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Abstract
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.
Highlights
Glioblastoma multiforme (GBM) is the most common form of primary brain cancer with a dismal median survival of approximately 18 months [1]
We demonstrate that using this custom 350 gene NanoString assay, we can rapidly profile GBM samples from various models and use it in conjunction with preclinical screening of novel therapeutics
We used classifier F-score with five-fold cross validation as our normalized, scaled GBM patient-derived xenografts (PDX) data
Summary
Glioblastoma multiforme (GBM) is the most common form of primary brain cancer with a dismal median survival of approximately 18 months [1]. There have been few advancements in the treatment of GBM since the findings of Stupp et al [2]. This may be due largely to the dearth of reliable preclinical models which accurately recapitulate the disease characteristics of GBM in patients. The best preclinical models of GBM are patient-derived xenografts (PDX). These models are expensive, time consuming, and not scalable for high-throughput screening of therapeutic compounds [3,4]
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