Establishment of a platform of non-small-cell lung cancer patient-derived xenografts with clinical and genomic annotation

Kang, Han Sol; Choi, Jae Woo; Shim, Hyo Sup; Kim, Jinna; Kim, Dae Joon; Lee, Chang Young; Hong, Min Hee; Park, Sung Yong; Park, A-Young; Shin, Eun Joo; Lee, Seo Yoon; Pyo, Kyoung‐Ho; Yun, Mi Ran; Choi, Hun Mi; Lee, Sung Sook; Kim, Seok-Young; Lee, Hanna; Paik, Soonmyung; Cho, Byoung Chul; Lee, Jin Gu; Kim, Hye Ryun

doi:10.1016/j.lungcan.2018.08.008

Cited by 27 publications

(40 citation statements)

References 34 publications

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“…14,15 Molecular characterization of lung cancer PDXs has demonstrated that these PDXs faithfully retain the genomic alterations found in their corresponding primary tumors. [18][19][20] Although several groups have devoted efforts to generating and characterizing NSCLC PDXs, the overall PDX engraftment rates for NSCLC range from 25% to 40%, 2,[20][21][22] which is lower than the engraftment rates reported for colorectal cancer 23 and melanoma PDXs. For example, the response rate to paclitaxel treatment in PDXs was equivalent to that reported in a clinical study of paclitaxel single-agent therapy for patients with advanced non-small cell lung cancer (NSCLC), 17 and PDXs harboring targetable driver mutations (EGFR, ALK, and ROS1) were found to have response patterns to target inhibitors similar to those of patient tumors.…”

Section: Introductionmentioning

confidence: 94%

“…For example, the response rate to paclitaxel treatment in PDXs was equivalent to that reported in a clinical study of paclitaxel single-agent therapy for patients with advanced non-small cell lung cancer (NSCLC), 17 and PDXs harboring targetable driver mutations (EGFR, ALK, and ROS1) were found to have response patterns to target inhibitors similar to those of patient tumors. [18][19][20] Although several groups have devoted efforts to generating and characterizing NSCLC PDXs, the overall PDX engraftment rates for NSCLC range from 25% to 40%, 2,[20][21][22] which is lower than the engraftment rates reported for colorectal cancer 23 and melanoma PDXs. 24 We previously reported an overall engraftment rate of 26% for NSCLC specimens in nonobese diabetic/ severe combined immunodeficiency (NOD SCID) mice.…”

Section: Introductionmentioning

confidence: 94%

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Tumor characteristics associated with engraftment of patient‐derived non–small cell lung cancer xenografts in immunocompromised mice

Chen

Zhang

Wang

et al. 2019

Cancer

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BACKGROUND: Patient-derived xenograft (PDX) models increasingly are used in translational research. However, the engraftment rates of patient tumor samples in immunodeficient mice to PDX models vary greatly. METHODS: Tumor tissue samples from 308 patients with non-small cell lung cancer were implanted in immunodeficient mice. The patients were followed for 1.5 to approximately 6 years. The authors performed histological analysis of PDXs and some residual tumor tissues in mice with failed PDX growth at 1 year after implantation. Quantitative polymerase chain reaction and enzyme-linked immunoadsorbent assay were performed to measure the levels of Epstein-Barr virus genes and human immunoglobulin G in PDX samples. Patient characteristics were compared for PDX growth and overall survival as outcomes using Cox regression analyses. Disease staging was based on the 7th TNM staging system. RESULTS:The overall engraftment rate for PDXs from patients with non-small cell lung cancer was 34%. Squamous cell carcinomas had a higher engraftment rate (53%) compared with adenocarcinomas. Tumor samples from patients with stage II and stage III disease and from larger tumors were found to have relatively high engraftment rates. Patients whose tumors successfully engrafted had worse overall survival, particularly those individuals with adenocarcinoma, stage III or stage IV disease, and moderately differentiated tumors. Lymphoma formation was one of the factors associated with engraftment failure. Human CD8-positive and CD20-positive cells were detected in residual samples of tumor tissue that failed to generate a PDX at 1 year after implantation. Human immunoglobulin G was detected in the plasma of mice that did not have PDX growth at 14 months after implantation. CONCLUSIONS: The results of the current study indicate that the characteristics of cancer cells and the tumor immune microenvironment in primary tumors both can affect engraftment of a primary tumor sample. Cancer 2019;125:3738-3748.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 94%

Tumor characteristics associated with engraftment of patient‐derived non–small cell lung cancer xenografts in immunocompromised mice

