MECCA: Metastatic Evolutionary dynamics of Colorectal CAncer

BACKGROUND People with metastatic colorectal cancer (mCRC) are unlikely to survive for 5-years following their diagnosis.

Metastasis is an evolutionary process, but the lack of understanding of the dynamics and drivers of mCRC evolution hinders efforts to better prognose and treat the disease.

Our work over the past decade has delineated evolutionary dynamics in primary CRC (pCRC), identifying key driving roles for copy number and epigenetic alterations, plasticity and tumour-immune interactions. It is unknown how these processes alter when cells metastasise to other organs.

AIMS We aim to determine the evolutionary dynamics of CRC metastasis, and how these dynamics are governed by molecular changes within tumour cells and interactions with the microenvironment. Specifically: 1. Determine the evolutionary paths and tumour cell-intrinsic molecular mechanisms controlling CRC metastasis. 2.

Quantitatively explore the role of the changing tumour microenvironment, and its influence on tumour cell plasticity, in metastasis. 3.

Determine the translational value of measures of metastasis evolution via interrogation of existing very large datasets.

METHODS We have assembled a large cohort of CRC metastasis samples and matched primaries that represent the diversity of clinical groups.

We will pursue an interdisciplinary approach led by mathematical modelling to decipher the multi-omic data we will generate from these samples.

Specifically: timing, clonality and routes of metastatic dissemination will be inferred using phylogenetic methods applied to genome sequencing, and mathematical modelling will be used to detect evidence of ongoing selection.

We will uncover epi(genetic) mechanisms that dictate gene expression in metastases, in a tissue-specific, disease course and selection-associated manner.

We will learn the dynamics of tumour-microenvironment (TME) interactions by creating stochastic models of the TME, followed by parameterisation and refinement of the models by fitting to high dimensional spatial multiplexed imaging data. We will detect plasticity by comparing genotype-phenotype data to expectations from mathematical modelling.

We will use existing molecular and clinical data assembled from ~5000 CRCs to determine the translational relevance of measures of metastasis evolution.

HOW THE RESULTS OF THIS RESEARCH WILL BE USED Our mathematics-led analysis will enable measurement of the dynamics of metastatic spread, tumour-microenvironment interactions and tumour cell plasticity directly from patient samples. The large repository of multi-omic data generated will become a disease-defining dataset.

Knowledge of the role of the epigenome and tumour-microenvironment interactions driving CRC metastasis has the potential to open up new therapeutic opportunities and allow early identification of high-risk pCRCs.