Clear cell renal cell carcinoma (ccRCC) is a significant global cancer, particularly impacting individuals in Western countries. Despite that, the molecular mechanisms driving renal cell carcinoma progression remain poorly understood, highlighting the need to investigate these mechanisms and identify novel therapeutic targets. Literature evidence suggests that elongation factor Tu GTP binding domain containing 2 (EFTUD2) and prominin (PROM1) gene expression have significant diagnostic potential in early tumor detection, potentially reflecting the trends in progression, and may become a novel therapeutic target. Therefore, this study aimed to evaluate EFTUD2 and PROM1 protein expression on clinical characteristics of ccRCC patients, especially overall and progression-free survival. To achieve that goal, we have combined publicly available liquid chromatography–mass spectrometry (LC-MS/MS) protein expression data with a comprehensive literature review to identify key protein markers for further study and immunohistochemical (IHC) analysis. Our findings highlight the importance of considering protein expression heterogeneity within tumors. The significant variation in EFTUD2 expression, its association with PFS, and its intricate connections with the mRNA splicing machinery underscore the need for a more nuanced understanding of its role in ccRCC. Similarly, the downregulation of PROM1 and its potential effects on cell surface interactions warrant further exploration. Future studies should focus on elucidating the mechanisms underlying these observations, exploring their potential as therapeutic targets, and investigating the specific pathways affected by their dysregulation.

Cancer progression and therapeutic resistance are closely linked to a stemness phenotype. Here, we introduce a protein-expression-based stemness index (PROTsi) to evaluate oncogenic dedifferentiation in relation to histopathology, molecular features, and clinical outcomes. Utilizing datasets from the Clinical Proteomic Tumor Analysis Consortium across 11 tumor types, we validate PROTsi's effectiveness in accurately quantifying stem-like features. Through integration of PROTsi with multi-omics, including protein post-translational modifications, we identify molecular features associated with stemness and proteins that act as active nodes within transcriptional networks, driving tumor aggressiveness. Proteins highly correlated with stemness were identified as potential drug targets, both shared and tumor specific. These stemness-associated proteins demonstrate predictive value for clinical outcomes, as confirmed by immunohistochemistry in multiple samples. The findings emphasize PROTsi's efficacy as a valuable tool for selecting predictive protein targets, a crucial step in customizing anti-cancer therapy and advancing the clinical development of cures for cancer patients.

Background/Objectives: Clear cell renal cell carcinoma (ccRCC) is a common kidney cancer with limited therapeutic options. This study investigated the expression of HEAT repeat-containing protein 1 (HEATR1) and solute carrier family 27 member 2 (SLC27A2) in ccRCC and their potential as prognostic markers and therapeutic targets. Methods: Analysis of a public proteomic dataset (CPTAC) and immunohistochemistry (IHC) validation in an independent cohort of 52 ccRCC patients was performed. HEATR1 and SLC27A2 expression were correlated with survival outcomes. Reactome pathway analysis was conducted to explore the functional roles of HEATR1 and SLC27A2. Results: The analysis showed that HEATR1 is upregulated and associated with poor prognosis, while SLC27A2 is downregulated and similarly linked to shorter progression-free survival. High HEATR1 expression and low SLC27A2 expression correlated with shorter progression-free survival (PFS) and overall survival (OS) in patients with high-grade ccRCC. Reactome analysis indicated HEATR1’s involvement in RNA metabolism and SLC27A2’s role in lipid metabolism, particularly peroxisomal lipid metabolism and fatty acyl-CoA biosynthesis. HEATR1 exhibited a dual localization in both the cytoplasm and nucleus, while SLC27A2 was primarily observed at the cell membrane and the nucleus. This different subcellular distribution suggests multifaceted roles for both proteins in ccRCC pathogenesis. Conclusions: HEATR1 and SLC27A2 are potential prognostic markers in ccRCC. Further research is needed to validate these findings in larger, more diverse cohorts and elucidate their roles in ccRCC progression.

