Sir Peter MacCallum Department of Oncology - Theses
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ItemPan-cancer reconstruction of clonal evolution in 1,800 patients using the discrete time-branching process( 2020)Intra and inter-tumour heterogeneity poses a challenge for associating molecular and immunohistochemical markers with clinical outcomes. Sequencing technologies has enabled detailed assessment of tumour heterogeneity, facilitating the genomic characterisation of tumours. Whilst such technologies have revealed mutational landscapes and have identified key driver alterations for tumorigenesis, pan-cancer clonal evolution reconstructions are lacking. In order to bridge this gap, I used discrete-time branching models to derive biological insights into tumour progression and reconstructed the clonal evolution in 1,800 patients, successfully linking mutations with growth patterns of disease progression. I first modified a discrete time-branching process to account for individual clonal subpopulations and derived analytical solutions for expectation and variance of both clonal and tumour expansions. Additionally, I derived the expected time for any given clone to successfully expand as \hat{\tau}, and with the use of these analytical solutions, I showed the likely driver and clonal compositions of the tumours and their phylogenies. Secondly, I generated a database of results from four different versions of time-branching process models that covered multiple parameters. Here I identified how an increase in diversity arises by both increased mutation rate and reduced fitness. I further corroborated that total number of drug resistant cells is directly proportional to lineage extinction probability (\delta) and tumour size as shown in previous studies. I also showed that this effect can be extrapolated to other types of functional passenger mutations involved in cancer-specific mortality. Moreover, I showed how commonly used sequencing cut-offs limit the accurate inference of tumour’s average selective advantage and driver mutation rate. Thirdly, I identified that a minimum distance metric can provide accurate fits of simulated cancer cell fractions to real patient tumour data. This metric showed at least 80% accuracy to identify the initial parameters of s and u and at least 40% accuracy to recover the correct evolutionary trajectory. Fourthly, I applied this fitting procedure to reconstruct the evolutionary trajectories of 1,800 tumours from different cancer sequencing studies. The best fits derived suggests that the most likely parameters for the evolution of solid tumours are high driver mutation rates and weak driver effects of fitness. Fifthly, using The Cancer Genome Atlas cohort, I identified an association between predicted degree of clonality and survival, and found branched topologies are common in malignancies with adverse prognosis. In the TRACERx non-small-cell lung cancer cohort, I identified that clonal reconstructions agreed with previously reported phylogenies. Additionally, using data from the Breast International Group 1-98, I identified the role of tumour fitness in determining clinical outcome, and the evolutionary dynamics of TP53 and PIK3CA mutations conducive to distant metastasis. Finally, using data from a metastatic melanoma patient collected through the CASCADE melanoma study, I was able to propose a pattern of dissemination from the primary to metastatic sites in the liver and brain based on the phylogenies recovered from my data-fitting procedure. This study demonstrates the power of the discrete-time branching process in reconstructing tumour evolution, and its potential to uncover insights in the dynamics of tumour growth that are missed by current methods.