Proteogenomics of glioblastoma associates molecular patterns with survival

Arad, Gali; Ofek, Paula; Yeini, Eilam; Danilevsky, Artem; Shomron, Noam; Grossman, Rachel; Satchi‐Fainaro, Ronit; Geiger, Tamar

doi:10.1101/2020.04.28.20083501

Cited by 4 publications

(1 citation statement)

References 73 publications

(102 reference statements)

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“…High-throughput multi-omic data generation has proven to be a useful resource for understanding disease biology and the identification of therapeutic targets 11,14,[55][56][57][58][59][60][61][62] . By combining serial multi-ome enrichments with HLA-I and HLA-II immunopeptidomics into a single workflow, we have provided a path forward to understanding connections between antigen presentation and protein expression, signaling, protein degradation, and epigenetic regulation from each single sample, which was not previously possible.…”

Section: Discussionmentioning

confidence: 99%

MONTE enables serial immunopeptidome, ubiquitylome, proteome, phosphoproteome, acetylome analyses of sample-limited tissues

Abelin

Bergstrom

Taylor

et al. 2021

Preprint

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Multiomic characterization of patient tissues provides insights into the function of different biological pathways in the context of disease. Much work has been done to serialize proteome and post-translational modification (PTM) analyses to conserve precious patient samples. However, characterizing clinically relevant tissues with multi-ome workflows that have distinct sample processing requirements remains challenging. To overcome the obstacles of combining enrichment workflows that have unique input amounts and utilize both label free and chemical labeling strategies, we developed a highly-sensitive multi-omic networked tissue enrichment (MONTE) workflow for the full analysis of HLA-I and HLA-II immunopeptidome, ubiquitylome, proteome, phosphoproteome and acetylome all from the same tissue sample. The MONTE workflow enables identification of a median of 9,000 HLA-I peptides, 6,000 HLA-II peptides, 10,000 Ub sites, 12,000 proteins, 20,000 phosphorylation sites and 15,000 acetylation sites from patient LUAD tumors. Because all omes are generated from the exact same tissue sample, there is less biological variability in the data enabling more robust integration. The information available in MONTE datasets facilitates the identification of putative immunotherapeutic targets, such as CT antigens and neoantigens presented by HLA complexes, as well as reveal insights into how disease-specific changes in protein expression, protein degradation, cell signaling, metabolic, and epigenetic pathways are involved in disease pathology and treatment.

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Section: Discussionmentioning

confidence: 99%

MONTE enables serial immunopeptidome, ubiquitylome, proteome, phosphoproteome, acetylome analyses of sample-limited tissues

Abelin

Bergstrom

Taylor

et al. 2021

Preprint

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Alternative RNA splicing modulates ribosomal composition and determines the spatial phenotype of glioblastoma cells

et al. 2022

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Overall patient’s survival of glioblastoma associated to molecular markers: a pan-proteomic prospective study

Drelich

Duhamel

Wisztorski

et al. 2020

Preprint

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SUMMARYMolecular heterogeneities are a key feature of glioblastoma (GBM) pathology impeding patient’s stratification and leading to high discrepancies between patients mean survivals. Here, we established a molecular classification of GBM tumors using a pan-proteomic analysis. Then, we identified, from our proteomic data, 2 clusters of biomarkers associated with good or bad patient survival from 46 IDH wild-type GBMs. Three molecular groups have been identified and associated with systemic biology analyses. Group A tumors exhibit neurogenesis characteristics and tumorigenesis. Group B shows a strong immune cell signature and express poor prognosis markers while group C tumors are characterized by an anti-viral signature and tumor growth proteins. 124 proteins were found statistically different based on patient’s survival times, of which 10 are issued from alternative AltORF or non-coding RNA. After statistical analysis, a panel of markers associated to higher survival (PPP1R12A, RPS14, HSPD1 and LASP1) and another panel associated to lower survival (ALCAM, ANXA11, MAOB, IP_652563 and IGHM) has been validated by immunofluorescence. Taken together, our data will guide GBM prognosis and help to improve the current GBM classification by stratifying the patients and may open new opportunities for therapeutic development.SignificanceGlioblastoma are very heterogeneous tumors with median survivals usually inferior to 20 months. We conducted a pan-proteomics analysis of glioblastoma (GBM) in order to stratify GBM based on the molecular contained. Forty-six GBM cases were classified into three groups where proteins are involved in specific pathways i.e. the first group has a neurogenesis signature and is associated with a better prognosis while the second group of patients has an immune profile with a bad prognosis. The third group is more associated to tumorigenesis. We correlated these results with the TCGA data. Finally, we have identified 28 new prognostic markers of GBM and from these 28, a panel of 4 higher and 5 lower survival markers were validated. With these 9 markers in hand, now pathologist can stratify GBM patients and can guide the therapeutic decision.HighlightsA novel stratification of glioblastoma based on mass spectrometry was established.Three groups with different molecular features and survival were identified.This new classification could improve prognostication and may help therapeutic options.8 prognosis markers for oncologist therapeutic decision have been validated.

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Proteogenomics of glioblastoma associates molecular patterns with survival

Cited by 4 publications

References 73 publications

MONTE enables serial immunopeptidome, ubiquitylome, proteome, phosphoproteome, acetylome analyses of sample-limited tissues

MONTE enables serial immunopeptidome, ubiquitylome, proteome, phosphoproteome, acetylome analyses of sample-limited tissues

Alternative RNA splicing modulates ribosomal composition and determines the spatial phenotype of glioblastoma cells

Overall patient’s survival of glioblastoma associated to molecular markers: a pan-proteomic prospective study

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