Clear-cell renal cell carcinoma (ccRCC) is a kidney cancer associated with poor prognosis and limited treatment options. Identifying new prognostic markers is crucial. This study investigates the potential of BCL9 and TPX2, two proteins involved in cancer progression, to predict patient outcomes This study analyzed protein abundance data from the CPTAC cohort (110 ccRCC and 84 NAT samples) using LC-MS/MS. BCL9 and TPX2 were validated via immunohistochemistry (IHC) in an independent cohort (52 ccRCC samples). Patients were stratified into high- and low-expression groups based on IHC scores. Survival analyses were conducted, and Reactome pathway enrichment analysis was performed. BCL9 and TPX2 were significantly upregulated in ccRCC compared to NAT. In the validation cohort, high BCL9 levels were associated with shorter progression-free survival (PFS) but not OS, while high TPX2 levels correlated with shorter overall survival (OS) but not PFS. Pathway analysis linked BCL9 to Wnt signaling and TPX2 to cell cycle regulation. Elevated BCL9 and TPX2 are associated with poor prognosis in ccRCC. These proteins are potential prognostic markers and therapeutic targets.

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan -cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype -specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Abstract Cancer progression and therapeutic resistance are closely linked to the acquisition of a stemness phenotype. In this context, we introduce a novel protein expression-based stemness index (PROTsi) designed to evaluate oncogenic dedifferentiation in relation to histopathology, molecular features, and clinical outcomes in tumor samples. The methodology involved the application of a machine learning model to predict the stemness molecular phenotype, leveraging proteomic data. The prediction model was built from human pluripotent stem cells from the Human Induced Pluripotent Stem Cells Consortium (HipSci). Using datasets sourced from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) across eleven distinct tumor types, we validate PROTsi's effectiveness in accurately quantifying stem-like features. Integrating the PROTsi with gene expression, DNA methylation, microRNA expression, copy number alteration, and protein post-translational modification data such as protein acetylation, glycosylation, and phosphorylation, we unveiled proteogenomic associations related to stemness. This comprehensive analysis enabled the identification of proteins and modified proteins functioning as active nodes within transcriptional networks, thereby steering the cancer aggressiveness. Proteins highly correlated with stemness were identified as potential drug targets both tumor-specific and shared among tumor types. The stemness-associated proteins we identified demonstrate prognostic value for clinical outcomes, as confirmed through immunohistochemistry in independent samples. This underscores PROTsi's efficacy as a valuable tool for selecting both prognostic protein and drug targets. This dual functionality enhances its utility in customizing anti-cancer therapy and propels the clinical development of effective cures for diverse cancer patient populations. In conclusion, our study not only introduces PROTsi as a robust method for evaluating stemness in cancer but also sheds light on potential opportunities for targeted therapies, emphasizing the significance of personalized treatment strategies in the pursuit of more effective cancer interventions. Citation Format: Iga Kołodziejczak-Guglas, Renan Simões, Elizabeth G. Demicco, Rossana L. Segura, Alexander J. Lazar, Weiping Ma, Erik Storrs, Francesca Petralia, Antonio Colaprico, Felipe da Leprevost, Pietro Pugliese, Michele Ceccarelli, Alexey I. Nesvizhski, Bożena Kamińska, Bing Zhang, Henry Rodriguez, Mehdi Mesri, Ana I. Robles, Clinical Proteomic Tumor Analysis Consortium, Li Ding, Tathiane M. Malta, Maciej Wiznerowicz. Deciphering oncogenic dedifferentiation: A proteomic approach to quantifying stemness scores and unveiling druggable targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB004.

Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas.

