Capture tumor evolution on chip
Purpose
Cancer biologists predominantly analyse fully grown malignant populations to infer their genetic evolutionary history. As such, key questions such as the extent of negative selection and mutation rate per cell division remain unanswered, as current studies are biased towards positively selected cell populations. To address these questions, this project requires technology that enables live-cell imaging of tumor growth with single-cell genomic analysis of all the individual cells that originate from said imaged tumor.
Technology gap
Organoid cultures, named method of the year in 2017 by Nature, are increasingly being adopted in cancer research labs world-wide as it is currently the closest representative of human tumors that is amenable for in vitro studies. Unfortunately, organoid cultures are resource and time-intensive to culture, with many classical assays in research laboratories, such as drug screens and cell-type purification by FACS, requiring large-scale culture platforms to compensate for significant cell losses inherent to those procedures.
Microfluidic solution
With C-TECH, a new microfluidic platform based on VyCAP’s proprietary technology will be developed and tested that fulfils two unmet demands for organoid technology. First, it enables capture and isolation of single-cells for prospective (genetic) analysis without cell loss, thereby making widely-used cell sorting procedures available for small-scale (50-5000 cells) organoid cultures. Second, they will establish ‘on chip’ organoid cultures from (tumor) cells that are captured in individual microwells (6400 cups/chip) to correlate cell fate with genetic mutations. Importantly, limited organoid material is required to fill their microfluidic chips, making time-and resource consuming expansion of organoid cultures obsolete for personalised drug screens with cancer therapeutics.
OoC Showcase
C-TECH will create a unique opportunity to superimpose single-cell mutational datasets on the complete cellular lineage tree that is inferred from live-imaged tumor organoids to delineate how environmental pressures (e.g. chemotherapies) shape the course of tumor evolution with cell-cycle resolution.
