Establishing and Maintaining an Extensive Library of Patient-Derived Xenograft Models

Mattar, Marissa S.; McCarthy, Craig; Kulick, Amanda; Qeriqi, Besnik; Guzman, Sean; Stanchina, Elisa de

doi:10.3389/fonc.2018.00019

Cited by 43 publications

(71 citation statements)

References 63 publications

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“…The patient was previously described 14 . The tumor samples were minced, mixed with matrigel, and implanted subcutaneously in the right flank of a female NOD/SCID gamma (NSG, Jackson Laboratory, Bar Harbor, ME) mouse (6-week old) to generate the patient-derived xenograft (PDX) 49 . PDX tumors were serially transplanted three times prior to being used for this study.…”

Section: Methodsmentioning

confidence: 99%

NTRK kinase domain mutations in cancer variably impact sensitivity to type I and type II inhibitors

et al. 2020

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Tyrosine kinase domains dynamically fluctuate between two main structural forms that are referred to as type I (DFG-in) or type II (DFG-out) conformations. Comprehensive data comparing type I and type II inhibitors are currently lacking for NTRK fusion-driven cancers. Here we used a type II NTRK inhibitor, altiratinib, as a model compound to investigate its inhibitory potential for larotrectinib (type I inhibitor)-resistant mutations in NTRK. Our study shows that a subset of larotrectinib-resistant NTRK1 mutations (V573M, F589L and G667C) retains sensitivity to altiratinib, while the NTRK1V573M and xDFG motif NTRK1G667C mutations are highly sensitive to type II inhibitors, including altiratinib, cabozantinib and foretinib. Moreover, molecular modeling suggests that the introduction of a sulfur moiety in the binding pocket, via methionine or cysteine substitutions, specifically renders the mutant kinase hypersensitive to type II inhibitors. Future precision treatment strategies may benefit from selective targeting of these kinase mutants based on our findings.

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Section: Methodsmentioning

confidence: 99%

NTRK kinase domain mutations in cancer variably impact sensitivity to type I and type II inhibitors

et al. 2020

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“…Another feature, source, also played a negative role in matching the genotype, different from that in tumor engraftments. Although uids source, including MPE and lymph, are proved to have a higher engraftment rate than the solid tumor tissues [32], we found that uid-derived tumor xenografts were more challenging to maintain driver genotypes from parental tumors.…”

Section: Discussionmentioning

confidence: 74%

Establishment of Prediction Models for Lung Cancer NOG/PDX Models: A Guideline for Machine Learning in Small Biomedical Datasets

Guo¹,

Li²,

Qi³

et al. 2020

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Introduction: Targeted therapy and immune checkpoint inhibitors are the most promising treatments for lung cancers but still facing multiple challenges, including resistance and individual difference. Therefore, patient-derived tumor xenografts (PDX) models are developed for drug discovery and screening. NOG mice is under the destruction of the interleukin-2 (IL-2) receptor common gamma chain, which is appropriate for building PDX models to test immunotherapies. However, current studies have little understanding of the causes of genotype mismatches in PDX or NOG/PDX models, which leads to a massive economic and time loss.Methods: Lung cancer tissues from 53 patients were obtained and engrafted into NOG mice. All of the patients' tumors and NOG/PDX models were detected for common gene mutations. Seventeen clinicopathological features were organized and input to stepwise logistic regression based on the lowest Akaike information criterion (AIC), least absolute shrinkage and selection operator (LASSO)-logistic regression, support vector machine recursive feature elimination (SVM-RFE), eXtreme Gradient Boosting (XGBoost), Gradient Boosting & Categorical Features (CatBoost), and synthetic minority over-sampling technique (SMOTE). Finally, the performance of all models was evaluated by the accuracy, area under the receiver operating characteristic curve (AUC), and F1 score in 100 testing groups.Results: Fifty-three lung cancer NOG/PDX models were successfully established, with a genotype matching rate of 79.2% (42/53). Two multivariable logistic regressions revealed that age, the number of driver mutations, epidermal growth factor receptor (EGFR) gene mutations, the type of prior chemotherapy, prior tyrosine kinase inhibitors (TKIs) therapy, and the source were potent predictors. Moreover, CatBoost (mean accuracy=0.960; mean AUC=0.939; mean F1 score=0.908) and 8-feature SVM (mean accuracy=0.950; mean AUC=0.934; mean F1 score=0.903) showed the best performance compared with the other algorithms. Moreover, the combination of SMOTE with SVM significantly improved the predictive capability (mean accuracy: 0.961 vs. 0.958, P=0.025; mean AUC: 0.940 vs. 0.935, P=0.045; mean F1 score: 0.909 vs. 0.903, P=0.047).Conclusions: We established an optimal predictive model to screen lung cancer patients for NOG/PDX models, and also offered a general approach for building prediction models in small unbalanced biomedical samples.

