Proneural – Mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in Glioblastoma DOI Creative Commons
Harshavardhan BV, Mohit Kumar Jolly

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 28, 2023

1 Abstract The aggressive nature of glioblastoma (GBM) – one the deadliest forms brain tumours is majorly attributed to underlying phenotypic heterogeneity. Early attempts classify this heterogeneity at a transcriptomic level in TCGA GBM cohort proposed existence four distinct molecular subtypes: Proneural, Neural, Classical and Mesenchymal. Further, single-cell RNA-seq analysis primary also reported similar 4 subtypes mimicking neuro-developmental lineages. However, it remains unclear whether these identified via bulk transcriptomics are mutually exclusive or not. Here, we perform pairwise correlations among individual genes gene signatures corresponding subtypes, show that not distinctly antagonistic either RNA-sequencing data. We observed proneural (or neural progenitor-like) mesenchymal axis most prominent pair, with other two lying on spectrum. These results reinforced through meta-analysis over 100 datasets as well terms functional association metabolic switching, cell cycle immune evasion pathways. suggest rethinking characterization for more effective therapeutic targeting efforts.

Language: Английский

Deciphering Aging, Genetic, and Epigenetic Heterogeneity in Cancer Evolution: Toward Personalized Precision Preventative Medicine DOI Creative Commons
Lamis Naddaf, Sheng Li

Aging and Cancer, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 28, 2025

ABSTRACT Background Cancer's inherent ability to evolve presents significant challenges for its categorization and treatment. Cancer evolution is driven by genetic, epigenetic, phenotypic diversity influenced microenvironment changes. Aging plays a crucial role altering the inducing substantial genetic epigenetic heterogeneity within an individual's somatic cells even before cancer initiation. Objectives This review highlights clinical significance of mechanisms in evolution, focusing on hematopoietic solid tumors. The aims explore opportunities integrating evolutionary principles data science into research. Methods synthesizes recent advancements omics technologies, single‐cell sequencing, barcoding elucidate aging's evolution. Results Epigenetic mechanisms' high plasticity generates heritable diversity, driving malignant toward poor prognosis. Advances sequencing enable precise detection tracking biomarkers, allowing early, personalized interventions. Incorporating research has potential map, predict, prevent effectively. Conclusion Understanding through novel technologies analysis offers proactive approach prevention By predicting key events leveraging strategies, patient outcomes can be improved, healthcare burdens reduced, marking transformative shift oncology.

Language: Английский

Citations

0
Ecological and evolutionary dynamics to design and improve ovarian cancer treatment DOI Creative Commons
Grace Han, Monica Alexander,

Julia Gattozzi

et al.

Clinical and Translational Medicine, Journal Year: 2024, Volume and Issue: 14(9)

Published: Aug. 29, 2024

Ovarian cancer ecosystems are exceedingly complex, consisting of a high heterogeneity cells. Development drugs such as poly ADP-ribose polymerase (PARP) inhibitors, targeted therapies and immunotherapies offer more options for sequential or combined treatments. Nevertheless, mortality in metastatic ovarian patients remains because cells consistently develop resistance to single combination therapies, urging need treatment designs that target the evolvability The evolutionary dynamics lead emerge from complex tumour microenvironment, heterogeneous populations, individual cell's plasticity. We propose successful management requires consideration ecological disease. Here, we review current challenges discuss principles evolution. conclude by proposing evolutionarily designed strategies cancer, with goal integrating longitudinal, quantitative data improve design drug resistance. KEY POINTS/HIGHLIGHTS: Tumours which non-cancer interact evolve dynamic ways. Conventional inevitably development they fail consider tumours' cellular Eco-evolutionarily should cell plasticity patient-specific characteristics clinical outcome prevent relapse.

Language: Английский

Citations

3
Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma DOI Creative Commons
Harshavardhan BV, Mohit Kumar Jolly

iScience, Journal Year: 2024, Volume and Issue: 27(3), P. 109184 - 109184

Published: Feb. 13, 2024

The aggressive nature of glioblastoma (GBM) - one the deadliest forms brain tumors is majorly attributed to underlying phenotypic heterogeneity. Early attempts classify this heterogeneity at a transcriptomic level in TCGA GBM cohort proposed existence four distinct molecular subtypes: Proneural, Neural, Classical, and Mesenchymal. Further, single-cell RNA sequencing (scRNA-seq) analysis primary also reported similar subtypes mimicking neurodevelopmental lineages. However, it remains unclear whether these identified via bulk transcriptomics are mutually exclusive or not. Here, we perform pairwise correlations among individual genes gene signatures corresponding show that not distinctly antagonistic either scRNA-seq data. We observed proneural (or neural progenitor-like)-mesenchymal axis most prominent pair, with other two lying on spectrum. These results reinforced through meta-analysis over 100 datasets as well terms functional association metabolic switching, cell cycle, immune evasion pathways. Finally, proneural-mesenchymal trend percolates relevant transcription factors patient survival. suggest rethinking characterization for more effective therapeutic targeting efforts.

Language: Английский

Citations

2
Proneural – Mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in Glioblastoma DOI Creative Commons
Harshavardhan BV, Mohit Kumar Jolly

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 28, 2023

1 Abstract The aggressive nature of glioblastoma (GBM) – one the deadliest forms brain tumours is majorly attributed to underlying phenotypic heterogeneity. Early attempts classify this heterogeneity at a transcriptomic level in TCGA GBM cohort proposed existence four distinct molecular subtypes: Proneural, Neural, Classical and Mesenchymal. Further, single-cell RNA-seq analysis primary also reported similar 4 subtypes mimicking neuro-developmental lineages. However, it remains unclear whether these identified via bulk transcriptomics are mutually exclusive or not. Here, we perform pairwise correlations among individual genes gene signatures corresponding subtypes, show that not distinctly antagonistic either RNA-sequencing data. We observed proneural (or neural progenitor-like) mesenchymal axis most prominent pair, with other two lying on spectrum. These results reinforced through meta-analysis over 100 datasets as well terms functional association metabolic switching, cell cycle immune evasion pathways. suggest rethinking characterization for more effective therapeutic targeting efforts.

Language: Английский

Citations

0