Recent advancements in proteomics have enhanced our understanding of clear cell renal cell carcinoma (CCRCC). Utilizing a combination of liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by immunohistochemical validation, we investigated the expression levels of UCHL1, PAK4, and SNRNP200 in high-grade CCRCC samples. Our analysis also integrated Reactome pathway enrichment to elucidate the roles of these proteins in cancer-related pathways. Our results revealed significant upregulation of UCHL1 and SNRNP200 and downregulation of PAK4 in high-grade CCRCC tissues compared to non-cancerous tissues. UCHL1, a member of the ubiquitin carboxy-terminal hydrolase family, showed variable expression across different tissues and was notably involved in the Akt signaling pathway, which plays a critical role in cellular survival in various cancers. SNRNP200, a key component of the RNA splicing machinery, was found to be essential for proper cell cycle progression and possibly linked to autosomal dominant retinitis pigmentosa. PAK4’s role was noted as critical in RCC cell proliferation and invasion and its expression correlated significantly with poor progression-free survival in CCRCC. Additionally, the expression patterns of these proteins suggested potential as prognostic markers for aggressive disease phenotypes. This study confirms the upregulation of UCHL1, SNRNP200, and PAK4 as significant factors in the progression of high-grade CCRCC, linking their enhanced expression to poor clinical outcomes. These findings propose these proteins as potential prognostic markers and therapeutic targets in CCRCC, offering novel insights into the molecular landscape of this malignancy and highlighting the importance of targeted therapeutic interventions.

We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.

One key barrier to improving efficacy of personalized cancer immunotherapies that are dependent on the tumor antigenic landscape remains patient stratification. Although patients with CD3 + CD8 + T cell-inflamed tumors typically show better response to immune checkpoint inhibitors, it is still unknown whether the immunopeptidome repertoire presented in highly inflamed and noninflamed tumors is substantially different. We surveyed 61 tumor regions and adjacent nonmalignant lung tissues from 8 patients with lung cancer and performed deep antigen discovery combining immunopeptidomics, genomics, bulk and spatial transcriptomics, and explored the heterogeneous expression and presentation of tumor (neo)antigens. In the present study, we associated diverse immune cell populations with the immunopeptidome and found a relatively higher frequency of predicted neoantigens located within HLA-I presentation hotspots in CD3 + CD8 + T cell-excluded tumors. We associated such neoantigens with immune recognition, supporting their involvement in immune editing. This could have implications for the choice of combination therapies tailored to the patient’s mutanome and immune microenvironment.

DNA methylation plays a critical role in establishing and maintaining cellular identity. However, it is frequently dysregulated during tumor development and is closely intertwined with other genetic alter-ations. Here, we leveraged multi-omic profiling of 687 tumors and matched non-involved adjacent tis-sues from the kidney, brain, pancreas, lung, head and neck, and endometrium to identify aberrant methylation associated with RNA and protein abundance changes and build a Pan-Cancer catalog. We uncovered lineage-specific epigenetic drivers including hypomethylated FGFR2 in endometrial cancer. We showed that hypermethylated STAT5A is associated with pervasive regulon downregula-tion and immune cell depletion, suggesting that epigenetic regulation of STAT5A expression consti-tutes a molecular switch for immunosuppression in squamous tumors. We further demonstrated that methylation subtype-enrichment information can explain cell-of-origin, intra-tumor heterogeneity, and tumor phenotypes. Overall, we identified cis-acting DNA methylation events that drive transcrip-tional and translational changes, shedding light on the tumor's epigenetic landscape and the role of its cell-of-origin.

