Spatial analysis of the glioblastoma proteome reveals specific molecular signatures and markers of survival

Duhamel, Marie; Drelich, Lauranne; Wisztorski, Maxence; Aboulouard, Soulaimane; Gimeno, Jean-Pascal; Ogrinc, Nina; Devos, Patrick; Cardon, Tristan; Weller, Michael; Escande, Fabienne; Zaïri, Fahed; Maurage, Claude‐Alain; Rhun, Émilie Le; Fournier, Isabelle

doi:10.1038/s41467-022-34208-6

Cited by 24 publications

(18 citation statements)

References 78 publications

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“…Cellular metabolism is a complex network containing thousands of reactions and thousands of genes [ 19 , 20 ]. Systematically characterizing the entire metabolic network by using routine gene expression analysis remains challenging [ 21 ], largely due to the poor correlation between cellular metabolite abundance and gene expression [ 11 ].…”

Section: Discussionmentioning

confidence: 99%

Metabolic Modeling Identifies a Novel Molecular Type of Glioblastoma Associated with Good Prognosis

et al. 2023

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Glioblastoma (GBM) is one of the most aggressive forms of cancer. Although IDH1 mutation indicates a good prognosis and a potential target for treatment, most GBMs are IDH1 wild-type. Identifying additional molecular markers would help to generate personalized therapies and improve patient outcomes. Here, we used our recently developed metabolic modeling method (genome-wide precision metabolic modeling, GPMM) to investigate the metabolic profiles of GBM, aiming to identify additional novel molecular markers for this disease. We systematically analyzed the metabolic reaction profiles of 149 GBM samples lacking IDH1 mutation. Forty-eight reactions showing significant association with prognosis were identified. Further analysis indicated that the purine recycling, nucleotide interconversion, and folate metabolism pathways were the most robust modules related to prognosis. Considering the three pathways, we then identified the most significant GBM type for a better prognosis, namely N+P-. This type presented high nucleotide interconversion (N+) and low purine recycling (P-). N+P--type exhibited a significantly better outcome (log-rank p = 4.7 × 10-7) than that of N-P+. GBM patients with the N+P−-type had a median survival time of 19.6 months and lived 65% longer than other GBM patients. Our results highlighted a novel molecular type of GBM, which showed relatively high frequency (26%) in GBM patients lacking the IDH1 mutation, and therefore exhibits potential in GBM prognostic assessment and personalized therapy.

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Section: Discussionmentioning

confidence: 99%

Metabolic Modeling Identifies a Novel Molecular Type of Glioblastoma Associated with Good Prognosis

et al. 2023

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“…Based on the technical advancements, the TME has been intensively analyzed because of transcriptomic, proteomic, metabolomic and spatial information. These innovative studies are giving new insights into how TME contributes to prognosis and therapy response [ 128 , 129 ]. Thus, therapies that use NPs are ongoing and are designed not only to disrupt the interplay of cancer cells with the surrounding TME, but also the remodeling of TME [ 130 ].…”

Section: Future Perspectivesmentioning

confidence: 99%

Nanoparticles for Drug and Gene Delivery in Pediatric Brain Tumors’ Cancer Stem Cells: Current Knowledge and Future Perspectives

et al. 2023

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Primary malignant brain tumors are the most common solid neoplasm in childhood. Despite recent advances, many children affected by aggressive or metastatic brain tumors still present poor prognosis, therefore the development of more effective therapies is urgent. Cancer stem cells (CSCs) have been discovered and isolated in both pediatric and adult patients with brain tumors (e.g., medulloblastoma, gliomas and ependymoma). CSCs are a small clonal population of cancer cells responsible for brain tumor initiation, maintenance and progression, displaying resistance to conventional anticancer therapies. CSCs are characterized by a specific repertoire of surface markers and intracellular specific pathways. These unique features of CSCs biology offer the opportunity to build therapeutic approaches to specifically target these cells in the complex tumor bulk. Treatment of pediatric brain tumors with classical chemotherapeutic regimen poses challenges both for tumor location and for the presence of the blood–brain barrier (BBB). Lastly, the application of chemotherapy to a developing brain is followed by long-term sequelae, especially on cognitive abilities. Novel avenues are emerging in the therapeutic panorama taking advantage of nanomedicine. In this review we will summarize nanoparticle-based approaches and the efficacy that NPs have intrinsically demonstrated and how they are also decorated by biomolecules. Furthermore, we propose novel cargoes together with recent advances in nanoparticle design/synthesis with the final aim to specifically target the insidious CSCs population in the tumor bulk.

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“…The detection of marker levels in patients prior to treatment is widely used to assess the occurrence of clinical events, disease recurrence or progression, and survival outcomes, thereby helping clinicians and researchers guide the monitoring of cancer patients and make decisions about personalized treatment [1][2][3]. Tumor prognostic markers are currently being studied in numerous areas, including genomics [4,5], transcriptomics [6][7][8], proteomics [9,10], metabolomics [11], and epigenomics [12,13]. Among these, the investigation of transcriptomic prognostic markers has been a hot area of research in the last decade owing to the rapid development of microarray and high-throughput sequencing technologies, which researchers have used to uncover a large number of potential molecular mechanisms of gene expression differences leading to disease development and clinical applications.…”

Section: Introductionmentioning

confidence: 99%

PanCanSurvPlot: A Large-scale Pan-cancer Survival Analysis Web Application

Lin

Yang

Shi

et al. 2022

Preprint

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The identification of reliable tumor prognostic markers can help clinicians and researchers predict tumor development and patient survival outcomes more accurately, which plays a vital role in clinical diagnosis, treatment effectiveness assessment, and prognostic evaluation. Existing web tools supporting online survival analysis are gradually failing to meet the increasing demands of researchers in terms of the dataset size, richness of survival analysis methods, and diversity of customization features. Therefore, there is an urgent need for a large-scale, one-stop pan-cancer survival analysis web server. We developed PanCanSurvPlot (https://smuonco.shinyapps.io/PanCanSurvPlot/), a Shiny web tool that has incorporated a total of 215 cancer-related datasets from the GEO and TCGA databases, covering nearly 100,000 genes (mRNAs, miRNAs, and lncRNAs), approximately 45,000 samples, 51 different cancer types, and 13 different survival outcomes. The website also provides two cutoff methods based on median and optimal cutpoints. All survival analysis results from the log-rank test and univariate Cox regression are presented in a clear and straightforward summary table. Finally, users can customize color schemes and cutpoint levels to quickly obtain high-quality Kaplan–Meier survival plots that meet publication requirements.

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Spatial analysis of the glioblastoma proteome reveals specific molecular signatures and markers of survival

Cited by 24 publications

References 78 publications

Metabolic Modeling Identifies a Novel Molecular Type of Glioblastoma Associated with Good Prognosis

Metabolic Modeling Identifies a Novel Molecular Type of Glioblastoma Associated with Good Prognosis

Nanoparticles for Drug and Gene Delivery in Pediatric Brain Tumors’ Cancer Stem Cells: Current Knowledge and Future Perspectives

PanCanSurvPlot: A Large-scale Pan-cancer Survival Analysis Web Application

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