Modelling the link between replication stress, chromosomal instabilityand aneuploidy

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Chromosomal instability (CIN), a condition frequently observed in cancer and in variou inheritable diseases, is defined as an increased rate of structural or whole chromosome alterations. Mitotic defects triggering chromosome missegregation are often considered as theroot cause of whole chromosome CIN (W-CIN), whereas structural CIN (S-CIN) is attributed to replication stress (RS). This prevailing view has been challenged by our finding that replication genes including GINS1 and CDC45 are overexpressed in W-CIN tumours. Indeed, a mechanism consistent with this observation has been identified in collaboration with Holger Bastians (FOR2800, SP2): increased replication origin firing triggered by these genes or by mild RS is sufficient to induce W-CIN. The details of how the increased origin firing is triggered by RS and how these disturbances of the replication machinery induce CIN are currently unexplored. We will develop and calibrate a mechanistic mathematical model for DNA replication and origin firing in human cells. We will use data from DNA combing experiments providing information on the fork speed and inter-origin distance to constrain the parameters of this model and our own mathematical techniques for handling structural model errors to suggest further data acquisition. By directly linking this model phenomenologically to mitotic defects, we will be able to simulate the effect of various interventions and perturbations of thereplication machinery (for instance GINS1 overexpression) on origin firing rate, mitotic chromosome segregation and CIN. A second version of the model will then include a model of the ATM pathway. We aim to study by computer simulations, how the ATM system transfer changes in origin firing and replication dynamics to mitotic defects and CIN. These model predictions will then be addressed experimentally by members of the FOR2800. Since aneuploidy and CIN have typically detrimental effects on cellular fitness, we aim to understand the longitudinal changes required to tolerate CIN to define the role of CIN in cancer. In collaboration with Zuzana Storchova (FOR2800, SP8), we will modify our recently developed MFmap machine learning framework to integrate high dimensional genomic data and to studychanges of the cell state during the adaptation to aneuploidy and CIN. The MFmap framework will allow us to predict cancer subtype labels and to compute low dimensional latent representations for samples at different time points during adaptation. By studying thesechanges and by correlating changes in the latent space to proliferation signatures we aim to identify and categorise key events along the adaptation process to CIN.