Reconstructing cancer evolution at the single-cell level in patient-derived organoids
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Date
2024-01-30ICR Author
Author
Sottoriva A
Parker, R
Sottoriva, A
Type
Thesis or Dissertation
Metadata
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Genetic mutations in cancer driver genes are heritable and can confer aggressive and therapy-resistant properties to cells. However genetic driver events do not fully explain the malignant phenotype, and it is becoming increasingly clear that epigenetic drivers can also confer these qualities. Furthermore, phenotypic switching and plasticity can also enable malignant cells to adapt to a changing environment. The degree to which these properties are inherited and related to genetic state is unclear. Smart-RRBS is a dual-omic single-cell technique capable of profiling methylation of cytosines and the transcriptome from the same cell. Methylation of cytosines can be used to both define the epigenetic state and to assemble the genealogy of somatic cells through phylogenetic inference. Smart-RRBS, therefore, allows phylogenetic reconstruction at the single-cell level, enabling resolution of cell-to-cell ancestral relations, coupled with a read out of their epigenetic and phenotypic state. Here I optimise and validate Smart-RRBS, then assess its performance in our hands compared to published data. Following establishment of this methodology in our lab, I apply Smart-RRBS to a patient-derived organoid model system of colorectal cancer, comparing a treatment naïve line to the same line following exposure to a MEK inhibitor. Leveraging an expressible lineage tracing barcoding system to validate the phylogenetic tree, I find the tree accurately captures phylogenetic architecture. I explore transcriptional and epigenetic phenotypic differences in treated and untreated cells and investigate to what degree these are present prior to drug treatment. I identify concordant copy number alterations from both the methylation data and the transcription data, defining a clone with a unique copy number profile that emerges after selection by treatment. I also apply a new single-cell whole genome amplification assay to treatment-naive and Akt-inhibitor treated colorectal patient derived organoids and construct a phylogeny using single-cell SNVs.
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Research team
Evol Genomics & Modelling
Language
eng
License start date
2024-01-30
Citation
2024
Publisher
Institute of Cancer Research (University Of London)