ArticleBMC medical genomics2019
Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs): challenges and guidelines.
Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 55 citations in OpenAlex.
- Review
- Xenomake: a pipeline for processing and sorting xenograft reads from spatial transcriptomic experiments.Bioinformatics (Oxford, England) · 2024Article
- PDX Models in Theranostic Applications: Generation and Screening for B Cell Lymphoma of Human Origin.Molecular imaging and biology · 2024Article
- A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis.Cancer research · 2024Article
- Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models.Nature communications · 2024Article
- Preclinical Modeling of Pathway-Targeted Therapy of Human Lung Cancer in the Mouse.Cold Spring Harbor perspectives in medicine · 2024Review
- Reference genomes for BALB/c Nude and NOD/SCID mouse models.G3 (Bethesda, Md.) · 2023Article
- Therapeutic implications of transcriptomics in head and neck cancer patient-derived xenografts.PloS one · 2023Article
- Patient-derived models facilitate precision medicine in liver cancer by remodeling cell-matrix interaction.Frontiers in immunology · 2023Review
- Article
- A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non-Small Cell Lung Cancer.Cancer research · 2022Article
- Article
- Challenges in genomic analysis of model systems and primary tumors of pancreatic ductal adenocarcinoma.Computational and structural biotechnology journal · 2022Review
- TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository.Journal of translational medicine · 2021Article
- Chromatin conformation capture (Hi-C) sequencing of patient-derived xenografts: analysis guidelines.GigaScience · 2021Article
- Article
- Identification of Prognostic Biomarkers Originating From the Tumor Stroma of Betel Quid-Associated Oral Cancer Tissues.Frontiers in oncology · 2021Article
- Genomic and Epigenomic Landscaping Defines New Therapeutic Targets for Adenosquamous Carcinoma of the Pancreas.Cancer research · 2020Article
- Critical evaluation of the subcutaneous engraftments of hormone naïve primary prostate cancer.Translational andrology and urology · 2020Article
- Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis.Cancer research · 2020Article
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Authors and funding
16 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundPatient-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.
resultsWe 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).
conclusionsThe 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 .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.