Location-dependent maintenance of intrinsic susceptibility to mTORC1-driven tumorigenesis

Rushing, Gabrielle V.; Brockman, Asa; Bollig, Madelyn K.; Leelatian, Nalin; Mobley, Bret C.; Irish, Jonathan M.; Ess, Kevin C.; Fu, Changlin; Ihrie, Rebecca A.

doi:10.26508/lsa.201800218

Cited by 12 publications

(14 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tsc2 deletion causes more severe phenotypes than Tsc1 in mice, but have only recently been performed in embryonic NSCs and GFAP positive postnatal astrocytes (Way et al, 2009 ; Zeng et al, 2011 ; Mietzsch et al, 2013 ; Moon et al, 2015 ). Recently, a group demonstrated that TSC patient tubers stain positive for excitatory NSC forebrain markers, that SEGAs stain positive for inhibitory NSC markers, and that inhibitory NSC Tsc2 deletion generates lesions recapitulating SENs (Rushing et al, 2019 ).…”

Section: Tsc Sega-genesismentioning

confidence: 99%

The Neurodevelopmental Pathogenesis of Tuberous Sclerosis Complex (TSC)

Feliciano

2020

Front. Neuroanat.

View full text Add to dashboard Cite

Tuberous sclerosis complex (TSC) is a model disorder for understanding brain development because the genes that cause TSC are known, many downstream molecular pathways have been identified, and the resulting perturbations of cellular events are established. TSC, therefore, provides an intellectual framework to understand the molecular and biochemical pathways that orchestrate normal brain development. The TSC1 and TSC2 genes encode Hamartin and Tuberin which form a GTPase activating protein (GAP) complex. Inactivating mutations in TSC genes (TSC1/TSC2) cause sustained Ras homologue enriched in brain (RHEB) activation of the mammalian isoform of the target of rapamycin complex 1 (mTORC1). TOR is a protein kinase that regulates cell size in many organisms throughout nature. mTORC1 inhibits catabolic processes including autophagy and activates anabolic processes including mRNA translation. mTORC1 regulation is achieved through two main upstream mechanisms. The first mechanism is regulation by growth factor signaling. The second mechanism is regulation by amino acids. Gene mutations that cause too much or too little mTORC1 activity lead to a spectrum of neuroanatomical changes ranging from altered brain size (micro and macrocephaly) to cortical malformations to Type I neoplasias. Because somatic mutations often underlie these changes, the timing, and location of mutation results in focal brain malformations. These mutations, therefore, provide gainof-function and loss-of-function changes that are a powerful tool to assess the events that have gone awry during development and to determine their functional physiological consequences. Knowledge about the TSC-mTORC1 pathway has allowed scientists to predict which upstream and downstream mutations should cause commensurate neuroanatomical changes. Indeed, many of these predictions have now been clinically validated. A description of clinical imaging and histochemical findings is provided in relation to laboratory models of TSC that will allow the reader to appreciate how human pathology can provide an understanding of the fundamental mechanisms of development.

show abstract

Section: Tsc Sega-genesismentioning

confidence: 99%

The Neurodevelopmental Pathogenesis of Tuberous Sclerosis Complex (TSC)

Feliciano

2020

Front. Neuroanat.

View full text Add to dashboard Cite

show abstract

“…Recent work using single cell gene expression has described the existence of multiple cellular states in glioblastoma tumors and the ability of cells to transition between states ( Neftel et al, 2019 ). Similar to most transcript-based studies, RAPID analyses were performed on cells collected at a single timepoint, precluding a direct investigation of the ability of GNP or GPP cells to transition to other phenotypes; however, it is possible that phosphorylated, active STAT3, STAT5, and S6 may enable transition between progenitor-like states as they do in earlier development, and thus influence patient outcome ( Rushing et al, 2019 ; Yoshimatsu et al, 2006 ). Another key research question for the future will be whether the signature features of the risk stratifying cells seen here will also be seen in other types of intractable human malignancies.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells

Leelatian

Sinnaeve

Mistry

et al. 2020

eLife

Self Cite

View full text Add to dashboard Cite

A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is unsupervised and automated, identifies phenotypically distinct cell populations, and determines whether these populations stratify patient survival. With a pilot mass cytometry dataset of 2 million cells from 28 glioblastomas, RAPID identified tumor cells whose abundance independently and continuously stratified patient survival. Statistical validation within the workflow included repeated runs of stochastic steps and cell subsampling. Biological validation used an orthogonal platform, immunohistochemistry, and a larger cohort of 73 glioblastoma patients to confirm the findings from the pilot cohort. RAPID was also validated to find known risk stratifying cells and features using published data from blood cancer. Thus, RAPID provides an automated, unsupervised approach for finding statistically and biologically significant cells using cytometry data from patient samples.

show abstract

“…Controversial studies have investigated this SVZ niche as a region originating glioma stem cells [36,37]. Among those publications, signaling via mTORC1 (mammalian target of rapamycin complex 1), protein activating HIF-1α, has been proposed to regulate self-renewal, proliferative divisions, differentiation, and brain ventricle morphogenesis in this SVZ [38]. HIF-1α was also described as a repressor of BMP (Bone Morphogenetic protein) and thus, promoting HGG precursors in the SVZ niche [21].…”

Section: Role Of Brain Physiological Hypoxia Location or Brain Physiomentioning

confidence: 99%

“…This can also explain why a really frequent invasive terminal process in this SVZ is present during pHGG progression. It results from mTor/HIF-1α expression in the tumors and its close hypoxic environment [13,38,44].…”

Section: Role Of Brain Physiological Hypoxia Location or Brain Physiomentioning

confidence: 99%

See 1 more Smart Citation

Hypoxia Inducible Factors’ Signaling in Pediatric High-Grade Gliomas: Role, Modelization and Innovative Targeted Approaches

Fuchs

Pierrevelcin

Messé

et al. 2020

Cancers

View full text Add to dashboard Cite

The brain tumor microenvironment has recently become a major challenge in all pediatric cancers, but especially in brain tumors like high-grade gliomas. Hypoxia is one of the extrinsic tumor features that interacts with tumor cells, but also with the blood-brain barrier and all normal brain cells. It is the result of a dramatic proliferation and expansion of tumor cells that deprive the tissues of oxygen inflow. However, cancer cells, especially tumor stem cells, can endure extreme hypoxic conditions by rescheduling various genes' expression involved in cell proliferation, metabolism and angiogenesis and thus, promote tumor expansion, therapeutic resistance and metabolic adaptation. This cellular adaptation implies Hypoxia-Inducible Factors (HIF), namely HIF-1α and HIF-2α. In pediatric high-grade gliomas (pHGGs), several questions remained open on hypoxia-specific role in normal brain during gliomagenesis and pHGG progression, as well how to model it in preclinical studies and how it might be counteracted with targeted therapies. Therefore, this review aims to gather various data about this key extrinsic tumor factor in pHGGs.

show abstract

Location-dependent maintenance of intrinsic susceptibility to mTORC1-driven tumorigenesis

Cited by 12 publications

References 47 publications

The Neurodevelopmental Pathogenesis of Tuberous Sclerosis Complex (TSC)

The Neurodevelopmental Pathogenesis of Tuberous Sclerosis Complex (TSC)

Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells

Hypoxia Inducible Factors’ Signaling in Pediatric High-Grade Gliomas: Role, Modelization and Innovative Targeted Approaches

Contact Info

Product

Resources

About