Periodic Reporting for period 2 - METCLL (Discovery and validation of ‘epidrivers’ of cancer evolution and resistance to therapy)
Reporting period: 2020-09-01 to 2021-08-31
Genetic and epigenetic intra-tumoral heterogeneity cooperate to shape the evolutionary course of cancer.
Chronic lymphocytic leukemia (CLL) is a highly informative model for cancer evolution as it undergoes substantial genetic diversification and evolution with therapy.
The CLL epigenome is also an important disease-defining feature, and growing CLL populations diversify through stochastic DNA methylation (DNAme) changes – epimutations.
However, previous studies based on bulk DNAme sequencing could not answer whether epimutations affect CLL populations homogenously.
To measure epimutation rate at single-cell resolution, we applied multiplexed single-cell reduced representation bisulfite sequencing (MscRRBS) to healthy donors B cell and CLL patient samples.
Chronic lymphocytic leukemia (CLL) is a highly informative model for cancer evolution as it undergoes substantial genetic diversification and evolution with therapy.
The CLL epigenome is also an important disease-defining feature, and growing CLL populations diversify through stochastic DNA methylation (DNAme) changes – epimutations.
However, previous studies based on bulk DNAme sequencing could not answer whether epimutations affect CLL populations homogenously.
To measure epimutation rate at single-cell resolution, we applied multiplexed single-cell reduced representation bisulfite sequencing (MscRRBS) to healthy donors B cell and CLL patient samples.
We observed that the common clonal CLL origin results in consistently elevated epimutation rate, with low cell-to-cell epimutation rate variability.
In contrast, variable epimutation rates across normal B cells reflect diverse evolutionary ages across the B cell differentiation trajectory, consistent with epimutations serving as a molecular clock.
Heritable epimutation information allowed high-resolution lineage reconstruction with single-cell data, applicable directly to patient samples.
CLL lineage tree shape revealed earlier branching and longer branch lengths than normal B cells, reflecting rapid drift after the initial malignant transformation and a greater proliferative history.
MscRRBS integrated with single-cell transcriptomes and genotyping confirmed that genetic subclones map to distinct clades inferred solely based on epimutation information.
Lastly, to examine potential lineage biases during therapy, we profiled serial samples during ibrutinib-associated lymphocytosis, and identified clades of cells preferentially expelled from the lymph node with therapy, marked by distinct transcriptional profiles.
The single-cell integration of genetic, epigenetic and transcriptional information thus charts CLL’s lineage history and its evolution with therapy.
In contrast, variable epimutation rates across normal B cells reflect diverse evolutionary ages across the B cell differentiation trajectory, consistent with epimutations serving as a molecular clock.
Heritable epimutation information allowed high-resolution lineage reconstruction with single-cell data, applicable directly to patient samples.
CLL lineage tree shape revealed earlier branching and longer branch lengths than normal B cells, reflecting rapid drift after the initial malignant transformation and a greater proliferative history.
MscRRBS integrated with single-cell transcriptomes and genotyping confirmed that genetic subclones map to distinct clades inferred solely based on epimutation information.
Lastly, to examine potential lineage biases during therapy, we profiled serial samples during ibrutinib-associated lymphocytosis, and identified clades of cells preferentially expelled from the lymph node with therapy, marked by distinct transcriptional profiles.
The single-cell integration of genetic, epigenetic and transcriptional information thus charts CLL’s lineage history and its evolution with therapy.
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.
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.