In this proposal we address a very substantial limitation in the ability of existing technologies to provide critical proteomic information from normal and cancer tissues. In standard proteomic analyses, proteins in complex protein mixtures such as total cell lysate are digested with proteases such as trypsin to produce peptides. These peptides are then analyzed by liquid chromatography and mass spectrometry (LC-MS), and the proteins present are identified by the presence of peptides contained within them. Relative or absolute quantification may be obtained if desired by a variety of different isotopic tagging strategies. While this strategy, termed ?bottom-up? proteomics, works well for identifying large numbers of proteins present in complex samples, it suffers from the loss of contextual information about the particular form of the proteins from which the peptides are derived, the ?proteoforms?. In order to understand the processes and pathways that are operative in cancer, it is essential to know the identities and abundances of these ?proteoforms? present in the tissues. Recently, a new approach has been developed for the identification and quantification of proteoforms in complex mixtures. In this approach two pieces of information are obtained for each proteoform in the sample: a highly accurate measurement of the proteoform intact mass, and the number of lysine residues it contains. This information allows identification and quantification of thousands of proteoforms. However, at present it can only be performed on cells grown in culture containing isotopically labeled lysine amino acids. This limitation makes it impossible to use the strategy for identification of proteoforms in cancer tissue samples. It is proposed here to develop an alternative means of introducing the isotopic tags needed for proteoform quantification and for the determination of the number of a targeted amino acid residue in each proteoform. Cysteine amino acids have been chosen in place of lysine amino acids because of their amenability to highly efficient tagging reactions, and suitable isotopic labels have been designed and will be synthesized and tested. This new chemistry will provide the power of the isotopic tagging strategy for proteomic analyses of cancer tissue samples. Such proteoform level knowledge of changes that occur in cancer will reveal a new universe of possible cancer biomarkers, as well as opening a currently closed avenue to understanding the proteomic changes that occur in cancer.
The proposed research will make possible the identification and quantification of intact proteoforms in clinically relevant cancer tissue samples. This will both open a new realm of potential cancer biomarkers for early cancer detection and provide critical new information on the precise molecular forms of proteins present in cancer tissues. This information is essential to understanding, and eventually treating, cancer.
