DIA-MS analysis of 24 breast cancer tissues

In recent years genomic and proteomic technologies have been employed in a combined effort to extrapolate key clinical and biological information of complex diseases such as cancer. Most integrative studies employ DNA and/or RNA sequencing technologies coupled to mass spectrometry-derived information to achieve deep information extraction levels, resulting in massive experimental efforts. In this context the employment of data independent acquisition (DIA) methods, which generally do not rely on fractionation, has seldom been tested. In this study, we evaluated the ability of DIA and data dependent acquisition (DDA) MS in determining key biological features of a set of 21 breast cancer tissues for whose RNA-sequencing data was also collected. We evaluated how proteomic data layers matched RNA analysis-derived genomic information, their degree of consensus, and their discrepancies.