Progression and therapeutic resistance in cancer have been strongly associated with the acquisition of a stemness phenotype. Here, we provide new stemness indices for assessing the degree of oncogenic dedifferentiation in tumor samples. We used a machine learning model to predict the stemness molecular phenotype based on proteomic data. The prediction model was built from human pluripotent stem cell from the Human Induced Pluripotent Stem Cells Consortium (HipSci) and applied to compute stemness indices on the Clinical Proteomic Tumor Analysis Consortium (CPTAC) tumor samples, consisting in their proteogenomic hallmarks of stemness. The obtained stemness scores based on protein expression are novel and original, and are significantly more robust compared to our previous published work. The obtained proteomic score is able to classify stem cells and non-stem cell classes. The initial analysis of over 2000 tumor samples obtained from twelve types of primary carcinomas of breast, ovary, lung, kidney, uterus, brain (pediatric and adult), head and neck, liver, stomach, colon, and pancreas has confirmed our previously published results. Indexing of CPTAC tumors with proteomic stemness score brought us with previously unappreciated findings. We integrated the stemness scores computed using proteins with gene expression, DNA methylation, microRNA, copy number alteration and protein post-translational modification to identify coherent proteogenomic stemness association. Our initial findings identified proteins and phospho-proteins as active nodes of signaling pathways and transcriptional networks that drive aggressiveness of the primary tumors that cause resistance to existing therapies. The correlation between stemness scores and protein expression resulted in the identification of potential drug targets for anti-cancer therapy both tumor-specific and shared among different tumor types. Our results also revealed stemness-associated proteins predictive of clinical outcome across analyzed tumor types. Finally, we validated some stemness targets by immunohistochemistry in independent samples and confirmed the association with clinical outcome. Targeting the proteins here identified and cellular mechanisms that drive a stemness phenotype with existing or novel drugs may eventually lead for clinical development of effective cures for cancer patients. Citation Format: Tathiane M. Malta, Iga Kołodziejczak, Renan Simões, Antonio Colaprico, Erik Storrs, Francesca Petralia, Felipe da v Leprevost, Rossana L. Segura, Elizabeth Demicco, Alexander J. Lazar, Weiping Ma, Pietro Pugliese, Michele Ceccarelli, Bozena Kamińska, Alexey I. Nesvizhski, Bing Zhang, Henry Rodriguez, Mehdi Mesri, Ana I. Robles, Clinical Proteomic Tumor Analysis Consortium, Li Ding, Maciej Wiznerowicz. Proteomic-based stemness score measure oncogenic dedifferentiation and enable the identification of druggable targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB061.

The National Cancer Institute Clinical Proteomic Atlas Consortium (CPTAC) herein reports our deep characterization of 228 grade IV IDH1 WT and mutant astrocytomas (including 28 matched primary and recurrent GBMs) using 15 proteogenomic and metabolomic platforms. Major advances over our first CPTAC GBM report (Wang et al., 2021, Cancer Cell), are the inclusion of many more samples including paired primary and recurrent tumors, application of new platforms, including glycoproteomics and targeted mass spectrometry methods, development and application of new computational techniques, and the integration of an atlas of experimentally determined, functional, kinase substrate interactions from Kinase Library. Paired primary-recurrent GBM analyses showed increased clonal diversity in recurrent tumors as a function of time, and treatment-induced mutation signatures. Proteomic and metabolomic analyses showed that different drivers can cause similar downstream effects. Only EGFR altered tumors were associated with dual EGFR glycosylation (N352 and N603) and EGFR phosphorylation (Y316) events. IDH1 mutation was associated with activated RTK signaling and decreased hypoxia pathway activities, concordant with epigenetic and metabolic profiles. Protein-protein interaction and kinase/phosphatase-substrate analyses uncovered detailed signaling events from different upstream drivers (e.g., EGFR, PDGFRA, and IDH1) converged through a PTPN11 hub to downstream effectors, including GAB1, IRS1, MAP3K5, and PTK2B. In summary, this multiscale resource presents new and deeper biological insights regarding treatment impact on tumor evolution, shared downstream consequences of independent drivers, and the potential importance of PTPN11 signaling circuitry across high-grade gliomas. We hope that reporting this new international resource to the SNO community will advance therapeutic development, including targeted therapies that may avoid known mechanisms of resistance.

