Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities

Garofano, Luciano; Migliozzi, Simona; Oh, Young Taek; D’Angelo, Fulvio; Najac, Ryan D.; Ko, Aram; Frangaj, Brulinda; Caruso, Francesca Pia; Yu, Kai; Yuan, Jinzhou; Zhao, Wenting; Stefano, Anna Luisa Di; Bielle, Franck; Jiang, Tao; Sims, Peter A.; Suvà, Mario L.; Tang, Fuchou; Su, Xiao‐Dong; Ceccarelli, Michele; Sanson, Marc; Lasorella, Anna; Iavarone, Antonio

doi:10.1038/s43018-020-00159-4

Cited by 187 publications

(208 citation statements)

References 77 publications

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“…While the mitochondrial subtypes are vulnerable to oxidative phosphorylation inhibitors, the glycolytic subtypes are unaffected. 58 Non-selective (non-target-specific) therapy regimens can allow mature, differentiated cancer cells of a patient's tumor population to revert to a highly plastic, CSC state. 57 These findings further blur the difference between a CSC and a well-differentiated cancer cell.…”

Section: Cancer Networkmentioning

confidence: 99%

A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

Uthamacumaran

2021

Patterns

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Cancers are complex dynamical systems. They remain the leading cause of disease-related pediatric mortality in North America. To overcome this burden, we must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. The review provides an overview of dynamical systems theory to steer cancer research in pattern science. While most of our current tools in network medicine rely on statistical correlation methods, causality inference remains primitively developed. As such, a survey of attractor reconstruction methods and machine algorithms for the detection of causal structures applicable in experimentally derived time series cancer datasets is presented. A toolbox of complex systems approaches are discussed for reconstructing the signaling state space of cancer networks, interpreting causal relationships in their time series gene expression patterns, and assisting clinical decision making in computational oncology. As a proof of concept, the applicability of some algorithms are demonstrated on pediatric brain cancer datasets and the requirement of their time series analysis is highlighted. ll

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Section: Cancer Networkmentioning

confidence: 99%

A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

Uthamacumaran

2021

Patterns

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“…Learning clustering represents the commonest approach to identify gene signatures representative of specific cell types [21,22,68,69]. As mentioned above, scRNA-seq data are highly heterogeneous, noisy and sparse.…”

Section: Deciphering Intra-tumor Heterogeneity 421 Defining Cell Types and Clonesmentioning

confidence: 99%

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

Giudice

Peirone

Perrone

et al. 2021

IJMS

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Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.

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“…Maintenance of heterogeneity may be driven by a population of cells within the tumor termed cancer stem cells (CSCs), which are highly plastic and responsive to their environment and hold self-renewal and tumor initiation capacity [ 4 , 5 ]. At the single cell level, GBM is highly heterogeneous with a spectrum of stem cell and metabolic phenotypes [ 6 , 7 ], and contains both fast-cycling and slow-cycling cells that have distinct metabolisms and cancerous phenotypes [ 8 ]. Critically for treatment, this diversity within the cell population means that while cells from one microenvironment or cellular state may respond to a therapy, others may not, resulting in therapeutic resistance of the overall tumor.…”

Section: Introductionmentioning

confidence: 99%

Altered lipid metabolism marks glioblastoma stem and non-stem cells in separate tumor niches

Shakya

Gromovsky

Hale

et al. 2021

acta neuropathol commun

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Glioblastoma (GBM) displays marked cellular and metabolic heterogeneity that varies among cellular microenvironments within a tumor. Metabolic targeting has long been advocated as a therapy against many tumors including GBM, but how lipid metabolism is altered to suit different microenvironmental conditions and whether cancer stem cells (CSCs) have altered lipid metabolism are outstanding questions in the field. We interrogated gene expression in separate microenvironments of GBM organoid models that mimic the transition between nutrient-rich and nutrient-poor pseudopalisading/perinecrotic tumor zones using spatial-capture RNA-sequencing. We revealed a striking difference in lipid processing gene expression and total lipid content between diverse cell populations from the same patient, with lipid enrichment in hypoxic organoid cores and also in perinecrotic and pseudopalisading regions of primary patient tumors. This was accompanied by regionally restricted upregulation of hypoxia-inducible lipid droplet-associated (HILPDA) gene expression in organoid cores and pseudopalisading regions of clinical GBM specimens, but not lower-grade brain tumors. CSCs have low lipid droplet accumulation compared to non-CSCs in organoid models and xenograft tumors, and prospectively sorted lipid-low GBM cells are functionally enriched for stem cell activity. Targeted lipidomic analysis of multiple patient-derived models revealed a significant shift in lipid metabolism between GBM CSCs and non-CSCs, suggesting that lipid levels may not be simply a product of the microenvironment but also may be a reflection of cellular state. CSCs had decreased levels of major classes of neutral lipids compared to non-CSCs, but had significantly increased polyunsaturated fatty acid production due to high fatty acid desaturase (FADS1/2) expression which was essential to maintain CSC viability and self-renewal. Our data demonstrate spatially and hierarchically distinct lipid metabolism phenotypes occur clinically in the majority of patients, can be recapitulated in laboratory models, and may represent therapeutic targets for GBM.

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Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities

Cited by 187 publications

References 77 publications

A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

Altered lipid metabolism marks glioblastoma stem and non-stem cells in separate tumor niches

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