Patient derived renal cell carcinoma xenografts exhibit distinct sensitivity patterns in response to antiangiogenic therapy and constitute a suitable tool for biomarker development

Schueler, Julia; Klingner, Kerstin; Bug, Daniel; Zoeller, Caren; Maier, Armin; Meng, Di; Willecke, K.V.; Peille, Anne-Lise; Steiner, E; Landesfeind, Manuel; Copland, John A.; Siegers, Gabrielle M.; Haferkamp, Axel; Boehm, Katharina; Tsaur, Igor; Schneider, Meike

doi:10.18632/oncotarget.25697

Cited by 8 publications

(6 citation statements)

References 54 publications

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“…In translational applications, panels of PDX models can screen for drug efficacy, resistance mechanisms, and aid in identifying biomarkers of predictive of drug sensitivity and response (Table 1). In a large cohort of mice grafted from a heterogeneous population of patient RCC tumors, Schueler et al found that they were able to predict tumor response to therapy based on HMGB1 levels [50]. Traditional systemic chemotherapies have been notoriously ineffective against advanced renal cell carcinoma.…”

Section: Correlation With Therapy Response and Chemosensitivitymentioning

confidence: 99%

Patient-Derived Xenograft Models in Urological Malignancies: Urothelial Cell Carcinoma and Renal Cell Carcinoma

Tracey

Murray

Coleman

et al. 2020

Cancers

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The engraftment of human tumor tissues into immunodeficient host mice to generate patient-derived xenograft (PDX) models has become increasingly utilized for many types of cancers. By capturing the unique genomic and molecular properties of the parental tumor, PDX models enable analysis of patient-specific clinical responses. PDX models are an important platform to address the contribution of inter-tumoral heterogeneity to therapeutic sensitivity, tumor evolution, and the mechanisms of treatment resistance. With the increasingly important role played by targeted therapies in urological malignancies, the establishment of representative PDX models can contribute to improved facilitation and adoption of precision medicine. In this review of the evolving role of the PDX in urothelial cancer and kidney cancer, we discuss the essential elements of successful graft development, effective translational application, and future directions for clinical models.

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Section: Correlation With Therapy Response and Chemosensitivitymentioning

confidence: 99%

Patient-Derived Xenograft Models in Urological Malignancies: Urothelial Cell Carcinoma and Renal Cell Carcinoma

Tracey

Murray

Coleman

et al. 2020

Cancers

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“…Existing and new PDX models brought onto the market are, however, often fully characterized in terms of mutational and transcriptomic status, growth rate, differentiation, vascularization, amount of stroma content, etc. which allow this wealth of information for each model to be exploited for understanding the specific traits of patient tumors that could be successfully targeted [ 9 ], and thereby give valuable insights for designing clinical trials [ 8 ]. As such, PDX models provide a better foundation for drug development and basic cancer research compared to fresh patient tumor samples.…”

Section: Discussionmentioning

confidence: 99%

Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer

Ali¹,

Vildevall²,

Rodriguez³

et al. 2022

J Exp Clin Cancer Res

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Background Accurate predictions of tumor dissemination risks and medical treatment outcomes are critical to personalize therapy. Patient-derived xenograft (PDX) models in mice have demonstrated high accuracy in predicting therapeutic outcomes, but methods for predicting tumor invasiveness and early stages of vascular/lymphatic dissemination are still lacking. Here we show that a zebrafish tumor xenograft (ZTX) platform based on implantation of PDX tissue fragments recapitulate both treatment outcome and tumor invasiveness/dissemination in patients, within an assay time of only 3 days. Methods Using a panel of 39 non-small cell lung cancer PDX models, we developed a combined mouse-zebrafish PDX platform based on direct implantation of cryopreserved PDX tissue fragments into zebrafish embryos, without the need for pre-culturing or expansion. Clinical proof-of-principle was established by direct implantation of tumor samples from four patients. Results The resulting ZTX models responded to Erlotinib and Paclitaxel, with similar potency as in mouse-PDX models and the patients themselves, and resistant tumors similarly failed to respond to these drugs in the ZTX system. Drug response was coupled to elevated expression of EGFR, Mdm2, Ptch1 and Tsc1 (Erlotinib), or Nras and Ptch1 (Paclitaxel) and reduced expression of Egfr, Erbb2 and Foxa (Paclitaxel). Importantly, ZTX models retained the invasive phenotypes of the tumors and predicted lymph node involvement of the patients with 91% sensitivity and 62% specificity, which was superior to clinically used tests. The biopsies from all four patient tested implanted successfully, and treatment outcome and dissemination were quantified for all patients in only 3 days. Conclusions We conclude that the ZTX platform provide a fast, accurate, and clinically relevant system for evaluation of treatment outcome and invasion/dissemination of PDX models, providing an attractive platform for combined mouse-zebrafish PDX trials and personalized medicine.

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“…Only the Vhldel Ink4a/Arf-del model produced some metastasis, but only in the liver. However, no suitable GEMMs of RCC recapitulating with high fidelity the metastatic process, as our model does, exists to date [28][29][30][31].…”

Section: Discussionmentioning

confidence: 99%

Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression

et al. 2021

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Background Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy. Methods In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data. Results Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance. Conclusion A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.

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Patient derived renal cell carcinoma xenografts exhibit distinct sensitivity patterns in response to antiangiogenic therapy and constitute a suitable tool for biomarker development

Cited by 8 publications

References 54 publications

Patient-Derived Xenograft Models in Urological Malignancies: Urothelial Cell Carcinoma and Renal Cell Carcinoma

Patient-Derived Xenograft Models in Urological Malignancies: Urothelial Cell Carcinoma and Renal Cell Carcinoma

Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer

Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression

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