Abstract
Patient-derived xenograft (PDX) mouse models are increasingly used for preclinical therapeutic testing of human cancer. A limitation in molecular and genetic characterization of PDX tumors is the presence of integral murine stroma. This is particularly problematic for genomic sequencing of PDX models. Rapid and dependable approaches for quantitating stromal content and purifying the malignant human component of these tumors are needed. We used a recently developed technique exploiting species-specific polymerase chain reaction (PCR) amplicon length (ssPAL) differences to define the fractional composition of murine and human DNA, which was proportional to the fractional composition of cells in a series of lung cancer PDX lines. We compared four methods of human cancer cell isolation: fluorescence-activated cell sorting (FACS), an immunomagnetic mouse cell depletion (MCD) approach, and two distinct EpCAM-based immunomagnetic positive selection methods. We further analyzed DNA extracted from the resulting enriched human cancer cells by targeted sequencing using a clinically validated multi-gene panel. Stromal content varied widely among tumors of similar histology, but appeared stable over multiple serial tumor passages of an individual model. FACS and MCD were superior to either positive selection approach, especially in cases of high stromal content, and consistently allowed high quality human-specific genomic profiling. ssPAL is a dependable approach to quantitation of murine stromal content, and MCD is a simple, efficient, and high yield approach to human cancer cell isolation for genomic analysis of PDX tumors.
Highlights
Since the early 1900’s, mice have emerged as the species of choice for cancer research due to their high breeding potential, low cost, small size, and ease of genetic manipulation
We determined the accuracy of ssPAL analysis by comparing the calculated ssPAL percentages of murine stromal contamination for three of our Patient-derived xenograft (PDX) lines to percentages calculated using fluorescence-activated cell sorting (FACS)
There are many factors that may contribute to failure of preclinical models to predict clinical outcomes
Summary
Since the early 1900’s, mice have emerged as the species of choice for cancer research due to their high breeding potential, low cost, small size, and ease of genetic manipulation. Common approaches include genetically engineered mouse models (GEMMs) which recapitulate malignancy through targeted modifications of driver oncogenes and tumor suppressors, carcinogen induced tumor models, and heterotopic or orthotopic injection of human cancer cell lines in immunocompromised strains [1, 2]. While each of these mouse models offer unique advantages, they display significant limitations that hinder their reliability as experimental models. Carcinogen-induced mouse models may have increased heterogeneity and genetic complexity, but are not of human origin, may not reflect relevant carcinogenic exposures, and their sporadic derivation and long latency markedly limits their utility as a preclinical platform for therapeutic research. Cell line xenograft models can be unreliable predictors of drug efficacy, with compounds that performed well in mouse models failing when translated to human clinical trials [7, 8]
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