Dataset Information

Characterization of patient-derived tumor xenografts (PDXs) as models for estrogen receptor positive (ER+HER2- and ER+HER2+) breast cancers.

ABSTRACT: The research was to appraise the utility of the patient-derived tumor xenografts (PDXs) as models of estrogen receptor positive (ER+HER2- and ER+HER2+) breast cancers. We compared protein expression profiles by Reverse Phase Protein Array (RPPA) in tumors that resulted in PDXs compared to those that did not. Our overall PDX intake rate for ER+ breast cancer was 9% (9/97). The intake rate for ER+HER2+ tumors (3/16, 19%) was higher than for ER+HER2- tumors (6/81, 7%). Heat map analyses of RPPA data showed that ER+HER2- tumors were divided into 2 groups by luminal A/B signature [protein expression of ER, AR, Bcl-2, Bim (BCL2L11), GATA3 and INPP4b], and this expression signature was also associated with the rate of PDX intake. Cell survival pathways such as the PI3K/AKT signaling and RAS/ERK pathways were more activated in the specimens that could be established as PDX in both classes. Expression of the ER protein itself may have a bearing on the potential success of an ER+ PDX model. In addition, HER2 and its downstream protein expressions were up-regulated in the ER+HER2+ patient tumors that were successfully established as PDX models. Moreover, the comparison of RPPA data between original and PDX tumors suggested that the selection/adaptation process required to grow the tumors in mice is unavoidable for generation of ER+ PDX models, and we identified differences between patient tumor samples and paired PDX tumors. A better understanding of the biological characteristics of ER+PDX would be the key to using PDX models in assessing treatment strategies in a preclinical setting.

SUBMITTER: Kanaya N

PROVIDER: S-EPMC5094906 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Publications

Characterization of patient-derived tumor xenografts (PDXs) as models for estrogen receptor positive (ER+HER2- and ER+HER2+) breast cancers.

Kanaya Noriko N Somlo George G Wu Jun J Frankel Paul P Kai Masaya M Liu Xueli X Wu Shang Victoria SV Nguyen Duc D Chan Nymph N Hsieh Meng-Yin MY Kirschenbaum Michele M Kruper Laura L Vito Courtney C Badie Behnam B Yim John H JH Yuan Yuan Y Hurria Arti A Peiguo Chu C Mortimer Joanne J Chen Shiuan S

The Journal of steroid biochemistry and molecular biology 20160503

The research was to appraise the utility of the patient-derived tumor xenografts (PDXs) as models of estrogen receptor positive (ER+HER2- and ER+HER2+) breast cancers. We compared protein expression profiles by Reverse Phase Protein Array (RPPA) in tumors that resulted in PDXs compared to those that did not. Our overall PDX intake rate for ER+ breast cancer was 9% (9/97). The intake rate for ER+HER2+ tumors (3/16, 19%) was higher than for ER+HER2- tumors (6/81, 7%). Heat map analyses of RPPA dat ...[more]

PMID: 27154416

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Project description:BACKGROUND:Patient-derived xenograft (PDX) models are in vivo models of human cancer that have been used for translational cancer research and therapy selection for individual patients. The Jackson Laboratory (JAX) PDX resource comprises 455 models originating from 34 different primary sites (as of 05/08/2019). The models undergo rigorous quality control and are genomically characterized to identify somatic mutations, copy number alterations, and transcriptional profiles. Bioinformatics workflows for analyzing genomic data obtained from human tumors engrafted in a mouse host (i.e., Patient-Derived Xenografts; PDXs) must address challenges such as discriminating between mouse and human sequence reads and accurately identifying somatic mutations and copy number alterations when paired non-tumor DNA from the patient is not available for comparison. RESULTS:We report here data analysis workflows and guidelines that address these challenges and achieve reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from PDX models that lack genomic data from paired non-tumor tissue for comparison. Our workflows incorporate commonly used software and public databases but are tailored to address the specific challenges of PDX genomics data analysis through parameter tuning and customized data filters and result in improved accuracy for the detection of somatic alterations in PDX models. We also report a gene expression-based classifier that can identify EBV-transformed tumors. We validated our analytical approaches using data simulations and demonstrated the overall concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA). CONCLUSIONS:The analysis workflows that we have developed to accurately predict somatic profiles of tumors from PDX models that lack normal tissue for comparison enable the identification of the key oncogenic genomic and expression signatures to support model selection and/or biomarker development in therapeutic studies. A reference implementation of our analysis recommendations is available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows .

Dataset Information

Characterization of patient-derived tumor xenografts (PDXs) as models for estrogen receptor positive (ER+HER2- and ER+HER2+) breast cancers.

Publications

Characterization of patient-derived tumor xenografts (PDXs) as models for estrogen receptor positive (ER+HER2- and ER+HER2+) breast cancers.

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