Tumours are complex ecosystems where several neoplastic and non-transformed cell populations (the tumour microenvironment – TME) interact in heterogeneous and dynamic ways. Descriptive studies in patient material, although extremely informative, often lack the temporal and spatial resolution needed to fully recapitulate this variability. Mouse models represent a natural complement to these correlative analyses, allowing longitudinal and spatially-extensive studies as well as perturbation experiments. Among pre-clinical models, patient-derived tumour xenografts (PDTXs) have been largely excluded from ecological descriptions due to the absence of a human TME and to the immune-compromised status of the mouse host. Evidence however suggests that PDTXs might be good candidates for ecosystem analyses because they contain composite tumour and TME compartments where cells may form functionally relevant interactions.
To demonstrate this, we used the extensive collection of breast cancer PDTXs established in our laboratory and performed the first in-depth description of ecosystem complexity in these model systems, confirming their potential as study tools for tumour ecology. After a global survey of all 100 available models, we thoroughly analysed a set of 15 xenografts using single cell RNA- and DNA-sequencing (scRNA/DNA-seq), three-dimensional imaging with serial two-photon tomography (STPT) and two-dimensional transcriptomic and proteomic profiling (the latter using imaging mass cytometry - IMC).
In this thesis, we will present part of this work, focusing in particular on the phenotypic and spatial complexity revealed by scRNA-seq, STPT, IMC and *in situ* transcriptomics. These analyses detail for the first time the features of the PDTX TME, which contains multiple cell types that significantly overlap with phenotypes described in other model systems and clinical samples. These cells have varying frequencies in different PDTXs and include multiple innate immune populations with tumour-reactive phenotypes. Expanding previous descriptions, we also show pervasive inter- and intra-model transcriptional heterogeneity in the PDTX tumour compartment. By applying ligand-receptor analysis, we study how different populations interact with each other and generate complex ecological dynamics. Using STPT, IMC and *in situ* transcriptomics we show that the geographical distribution of different neoplastic and TME cells is largely model-specific, indicating a concerted organisation of PDTX spatial ecosystems by the malignant cell-autonomous compartment. In agreement with this, we predict multiple exclusive interactions between cancer and stroma and show that regional variations in tumour phenotypes result in heterogeneous TME features within specific samples.
Overall, this thesis unveils the phenotypic and spatial heterogeneity of PDTX ecosystems, showing their variable organisation between and within models. By providing evidence of potential interactions between different cell populations, our study suggests the functional relevance of this variation. In general, these findings support a wider application of PDTXs for the study of tumour ecology and have strong implications for the interpretation of xenograft studies and for the characterisation of tumour-host interactions *in vivo*. The future integration of other data modalities (scDNA-seq) and extension to patient data will likely confirm these observations and offer complementary information on the processes controlling phenotypic and spatial cellular variation in cancer
