Dataset Information

PCR-based zebrafish model for personalised medicine in head and neck cancer.

ABSTRACT:

Background

Currently, in vivo model for personalised cancer drug testing is challenging. A zebrafish larvae xenograft model has been applied in recent years to cancer research, particularly for drug testing purposes, showing promising results in drug testing against patient-derived tumour xenografts. Currently, these xenograft models apply imaging techniques to measure drug efficacy. However, this method carries several limitations, including timely imaging, thereby reducing the available number of tested fish and drugs. Here, we propose a PCR-based fast assay to evaluate drug efficacy in a zebrafish larvae xenograft model.

Methods

We tested two primary and corresponding metastatic head and neck squamous cell carcinoma (HNSCC) cell lines and patient-derived tongue cancer sample applying zebrafish larvae xenograft model. Cisplatin efficacy was tested using imaging technique and compared the results with PCR-based methods. Drug screening of eight compounds was applied on both cell lines and patient sample using PCR.

Results

In a head-to-head comparison, all the three techniques (imaging, quantitative PCR, and droplet digital PCR) showed similar reduction of the cancer cells growth after cisplatin treatment. Using the quantitative PCR assay, we demonstrated a dose-dependent response of HNSCC cells to cisplatin. Drug screening results of four HNSCC cell lines and patient sample revealed different drug efficacy between tested cancer cells.

Conclusion

We introduce a novel, easy, fast and cost-effective PCR-based in vivo zebrafish larvae assay to test the response of cell lines and clinical tumour samples to anti-cancer drugs. This method goes hand-by-hand with the commonly used imaging assay.

SUBMITTER: Al-Samadi A

PROVIDER: S-EPMC6647158 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

PCR-based zebrafish model for personalised medicine in head and neck cancer.

Al-Samadi Ahmed A Tuomainen Katja K Kivimäki Anne A Salem Abdelhakim A Al-Kubati Sakhr S Hyytiäinen Aini A Parikka Mataleena M Mesimäki Karri K Wilkman Tommy T Mäkitie Antti A Grenman Reidar R Salo Tuula T

Journal of translational medicine 20190722 1

<h4>Background</h4>Currently, in vivo model for personalised cancer drug testing is challenging. A zebrafish larvae xenograft model has been applied in recent years to cancer research, particularly for drug testing purposes, showing promising results in drug testing against patient-derived tumour xenografts. Currently, these xenograft models apply imaging techniques to measure drug efficacy. However, this method carries several limitations, including timely imaging, thereby reducing the availabl ...[more]

PMID: 31331338

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Project description:Gaining insight into the mechanisms of signal transduction networks (STNs) by using critical features from patient-specific mathematical models can improve patient stratification and help to identify potential drug targets. To achieve this, these models should focus on the critical STNs for each cancer, include prognostic genes and proteins, and correctly predict patient-specific differences in STN activity. Focussing on colorectal cancer and the WNT STN, we used mechanism-based machine learning models to identify genes and proteins with significant associations to event-free patient survival and predictive power for explaining patient-specific differences of STN activity. First, we identified the WNT pathway as the most significant pathway associated with event-free survival. Second, we built linear-regression models that incorporated both genes and proteins from established mechanistic models in the literature and novel genes with significant associations to event-free patient survival. Data from The Cancer Genome Atlas and Clinical Proteomic Tumour Analysis Consortium were used, and patient-specific STN activity scores were computed using PROGENy. Three linear regression models were built, based on; (1) the gene-set of a state-of-the-art mechanistic model in the literature, (2) novel genes identified, and (3) novel proteins identified. The novel genes and proteins were genes and proteins of the extant WNT pathway whose expression was significantly associated with event-free survival. The results show that the predictive power of a model that incorporated novel event-free associated genes is better compared to a model focussing on the genes of a current state-of-the-art mechanistic model. Several significant genes that should be integrated into future mechanistic models of the WNT pathway are DVL3, FZD5, RAC1, ROCK2, GSK3B, CTB2, CBT1, and PRKCA. Thus, the study demonstrates that using mechanistic information in combination with machine learning can identify novel features (genes and proteins) that are important for explaining the STN heterogeneity between patients and their association to clinical outcomes.

Project description:Despite the current progress in the development of new concepts of precision medicine for head and neck squamous cell carcinoma (HNSCC), in particular targeted therapies and immune checkpoint inhibition (CPI), overall survival rates have not improved during the last decades. This is, on the one hand, caused by the fact that a significant number of patients presents with late stage disease at the time of diagnosis, on the other hand HNSCC frequently develop therapeutic resistance. Distinct intratumoral and intertumoral heterogeneity is one of the strongest features in HNSCC and has hindered both the identification of specific biomarkers and the establishment of targeted therapies for this disease so far. To date, there is a paucity of reliable preclinical models, particularly those that can predict responses to immune CPI, as these models require an intact tumor microenvironment (TME). The "ideal" preclinical cancer model is supposed to take both the TME as well as tumor heterogeneity into account. Although HNSCC patients are frequently studied in clinical trials, there is a lack of reliable prognostic biomarkers allowing a better stratification of individuals who might benefit from new concepts of targeted or immunotherapeutic strategies. Emerging evidence indicates that cancer stem cells (CSCs) are highly tumorigenic. Through the process of stemness, epithelial cells acquire an invasive phenotype contributing to metastasis and recurrence. Specific markers for CSC such as CD133 and CD44 expression and ALDH activity help to identify CSC in HNSCC. For the majority of patients, allocation of treatment regimens is simply based on histological diagnosis and on tumor location and disease staging (clinical risk assessments) rather than on specific or individual tumor biology. Hence there is an urgent need for tools to stratify HNSCC patients and pave the way for personalized therapeutic options. This work reviews the current literature on novel approaches in implementing three-dimensional (3D) HNSCC in vitro and in vivo tumor models in the clinical daily routine. Stem-cell based assays will be particularly discussed. Those models are highly anticipated to serve as a preclinical prediction platform for the evaluation of stable biomarkers and for therapeutic efficacy testing.

Dataset Information

PCR-based zebrafish model for personalised medicine in head and neck cancer.

Background

Methods

Results

Conclusion

Publications

PCR-based zebrafish model for personalised medicine in head and neck cancer.

Similar Datasets

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