Engineering model-based systems to monitor and steer subclonal dynamics

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ABSTRACT Primary tumors as well as cancer cell lines have been shown to exhibit extensive genetic and transcriptional heterogeneity, with multiple subclones co-existing in the same cancer population.1 Even after decades of in-vitro growth, established cell cultures continue to evolve.2 The heterogeneity of cancer cell lines over space and time crystallizes into three unmet needs: i) cell culture protocols that offer a high temporal resolution on in-vitro growth dynamics; ii) close monitoring of the temporal separation between genotypic and phenotypic measurements and iii) reconciling the cost-prohibitive nature of repeated high-throughput multi-omic measurements with ceaseless changes in subclonal composition. To fill these needs, we propose to engineer how in-vitro and in-silica experiments interact into a software solution called CLONEID. An SOL database in the backend, a Java core and an R user interface will come together to form two modules: One will record the pedigree of lineages grown in a lab and use computer vision to monitor phenotypic changes, such as variable growth rates. The second module will link subclonal multi-omics profiles from different high throughput assays to each other and to the phenotypes from the first module. In aim 1 we will develop the first module and use it to demonstrate feasibility of monitoring phenotypic transitions of cell lines with CLONE ID at high temporal resolution, without any specialized equipment. Aim 2 will use this data in conjunction with existing single cell sequencing of the same cell lines to develop and test the second module and use it to identify subclone-specific biomarkers of growth. Together these aims will pave the way to more complex mathematical models of carcinogenesis, that do not have to rely on the simplifying assumption that individual driver mutations have equal fitness effects and that individual subclones have a fixed growth rate. In the future, the framework developed here will serve as foundation to deploy computer vision for early detection of morphological changes, including adaptation from in-vivo to in-vitro growth and mycoplasma contamination.