Targeting cellular pathways in glioblastoma multiforme

Pearson, Joshua R.D.; Regad, Tarik

doi:10.1038/sigtrans.2017.40

Cited by 264 publications

(263 citation statements)

References 146 publications

(164 reference statements)

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“…Treatment of cell line and xenograft with METex14 or ZM fusion with PLB-1001 resulted in MET signaling inhibition and reduced tumor volume. Despite several ongoing MET-targeting clinical trials in glioma (Pearson and Regad, 2017), our study highlights a precision neurooncology approach by identifying patients with MET alterations. In our phase I clinical trial, PLB-1001 monotherapy demonstrated a safety profile for patients with ZM fusion and/or METex14.…”

Section: Discussionmentioning

confidence: 92%

Mutational Landscape of Secondary Glioblastoma Guides MET-Targeted Trial in Brain Tumor

Bao

et al. 2018

Cell

261

243

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Graphical AbstractHighlights d Characterization of the mutational landscape of secondary glioblastoma d Clonal and subclonal METex14 promote glioma progression and mark worse prognosis d PLB-1001 is a highly selective, efficient, and BBB-permeable MET kinase inhibitor d PLB-1001 provides a safe and efficacious therapeutic approach for glioma treatment SUMMARY Low-grade gliomas almost invariably progress into secondary glioblastoma (sGBM) with limited therapeutic option and poorly understood mechanism. By studying the mutational landscape of 188 sGBMs, we find significant enrichment of TP53 mutations, somatic hypermutation, MET-exon-14-skipping (METex14), PTPRZ1-MET (ZM) fusions, and MET amplification. Strikingly, METex14 frequently co-occurs with ZM fusion and is present in $14% of cases with significantly worse prognosis. Subsequent studies show that METex14 promotes glioma progression by prolonging MET activity. Furthermore, we describe a MET kinase inhibitor, PLB-1001, that demonstrates remarkable potency in selectively inhibiting MET-altered tumor cells in preclinical models. Importantly, this compound also shows blood-brain barrier permeability and is subsequently applied in a phase I clinical trial that enrolls MET-altered chemo-resistant glioma patients. Encouragingly, PLB-1001 achieves partial response in at least two advanced sGBM patients with rarely significant side effects, underscoring the clinical potential for precisely treating gliomas using this therapy.

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Section: Discussionmentioning

confidence: 92%

Mutational Landscape of Secondary Glioblastoma Guides MET-Targeted Trial in Brain Tumor

Bao

et al. 2018

Cell

261

243

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“…CQ and its derivative hydroxychloroquine (HCQ) are known inhibitors of autophagy, used in the clinics to treat GBM tumors, typically in combinatorial therapies together with the alkylating agent temozolomide (TMZ), and contribute, in some cases, to extend survival prognoses in patients affected by these aggressive, autophagy‐prone tumors . The N , N ‐(8‐hydroxyquinoline)methyl‐substituted benzylamine JLK1486 has been reported to inhibit cellular proliferation in B16F10 skin melanoma cells, via induction of cytodestructive autophagy .…”

Section: Introductionmentioning

confidence: 99%

Quinoline‐Conjugated Ruthenacarboranes: Toward Hybrid Drugs with a Dual Mode of Action

et al. 2019

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The role of autophagy in cancer is often complex, ranging from tumor-promoting to -suppressing effects. In this study, two novel hybrid molecules were designed, containing a ruthenacarborane fragment conjugated with a known modulator of autophagy, namely a quinoline derivative. The complex closo-[3-(η 6 -p-cymene)-1-(quinolin-8-yl-acetate)-3,1,2-RuC 2 B 9 H 10 ](4) showed a dual mode of action against the LN229 (human glioblastoma) cell line, where it inhibited tumor-promoting autophagy, and strongly inhibited cell proliferation, de facto blocking cellular division. These results, together with the tendency to spontaneously form nanoparticles in aqueous solution, make complex 4 a very promising drug candidate for further studies in vivo, for the treatment of autophagy-prone glioblastomas. [a] Dr. . Results from flow cytometric and fluorescence microscopy analysis, and wound healing assay of MCF-7 cells incubated (72 h) with 3 and 4 at 20 μM. (A) CFSE staining (left panel) and wound healing assay (right panel); (B) AnnV/PI double staining; (C) DAPI-stained cells observed under fluorescence microscope (magnification X200). Arrows indicate apoptotic cells; (D) ApoStat staining; (E) AO staining. Experiments were run in triplicate. One representative example per each experiment is shown. For each staining protocol, the respective control (untreated cells) is also shown. (FL1, green channel; FL2, orange channel; FL3, dark red channel).

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Section: Glioblastoma Multiforme (Gbm)mentioning

confidence: 99%

“…PDGFR and TGF-β pathways are known to be dysregulated in GBM and they contribute to its pathogenesis and progression [64]. PDGFR and TGF-β are are currently considered as therapeutic targets [64,65]. In addition, TGF-β activity is associated with differences in prognosis in gliomas, including GBM [65,66].…”

Section: Glioblastoma Multiforme (Gbm)mentioning

confidence: 99%

PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data

2020

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Background: Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics data increase the chance to robustly identify molecular patient sub-groups and hence open the door towards a better personalized treatment of diseases. Several methods have been proposed for unsupervised clustering of multi-omics data. However, a number of challenges remain, such as the magnitude of features and the large difference in dimensionality across different omics data sources. Results: We propose a multi-modal sparse denoising autoencoder framework coupled with sparse non-negative matrix factorization to robustly cluster patients based on multi-omics data. The proposed model specifically leverages pathway information to effectively reduce the dimensionality of omics data into a pathway and patient specific score profile. In consequence, our method allows us to understand, which pathway is a feature of which particular patient cluster. Moreover, recently proposed machine learning techniques allow us to disentangle the specific impact of each individual omics feature on a pathway score. We applied our method to cluster patients in several cancer datasets using gene expression, miRNA expression, DNA methylation and CNVs, demonstrating the possibility to obtain biologically plausible disease subtypes characterized by specific molecular features. Comparison against several competing methods showed a competitive clustering performance. In addition, post-hoc analysis of somatic mutations and clinical data provided supporting evidence and interpretation of the identified clusters. Conclusions: Our suggested multi-modal sparse denoising autoencoder approach allows for an effective and interpretable integration of multi-omics data on pathway level while addressing the high dimensional character of omics data. Patient specific pathway score profiles derived from our model allow for a robust identification of disease subgroups.

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Targeting cellular pathways in glioblastoma multiforme

Cited by 264 publications

References 146 publications

Mutational Landscape of Secondary Glioblastoma Guides MET-Targeted Trial in Brain Tumor

Mutational Landscape of Secondary Glioblastoma Guides MET-Targeted Trial in Brain Tumor

Quinoline‐Conjugated Ruthenacarboranes: Toward Hybrid Drugs with a Dual Mode of Action

PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data

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