Dataset Information

Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities.

ABSTRACT: We performed a deep proteogenomic analysis of bulk tumor and laser microdissection enriched tumor cell populations from high-grade serous ovarian cancer (HGSOC) tissue specimens spanning a broad spectrum of purity. We identified patients with longer progression-free survival had increased immune-related signatures and validated proteins correlating with tumor-infiltrating lymphocytes in 65 tumors from an independent cohort of HGSOC patients, as well as with overall survival in an additional 126 HGSOC patient cohort. We identified that homologous recombination deficient (HRD) tumors are enriched in pathways associated with metabolism and oxidative phosphorylation that we validated in independent patient cohorts. We further identified that polycomb complex protein BMI-1 is elevated in HR proficient (HRP) tumors, that elevated BMI-1 correlates with poor overall survival in HRP but not HRD HGSOC patients, and that HRP HGSOC cells are uniquely sensitive to BMI-1 inhibition.

SUBMITTER: Bateman NW

PROVIDER: S-EPMC10937683 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities.

Bateman Nicholas W NW Abulez Tamara T Soltis Anthony R AR McPherson Andrew A Choi Seongmin S Garsed Dale W DW Pandey Ahwan A Tian Chunqiao C Hood Brian L BL Conrads Kelly A KA Teng Pang-Ning PN Oliver Julie J Gist Glenn G Mitchell Dave D Litzi Tracy J TJ Tarney Christopher M CM Crothers Barbara A BA Mhawech-Fauceglia Paulette P Dalgard Clifton L CL Wilkerson Matthew D MD Pierobon Mariaelena M Petricoin Emanuel F EF Yan Chunhua C Meerzaman Daoud D Bodelon Clara C Wentzensen Nicolas N Lee Jerry S H JSH Huntsman David G DG Shah Sohrab S Shriver Craig D CD Phippen Neil T NT Darcy Kathleen M KM Bowtell David D L DDL Conrads Thomas P TP Maxwell G Larry GL

NPJ precision oncology 20240313 1

We performed a deep proteogenomic analysis of bulk tumor and laser microdissection enriched tumor cell populations from high-grade serous ovarian cancer (HGSOC) tissue specimens spanning a broad spectrum of purity. We identified patients with longer progression-free survival had increased immune-related signatures and validated proteins correlating with tumor-infiltrating lymphocytes in 65 tumors from an independent cohort of HGSOC patients, as well as with overall survival in an additional 126 ...[more]

PMID: 38480868

Similar Datasets

Project description:Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In our study, we first estimated the tumor microenvironment (TME) infiltration using the xCell algorithm. Based on TME infiltration, the three main TME clusters were identified using consensus clustering. Our results showed that the three main TME clusters cause significant differences in survival rates and TME infiltration patterns (log-rank test, p = 0.006). Then, multiple machine learning algorithms were used to develop a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer (BRCA) patients (the immune-related pathway-based risk score, defined as IPRS). Based on the IPRS, BRCA patients were divided into two subgroups, and patients in the IPRS-low group presented significantly better overall survival (OS) rates than the IPRS-high group (log-rank test, p < 0.0001). Correlation analysis revealed that the IPRS-low group was characterized by increases in immune-related scores (cytolytic activity (CYT), major histocompatibility complex (MHC), T cell-inflamed immune gene expression profile (GEP), ESTIMATE, immune, and stromal scores) while exhibiting decreases in tumor purity, suggesting IPRS-low patients may have a strong immune response. Additionally, the gene-set enrichment analysis (GSEA) result confirmed that the IPRS-low patients were significantly enriched in several immune-associated signaling pathways. Furthermore, multivariate Cox analysis revealed that the IPRS was an independent prognostic biomarker after adjustment by clinicopathologic characteristics. The prognostic value of the IPRS model was further validated in three external validation cohorts. Altogether, our findings demonstrated that the IPRS was a powerful predictor to screen out certain populations with better prognosis in breast cancer and may serve as a potential biomarker guiding clinical treatment decisions.

