| Autor: |
Stackhouse CT; Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL 35294, USA.; Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL 35294, USA., Rowland JR; Department of Physics, Ohio State University, Columbus, OH 43210, USA., Shevin RS; Center for Clinical and Translational Science, The University of Alabama at Birmingham, AL 35294, USA., Singh R; Institute of Regenerative Medicine, LifeNet Health, Virginia Beach, VA 23453, USA., Gillespie GY; Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL 35294, USA. gygillespie@uabmc.edu., Willey CD; Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL 35294, USA. cwilley@uabmc.edu. |
| Abstrakt: |
Accurate patient-derived models of cancer are needed for profiling the disease and for testing therapeutics. These models must not only be accurate, but also suitable for high-throughput screening and analysis. Here we compare two derivative cancer models, microtumors and spheroids, to the gold standard model of patient-derived orthotopic xenografts (PDX) in glioblastoma multiforme (GBM). To compare these models, we constructed a custom NanoString panel of 350 genes relevant to GBM biology. This custom assay includes 16 GBM-specific gene signatures including a novel GBM subtyping signature. We profiled 11 GBM-PDX with matched orthotopic cells, derived microtumors, and derived spheroids using the custom NanoString assay. In parallel, these derivative models underwent drug sensitivity screening. We found that expression of certain genes were dependent on the cancer model while others were model-independent. These model-independent genes can be used in profiling tumor-specific biology and in gauging therapeutic response. It remains to be seen whether or not cancer model-specific genes may be directly or indirectly, through changes to tumor microenvironment, manipulated to improve the concordance of in vitro derivative models with in vivo models yielding better prediction of therapeutic response. |
