Algorithmic reconstruction of glioblastoma network complexity

Uthamacumaran, Abicumaran; Craig, Morgan

doi:10.1016/j.isci.2022.104179

Cited by 7 publications

(21 citation statements)

References 70 publications

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“…Novel data science and network reconstruction techniques have enabled the identification and delineation of transcriptional networks that reprogram high-grade glioma behaviour patterns. These transcriptional regulatory networks act as enhances and regulators of oncogenes and oncohistone variants observed in paediatric glioma (i.e., K27M and G34 V/R) (86). Moreover, three-dimensional genomic structural variations have the potential to hijack transcriptional enhancers and gene coamplification contributing to the epigenetic landscape and contributing to tumorigenesis in pHGG (87).…”

Section: Molecular Genetics Of Phggmentioning

confidence: 99%

Molecular Genetics and Targeted Therapies for Paediatric High-grade Glioma

Rallis

George

Woźniak

et al. 2022

Cancer Genomics Proteomics

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Brain tumours are the leading cause of paediatric cancer-associated death worldwide. High-grade glioma (HGG) represents a main cause of paediatric brain tumours and is associated with poor prognosis despite surgical and chemoradiotherapeutic advances. The molecular genetics of paediatric HGG (pHGG) are distinct from those in adults, and therefore, adult clinical trial data cannot be extrapolated to children. Compared to adult HGG, pHGG is characterised by more frequent mutations in PDGFRA, TP53 and recurrent K27M and G34R/V mutations on histone H3. Ongoing trials are investigating novel targeted therapies in pHGG. Promising results have been achieved with BRAF/MEK and PI3K/mTOR inhibitors. Combination of PI3K/mTOR, EGFR, CDK4/6, and HDAC inhibitors are potentially viable options. Inhibitors targeting the UPS proteosome, ADAM10/17, IDO, and XPO1 are more novel and are being investigated in early-phase trials. Despite preclinical and clinical trials holding promise for the discovery of effective pHGG treatments, several issues persist. Inadequate blood-brain barrier penetration, unfavourable pharmacokinetics, dose-limiting toxicities, long-term adverse effects in the developing child, and short-lived duration of response due to relapse and resistance highlight the need for further improvement. Future pHGG management will largely depend on selecting combination therapies which work synergistically based on a sound knowledge of the underlying molecular target pathways. A systematic investigation of multimodal therapy with chemoradiotherapy, surgery, target agents and immunotherapy is paramount. This review provides a comprehensive overview of pHGG focusing on molecular genetics and novel targeted therapies. The diagnostics, genetic discrepancies with adults and their clinical implications, as well as conventional treatment approaches are discussed.Intracranial and intraspinal tumours are the most common solid tumours in paediatrics (1). They are the second leading cause of cancer-associated death in children and adolescents under the age of 19 in the USA and Canada (1), with an average annual age-adjusted incidence of 6.06 per 100,000 in the USA between 2012 and 2016 (2). Brain tumours are now the leading cause of paediatric-cancer-associated death worldwide, surpassing deaths from childhood leukaemia (3). Gliomas represent the highest proportion of childhood brain tumours (4, 5) accounting for 60% of paediatric brain tumour cases (5, 6), of which approximately half are classified as high grade (6, 7).

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Section: Molecular Genetics Of Phggmentioning

confidence: 99%

Molecular Genetics and Targeted Therapies for Paediatric High-grade Glioma

Rallis

George

Woźniak

et al. 2022

Cancer Genomics Proteomics

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“…In recent years, cancer has been studied as a complex system by taking into account the interacting TFs, cytokines emitted at different states, and other intracellular signals coming from the TME ( 55 – 57 ). Nevertheless, to our knowledge, there are very few studies that explore the role that immune cells have in cancer progression, specifically the macrophage.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Boolean modeling reveals that cyclic attractors in macrophage polarization serve as reservoirs of states to balance external perturbations from the tumor microenvironment

