Elucidating intratumour heterogeneity through investigation of clonal, transcriptomic, and proteomic features across models of triple negative breast cancer and diffuse intrinsic pontine glioma

Abstract

Intratumour heterogeneity defines the diversity found within those malignant populations that take up residence within our bodies as cancer. These cellular societies comprise actors of diverse talents, from those suited to rapid proliferation, to drug resistance, to the cells that leave the commune to seed new metastatic colonies. Intratumour heterogeneity has come to be understood as a major driver of disease progression, and its connection to these traits ties it explicitly to increased patient mortality. These diverse genetic or phenotypic states enable the collective evolution of tumours towards more aggressive and treatment refractory disease. Further, these cells influence the tumour microenvironment, initiating a reciprocal feedback loop that adds additional complexity to the tumour milieu. Together, this melting pot of cellular diversity provides the perfect substrate for evolutionary selection to encourage the development of increasingly well adapted subpopulations. In many cancers, despite a collection of knowledge on the importance of intratumour heterogeneity in tumour progression, knowledge of the offending populations remains scarce; in others, understanding of the contribution of this phenomenon to disease state is still in its infancy. This work spans two major disease contexts– mouse models of triple negative breast cancer, in which the contribution of intratumour heterogeneity to disease progression has been well described; and diffuse intrinsic pontine glioma, a paediatric high-grade glioma where nascent insights have revealed a compelling link between heterogeneity and disease severity. To explore these challenges, this work utilises a combination of RNA sequencing based approaches, proteomics, and imaging techniques. The previously developed WILD-seq lineage tracing technology is applied to the D2A1-m2 model of breast cancer metastasis, enabling the tracing of barcoded clones through RNA sequencing. With this, the utility of this technology is demonstrated by identifying clonal populations derived from lung metastases. Further, this work explores builds upon the WILD-seq technology by marrying it with single-nuclei RNA sequencing, and raises valuable points for consideration in the development of clonal tracing experiments, including the importance of molecular reporter selection. Furthermore, this work involved the opportunity to evaluate a novel murine model of diffuse intrinsic pontine glioma for its heterogeneous composition by scRNA-seq. This thesis describes the identification of heterogenous cell populations derived from this model, and identifies cell clusters expressing genes of potential disease significance. This work is built upon by demonstrating the application of imaging mass cytometry to sections of these samples, positively identifying canonical marker genes in space. Beyond this, this work also produced the first serial two photon tomography volume of a DIPG model, revealing diffuse spread from the hindbrain towards the frontal lobe, and setting the stage for exciting subsequent developments. This thesis builds upon the existing work documenting intratumour heterogeneity in the 4T1 model of triple negative breast cancer. In this work, knowledge of transcriptomic heterogeneity in the 4T1 clonal model is built upon with full proteome analysis of selected isolated clones, revealing distinct differential expression that is robustly captured on the protein level. Finally, this work presents a potential protein-based barcoding methodology that may be developed to enhance the spatial deconvolution of clonality, alongside the spatial delineation of proteins of interest within tissue samples. Collectively, this thesis contributes to the body of intratumour heterogeneity knowledge by building upon the tools to interrogate clonal architecture of these societies, and by laying the foundations to unveil their participants. This work has enabled the refinement of important methods used to understand cellular heterogeneity, and has demonstrated the biological reality of this diversity across two disease models. This project has strong implications for both technology development and biological significance, which are clearly highlighted throughout this work.