Proteogenomics of glioblastoma associates molecular patterns with survival

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

doi:10.1016/j.celrep.2021.108787

Cited by 41 publications

(38 citation statements)

References 78 publications

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“…We then assessed at the individual gene level Spearman correlations between RNA and protein expression levels in seven human cancer types. We obtained 0.17 mean Spearman correlation that varied from 0.017 for Hepatocellular Carcinoma till 0.27 for Lung Adenocarcinoma samples ( Figure 2 ), which is in line with the previous results, i.e., ~0.16–0.35 correlation between the RNA- and proteome-based gene expression levels [ 6 , 7 ].…”

Section: Resultssupporting

confidence: 91%

“…At the gene level, the correlations observed were congruent with the literature data (e.g., we observed ~0.23–0.24 correlations for ovarian cancer at the gene level, compared to ~0.3 in the previous report [ 7 ], or ~0.24–0.29 for glioblastoma in this study compared to previously observed ~0.15 [ 6 ]).…”

Section: Discussionsupporting

confidence: 92%

“…Transcriptomics offer another way of measuring expression of protein-coding genes [ 5 ]. It has been shown that RNA- and proteomic raw gene expression levels statistically significantly correlate with mean average Speaman Rho 0.16–0.52 [ 6 , 7 , 8 ]. Furthermore, RNA sequencing (RNAseq) can be used as an alternative to immunohistochemical methods for the assessment of biomarker functional status in clinical cancer samples for proteins such as HER2, ESR1, PGR, and PD-L1 [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Better Agreement of Human Transcriptomic and Proteomic Cancer Expression Data at the Molecular Pathway Activation Level

Raevskiy

Sorokin

Захарова³

et al. 2022

IJMS

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Previously, we have shown that the aggregation of RNA-level gene expression profiles into quantitative molecular pathway activation metrics results in lesser batch effects and better agreement between different experimental platforms. Here, we investigate whether pathway level of data analysis provides any advantage when comparing transcriptomic and proteomic data. We compare the paired proteomic and transcriptomic gene expression and pathway activation profiles obtained for the same human cancer biosamples in The Cancer Genome Atlas (TCGA) and the NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects, for a total of 755 samples of glioblastoma, breast, liver, lung, ovarian, pancreatic, and uterine cancers. In a CPTAC assay, expression levels of 15,112 protein-coding genes were profiled using the Thermo QE series of mass spectrometers. In TCGA, RNA expression levels of the same genes were obtained using the Illumina HiSeq 4000 engine for the same biosamples. At the gene level, absolute gene expression values are compared, whereas pathway-grade comparisons are made between the pathway activation levels (PALs) calculated using average sample-normalized transcriptomic and proteomic profiles. We observed remarkably different average correlations between the primary RNA- and protein expression data for different cancer types: Spearman Rho between 0.017 (p = 1.7 × 10−13) and 0.27 (p < 2.2 × 10−16). However, at the pathway level we detected overall statistically significantly higher correlations: averaged Rho between 0.022 (p < 2.2 × 10−16) and 0.56 (p < 2.2 × 10−16). Thus, we conclude that data analysis at the PAL-level yields results of a greater similarity when comparing high-throughput RNA and protein expression profiles.

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Section: Resultssupporting

confidence: 91%

Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Better Agreement of Human Transcriptomic and Proteomic Cancer Expression Data at the Molecular Pathway Activation Level

Raevskiy

Sorokin

Захарова³

et al. 2022

IJMS

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“…More recently, integrated pharmaco-proteogenomic studies were performed on GBM resulting in two subgroups within the IDH wild-type group showing different prognostic and therapeutic opportunities ( Oh et al, 2020 ). Moreover, the integrative analysis of the protein profile, the RNA expression, and the patient clinical information enable the identification of specific signaling pathways related to immune, metabolic, and developmental processes and associated with a patient survival ( Yanovich-Arad et al, 2021 ). Recently, subclassification of GBM using MALDI mass spectrometry imaging associated with spatially resolved proteomics and patient survival has established the presence of three GBM subtypes corresponding to antiviral immune response, neurogenesis processes, and immune infiltration.…”

Section: Introductionmentioning

confidence: 99%

Unveiling a Ghost Proteome in the Glioblastoma Non-Coding RNAs

Cardon

Fournier

Salzet

2021

Front. Cell Dev. Biol.

