Next-generation quantitative proteomics based on data-independent acquisition mass spectrometry (DIA-MS) enables large-scale sample profiling, a necessity for (pre)clinical studies with heterogeneous samples. To date, most cancer protein profiling studies have focussed on a single cancer type. Here we report on The PanCancer Proteome Atlas (TPCPA) project, a multi-laboratory cancer proteome profiling effort based on DIA-MS. We generated a pan-cancer proteome landscape consisting of 1129 samples including 999 primary cancers representing 22 cancer types, to better understand cancer biology and to identify core and cancer type-enriched molecular therapeutic targets and biomarkers.