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Abstract Immunotherapies are revolutionizing cancer care, but many patients do not achieve durable responses and immune-related adverse events are difficult to predict. Quantifying the hundreds of proteins involved in cancer immunity has the potential to provide biomarkers to monitor and predict tumor response. We previously developed robust, multiplexed quantitative assays for immunomodulatory proteins using targeted mass spectrometry, providing measurements that can be performed reproducibly and harmonized across laboratories. Here, we expand upon those efforts in presenting data from a multiplexed immuno-oncology (IO)-3 assay panel targeting 43 peptides representing 39 immune- and inflammation-related proteins. A suite of novel monoclonal antibodies was generated as assay reagents, and the fully characterized antibodies are made available as a resource to the community. The publicly available dataset contains complete characterization of the assay performance, as well as the mass spectrometer parameters and reagent information necessary for implementation of the assay. Quantification of the proteins will provide benefit to correlative studies in clinical trials, identification of new biomarkers, and improve understanding of the immune response in cancer.

Abstract A wealth of proteogenomic data has been generated using cancer samples to deepen our understanding of the mechanisms of cancer and how biological networks are altered in association with somatic mutation of tumor suppressor genes, such as TP53 and PTEN. To generate functional signatures of TP53 or PTEN loss, we profiled the RNA and phosphoproteomes of the MCF10A epithelial cell line, along with its congenic TP53- or PTEN-knockout derivatives, upon perturbation with the monofunctional DNA alkylating agent methyl methanesulfonate (MMS) vs. mock treatment. To enable quantitative and reproducible mass spectrometry data generation, the cell lines were SILAC-labeled (stable isotope labeling with amino acids in cell culture), and the experimental design included label swapping and biological replicates. All data are publicly available and may be used to advance our understanding of the TP53 and PTEN tumor suppressor genes and to provide functional signatures for bioinformatic analyses of proteogenomic datasets.