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Glioblastoma is the most common brain cancer in adults. Nevertheless, the median survival time is 15 months, if treated with at least a near total resection and followed by radiotherapy in association with temozolomide. In glioblastoma (GBM), variations of non-coding ribonucleic acid (ncRNA) expression have been demonstrated in tumor processes, especially in the regulation of major signaling pathways. Moreover, many ncRNAs present in their sequences an Open Reading Frame (ORF) allowing their translations into proteins, so-called alternative proteins (AltProt) and constituting the “ghost proteome.” This neglected world in GBM has been shown to be implicated in protein–protein interaction (PPI) with reference proteins (RefProt) reflecting involvement in signaling pathways linked to cellular mobility and transfer RNA regulation. More recently, clinical studies have revealed that AltProt is also involved in the patient’s survival and bad prognosis. We thus propose to review the ncRNAs involved in GBM and highlight their function in the disease.

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“…Primary GBM stem cells (GSCs) have shown to reflect the diversity of GBM, recapitulate the tumour subtypes at mRNA level, and represent a good model to study the molecular profile of this cancer and explore new therapeutic targets (Johansson et al, 2020). Many efforts were undertaken to uncover gene expression signatures that are pivotal for GSC functions, expanding our understanding of the transcriptome and proteome of GBM and GSCs (Asif et al, 2019; Guardia et al, 2020; Johansson et al, 2020; Kozuka-Hata et al, 2012; MacLeod et al, 2019; Marziali et al, 2016; Mostovenko et al, 2018; Rheinbay et al, 2013; Song et al, 2017; Yanovich-Arad et al, 2021). Single-cell RNA-sequencing (scRNAseq) studies have demonstrated that GSCs are plastic and can switch between different subtypes 8 .…”

Section: Introductionmentioning

confidence: 99%

Glioblastoma stem cells express non-canonical proteins and exclusive mesenchymal-like or non-mesenchymal-like protein signatures

Babačić

Galardi

Umer

et al. 2022

Preprint

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Glioblastoma’s (GBM) origin, recurrence and resistance to treatment are driven by GBM cancer stem cells (GSCs). Existing transcriptomic characterisations of GBM classify the tumours to three subtypes: classical, proneural, and mesenchymal. The comprehension of how expression patterns of the GBM subtypes are reflected at global proteome level in GSCs is limited. To characterise protein expression in GSCs, we performed in-depth proteogenomic analysis of patient-derived GSCs by RNA-sequencing and mass-spectrometry proteomics. We identified and quantified over 10,000 proteins in two independent GSCs panels, and propose a GSC-associated proteomic signature (GSAPS) that defines two distinct morphological conditions; one defined by a set of proteins expressed in non-mesenchymal - proneural and classical - GSCs (GPC-like), and another expressed in mesenchymal GSCs (GM-like). The expression of GM-like protein set in GBM tissue was associated with hypoxia, necrosis, recurrence, and worse overall survival in GBM patients. In a proof-of-concept proteogenomic approach, we discovered 252 non-canonical peptides expressed in GSCs, i.e., protein sequences that are variant or derive from genome regions previously considered protein-non-coding. We report new variants of the heterogeneous ribonucleoproteins (HNRNPs), which are implicated in mRNA splicing. Furthermore, we show that per-gene mRNA-protein correlations in GSCs are moderate and vary compared to GBM tissue.

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Proteogenomics of glioblastoma associates molecular patterns with survival

Cited by 41 publications

References 78 publications

Better Agreement of Human Transcriptomic and Proteomic Cancer Expression Data at the Molecular Pathway Activation Level

Better Agreement of Human Transcriptomic and Proteomic Cancer Expression Data at the Molecular Pathway Activation Level

Unveiling a Ghost Proteome in the Glioblastoma Non-Coding RNAs

Glioblastoma stem cells express non-canonical proteins and exclusive mesenchymal-like or non-mesenchymal-like protein signatures

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