Chen

Zhang

Wang

et al. 2019

Cancer

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“…We established the HNSCC PDX model using a similar method as that used to generate lung cancer PDX [8]. PDX models were generated using 6-to 8-week-old female nude (nu/nu) and severe combined immunodeficient (NOG, NOD/Shi-scid/IL-2Rγ null ) mice (OrientBio, Seoul, Korea).…”

Section: Establishment Of Pdx Modelsmentioning

confidence: 99%

“…The stored fragments were used for genetic profiling and/or conserved in RNAlater RNA stabilization reagent (Qiagen). Moreover, we fixed fragments in 10% neutral-buffered formalin and made paraffin embedded tissue samples for further additional pathological analysis [8].…”

Section: Sample Storagementioning

confidence: 99%

“…Therefore, we can anticipate patient's clinical outcome by referring to PDXs, and thus are useful for clinical translational research, drug screening, and biomarker discovery and validation. PDXs are in vivo models to test clinically guided hypotheses, capable of evaluating drug activity and novel drug combined strategies, and also to elucidate their predictive biomarkers [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Establishment and characterization of patient-derived xenografts as paraclinical models for head and neck cancer

et al. 2020

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Background: We investigated whether head and neck squamous cell carcinoma (HNSCC) patient-derived xenografts (PDXs) reaffirm patient responses to anti-cancer therapeutics. Methods: Tumors from HNSCC patients were transplanted into immunodeficient mice and propagated via subsequent implantation. We evaluated established PDXs by histology, genomic profiling, and in vivo anti-cancer efficacy testing to confirm them as the authentic in vivo platform. Results: From 62 HNSCCs, 15 (24%) PDXs were established. The primary cancer types were tongue (8), oropharynx (3), hypopharynx (1), ethmoid sinus cancer (1), supraglottic cancer (1), and parotid gland (1); six PDXs (40%) were established from biopsy specimens from advanced HNSCC. PDXs mostly retained donor characteristics and remained stable across passages. PIK3CA (H1047R), HRAS (G12D), and TP53 mutations (H193R, I195T, R248W, R273H, E298X) and EGFR, CCND1, MYC, and PIK3CA amplifications were identified. Using the acquisition method, biopsy showed a significantly higher engraftment rate when compared with that of surgical resection (100% [6/6] vs. 16.1% [9/56], P < 0.001). Specimens obtained from metastatic sites showed a significantly higher engraftment rate than did those from primary sites (100% [9/9] vs. 11.3% [6/53], P < 0.001). Three PDX models from HPV-positive tumors were established, as compared to 12 from HPV-negative (15.8% [3/19] and 27.9% [12/43] respectively, P = 0.311), suggesting that HPV positivity tends to show a low engraftment rate. Drug responses in PDX recapitulated the clinical responses of the matching patients with pan-HER inhibitors and pan-PI3K inhibitor.(Continued on next page)

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Integration of Heterogeneous Biological Data in Multiscale Mechanistic Model Calibration: Application to Lung Adenocarcinoma

Palgen¹,

Perrillat-Mercerot²,

Ceres³

et al. 2022

Acta Biotheor

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Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data. Determining parameters and their values is particularly challenging in the case of models of pathophysiology, for which data for calibration is sparse. Multiple data sources might be required, and data may not be in a uniform or desirable format. We describe a calibration strategy to address the challenges of scarcity and heterogeneity of calibration data. Our strategy focuses on parameters whose initial values cannot be easily derived from the literature, and our goal is to determine the values of these parameters via calibration with constraints set by relevant data. When combined with a covariance matrix adaptation evolution strategy (CMA-ES), this step-by-step approach can be applied to a wide range of biological models. We describe a stepwise, integrative and iterative approach to multiscale mechanistic model calibration, and provide an example of calibrating a pathophysiological lung adenocarcinoma model. Using the approach described here we illustrate the successful calibration of a complex knowledge-based mechanistic model using only the limited heterogeneous datasets publicly available in the literature.

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Establishment of a platform of non-small-cell lung cancer patient-derived xenografts with clinical and genomic annotation

Cited by 27 publications

References 34 publications

Tumor characteristics associated with engraftment of patient‐derived non–small cell lung cancer xenografts in immunocompromised mice

Tumor characteristics associated with engraftment of patient‐derived non–small cell lung cancer xenografts in immunocompromised mice

Establishment and characterization of patient-derived xenografts as paraclinical models for head and neck cancer

Integration of Heterogeneous Biological Data in Multiscale Mechanistic Model Calibration: Application to Lung Adenocarcinoma

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