Combigem for Systematic and Massively Parallel Analysis of Drug-Gene Combinations for Cancer Therapeutics | AIChE

Combigem for Systematic and Massively Parallel Analysis of Drug-Gene Combinations for Cancer Therapeutics

Authors 

Choi, G. - Presenter, Massachusetts Institute of Technology
Lu, T. K., Massachusetts Institute of Technology
Wong, A., Massachusetts Institute of Technology



Paper_403478_abstract_68907_0.docx

CombiGEM for Systematic and Massively Parallel Analysis of Drug-Gene Combinations for Cancer Therapeutics

Alan S. L. Wong, Gigi C. G. Choi, Allen A. Cheng, Oliver Purcell, Timothy K. Lu
Synthetic Biology Group, MIT Synthetic Biology Center, Research Laboratory of Electronics, Department of Biological Engineering and Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
The chemosensitivity of cancer cells is under the influence of multiple cellular pathways and genetic factors. Combinatorial drug therapies can achieve higher efficacy than individual drugs since targeting multiple pathways can be synergistic. Understanding drug- gene interactions should enable us to devise optimized therapeutics for sensitizing cancer cells to drugs and reversing drug resistance. Conventional approaches for analyzing a large number of drug-gene combinations are low-throughput, and require huge effort and resources. Harnessing the power of synthetic biology and next-generation sequencing technologies, we developed CombiGEM (Combinatorial Genetics En Masse) as a powerful platform for high-throughput functional characterization of combinatorial genetic perturbations in human cells, which can be broadly applied in biomedical research. We applied
the CombiGEM technology to achieve scalable assembly of microRNA (miRNA) overexpression constructs. We generated high-coverage combinatorial miRNA libraries and performed systematic screens to identify combinatorial miRNA effectors that sensitized drug- resistant ovarian cancer cells to chemotherapy and/or inhibited cancer cell proliferation. We identified and validated a list of miRNA combinations that act synergistically to achieve anti- cancer phenotypes when expressed in combination. This effort revealed new insights into complex miRNA interaction networks, including previously unknown interactions between miRNAs that modulate drug resistance and cell growth phenotypes.
This work was supported by the NIH New Innovator Award (DP2 OD008435), the Office of Naval Research, and the Ellison Foundation New Scholar in Aging Award, and the Croucher Foundation.