Abstract
Patient-derived tumor xenograft (PDX) mouse models are widely used for drug screening. The underlying assumption is that PDX tissue is very similar with the original patient tissue, and it has the same response to the drug treatment. To investigate whether the primary tumor site information is well preserved in PDX, we analyzed the gene expression profiles of PDX mouse models originated from different tissues, including breast, kidney, large intestine, lung, ovary, pancreas, skin, and soft tissues. The popular Monte Carlo feature selection method was employed to analyze the expression profile, yielding a feature list. From this list, incremental feature selection and support vector machine (SVM) were adopted to extract distinctively expressed genes in PDXs from different primary tumor sites and build an optimal SVM classifier. In addition, we also set up a group of quantitative rules to identify primary tumor sites. A total of 755 genes were extracted by the feature selection procedures, on which the SVM classifier can provide a high performance with MCC 0.986 on classifying primary tumor sites originated from different tissues. Furthermore, we obtained 16 classification rules, which gave a lower accuracy but clear classification procedures. Such results validated that the primary tumor site specificity was well preserved in PDX as the PDXs from different primary tumor sites were still very different and these PDX differences were similar with the differences observed in patients with tumor. For example, VIM and ABHD17C were highly expressed in the PDX from breast tissue and also highly expressed in breast cancer patients.
Highlights
Patient-derived tumor xenograft (PDX) mouse models, developed by implanting patients’ in vivo tumor tissues into immune-deficient mice (Harris et al, 2016), are widely used in tumor biology and drug screening
To evaluate the investigated features mentioned in the section Dataset on discriminating samples from different tissues, the Monte Carlo feature selection (MCFS) method was used to analyze and rank them in descending order according to their relative importance (RI) values
The predicted results were counted as individual accuracy for each tissue, overall accuracy, and Matthew’s correlation coefficient (MCC) described in the section Performance Measurement, which are provided in Supplementary Table 2
Summary
Patient-derived tumor xenograft (PDX) mouse models, developed by implanting patients’ in vivo tumor tissues into immune-deficient mice (Harris et al, 2016), are widely used in tumor biology and drug screening. Such study confirmed that the PDX mouse model can basically reflect the same pathological processes during the initiation and progression of breast cancer, validating the significance of such model in the field of tumor research. PDX mouse models have been applied to various tumor subtypes, including colorectal cancer, pancreatic cancer, and pediatric cancer (Scott et al, 2017). Studies on such tumor subtypes have confirmed that tumor tissues developed in a PDX mouse model have quite similar pathological and biological characteristics with tumor tissues in situ, though without immune selective pressure. PDX mouse models have been accepted as one of the most significant methods for tumor research
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