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In this issue of Cell, articles by Gillette et al., Chen et al., and Xu, et al. collectively provide a deep and comprehensive proteogenomic analysis of lung adenocarcinoma, addressing differences in patient ethnicity and smoking background. They highlight the importance of associating genomics with the functional proteomic outcome.

Summary Glioblastoma (GBM) is the most aggressive form of glioma, with poor prognosis exhibited by most patients, and a median survival time of less than 2 years. We assemble a cohort of 87 GBM patients whose survival ranges from less than 3 months and up to 10 years and perform both high-resolution mass spectrometry proteomics and RNA sequencing (RNA-seq). Integrative analysis of protein expression, RNA expression, and patient clinical information enables us to identify specific immune, metabolic, and developmental processes associated with survival as well as determine whether they are shared between expression layers or are layer specific. Our analyses reveal a stronger association between proteomic profiles and survival and identify unique protein-based classification, distinct from the established RNA-based classification. By integrating published single-cell RNA-seq data, we find a connection between subpopulations of GBM tumors and survival. Overall, our findings establish proteomic heterogeneity in GBM as a gateway to understanding poor survival.

Necroptosis is a regulated and inflammatory form of cell death. We, and others, have previously reported that necroptotic cells release extracellular vesicles (EVs). We have found that necroptotic EVs are loaded with proteins, including the phosphorylated form of the key necroptosis-executing factor, mixed lineage kinase domain-like kinase (MLKL). However, neither the exact protein composition, nor the impact, of necroptotic EVs have been delineated. To characterize their content, EVs from necroptotic and untreated U937 cells were isolated and analyzed by mass spectrometry-based proteomics. A total of 3337 proteins were identified, sharing a high degree of similarity with exosome proteome databases, and clearly distinguishing necroptotic and control EVs. A total of 352 proteins were significantly upregulated in the necroptotic EVs. Among these were MLKL and caspase-8, as validated by immunoblot. Components of the ESCRTIII machinery and inflammatory signaling were also upregulated in the necroptotic EVs, as well as currently unreported components of vesicle formation and transport, and necroptotic signaling pathways. Moreover, we found that necroptotic EVs can be phagocytosed by macrophages to modulate cytokine and chemokine secretion. Finally, we uncovered that necroptotic EVs contain tumor neoantigens, and are enriched with components of antigen processing and presentation. In summary, our study reveals a new layer of regulation during the early stage of necroptosis, mediated by the secretion of specific EVs that influences the microenvironment and may instigate innate and adaptive immune responses. This study sheds light on new potential players in necroptotic signaling and its related EVs, and uncovers the functional tasks accomplished by the cargo of these necroptotic EVs.

Summary Immunotherapy has revolutionized cancer treatment, yet most patients do not respond. Here, we investigated mechanisms of response by profiling the proteome of clinical samples from advanced stage melanoma patients undergoing either tumor infiltrating lymphocyte (TIL)-based or anti- programmed death 1 (PD1) immunotherapy. Using high-resolution mass spectrometry, we quantified over 10,300 proteins in total and ∼4,500 proteins across most samples in each dataset. Statistical analyses revealed higher oxidative phosphorylation and lipid metabolism in responders than in non-responders in both treatments. To elucidate the effects of the metabolic state on the immune response, we examined melanoma cells upon metabolic perturbations or CRISPR-Cas9 knockouts. These experiments indicated lipid metabolism as a regulatory mechanism that increases melanoma immunogenicity by elevating antigen presentation, thereby increasing sensitivity to T cell mediated killing both in vitro and in vivo. Altogether, our proteomic analyses revealed association between the melanoma metabolic state and the response to immunotherapy, which can be the basis for future improvement of therapeutic response.

The identification of molecular pathways driving cancer progression is a fundamental challenge in cancer research. Most approaches to address it are limited in the number of data types they employ and perform data integration in a sequential manner. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating protein-protein interactions, mutual exclusivity of mutations and copy number alterations, transcriptional coregulation, and RNA coexpression into a single probabilistic model. To efficiently search and score the large space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Applied across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods and demonstrating the power of using multiple omics data types simultaneously. On breast cancer subtypes, ModulOmics proposes unexplored connections supported by an independent patient cohort and independent proteomic and phosphoproteomic datasets.