Given the major role of proteins in determining the function of all tissues including cancers, it seems anomalous that proteomic testing is not deployed routinely in the cancer clinic. The reasons for this appear to relate both to technical aspects of proteomic technology and to the current lack of a substantial evidence base to underpin the interpretation of proteomic data in a clinical setting. We will describe here the steps being taken by the ProCan® research program to enable generation of clinically-actionable cancer tissue proteomic data. The first priority was to develop a robust and reproducible proteomic analytic pipeline where the most common input is tumour tissue that has been processed by the method used almost universally in clinical anatomical pathology laboratories – formalin fixation and paraffin embedding (FFPE) – and the output is a data matrix providing relative quantitation of the peptides or proteins detectable by Data-Independent Acquisition (DIA) mass spectrometry (MS). The next priority is to generate a database of cancer proteomes with corresponding clinical outcome data of sufficient scale to permit discovery of peptide and/or protein signatures that can predict patient outcomes. This is being attempted through a “hyper-collaborative” approach where many dozens of collaborating cancer research groups are providing stored tumour samples for patient cohorts assembled to address questions relevant to specific cancer types and for which the outcome of treatment has already been documented. Wherever possible, predictive proteomic signatures obtained in this way are validated in independent patient cohorts. More than 20,000 cancer samples have been analysed to date, and this is being complemented by proteomic analysis of cancer model systems from which extensive drug treatment and gene dependency data can be obtained. The use of targeted proteomic methodologies and the prospective testing of cancer samples are both being explored at present. The overall objective is to develop a proteomic/clinical knowledge base that can underpin the use of proteomic testing that will assist cancer clinicians to choose the best available treatment options for individual cancer patients.