The advent of SWATH-MS analysis has led to technological advances as well as significantly improved data quality and quantity. Each SWATH experiment covers a large swathe of the proteomics discovery space. In practice, however, the only peptides (and by inference proteins) able to be identified are those that are present in the SWATH library from previous DDA experiments. Multiple studies have attempted to increase library depth and quality, including advanced peptide fractionation, abundant protein depletion and other methods using the same sample type. Low abundance plasma proteins suffer particularly due to the prevalence of high abundant proteins that mask these during DDA experiments required to construct SWATH libraries. Additionally, disparate results from different search engines make conclusions requiring extensive validation using orthogonal techniques time-consuming and not always accurate. Here, we overcame these obstacles by combining libraries using iSwathX  from two distinct samples (blood plasma and cells) that were run on the same machine and platform to generate a large extended SWATH library. We used two commonly-available search engine pipelines to design a high coverage library dataset against which SWATH patient data from 4 stages of colorectal cancer were searched. Our extended cross-sample SWATH library was used to; (i) identify and quantify a large number of proteins from neat “undepleted” plasma (~2000 proteins reliably quantified using ~4500 unique peptides of at least 8 amino acids long) and (ii) detect ~70 differentially-expressed proteins. SWATH library concatenation can lead to the discovery of new disease biomarkers by enhancement of proteomics discovery space and by significantly increasing resolution.