaut and DIA-NN software.
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