• Post-Doc Research Associate

    University of North Carolina- Chapel Hill (Chapel Hill, NC)


    Vacancy ID: PDS004498

    Position Summary/Description:

    Lineberger Comprehensive Cancer Center is inviting applications for a funded position to begin as early as May 1 2025.

     

    The Spanheimer lab studies how gene regulation impacts response to treatment in breast cancer. Particular areas of emphasis are investigating how the program of estrogen response is maintained in estrogen receptor positive breast cancer and identifying novel therapeutic vulnerabilities in tumors that become unresponsive to antiestrogen treatment (Spanheimer et al. Clin Cancer Research 2014, Spanheimer et al. Ann Surg 2014, Spanheimer et al. Ann Surg Oncol 2015). We work with cell line and in vitro models, but also innovative translational models. As a breast cancer surgeon, I have developed important infrastructure for fresh tissue acquisition which has facilitated generating translational models and single cell genomics used in this proposal. Specifically, we recently showed that single cell based analyses can identify unique drug resistant subpopulations within heterogeneous tumors (Kim et al. Clinical Cancer Research, Zikry et al. PNAS ) and that characterizing these subpopulations identifies treatment targets to overcome resistance. Our current research focuses on how resistant trajectories arise from tumors and how we can prevent resistance by targeting unique vulnerabilities of those cells.

     

    The purpose of this position will be to further our projects into understanding how tumor cell subpopulations give rise to resistant trajectories. Special emphasis will be on developing and testing in vitro models including CRISPR -CAS9 knockouts and gene regulation assays including mRNA sequencing and CUT and RUN , and using proliferation assays to test therapeutic strategies.

    Education and Experience:

    The ideal candidate will be highly motivated to join a vibrant research laboratory to take advantage of sophisticated model systems that we have developed. Candidates with prior experience in molecular biology, molecular genetics, cancer genomics, and/or computational biology will be preferred.