Supporting clinical decision making in advanced melanoma by preclinical testing in personalized immune-humanized xenograft mouse models

Ny, Lars; Rizzo, Larissa Yokota; Belgrano, Valerio; Karlsson, Jan Ch; Jespersen, Henrik; Carstam, Louise; Bagge, Roger Olofsson; Nilsson, Lisa M.; Nilsson, Jonas A.

doi:10.1016/j.annonc.2019.11.002

Cited by 36 publications

(21 citation statements)

References 21 publications

(34 reference statements)

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“…Along these lines, adoptive transfer of autologous TILs inhibited melanoma PDX growth in hIL2-NOG mice following an analogous responder/nonresponder pattern in the patient similarly treated with ACT ( Jespersen et al 2017). Additionally, the anti-PD1 response observed in an avatar model using the patient's own melanoma PDX and TILs supported therapeutic decision for this patient (Ny et al 2020). The avatar approach using HIS models still needs to be validated, but it could eventually guide the future use of immunotherapies as precision medicine and help to identify predictive biomarkers of clinical responses.…”

Section: Personalized Medicine: Avatar Modelsmentioning

confidence: 96%

Humanized Mouse Models to Evaluate Cancer Immunotherapeutics

Guil‐Luna

Sedlik

Piaggio

2021

Annu. Rev. Cancer Biol.

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Immunotherapy is at the forefront of cancer treatment. The advent of numerous novel approaches to cancer immunotherapy, including immune checkpoint antibodies, adoptive transfer of CAR (chimeric antigen receptor) T cells and TCR (T cell receptor) T cells, NK (natural killer) cells, T cell engagers, oncolytic viruses, and vaccines, is revolutionizing the treatment for different tumor types. Some are already in the clinic, and many others are underway. However, not all patients respond, resistance develops, and as available therapies multiply there is a need to further understand how they work, how to prioritize their clinical evaluation, and how to combine them. For this, animal models have been highly instrumental, and humanized mice models (i.e., immunodeficient mice engrafted with human immune and cancer cells) represent a step forward, although they have several limitations. Here, we review the different humanized models available today, the approaches to overcome their flaws, their use for the evaluation of cancer immunotherapies, and their anticipated evolution as tools to help personalized clinical decision-making.

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Section: Personalized Medicine: Avatar Modelsmentioning

confidence: 96%

Humanized Mouse Models to Evaluate Cancer Immunotherapeutics

Guil‐Luna

Sedlik

Piaggio

2021

Annu. Rev. Cancer Biol.

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“…The one mouse per model per treatment scheme were adopted since the reproducibility of this scheme has been demonstrated. (25)(26)(27) For each tumor, at least 4 unique therapeutic entities were screened for both PDTX and FastPDTX. The schematic paradigm for PDTX and FastPDTX modeling were illustrated in Figure 1A.…”

Section: Fastpdtx Modelingmentioning

confidence: 99%

“…We adapted one mouse per model per treatment method in FastPDTX since the reproducibility of this scheme has been demonstrated. (25)(26)(27) Because the cellular necrosis and the proliferating inhibition could be induced by other conditions such as previous neoadjuvant chemotherapy or an insufficient blood supply in tumorgrafts we used untreated tumorgrafts as a control to abate those potential disturbing effects. We quantified the percentage of necrotic and proliferative cells and compared those two indices pairwise in control 246 and treated samples.…”

Section: Drug Efficacy Can Be Determined By Pathological Indicesmentioning

confidence: 99%

A fast and efficient model for real-time anti-tumor drug efficacy test in the clinical setting based on patient-derived tumor xenograft

Zhu¹,

Wang²,

Wang³

et al. 2021

Preprint

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Drug response rates in cancer therapies are frequently unsatisfactory (first-line 30-40%, subsequent-line ~10% or less). The mouse model, patient-derived tumor xenograft (PDTX), can help promote drug effective rate to over 80% by precisely electing efficacious agents. However, the low engraftment rates, extended testing cycle, and high cost limit its utilization in the clinical setting. A novel method, FastPDTX, was developed, in which a substitutive engrafting procedure was applied, and the labor-intensive and time-consuming propagation of tumorgrafts was circumvented by pathological analysis. A comprehensive evaluation involving tumor biology and clinical performance was performed for this new model. The clinical evaluation engaged 431 PDTX cases, 1050 FastPDTX cases, 531 therapeutic entities, 6535 regimens, and a broad spectrum of solid tumors. Typical cases were also studied, covering various refractory malignant tumors. FastPDTX kept biological architectures and genetic characteristics of the primary tumor and displayed an equivalent positive predictive value as PDTX for the drug efficacy test. The testing cycle was reduced from 3-6 months to 3 weeks, and the cost was decreased by 85%. More importantly, the obstacle of low engraftment rate was circumvented, resulting in the percentage of appliable tests nearly 5-fold higher (94.8% vs. 16.7%) than PDTX. FastPDTX is particularly suitable in circumstances where standard care is not available and the therapeutic window is short, and for cases where tumors have indolent growth in mice. FastPDTX preserved the advantages of classical PDTX, overcame its major limitations, and showed great potential for real-time clinic drug selection in cancer therapy.

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“…xenografts develop [14,15]. Nonetheless, the low success rate of PDX establishment (20-40%) [16][17][18] with the notably inconsistent rate of diver gene-mutations between established PDX and original tumors (10-20%) [19,20] is quite a worrisome problem, but not well studied.…”

Section: Introductionmentioning

confidence: 99%

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

Preprint

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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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Supporting clinical decision making in advanced melanoma by preclinical testing in personalized immune-humanized xenograft mouse models

Cited by 36 publications

References 21 publications

Humanized Mouse Models to Evaluate Cancer Immunotherapeutics

Humanized Mouse Models to Evaluate Cancer Immunotherapeutics

A fast and efficient model for real-time anti-tumor drug efficacy test in the clinical setting based on patient-derived tumor xenograft

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

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