ArticleOncotarget2018
Patient derived renal cell carcinoma xenografts exhibit distinct sensitivity patterns in response to antiangiogenic therapy and constitute a suitable tool for biomarker development.
Article in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- SETD2 Deficiency Drives Mitochondrial DNA Leakage and Creates a Druggable Dependency on BCL-xL in Clear Cell Renal Cell Carcinoma.Cancer research · 2026Article
- Immune cell infiltration pattern in non-small cell lung cancer PDX models is a model immanent feature and correlates with a distinct molecular and phenotypic make-up.Journal for immunotherapy of cancer · 2022Article
- Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer.Journal of experimental & clinical cancer research : CR · 2022Article
- A Molecularly Characterized Preclinical Platform of Subcutaneous Renal Cell Carcinoma (RCC) Patient-Derived Xenograft Models to Evaluate Novel Treatment Strategies.Frontiers in oncology · 2022Article
- Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression.Molecular cancer · 2021Article
- Patient-Derived Xenograft Models in Urological Malignancies: Urothelial Cell Carcinoma and Renal Cell Carcinoma.Cancers · 2020Review
- Molecular Inhibitor of QSOX1 Suppresses Tumor GrowthMolecular cancer therapeutics · 2020Article
- Precision Medicine Tools to Guide Therapy and Monitor Response to Treatment in a HER-2+ Gastric Cancer Patient: Case Report.Frontiers in oncology · 2019Article
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Authors and funding
16 authors.
Funding
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Abstract
Systemic treatment is necessary for one third of patients with renal cell carcinoma. No valid biomarker is currently available to tailor personalized therapy. In this study we established a representative panel of patient derived xenograft (PDX) mouse models from patients with renal cell carcinomas and determined serum levels of high mobility group B1 (HMGB1) protein under treatment with sunitinib, pazopanib, sorafenib, axitinib, temsirolimus and bevacizumab. Serum HMGB1 levels were significantly higher in a subset of the PDX collection, which exhibited slower tumor growth during subsequent passages than tumors with low HMGB1 serum levels. Pre-treatment PDX serum HMGB1 levels also correlated with response to systemic treatment: PDX models with high HMGB1 levels predicted response to bevacizumab. Taken together, we provide for the first time evidence that the damage associated molecular pattern biomarker HMGB1 can predict response to systemic treatment with bevacizumab. Our data support the future evaluation of HMGB1 as a predictive biomarker for bevacizumab sensitivity in patients with renal cell carcinoma.
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