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“…Heparin was added to fluids at time of collection and cells were collected by centrifugation (1000 RPM, 5 min), washed once with ice-cold PBS and then either resuspended in growth media (DMEM:F12 + 10% FBS + 1% antibiotic/antimycotic solution) or prepared as described next for solid tumors for implantation into mice. Fresh tumor samples were cleaned and then minced, mixed with Matrigel and implanted into a subcutaneous flank of female NOD/SCID gamma (NSG, Jackson Laboratory, Bar Harbor, ME) mice to generate xenografts (26). For cell line xenografts, 10 million cells were mixed with Matrigel (1:1) and injected subcutaneously into a single flank of female NSG mice.…”

Section: Methodsmentioning

confidence: 99%

Establishment of multiple novel patient-derived models of desmoplastic small round cell tumor enabling functional characterization of ERBB pathway signaling and pre-clinical evaluation of a novel targeted therapy approach

Smith

Odintsov

Liu

et al. 2020

Preprint

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Desmoplastic small round cell tumor (DSRCT) is characterized by the t(11;22)(p13;q12) chromosomal translocation, which fuses the transcriptional regulatory domain of EWSR1 with the zinc finger DNA-binding domain of WT1, resulting in the oncogenic transcription factor EWS-WT1. DSRCT primarily affects young males and has a 5-year overall survival of about 15%. Typical treatment approaches for patients with DSRCT involve a multi-modal combination of surgery, chemotherapy and radiation. The paucity of DSRCT disease models has hampered functional and pre-clinical therapeutic studies in this aggressive cancer. Here, we developed robust preclinical disease models and mined DSRCT expression profiling data to identify genetic vulnerabilities that could be leveraged for the identification of rational therapies. Specifically, we developed four new DSRCT cell lines and one patient-derived xenograft (PDX) model. Transcriptomic and proteomic profiling showed evidence of activation of the ERBB pathway. Ectopic expression of EWSR1-WT1 resulted in upregulation of ERRB family ligands and downstream signaling. Treatment of DSRCT cell lines with ERBB ligands resulted in activation of EGFR, ERBB2, ERK1/2 and AKT, and stimulation of cell growth. Conversely, targeting of EGFR using shRNA, small molecule inhibitors (afatinib, neratinib) or an anti-EGFR antibody (cetuximab) inhibited growth and induced apoptosis in DSRCT cells. Finally, treatment of mice bearing DSRCT xenografts with a combination of cetuximab and afatinib significantly reduced tumor growth. These data provide a rationale for the clinical evaluation of EGFR antagonists in patients with DSRCT.

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Establishing and Maintaining an Extensive Library of Patient-Derived Xenograft Models

Cited by 43 publications

References 63 publications

NTRK kinase domain mutations in cancer variably impact sensitivity to type I and type II inhibitors

NTRK kinase domain mutations in cancer variably impact sensitivity to type I and type II inhibitors

Establishment of Prediction Models for Lung Cancer NOG/PDX Models: A Guideline for Machine Learning in Small Biomedical Datasets

Establishment of multiple novel patient-derived models of desmoplastic small round cell tumor enabling functional characterization of ERBB pathway signaling and pre-clinical evaluation of a novel targeted therapy approach

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