Collectively, by leveraging the heritable information captured through epimutation, we retraced the evolutionary histories of CLL and charted its evolution with therapy, demonstrating how different lineages may be preferentially impacted by a therapeutic intervention, even in genetically homogenous cell populations.
We foresee that future application of multi-modality single-cell sequencing will enable the annotation of intra-tumoral transcriptional disparities in response to therapy with precise lineage history information, as well as the integration of genetic, epigenetic and transcriptional information at the atomic unit of somatic evolution – the single cell.
To expand this discovery to solid tumor samples and prepare the ground to apply this innovative method on breast cancer samples (Aim 4), I started a collaboration with Mario Suva (Broad Institute) and generated scDNAme + scRNAseq dataset from diffuse gliomas samples.
Diffuse gliomas are incurable malignancies, where cellular state diversity fuels tumor progression and resistance to therapy.
Single-cell RNA-Sequencing (scRNAseq) studies recently charted the cellular states of IDH-mutant gliomas (IDH-MUT) and IDH-wildtype glioblastoma (GBM), showing that malignant cells partly recapitulate neurodevelopmental trajectories.
This raises the central questions of how cell states are encoded epigenetically and whether unidirectional hierarchies or more plastic state transitions govern glioma cellular architectures.
To address these questions, we generated multi-omics single-cell profiling, integrating DNA methylation (DNAme), transcriptome and genotyping of 1,728 cells from 11 GBM and IDH-MUT patient samples.