et al. 2022

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Cyclic attractors generated from Boolean models may explain the adaptability of a cell in response to a dynamical complex tumor microenvironment. In contrast to this idea, we postulate that cyclic attractors in certain cases could be a systemic mechanism to face the perturbations coming from the environment. To justify our conjecture, we present a dynamic analysis of a highly curated transcriptional regulatory network of macrophages constrained into a cancer microenvironment. We observed that when M1-associated transcription factors (STAT1 or NF-κB) are perturbed and the microenvironment balances to a hyper-inflammation condition, cycle attractors activate genes whose signals counteract this effect implicated in tissue damage. The same behavior happens when the M2-associated transcription factors are disturbed (STAT3 or STAT6); cycle attractors will prevent a hyper-regulation scenario implicated in providing a suitable environment for tumor growth. Therefore, here we propose that cyclic macrophage phenotypes can serve as a reservoir for balancing the phenotypes when a specific phenotype-based transcription factor is perturbed in the regulatory network of macrophages. We consider that cyclic attractors should not be simply ignored, but it is necessary to carefully evaluate their biological importance. In this work, we suggest one conjecture: the cyclic attractors can serve as a reservoir to balance the inflammatory/regulatory response of the network under external perturbations.

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“…Sixteen transition markers identified from MuTrans were analyzed independently using the partial information decomposition (PIDC) network inference algorithm described in Uthamacumaran and Craig (2022). This independent analysis was used as a blindfolded validation tool (i.e., a distinct Waddington/attractor landscape reconstruction algorithm) to determine whether matching or overlapping transition markers with our other lineage tracing algorithms will be identified by use of a complementary cell fate attractor reconstruction method.…”

Section: Gene Regulatory Network Inferencementioning

confidence: 99%

“…Cancers are prime examples of these complex adaptive systems as they learn and adapt from their dynamic environments (Gros, 2011). To better understand transcriptional regulation in glioblastoma, we have previously applied complex systems theory to discriminate between cell fate decisions and transcriptional networks in adult and pediatric glioblastoma, and adult GSCs (Uthamacumaran and Craig, 2022). Previous work has also shown that the collective behaviors/dynamics of glioblastoma cell fate decisions self-organize towards causal patterns in gene expression state-space (attractors) in their developmental progression (Janson, 2012;Strogatz, 2015).…”

Section: Introductionmentioning

confidence: 99%

Cell fate dynamics reconstruction identifies TPT1 and PTPRZ1 feedback loops as master regulators of differentiation in pediatric glioblastoma-immune cell networks

Uthamacumaran

2023

Preprint

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Pediatric glioblastoma is a complex dynamical disease that is difficult to treat due to its multiple adaptive behaviors driven largely by phenotypic plasticity. Integrated data science and network theory pipelines offer novel approaches to study glioblastoma cell fate dynamics, particularly phenotypic transitions over time. Here we used various single-cell trajectory inference algorithms to infer signaling dynamics regulating pediatric glioblastoma-immune cell networks. We identified GATA2, PTPRZ1, TPT1, MTRNR2L1/2, OLIG1/2, SOX11, PDGFRA, EGFR, S100B, WNT, TNFα, and NF-kB as critical transition genes or signals regulating glioblastoma-immune network dynamics, revealing potential clinically relevant targets. Further, we reconstructed glioblastoma cell fate attractors and found complex bifurcation dynamics within glioblastoma phenotypic transitions, suggesting that a causal pattern may be driving glioblastoma evolution and cell fate decision-making. Together, our findings have implications for the development of targeted therapies against glioblastoma, and the continued integration of quantitative approaches to understand pediatric glioblastoma tumour-immune interactions.

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Algorithmic reconstruction of glioblastoma network complexity

Cited by 7 publications

References 70 publications

Molecular Genetics and Targeted Therapies for Paediatric High-grade Glioma

Molecular Genetics and Targeted Therapies for Paediatric High-grade Glioma

Boolean modeling reveals that cyclic attractors in macrophage polarization serve as reservoirs of states to balance external perturbations from the tumor microenvironment

Cell fate dynamics reconstruction identifies TPT1 and PTPRZ1 feedback loops as master regulators of differentiation in pediatric glioblastoma-immune cell networks

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