Deciphering tumour heterogeneity in acute myeloid leukaemia at the single cell level

Abstract

The advent of next-generation sequencing (NGS) has allowed researchers to appreciate the enormous heterogeneity that exists between cells within a single tumour. This intratumour heterogeneity leads to diverse phenotypic outcomes, resulting in functionally distinct subpopulations of cancer cells. This functional heterogeneity fuels tumour evolution and therapeutic resistance and is thus a major barrier to producing cures in cancer.

Acute myeloid leukaemia (AML) is an aggressive and heterogeneous malignancy with a high relapse rate. The prevailing paradigm to explain relapse in AML posits that genetic heterogeneity leads to pre-existing or acquired mutations that render certain cells refractory to therapy, resulting in the outgrowth of a resistant clone. Large-scale sequencing studies aimed at cataloguing genetic heterogeneity in AML have revealed several important observations. Firstly, AML has one of the lowest mutational burdens of any cancer. Secondly, a significant proportion of clinical relapse events cannot be attributed to an underlying genetic change. These important findings raise the possibility that mutations alone are insufficient to fully explain therapeutic resistance in AML. Indeed, we are now beginning to appreciate that both tumour evolution and clinical relapse can be driven by non-genetic processes. However, characterising the full extent of non-genetic heterogeneity and its relative contribution to both the evolutionary trajectory of the disease and therapeutic resistance requires innovative single cell methodologies.

Single-cell RNA sequencing (scRNA-seq) has been instrumental in revealing the phenotypic heterogeneity of rare subpopulations of cells within a complex tumour. However, it is difficult to infer clonal relationships from scRNA-seq alone and this has hampered our ability to understand how individual malignant cells evolve over time. To overcome some of these challenges, we present a lentiviral method of tagging cells with unique heritable barcodes that are stably transcribed into RNA molecules in cells and therefore highly detected in microfluidic scRNA-seq workflows. This strategy, termed Single-cell Profiling and LINeage TRacking with expressed barcodes (SPLINTR), offers the ability to match the gene expression programmes of individual cells to their clonal lineage, in order to establish how initial transcriptional differences amongst heterogeneous malignant cells can shape thier future clonal behaviour during cancer progression. We apply our SPLINTR barcoding system to an in vivo model of clonal competition in order to determine the early transcriptional signatures that are associated with future clonal dominance in AML. We discover that clonal dominance is largely an intrinsic property amongst genetically identical clones. However, we find the deterministic nature of dominance is altered by the presence of other distinct competing mutational clones. Furthermore, SPLINTR enabled us to retrospectively identify a novel set of differentially expressed genes contained within certain clones prior to transplantation, which distinguished them from losing clones and was associated with their future dominance during disease progression.

Finally, we find that resistance occurs to BET inhibitor therapy in the clinic in the absence of a clear genetic event. scRNA-seq of paired baseline and relapse AML patient bone marrow samples revealed than non-genetic resistance originates from either a population of pre-existing cells that phenotypically resemble LSCs, or through transcriptional adaptation as a result of therapeutic pressure. We then use SPLINTR coupled with scRNA-seq to interrogate our previously published in vitro model of non-genetic resistance to BET bromodomain inhibition. This provided further evidence that Lamarckian evolution in the form of gradual transcriptional adaptation drives non-genetic resistance. Future work aims to unravel the epigenetic states that mediate non-genetic transcriptional adaptation in a broader therapeutic context in AML.

Collectively, the research presented in this thesis demonstrates the importance of applying novel single cell technologies to investigations of cellular diversity in cancer and highlights the underappreciated role of non-genetic heterogeneity in driving both disease evolution and therapeutic resistance in AML. These studies provide the molecular tools and rationale to further define the mechanisms by which non-genetic heterogeneity shapes cellular behaviour in cancer.