This is the first of three abstracts that will be presented as posters at AACR in June. During the up-coming weeks, all abstracts will be presented in a series here at our homepage. If you are interested in learning more or how zebrafish models could help you in your development process, do not hesitate to contact us at email@example.com or in the contact form below.
Title: Zebrafish patient tumor-derived xenograft models synergize with mouse-PDX models for understanding variation in anti-cancer drug responses
Authors: Zaheer Ali, Malin Vildevall, Anna Nilsson, Julia Schueler, Anna Fahlgren, Lasse DE Jensen
This study was made as a collaboration between BioReperia AB and Charles River Laboratories.
Lung cancer accounts for the 2nd most common cancer among men and women, representing 24% of cancer deaths worldwide. Standard-of-care treatments vary considerably depending on the tumor type and staging. Identifying which patients will benefit from treatment with a certain drug remains one of the major challenges in the clinic. Genetic analyses are widely used but have low applicability as only ~10% of the patients have mutations coupled to available targeted therapies and relatively low sensitivity as therapeutic effects are absent in ~50% of the predicted responders. Mouse-PDX models can accurately determine drug response rates for 50-60% of the patients, but are not well suited for evaluating metastatic risk. As metastasis is a major cause of disease-associated mortality and no drugs that target metastasis exist today, there is considerable need to develop new drugs able to impair metastatic dissemination in lung (and other) cancers.
To meet this need, zebrafish-PDX (ZTX) models are ideally positioned as a synergistic complement to mouse-PDX models allowing evaluation of drug responses, in a non-rodent in vivo system with the turnaround time and scalability of an in vitro platform.
Here we generated zebrafish- and mouse-PDX models based on 20 patient NSCLC samples and compared the efficacy of standard-of-care treatment (erlotinib and paclitaxel) on primary tumor growth/regression as well as metastatic dissemination in the zebrafish-PDX models.
The ZTX models exhibited variable sensitivity to the drugs tested with 11 of 20 and 16 of 20 models being sensitive to erlotinib and paclitaxel respectively. The efficacy of erlotinib and/or paclitaxel was identical in 9 of 11 mouse- and zebrafish-PDX models where these drugs were compared head-to-head.
The models metastasized within three days of tumor implantation in the zebrafish larvae, seeding an average of 2 - 8 metastatic lesions per model in the caudal hematopoietic plexus. Paclitaxel and erlotinib inhibited metastasis in 7 of 20 and 6 of 20 models respectively. The anti-metastatic activity did not correlate with the activity against the primary tumor. Investigations as to what extent this correlates with invasive phenotypes observed in histological preparations of the mouse-PDX models, and clinical data on metastasis in the patients, are currently ongoing.*
* Since the abstract was submitted, this data has been generated where that the invasiveness of the tumors in the zebrafish is correlated to clinical data on metastasis in the patients from which the PDX models were generated and the results shows that the ZTX model could predict 100% of patients with invasive tumor phenotype.
In conclusion we provide evidence of the accuracy of the ZTX models in predicting anti-tumor responses to commonly used drugs in NSCLC compared to mouse-PDX models and demonstrate that ZTX models provide a sensitive method for determining metastatic risk and the anti-metastatic efficacy of NSCLC relevant drugs.
The poster will be presented in session PO.TB01.06- Model organisms for Cancer Research and have the code 6125/9.