Abstract Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem cell-like features. Here, we provide new stemness indices for assessing the degree of oncogenic dedifferentiation. We used machine learning approaches to extract epigenetic, transcriptomic, and proteomic features from human stem cells and applied the computed result to index the Clinical Proteomic Tumor Analysis Consortium (CPTAC) tumor samples by their proteogenomic hallmarks of stemness. Leveraging the resource generated by Human Induced Pluripotent Stem Cells Consortium (HipSci), we extracted stemness signatures from the coherent epigenetic, transcriptomic, and proteomic datasets, using one-class logistic regression machine learning algorithm. The newly obtained stemness scores based on the DNA methylation and gene expression are significantly more robust compared to our previous published work based on the dataset obtained from a smaller number of samples with only genomics data. The stemness score computed by machine learning algorithms using proteomic datasets is novel and original. The obtained proteomic score is able to classify stem cells and non-stem cell classes. Indexing of CPTAC tumors with proteomic stemness score brought us with previously unappreciated findings. We have used the stemness scores computed using gene expression, DNA methylation, and proteins and their modifications to interrogate the coherent proteogenomic CPTAC datasets. The initial analysis of over 2000 tumor samples obtained from twelve types of primary carcinomas of breast, ovary, lung, kidney, uterus, brain (pediatric and adult), head and neck, liver, stomach, colon, and pancreas has confirmed our previously published results. Importantly, our original proteomic-based score brought the analyses to a novel dimension, far beyond previously described results. The initial findings of our work identified proteins and phospho-proteins as active nodes of signaling pathways and transcriptional networks that drive aggressiveness of the primary tumors that cause resistance to existing therapies. Our results indicate that cancer stemness is associated with the engagement of developmental pathways shaping tumor plasticity. Progressive oncogenic de-differentiation of cancer cells impacts tumor microenvironment and is associated with “cold” tumors thus impairing anti-tumor immune response and limiting the efficacy of immunotherapies. Hallmarks of cancer stemness correlate with increased tumor pathology grade and clinical stage and results in worse survival of the cancer patients across analyzed tumor types. Targeting identified proteins and cellular mechanisms that drive lethal phenotype of de-differentiated tumors with existing or novel drugs may pave the way for clinical development of effective cures for cancer patients. Citation Format: Maciej Wiznerowicz, Antono Colaprico, Erik Storrs, Francesca Petralia, Iga Kołodziejczak, Felipe da Veiga Leprevost, Weiping Ma, Daniel Cui Zhou, Bo Wen, Alexander Lazar, Pietro Pugliese, Michele Ceccarelli, Bożena Kamińska, Jan Lubiński, Alexey Nesvizhskii, Bing Zhang, Henry Rodriguez, Ana L. Robles, Mehdi Mesri. Pan-cancer stemness defined by CPTAC proteogenomics guides personalized therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3113.

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment.

TRIM28, a multi-domain protein, is crucial in the development of mouse embryos and the maintenance of embryonic stem cells' (ESC) self-renewal potential. As the epigenetic factor modulating chromatin structure, TRIM28 regulates the expression of numerous genes and is associated with progression and poor prognosis in many types of cancer. Because of many similarities between highly dedifferentiated cancer cells and normal pluripotent stem cells, we applied human induced pluripotent stem cells (hiPSC) as a model for stemness studies. For the first time in hiPSC, we analyzed the function of individual TRIM28 domains. Here we demonstrate the essential role of a really interesting new gene (RING) domain and plant homeodomain (PHD) in regulating pluripotency maintenance and self-renewal capacity of hiPSC. Our data indicate that mutation within the RING or PHD domain leads to the loss of stem cell phenotypes and downregulation of the FGF signaling. Moreover, impairment of RING or PHD domain results in decreased proliferation and impedes embryoid body formation. In opposition to previous data indicating the impact of phosphorylation on TRIM28 function, our data suggest that TRIM28 phosphorylation does not significantly affect the pluripotency and self-renewal maintenance of hiPSC. Of note, iPSC with disrupted RING and PHD functions display downregulation of genes associated with tumor metastasis, which are considered important targets in cancer treatment. Our data suggest the potential use of RING and PHD domains of TRIM28 as targets in cancer therapy.

We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

The integration of mass spectrometry-based proteomics with next-generation DNA and RNA sequencing profiles tumors more comprehensively. Here this "proteogenomics" approach was applied to 122 treatment-naive primary breast cancers accrued to preserve post-translational modifications, including protein phosphorylation and acetylation. Proteogenomics challenged standard breast cancer diagnoses, provided detailed analysis of the ERBB2 amplicon, defined tumor subsets that could benefit from immune checkpoint therapy, and allowed more accurate assessment of Rb status for prediction of CDK4/6 inhibitor responsiveness. Phosphoproteomics profiles uncovered novel associations between tumor suppressor loss and targetable kinases. Acetylproteome analysis highlighted acetylation on key nuclear proteins involved in the DNA damage response and revealed cross-talk between cytoplasmic and mitochondrial acetylation and metabolism. Our results underscore the potential of proteogenomics for clinical investigation of breast cancer through more accurate annotation of targetable pathways and biological features of this remarkably heterogeneous malignancy.