To address the complexity of cancer, liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) panels provide the capability to quantify clinical biomarkers and emerging protein markers to establish the context of tumor phenotypes that provide highly relevant supporting information. The resulting targeted proteomics data will empower translational researchers to move protein biomarker panels through the process from discovery to clinical use. Here, targeted proteomics panel measurements provide pathway-level evaluations of key biological drivers (e.g., EGFR signaling), phenotypes (e.g., EMT) and the ability to quantify specific drug targets within the context of a sample cohort of lung squamous cell carcinomas.

An in-house dataset quantifying 97 proteins in 108 tumor samples was generated with liquid chromatography-multiple reaction monitoring mass spectrometry analysis of 1 microgram of total tumor protein digest spiked with 20 fmol of each stable isotope-labeled standard peptide. Both the cohort of 108 lung squamous cell carcinomas as well as the proteomics sample preparation have been described in a previous publication describing proteogenomics (Stewart et al. Nat Commun 2019, 10 (1), 3578); de-identified patient number is the same in both studies to enable data comparison. The LC-MRM data acquisition method has also been previously published (Whiteaker, Sharma, Hoffman, et al. Cell Rep Methods 2021, 1 (3), 100015). Skyline software (MacLean et al. Bioinformatics 2010, 26 (7), 966-968.) was used for quality control, peak selection, and relative quantification. Data were exported from Skyline14 and imported into Excel to calculate protein expression based on comparison of ion signal for the proteolytic peptide from the endogenous protein to the stable isotope-labeled standard peptide.

Frozen tumor digests were spiked with stable isotope-labeled standards prior to LC-MRM analysis; the same digests were already used for LC-MS/MS relative quantification experiments with tandem mass tag chemical labeling (Stewart et al. Nat Commun 2019, 10 (1), 3578).