Clinical management of cancer types relies on molecular subtyping in a significant number of cancer types, e.g. estrogen receptor-positive (ER+), HER2-amplified, and triple-negative (TNBC) for breast cancers, yet substantial heterogeneity within molecular subtypes drives variable treatment responses that remain poorly understood at the protein level. We have generated data-independent acquisition (DIA) mass spectrometry proteomics data for a number of different cancer cohorts, and deep as well as tandem mass tag (TMT)-labelled proteomics and phosphoproteomics data for a large number of samples. The primary computational task is processing raw mass spectrometry data to generate quantitative protein matrices for downstream analysis. This involves spectral library generation, peptide identification, and protein quantification across all ~2,200 DIA acquisitions using tools such as DIA-NN and Spectronaut. Processing requires iterative searching of each acquisition against comprehensive protein sequence databases, retention time alignment across large sample batches, and multi-level quality control filtering. For TMT-labelled data, additional processing includes reporter ion quantification and batch correction across fractionated runs. Proteogenomic searches add substantial computational load by requiring each sample's spectra to be matched against individualized protein databases derived from matched WGS variant calls, enabling identification of novel coding sequences and variant peptides across the cohort. These data processing steps are highly parallelizable but require significant CPU time and memory that exceeds local workstation capacity. Combining the quantitative protein matrices with proteogenomics results will enable a broad range of biological and clinical investigations across cancer subtypes. These include characterization of proteomic diversity within subtypes and their emerging molecular subclassifications; identification of subtype-specific protein signatures associated with clinical outcomes and treatment response; integration with matched genomic, transcriptomic, and epigenomic data for multi-omic analysis of gene-to-protein regulation; phosphoproteomics-based signaling pathway and kinase activity inference in e.g. TNBC; sample-specific protein module detection using topology-based network methods; and proteogenomic discovery of novel protein-coding sequences and neoantigen candidates. Results will contribute to ongoing manuscripts characterizing the population-based breast cancer proteogenomic landscape. Certain cohorts can additionally serve as a reference for parallel immunotherapy biomarker studies in patients. All results will be made available to the SCANB consortium and deposited in appropriate public repositories upon publication.