Multi-omics approaches to characterise and model the tumour micro-environment" by Vera Pancaldi

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Multi-omics approaches to characterise and model the tumour micro-environment

The complex interrelations of cells in the tumour micro-environment (TME) remain hidden in population bulk datasets. Computational cell type deconvolution approaches can be used to list the types and amounts of cells present in each tumour sample, either based purely on transcriptomics, or on chromatin level information⁠. However, these approaches remain approximate and new single-cell methods are bound to make population-based approaches obsolete. A powerful complementary approach to these sequencing-based approaches is given by imaging. Using specific antibodies in immunofluorescence multiplex imaging, proteins are detected in single cells in tissues, defining cell identity and phenotype in a spatial context.

We are involved in projects describing and modelling interactions between different cells found in tumour samples, including macrophages that can either promote or hinder tumour growth, depending on their polarization state. More specifically, we are working on a detailed characterization of tumour infiltrating cells in solid cancers (pancreatic and lung cancer) as well as using ex-vivo and in-vitro simplified models of the tumour microenvironment.

In terms of modelling, we are working on an agent-based model of cellular interactions inside the TME, which considers the different cell types (cancer cells, lymphocytes, macrophages, etc.) as agents with specific behaviours (phagocytosis, migration, apoptosis …) and especially aims to simulate the interplay between pro- and anti-tumoral functions of myeloid cells. We have started applying this model to co-cultures of monocytes and cancer cells from Chronic Lymphocytic Leukaemia patients, which are known to produce a specific type of tumour associated macrophages. These cultures are monitored by imaging and RNA is extracted for bulk and single-cell transcriptomics.

Finally, we are exploring new spatial transcriptomic approaches which will enable us to define cellular types and states within the spatial context of the tissue with a single technology.