Highlights d Novel mouse system to uncouple tumor mutational load and tumor heterogeneity d Lower tumor heterogeneity leads to decreased tumor growth because of immune rejection d Both clone numbers and their genetic diversity mediate tumor growth and rejection d Tumor heterogeneity is linked to patient survival and checkpoint blockade response

The urea cycle (UC) is the main pathway by which mammals dispose of waste nitrogen. We find that specific alterations in the expression of most UC enzymes occur in many tumors, leading to a general metabolic hallmark termed "UC dysregulation" (UCD). UCD elicits nitrogen diversion toward carbamoyl-phosphate synthetase2, aspartate transcarbamylase, and dihydrooratase (CAD) activation and enhances pyrimidine synthesis, resulting in detectable changes in nitrogen metabolites in both patient tumors and their bio-fluids. The accompanying excess of pyrimidine versus purine nucleotides results in a genomic signature consisting of transversion mutations at the DNA, RNA, and protein levels. This mutational bias is associated with increased numbers of hydrophobic tumor antigens and a better response to immune checkpoint inhibitors independent of mutational load. Taken together, our findings demonstrate that UCD is a common feature of tumors that profoundly affects carcinogenesis, mutagenesis, and immunotherapy response.

No abstract

Tandem mass spectrometry (MS/MS), coupled with liquid chromatography (LC), is a powerful tool for the analysis and comparison of complex protein and peptide mixtures. However, the extremely large amounts of data that result from the process are very complex and difficult to analyze. We show how the clustering of similar spectra from multiple LC-MS/MS runs can help in data management and improve the analysis of complex peptide mixtures. The major effect of spectrum clustering is the reduction of the huge amounts of data to a manageable size. As a result, analysis time is shorter and more data can be stored for further analysis. Furthermore, spectrum quality improvement allows the identification of more peptides with greater confidence, the comparison of complex peptide mixtures is facilitated, and the entire proteomics project is presented in concise form. Pep-Miner is an advanced software tool that implements these clustering-based applications. It proved useful in several comparative proteomics projects involving lung cancer cells and various other cell types. In one of these projects, Pep-Miner reduced 517 000 spectra to 20 900 clusters and identified 2518 peptides derived from 830 proteins. Clustering and identification lasted less than two hours on an IBM Thinkpad T23 computer (laptop). Pep-Miner's unique properties make it a very useful tool for large-scale shotgun proteomics projects.

The HLA molecules are membrane-bound transporters that carry peptides from the cytoplasm to the cell surface for surveillance by circulating T lymphocytes. Although low levels of soluble HLA molecules (sHLA) are normally released into the blood, many types of tumor cells release larger amounts of these sHLA molecules, presumably to counter immune surveillance by T cells. Here we demonstrate that these sHLA molecules are still bound with their authentic peptide repertoires, similar to those of the membranal HLA molecules (mHLA). Therefore, a single immunoaffinity purification of the plasma sHLA molecules, starting with a few milliliters of patients' blood, allows for identification of very large sHLA peptidomes by mass spectrometry, forming a foundation for development of a simple and universal blood-based cancer diagnosis. The new methodology was validated using plasma and tumor cells of multiple-myeloma and leukemia patients, plasma of healthy controls, and with cultured cancer cells. The analyses identified thousands of sHLA peptides, including some cancer-related peptides, present among the sHLA peptidomes of the cancer patients. Furthermore, because the HLA peptides are the degradation products of the cellular proteins, this sHLA peptidomics approach opens the way for investigation of the patterns of protein synthesis and degradation within the tumor cells.