Spatial genomic, biochemical and cellular mechanisms underlying meningioma heterogeneity and evolution.
Calixto-Hope G LucasKanish MirchiaKyounghee SeoHinda NajemWilliam C ChenNaomi ZakimiKyla FosterCharlotte D EatonMartha A CadyAbrar ChoudhurySiyuan John LiuJoanna J PhillipsStephen T MagillCraig M HorbinskiDavid A SolomonArie PerryHarish N VasudevanAmy B HeimbergerDavid R RaleighPublished in: Nature genetics (2024)
Intratumor heterogeneity underlies cancer evolution and treatment resistance, but targetable mechanisms driving intratumor heterogeneity are poorly understood. Meningiomas are the most common primary intracranial tumors and are resistant to all medical therapies, and high-grade meningiomas have significant intratumor heterogeneity. Here we use spatial approaches to identify genomic, biochemical and cellular mechanisms linking intratumor heterogeneity to the molecular, temporal and spatial evolution of high-grade meningiomas. We show that divergent intratumor gene and protein expression programs distinguish high-grade meningiomas that are otherwise grouped together by current classification systems. Analyses of matched pairs of primary and recurrent meningiomas reveal spatial expansion of subclonal copy number variants associated with treatment resistance. Multiplexed sequential immunofluorescence and deconvolution of meningioma spatial transcriptomes using cell types from single-cell RNA sequencing show decreased immune infiltration, decreased MAPK signaling, increased PI3K-AKT signaling and increased cell proliferation, which are associated with meningioma recurrence. To translate these findings to preclinical models, we use CRISPR interference and lineage tracing approaches to identify combination therapies that target intratumor heterogeneity in meningioma cell co-cultures.
Keyphrases
- single cell
- copy number
- high grade
- rna seq
- pi k akt
- mitochondrial dna
- genome wide
- cell proliferation
- high throughput
- low grade
- signaling pathway
- optic nerve
- dna methylation
- healthcare
- machine learning
- public health
- cell cycle arrest
- combination therapy
- cell death
- transcription factor
- squamous cell carcinoma
- gene expression
- young adults
- mesenchymal stem cells
- stem cells
- genome editing
- deep learning
- single molecule