Project description:Therapeutic targeting of tumor vulnerabilities is emerging as a key area of research. This review is focused on exploiting the vulnerabilities of tumor cells and the immune cells in the tumor immune microenvironment (TIME), including tumor hypoxia, tumor acidity, the bidirectional proton-coupled monocarboxylate transporters (MCTs) of lactate, mitochondrial oxidative phosphorylation (OXPHOS), and redox enzymes in the tricarboxylic acid cycle. Cancer cells use glucose for energy even under normoxic conditions. Although cancer cells predominantly rely on glycolysis, many have fully functional mitochondria, suggesting that mitochondria are a vulnerable target organelle in cancer cells. Thus, one key distinction between cancer and normal cell metabolism is metabolic reprogramming. Mitochondria-targeted small molecule inhibitors of OXPHOS inhibit tumor proliferation and growth. Another hallmark of cancer is extracellular acidification due lactate accumulation. Emerging results show that lactate acts as a fuel for mitochondrial metabolism and supports tumor proliferation and growth. Metabolic reprogramming occurs in glycolysis-deficient tumor phenotypes and in kinase-targeted, drug-resistant cancers overexpressing OXPHOS genes. Glycolytic cancer cells located away from the vasculature overexpress MCT4 transporter to prevent overacidification by exporting lactate, and the oxidative cancer cells located near the vasculature express MCT1 transporter to provide energy through incorporation of lactate into the tricarboxylic acid cycle. MCTs are, therefore, a vulnerable target in cancer metabolism. MCT inhibitors exert synthetic lethality in combination with metformin, a weak inhibitor of OXPHOS, in cancer cells. Simultaneously targeting multiple vulnerabilities within mitochondria shows synergistic antiproliferative and antitumor effects. Developing tumor-selective, small molecule inhibitors of OXPHOS with a high therapeutic index is critical to fully exploiting the mitochondrial vulnerabilities. We and others developed small-molecule inhibitors containing triphenylphosphonium cation that potently inhibit OXPHOS in tumor cells and tissues. Factors affecting tumor cell vulnerabilities also impact immune cells in the TIME. Glycolytic tumor cells supply lactate to the tumor-suppressing regulatory T cells overexpressing MCTs. Therapeutic opportunities for targeting vulnerabilities in tumor cells and the TIME, as well as the implications on cancer health disparities and cancer treatment, are addressed.

Project description:Purpose: Increasing evidence has elucidated the significance of lipid metabolism in predicting therapeutic efficacy. Obviously, a systematic analysis of lipid metabolism characterizations of gastric cancer (GC) needs to be reported. Experimental design: Based on two proposed computational algorithms (TCGA-STAD and GSE84437), the lipid metabolism characterization of 367 GC patients and its systematic relationship with genomic characteristics, clinicopathologic features, and clinical outcomes of GC were analyzed in our study. Differentially expressed genes (DEGs) were identified based on the lipid metabolism cluster. At the same time, we applied single-factor Cox regression and random forest to screen signature genes to construct a prognostic model, namely, the lipid metabolism score (LMscore). Next, we deeply explored the predictive value of the LMscore for GC. To verify the specific changes in lipid metabolism, a total of 90 serum, 30 tumor, and non-tumor adjacent tissues from GC patients, were included for pseudotargeted metabolomics analysis via SCIEX triple quad 5500 LC-MS/MS system. Results: Five lipid metabolism signature genes were identified from a total of 3,104 DEGs. The LMscore could be a prognosticator for survival in different clinicopathological GC cohorts. As well, the LMscore was identified as a predictive biomarker for responses to immunotherapy and chemotherapeutic drugs. Additionally, significant changes in sphingolipid metabolism and sphingolipid molecules were discovered in cancer tissue from GC patients by pseudotargeted metabolomics. Conclusion: In conclusion, multivariate analysis revealed that the LMscore was an independent prognostic biomarker of patient survival and therapeutic responses in GC. Depicting a comprehensive landscape of the characteristics of lipid metabolism may help to provide insights into the pathogenesis of GC, interpret the responses of gastric tumors to therapies, and achieve a better outcome in the treatment of GC. In addition, significant alterations of sphingolipid metabolism and increased levels of sphingolipids, in particular, sphingosine (d16:1) and ceramide, were discovered in GC tissue by lipidome pseudotargeted metabolomics, and most of the sphingolipid molecules have the potential to be diagnostic biomarkers for GC.

Dataset Information

Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities.

Publications

Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets